The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences

Whitfield, Keith, E;Forrester,, Sarah;Thorpe, Roland, J

doi: 10.1093/gerona/glz214pmid: 31724054

Abstract Background African American life expectancy at age 65 is about 2 years less than that of Caucasians, but by age 85, African Americans may have a longevity advantage. One possible explanation for this cross-over effect is that African Americans who make it to the oldest ages have done so by handling stressful contextual and health disadvantages. The purpose of this study was to examine possible within group cohort differences that lead to exceptional longevity among older African Americans. Methods Data came from three cohorts of older African Americans: the Carolina African American Twin Study of Aging (CAATSA), the Baltimore Study of Black Aging-Patterns of Cognitive Aging (BSBA-PCA), and the Study of Longevity and Stress in African American Families (SOLSAA). Of the 533 participants, we compared two age cohorts (60–79 and 80+) with an average age of 73.2 (SD = 8.33) and 26.3% are men. Variables included measures of stress, depression, coping, cognition, and health indicators. Results The variance for depression and average peak expiratory flow (APEF) was significantly larger for the older cohort but after controlling for demographic factors, the measure of depressive symptoms was not. The Alpha Span test showed a significant difference with the older cohort having larger variances after controlling for demographic factors. Conclusions The findings suggest that there are changes in the characteristics of who makes it to later life, but counter to our hypothesis, there was greater variability in the oldest group relative to the younger. Longevity, African Americans, Age cohorts Although remaining life expectancy of African American men and women at age 65 is about 2 years less than that of Caucasian men and women at the same age, some estimates suggest that by age 85, African Americans may have a longevity advantage (1–4). One possible explanation for this convergence, or cross-over effect, is that African Americans who make it to the oldest ages have done so despite many disadvantages by effectively managing sources of stress such as discrimination. They represent “exceptional survivors” who have developed or possess physiological and/or psychological survival advantages (5). The African Americans who survive into late life have done so by handling a lifetime of stressful contextual and health disadvantages. Exceptional survivorship is often thought to be the result of genetic predisposition. While the National Institute on Aging has presented a fairly substantial list of candidate genes for longevity (cf. http://elcapitan.ucsd.edu/cgi-bin/longevity/GeneData.cgi?org=Hs) thought to explain population longevity, heritability of life span has been found to vary significantly by age and by ethnic group with African Americans having the lowest heritability (6). This suggests that environmental sources of variability account for more of the individual variability in longevity in African Americans. This does not suggest that genes do not impact survival among this group, but rather, complex relationships likely exist between genes and other salient factors, such as family environment and unique environmental factors to play critical roles in longevity in this population. These environmental components are likely to represent complex interactions that result from interactions between individual intrinsic and shared extrinsic factors. Intrinsic factors, for example, might be personality characteristics and extrinsic factors such as family income that result in an individual by family interaction creating variability in longevity. Social scientists have identified many different causes of health disparities and mortality (7). One of the most formidable and well recognized biobehavioral variables related to physiological damage and risk for poor health is stress. Stress can be considered both an intrinsic and extrinsic factor. The relationship between health and stress, particularly perceived stress, is a central mechanism that accounts for the health disparities experienced by African Americans. Perceived stress is typically studied in relation to racial discrimination, social inequities, disadvantaged contexts (poor neighborhoods), and perceptions about the health care system (8). What has not been clear is how the impact of perceptions of stress from our environment that have changed over time and different sources across generations continue to exert the negative persistent influence on longevity of African Americans. These generational similarities and differences can be implicitly observed in age group comparisons. The purpose of this study was to examine whether variances in health and psychosocial factors change with age. We hypothesized that age group variances will be different and decrease from younger to older cohorts. Our conjecture is that selection effects produce reductions in the variability in older adults. This is based on the well-known patterns of mortality observed in older African Americans. We believe that excess mortality results in older adult populations who have survived into later life, who do not have a variety of health conditions and are similar to one another in how they perform on psychosocial measures. We also believe that stress and depression will account for the differences between the age groups. Psychosocial factors such as stress contribute to the development of chronic health conditions and lifetime stress and discrimination can lead to poor mental health (depression), which produces poor physical health (9). Methods Data for this study comes from individuals 60 years of age and older from three cohorts of older African Americans: the Carolina African American Twin Study of Aging (CAATSA) (10), the Baltimore Study of Black Aging-Patterns of Cognitive Aging (BSBA-PCA) (11) and the Study of Stress and Longevity in African American Families (SOLSAA). CAATSA was designed to examine the sources of individual variability in health status, cognition, and physical and psychosocial functioning of adult African American twins) (10). This population-based sample of participants was identified from birth records between the years of 1913 and 1975 from 23 vital statistics offices in North Carolina counties. Birth records were then entered into a computerized database of twin births. After the records were computerized, potential participants were located through voter registries and telephone White page searches. The protocol for data collection consisted of two parts. The survey was administered in person by a trained interviewer and consisted of a structured questionnaire that included demographic and socioeconomic information, self-reported health behaviors, chronic conditions, perceived stress, personality, memory, and well-being. Additionally, assessments of blood pressure and average peak expiratory flow (APEF) were obtained following the survey. Participants were enrolled between 1999 and 2003. All participants provided informed consent and the study was approved by the Institutional Review Board at the University of North Carolina Chapel Hill and Pennsylvania State University. Of the 116 CAATSA participants who were included in this study, 41.4% were men. The average age of the CAATSA participants was 69.2 ± 7.2 years. Additional information regarding the CAATSA study design can be found elsewhere (10). BSBA-PCA was designed to examine patterns and individual factors that contribute to individual differences in cognitive function in older African Americans (11). The sample consisted of 602 community-dwelling African American men and women between the ages of 48 and 92 at the study’s inception. These participants were recruited from 29 senior apartment complexes in the city of Baltimore, Maryland. Data collection lasted 18 months and took place between 2006 and 2008. The interviews lasted 2.5 hours on average and consisted of a face-to-face interview in which there were three blood pressure measurements, three lung function measurements, a battery of cognitive tests and information collected on physical and mental health. All participants signed a written informed consent agreement approved by the institutional review board at Duke University and received monetary compensation for their participation. For this study, 393 BSBA-PCA participants were included in this study. The average age of the participants was 73.7 ± 7.4 years and 21.4% of the BSBA-PCA participants were men. SOLSAA was designed to examine similarity and differences in stress among siblings and between parents and their children to obtain information about factors that contribute to longevity in older African Americans. At the time of this manuscript, data collection was nearly half complete with the goal of interviewing 750 participants. The study is designed to collect data on siblings whose parents had passed away (short-lived) and those sibling pairs who have at least one living parent and who was willing to participate (long-lived). There were 59 SOLSAA participants who were included in this study. The average age of the SOLSAA participants was 77.4 ± 12.3 years and 28.8% of the SOLSAA participants were men. Only participants who were either 60–79 or 80–99 were included in the analysis. This resulted in 568 participants with an average age of 73.2 (SD = 8.3) and 26.2% were men. Measures Stress The Perceived Stress Scale (PSS) (12) is a global measure of perceived stress designed to quantify the degree to which situations in one’s life are appraised as stressful. The PSS consists of 14 items that use a 5-point Likert-scale for responses about the amount of stress the individual experienced during the previous month. Participants scores ranged from 0 to 60 (12). Depressive symptomatology Depressive symptomatology was assessed using the Center for Epidemiologic Studies-Depression (CES-D) scale, the 11-item version, which is designed to assess both frequency and severity of depressive symptoms during the previous week (13). Scores can range from 0 (reporting no depressive symptoms) to 33 (reporting more depressive symptoms). Blood pressure Blood pressure was taken by using an oscillometric automated device (A & D model UA-767; Milpitas California). Three measurements were taken in a sitting position, from the same arm, using a cuff of appropriate size for the participant’s arm (14). The average systolic blood pressure (SBP) and diastolic blood pressure (DBP) values were used in the analysis. Average peak expiratory flow Lung function or APEF status was measured using the Mini-Wright peak flow meter, which assessed participants’ peak expiratory flow (15). Participants stood and covered the end of the tube of the peak flow meter with their lips and blew as hard as possible after taking a deep breath for 1 second. The APEF was calculated as the average of three trials. There was at least a 30-second interval between each measurement. Health status Health status was based on self-report of chronic health conditions. Chronic health conditions were based on participants’ report of physician diagnoses of the following: arthritis, cancer, diabetes, stroke, heart attack, or high blood pressure. Each of the chronic conditions was coded as binary variables (1 = present; 0 = absent). All six conditions were summed to create a variable representing the total number of chronic health conditions. Body mass index Height and weight were assessed by interviewers from subjects dressed in lightweight clothes with their shoes removed. Body mass index was calculated as weight (kg) divided by height squared (m2). Cognitive speed The Digit Symbol Test (16) is a measure of cognitive speed that requires participants to reproduce, within 120 seconds (60 seconds per trial), as many coded symbols as possible in blank boxes beneath randomly generated digits, according to a coding scheme for pairing digits with symbols. The number correct and incorrect are recorded and used as variables. Memory The Alpha Span (17) is a task that measures short-term memory. Participants are read a list of words (from two to eight words). After each list is read, participants are asked to repeat the list in alphabetical order. Responses are recorded as pass or fail. If a subject fails two consecutive attempts, the test is ended. Covariates Covariates included age, gender, and education. Age was the main independent variable and although age was assessed as a continuous variable, a dichotomous variable was created to identify individuals who were between the ages of 60 and 79, and 80 to 99 years of age. Gender was included as a dichotomous variable where men were indicated as 0 and women as 1. Education level was based on the number of years of education completed. Analysis Frequencies, means, and standard errors were calculated to describe the sample. Analysis of variance was conducted to determine whether there were differences between the two age groups relative to the health outcomes. Levene’s test for homogeneity of variance was conducted to determine whether variance was equal across the age groups (18,19). The Breusch-Pagan’s Test was used to examine the heteroskedasticity across age groups in our adjusted models (19). All analyses were conducted using STATA v.14 (College Station, TX) and p values less than .05 were considered statistically significant. Results Demographic and health-related characteristics by age cohort are given in Table 1. The average age of the 568 participants was 73.2 ± 0.3 years. Regarding demographic variables, approximately three-fourth of the participants were female and had on average 11.2 ± 0.1 years of education. As it relates to health characteristics, the participants exhibited the following averages: height 65.0 ± 0.1 inches, weight 184.4 ± 1.8 pounds, body mass index 30.6 ± 0.3, number of chronic conditions 2.2 ± 0.1, FEV 232.4 ± 4.1, systolic blood pressure 146.1 ± 1.0 mmHg, diastolic blood pressure 82.9 ± 0.5 mmHg, perceived stress score 19.5 ± 0.3, depressive symptoms score 6.5 ± 0.1, digital symbol substitution test 4.5 ± 0.1, and alpha span test 4.4 ± 0.1. When examining the participants by age cohort, in the younger cohort, there were fewer women and the participants had a higher educational attainment, were taller, were heavier, and had better lung function than participants in the older cohort. There were no observed significant differences between the age cohorts with regard to number of health conditions, systolic or diastolic blood pressure, perceived stress score, depressive symptoms score, digital symbol substitution test, or the alpha span test. Table 1. Demographic and Health-Related Characteristics for the Total Sample and by Age Cohort of Participants in CAATSA, BSBA-PCA, and SOLSAA Age Cohort Characteristic 60–79 Years (N = 425) 80–99 Years (N = 143) p Value Levene’s Test p Valuea Breusch-Pagan’s Test p Valueb Breusch-Pagan’s Test p Valuec Demographic  Age (years) — — —  Female (%) 71.0 81.8 .011  Married (%) 22.4 12.5 .011  Education attainment (years) 11.4 ± 0.1 10.6 ± 0.2 .019 Health-related characteristics  Body mass index 31.3 ± 0.3 28.5 ± 0.5 <.001 .063 .140 .236  Number of health conditions 2.1 ± 0.1 2.3 ± 0.1 .072 .958 .701 .923  Mean APEF (mm/l) 246.8 ± 4.9 188.9 ± 5.9 <.001 <.001 <.001 .005  Mean systolic blood pressure (mmHg) 145.4 ± 1.1 148.1 ± 1.9 .255 .248 .550 .795  Mean diastolic blood pressure (mmHg) 83.4 ± 0.6 81.4 ± 1.0 .137 .255 .614 .946  Mean Perceived Stress Score 19.7 ± 0.3 19.0 ± 0.6 .330 .110 .231 —  Mean Depressive Symptoms Score 6.7 ± 0.1 6.1 ± 0.2 .136 .041 .179 —  Digital Symbol Substitution Test 4.6 ± 0.1 4.2 ± 0.1 .047 .998 .554 .617  Alpha Span Test 4.5 ± 0.1 4.2 ± 0.1 .098 .017 .026 .165 Age Cohort Characteristic 60–79 Years (N = 425) 80–99 Years (N = 143) p Value Levene’s Test p Valuea Breusch-Pagan’s Test p Valueb Breusch-Pagan’s Test p Valuec Demographic  Age (years) — — —  Female (%) 71.0 81.8 .011  Married (%) 22.4 12.5 .011  Education attainment (years) 11.4 ± 0.1 10.6 ± 0.2 .019 Health-related characteristics  Body mass index 31.3 ± 0.3 28.5 ± 0.5 <.001 .063 .140 .236  Number of health conditions 2.1 ± 0.1 2.3 ± 0.1 .072 .958 .701 .923  Mean APEF (mm/l) 246.8 ± 4.9 188.9 ± 5.9 <.001 <.001 <.001 .005  Mean systolic blood pressure (mmHg) 145.4 ± 1.1 148.1 ± 1.9 .255 .248 .550 .795  Mean diastolic blood pressure (mmHg) 83.4 ± 0.6 81.4 ± 1.0 .137 .255 .614 .946  Mean Perceived Stress Score 19.7 ± 0.3 19.0 ± 0.6 .330 .110 .231 —  Mean Depressive Symptoms Score 6.7 ± 0.1 6.1 ± 0.2 .136 .041 .179 —  Digital Symbol Substitution Test 4.6 ± 0.1 4.2 ± 0.1 .047 .998 .554 .617  Alpha Span Test 4.5 ± 0.1 4.2 ± 0.1 .098 .017 .026 .165 Notes. APEF = average peak expiratory flow; BSBA-PCA = Baltimore Study of Black Aging-Patterns of Cognitive Aging; CAATSA = Carolina African American Twin Study of Aging; SOLSAA = Study of Longevity and Stress in African American Families. aLevene’s test for the unadjusted test of homogeneity of variance across age cohorts. bThe Breusch-Pagan’s test is a test for homogeneity of variance across age cohorts adjusting for gender and education. cThe Breusch-Pagan’s test is a test for homogeneity of variance across age cohorts adjusting for gender, education, depressive symptoms, and perceived stress. Open in new tab Table 1. Demographic and Health-Related Characteristics for the Total Sample and by Age Cohort of Participants in CAATSA, BSBA-PCA, and SOLSAA Age Cohort Characteristic 60–79 Years (N = 425) 80–99 Years (N = 143) p Value Levene’s Test p Valuea Breusch-Pagan’s Test p Valueb Breusch-Pagan’s Test p Valuec Demographic  Age (years) — — —  Female (%) 71.0 81.8 .011  Married (%) 22.4 12.5 .011  Education attainment (years) 11.4 ± 0.1 10.6 ± 0.2 .019 Health-related characteristics  Body mass index 31.3 ± 0.3 28.5 ± 0.5 <.001 .063 .140 .236  Number of health conditions 2.1 ± 0.1 2.3 ± 0.1 .072 .958 .701 .923  Mean APEF (mm/l) 246.8 ± 4.9 188.9 ± 5.9 <.001 <.001 <.001 .005  Mean systolic blood pressure (mmHg) 145.4 ± 1.1 148.1 ± 1.9 .255 .248 .550 .795  Mean diastolic blood pressure (mmHg) 83.4 ± 0.6 81.4 ± 1.0 .137 .255 .614 .946  Mean Perceived Stress Score 19.7 ± 0.3 19.0 ± 0.6 .330 .110 .231 —  Mean Depressive Symptoms Score 6.7 ± 0.1 6.1 ± 0.2 .136 .041 .179 —  Digital Symbol Substitution Test 4.6 ± 0.1 4.2 ± 0.1 .047 .998 .554 .617  Alpha Span Test 4.5 ± 0.1 4.2 ± 0.1 .098 .017 .026 .165 Age Cohort Characteristic 60–79 Years (N = 425) 80–99 Years (N = 143) p Value Levene’s Test p Valuea Breusch-Pagan’s Test p Valueb Breusch-Pagan’s Test p Valuec Demographic  Age (years) — — —  Female (%) 71.0 81.8 .011  Married (%) 22.4 12.5 .011  Education attainment (years) 11.4 ± 0.1 10.6 ± 0.2 .019 Health-related characteristics  Body mass index 31.3 ± 0.3 28.5 ± 0.5 <.001 .063 .140 .236  Number of health conditions 2.1 ± 0.1 2.3 ± 0.1 .072 .958 .701 .923  Mean APEF (mm/l) 246.8 ± 4.9 188.9 ± 5.9 <.001 <.001 <.001 .005  Mean systolic blood pressure (mmHg) 145.4 ± 1.1 148.1 ± 1.9 .255 .248 .550 .795  Mean diastolic blood pressure (mmHg) 83.4 ± 0.6 81.4 ± 1.0 .137 .255 .614 .946  Mean Perceived Stress Score 19.7 ± 0.3 19.0 ± 0.6 .330 .110 .231 —  Mean Depressive Symptoms Score 6.7 ± 0.1 6.1 ± 0.2 .136 .041 .179 —  Digital Symbol Substitution Test 4.6 ± 0.1 4.2 ± 0.1 .047 .998 .554 .617  Alpha Span Test 4.5 ± 0.1 4.2 ± 0.1 .098 .017 .026 .165 Notes. APEF = average peak expiratory flow; BSBA-PCA = Baltimore Study of Black Aging-Patterns of Cognitive Aging; CAATSA = Carolina African American Twin Study of Aging; SOLSAA = Study of Longevity and Stress in African American Families. aLevene’s test for the unadjusted test of homogeneity of variance across age cohorts. bThe Breusch-Pagan’s test is a test for homogeneity of variance across age cohorts adjusting for gender and education. cThe Breusch-Pagan’s test is a test for homogeneity of variance across age cohorts adjusting for gender, education, depressive symptoms, and perceived stress. Open in new tab The Levene’s test and the Breusch-Pagan’s test for constant variance as it relates to age cohorts is also displayed in Table 1. There were differences in variances across the age cohorts for APEF (p = .041), depressive symptoms score (p = .017), and the Alpha Span test (p < .001) with the 80- to 99-year-old cohort having larger variances compared to the younger cohort. To adjust for possible demographic differences, the Breusch-Pagan’s test was used to adjust for gender and education. The results showed that the variance of the APEF (p < .001) and the Alpha Span test(p = .026) remained significant with larger variances for the older cohort. Next, we used these findings to examine whether psychosocial factors (ie, perceived stress and depression) accounted for differences in variances between the two age groups. In addition to gender and education, we accounted for stress and depressive symptoms score. With respect to APEF, the differences in the variances in age group remained significant (p = .005) with the larger variances for the older cohort. However, the differences in the variances in age group for Alpha Span test were no longer significant (p = .165) after accounting for stress and depressive symptoms score. Discussion The goal of this article was to examine similarities and differences in cognitive, psychosocial, and health indices in two age cohorts with the implied goal of gaining insights about changes in variability with advanced age. We hypothesized that variances would be larger in younger cohorts compared to older cohorts. If variances decrease, this would suggest that selection effects may be present as age increases resulting in a clustering of performance. Our results, however, showed that variances increased in the older age groups for a cognitive measure (alpha span) and a health indicator (average peak expiratory flow) relative to the younger age group. These finding emphasize the importance of both psychosocial factors and health indices as possible explanatory factors of exceptional survivorship among African Americans. With respect to APEF, the differences in the variances by age group remained significant even after controlling for psychosocial factors with larger variances remaining for the older cohort. This finding is particularly interesting given the increased mortality that has been observed in chronic obstructive pulmonary disease in the last couple of decades (20). The differences in the variances by age group for Alpha Span test, however, were no longer significant after accounting for stress and depressive symptom score. The results suggest that psychosocial factors are important explanatory factors of individual variability in some cognitive tests with advancing age. We believe that these indicators may represent factors that are central to longevity and exceptional survival in African Americans. Given the significant health disparities observed in African Americans compared to Whites, these psychosocial factors are important indicators to understand the source and course of health disparities and how they may mollify other factors that produce poor health over the life course. This resistance to age-related variability may indicate that after African Americans surpass their life expectancy (in their 70s), the pressures that increase their likelihood of memory problems are due in part to stress and depressive symptoms. Stress and depressive symptoms on are well-known contributors to cognitive dysfunction and mental health problems by way of hormonal activity across the lifespan that impact the brain (21). It also implies that there are multiple ways African Americans preserve memory functioning into later life and that the threats to memory problems that might be experienced in earlier life have either been avoided and no longer pose a threat or reduce in their level of effect to cause age differences in memory functioning in very late life. There are a few possible explanations for our findings. First, this work suggests that our analysis did not capture selection effects that might have occurred in the sample analyzed here. This may mean that the psychosocial and health variables we chose do not represent factors that produce exceptional aging in African Americans. These findings suggest that trajectories for cognitive and health factors show increasing variability with age and are not impacted by factors that cause early mortality, which might keep an individual from being in later aged cohorts. Another alternative explanation is the age groups we examined represent two distinct groups in relation to biological substrates. Following this rationale, we hypothesize that the younger group has not been impacted by physiological changes that create variability in cognitive and health indicators, which increase in their effects with advancing age. Thus, the older age group was more impacted by those factors than the young. We also considered that while the factors creating variability might be present in both groups, they create greater variability in the older group by way of a longer time to manifest and perhaps differentially impact the older group because they are more frail. Frail but still living. These are untested alternative hypotheses that require additional investigation. The study has some limitations. One of the limitations was the type and size of the sample. While the size was not large compared to epidemiological studies, data on very old African Americans are not readily available. Even less available are longitudinal studies with large samples of older African Americans, particularly ones who could be considered exceptional survivors. The sample used here was collected from three different sources to get a sufficient sample size (BSBA, CAATSA, SOLSAA). Another limitation is the number of factors examined. This limit was due in part to obtaining data from three different studies. While we only had two to three indicators in each of the collections of measures (health, cognition, and psychosocial factors), the factors available resulted in significant findings. This suggests there may be more to learn from a larger and more diverse analysis of older African Americans of how variances change across age groups. Nevertheless, this study has several strengths. The authors are unaware of any other study seeking to understand if there is variability in health and cognitive outcomes across age groups of older African Americans. While the examination of mean differences in age groups provides information about level effects it does not provide information about patterns of variability that may occur. Understanding this variability may provide insights about key factors about how selection effects may play a role in longevity among older African Americans. This study identified exceptional survivors for African Americans for the purpose of learning and writing from a positive perspective on this racial group. The health and well-being of older African Americans is an often-understudied group due to the high rates of premature mortality for African Americans. Chaos theory applied to our results would suggest that as systems age, they become less regulated and increase in variability (22). Attempting to identify a discrete collection of causes for increased variability in exceptional survivors or for longevity is likely mired in improbability. The complexity of both psychosocial and biological underpinnings to exceptional survival will require additional complex biobehavioral approaches. This may be particularly true for African Americans who begin the first half of life with an increased probability for mortality and a second half of life with a far decreased probability for mortality when compared to other racial/ethnic groups. In this study, the investigators sought to determine the similarities and differences in age cohorts of older African Americans in cognitive, psychosocial, and health indices and longevity. Our findings underscore the importance of examining psychosocial factors as possible explanatory factors of exceptional survivorship. Future work should examine longitudinal data on individuals as well as including genetic markers as possible predictors or modifiers of the results found here. In addition, generational effects might also be studied using a within-family approach examine how historical events might be contributing to the variability observed in different age cohorts of African Americans. Future data from SOLSA study, which when completed will provide genetic, psychosocial, and health data on short- and long-lived families, will provide important additional insights to understand such effects. Funding This paper was published as part of a supplement sponsored and funded by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP Conflict of Interest None declared. References 1. Corti MC , Guralnik JM , Ferrucci L , et al. Evidence for a black-white crossover in all-cause and coronary heart disease mortality in an older population: the North Carolina EPESE . Am J Public Health . 1999 ; 89 ( 3 ): 308 – 314 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 2. Manton KG , Poss SS , Wing S . The black/white mortality crossover: investigation from the perspective of the components of aging . Gerontologist . 1979 ; 19 ( 3 ): 291 – 300 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 3. Manton KG , Stallard E , Vaupel JW . Methods for comparing the mortality experience of heterogeneous populations . Demography . 1981 ; 18 ( 3 ): 389 – 410 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 4. McCord C , Freeman HP . Excess mortality in Harlem . N Engl J Med. 1990 ; 322 : 173 – 177 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 5. Sebastiani P , Sun FX , Andersen SL , et al. Families enriched for exceptional longevity also have increased health-span: findings from the long life family study . Front Public Health . 2013 ; 1 : 38 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 6. Lee JH , Flaquer A , Costa R , et al. Genetic influences on life span and survival among elderly African‐Americans, Caribbean Hispanics, and Caucasians . Am J Med Genet A . 2004 ; 128 ( 2 ): 159 – 164 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 7. Whitfield K. Focus on Biobehavioral Aspects of Health In Later Life . New York, NY : Springer ; 2010 . Google Preview WorldCat COPAC 8. Williams DR . Race, socioeconomic status, and health. The added effects of racism and discrimination . Ann N Y Acad Sci. 1999 ; 896 : 173 – 188 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 9. Brown TN , Williams DR , Jackson JS , et al. “Being black and feeling blue”: the mental health consequences of racial discrimination . Race and Society . 2000 ; 2 ( 2 ): 117 – 131 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 10. Whitfield KE . A registry of adult African American twins: The Carolina African American twin study of aging . Twin Res Hum Genet. 2013 ; 16 ( 1 ): 476 – 480 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 11. Sims RC , Whitfield KE , Ayotte BJ , Gamaldo AA , Edwards CL , Allaire JC . Subjective memory in older African Americans . Exp Aging Res . 2011 ; 37 ( 1 ): 220 – 240 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 12. Cohen S , Kamarck T , Mermelstein R . A global measure of perceived stress . J Health Soc Behav . 1983 ; 24 ( 4 ): 385 – 396 .doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 13. Suthers KM , Gatz M , Fiske A . Screening for depression: a comparative analysis of the 11-item CES-D and the CIDI-SF . J Ment Health Aging . 2004 ; 10 ( 3 ): 209 – 220 . WorldCat 14. Beevers G , Lip GY , O’brien E . Blood pressure measurement: Part I-Sphygmomanometry: factors common to all techniques . BMJ . 2001 ; 322 ( 7292 ): 981 – 985 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 15. Cook NR , Evans DA , Scherr PA , Speizer FE , Taylor JO , Hennekens CH . Peak expiratory flow rate and 5-year mortality in an elderly population . Am J Epidemiol. 1991 ; 133 ( 8 ): 784 – 794 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 16. Wechsler D . Manual for the Wechsler Adult Intelligence Scale, Revised . New York : Psychological Corporation ; 1981 . 17. Craik FI . Changes in memory with normal aging: a functional view . Adv Neurol. 1990 ; 51 : 201 – 205 . Google Scholar PubMed WorldCat 18. Moore DS McCabe GP. Introduction to the Practice of Statistics . New York, NY : W.H. Freeman and Company ; 1998 . Google Preview WorldCat COPAC 19. Neter J , Kutner MH , Nachtsheim CJ , Wasserman W. Applied Linear Statistical Models . Vol 4 . Chicago, IL : Irwin ; 1996 . Google Preview WorldCat COPAC 20. Ford ES . Trends in mortality from COPD among adults in the United States . Chest. 2015 ; 148 ( 4 ): 962 – 970 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 21. Lupien SJ , McEwen BS , Gunnar MR , Heim C . Effects of stress throughout the lifespan on the brain, behaviour and cognition . Nat Rev Neurosci . 2009 ; 10 ( 6 ): 434 – 445 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 22. Resnicow K , Page SE . Embracing chaos and complexity: a quantum change for public health . Am J Public Health. 2008 ; 98 : 1382 – 1389 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Perls, Thomas, T;Tan, Erwin, J

doi: 10.1093/gerona/glz237pmid: 31724053

Across the world, more people are living longer. Yet as Victor Dzau and Jo Ann Jenkins point out in their editorial, “whether the extra years will be good ones—and whether societies and economies will benefit as a result–depends on the actions we take now” (1). This special issue of the Journals of Gerontology Series A: Biological and Medical Sciences examines enablers of healthy longevity as well as the accompanying opportunities and challenges. Framing this conversation are two articles about human life span. In defending the veracity of the well-publicized 122-year life of Frenchwoman Jeanne Calment’s age at death, Jean-Marie Robine and coauthors define the current human life span (2). A highly publicized theory challenging Jeanne Calment’s age claim was based in part upon the ageist contention that she “didn’t look frail enough to be a supercentenarian.” One of many consistent forms of evidence supporting the claim of a 122-year life is the finding that photos of Jeanne Calment in her tenth and part of her 11th decades of life correlate with real-time descriptions of her remarkable functional status at those ages. Both her appearance and independent function for the vast majority of her extremely long life are consistent with what must have been a relatively slow biological process of aging and increased resistance to aging-related diseases and syndromes. This prolongation of good health and function is consistent with James Fries’ theory of “compression of morbidity hypothesis,” which states that as one approaches the limit of life span, long-lived individuals must necessarily compress the period of time in which they experience diseases associated with increased mortality towards the end of their life (3). In the second article, Jay Olshansky and Bruce Carnes argue that although humans have experienced a relatively recent and rapid increase in average life expectancy, especially in countries with advanced economies, this increase is now slowing with further technological and medical advances yielding diminishing returns (4). With Jean-Marie Robine and coauthors’ calculation of the mathematical plausibility of a human living to the age of 122 years (2), it is not surprising that we have not seen any human exceed Jeanne Calment’s record set in 1997 when she died more than 20 years ago. Jay Olshansky and Bruce Carnes predict that a major breakthrough with the basic biology of aging might help increase life expectancy for some individuals, but we are unlikely to observe any appreciable increase in life span in the near future. One of the leaders in the study of compression of morbidity is Eileen Crimmins’ research group at the University of Southern California. By analyzing Health and Retirement Study data, Eileen Crimmins and coauthors reveal a decline in heart attack and stroke rates among people over the age of 70. Although this encouraging decline probably reflects both reductions in risk factors such as smoking and more effective medical interventions such as lipid therapy and better screening and treatment of high blood pressure (5), other counter trends are dampening the optimism. This progress, the authors point out, is undermined by increased rates of cancer, stroke, and diabetes among younger age groups. The future net impact: leveling off or even decline in average life expectancy. This potential harbinger of declines in health and longevity in the United States is echoed by Jay Olshansky’s work showing that obesity rates are on the rise and with them, so are rates of obesity-related diseases, the primary killers of adults—namely diabetes, cardiovascular disease, and cancer (6). As indicated by Keith Whitfield and colleagues, and by other studies, these problems are amplified among African Americans who on average have a 2-year lower life expectancy at age 65 compared with Caucasians (7,8). Notably, obesity, smoking, exercise, healthy diets, stress management, adequate sleep, and other potentially modifiable risk factors are primary influences of mortality. Thus, Victor Dzau’s and Jo Ann Jenkins’ call to arms to educate people about modifiable risk factors and to enable them to decrease their risks and increase their resilience comes with a critical urgency. Today’s boomers, in their 50s–70s, and members of generation X, in their 40s–50s, are at critical stages of their lives in determining their health spans. A societal embracing of a paradigm shift—the understanding that as the potential for longevity increases, so does the importance of health-related behaviors at all ages (including middle and older ages)—could extend health span and make aging an opportunity rather than an adversity. From Keith Whitfield and colleagues come a further call to arms, asserting that understanding health disparities requires more data from the oldest members of communities with otherwise lower life expectancies. They hypothesized that African Americans age 85 and older are a select cohort with shared attributes who have somehow overcome the various causes of decreased average life expectancy. One expect that these individuals have survival-related factors in common. However, these researchers report greater variability in older cohorts when compared with younger cohorts. This result suggest that although there are similarities among the oldest old, we still have much to learn about what allows some individuals to experience different rates of “biological aging” (9). In these pages, we also examine how we measure the biological process of aging and how these additional data could enable us to extend healthy longevity. Although the policy concerns of data collection from hundreds of thousands of people is a continuing discussion, we cannot deny that we are now in the age of “big data”. Today wearable devices can capture hundreds of data points about physical function. In the area of biology, a small sample of blood can now yield not only your entire genetic code, but also information about thousands of proteins, enzymes, metabolic markers and other indicators of both disease and health. Analysis of a small stool sample can quantify thousands of different bacteria populations (called the microbiome) which are increasingly being found to strongly influence both health and disease. Just as important, the fields of biostatistics and bioinformatics are catching up with this plethora of data, and new techniques will be able to integrate increasing amounts of data from different modalities. Monty Montano’s research group at Harvard provides insights into the power of data produced from a wearable device, an accelerometer that can capture a wide range of measures about physical activity while people conduct their everyday lives. Novel data such as the period of time a foot is not touching the ground while walking or the change over time in the span of a person’s gait show promise as markers of health or disease and may be especially sensitive to early changes in physical function. In the Pencina and colleagues study, gait speed was found to be a good proxy for both immune and muscle function at basic biological levels (10). In the future, the wide array of clinical and biological data being captured could be combined to potentially enhance the characterization of a person’s health status, to potentially discover a disease process or even to measure the biological rate of aging much earlier and more accurately than we can with current technology. Addressing the biological underpinnings of such work is a contribution by Annibale Puca’s laboratory, which concentrates on the biological characterization of the metabolic and immune systems and what features and mechanisms are associated with successful aging into the 10th and 11th decades of life. The authors describe how some long-lived individuals exhibit regulatory mechanisms that limit the impact of inflammation on mortality and vascular disease (11). As noted above in the article by Whitfield and colleagues, there is a great deal of heterogeneity in how people age. Understanding how these heterogeneous determinants of healthy longevity interact with one another and the environment elevates the importance of personalizing assessment and treatment. The article by Anastasia Gurinovich and colleagues identifies how the effects of specific genetic variants can differ by ethnicity as well as, likely, the geographic distribution of where that variant evolved (12). The apolipoprotein E gene can occur as one of the three common variants, and the E4 variant is a noted risk factor for both Alzheimer’s disease and vascular disease. Previous work has noted that the E4 variant or allele is, as one would predict, very uncommon in centenarians or people who live to 100 years or older. Other groups have found that another variant which is associated with living longer, E2, has increased frequency among centenarians. However, in the study by Anastasia Gurinovich and her colleagues, the effects of these variants can greatly vary according to ethnicity and geography. These and other gene-based differences according to ethnicity and geography may be an important mechanism by which nutrition and other local environmental exposures can have markedly different effects on a person’s health span and susceptibility to disease. John Earls and colleagues describe how clinical and laboratory data can be used to construct a measure of “biological age” (13). This methodology could leverage ongoing work by the National Institute on Aging and large health care systems, from Kaiser Permanente and Geisinger to the Veterans Administration which are archiving broad ranges of clinical and laboratory data from millions of patients and study participants. Future research could then test if combinations of data can enhance our ability to quantify the biological rate of aging and measure a person’s risk of common diseases much earlier than ever before. Large numbers of participants and patients may be required to account for the heterogeneity found worldwide in genetic, cultural, and environmental enablers of healthy longevity. Future research should seek to determine whether these measures of “biological age” can contribute to personalized wellness interventions and treatment plans that could enable health longevity. Jamison McCorrison and colleagues discuss the potential strategies to identify interventions that slow the biological rate of aging and suggest that more comprehensive data sets and studies are needed to translate genetic association data into clinical interventions (14). They propose that additional research is required to determine how specific longevity associated genes are activated and under what circumstances. Genetic and environmental associations will also require understanding cellular mechanisms and metabolic pathways that are reacting to specific stresses or optimal conditions. The authors then describe how additional data could inform us about potential therapeutic drugs and prevention strategies for aging-related diseases and hopefully healthy longevity. Current studies are underway, such as one from the National Institute on Aging’s Longevity Consortium, to gather clinical, biochemical, and biological data over time from both people with aging-related diseases and those who have prolonged health span such as centenarians and supercentenarians (those who live to 110 years old and older) like the 122 year-old Jeanne Calment. The National Academy of Medicine's Global Roadmap for Health Longevity provides a call to action to understand how individual biology, societal enablers, medical science, and technology could be harnessed to ensure that people worldwide can live longer, healthier, and more fulfilling lives. The potential for increased longevity elevates the importance of our health at all ages. Although historical reductions in childhood and adolescent mortality are key drivers of our longevity today, increasing health span in middle age and beyond is required if we seek to expand healthy longevity. Healthy longevity will require a new paradigm that increases research and resources that continue to support increased health span in midlife and older ages. It has long been noted that the “populations with high life expectancy enjoy low life disparity” (15). As the United States continues to experience persistent disparities in life expectancy by race (7), there is also now evidence that geographic disparities in life expectancy among U.S. counties are large and increasing (9). Additional advancements in addressing disparities by income, race, ethnicity, and geography are required if we are to reverse the recently observed reduction in U.S. life expectancy. As we are reminded in the editorial by Victor Dzau and Jo Ann Jenkins, many people across the world are depending on gerontologists to take action now. The call to action comes in response to the unprecedented and exciting opportunity that extended health spans and enhanced longevity present to us. Funding Funding for this supplement was provided by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP. Conflict of Interest None reported. References 1. Dzau VJ , Jenkins JAC . Creating a global roadmap for healthy longevity . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S4–S6. doi: WorldCat Crossref 2. Robine JM , Allard M , Herrmann FR , Jeune B . The real facts supporting Jeanne Calment as the oldest ever human . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S13–S20. doi: WorldCat Crossref 3. Fries JF . Aging, natural death, and the compression of morbidity . N Engl J Med . 1980 ; 303 : 130 – 135 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 4. Olshansky SJ , Carnes B . Inconvenient truths about human longevity . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S52–S60. doi: WorldCat Crossref 5. Crimmins EM , Zhang YS , Kim JK , Levine ME . Changing disease prevalence, incidence, and mortality among older cohorts: the Health and Retirement Study . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S21–S26. doi: WorldCat Crossref 6. Olshansky SJ , Passaro DJ , Hershow RC , et al. A potential decline in life expectancy in the United States in the 21st century . N Engl J Med . 2005 ; 352 : 1138 – 1145 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 7. Cunningham TJ , Croft JB , Liu Y , Lu H , Eke PI , Giles WH . Vital signs: racial disparities in age-specific mortality among blacks or African Americans – United States, 1999–2015 . MMWR Morb Mortal Wkly Rep . 2017 ; 66 : 444 – 456 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 8. Whitfield KE , Forrester S , Thorpe RJ . A comparison of variances in age cohorts to understand longevity in African Americans . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S27–S31. doi: WorldCat Crossref 9. Dwyer-Lindgren L , Bertozzi-Villa A , Stubbs RW , et al. Inequalities in life expectancy among US counties, 1980 to 2014: temporal trends and key drivers . JAMA Intern Med . 2017 ; 177 : 1003 – 1011 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 10. Pencina KM , Li Z , Montano M . Objectively measured physical activity in asymptomatic middle-aged men is associated with routine bood-based biomarkers . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S32–S37. doi: WorldCat Crossref 11. Ciaglia E , Montella F , Maciag A , et al. Longevity associated variant of bpifb4 mitigates monocyte mediated acquired immune response: a possible explanation of how long-living individuals escape age-related diseases? J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S38–S44. doi: WorldCat Crossref 12. Gurinovich A , Bae H , Andersen SL , et al. Varying effect of APOE alleles on extreme longevity in European ethnicities . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S45–S51. doi: WorldCat Crossref 13. Earls JC , Rappaport N , Heath L , et al. Multi-omic biological age estimation and its correlation with wellness and disease phenotypes: a longitudinal study of 3558 individuals . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S52–S60. doi: WorldCat Crossref 14. McCorrison J , Girke T , Goetz LH , Miller RA , Schork NJ . Genetic support for longevity-enhancing drug targets: issues, preliminary data, and future directions . J Gerontol A Biol Sci Med Sci . 2019;74(suppl 1):S61–S71. doi: WorldCat Crossref 15. Vaupel JW , Zhang Z , van Raalte AA . Life expectancy and disparity: an international comparison of life table data . BMJ Open . 2011 ; 1 : e000128 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

McCorrison,, Jamison;Girke,, Thomas;Goetz, Laura, H;Miller, Richard, A;Schork, Nicholas, J

doi: 10.1093/gerona/glz206pmid: 31724058

Abstract Interventions meant to promote longevity and healthy aging have often been designed or observed to modulate very specific gene or protein targets. If there are naturally occurring genetic variants in such a target that affect longevity as well as the molecular function of that target (eg, the variants influence the expression of the target, acting as “expression quantitative trait loci” or “eQTLs”), this could support a causal relationship between the pharmacologic modulation of the target and longevity and thereby validate the target at some level. We considered the gene targets of many pharmacologic interventions hypothesized to enhance human longevity and explored how many variants there are in those targets that affect gene function (eg, as expression quantitative trait loci). We also determined whether variants in genes associated with longevity-related phenotypes affect gene function or are in linkage disequilibrium with variants that do, and whether pharmacologic studies point to compounds exhibiting activity against those genes. Our results are somewhat ambiguous, suggesting that integrating genetic association study results with functional genomic and pharmacologic studies is necessary to shed light on genetically mediated targets for longevity-enhancing drugs. Such integration will require more sophisticated data sets, phenotypic definitions, and bioinformatics approaches to be useful. Longevity, Mortality, Human Aging The identification of interventions, such as nutritional supplements, specific diets, and drugs that can reduce age-related disease risk and enhance longevity, is receiving a great deal of attention. The reasons for this are not just rooted in an age-old fascination with mortality, but also the belief that it might be possible to slow the aging process, simultaneously reducing the risk of many age-related diseases and morbidities, maintaining health, and ultimately increasing longevity (1–5). However, the identification of relevant targets for the development of longevity-enhancing drugs, such as specific genes or proteins, is complicated by the fact that human longevity and the aging process are complex and ultimately influenced by a number of genetic and nongenetic factors (3,6–9). This makes it difficult to identify compelling longevity-enhancing drug targets because the effects of any one potential gene or protein target could be obscured by the effects of others. There are strategies to identify longevity-enhancing drug targets that overcome this complexity, and many have been used with some level of success. For example, many researchers have studied longevity in nonhuman species since relevant experiments can be performed in ways that are not feasible in humans. These studies range from the comparison of, for example, genes and their expression levels across species exhibiting variation in life span (10–13), exploring natural variation in life span among individual animals within a species (14–16), using contrived gene manipulation techniques (such as knocking out a gene or controlling its expression using various genetic engineering strategies) and assessing their effects on life span (17,18), or simply screening drugs against individual animals to see which have a positive effect (19,20). The biological insights into the aging processes from these studies have been varied, with many offering important observations on evolutionarily conserved processes involved in aging. However, given the fundamental differences between humans and other species at the molecular and physiologic levels, it is still an open question as to whether these insights can be readily translated into findings that can form the basis for human longevity-enhancing interventions (21). An alternative to identifying drug targets involving nonhuman species is to use human genetic studies, in particular genome-wide association studies (GWAS), which seek to identify naturally occurring DNA sequence variants that are associated with, for example, longevity, healthspan, and susceptibility/resistance to diseases. A number of GWAS have been pursued to date that have focused on human longevity, healthspan, and protection from disease (3,14,22–26). Unfortunately, the results of many of these studies have not been replicated, in part due to the multifactorial nature of human longevity, but also due to difficulties in assembling relevant cohorts necessary for such studies (eg, large numbers of very long-lived individuals; large cohorts with longitudinal data reflecting health trajectories over time, etc.) (27). Despite complications with many GWAS initiatives, it has been shown that a drug designed to modulate or affect a gene or protein target, which harbors variants associated with the specific disease that the drug was designed to treat, actually yields better outcomes during the drug development process than a drug that targets a gene that does not harbor such variants (28–31). This is plausible because naturally occurring genetic variations that have an impact on a phenotype of relevance must work through some mechanism that could, in theory, be modulated pharmacologically (32). Many success stories exist in which drugs have been developed that target or modulate genes harboring variants associated with a specific disease (see, eg, research on the development of Ivacaftor for cystic fibrosis (33) and PCSK9 inhibitors for treating hypercholesterolemia (34)). In fact, these successes are consistent with, and have motivated, the growing interest in tailoring medicine to individuals' genetic (and other) profiles via precision medicine initiatives (35,36). Identifying drug targets based on genetic association study results is not trivial, however, because the mere association of a genetic variant with a phenotype, especially one as complex as longevity, is insufficient. One must identify the actual molecular mechanism or process through which the variant alters the phenotype before a genetic association can reveal a viable drug target. Unfortunately, many variants found to be associated with longevity-related phenotypes via GWAS—as well as most other phenotypes—do not actually reside in genes or their more obvious surrounding regulatory elements. As an example, these variants may reside in intergenic regions or regulatory elements, which are not well characterized, making their immediate functional effects hard to discern (37). In addition, the genes that harbor variants are not always found to be amenable to pharmacologic modulation (ie, they might not be “druggable”) (38). Finally, many variants associated with a disease or trait, even those in genes that are thought to be druggable, do not implicate or suggest specific or obvious mechanisms for pharmacologic modulation. For example, it might not be obvious whether or not a variant causes overexpression of a gene in a specific tissue of relevance whose pharmacologic inhibition would lead to consistent and favorable phenotypic outcomes (39). As a result, the use of genetic information to identify or prioritize drug targets is likely to require integrated approaches which draw on the insights from a number of disciplines beyond genetics, including molecular, systems and evolutionary biology, genomics, pharmacology, and chemoinformatics (see the Discussion section for more detail) (2,3,27,40). One approach to determining whether a variant found to be associated with, for example, longevity is likely to reveal a viable drug target is to determine whether that variant is also known to influence, or correlate with, a molecular phenotype that could be amenable to pharmacologic modulation in a tissue of relevance. For example, if the associated variant is known to affect the expression level of a gene (ie, is an “expression quantitative trait loci” or “eQTL”) or the abundance of a particular protein (ie, is a “pQTL”) in muscle, cardiac, or brain cells, there is a possibility that the variant influences this molecular phenotype in a causal pathway leading from the variant to the longevity phenotype. Evidence for a causation would make that molecular phenotype a logical longevity-enhancing drug target (41). In fact, databases are available that catalog variants that have been found to be eQTLs, pQTLs, or other molecular phenotypes, as in the GTex database (42). In addition, statistical strategies have been developed to test the hypothesis that, for example, an eQTL, or other molecular (or “intermediate”) phenotype, is in a causal pathway leading from the relevant variant to an overt, clinically meaningful, phenotype like longevity (43). We surveyed the available literature and interrogated a number of resources focused on associations with genetic variants, and their known effects, to determine whether the research community might be able to exploit information about genetic associations involving longevity and longevity-related phenotypes to identify possible longevity-enhancing drug targets. We pursued this in two ways. We first identified a number of drugs thought to be candidates for enhancing longevity in humans based on their effects on longevity in nonhuman species, their mechanisms of action, and/or their impacts on age-related diseases. We then determined whether there was evidence that those drugs modulate or target genes harboring variants associated with human longevity-associated phenotypes and/or a potential mechanism amenable to pharmacologic modulation (eg, if the variants are known eQTLs or pQTLs.) We also identified individual variants found to be associated with human longevity, healthspan, and disease protection based on GWAS (3,22–25,44). We then determined whether these longevity phenotype-associated variants, or more precisely the alternate alleles at the locus harboring the variants, are associated with age-related disease phenotypes. We also determined whether these variants reside in druggable genes, are known QTLs (eQTLs, pQTLs, etc.), or are in linkage disequilibrium (LD > 0.8) with variants that are QTLs and/or associated with other phenotypes. We cataloged eQTLs in LD with the sourced eQTLs, as well as variants associated with longevity phenotypes because they give an indication of how complex a regulatory setting a target gene may be operating within. For example, if perturbations within a gene induced by naturally occurring sequence variants have ripple effects involving a number of other genes, then modulating that gene pharmacologically could affect multiple pathways or molecular networks, for better or worse. For genes harboring associated variants, we also determined whether there were pharmacology studies published in the chemoinformatics literature suggesting that a drug or compound exhibited activity against genes whose DNA sequences were homologous to that gene (45,46). Activity against a homologous gene sequence may suggest that the drug or compound in question may also exhibit activity against the gene harboring the associated variant. It may also indicate that, if the homologous gene has a similar function to the gene harboring the associated variant, the modulation of that gene may affect the longevity-related phenotype. We emphasize eQTLs as relevant molecular phenotypes in much of our analyses because they have received the most attention in the literature, have the most information about their influence on different tissues, and have the most resources cataloging them. In addition, by focusing on eQTLs in potential target genes and their LD relationships to other variants, we expose the potential that a variant associated with longevity could reveal a molecular mechanism worthy of scrutiny as a drug target. We admit that there may be other variants in the genes of interest that are not themselves eQTLs, nor in LD with eQTLs, that may actually induce or contribute to an as-yet uncharacterized molecular function that could be pharmacologically modulated. We also emphasize that the definition of longevity is widely debated and a crucially important topic for putting aspects of our findings into perspective. We make no attempt to resolve this debate but rather use the published data based on the authors' definitions of longevity to make broader claims about genetic information and putative longevity-enhancing drugs and drug targets. Figure 1 provides a schematic summarizing our sources of information for longevity-associated variants as well as putative longevity-enhancing drugs. Figure 1 also provides the main databases and query tools used in our analyses, which are discussed in greater detail in the Methods section. Figure 1. Open in new tabDownload slide Schematic of the resources used for both determining: (i) if a gene harboring a longevity-associated variant is a reasonable drug target and (ii) if there is genetic evidence supporting the targets of potential longevity-enhancing drugs. Note that numbers in parentheses denote references. Figure 1. Open in new tabDownload slide Schematic of the resources used for both determining: (i) if a gene harboring a longevity-associated variant is a reasonable drug target and (ii) if there is genetic evidence supporting the targets of potential longevity-enhancing drugs. Note that numbers in parentheses denote references. Methods Longevity Drug and Compound Data Sources There are many drugs and compounds that have been hypothesized to influence human longevity based on a wide variety of studies (see, eg, the DrugAge database (47)). We limited the number of drugs we considered in the present analysis to those receiving the most attention based on our internet and literature searchers, although we are pursuing more complete analyses of a larger collection of drugs. Note that many “drugs” we list are actually experimental compounds not yet approved for use but are rather in various stages of development. We first considered the drugs and compounds found to significantly increase longevity in mice from the NIA-sponsored Interventions Testing Program (ITP) (20). The ITP follows a rigorous standard protocol to test drugs and compounds for their effects on mouse longevity. We also considered drugs and compounds that have been proposed to be evaluated in human clinical trials based on the credibility of the published science behind them. URLs and relevant references with information describing these efforts are provided in Supplementary Tables where appropriate. Finally, we considered the drugs and compounds ranking highly as likely to affect human longevity based on the systems biology and cross-species analysis of Fuentealba and colleagues (48) because the authors did not include more comprehensive human genetic association study result information in their otherwise very thorough analysis of candidacy and properties of the drug targets. Identifying Variants and Their Associations in the Gene Targets of Longevity-Enhancing Drugs For each putative longevity-enhancing drug and compound we considered, we identified the primary gene targets of those drugs and compounds using the Therapeutic Target Database (TTD) (45). We emphasize that TTD, although well curated, does not contain exhaustive information about drug targets that could be obtained from an analysis of, for example, the downstream effects of a drug on genes in a particular pathway. For each gene target, we used the LinDA web-based query tools (49) to determine the number of eQTL variants within them that could reflect compelling genetically mediated molecular phenotypes for drug development purposes (eg, pharmacologic modulation studies of the expression of a gene that varies naturally between long-lived and short-lived individuals or between carriers and noncarriers of specific genetic variants). Note that we used conventional statistical significance criteria also used on the website to determine eQTL status, though of course it would be important to explore how the use of different criteria would change our findings. For each of the eQTL variants we also used the LinDA query tools to identify variants that were in LD with these eQTLs (LD > 0.8) that were associated with (i) longevity; (ii) diseases and other clinical phenotypes; and/or (iii) other molecular phenotypes (eQTLs; sQTLs [splicing QTLS]; aseQTLs [allele-specific expression QTLs]; polyAQTLs [alternative polyA QTLs]; repeatQTLs [repeats expansion expression-level QTLs]; pQTLs: dhsQTL [DnaseI hypersensitive sites QTLs]; hQTLs [Histone modifications QTLs]; mQTLs [DNA methylation QTLs]). Variants in LD with eQTLs were based on the use of the European cohort from the 1000 Genomes Project (50). Note that we chose an LD cutoff of 0.8 because the effect sizes of most variants associated with longevity are weak. If a true functional variant is in weak LD with a variant with a weak effect on longevity, it is difficult to argue that the influence of that variant on longevity is due to the molecular phenotype induced by the variant for which it is in weak LD. Of course, further studies assuming different LD strengths could be revealing and should be pursued. We did not weigh the evidence for reported phenotypic associations, but merely point to published studies claiming an association with a particular phenotype. Further exploration is needed to accurately assess the strength of the evidence for each association and how it may support the belief that the gene harboring that variant is a reasonable drug target. For eQTL information, we summarized studies involving different tissues using the GTex database (42). To summarize genetic association information, we summed the number of associated variants (for longevity and other phenotypes, including the molecular phenotypes) falling into various categories and report these categories here. Associated Variants With Longevity, Healthspan, and Disease Protection Sources We gathered information about genetic variants associated with human longevity, healthspan, and protection against disease from multiple publications. For variants associated with longevity, we used the recent review by Partridge and colleagues focusing on variants with replication studies (3,22,51–54), the meta-analysis of GWAS studies by Sebastiani and colleagues (22), and the GWAS study of parental life span using the UK Biobank and LifeGen study data by Timmers and colleagues (23). For variants associated with healthspan, we used the study involving the UK Biobank by Zenin and colleagues (24) as well as the study on TTR gene variants by Hornstrup and colleagues (25). For genes harboring rare variants that appear to protect individuals from getting certain diseases, we used the list in the review by Harper and colleagues (44). Characteristics and Drug Information for Genes Harboring Variants Associated With Longevity-Related Phenotypes For variants associated with longevity, healthspan, and protection against diseases, we first identified the genes reported to harbor, be near, or modulated by, the variant from the publications cited. We then determined these genes' druggability based on information in Chembl (45) and TTD (46). We determined if the associated variants were themselves eQTLs using the LinDA resources (49). We also used the LinDA resources to determine whether the associated variants were in LD (>0.8) with variants associated with longevity, other age-related phenotypes, or various molecular phenotypes (eg, eQTLs or pQTLs). To capture additional information about the various tissues affected by the eQTLs, we used the LDLink resources and query tools using the European cohort information of the 1000 Genomes Project (50,55). We queried each of the single-nucleotide polymorphisms (SNPs) in LD with other variants associated with various phenotypes, and summed the count of disease/trait associations across different disease categories to get a total number of SNPs. When eQTLS were in LD with a longevity phenotype-associated variant, we also catalogued the various tissues that these eQTLs affected—based on the GTex database (42). For each protein-coding gene harboring the variants, we identified its protein equivalent in UniProt (56) using the BioConductor package “Uniprot.ws.” Known and experimental drugs targeting these proteins were identified with custom functions querying a downloaded SQLite instance of the ChEMBL database (Version 24) (45). The drug-target annotation functions developed for this step have been implemented as an R software package (Girke and colleagues, manuscript in review). Representative drugs were provided for any gene encoding druggable proteins identified from both TTD and ChEMBL. Because many genes in the human genome are part of gene families, we included in an extra panel of our drug-target annotation routine all nearest neighbor proteins. They had to share with each protein, encoded by a variant harboring gene, a sequence identity of at least 90% based on the UniRef90 entries in UniProt. Including nearest neighbor protein sequences is important because closely related proteins are usually targeted by the same drugs. Yet, drug development and screening efforts can only focus on one or a few targets within a protein family. Thus, incorporating these family relationships reduces the false-negative rate of our approach. Results Candidate Longevity-Enhancing Drugs and Compounds ITP drugs We first considered the drugs and compounds shown to have an effect on longevity in mice from the ITP (20). Table 1 summarizes the results. For some drugs and compounds, multiple target proteins are listed in the TTD (46). We note that experimental evidence may not suggest that a drug exhibits activity against all of these targets. In addition, information in the TTD (and other databases) may simply be wrong. Note that despite its having a positive effect on longevity based on ITP studies, we did not include the dietary supplement Protandim (a mixture of milk thistle, bacopa extract, ashwagandha, green tea extract, and turmeric extract) because it is not a defined active small molecule with a specific target as indicated in the TTD. Protandim may affect a number of genes possibly relevant to longevity based on association studies; however, from Table 1, it can be seen that none of the drugs and compounds with an effect on mice interact with human gene targets that harbor variants associated with longevity. However, some drugs target genes that harbor variants associated with age-related diseases (eg, 17-alpha-estradiol and target gene ESR1). In addition, it is important to point out that 17-alpha-estradiol and acarbose exhibited sex-specific effects in mice based on the ITP studies, complicating their relationships to a target gene or protein. Many other target genes harbor eQTLs and other variants that are in LD with many other phenotypes and eQTLs that affect longevity-related tissues (eg, acarbose targets gene MGAM with variants in LD with eQTLs affecting the expression levels of genes in the brain). These findings do not suggest that the drugs found to affect longevity, as defined by the ITP, in mice will not affect human longevity, but rather that variation in the genes they target are not overtly associated with human longevity. Table 1. Human Genetic Information Related to Drugs That Exhibited Statistically Significant Positive Effects on Mouse Longevity From the NIA-Sponsored Interventions Testing Program (20) Drug/Compound Information Variants Associations Involving Variants in LD With Target Gene eQTLs eQTL Tissues Drug Gene Target TTD Mechanism Indication eQTLs # LD Long Age Rel Other LD eQTLs LD pQTLs LD mQTLs LD Other Adipose Artery Brain Heart Muscle Skin WB Total Rapamycin OPRK1 A C 8 185 0 0 0 27 0 8 12 0 0 59 0 0 0 0 59 Rapamycin MTOR I C 24 1,753 0 22 7 548 0 143 217 0 0 2 0 9 17 212 437 Rapamycin FKBP1A B C 33 524 0 0 0 178 0 16 82 15 32 1 0 9 27 1 132 17Aalpha estradiol ESR1 A M 15 890 0 42 8 48 0 206 47 0 53 0 0 1 3 0 204 17-alpha-estradiol ESR2 A M 28 881 0 10 25 360 0 81 284 0 0 58 9 226 486 76 1,348 Acarbose MGAM M D 41 3,167 0 1 2 4,789 26 143 1,256 0 0 1,599 0 0 0 9 2,134 NDGA ERBB2 M P 12 531 0 0 25 569 0 159 134 7 0 0 110 0 161 0 736 Drug/Compound Information Variants Associations Involving Variants in LD With Target Gene eQTLs eQTL Tissues Drug Gene Target TTD Mechanism Indication eQTLs # LD Long Age Rel Other LD eQTLs LD pQTLs LD mQTLs LD Other Adipose Artery Brain Heart Muscle Skin WB Total Rapamycin OPRK1 A C 8 185 0 0 0 27 0 8 12 0 0 59 0 0 0 0 59 Rapamycin MTOR I C 24 1,753 0 22 7 548 0 143 217 0 0 2 0 9 17 212 437 Rapamycin FKBP1A B C 33 524 0 0 0 178 0 16 82 15 32 1 0 9 27 1 132 17Aalpha estradiol ESR1 A M 15 890 0 42 8 48 0 206 47 0 53 0 0 1 3 0 204 17-alpha-estradiol ESR2 A M 28 881 0 10 25 360 0 81 284 0 0 58 9 226 486 76 1,348 Acarbose MGAM M D 41 3,167 0 1 2 4,789 26 143 1,256 0 0 1,599 0 0 0 9 2,134 NDGA ERBB2 M P 12 531 0 0 25 569 0 159 134 7 0 0 110 0 161 0 736 Notes: TTD mechanism = mechanism of action per the Therapeutic Target Database, where A = agonist, I = inhibitor, B = binder, and M = modulator (TTD) (46). Indication = indication of drug on disease where C = CAS, multiple myeloma, M = menopause, D = diabetes, cardiovascular disease, and P = prostate cancer. eQTLs = number of eQTLs in the gene target based on the LinDA eGENE query tool (49). # LD = number of variants in linkage disequilibrium (LD > 0.8 among the 100 genomes European cohort) with the eQTL variants in the gene target per the LinDA eGENE query. Long = number LD variants associated Longevity from the literature based on the LinDA eGENE GWAS summary. Age Rel = comparable number LD variants with GWAS associations to age-related diseases (cancer, cardiovascular disease, metabolic diseases such as diabetes, osteoporosis, and other age-related bone diseases, and Alzheimer's and Parkinson's diseases). Other = number LD variants with GWAS associations associated other phenotypes; LD eQ, LD pQ, LD mQ, LD Other = number of variants eQTL, pQTL and mQTLs themselves in LD (>0.8) with variants with eQTLs in the gene target based on the LinDA molecular QTL summary. eQTL Tissues = number of variants associated with the Gene Target in GTex that affect certain tissues where WB = whole blood and Total = sum of eQTLs including all other tissues. Open in new tab Table 1. Human Genetic Information Related to Drugs That Exhibited Statistically Significant Positive Effects on Mouse Longevity From the NIA-Sponsored Interventions Testing Program (20) Drug/Compound Information Variants Associations Involving Variants in LD With Target Gene eQTLs eQTL Tissues Drug Gene Target TTD Mechanism Indication eQTLs # LD Long Age Rel Other LD eQTLs LD pQTLs LD mQTLs LD Other Adipose Artery Brain Heart Muscle Skin WB Total Rapamycin OPRK1 A C 8 185 0 0 0 27 0 8 12 0 0 59 0 0 0 0 59 Rapamycin MTOR I C 24 1,753 0 22 7 548 0 143 217 0 0 2 0 9 17 212 437 Rapamycin FKBP1A B C 33 524 0 0 0 178 0 16 82 15 32 1 0 9 27 1 132 17Aalpha estradiol ESR1 A M 15 890 0 42 8 48 0 206 47 0 53 0 0 1 3 0 204 17-alpha-estradiol ESR2 A M 28 881 0 10 25 360 0 81 284 0 0 58 9 226 486 76 1,348 Acarbose MGAM M D 41 3,167 0 1 2 4,789 26 143 1,256 0 0 1,599 0 0 0 9 2,134 NDGA ERBB2 M P 12 531 0 0 25 569 0 159 134 7 0 0 110 0 161 0 736 Drug/Compound Information Variants Associations Involving Variants in LD With Target Gene eQTLs eQTL Tissues Drug Gene Target TTD Mechanism Indication eQTLs # LD Long Age Rel Other LD eQTLs LD pQTLs LD mQTLs LD Other Adipose Artery Brain Heart Muscle Skin WB Total Rapamycin OPRK1 A C 8 185 0 0 0 27 0 8 12 0 0 59 0 0 0 0 59 Rapamycin MTOR I C 24 1,753 0 22 7 548 0 143 217 0 0 2 0 9 17 212 437 Rapamycin FKBP1A B C 33 524 0 0 0 178 0 16 82 15 32 1 0 9 27 1 132 17Aalpha estradiol ESR1 A M 15 890 0 42 8 48 0 206 47 0 53 0 0 1 3 0 204 17-alpha-estradiol ESR2 A M 28 881 0 10 25 360 0 81 284 0 0 58 9 226 486 76 1,348 Acarbose MGAM M D 41 3,167 0 1 2 4,789 26 143 1,256 0 0 1,599 0 0 0 9 2,134 NDGA ERBB2 M P 12 531 0 0 25 569 0 159 134 7 0 0 110 0 161 0 736 Notes: TTD mechanism = mechanism of action per the Therapeutic Target Database, where A = agonist, I = inhibitor, B = binder, and M = modulator (TTD) (46). Indication = indication of drug on disease where C = CAS, multiple myeloma, M = menopause, D = diabetes, cardiovascular disease, and P = prostate cancer. eQTLs = number of eQTLs in the gene target based on the LinDA eGENE query tool (49). # LD = number of variants in linkage disequilibrium (LD > 0.8 among the 100 genomes European cohort) with the eQTL variants in the gene target per the LinDA eGENE query. Long = number LD variants associated Longevity from the literature based on the LinDA eGENE GWAS summary. Age Rel = comparable number LD variants with GWAS associations to age-related diseases (cancer, cardiovascular disease, metabolic diseases such as diabetes, osteoporosis, and other age-related bone diseases, and Alzheimer's and Parkinson's diseases). Other = number LD variants with GWAS associations associated other phenotypes; LD eQ, LD pQ, LD mQ, LD Other = number of variants eQTL, pQTL and mQTLs themselves in LD (>0.8) with variants with eQTLs in the gene target based on the LinDA molecular QTL summary. eQTL Tissues = number of variants associated with the Gene Target in GTex that affect certain tissues where WB = whole blood and Total = sum of eQTLs including all other tissues. Open in new tab Proposed longevity-enhancing clinical trial drugs We identified multiple drugs that have been proposed as potential longevity-enhancing compounds and for which some claim about them being evaluated in a clinical trial has been made (see Supplementary Tables for references). Supplementary Table 1 describes the results. None of the reported drugs targets a gene that harbors a variant associated with longevity. A few genes (eg, Alki5i, alternatively named TGFBR1) harbor variants that have been shown to be associated with age-related diseases, but the reproducibility of these associations needs to be considered and explored. Many of the drugs listed in Supplementary Table 1 target gene products that harbor variants that are themselves eQTLs, are in LD with variants that affect gene function, or are associated with age-related diseases and phenotypes (eg, Fisetin and the FABG gene; J147 and the ATP5A1 gene). This provides evidence that a rich genetically mediated set of phenomena exists that could make these genes even more compelling longevity-enhancing drug targets, either through their ability to stave off age-related diseases, slow the aging rate, or both, if explored in greater depth. Highly ranked drugs by Fuentealba and colleagues Fuentealba and colleagues (48) conducted a series of analyses to evaluate the evidence that certain drugs target genes that, if modulated, are likely to affect fundamental processes implicated in aging and longevity. These analyses leveraged state-of-the-art systems biology analyses and databases and resulted in two lists of prioritized drugs. The first list (Table 1 in Fuentealba and colleagues (48)) considers drugs whose gene targets contribute to processes and networks of relevance to longevity and aging. Of the drugs in this list, six drugs had been shown to influence longevity in nonhuman species (resveratrol, genistein, simvastatin, epigallocatechin gallate, celecoxib, and sirolimus). The second list of drugs (Table 2 in Fuentealba and colleagues (48)) was based on multiple criteria including their reported biological activity. Of the drugs in this list, three drugs had been shown to influence longevity in nonhuman species: trichostatin, geldanamycin, and celecoxib. Despite the sophistication of the approach taken to identify candidate drugs for enhancing human longevity, Fuentealba and colleagues (48) did not consider human genetic support in the form of GWAS, eQTL, and other association studies. We note that we could not identify information necessary to conduct our assessments for a few of the drugs listed by Fuentealba and colleagues (48) including cAMP, epigallocatechin gallate, dorsomorphin, doxorubicin, selenium, indole-3 carbinol, cisplatin, and etoposide. Also, the potential side effects of many of these drugs in humans need further attention given their use as chemotherapeutic agents. Table 2. Human Genetic Information Related to Drugs Identified by Fuentealba and Colleagues as Being Good Candidate for Promoting Healthy Aging Based on These Drugs' Multiple Levels of Biological Action (48) Drug/Compound Information Variants Associations Involving Variants in LD With Target Gene eQTLs eQTL Tissues Drug Extend Toxic Status Gene Target Mechanism eQTLs # LD Long Age Rel Other LD eQTLs LD pQTLs LD mQTLs LD Other Adipose Artery Brain Heart Muscle Skin WB Sum Tanespimycin N N I HSP90AA1 I 19 401 0 0 4 103 0 18 57 35 4 3 0 92 250 0 393 Imatinib N N A KIT I 8 511 0 0 11 133 0 133 42 0 0 0 0 0 0 0 114 Imatinib N N A PDGFRB I 22 425 0 1 7 160 0 404 106 0 32 0 22 1 0 0 107 Imatinib N N A ABL1 I 18 734 0 0 3 32 0 7 9 0 3 0 4 0 0 0 19 Sunitinib N N A KDR M 13 309 0 0 0 16 0 4 0 0 3 5 0 0 0 0 812 Trichostatin Y N E HDAC1 I 15 1,054 0 0 1 50 0 3 11 4 4 5 4 2 5 0 32 Geldanamycin Y N I HSP90AA1 I 19 401 0 0 4 103 0 18 57 0 4 3 0 92 250 0 358 Sorafenib N N A KDR M 13 309 0 0 0 16 0 4 0 0 3 5 0 0 0 0 812 Sorafenib N N A KIT M 8 511 0 0 11 133 0 133 42 0 0 0 0 0 0 0 114 Sorafenib N N A PDGFRB M 22 425 0 1 7 160 0 404 106 0 32 0 22 1 0 0 107 Dasatinib N N A SRC I 20 216 0 0 2 273 0 46 25 0 0 0 0 0 0 0 6 Dasatinib N N A ABL1 I 18 734 0 0 3 32 0 7 9 0 3 0 4 0 0 0 19 Dasatinib N N A LCK I 15 1,792 0 0 0 52 2 28 19 0 0 10 0 0 0 0 36 Dasatinib N N A FYN I 34 618 0 1 5 193 0 56 46 0 184 0 1 0 0 0 373 Erlotinib N N A EGFR I 17 87 0 1 0 23 0 6 0 0 0 8 9 7 146 0 245 Celecoxib Y N A PTGS2 I 17 1,154 0 0 10 194 5 21 130 0 0 0 0 0 0 0 9 Drug/Compound Information Variants Associations Involving Variants in LD With Target Gene eQTLs eQTL Tissues Drug Extend Toxic Status Gene Target Mechanism eQTLs # LD Long Age Rel Other LD eQTLs LD pQTLs LD mQTLs LD Other Adipose Artery Brain Heart Muscle Skin WB Sum Tanespimycin N N I HSP90AA1 I 19 401 0 0 4 103 0 18 57 35 4 3 0 92 250 0 393 Imatinib N N A KIT I 8 511 0 0 11 133 0 133 42 0 0 0 0 0 0 0 114 Imatinib N N A PDGFRB I 22 425 0 1 7 160 0 404 106 0 32 0 22 1 0 0 107 Imatinib N N A ABL1 I 18 734 0 0 3 32 0 7 9 0 3 0 4 0 0 0 19 Sunitinib N N A KDR M 13 309 0 0 0 16 0 4 0 0 3 5 0 0 0 0 812 Trichostatin Y N E HDAC1 I 15 1,054 0 0 1 50 0 3 11 4 4 5 4 2 5 0 32 Geldanamycin Y N I HSP90AA1 I 19 401 0 0 4 103 0 18 57 0 4 3 0 92 250 0 358 Sorafenib N N A KDR M 13 309 0 0 0 16 0 4 0 0 3 5 0 0 0 0 812 Sorafenib N N A KIT M 8 511 0 0 11 133 0 133 42 0 0 0 0 0 0 0 114 Sorafenib N N A PDGFRB M 22 425 0 1 7 160 0 404 106 0 32 0 22 1 0 0 107 Dasatinib N N A SRC I 20 216 0 0 2 273 0 46 25 0 0 0 0 0 0 0 6 Dasatinib N N A ABL1 I 18 734 0 0 3 32 0 7 9 0 3 0 4 0 0 0 19 Dasatinib N N A LCK I 15 1,792 0 0 0 52 2 28 19 0 0 10 0 0 0 0 36 Dasatinib N N A FYN I 34 618 0 1 5 193 0 56 46 0 184 0 1 0 0 0 373 Erlotinib N N A EGFR I 17 87 0 1 0 23 0 6 0 0 0 8 9 7 146 0 245 Celecoxib Y N A PTGS2 I 17 1,154 0 0 10 194 5 21 130 0 0 0 0 0 0 0 9 Notes: See Table 1. Extend = evidence that the drug can increase life span in model species per Fuentealba and colleagues (48); toxic = evidence exists that the drug is toxic per Fuentealba and colleagues (48). Extend: Y = yes, N = no. Toxic: Y = yes, N = no. Status: I = Investigational, A = approved, E = experimental. Mechanism: I = inhibitor, M = modulator. Open in new tab Table 2. Human Genetic Information Related to Drugs Identified by Fuentealba and Colleagues as Being Good Candidate for Promoting Healthy Aging Based on These Drugs' Multiple Levels of Biological Action (48) Drug/Compound Information Variants Associations Involving Variants in LD With Target Gene eQTLs eQTL Tissues Drug Extend Toxic Status Gene Target Mechanism eQTLs # LD Long Age Rel Other LD eQTLs LD pQTLs LD mQTLs LD Other Adipose Artery Brain Heart Muscle Skin WB Sum Tanespimycin N N I HSP90AA1 I 19 401 0 0 4 103 0 18 57 35 4 3 0 92 250 0 393 Imatinib N N A KIT I 8 511 0 0 11 133 0 133 42 0 0 0 0 0 0 0 114 Imatinib N N A PDGFRB I 22 425 0 1 7 160 0 404 106 0 32 0 22 1 0 0 107 Imatinib N N A ABL1 I 18 734 0 0 3 32 0 7 9 0 3 0 4 0 0 0 19 Sunitinib N N A KDR M 13 309 0 0 0 16 0 4 0 0 3 5 0 0 0 0 812 Trichostatin Y N E HDAC1 I 15 1,054 0 0 1 50 0 3 11 4 4 5 4 2 5 0 32 Geldanamycin Y N I HSP90AA1 I 19 401 0 0 4 103 0 18 57 0 4 3 0 92 250 0 358 Sorafenib N N A KDR M 13 309 0 0 0 16 0 4 0 0 3 5 0 0 0 0 812 Sorafenib N N A KIT M 8 511 0 0 11 133 0 133 42 0 0 0 0 0 0 0 114 Sorafenib N N A PDGFRB M 22 425 0 1 7 160 0 404 106 0 32 0 22 1 0 0 107 Dasatinib N N A SRC I 20 216 0 0 2 273 0 46 25 0 0 0 0 0 0 0 6 Dasatinib N N A ABL1 I 18 734 0 0 3 32 0 7 9 0 3 0 4 0 0 0 19 Dasatinib N N A LCK I 15 1,792 0 0 0 52 2 28 19 0 0 10 0 0 0 0 36 Dasatinib N N A FYN I 34 618 0 1 5 193 0 56 46 0 184 0 1 0 0 0 373 Erlotinib N N A EGFR I 17 87 0 1 0 23 0 6 0 0 0 8 9 7 146 0 245 Celecoxib Y N A PTGS2 I 17 1,154 0 0 10 194 5 21 130 0 0 0 0 0 0 0 9 Drug/Compound Information Variants Associations Involving Variants in LD With Target Gene eQTLs eQTL Tissues Drug Extend Toxic Status Gene Target Mechanism eQTLs # LD Long Age Rel Other LD eQTLs LD pQTLs LD mQTLs LD Other Adipose Artery Brain Heart Muscle Skin WB Sum Tanespimycin N N I HSP90AA1 I 19 401 0 0 4 103 0 18 57 35 4 3 0 92 250 0 393 Imatinib N N A KIT I 8 511 0 0 11 133 0 133 42 0 0 0 0 0 0 0 114 Imatinib N N A PDGFRB I 22 425 0 1 7 160 0 404 106 0 32 0 22 1 0 0 107 Imatinib N N A ABL1 I 18 734 0 0 3 32 0 7 9 0 3 0 4 0 0 0 19 Sunitinib N N A KDR M 13 309 0 0 0 16 0 4 0 0 3 5 0 0 0 0 812 Trichostatin Y N E HDAC1 I 15 1,054 0 0 1 50 0 3 11 4 4 5 4 2 5 0 32 Geldanamycin Y N I HSP90AA1 I 19 401 0 0 4 103 0 18 57 0 4 3 0 92 250 0 358 Sorafenib N N A KDR M 13 309 0 0 0 16 0 4 0 0 3 5 0 0 0 0 812 Sorafenib N N A KIT M 8 511 0 0 11 133 0 133 42 0 0 0 0 0 0 0 114 Sorafenib N N A PDGFRB M 22 425 0 1 7 160 0 404 106 0 32 0 22 1 0 0 107 Dasatinib N N A SRC I 20 216 0 0 2 273 0 46 25 0 0 0 0 0 0 0 6 Dasatinib N N A ABL1 I 18 734 0 0 3 32 0 7 9 0 3 0 4 0 0 0 19 Dasatinib N N A LCK I 15 1,792 0 0 0 52 2 28 19 0 0 10 0 0 0 0 36 Dasatinib N N A FYN I 34 618 0 1 5 193 0 56 46 0 184 0 1 0 0 0 373 Erlotinib N N A EGFR I 17 87 0 1 0 23 0 6 0 0 0 8 9 7 146 0 245 Celecoxib Y N A PTGS2 I 17 1,154 0 0 10 194 5 21 130 0 0 0 0 0 0 0 9 Notes: See Table 1. Extend = evidence that the drug can increase life span in model species per Fuentealba and colleagues (48); toxic = evidence exists that the drug is toxic per Fuentealba and colleagues (48). Extend: Y = yes, N = no. Toxic: Y = yes, N = no. Status: I = Investigational, A = approved, E = experimental. Mechanism: I = inhibitor, M = modulator. Open in new tab Supplementary Table 2 (reflecting drugs in Table 1 of Feuntealba and colleagues (48)) and Table 2 (reflecting drugs in Table 2 of Feuntealba and colleagues (48)) provide the results of our assessments of the genetic support for the two lists of drugs. It is interesting that none of the drugs/compounds target a gene that harbors variants associated with longevity. However, all of the target genes harbor eQTLs, suggesting that functional variants affect those genes. In addition, several of the drugs and compounds target genes, which contain variants in LD with eQTLs that are associated with many age-related diseases and additional functional variants such as eQTLs, pQTLs, and mQTLs. For those target genes enriched for eQTLs, many of the tissues affected by these eQTLs are important in aging (eg, the NOS2 gene targeted by resveratrol; the HSP90AA1 gene targeted by tanespimycin and geldanamycin). Variants Associated With Longevity-Related Phenotypes Variants associated with longevity The review on aging research by Partridge and colleagues (3) discusses a number of variants in specific genes that have been shown to be strongly associated with human longevity. Table 3 provides our assessment of those variants and genes. We find that at least two of the genes harboring longevity-associated variants are not considered druggable because they are noncoding genes and hence not considered in the TTD (LINC02227 and USP2-AS1). Two of the variants are themselves eQTLs, suggesting that they could reveal mechanisms for their association with longevity that could motivate the genes they reside in as potential drug targets (FOXO3A and RAD50/IL13). Many of the variants were in LD with eQTLs, and other variants were associated with a wide variety of phenotypes, with the exception of the rs28926173 variant in the MC2R gene and the rs139137459 variant in USP2-AS1. These SNPs do not appear to be in strong LD with other variants of functional significance, raising questions about the biology behind their associations with longevity. Interestingly, two of the genes harboring longevity-associated variants have been the focus of pharmacologic studies (eg, the #Ch columns in Table 3): FOXO3A and MC2R. Further exploration of the studies focused on FOXO3A suggests that resveratrol (which has been extensively studied and considered a candidate longevity-enhancing drug despite not exhibiting effects on longevity in mice) has an effect on that gene (57) and that the efficacy of corticotropin administration is influenced by variants in the MC2R gene (58). The eQTL effects of the longevity-associated FOXO3A variant rs10457180 are on tibial nerve and artery tissue, making their relevance to pharmacologic modulation and potential connection to resveratrol in need of further investigation. Table 3. Variant Effect Annotations and Drug-Target Information on Variants Found to Be Associated With Human Longevity as Reviewed by Partridge and Colleagues (3) Associated Variant Information Druggable? Annotations Associations Involving Variants in LD With Target SNP Chem Studies on Gene SNP Gene Refs Chrom PCh TTD eQTL? # LD Long Age Rel Other LD eQ LD pQ LD mQ LD O #Ch #Ch A #TTD #I/M #Ant rs6857 APOE 22 19 N Y N 1 0 18 2 0 0 0 0 0 0 2 2 0 rs2149954 LINC02227 51 5 N N Y 34 2 10 0 1 0 1 3 0 0 0 0 0 rs10457180 FOXO3A 52 6 Y N Y 26 0 17 8 2 0 1 0 9 2 0 0 0 rs2706372 RAD50/IL13 53 5 Y N Y 106 0 0 17 4 0 18 5 0 6 8 5 1 rs2892613 MC2R 54 18 Y Y N 1 0 0 0 0 0 0 0 34 4 1 0 0 rs139137459 USP2-AS1 54 11 N N N 1 0 0 0 0 0 0 0 0 0 0 0 0 Associated Variant Information Druggable? Annotations Associations Involving Variants in LD With Target SNP Chem Studies on Gene SNP Gene Refs Chrom PCh TTD eQTL? # LD Long Age Rel Other LD eQ LD pQ LD mQ LD O #Ch #Ch A #TTD #I/M #Ant rs6857 APOE 22 19 N Y N 1 0 18 2 0 0 0 0 0 0 2 2 0 rs2149954 LINC02227 51 5 N N Y 34 2 10 0 1 0 1 3 0 0 0 0 0 rs10457180 FOXO3A 52 6 Y N Y 26 0 17 8 2 0 1 0 9 2 0 0 0 rs2706372 RAD50/IL13 53 5 Y N Y 106 0 0 17 4 0 18 5 0 6 8 5 1 rs2892613 MC2R 54 18 Y Y N 1 0 0 0 0 0 0 0 34 4 1 0 0 rs139137459 USP2-AS1 54 11 N N N 1 0 0 0 0 0 0 0 0 0 0 0 0 Notes: See Table 1. SNP = SNP found to exhibit a statistically significant association with human longevity. Druggable PCh, TTD = the gene harboring the associated variants status as “druggable” according to ChEMBL (45) and the TTD (46), respectively, where Y = yes and N = no. eQTL = whether or not the associated variant is an eQTL based on GTEX query, where Y = yes and N = no (42). Chem Studies on Genes: #Ch = number of ChEMBL (45) entries with reported activity against gene sequences homologous to the sequence of the gene harboring the longevity-associated variant. #Ch A = number of ChEMBL entries with in-depth annotation information without leveraging paralogs, #TTD = number of entries for the gene in the TTD (46). #I/M = number of drugs in the TTD that modulate or agonize the gene. #Ant = number of drugs in the TTD antagonize the gene. Open in new tab Table 3. Variant Effect Annotations and Drug-Target Information on Variants Found to Be Associated With Human Longevity as Reviewed by Partridge and Colleagues (3) Associated Variant Information Druggable? Annotations Associations Involving Variants in LD With Target SNP Chem Studies on Gene SNP Gene Refs Chrom PCh TTD eQTL? # LD Long Age Rel Other LD eQ LD pQ LD mQ LD O #Ch #Ch A #TTD #I/M #Ant rs6857 APOE 22 19 N Y N 1 0 18 2 0 0 0 0 0 0 2 2 0 rs2149954 LINC02227 51 5 N N Y 34 2 10 0 1 0 1 3 0 0 0 0 0 rs10457180 FOXO3A 52 6 Y N Y 26 0 17 8 2 0 1 0 9 2 0 0 0 rs2706372 RAD50/IL13 53 5 Y N Y 106 0 0 17 4 0 18 5 0 6 8 5 1 rs2892613 MC2R 54 18 Y Y N 1 0 0 0 0 0 0 0 34 4 1 0 0 rs139137459 USP2-AS1 54 11 N N N 1 0 0 0 0 0 0 0 0 0 0 0 0 Associated Variant Information Druggable? Annotations Associations Involving Variants in LD With Target SNP Chem Studies on Gene SNP Gene Refs Chrom PCh TTD eQTL? # LD Long Age Rel Other LD eQ LD pQ LD mQ LD O #Ch #Ch A #TTD #I/M #Ant rs6857 APOE 22 19 N Y N 1 0 18 2 0 0 0 0 0 0 2 2 0 rs2149954 LINC02227 51 5 N N Y 34 2 10 0 1 0 1 3 0 0 0 0 0 rs10457180 FOXO3A 52 6 Y N Y 26 0 17 8 2 0 1 0 9 2 0 0 0 rs2706372 RAD50/IL13 53 5 Y N Y 106 0 0 17 4 0 18 5 0 6 8 5 1 rs2892613 MC2R 54 18 Y Y N 1 0 0 0 0 0 0 0 34 4 1 0 0 rs139137459 USP2-AS1 54 11 N N N 1 0 0 0 0 0 0 0 0 0 0 0 0 Notes: See Table 1. SNP = SNP found to exhibit a statistically significant association with human longevity. Druggable PCh, TTD = the gene harboring the associated variants status as “druggable” according to ChEMBL (45) and the TTD (46), respectively, where Y = yes and N = no. eQTL = whether or not the associated variant is an eQTL based on GTEX query, where Y = yes and N = no (42). Chem Studies on Genes: #Ch = number of ChEMBL (45) entries with reported activity against gene sequences homologous to the sequence of the gene harboring the longevity-associated variant. #Ch A = number of ChEMBL entries with in-depth annotation information without leveraging paralogs, #TTD = number of entries for the gene in the TTD (46). #I/M = number of drugs in the TTD that modulate or agonize the gene. #Ant = number of drugs in the TTD antagonize the gene. Open in new tab The meta-analysis of four GWAS studies described by Sebastiani and colleagues (22) led to the identification of 8 longevity-associated variants, including an APOE variant. Supplementary Table 3 provides our assessment of those variants. We note that Sebastiani and colleagues (22) did compile some information about eQTLs in LD with those variants but did not have access to the most recently developed tools and databases. Unfortunately, only one of the genes harboring longevity-associated variants is thought to be druggable, though a few of the genes have variants, which are in LD with other variants exhibiting functional effects (eg, the rs7185375 variant in the SIAH1 gene and the rs72834698 variant in the HIST1H2BD gene). Timmers and colleagues (23) conducted a very large GWAS on parental life spans as a proxy for an individual life span among participants in the UK Biobank Study and LifeGen study data (23). This study focused on natural variation in life span and not exceptional longevity as a unique phenotype. They identified 12 associated variants using standard GWAS (reviewed in our Supplementary Table 4a) as well as 7 additional variants using a Bayesian analysis that accommodated mortality risk factors in the association test with longevity (reviewed in our Supplementary Table 4b). Our assessment of these variants again suggests that many are within genes that are not thought to be druggable, or at least within gene products for which no known or experimental drugs are available, despite many being in LD with a variant associated with a wide variety of age-related diseases, phenotypes, and functional effects. A few of the genes harboring longevity-associated variants have been the focus of a large number of pharmacologic studies (eg, the HTT gene harboring the rs61348208 variants and the ATXN2 gene harboring the rs11065979 variant). Timmers and colleagues (23) also pursued an in-depth set of analyses seeking to identify genes whose expression levels are in likely variant-mediated causal pathways involving longevity based on Mendelian randomization tests (43). Table 4 provides the results of our assessments of these variants. Note that the authors identified multiple genes whose expression levels passed statistical criteria for being in a causal pathway from an associated variant to longevity. We present information about the reported associated variants only, but note that other variants in each of the implicated genes that might be of interest. Unfortunately, only a few of these variants implicate genes in a causal pathway involving longevity that are thought to be druggable, although all of them, being eQTLs themselves, are in LD with a number of variants associated with other phenotypes and a wide variety of functional variants. Many of the tissues affected by the longevity-associated eQTL variants are relevant to aging (eg, skeletal muscle, associated variant rs4970836 within gene CELSR2). Table 4. Variant Effect Annotations and Drug-Target Information on eQTL Variants With Statistically Significant Evidence That They Are in a Causal Pathway Associated With Human Longevity Based on Analyses by Timmers and Colleagues (23) Associated Variant Information Drug? Annot. Associations Involving Variants in LD With Target SNP Chem Studies on Gene SNP Gene Chr Example Tissue Number of Tissues PCh TTD eQTL? # LD Long Age Rel Other LD eQ LD pQ LD mQ LD O #Ch #Ch A #TTD #I/M #Ant rs429358 AC006126.4 19 Testis 1 N N Y 1 2 14 32 0 0 1 0 0 0 0 0 — rs429358 CEACAM19 19 Thyroid 2 N N Y 1 2 14 32 0 0 1 0 0 0 0 — — rs429358 APOC1 19 Esophagus mucosa 1 N N Y 1 2 14 32 0 0 1 0 0 0 0 — — rs11065979 ALDH2 12 Sun-exposed skin 1 Y Y Y 24 0 95 93 22 0 39 1 0 1 3 2 6 rs11065979 CUX2 12 Muscle skeletal 1 N N Y 24 0 95 93 22 0 39 1 0 0 0 — — rs1230666 AP4B1-AS1 1 Transformed fibroblasts 1 N N Y 1 0 1 2 0 0 0 0 353 0 0 — — rs3130507 CCHCR1 6 Sun-exposed skin 2 N N Y 18 0 0 0 2 0 5 2 0 0 0 — — rs3130507 PSORS1C1 6 Artery aorta 4 N N Y 18 0 0 0 2 0 5 2 0 0 0 — — rs4970836 CELSR2 1 Muscle skeletal 3 N N Y 11 0 16 56 52 0 2 11 0 0 0 — — rs4970836 PSRC1 1 Liver 3 N N Y 11 0 16 56 52 0 2 11 0 0 0 — — rs6224 FES 15 Transformed fibroblasts 9 N Y Y 10 0 6 0 11 0 3 0 1,835 0 2 0 2 rs6224 FURIN 15 Artery aorta 1 N Y Y 10 0 6 0 11 0 3 0 511 0 3 0 3 rs6224 RCCD1 15 Brain cerebellum 2 N N Y 10 0 6 0 11 0 3 0 0 0 0 — — rs113160991 PMS2P3 7 Esophagus muscularis 6 N N Y 13 0 0 0 30 0 1 15 0 0 0 — — rs8042849 RP11-650L12.2 15 Lung 2 N N Y 41 2 13 149 13 0 10 16 0 0 0 — — rs111333005 SLC22A1 6 Pituitary 6 N N Y 33 0 0 0 6 0 0 0 437 0 0 — — Associated Variant Information Drug? Annot. Associations Involving Variants in LD With Target SNP Chem Studies on Gene SNP Gene Chr Example Tissue Number of Tissues PCh TTD eQTL? # LD Long Age Rel Other LD eQ LD pQ LD mQ LD O #Ch #Ch A #TTD #I/M #Ant rs429358 AC006126.4 19 Testis 1 N N Y 1 2 14 32 0 0 1 0 0 0 0 0 — rs429358 CEACAM19 19 Thyroid 2 N N Y 1 2 14 32 0 0 1 0 0 0 0 — — rs429358 APOC1 19 Esophagus mucosa 1 N N Y 1 2 14 32 0 0 1 0 0 0 0 — — rs11065979 ALDH2 12 Sun-exposed skin 1 Y Y Y 24 0 95 93 22 0 39 1 0 1 3 2 6 rs11065979 CUX2 12 Muscle skeletal 1 N N Y 24 0 95 93 22 0 39 1 0 0 0 — — rs1230666 AP4B1-AS1 1 Transformed fibroblasts 1 N N Y 1 0 1 2 0 0 0 0 353 0 0 — — rs3130507 CCHCR1 6 Sun-exposed skin 2 N N Y 18 0 0 0 2 0 5 2 0 0 0 — — rs3130507 PSORS1C1 6 Artery aorta 4 N N Y 18 0 0 0 2 0 5 2 0 0 0 — — rs4970836 CELSR2 1 Muscle skeletal 3 N N Y 11 0 16 56 52 0 2 11 0 0 0 — — rs4970836 PSRC1 1 Liver 3 N N Y 11 0 16 56 52 0 2 11 0 0 0 — — rs6224 FES 15 Transformed fibroblasts 9 N Y Y 10 0 6 0 11 0 3 0 1,835 0 2 0 2 rs6224 FURIN 15 Artery aorta 1 N Y Y 10 0 6 0 11 0 3 0 511 0 3 0 3 rs6224 RCCD1 15 Brain cerebellum 2 N N Y 10 0 6 0 11 0 3 0 0 0 0 — — rs113160991 PMS2P3 7 Esophagus muscularis 6 N N Y 13 0 0 0 30 0 1 15 0 0 0 — — rs8042849 RP11-650L12.2 15 Lung 2 N N Y 41 2 13 149 13 0 10 16 0 0 0 — — rs111333005 SLC22A1 6 Pituitary 6 N N Y 33 0 0 0 6 0 0 0 437 0 0 — — Notes: See Table 3. A dash (—) in a cell indicates that we could not find information about the gene or variants in the gene with the resources we used. Drug = Druggable? Annot. = Annotations. PCh: Y = yes, N = no. TTD: Y = yes, N = no. eQTL: Y = yes, N = no. Open in new tab Table 4. Variant Effect Annotations and Drug-Target Information on eQTL Variants With Statistically Significant Evidence That They Are in a Causal Pathway Associated With Human Longevity Based on Analyses by Timmers and Colleagues (23) Associated Variant Information Drug? Annot. Associations Involving Variants in LD With Target SNP Chem Studies on Gene SNP Gene Chr Example Tissue Number of Tissues PCh TTD eQTL? # LD Long Age Rel Other LD eQ LD pQ LD mQ LD O #Ch #Ch A #TTD #I/M #Ant rs429358 AC006126.4 19 Testis 1 N N Y 1 2 14 32 0 0 1 0 0 0 0 0 — rs429358 CEACAM19 19 Thyroid 2 N N Y 1 2 14 32 0 0 1 0 0 0 0 — — rs429358 APOC1 19 Esophagus mucosa 1 N N Y 1 2 14 32 0 0 1 0 0 0 0 — — rs11065979 ALDH2 12 Sun-exposed skin 1 Y Y Y 24 0 95 93 22 0 39 1 0 1 3 2 6 rs11065979 CUX2 12 Muscle skeletal 1 N N Y 24 0 95 93 22 0 39 1 0 0 0 — — rs1230666 AP4B1-AS1 1 Transformed fibroblasts 1 N N Y 1 0 1 2 0 0 0 0 353 0 0 — — rs3130507 CCHCR1 6 Sun-exposed skin 2 N N Y 18 0 0 0 2 0 5 2 0 0 0 — — rs3130507 PSORS1C1 6 Artery aorta 4 N N Y 18 0 0 0 2 0 5 2 0 0 0 — — rs4970836 CELSR2 1 Muscle skeletal 3 N N Y 11 0 16 56 52 0 2 11 0 0 0 — — rs4970836 PSRC1 1 Liver 3 N N Y 11 0 16 56 52 0 2 11 0 0 0 — — rs6224 FES 15 Transformed fibroblasts 9 N Y Y 10 0 6 0 11 0 3 0 1,835 0 2 0 2 rs6224 FURIN 15 Artery aorta 1 N Y Y 10 0 6 0 11 0 3 0 511 0 3 0 3 rs6224 RCCD1 15 Brain cerebellum 2 N N Y 10 0 6 0 11 0 3 0 0 0 0 — — rs113160991 PMS2P3 7 Esophagus muscularis 6 N N Y 13 0 0 0 30 0 1 15 0 0 0 — — rs8042849 RP11-650L12.2 15 Lung 2 N N Y 41 2 13 149 13 0 10 16 0 0 0 — — rs111333005 SLC22A1 6 Pituitary 6 N N Y 33 0 0 0 6 0 0 0 437 0 0 — — Associated Variant Information Drug? Annot. Associations Involving Variants in LD With Target SNP Chem Studies on Gene SNP Gene Chr Example Tissue Number of Tissues PCh TTD eQTL? # LD Long Age Rel Other LD eQ LD pQ LD mQ LD O #Ch #Ch A #TTD #I/M #Ant rs429358 AC006126.4 19 Testis 1 N N Y 1 2 14 32 0 0 1 0 0 0 0 0 — rs429358 CEACAM19 19 Thyroid 2 N N Y 1 2 14 32 0 0 1 0 0 0 0 — — rs429358 APOC1 19 Esophagus mucosa 1 N N Y 1 2 14 32 0 0 1 0 0 0 0 — — rs11065979 ALDH2 12 Sun-exposed skin 1 Y Y Y 24 0 95 93 22 0 39 1 0 1 3 2 6 rs11065979 CUX2 12 Muscle skeletal 1 N N Y 24 0 95 93 22 0 39 1 0 0 0 — — rs1230666 AP4B1-AS1 1 Transformed fibroblasts 1 N N Y 1 0 1 2 0 0 0 0 353 0 0 — — rs3130507 CCHCR1 6 Sun-exposed skin 2 N N Y 18 0 0 0 2 0 5 2 0 0 0 — — rs3130507 PSORS1C1 6 Artery aorta 4 N N Y 18 0 0 0 2 0 5 2 0 0 0 — — rs4970836 CELSR2 1 Muscle skeletal 3 N N Y 11 0 16 56 52 0 2 11 0 0 0 — — rs4970836 PSRC1 1 Liver 3 N N Y 11 0 16 56 52 0 2 11 0 0 0 — — rs6224 FES 15 Transformed fibroblasts 9 N Y Y 10 0 6 0 11 0 3 0 1,835 0 2 0 2 rs6224 FURIN 15 Artery aorta 1 N Y Y 10 0 6 0 11 0 3 0 511 0 3 0 3 rs6224 RCCD1 15 Brain cerebellum 2 N N Y 10 0 6 0 11 0 3 0 0 0 0 — — rs113160991 PMS2P3 7 Esophagus muscularis 6 N N Y 13 0 0 0 30 0 1 15 0 0 0 — — rs8042849 RP11-650L12.2 15 Lung 2 N N Y 41 2 13 149 13 0 10 16 0 0 0 — — rs111333005 SLC22A1 6 Pituitary 6 N N Y 33 0 0 0 6 0 0 0 437 0 0 — — Notes: See Table 3. A dash (—) in a cell indicates that we could not find information about the gene or variants in the gene with the resources we used. Drug = Druggable? Annot. = Annotations. PCh: Y = yes, N = no. TTD: Y = yes, N = no. eQTL: Y = yes, N = no. Open in new tab Variants associated with healthspan Zenin and colleagues recently pursued a GWAS exploring the age at which an individual likely succumbed to disease and was in a healthy state prior to that age over their life and identified 12 “healthspan”-associated variants (24). Along with these health-enhancing variants, we also gathered information about a variant in the TTR gene that has been shown to influence healthspan and longevity as discussed by Hornstrup and colleagues (25). Supplementary Table 5 provides the results, with the first 12 rows corresponding to the variants identified by Zenin and colleagues (24) and the last row corresponding to the TTR variant discussed by Hornstrup and colleagues (25). As with the longevity-associated variants, many of the variants found to be associated with healthspan are not located in genes coding for proteins for which drug-target information is available, although many themselves are eQTLs or in LD with eQTLs and variants associated with nonlongevity phenotypes. Variants shown to be protective against diseases We finally considered genes that harbor multiple rare variants that have been associated with protection against diseases (ie, they seem to confer health benefits to those that possess them) as reviewed Harper and colleagues (44). These protective variants may or may not protect against all or most age-related diseases, however. Given the number and rarity of the variants exhibiting protective effects, we considered the properties of the genes they were identified in, rather than the individual variants themselves. Supplementary Table 6 provides the results. All but one (SLC30A8) is considered to be druggable, which makes sense since these genes have gathered a great deal of interest as drug targets. There are many eQTLs and variants associated with phenotypes other than longevity, but whether these variants are in LD with the rare variants thought to have functional effects, and induce the positive phenotypes they are associated with, needs further exploration. Discussion The genomics era has resulted in a number of major initiatives focusing on naturally occurring human genetic variation, such as the Human Genome Project (59), the International Hapmap Project (60), the 1000 Genomes Project (50), and The Cancer Genome Atlas (TCGA) project (61), among many others. We considered the utility of genetic information in prioritizing or validating drug targets for longevity-enhancing interventions. We identified drugs and compounds thought to have potential to enhance human longevity, collated naturally occurring variants in the gene and protein targets for these drugs, and looked to see whether these variants have been associated with longevity, or if they influence the molecular functions of those targets. We also brought together, from published literature, lists of variants allegedly associated with longevity, healthspan, or protection from disease, and asked if the genes they reside in are reasonable targets for drug development. The fact that we found that no drug hypothesized to modify human longevity targets a gene that harbors variants found to be associated with longevity to date and that no associated variants have led to a longevity-enhancing drug development campaign, suggests, among other things, that (i) many proposed drugs are not targeting relevant biology related to human longevity (at least as revealed by GWAS); (ii) the genetic associations from GWAS are too weak and ambiguous to reveal compelling targets; and/or (iii) more comprehensive data sets and studies are needed to make genetic association data “actionable” at some level. We believe that the third explanation is likely the best because we did find that there is an incredibly rich biology uncovered by the effects of genetic variants on mechanisms, like gene expression levels, that could be exploited in drug-target identification studies with more systematic analysis. In this light, our work can be seen as a starting point for more comprehensive assessments of genetically mediated biological targets for longevity-enhancing drugs. For example, we believe our work can motivate more sophisticated consideration of genetic association and, for example, eQTL and pQTL studies in work like that of Partridge and colleagues (48) and Cardoso and colleagues (62), which seek to integrate different sources of information in analyses designed to prioritize drugs and biomarkers for further study (27). In fact, a very recent study exploring the utility of genetic association studies in drug target analysis for immune-related diseases provides an excellent example of the type of integration that we feel is necessary (63). Unfortunately, the data sets and information that the authors exploited, including study results using assays on humans interrogating processes known to be of fundamental importance to immune diseases, are lacking with respect to human longevity. It is noteworthy, however, that the National Institutes on Aging (NIA) of the U.S. National Institutes of Health (NIH) have recently funded initiatives designed to generate more sophisticated data and methods that could enable longevity-enhancing drug-target identification and validation (eg, the Longevity Consortium (https://www.longevityconsortium.org) and the Longevity Genomics (https://www.longevitygenomics.org) initiatives. Given the hype surrounding genetic studies and the somewhat humbling results of our studies, which suggest no obvious connections between genetic associations and drugs currently hypothesized to enhance human longevity exist, we believe our work exposes a number of serious shortcomings with the use of genetic data for identifying, prioritizing, or validating drug targets for human longevity that are also touched on in the study by Fang and colleagues (63). We describe a few of these shortcomings below—many of which are relevant to our very specific analyses—but feel these descriptions are less of a focused critique of what we have produced and more of an indication of what needs to be done going forward, so that better integration of genetic information into bioinformatics analyses can be pursued (27). Exploitation of Results of GWAS Involving Other Ancestral Groups We used variant and LD information obtained from individuals of European descent, though many variants are population specific and/or exhibit different LD relationships in individuals of non-European descent. Consideration of Different Levels of LD We chose to only consider variants with an LD strength > 0.8 for target variants or those in LD with eQTLs within a gene. Different LD strengths could provide a different picture of the functional landscape of a gene. Consideration of the Direction of Effect of a Variant's Associations A variant could increase or decrease, for example, the expression level of a gene. If this variant is associated with a relevant phenotype as well, then the direction of effect on gene expression level could indicate whether a drug should enhance or antagonize the expression of the gene to achieve the same phenotypic effect. Leveraging Pleiotropy and Unpacking Diseases Associated With Variants We cataloged variants associated with many age-related diseases, but if many variants are associated with the same disease, this provides a different picture of the pleiotropic effects of the gene than if many variants are associated with different diseases. Unpacking the Number of Associations for a Gene We summed up the number of variants associated with different phenotypes, but the resulting sum may involve different variants in varying degrees of LD or variants in very strong LD. These two scenarios have different biological consequences, wherein variation induced by a gene's functional differences attributable to individual variants is due to a single haplotype that deviates from the others functionally or whether there are multiple haplotypes (alleles) that each differs from the others. In addition, the mere assignment of a variant to an individual gene can be problematic if the variant resides in DNA sequence that does not encode a particular gene or if the sequence does encode a gene but that gene is alternatively spliced such that the variant may not affect all forms of protein translated from that gene's sequence. Exploring the Effects of Multiple eQTLs Within a Gene A gene that harbors many eQTLs, pQTLs, etc. is likely to regulate a wider range of molecular phenomena. This could indicate that the gene participates in a network filled with feedback and redundancy mechanisms, which could affect its candidacy as a drug target. Making Better Use of Orthology Information Many drugs and compounds have been tested in model organisms for their effects on longevity, such as those pursued by the ITP (20), but the relevance of the effects observed in model species to humans is an open question. Exploring the degree of homology between nonhuman and human genes and incorporating this information into cross-species analyses may be useful in this context. Better Phenotyping and Indices of Health Individual life span is a very crude phenotype and does not capture the underlying “subclinical” aspects of health that might exist in people who die early of nongenetic causes (eg, accidents, malnutrition, war, etc.) nor what might be possessed by people who would have died earlier without extensive health care or a favorable but rare environment. Therefore, better measures of underlying robustness, vitality, and functional enhancements (eg, muscle strength, excellent vision, etc.) are needed for GWAS and related studies. Better Molecular Phenotyping of Longevity-Related Processes Disease-focused research communities often exploit extensive molecular phenotyping (eg, lipid biology in cardiovascular disease, inflammation in rheumatic disease, etc.) to help put drug targets and potential interventions into biological perspective. Researchers investigating longevity need better phenotyping of aging-related processes, such as “rate of aging measures” or measurable facets of the hallmarks of aging, that could be subjected to GWAS (64,65). This activity could lead to better biomarkers of aging to be considered in causal analyses of longevity (see below). Incorporating Biomarker Data eQTLs, pQTLs, and so forth capture the effects of variants on measurable molecular phenotypes. These molecular phenotypes could themselves be tested for association with longevity (eg, a gene's expression level in whole blood or skin may correlate with longevity). Many molecular phenotypes have been treated as biomarkers and tested for association with longevity and aging-related phenotypes (62). Information about whether such biomarkers are associated with longevity-related phenotypes could help solidify causal chains leading from a variant to a longevity-related phenotype, but the tissue in which that biomarker has been measured is important to consider. Note that systematically testing such causal chains for prioritization is crucial if there are many such potential causal connections (43). Exploiting the Power of Network Biology The role of a gene within a broader network of genes is important for placing drug candidates into context. For example, a gene may harbor a variant associated with a longevity phenotype, but its druggability has not been demonstrated yet. However, if that gene is known to modulate another gene in an extended causal chain leading from the variant to the longevity phenotype, then the gene that is modulated by the other could be thought of as a drug target. Thus, including network module and pathway information into studies like ours may be of crucial importance. As we have emphasized, although our efforts to compile and process relevant information on the genetic support for longevity-enhancing drug targets are hardly exhaustive, we pursued it to show the potential, and limitations, of the use of such information. We ultimately find that there is a great deal of potential in using genetic association information for longevity-enhancing drug-target studies, particularly with respect to prioritization and lead development. However, we also believe that more detailed and focused mining of the information, along with relevant query tool and resource development, will be necessary to have a broader impact. We hope that our efforts will motivate the pursuit of appropriate studies and tool development. Acknowledgments The authors want to thank the members of the Longevity Consortium, including associated NIA staff, for comments and suggestions as this work was being pursued. Again, we emphasize, however, that the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Funding This paper was published as part of a supplement sponsored and funded by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP. Conflict of Interest None reported. References 1. Vaiserman A , Lushchak O . Implementation of longevity-promoting supplements and medications in public health practice: achievements, challenges and future perspectives . J Transl Med . 2017 ; 15 : 160 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 2. Kaeberlein M , Rabinovitch PS , Martin GM . Healthy aging: the ultimate preventative medicine . Science. 2015 ; 350 : 1191 – 1193 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 3. Partridge L , Deelen J , Slagboom PE . Facing up to the global challenges of ageing . Nature. 2018 ; 561 : 45 – 56 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 4. Mahmoudi S , Xu L , Brunet A . Turning back time with emerging rejuvenation strategies . Nat Cell Biol . 2019 ; 21 : 32 – 43 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 5. Longo VD , Antebi A , Bartke A , et al. Interventions to slow aging in humans: are we ready? Aging Cell . 2015 ; 14 : 497 – 510 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 6. Broer L , van Duijn CM . GWAS and meta-analysis in aging/longevity . Adv Exp Med Biol . 2015 ; 847 : 107 – 125 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 7. Brooks-Wilson AR . Genetics of healthy aging and longevity . Hum Genet . 2013 ; 132 : 1323 – 1338 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 8. Crocco P , Montesanto A , Dato S , et al. Inter-individual variability in xenobiotic-metabolizing enzymes: implications for human aging and longevity . Genes (Basel) . 2019 ; 10 . doi: WorldCat Crossref 9. Dato S , Rose G , Crocco P , et al. The genetics of human longevity: an intricacy of genes, environment, culture and microbiome . Mech Ageing Dev . 2017 ; 165 ( Pt B ): 147 – 155 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 10. Ma S , Gladyshev VN . Molecular signatures of longevity: insights from cross-species comparative studies . Semin Cell Dev Biol . 2017 ; 70 : 190 – 203 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 11. Cohen AA . Aging across the tree of life: the importance of a comparative perspective for the use of animal models in aging . Biochim Biophys Acta Mol Basis Dis . 2018 ; 1864 : 2680 – 2689 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 12. Tian X , Seluanov A , Gorbunova V . Molecular mechanisms determining lifespan in short- and long-lived species . Trends Endocrinol Metab . 2017 ; 28 : 722 – 734 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 13. Singh PP , Demmitt BA , Nath RD , Brunet A . The genetics of aging: a vertebrate perspective . Cell . 2019 ; 177 : 200 – 220 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 14. Hook M , Roy S , Williams EG , et al. Genetic cartography of longevity in humans and mice: current landscape and horizons . Biochim Biophys Acta Mol Basis Dis . 2018 ; 1864 : 2718 – 2732 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 15. Bogue MA , Peters LL , Paigen B , et al. Accessing data resources in the mouse phenome database for genetic analysis of murine life span and health span . J Gerontol A Biol Sci Med Sci . 2016 ; 71 : 170 – 177 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 16. Ackert-Bicknell CL , Anderson LC , Sheehan S , et al. Aging research using mouse models . Curr Protoc Mouse Biol . 2015 ; 5 : 95 – 133 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 17. Zhang P , Judy M , Lee SJ , Kenyon C . Direct and indirect gene regulation by a life-extending FOXO protein in C. elegans: roles for GATA factors and lipid gene regulators . Cell Metab . 2013 ; 17 : 85 – 100 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 18. Ocampo A , Reddy P , Martinez-Redondo P , et al. In vivo amelioration of age-associated hallmarks by partial reprogramming . Cell . 2016 ; 167 : 1719 – 1733.e12 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 19. Ye X , Linton JM , Schork NJ , Buck LB , Petrascheck M . A pharmacological network for lifespan extension in Caenorhabditis elegans . Aging Cell. 2014 ; 13 : 206 – 215 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 20. Nadon NL , Strong R , Miller RA , Harrison DE . NIA Interventions Testing Program: investigating putative aging intervention agents in a genetically heterogeneous mouse model . EBioMed . 2017 ; 21 : 3 – 4 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 21. Hackam DG , Redelmeier DA . Translation of research evidence from animals to humans . JAMA . 2006 ; 296 : 1731 – 1732 . doi: Google Scholar PubMed WorldCat Crossref 22. Sebastiani P , Gurinovich A , Bae H , et al. Four genome-wide association studies identify new extreme longevity variants . J Gerontol A Biol Sci Med Sci . 2017 ; 72 : 1453 – 1464 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 23. Timmers PR , Mounier N , Lall K , et al. Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances . Elife. 2019 ; 8 . doi: WorldCat Crossref 24. Zenin A , Tsepilov Y , Sharapov S , et al. Identification of 12 genetic loci associated with human healthspan . Commun Biol . 2019 ; 2 : 41 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 25. Hornstrup LS , Frikke-Schmidt R , Nordestgaard BG , Tybjærg-Hansen A . Genetic stabilization of transthyretin, cerebrovascular disease, and life expectancy . Arterioscler Thromb Vasc Biol . 2013 ; 33 : 1441 – 1447 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 26. Sebastiani P , Solovieff N , Dewan AT , et al. Genetic signatures of exceptional longevity in humans . PLoS One . 2012 ; 7 : e29848 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 27. Schork NJ , Raghavachari N ; Workshop Speakers and Participants . Report: NIA workshop on translating genetic variants associated with longevity into drug targets . Geroscience . 2018 ; 40 : 523 – 538 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 28. Sanseau P , Agarwal P , Barnes MR , et al. Use of genome-wide association studies for drug repositioning . Nat Biotechnol . 2012 ; 30 : 317 – 320 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 29. Nelson MR , Tipney H , Painter JL , et al. The support of human genetic evidence for approved drug indications . Nat Genet . 2015 ; 47 : 856 – 860 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 30. Hurle MR , Nelson MR , Agarwal P , Cardon LR . Impact of genetically supported target selection on R&D productivity . Nat Rev Drug Discov . 2016 ; 15 : 596 – 597 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 31. King EA , Davis JW , Degner JF . Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval . bioRxiv . 2019 . doi: WorldCat Crossref 32. Plenge RM , Scolnick EM , Altshuler D . Validating therapeutic targets through human genetics . Nat Rev Drug Discov . 2013 ; 12 : 581 – 594 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 33. Feng LB , Grosse SD , Green RF , Fink AK , Sawicki GS . Precision medicine in action: the impact of ivacaftor on cystic fibrosis-related hospitalizations . Health Aff (Millwood) . 2018 ; 37 : 773 – 779 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 34. Paton DM . PCSK9 inhibitors: monoclonal antibodies for the treatment of hypercholesterolemia . Drugs Today (Barc). 2016 ; 52 : 183 – 192 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 35. Aronson SJ , Rehm HL . Building the foundation for genomics in precision medicine . Nature . 2015 ; 526 : 336 – 342 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 36. Zahn LM . Unleashing the power of precision medicine . Science. 2016 ; 354 : 1546 – 1548 . doi: WorldCat Crossref 37. Schork AJ , Thompson WK , Pham P , et al. ; Tobacco and Genetics Consortium; Bipolar Disorder Psychiatric Genomics Consortium; Schizophrenia Psychiatric Genomics Consortium . All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs . PLoS Genet . 2013 ; 9 : e1003449 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 38. Finan C , Gaulton A , Kruger FA , et al. The druggable genome and support for target identification and validation in drug development . Sci Transl Med. 2017 ; 9 . doi: WorldCat Crossref 39. Floris M , Olla S , Schlessinger D , Cucca F . Genetic-driven druggable target identification and validation . Trends Genet . 2018 ; 34 : 558 – 570 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 40. Oprea TI , Bologa CG , Brunak S , et al. Unexplored therapeutic opportunities in the human genome . Nat Rev Drug Discov . 2018 ; 17 : 377 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 41. Brown AA , Viñuela A , Delaneau O , Spector TD , Small KS , Dermitzakis ET . Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues . Nat Genet . 2017 ; 49 : 1747 – 1751 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 42. eGTEx_Project . Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease . Nat Genet. 2017 ; 49 : 1664 – 1670 . doi: Crossref Search ADS PubMed WorldCat Crossref 43. Zheng J , Baird D , Borges MC , et al. Recent developments in Mendelian randomization studies . Curr Epidemiol Rep . 2017 ; 4 : 330 – 345 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 44. Harper AR , Nayee S , Topol EJ . Protective alleles and modifier variants in human health and disease . Nat Rev Genet . 2015 ; 16 : 689 – 701 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 45. Gaulton A , Hersey A , Nowotka M , et al. The ChEMBL database in 2017 . Nucleic Acids Res . 2017 ; 45 : D945 – D954 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 46. Li YH , Yu CY , Li XX , et al. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics . Nucleic Acids Res . 2018 ; 46 : D1121 – D1127 . doi: Google Scholar PubMed WorldCat Crossref 47. Tacutu R , Thornton D , Johnson E , et al. Human ageing genomic resources: new and updated databases . Nucleic Acids Res . 2018 ; 46 : D1083 – D1090 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 48. Fuentealba M , Dönertaş HM , Williams R , Labbadia J , Thornton JM , Partridge L . Using the drug-protein interactome to identify anti-ageing compounds for humans . PLoS Comput Biol . 2019 ; 15 : e1006639 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 49. IRGB . LinDA (LINkage Disequilibrium-based Annotation) Browser . Cagliari, Province of Cagliari, Italy : Università degli Studi di Cagliari ; 2019 . http://linda.irgb.cnr.it/. COPAC 50. 1000_Genomes_Project_Consortium , Auton A , Brooks LD , Durbin RM , et al. A global reference for human genetic variation . Nature . 2015 ; 526 : 68 – 74 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 51. Deelen J , Beekman M , Uh HW , et al. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age . Hum Mol Genet . 2014 ; 23 : 4420 – 4432 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 52. Broer L , Buchman AS , Deelen J , et al. GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy . J Gerontol A Biol Sci Med Sci . 2015 ; 70 : 110 – 118 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 53. Flachsbart F , Ellinghaus D , Gentschew L , et al. Immunochip analysis identifies association of the RAD50/IL13 region with human longevity . Aging Cell . 2016 ; 15 : 585 – 588 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 54. Pilling LC , Kuo CL , Sicinski K , et al. Human longevity: 25 genetic loci associated in 389,166 UK biobank participants . Aging (Albany, NY) 2017 ; 9 : 2504 – 2520 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 55. Machiela MJ , Chanock SJ . LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants . Bioinformatics. 2015 ; 31 : 3555 – 3557 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 56. The_UniProt_Consortium . UniProt: the universal protein knowledgebase . Nucleic Acids Res. 2017 ; 45 : D158 – D169 . doi: Crossref Search ADS PubMed WorldCat Crossref 57. Kwon SH , Choi HR , Kang YA , Park KC . Depigmenting effect of resveratrol is dependent on FOXO3a activation without SIRT1 activation . Int J Mol Sci. 2017 ; 18 . doi: WorldCat Crossref 58. Ding YX , Zou LP , He B , Yue WH , Liu ZL , Zhang D . ACTH receptor (MC2R) promoter variants associated with infantile spasms modulate MC2R expression and responsiveness to ACTH . Pharmacogenet Genomics . 2010 ; 20 : 71 – 76 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 59. Green ED , Watson JD , Collins FS . Human genome project: twenty-five years of big biology . Nature. 2015 ; 526 : 29 – 31 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 60. International HapMap Consortium . The international hapMap project . Nature. 2003 ; 426 : 789 – 796 . doi: Crossref Search ADS PubMed WorldCat Crossref 61. Cancer Genome Atlas Research Network , Weinstein JN , Collisson EA , Mills GB , et al. The Cancer Genome Atlas Pan-Cancer analysis project . Nat Genet. 2013 ; 45 : 1113 – 1120 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 62. Cardoso AL , Fernandes A , Aguilar-Pimentel JA , et al. Towards frailty biomarkers: candidates from genes and pathways regulated in aging and age-related diseases . Ageing Res Rev . 2018 ; 47 : 214 – 277 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 63. Fang H , De Wolf H , Knezevic B , et al. ; ULTRA-DD Consortium . A genetics-led approach defines the drug target landscape of 30 immune-related traits . Nat Genet . 2019 ; 51 : 1082 – 1091 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 64. Marioni RE , Harris SE , Shah S , et al. The epigenetic clock and telomere length are independently associated with chronological age and mortality . Int J Epidemiol. 2016 . doi: WorldCat Crossref 65. López-Otín C , Blasco MA , Partridge L , Serrano M , Kroemer G . The hallmarks of aging . Cell. 2013 ; 153 : 1194 – 1217 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Pencina, Karol, M;Li,, Zhuoying;Montano,, Monty

doi: 10.1093/gerona/glz151pmid: 31724056

Abstract Background The use of circulating clinically routine biomarkers and volitional physical activity using wristband accelerometry in preclinical middle-aged adults may provide sensitive measures of physical function and predict sooner the onset of age- and HIV-related physical decline. Methods Nested cross-sectional cohort study of adult men 50–65 years old with HIV infection on potent antiretroviral therapy and uninfected control participants within the Boston metropolitan area. Gait speed derived from wristband accelerometry, gait speed derived from a standardized 6-minute walk test, cellular immune biomarker levels (CD4 T cell, CD8 T cell), and serum anabolic biomarker levels (total and free testosterone, and sex-hormone-binding globulin) were measured. Results Of the five measured biomarkers, four were significantly associated with volitional gait speed based on accelerometry, whereas only one was associated with gait speed based on the 6-minute walk test collected in a laboratory environment. Conclusion Levels of selected immune and anabolic biomarkers were associated with volitional physical activity in middle-aged individuals. Digital and circulating biomarkers may be useful in future studies designed to identify presymptomatic individuals at increased risk for age- and HIV-associated functional decline. Accelerometry, Gait speed, HIV, Digital biomarkers, Blood The trajectory of functional aging varies based on genetic and environmental factors that shape the life history of an individual, but can be generally characterized by a maintenance of functional capacity for decades that is followed by a steep decline in function occurring at variable times later in life (1,2). There is a growing interest in functional aging as an outcome of the balance between life stressors, both acute (eg, injury) and chronic conditions (eg, infection) and physiologic reserve, and capacity for compensation to delay stressor-driven functional decline. Identifying early biomarkers that reflect this dynamic balance and that precede clinical evidence for functional decline would be useful in preventative care designed to optimize healthy aging. The onset and burden of age-related morbidities (eg, renal failure, diabetes, loss in bone density, hypertension, heart disease) that limit healthy aging all occur at earlier ages and with a higher prevalence in people aging with HIV infection (PAWH), even despite achieving undetectable viral load with combination antiretroviral therapy (3). We recently described a cohort of 170 asymptomatic middle-aged (50–65 years old) men and women with or without HIV infection, the Muscle and Aging Treated Chronic HIV (MATCH) cohort (4). Although overall the HIV-infected participants had subclinical deficits in physical function with persistent inflammation and immune activation (4), the men did not differ significantly in their gait speed, based on a 6-minute walk test (6-MWT) that was administered in a laboratory setting. Provocatively, in a follow-up substudy, volitional gait speed (VGS) using wristband accelerometry revealed that male PAWH were slower and less active compared to uninfected men, despite similar 6-MWT results (5). This difference detected by activity monitors raises the question of whether preclinical changes in activity patterns using accelerometry as a digital biomarker might be correlated to circulating age-related biomarkers. In this study, middle-aged men with and without HIV (n = 46, 50% HIV+ men, age 50–65 years) without evidence for loss in functional performance were enrolled in a substudy to characterize physical activity profiles and gait speed under free-living conditions for three continuous weeks using wristband accelerometers. Circulating biomarkers for anabolic and immune status were measured in blood to identify associations with physical activity and gait speed. The objective of the current study was to determine whether levels of routinely collected biomarkers (ie, CD4 and CD8 T-cell count and serum free and total testosterone, and sex-hormone-binding globulin) vary in association with PA measured as motion detected by the accelerometer, including fragmented activity, activity defined as walking (54–144 steps per minute), and activity defined as running (>144 steps per minute), using a formula developed by Withings (Eva Roitmann, Withings, E-mail communication; March 28, 2018) and expressed as VGS using data from wristband accelerometry and gait speed measured using a standardized laboratory-based 6-MWT. We hypothesized that circulating biomarkers would be associated with volitional PA, which in a prior study distinguished HIV+ from HIV− individuals that were asymptomatic and did not differ in their 6-MWT results. Identifying digital and blood-based biomarkers for functional aging may help to inform the care of aging persons at increased risk for frailty. Methods Study Population Participants in this substudy consisted of 46 adults that included 23 men with HIV infection (HIV+) and 23 men without HIV infection (HIV−), all between the ages of 50–65 years and enrolled in the MATCH study (4) and who consented to this study. Eligible participants were required to have sufficient capacity to engage in activities of daily living (bathing, grooming) and to participate in functional assessment. Participants were excluded if they reported use of anabolic therapy in the last 6 months. Participants in this study were enrolled randomly from among subjects currently enrolled in the parental MATCH study. Subjects enrolled in MATCH were approached at their next MATCH study visit. Enrollment was balanced to achieve equivalent numbers of HIV+ men and controls. The MATCH cohort is a longitudinal observational study of middle-aged HIV+ individuals on effective antiretroviral therapy, along with aged-matched uninfected controls, all living in the Boston metropolitan area in Massachusetts (4). The parental study and this cross-sectional follow-up substudy sample have been recently described (4,5). All study procedures were approved by the Partners Human Research Committee Institutional Review Board. Data Collection Blood biomarkers that are routinely measured were chosen (CD4 T-cell count, CD8 T-cell count, free testosterone (FT), total testosterone (TT), and sex-hormone-binding globulin (SHBG) and were measured and reported as described (4,5). The accelerometer used in this study was the Nokia Pulse Ox, a consumer-grade triaxial accelerometer with published validity for step counts when compared to research-grade accelerometers (6,7). Data were collected in 1-minute intervals when any step activity was detected by the accelerometer, including low-intensity levels of activity that are not typically analyzed or accessible using commercial trackers. Mean gait speed in meters per second was based on the number of steps per minute and a formula using participant’s height in meters: ([steps × height × 0.414]/60) (8). Participants were instructed to wear the accelerometer on their nondominant wrist 24 hours per day, 7 days per week for 3 consecutive weeks, as described (5). Statistical Analysis Data distributions were inspected graphically and variables (VGS, 6-MWT, CD4 T cell, CD8 T cell, FT, TT, and SHBG) were log-transformed prior to analysis to remove the effect of skewness on the analysis. Crude association between biomarkers and volitional or laboratory-based gait speed was evaluated in univariate linear regression models. Furthermore, multivariate models were used to examine potential effect modification due to HIV serostatus and age. Slopes of the regressions with 95% CIs and R2 were reported to examine magnitude of the association. Correlations between gait speed measured by tracker and the 6-MWT were evaluated using Pearson’s coefficient. The hypotheses were constructed with proof-of-concept approach and p values were not adjusted due to multiple testing. Analyses were conducted using two-sided alpha level of 0.05 and performed using SAS, v.9.4 (SAS Institute, Cary, NC) and Stata, v15.0 (StataCorp LLC, College Station, TX). Results Baseline characteristics of the study participants have been recently published (4,5). Briefly, the baseline values for outcome and predictive variables were as follows: VGS mean = 0.48 ± 0.15 meters/second (m/s); laboratory gait speed from 6-MWT mean = 1.52 ± 0.27 m/s; CD8 T-cell mean = 608.20 ± 388.82 cells/µL; CD4 T-cell mean = 813.04 ± 313.95 cells/µL; FT mean = 15.22 ± 8.62 ng/dL; TT mean = 18.61 ± 10.38 nmol/L, and SHBG mean = 58.42 ± 28.90 nmol/L. Significant differences based on HIV status were observed for VGS, CD8 T cell, and CD4 T cells. There were no significant differences based on HIV status in laboratory gait speed, SHBG, TT, or FT (4,5). Correlation in our sample between gait speed measured by tracker and 6-MWT was r = .35 (in the HIV− group was r = .49 and in the HIV+ group was r = .15). The HIV+ men differed in VGS but not in the 6-MWT, as previously reported (5). Figure 1 illustrates the associations between levels of blood-based biomarkers (immune: CD8 T cell, CD4 T cell; anabolic: FT, TT, and SHBG) and gait speed based on wristband accelerometry (ie, VGS). Data for HIV positive and negative participants were combined as interaction between biomarkers and HIV status was nonsignificant (all ps > .2). CD8 T-cell levels declined in association with increasing VGS (R2 = .157, p = .006) (Figure 1A) and CD4 T-cell levels declined in association with VGS (R2 = .02, p = .327) (Figure 1B); however, this relationship was not statistically significant. Association between FT, TT, and SHBG and VGS was positive and had moderate strength (R2 = .10, p = .042; R2 = .11, p = .032; R2 = .09, p = .041, respectively) (Figure 1C–E). Figure 1. Open in new tabDownload slide Two-way scatterplots of log-transformed volitional gait speed with levels of log-transformed CD8 T cells (A), CD4 T cells (B), free testosterone (C), total testosterone (D), and SHBG (E). Linear fitted plots with 95% confidence intervals are shown for study participants (A–E). p values and R2 are extracted from univariate linear regression models. Figure 1. Open in new tabDownload slide Two-way scatterplots of log-transformed volitional gait speed with levels of log-transformed CD8 T cells (A), CD4 T cells (B), free testosterone (C), total testosterone (D), and SHBG (E). Linear fitted plots with 95% confidence intervals are shown for study participants (A–E). p values and R2 are extracted from univariate linear regression models. Results remained similar after adjusting for HIV serostatus and age with CD4 T cell showing statistically significant association with VGS in multivariate regression model (Table 1) Table 1. Linear Regression Model for Volitional Gait Speed Based on Blood Biomarkers Volitional Gait Speed Variable Univariate HIV-Adjusted HIV- and Age-Adjusted Domain β (95% CI) p value β (95% CI) p value β (95% CI) p value Immune CD8 T cell (n = 46) −0.209 (−0.357, −0.062) .006 −0.205 (−0.392, −0.017) .033 −0.198 (−0.389, −0.007) .042 CD4 T cell (n = 46) −0.127 (−0.385, 0.131) .327 −0.275 (−0.479, −0.072) .026 −0.313 (−0.605, −0.021) .037 Anabolic FT (n = 42) 0.190 (0.007, 0.374) .042 0.185 (0.003, 0.367) .046 0.183 (−0.023, 0.389) .079 TT (n = 42) 0.197 (0.018, 0.375) .032 0.202 (0.026, 0.378) .026 0.205 (0.006, 0.405) .044 SHBG (n = 46) 0.179 (0.007, 0.351) .041 0.210 (0.043, 0.377) .015 0.214 (0.031, 0.396) .023 Volitional Gait Speed Variable Univariate HIV-Adjusted HIV- and Age-Adjusted Domain β (95% CI) p value β (95% CI) p value β (95% CI) p value Immune CD8 T cell (n = 46) −0.209 (−0.357, −0.062) .006 −0.205 (−0.392, −0.017) .033 −0.198 (−0.389, −0.007) .042 CD4 T cell (n = 46) −0.127 (−0.385, 0.131) .327 −0.275 (−0.479, −0.072) .026 −0.313 (−0.605, −0.021) .037 Anabolic FT (n = 42) 0.190 (0.007, 0.374) .042 0.185 (0.003, 0.367) .046 0.183 (−0.023, 0.389) .079 TT (n = 42) 0.197 (0.018, 0.375) .032 0.202 (0.026, 0.378) .026 0.205 (0.006, 0.405) .044 SHBG (n = 46) 0.179 (0.007, 0.351) .041 0.210 (0.043, 0.377) .015 0.214 (0.031, 0.396) .023 Notes: Slopes, 95% CIs, and p values are extracted from univariate and adjusted linear regression models for volitional gait speed. All variables were log-transformed. FT = Free testosterone; TT = Total testosterone; SHBG = Sex-hormone-binding globulin. Open in new tab Table 1. Linear Regression Model for Volitional Gait Speed Based on Blood Biomarkers Volitional Gait Speed Variable Univariate HIV-Adjusted HIV- and Age-Adjusted Domain β (95% CI) p value β (95% CI) p value β (95% CI) p value Immune CD8 T cell (n = 46) −0.209 (−0.357, −0.062) .006 −0.205 (−0.392, −0.017) .033 −0.198 (−0.389, −0.007) .042 CD4 T cell (n = 46) −0.127 (−0.385, 0.131) .327 −0.275 (−0.479, −0.072) .026 −0.313 (−0.605, −0.021) .037 Anabolic FT (n = 42) 0.190 (0.007, 0.374) .042 0.185 (0.003, 0.367) .046 0.183 (−0.023, 0.389) .079 TT (n = 42) 0.197 (0.018, 0.375) .032 0.202 (0.026, 0.378) .026 0.205 (0.006, 0.405) .044 SHBG (n = 46) 0.179 (0.007, 0.351) .041 0.210 (0.043, 0.377) .015 0.214 (0.031, 0.396) .023 Volitional Gait Speed Variable Univariate HIV-Adjusted HIV- and Age-Adjusted Domain β (95% CI) p value β (95% CI) p value β (95% CI) p value Immune CD8 T cell (n = 46) −0.209 (−0.357, −0.062) .006 −0.205 (−0.392, −0.017) .033 −0.198 (−0.389, −0.007) .042 CD4 T cell (n = 46) −0.127 (−0.385, 0.131) .327 −0.275 (−0.479, −0.072) .026 −0.313 (−0.605, −0.021) .037 Anabolic FT (n = 42) 0.190 (0.007, 0.374) .042 0.185 (0.003, 0.367) .046 0.183 (−0.023, 0.389) .079 TT (n = 42) 0.197 (0.018, 0.375) .032 0.202 (0.026, 0.378) .026 0.205 (0.006, 0.405) .044 SHBG (n = 46) 0.179 (0.007, 0.351) .041 0.210 (0.043, 0.377) .015 0.214 (0.031, 0.396) .023 Notes: Slopes, 95% CIs, and p values are extracted from univariate and adjusted linear regression models for volitional gait speed. All variables were log-transformed. FT = Free testosterone; TT = Total testosterone; SHBG = Sex-hormone-binding globulin. Open in new tab In contrast to VGS, analyses examining 6-MWT showed only CD4 T-cell levels being significantly associated with outcome in HIV-adjusted and HIV- and age-adjusted models (univariate β = −0.138, p = .060; HIV-adjusted β = −0.195, p = .023; HIV- and age-adjusted β = −0.212, p = .017). Associations between gait speed calculated from 6-MWT and other biomarkers (CD8 T cell, FT, TT, and SHBG) were statistically not significant in any of the analyzed regressions (Table 2, Figure 2). Table 2. Linear Regression Model for 6-Minute Walk Test (6-MWT)-Derived Gait Speed and Blood Biomarkers 6-MWT Variable Univariate HIV-Adjusted HIV- and Age-Adjusted Domain β (95% CI) p value β (95% CI) p value β (95% CI) p value Immune CD8 T cell (n = 46) −0.060 (−0.151, 0.031) .191 −0.089 (−0.204, 0.026) .125 −0.093 (−0.210, 0.024) .115 CD4 T cell (n = 46) −0.138 (−0.283, 0.006) .060 −0.195 (−0.363, −0.028) .023 −0.212 (−0.385, −0.040) .017 Anabolic FT (n = 42) 0.049 (−0.067, 0.165) .398 0.049 (−0.068, 0.167) .402 0.065 (−0.067, 0.198) .325 TT (n = 42) 0.061 (−0.052, 0.174) .281 0.061 (−0.054, 0.176) .288 0.080 (−0.049, 0.209) .217 SHBG (n = 46) 0.058 (−0.045, 0.160) .261 0.061 (−0.044, 0.166) .251 0.078 (−0.036, 0.192) .175 6-MWT Variable Univariate HIV-Adjusted HIV- and Age-Adjusted Domain β (95% CI) p value β (95% CI) p value β (95% CI) p value Immune CD8 T cell (n = 46) −0.060 (−0.151, 0.031) .191 −0.089 (−0.204, 0.026) .125 −0.093 (−0.210, 0.024) .115 CD4 T cell (n = 46) −0.138 (−0.283, 0.006) .060 −0.195 (−0.363, −0.028) .023 −0.212 (−0.385, −0.040) .017 Anabolic FT (n = 42) 0.049 (−0.067, 0.165) .398 0.049 (−0.068, 0.167) .402 0.065 (−0.067, 0.198) .325 TT (n = 42) 0.061 (−0.052, 0.174) .281 0.061 (−0.054, 0.176) .288 0.080 (−0.049, 0.209) .217 SHBG (n = 46) 0.058 (−0.045, 0.160) .261 0.061 (−0.044, 0.166) .251 0.078 (−0.036, 0.192) .175 Notes: Slopes, 95% CLs, and p values are extracted from univariate and adjusted linear regression models for gait speed based on the 6-MWT. All variables were log-transformed. FT = Free testosterone; TT = Total testosterone; SHBG = Sex-hormone-binding globulin. Open in new tab Table 2. Linear Regression Model for 6-Minute Walk Test (6-MWT)-Derived Gait Speed and Blood Biomarkers 6-MWT Variable Univariate HIV-Adjusted HIV- and Age-Adjusted Domain β (95% CI) p value β (95% CI) p value β (95% CI) p value Immune CD8 T cell (n = 46) −0.060 (−0.151, 0.031) .191 −0.089 (−0.204, 0.026) .125 −0.093 (−0.210, 0.024) .115 CD4 T cell (n = 46) −0.138 (−0.283, 0.006) .060 −0.195 (−0.363, −0.028) .023 −0.212 (−0.385, −0.040) .017 Anabolic FT (n = 42) 0.049 (−0.067, 0.165) .398 0.049 (−0.068, 0.167) .402 0.065 (−0.067, 0.198) .325 TT (n = 42) 0.061 (−0.052, 0.174) .281 0.061 (−0.054, 0.176) .288 0.080 (−0.049, 0.209) .217 SHBG (n = 46) 0.058 (−0.045, 0.160) .261 0.061 (−0.044, 0.166) .251 0.078 (−0.036, 0.192) .175 6-MWT Variable Univariate HIV-Adjusted HIV- and Age-Adjusted Domain β (95% CI) p value β (95% CI) p value β (95% CI) p value Immune CD8 T cell (n = 46) −0.060 (−0.151, 0.031) .191 −0.089 (−0.204, 0.026) .125 −0.093 (−0.210, 0.024) .115 CD4 T cell (n = 46) −0.138 (−0.283, 0.006) .060 −0.195 (−0.363, −0.028) .023 −0.212 (−0.385, −0.040) .017 Anabolic FT (n = 42) 0.049 (−0.067, 0.165) .398 0.049 (−0.068, 0.167) .402 0.065 (−0.067, 0.198) .325 TT (n = 42) 0.061 (−0.052, 0.174) .281 0.061 (−0.054, 0.176) .288 0.080 (−0.049, 0.209) .217 SHBG (n = 46) 0.058 (−0.045, 0.160) .261 0.061 (−0.044, 0.166) .251 0.078 (−0.036, 0.192) .175 Notes: Slopes, 95% CLs, and p values are extracted from univariate and adjusted linear regression models for gait speed based on the 6-MWT. All variables were log-transformed. FT = Free testosterone; TT = Total testosterone; SHBG = Sex-hormone-binding globulin. Open in new tab Figure 2. Open in new tabDownload slide Two-way scatterplots of log-transformed lab-based gait speed using the 6-MWT with levels of log-transformed CD8 T cells (A), CD4 T cells (B), free testosterone (C), total testosterone (D), and SHBG (E). Linear fitted plots with 95% confidence intervals are shown for study participants (A–E). p values and R2 are extracted from univariate linear regression models. Figure 2. Open in new tabDownload slide Two-way scatterplots of log-transformed lab-based gait speed using the 6-MWT with levels of log-transformed CD8 T cells (A), CD4 T cells (B), free testosterone (C), total testosterone (D), and SHBG (E). Linear fitted plots with 95% confidence intervals are shown for study participants (A–E). p values and R2 are extracted from univariate linear regression models. Discussion This study of asymptomatic middle-aged men living with and without HIV infection revealed that physical activity measured using wristband accelerometry detects differences that are not yet evident in laboratory-based assessment and that physical activity levels measured using wristband accelerometry were associated with levels of immune and anabolic biomarkers measured in blood. Accelerometry-based gait speed, after adjusting for HIV serostatus, was associated with cell-surface immune biomarkers (CD8 and CD4 expressing T cells) and circulating anabolic biomarkers, that is, TT and FT and SHBG. When also adjusted for age, CD8 and CD4 expressing T cells, TT, and SHBG remained significant predictors. By contrast, gait speed based on the 6-MWT was only associated with CD4 T-cell levels, which remained significant when adjusted for HIV serostatus and age. Interestingly, correlation between the 6-MWT and VGS was lower in the male PAWH compared to the uninfected. This may reflect a reduced exercise tolerance and fatigability in the HIV+, as VGS reflects longer-term activity, whereas the 6-MWT is a shorter-term assessment. Future studies will need to directly confirm whether PAWH have reduced exercise tolerance and increased fatigability compared to their uninfected counterparts. Prior to the onset of a frail phenotype, multiple studies have observed a more rapid decline in gait speed and strength in PAWH compared to individuals of similar age that are not infected (9,10). Identifying convenient biomarkers that precede declines in gait speed and strength would provide an early window of intervention to delay (or prevent) impairment and frailty. Age-related decline in physical function can reflect the accumulation of impairments across multiple biological systems that eventually overcome compensatory reserve mechanisms resulting in clinical presentation and eventually overt disability. A conceptual model for functional aging was proposed by Lopez-Otin (11) wherein underlying biological aging (eg, molecular damage, defective repair, energy exhaustion, and nutrient sensing) and phenotypic aging (eg, body composition, energetics, homeostatic mechanisms, and brain health) accumulate until a threshold is reached with subsequent overt presentation of functional impairment. Whether HIV infection accelerates or alters the deficit accumulation that occurs with functional aging remains unclear, but the conceptual model proposed by Lopez-Otin (11) and developed by Ferrucci and colleagues (1,2) does provide a framework for evaluating causative models in the context of chronic infection. We and others have shown that biomarkers for inflammation are upregulated with aging (12–15). Circulating levels of inflammatory factors have also been associated with physical activity, both in the general population (16,17) and in PAWH, reviewed by d’Ettorre and colleagues (18). Notably, laboratory-based assessment of gait speed has been associated with levels of inflammation (19) and anabolic levels (20). Regulators of anabolic activity have been previously linked to gait speed (21,22), however, there is a paucity of data relating circulating cellular immune and anabolic factor levels with VGS based on accelerometry. Our study builds off an existing cohort of aging participants with and without HIV infection, and for which comprehensive data are being collected including multiple metrics of physical function. Furthermore, the accelerometry data are based on 3 weeks of continuous monitoring providing a robust assessment of volitional physical activity. Our study also has many limitations. First, our study population was relatively small and comprises only males; hence association between biomarkers and VGS should be validated in larger populations of men and women. Second, the study population is relatively young, 50–65 years old, therefore, reproducibility of our findings should be also assessed in different cohorts. Third, the number of biomarkers measured is limited and were evaluated individually, given the small sample size affecting statistical power to develop a more complex model. The role of body mass index in our study outcomes was not addressed directly, but was reported in a prior study (5). Biomarkers were chosen based on clinical convenience of sampling routine levels of immune and anabolic biomarkers, as well as data implicating these biomarkers in our prior studies (4,5) and their general role as different physiologic domains of healthy aging (15). Fourth, in this study, we did not evaluate different levels of activity detected by the tracker. Recent studies suggest that fragmented physical activity captured by trackers may reflect a key phenotype of higher fatigability (23,24) that should be tested directly in a future study. As our study is cross-sectional in nature, future research is necessary to examine longitudinal trends in functional decline and its early indication using a simple panel of biomarkers. In summary, our findings suggest VGS measured with accelerometry correlated better with immune and anabolic biomarkers than did laboratory-based assessment using the 6-MWT. Blood-based and digital biomarkers may provide presymptomatic tools for identifying risk of functional decline. Funding This paper was published as part of a supplement sponsored and funded by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP. Acknowledgments We thank the MATCH study participants, the Boston Pepper Center, and the Harvard University Center for AIDS Research. We also thank Withings (Paris, France) for their gift supporting this research. Conflict of Interest None reported. References 1. Ferrucci L , Cooper R , Shardell M , Simonsick EM , Schrack JA , Kuh D . Age-related change in mobility: perspectives from life course epidemiology and geroscience . J Gerontol A Biol Sci Med Sci. 2016 ; 71 : 1184 – 1194 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 2. Ferrucci L , Levine ME , Kuo PL , Simonsick EM . Time and the metrics of aging . Circ Res. 2018 ; 123 : 740 – 744 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 3. Guaraldi G , Orlando G , Zona S , et al. Premature age-related comorbidities among HIV-infected persons compared with the general population . Clin Infect Dis. 2011 ; 53 : 1120 – 1126 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 4. Tran T , Guardigni V , Pencina KM , et al. Atypical skeletal muscle profiles in human immunodeficiency virus-infected asymptomatic middle-aged adults . Clin Infect Dis. 2018 ; 66 : 1918 – 1927 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 5. Hale TM , Guardigni V , Roitmann E , et al. Middle-aged men with HIV have diminished accelerometry-based activity profiles despite similar lab-measured gait speed: pilot study . JMIR Mhealth Uhealth. 2019 ; 7 : e11190 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 6. Ferguson T , Rowlands AV , Olds T , Maher C . The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study . Int J Behav Nutr Phys Act. 2015 ; 12 : 42 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 7. Kooiman TJ , Dontje ML , Sprenger SR , Krijnen WP , van der Schans CP , de Groot M . Reliability and validity of ten consumer activity trackers . BMC Sports Sci Med Rehabil. 2015 ; 7 : 24 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 8. Murray MP , Drought AB , Kory RC . Walking patterns of normal men . J Bone Joint Surg Am. 1964 ; 46 : 335 – 360 . PubMed PMID: 14129683. Google Scholar Crossref Search ADS PubMed WorldCat 9. Desquilbet L , Jacobson LP , Fried LP , et al. ; Multicenter AIDS Cohort Study . HIV-1 infection is associated with an earlier occurrence of a phenotype related to frailty . J Gerontol A Biol Sci Med Sci. 2007 ; 62 : 1279 – 1286 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 10. Schrack JA , Althoff KN , Jacobson LP , et al. Accelerated longitudinal gait speed decline in HIV-infected older men . J Acquir Immune Defic Syndr (1999) . 2015 ; 70 ( 4 ): 370 – 376 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 11. López-Otín C , Blasco MA , Partridge L , Serrano M , Kroemer G . The hallmarks of aging . Cell. 2013 ; 153 : 1194 – 1217 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 12. Howcroft TK , Campisi J , Louis GB , et al. The role of inflammation in age-related disease . Aging (Albany NY). 2013 ; 5 : 84 – 93 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 13. Peters MJ , Joehanes R , Pilling LC , et al. ; NABEC/UKBEC Consortium . The transcriptional landscape of age in human peripheral blood . Nat Commun. 2015 ; 6 : 8570 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 14. Pilling LC , Joehanes R , Melzer D , et al. Gene expression markers of age-related inflammation in two human cohorts . Exp Gerontol. 2015 ; 70 : 37 – 45 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 15. Sebastiani P , Thyagarajan B , Sun F , et al. Age and sex distributions of age-related biomarker values in healthy older adults from the long life family study . J Am Geriatr Soc. 2016 ; 64 : e189 – e194 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 16. Gleeson M , Bishop NC , Stensel DJ , Lindley MR , Mastana SS , Nimmo MA . The anti-inflammatory effects of exercise: mechanisms and implications for the prevention and treatment of disease . Nat Rev Immunol. 2011 ; 11 : 607 – 615 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 17. Kasapis C , Thompson PD . The effects of physical activity on serum C-reactive protein and inflammatory markers: a systematic review . J Am Coll Cardiol. 2005 ; 45 : 1563 – 1569 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 18. d’Ettorre G , Ceccarelli G , Giustini N , Mastroianni CM , Silvestri G , Vullo V . Taming HIV-related inflammation with physical activity: a matter of timing . AIDS Res Hum Retroviruses. 2014 ; 30 : 936 – 944 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 19. Zuo X , Luciano A , Pieper CF , et al. Combined inflammation and metabolism biomarker indices of robust and impaired physical function in older adults . J Am Geriatr Soc. 2018 ; 66 : 1353 – 1359 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 20. Bhasin S , Ellenberg SS , Storer TW , et al. Effect of testosterone replacement on measures of mobility in older men with mobility limitation and low testosterone concentrations: secondary analyses of the Testosterone Trials . Lancet Diabetes Endocrinol. 2018 ; 6 : 879 – 890 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 21. Liaw FY , Kao TW , Fang WH , Han DS , Chi YC , Yang WS . Increased follistatin associated with decreased gait speed among old adults . Eur J Clin Invest. 2016 ; 46 : 321 – 327 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 22. Fife E , Kostka J , Kroc Ł , et al. Relationship of muscle function to circulating myostatin, follistatin and GDF11 in older women and men . BMC Geriatr. 2018 ; 18 : 200 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 23. Schrack JA , Kuo PL , Wanigatunga AA , et al. Active-to-sedentary behavior transitions, fatigability, and physical functioning in older adults . J Gerontol A Biol Sci Med Sci. 2019 ; 74 : 560 – 567 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 24. Wanigatunga AA , Ferrucci L , Schrack JA . Physical activity fragmentation as a potential phenotype of accelerated aging . Oncotarget. 2019 ; 10 : 807 – 809 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Robine,, Jean-Marie;Allard,, Michel;Herrmann, François, R;Jeune,, Bernard

doi: 10.1093/gerona/glz198pmid: 31529019

Abstract Background The 122 years and 164 days age claim of Jeanne Calment, the world oldest person who died in 1997, is the most thoroughly validated age claim. Recently the claim that families Calment and Billot organized a conspiracy concerning tax fraud based on identity fraud between mother and daughter gained international media attention. Methods Here, we reference the original components of the validation as well as additional documentation to address various claims of the conspiracy theory and provide evidence for why these claims are based on inaccurate facts or unrelated to the death of Yvonne Billot-Calment, the daughter of Jeanne Calment, in 1934. Results Also, countering the contention that the occurrence of a 122 year old person is statistically impossible, mathematical models are presented which also supports the hypothesis that though extremely rare, as would be expected for the oldest person ever, Jeanne Calment’s age claim is plausible. Conclusions In total, the quality of the investigation supporting the claim of conspiracy as well as the mathematical analysis aiming to back it do not reach the level expected for a scientific publication. Centenarian, Supercentenarian, Demography, Age validation, Mathematical simulation Since she died at the age of 122 in 1997, Jeanne Calment (JC) has been recognized as the record-holder for longevity of the human species. Though a few researchers have been skeptical based on statistical arguments, most researchers in the field have been convinced by the thorough documentation and validation carried out and published by French researchers, Michel Allard (MA) and Jean-Marie Robine (JMR) with the help of her Doctor Victor Lèbre (1–6). She was at that time and still is among the best documented supercentenarians, that is, people over 110 years old (5,7–9). However, in December 2018, JC’s record of longevity was contested by the gerontologist Valery Novoselov in an interview published on a website (10) and by the laboratory technician Nikolay Zak in a manuscript posted on ResearchGate.net (11). Based upon inaccurate or unrelated facts, they propose a conspiracy theory, claiming that JC died in 1934 and that her daughter, Yvonne Billot (YB), committed an identity fraud in order to avoid paying inheritance taxes. Novoselov and Zak accused the Calment and Billot families having conspired to commit this identity fraud. Eventually, Zak revised his preprint and it was accepted for publication by Aubrey De Grey, for the journal Rejuvenation Research (12). Most of age claims 115 years and older are false, either intentionally or unintentionally, and thus, it is particularly important to compile multiple forms of proof that agree with one another in order to sufficiently substantiate such claims (13–16). This is even more so the case when a claim would establish a new record for the world’s ever-oldest person. JMR and MA therefore compiled and described multiple pieces of intraconsistent evidence supporting the age of death of JC at 122 years (1,2,4). In the process of validating the claim, particularly one as rare as the oldest person ever, one must carefully rule out all the possible causes of a false positive. It is particularly helpful, as was the case with JC, for the claimant to be alive at the time of the validation and to be able to ask them questions to both rule in a true positive and rule out a false positive. During the 3 years, from 1993 to 1995, JMR and MA regularly visited and interviewed JC and despite few small inconsistencies not once did these conversations produce a suspicion of fraud and especially not a possibility of an identity switch between mother and daughter. The hypothesis of an identity switch between mother and daughter is not new. It was already considered in 1995 when the epidemiologist Bernard Jeune (BJ) and demographers Väinö Kannisto (VK) and James W. Vaupel (JWV) went to visit JC the day after her 120 years anniversary and went through all the 30 documents that the French researchers had found in the archives of Arles. In their book about her (1), MA and JMR had included a photo of JC and her daughter Yvonne with the following caption in French “Quelle est l’une, quelle est l’autre?”, meaning “Who is who?” The purpose of the photo was to show just how young JC appeared for her age and thus how slowly she was aging. However, in 1995 when BJ, VK, and JWV were working in the archives of Arles, they focused on any possible confusion of birth records among family members particularly between siblings, but also JC and YB. They found a fraud improbable given the position and the life circumstances of the Calment family in this relatively small city of Arles where many people knew JC and her daughter. BJ, VK, and JWV were more looking for an identity switch with a younger sister than one between mother and daughter. In this current paper, we are more specifically focused on the switch hypothesis between daughter and mother raised by Zak and Novoselov. After a summary of the steps taken to validate JC’s age in the 1990s, we specifically address Zak’s contention with new facts of the case. Finally, we discuss why his belief that her claim is mathematically too improbable is incorrect. Summary of the Validation of JC’s Age Several publications provide the details of the validation of JC’s age at death of 122 years. Much of these validation efforts occurred while JC was alive, once she surpassed the age of 117 years. The first step to validate her age claim was to look up her birth record. Since the 16th century and the French Revolution and in the case of Catholics, there are usually two birth registrations, one civil and one religious. In the case of JC, we located both the civil birth record and the baptism record and they exactly corresponded with one another. At that time, the civil vital events were noted in sequential individually bound books, with a new book created for each year. We located her civil birth record in the 1875 book on the correct page of the book corresponding to her birthdate. Familial Reconstitution Method Familial reconstitution is a critical part of the validation process, in which one determines that all the relative’s ages make reproductive sense (13–16). For example, a case would not be valid if a mother appeared to have a child before the age of 10 or if one sibling was 60 years older than another. In the case of JC’s pedigree, all of the ages make sense. Beginning with her birth documents and the information she provided, we constructed a family pedigree that included JC’s parents, siblings, and descendants. All of the birthdates made sense. Her parents married at the age of 23 years. They had four children born in 1862, 1863, 1865, and 1875. The two oldest siblings, Antoine and Marie died in infancy. Her brother, François was born 10 years before her (1,2,4–6). Regarding the family pedigree, Zak argues that a case such as JC should demonstrate familial evidence of exceptional longevity. Indeed, about 50% of current centenarians have a history of an ancestor living into their nineties or older, but the other half do not, and in the case of supercentenarians (those ages 110+ years), the frequency appears to be even less (Thomas Perls, personal communication). Many potential centenarians died as infants when infant mortality was so high. Also, many adults who had a familial predisposition for longevity did not achieve such ages because of what today would be a readily reversible cause of death (eg, infection, trauma). Still, in the case of JC, JMR and MA have disclosed an exceptional concentration of long-living ancestors. Indeed, JC had a Total Immediate Ancestral Longevity (TIAL) of 477 compared with 289 for the sum of the ages at death of the six immediate controls of the reference family (6). Multiple Documents from Throughout the Person’s Life That Are Consistent in Their Documentation of the Claimant’s Age One of the most important tenets of age validation is that the claimant should have been known within the local community well before the age of 100 years, thus not appearing suddenly at a claimed extraordinary age. Thus, JMR and MA searched for proofs of the existence of JC well before she surpassed the age of 100 years. The general census of the French population, administered by each municipality, offered such proofs. From 1801 to 1946, the census was conducted every 5 years except for 1916 and 1941 because of the wars. Since 1946, the census was carried out irregularly in 1954, 1962, 1968, 1975, 1982, and 1990. The details of the comprehensive multiple forms of written proof of JC’s ages throughout her life, including the above, are noted in reference (4). In the discussion of that article, the gathered evidence was summarized as: …seven direct civil status documents (i) bear witness to the identity of Jeanne Louise Calment from her birth date to her death. (ii) They are complemented by numerous indirect civil status documents which confirm the sequence of the information (iii) by 14 direct documents from censuses that confirm the age or the birth date of Jeanne Calment [1876, 1881, 1886, 1901, 1906, 1911, 1921, 1926, 1936, 1946, 1954, 1962, 1968, 1975], (iv) and by direct or indirect parish documents, (v) by notarial documents (marriage contracts, sale of a house), (vi) by a medical doctorate dissertation, (vii) and by newspaper articles. Links to copies of these censuses are provided in the Supplementary Material (SOM, A1). Though we discuss Zak’s arguments below regarding what he views as inconsistencies in the JC age claim, we point out here one issue that he asserts, which is the lack of mention of JC’s 100th birthday in the local newspaper. However, this is clearly countered by the 1975 census indicating her date of birth and the fact that many centenarians do not have their birthdays celebrated in the news for a range of reasons. Furthermore, Zak’s coinvestigator, Novoselov, in a published interview, indicated that JC was visited by the Mayor of Arles on the occasion of her 100th birthday (10). Zak also believed that JC’s apparent absence from the 1931 census was supportive of his accusation of identity fraud. 1931 was a difficult year for the census since hand-written entries were replaced with typewritten entries and that year’s entries are well known for many mistakes; so much so that census officials returned to hand-written entries in 1936 and did not begin to use typed entries until 1962. Some of the many incorrect entries in the 1931 census are shown in the SOM (see SOM, A2) Claimant Interviews From their 1993 and 1994 interviews with JC (ages 117 and 118 years), JMR and MA listed the following facts stated by JC who provided these details from memory (Table 1) (4): Table 1. Facts Provided by Jeanne Calment to Jean Marie Robine and Michel Allard During Their 1993 and 1994 Interviews with Her (i) She was born on February 21st, 1875 in Arles; (ii) Her father, Nicolas Calment, was a shipbuilder; (iii) Her mother, Marguerite Gilles, was from a family of millers; (iv) Her godfather was named Louis Pages [or Paget]; (v) Her godmother was Jeanne Gilles, her maternal aunt; (vi) She had a brother, François Calment, who was ten years older than her and who died at 97 years of age; (vii) She married her cousin, Fernand Calment; (viii) They lived for some time with Mrs. Maria Calment, [maiden name Felix], her mother-in-law; (ix) Ever since, she lived in the same house in Gambetta Street [at the corner of St-Estève Street, next to Rue de la République], in an apartment located above their department store named “Grand Magasin Calment”; (x) She left this house when she was 110 years old to live in a nursing home, “La Maison du Lac” where we met her; (xi) She gave birth to one child, a daughter, Yvonne, [Calment], born in Arles; (xii) Her daughter married [Captain] Joseph Billot; (xiii) They had one son, Frédéric [born Billot]; (xiv) Her daughter Yvonne died when she was 36 years old; (xv) Her grandson, Frédéric Billot, also died at 36 years old; (xvi) She mentioned Marthe Fousson, one of the first servants she had in her service, when she was newly married [Marthe was noted in the 1906 and 1911 censuses as living with the family]. (i) She was born on February 21st, 1875 in Arles; (ii) Her father, Nicolas Calment, was a shipbuilder; (iii) Her mother, Marguerite Gilles, was from a family of millers; (iv) Her godfather was named Louis Pages [or Paget]; (v) Her godmother was Jeanne Gilles, her maternal aunt; (vi) She had a brother, François Calment, who was ten years older than her and who died at 97 years of age; (vii) She married her cousin, Fernand Calment; (viii) They lived for some time with Mrs. Maria Calment, [maiden name Felix], her mother-in-law; (ix) Ever since, she lived in the same house in Gambetta Street [at the corner of St-Estève Street, next to Rue de la République], in an apartment located above their department store named “Grand Magasin Calment”; (x) She left this house when she was 110 years old to live in a nursing home, “La Maison du Lac” where we met her; (xi) She gave birth to one child, a daughter, Yvonne, [Calment], born in Arles; (xii) Her daughter married [Captain] Joseph Billot; (xiii) They had one son, Frédéric [born Billot]; (xiv) Her daughter Yvonne died when she was 36 years old; (xv) Her grandson, Frédéric Billot, also died at 36 years old; (xvi) She mentioned Marthe Fousson, one of the first servants she had in her service, when she was newly married [Marthe was noted in the 1906 and 1911 censuses as living with the family]. Note: Annotations from JMR are provided in brackets (5). Open in new tab Table 1. Facts Provided by Jeanne Calment to Jean Marie Robine and Michel Allard During Their 1993 and 1994 Interviews with Her (i) She was born on February 21st, 1875 in Arles; (ii) Her father, Nicolas Calment, was a shipbuilder; (iii) Her mother, Marguerite Gilles, was from a family of millers; (iv) Her godfather was named Louis Pages [or Paget]; (v) Her godmother was Jeanne Gilles, her maternal aunt; (vi) She had a brother, François Calment, who was ten years older than her and who died at 97 years of age; (vii) She married her cousin, Fernand Calment; (viii) They lived for some time with Mrs. Maria Calment, [maiden name Felix], her mother-in-law; (ix) Ever since, she lived in the same house in Gambetta Street [at the corner of St-Estève Street, next to Rue de la République], in an apartment located above their department store named “Grand Magasin Calment”; (x) She left this house when she was 110 years old to live in a nursing home, “La Maison du Lac” where we met her; (xi) She gave birth to one child, a daughter, Yvonne, [Calment], born in Arles; (xii) Her daughter married [Captain] Joseph Billot; (xiii) They had one son, Frédéric [born Billot]; (xiv) Her daughter Yvonne died when she was 36 years old; (xv) Her grandson, Frédéric Billot, also died at 36 years old; (xvi) She mentioned Marthe Fousson, one of the first servants she had in her service, when she was newly married [Marthe was noted in the 1906 and 1911 censuses as living with the family]. (i) She was born on February 21st, 1875 in Arles; (ii) Her father, Nicolas Calment, was a shipbuilder; (iii) Her mother, Marguerite Gilles, was from a family of millers; (iv) Her godfather was named Louis Pages [or Paget]; (v) Her godmother was Jeanne Gilles, her maternal aunt; (vi) She had a brother, François Calment, who was ten years older than her and who died at 97 years of age; (vii) She married her cousin, Fernand Calment; (viii) They lived for some time with Mrs. Maria Calment, [maiden name Felix], her mother-in-law; (ix) Ever since, she lived in the same house in Gambetta Street [at the corner of St-Estève Street, next to Rue de la République], in an apartment located above their department store named “Grand Magasin Calment”; (x) She left this house when she was 110 years old to live in a nursing home, “La Maison du Lac” where we met her; (xi) She gave birth to one child, a daughter, Yvonne, [Calment], born in Arles; (xii) Her daughter married [Captain] Joseph Billot; (xiii) They had one son, Frédéric [born Billot]; (xiv) Her daughter Yvonne died when she was 36 years old; (xv) Her grandson, Frédéric Billot, also died at 36 years old; (xvi) She mentioned Marthe Fousson, one of the first servants she had in her service, when she was newly married [Marthe was noted in the 1906 and 1911 censuses as living with the family]. Note: Annotations from JMR are provided in brackets (5). Open in new tab Again, regarding Zak’s hypotheses, he posited that JC’s daughter, Yvonne, could have provided facts to JMR and MA by perhaps access to a diary or other set of records previously in the possession of her mother. In considering this hypothesis, note that had she not died in 1934, at the age of 36, in 1993 YB would have been 95 years old. It is not reasonable to claim that she could provide, for example, the names of JC’s godparents based upon her recollection of what she might have read many years previously in an attempt to maintain a fraud. Additionally, Zak indicated that there was a family move in 1888, when JC was 13, that was not mentioned by JC in the interviews of her by JMR and MA and thus this oversight is an important inconsistency that supports an assertion of a conspiracy to commit fraud. Indeed, we did not learn about this move during our interviews with JC, but we have since determined that the move was only 150 m away in the same neighborhood in 1884 or 1885 when JC was 9 or 10 (not 13) years old (see SOM B). We do not feel it is remarkable that a minor move such as this at the age of ten would not be mentioned. The Conspiracy Theory of an Identity Switch According to a published interview, Novoselov asserted that JC did not look frail enough to be a supercentenarian (10). He referred to the surprise of the Mayor, who made it a habit to visit centenarians on their birthdays, that at age 100 years, JC appeared so spry. Actually, such spryness is exactly what one would expect from a 100-year-old destined to live another 22 years, to the age of 122 years. In their study of people living to supercentenarians, New England Centenarian Study (NECS) investigators noted that the morbidity and disability profiles of these individuals support James Fries compression of morbidity hypothesis (17,18). That is at the limit of human life span, aging-related diseases and syndromes that increase mortality risk must be delayed towards the time of death. The NECS found that supercentenarians generally require minimal or no assistance with their activities of daily living or clinically manifest any of the major aging-related diseases (heart disease, stroke, diabetes, hypertension, cancer, or chronic obstructive pulmonary disease) at the mean age of 106 years (17). Photos of both JC and Sarah Knauss, who died at the age of 119 years in 1999, are examples of how supercentenarians have a history of aging so remarkably slowly. Supercentenarians are typically characterized as having delayed or escaped major diseases and compressed the time they experience severe decline towards the end of their extremely long lives, and it is not until the relatively short time at the end of their lives that they demonstrate frailty (Figure 1). Figure 1. Open in new tabDownload slide Photos of Sarah Knaus, ages 99 years and 119 years (the latter by Theo Westenberger) and Jeanne Calment at 116 years and 122 years. Figure 1. Open in new tabDownload slide Photos of Sarah Knaus, ages 99 years and 119 years (the latter by Theo Westenberger) and Jeanne Calment at 116 years and 122 years. Nonetheless, Novoselov approached Zak to analyze available mortality data to determine whether mathematically, JC was an impossible outlier (10). From his preprint uploaded on Researchgate.net, it appears that Zak concludes that it was mathematically impossible for a person to reach the age of 122 years. He then researched what had been written about JC and subsequently constructed a story that he believes supports a case of fraud (11,12). In the preprint, Zak devotes just a few paragraphs to his mathematical refute of JC’s age claim. He bases his conclusion on two assumptions in both the preprint and later in his article, that “the force of mortality is almost constant after 105 years” thus half of supercentenarians die “during any year of follow up,” and that it “does not vary much with sex, country and year of birth” (11,12). Initial studies argued for an exponential increase in the mortality rate at older age but then a deceleration at extreme ages. In fact, it is difficult to assess if mortality rates keep increasing with age or tend towards some mortality plateau. Data quality issues combine with small numbers to render these issues to be quite complex and thus approaching the problem with one possible model is incorrect. Zak declined to investigate multiple possible models of mortality. Also, given that generally 85% of centenarians and 90% of supercentenarians are female, considering that there are no differences in sex is grossly incorrect. We formally address the statistical plausibility of a person living to age 122 years later in this paper. To support the hypothesis of an identity switch one must have a motive justifying such a fraud, and then show that such a substitution was practically possible. As for a motive for why the Calment and Billot families would conspire to commit fraud, Zak wrote, “Perhaps the Calment family suffered from taxation after the death of Maria Felix (widow of the founder of the store, Jacques Calment) and especially after the death of Jeannes father Nicolas Calment, the owner of land and real estate in the surrounding villages in 1931. The inheritance tax for the farm in Saint Martin de Crau could amount to hundreds of thousands of dollars in modern money. It is not hard to imagine that the family had neither desire nor ability to pay the tax, especially twice in a row (here, one should recall that Jeanne hated socialists).” (12) This motive is not only speculation, it has no basis. In actuality, French real estate transactions have resulted in notarial deeds for centuries. These documents are publicly available after 70 years and in the case of JC a dozen transactions established before 1949 are available (see SOM B). Zak is negligent in not noting that Nicolas Calment (NC) had given all his property to his children on March 15, 1926 in exchange for an annual life annuity of 5,000 francs that his children had to pay him until his death. The only financial consequence upon the death of NC in 1931 is the extinction of the life annuity. According to Zak, another motive could be an annuity contract presumably signed before 1934 and still in operation at the time of her death in 1997 (12). This second motive seems also to have no basis as it is not reported in the declaration of assets that JC made in 1946 on the occasion of the national solidarity tax in application of the Ordinance of August 15, 1945 (see SOM B). An important argument against Zak’s conspiracy theory is the fact that the Calment family was a well-known family in Arles. Her father-in-law had established the local and prominent department store, Grand Magasin Calment, and her father was a city councilman. Yvonne’s husband was a member of the French Legion of Honor. Novoselov, in his interview (10) stated that the community would not have noticed a switch because Arles is actually based in a large county and that JC and her daughter spent some of their time in a homestead 16 km away from Arles. Zak himself negates this notion in his preprint stating that “Jeanne Louise Calment had been alive for 12 years and 164 days after her 110th anniversary and was under close (and with growing age) scrutiny from the general public and scientific community” (12). Since Zak’s accusations of fraud and conspiracy, at least four relatives have released photos showing that Yvonne was, before her marriage in 1926, active and well- integrated within her social group of young women (See SOM C). Of course, this social circle of friends would have been deceived into believing that it was JC who lived beyond 1934, rather than her daughter, YB. According to the local press, at the funeral of YB in 1934, “A huge crowd drove last Saturday to her last home, Mrs. Billot Calment died at the age of 36 years.” (see SOM C). The death notice of YB has been sent on behalf of 34 people and their children, the staff of the House Calment and 13 different families (see SOM C for the notice). People were invited to gather at the family house and we can guess that many of them attended the funeral wake. In these circumstances, unless we accept the idea of the complicity of dozens of people, a substitution between the bodies of JC and YB was virtually impossible. Also, such a substitution would have led to an incestuous family configuration. Frédéric Billot, the son of YB, was 7 years old when his mother died in 1934 and Fernand Calment, the husband of JC was still alive for several years beyond Yvonne’s death. Therefore, according to Zak, YB took the place of her mother and therefore Fernand and Yvonne would have had to act as if they were married. Frédéric, at the age of seven would have had to act as if his mother had died and pretend that Yvonne was really his grandmother, rather than mother. His own father, Captain Joseph Billot, the husband of Yvonne, would have had to be complicit with the ruse. A conspiracy to commit identity fraud would have required the participation of many people. Zak also asserted that Yvonne’s absence from the 1931 census was evidence of fraudulent behavior. In fact, the reason she is not listed is because at the time, she was staying in Leysin, Switzerland, being possibly treated for tuberculosis (Figure 2). Leysin was home to a number of sanatoriums for the treatment of tuberculosis, and a famous one in particular, the Sanatorium Universitaire (19). Figure 2. Open in new tabDownload slide Photo of Yvonne in 1931, staying in Leysin, likely at one of its numerous sanatoriums for treatment of tuberculosis. Figure 2. Open in new tabDownload slide Photo of Yvonne in 1931, staying in Leysin, likely at one of its numerous sanatoriums for treatment of tuberculosis. Yvonne’s stay in Leysin, 3 years before her death underlies that she was an ill woman at the time and speaks to the authenticity of her death at the age of 36 in 1934. JC had told MA and JMR that her daughter’s illness had started after the birth of Frédéric. At the end of this conversation, JC turned to Victor Lèbre, her doctor, saying, “When they put me in the coffin, put the photo of my grandson at my right, and the one of my daughter at my left, and they will be buried with me. Oh, that will only be an imaginary burial, they are both in the ground already, but that way, they will be beside me.” (1,2) A military file of Joseph Billot, YB’s husband, indicates a granted leave on personal grounds for 5 years from June 10, 1928 and then renewed for another 5 years on June 10, 1933 (See SOM C). A 1928 letter written by Joseph Billot’s superior, indicates the reasons for the requested leave; namely the poor health of his wife. He wrote “Captain Billot made an application on March 30, 1928, to be admitted for a granted leave. It is with regret that he leaves the army, but his interests and the health of his wife oblige him to go to live in the South, near Arles.” (see SOM C). The new documents consulted since Zak’s story are consistent with the fact that Yvonne was sick, presumably suffering from tuberculosis, a major cause of death at the time (20). The paragraph of Zak on a possible fibroma is another weakness “In one of the few photographs of the young Yvonne that exist, one can see a small fibroma on the nose (it could be a scan defect, but it is also visible on different scans). A similar fibroma can be seen in one of the photos of the old Calment. Interestingly, it is absent from later photos, indicating that it was removed. If Yvonne removed it more than once, that could explain its slightly different locations inFigure 3A and Band also the fact that the fibroma appears smaller in the older woman, even though fibromas grow over time.” None of the photos of the young YB (see SOM D) and the many photos of JC taken after 1936 (see SOM D) shows a fibroma. In Figure 3B, of the Zak paper, JC is 114 years old. Zak suggests that the fibroma has been removed when JC was a resident of the nursing home Maison du Lac. Of course, there is no mention of such intervention in the dissertation of Catherine Levraud (21) where she summarizes, with great detail, the medical history of JC after her arrival in the nursing home, between the ages of 111 and 118. Other weaknesses of his arguments, for instance about JC’s size and JC’s eye color have already been pointed out by the publication by Le Bourg (22). Figure 3. Open in new tabDownload slide Number of deaths at aged 100 years and older, France 1818–2016, according to the Human Mortality Database. Source: https://www.mortality.org. Figure 3. Open in new tabDownload slide Number of deaths at aged 100 years and older, France 1818–2016, according to the Human Mortality Database. Source: https://www.mortality.org. The Statistical Plausibility of a Human Surviving to Age 122 Years Zak’s interest in the sociodemographic aspects of the JC age claim began with his contention that JC’s ability to survive to age 122+ years is mathematically impossible. Thus, here we also refute his mathematical argument. In the broadest sense, the statistical universe of JC is composed of all the lifetimes of human beings throughout the world. Of course, the ages of survival for the vast majority of these lives are unknown. Therefore, we consider a subset within a defined period of time where we have some degree of confidence in its completeness and a substantial similarity in terms of sociodemographic and culture to JC. Thus, we chose France where JC was born and where she lived, and for which we have continuous information on the ages at death since 1816. These data, assembled by the General Statistics of France, and then by the National Institute of Statistics and Economic Studies are available at the Human Mortality Database (HMD, https://www.mortality.org). In this database, approximately 142 million deaths were recorded in France between 1816 and 2016 and are segregated by sex, by single age from age 0 to an open-ended age group “110+,” by year of birth from 1706 to 2016 and by year of death. Figure 3 displays the changes over time in the number of deaths recorded at aged 100 years and older for both males and females. Overall, the number of deaths of centenarians did not increase in France until the post WW II period for women and the 1990s for men. The zoomed in view on the right panel shows that the number of centenarians decreased during the initial period, from 1816 to 1880, probably due to the improvement in the quality of the statistics and then remains stable during a second period, from 1880 to the end of the WWII. In 1975, when JC turned 100 years old, 684 deaths of centenarian women were registered, and in 1997, the year of her death, there were 2,727 deaths recorded. As the HMD data are censored beyond the age of 109, we have supplemented them with data from the International Database on Longevity – IDL (https://www.supercentenarians.org), compiling lifetimes greater than 105 years. These data relate to 9,466 individual records of people who died between 1982 and 2017 in France. Although virtually no one died at age 105 or older in the 1980s, there are several 100 deaths recorded above this age in the most recent years. They are mostly women. The Exceptional Case of JC Figure 4 depicts the age at death of JC (1997, age 122 years) in the context of the above described data. Over the long term, changes in the Maximum Reported Age at Death (MRAD) demonstrated large fluctuations and much smoother changes were observed with the measure Highest Age Providing at Least 30 deaths (HAPaL_30). Thus, we chose HAPaL_30 to assess the limits of longevity without its extreme fluctuations. The 122-year lifetime of JC is not only quite far from the value of HAPaL_30 which reached 106 years in 1997 when JC died but also from the values of the MRAD in the neighboring years, that is, 112 years. Obviously, 122 years appears as an outlier, even among the extreme values measured by the MRAD. Figure 4. Open in new tabDownload slide Individual observations and statistical indicator of Maximum Life Span (MLS): Maximum Reported Age at Death (MRAD) and Highest Age Providing at Least 30 deaths (HAPaL_30), females, France, 1818–2016. Source: https://www.mortality.org and https://www.supercentenarians.org. Figure 4. Open in new tabDownload slide Individual observations and statistical indicator of Maximum Life Span (MLS): Maximum Reported Age at Death (MRAD) and Highest Age Providing at Least 30 deaths (HAPaL_30), females, France, 1818–2016. Source: https://www.mortality.org and https://www.supercentenarians.org. Cohort Reconstruction and Modeling Using the death data described above, organized by single age, period, and cohort of birth (Lexis triangles), we reconstructed the cohorts born in 1875 as in the case of JC and in 1903, the most recent extinct cohort, and computed the probability (qx) that someone aged A[x] will die before reaching age A[x+1]. Then, we plotted the mortality risk expressed as the line plots of qx over age and modelized it using three usual functions: three-parameter exponential (b0 + b1*b2age), four-parameter Gompertz function = b0 + b1*exp(-exp(-b2*(age - b3))), and four-parameter logistic function = b0 + b1/(1 + exp(-b2*(age - b3))) (equations 1–3). Equation 1: Exponential qx=b0+b1b2Age Equation 2: Logistic qx=b0+b11+e−b2(Age−b3) Equation 3: Gompertz qx=b0+b1e−e−b2(Age−b3) Modeling of the mortality risk of the 1875 and 1903 birth cohorts using exponential (short dash line), Gompertz (plain line), and logistic (long dash line) functions are shown in Figure 5. The open dots correspond to the one used to compute the models. The plain dots correspond to ages above (HAPaL_30), those with less than 30 deaths occurrence and excluded from the models. The parameters of the models are given in Table 2 along with their coefficient of determination, which exceeds 99%. Table 2. Parameters of the Mortality Models with Their Coefficient of Determination (R2) Model b0 b1 b2 b3 R2 Logistic 1875 0.0071 0.6819 0.1078 97.8033 0.9921 Logistic 1903 0.0062 0.6403 0.1241 99.7883 0.9986 Gompertz 1875 0.0160 1.4365 0.0379 108.0642 0.9917 Gompertz 1903 0.0138 1.1814 0.0463 106.1697 0.9979 (R2) Model b0 b1 b2 b3 R2 Logistic 1875 0.0071 0.6819 0.1078 97.8033 0.9921 Logistic 1903 0.0062 0.6403 0.1241 99.7883 0.9986 Gompertz 1875 0.0160 1.4365 0.0379 108.0642 0.9917 Gompertz 1903 0.0138 1.1814 0.0463 106.1697 0.9979 Open in new tab Table 2. Parameters of the Mortality Models with Their Coefficient of Determination (R2) Model b0 b1 b2 b3 R2 Logistic 1875 0.0071 0.6819 0.1078 97.8033 0.9921 Logistic 1903 0.0062 0.6403 0.1241 99.7883 0.9986 Gompertz 1875 0.0160 1.4365 0.0379 108.0642 0.9917 Gompertz 1903 0.0138 1.1814 0.0463 106.1697 0.9979 (R2) Model b0 b1 b2 b3 R2 Logistic 1875 0.0071 0.6819 0.1078 97.8033 0.9921 Logistic 1903 0.0062 0.6403 0.1241 99.7883 0.9986 Gompertz 1875 0.0160 1.4365 0.0379 108.0642 0.9917 Gompertz 1903 0.0138 1.1814 0.0463 106.1697 0.9979 Open in new tab Figure 5. Open in new tabDownload slide Modeling of the mortality risk of the 1875 and 1903 birth cohorts. Figure 5. Open in new tabDownload slide Modeling of the mortality risk of the 1875 and 1903 birth cohorts. For determining the probability of reaching JC’s age, we used the fitted parameters to generate three sets of simulations. The exponential function diverging too quickly was not used here. Simulations set 1: starting with a sample of 10 centenarians, we applied at each age the mortality risk of the logistic and Gompertz functions with a pseudo-random number generator determining the death or living status of each virtual centenarian until the extinction of the whole sample. The simulations were run 500,000 times to determine the maximum age that could be reached and the probability of reaching it with its binomial exact 95% confidence interval. Simulations set 2: same as above but starting with 500 centenarians and 100,000 runs. Simulations set 3: same as above but starting with 100,000 centenarians and 500 runs. All computations were performed with the Stata software release 15.1. Results of the 12 sets of simulations (3 samples size × 2 models × 2 cohorts) are given in Table 3. Table 3. Maximum Age Reached According to 12 Simulations Sets with Varying Sample Size and Resampling Model Simulations Set 1 Simulations Set 2 Simulations Set 3  Sample size (number of centenarian) 10 500 100,000  Number of resampling 500,000 100,000 500 Logistic 1875  Maximum age 119 121 121  Probability of maximum age or 122+ 3/50,000,000 4/50,000,000 3/50,000,000  Lower bound 95% CI 0.0000000124 0.0000000218 0.0000000124  Upper bound 95% CI 0.0000001750 0.0000002050 0.0000001750 Logistic 1903  Maximum age 121 123 123  Probability of maximum age or 122+ 2/50,000,000 2/50,000,000 5/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000325  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000002330 Gompertz 1875  Maximum age 120 122 120  Probability of maximum age or 122+ 1/50,000,000 1/50,000,000 1/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000005  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000001110 Gompertz 1903  Maximum age [year] 119 122 121  Probability of maximum age or 122+ 1/50,000,000 1/50,000,000 2/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000005  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000001110 Model Simulations Set 1 Simulations Set 2 Simulations Set 3  Sample size (number of centenarian) 10 500 100,000  Number of resampling 500,000 100,000 500 Logistic 1875  Maximum age 119 121 121  Probability of maximum age or 122+ 3/50,000,000 4/50,000,000 3/50,000,000  Lower bound 95% CI 0.0000000124 0.0000000218 0.0000000124  Upper bound 95% CI 0.0000001750 0.0000002050 0.0000001750 Logistic 1903  Maximum age 121 123 123  Probability of maximum age or 122+ 2/50,000,000 2/50,000,000 5/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000325  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000002330 Gompertz 1875  Maximum age 120 122 120  Probability of maximum age or 122+ 1/50,000,000 1/50,000,000 1/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000005  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000001110 Gompertz 1903  Maximum age [year] 119 122 121  Probability of maximum age or 122+ 1/50,000,000 1/50,000,000 2/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000005  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000001110 Note: CI = Confidence interval. Open in new tab Table 3. Maximum Age Reached According to 12 Simulations Sets with Varying Sample Size and Resampling Model Simulations Set 1 Simulations Set 2 Simulations Set 3  Sample size (number of centenarian) 10 500 100,000  Number of resampling 500,000 100,000 500 Logistic 1875  Maximum age 119 121 121  Probability of maximum age or 122+ 3/50,000,000 4/50,000,000 3/50,000,000  Lower bound 95% CI 0.0000000124 0.0000000218 0.0000000124  Upper bound 95% CI 0.0000001750 0.0000002050 0.0000001750 Logistic 1903  Maximum age 121 123 123  Probability of maximum age or 122+ 2/50,000,000 2/50,000,000 5/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000325  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000002330 Gompertz 1875  Maximum age 120 122 120  Probability of maximum age or 122+ 1/50,000,000 1/50,000,000 1/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000005  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000001110 Gompertz 1903  Maximum age [year] 119 122 121  Probability of maximum age or 122+ 1/50,000,000 1/50,000,000 2/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000005  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000001110 Model Simulations Set 1 Simulations Set 2 Simulations Set 3  Sample size (number of centenarian) 10 500 100,000  Number of resampling 500,000 100,000 500 Logistic 1875  Maximum age 119 121 121  Probability of maximum age or 122+ 3/50,000,000 4/50,000,000 3/50,000,000  Lower bound 95% CI 0.0000000124 0.0000000218 0.0000000124  Upper bound 95% CI 0.0000001750 0.0000002050 0.0000001750 Logistic 1903  Maximum age 121 123 123  Probability of maximum age or 122+ 2/50,000,000 2/50,000,000 5/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000325  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000002330 Gompertz 1875  Maximum age 120 122 120  Probability of maximum age or 122+ 1/50,000,000 1/50,000,000 1/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000005  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000001110 Gompertz 1903  Maximum age [year] 119 122 121  Probability of maximum age or 122+ 1/50,000,000 1/50,000,000 2/50,000,000  Lower bound 95% CI 0.0000000005 0.0000000005 0.0000000005  Upper bound 95% CI 0.0000001110 0.0000001110 0.0000001110 Note: CI = Confidence interval. Open in new tab With samples of 500 and 100,000 centenarians, the maximum age ranged from 120 to 123. The Logistic and Gompertz models yield similar results, with the Logistic 1903 outperforming the others by a small margin. It must be underlined that these maximum ages were observed even when extrapolation of the functions leads to qx exceeding 0.6 that is a probability of dying above 60%. Thus, in silico, JC’s age can be reached and even exceeded, even though the probability remains very low. In the best simulation set (set 3 and Logistic 1903), this event occurs on average once every 10 million centenarians. Such a result is compatible with previous estimations (23,24) including a first estimation proposed by Väinö Kannisto and Roger Thatcher (unpublished in 1993, available on request). Based on these data, the survival of JC to 122 years is possible. Considering that the world has experienced somewhere between 8 and 10 million centenarians since at least the 1700s, then a person age 122 years by around the late 1900s is reasonable. According to the Population Division of the United Nations, by 2100, the global number of centenarians within a single year could be as high as 25 millions (25) and so the observance of yet another person age 122 or perhaps even a little older is also reasonable. Conclusion Jeanne Calment claim as the record-holder for longevity for the human species, at the 122 years and 165 days, remains valid. The material gathered in favor of its accuracy far outweigh Zak’s hypothesis of identity fraud. Our mathematical models show that a person achieving the age of 122 years by the late 1990’s would be possible. Empirically though, the only way to assess that it is an accurate observation is to examine the evidence supporting and challenging the age claim. Zak never mentioned the existence of well-validated extreme cases of longevity. Sarah Knauss, who passed away at age 119 in 1999, and Marie-Louise Meilleur, who died at age 117 years in 1998, make JC appears less exceptional than if, for example, the next oldest person was 115 years old. Since 2015, five other women have reached the age of 117 years: three of them in 2017 (the Japanese woman Misao Okawa (26), the Italian woman Emma Morano (27) and Violet Brown from Jamaica) and two of them in 2018 (the Japanese women Nabi Tajima and Chiyo Miyako (26)). The cases of Violet Brown and Nabi Tajima have not yet been thoroughly validated. Should Zak and his colleagues wish to contest Sarah Knauss’ age, they should keep in mind that not only was her age also well validated with multiple documents throughout her life, the age of her daughter Kitty, who lived to 101 years, was also validated (correspondence with the New England Centenarian Study). We have identified three main limitations of this study. First, time and space limitations to answer in this journal to the dozens of arguments challenging the reported age of JC gathered by Zak and Novoselov. Therefore, we only focused on the main arguments concerning the motive and the practicability of the identity fraud. However, we examined many other arguments and we discovered some errors which at least show great negligence in their work. Second, legal limitations as the most recent financial transactions concerning JC will not be publicly available before 2067, 70 years after her death. Third, ethical limitations as the most recent transactions involved people who are still alive. For instance, some allied families close to the Calment/Billot family informed us about the most recent financial transactions but asking us not to circulate this information. In other words, there is still many material to confirm the biography of JC. As expected for the age of the oldest person in the world, the probability of her occurrence is extremely small. In his published interview in leafscience.com, Novoselov requests a revalidation of the case of Jeanne Calment. In a way, it is what we have provided with this paper showing that JC remains the oldest human whose age is well-documented. In conclusion and coming back to the paper published by Zak, we would like to stress the unacceptability of publishing an article with such unfounded accusations claiming that members of the Calment and Billot families collectively committed fraud. How was it possible that a paper so full of unsubstantiated assertions could survive a responsible peer review and subsequently be published in Rejuvenation Research? Based on the evidence that we bring in this paper, we call for a retraction of Zak’s article. Acknowledgments We thank the members of the families allied with the family Calment/Billot, especially the family Billot/Guillet with Robert Billot and Frédérique Skyronka, the family Billot/Taques with Claudie et Christian Taque, the family Fassin with Claude Chaix, and the family Mery with Gilberte Mery and her grand-son, who provided photographs and new documents concerning JC, YB, and Captain Joseph Billot. We thank national, regional, and municipal institutions which opened their archives providing marriage contracts and other private financial transactions, personal military files as well as personal tax files, especially Les Archives départementales des Bouches-du-Rhône. We thank Rémi Venture, director of the Arles library (Médiathèque) and Aurélie Samson, acting director of the Arlaten museum for their help and advice, as well as Les Amis du Vieil Arles (AVA) and Bernadette Murphy. We thank our more regular collaborators, Caroline Bisson genealogist in Marseille, Laurent Toussaint, a passionate researcher on the supercentenarians and centenarians of ancient times, and Cyril Depoudent, French correspondent of the Gerontological Research Group (GRG), who helped us to collect and read these new documents. We thank François Robin-Champigneul who read our initial manuscript and spotted a few errors. We would like to thank the journalists who discovered and circulated new documents such as Prescillia Michel and Olivier Sibille, journalist at France 2, and Camille Le Pomellec, who conducted an investigation into the JC case for Paris-Match, as well as all the members of the Facebook group “Contre-enquête sur l’enquête Jeanne Calment” who searched and provided some of the new documents. Finally, we would like to thank Dr. George Garoyan and Dr. Catherine Levraud for the time spent remembering JC’s health status in the 1990s. Author Contributions All authors, J.M.R., M.A., F.R.H., and B.J., equally contributed to the design of the study and the writing of the paper. J.M.R. and B.J. drafted the first version of the paper. F.R.H. and J.M.R. coordinated the mathematical part. Funding This paper was published as part of a supplement sponsored and funded by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP. Conflict of Interest None reported. References 1. Allard M , Lèbre V , Robine JM. Les 120 Ans De Jeanne Calment . Paris, France : Le Cherche-Midi Editeur ; 1994 . Google Preview WorldCat COPAC 2. Allard M , Lèbre V , Robine JM. Jeanne Calment: From Van Gogh’s Time to Ours, 122 Extraordinary Years . New York, NY : WH Freeman and Company ; 1998 . Google Preview WorldCat COPAC 3. Allard M , Robine JM. Les Centenaires Français. Etude de la Fondation IPSEN 1990–2000 Rapport Final . Paris, France : Annee Gerontologique ; 2000 . Google Preview WorldCat COPAC 4. Robine JM , Allard M . Validation of the exceptional longevity case of a 120 years old woman . Facts and Research in Gerontology. 1995 ; 9: 363 – 367 . WorldCat 5. Robine JM , Allard M . Jeanne Calment: validation of the duration of her life . In: Validation of Exceptional Longevity. Odense Mongraphs on Population Aging 6 . Odense, Denmark : Odense University Press ; 1999 . https://www.demogr.mpg.de/books/odense/6/09.htm. Accessed July 23, 2019. Google Preview WorldCat COPAC 6. Robine JM , Allard M . The oldest human . Science. 1998 ; 279 : 1834 – 1835 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 7. Jeune B . In Search of the First Centenarians . In: Jeune B , Vaupel JW , eds. Exceptional Longevity: From Prehistory to the Present. Odense Monographs on Population Aging . Odense, Denmark: Odense University Press; 1995 : 2 . https://www.demogr.mpg.de/Papers/Books/Monograph2/start.htm. Accessed July 23, 2019. Google Preview WorldCat COPAC 8. Jeune B , Vaupel JW . Species of evidence of exceptional longevity . In: Jeune B , Vaupel JW , eds. Validation of Exceptional Longevity Odense Monographs on Population Aging . Odense, Denmark: Odense University Press; 1999 : 6 . https://www.demogr.mpg.de/books/odense/6/index.htm. Accessed July 23, 2019. Google Preview WorldCat COPAC 9. Maier H , Jeune B , Vaupel JW. Exceptional Longevity . Heidelberg, Germany : Springer Verlag ; 2020 . Google Preview WorldCat COPAC 10. Milova E . Valery Novoselov: Investigating Jeanne Calment’s Longevity Record . Life Extension Advocacy Foundation . https://www.leafscience.org/valery-novoselov-investigating-jeanne-calments-longevity-record/. Accessed July 23, 2019. COPAC 11. Zak N . Jeanne Calment: the Secret of Longevity . Researchgate ; 2018 . doi:. https://www.researchgate.net/publication/329773795_Jeanne_Calment_the_secret_of_longevity. Accessed July 23, 2019. COPAC Crossref 12. Zak N . Evidence that Jeanne Calment died in 1934 -not 1997 . Rejuvenation Res. 2019 ; 22 : 3 – 12 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 13. Desjardins B . Validation of extreme longevity cases in the past: the French-Canadian experience . In: Jeune B , Vaupel JW , eds. Validation of Exceptional Longevity, Odense Monographs on Population Aging No. 6 . Odense, Denmark : Odense University Press ; 1999 . https://www.demogr.mpg.de/books/odense/6/04.htm. Accessed July 23, 2019. Google Preview WorldCat COPAC 14. Desjardins B , Bourbeau R . The emergence of supercentenarians in Canada . In: Maier H , Gampe J , Jeune B , Robine J-M , Vaupel JW , eds. Supercentenarians . Berlin, Germany : Springer ; 2010 : 59 – 74 . https://link.springer.com/content/pdf/bfm%3A978-3-642-11520-2%2F1.pdf. Accessed July 23, 2019. Google Scholar Crossref Search ADS Google Preview WorldCat COPAC 15. Poulain M . On the age validation of supercentenarians . In: Maier H , Gampe J , Jeune B , Robine J-M , Vaupel JW , editors. Supercentenarians . Berlin, Germany : Springer ; 2010 . p. 3 – 30 . https://link.springer.com/content/pdf/bfm%3A978-3-642-11520-2%2F1.pdf. Accessed July 23, 2019. Google Scholar Crossref Search ADS Google Preview WorldCat COPAC 16. Young RD , Desjardins B , McLaughlin K , Poulain M , Perls T . Typologies of Extreme Longevity Myths . Curr Gerontol Geriatr Res 2011 ;2010: 1 – 12 . http://www.hindawi.com/journals/cggr/2010/423087/. Accessed July 23, 2019. doi:10.1155/2010/423087 WorldCat 17. Andersen SL , Sebastiani P , Dworkis DA , Feldman L , Perls TT . Health span approximates life span among many supercentenarians: compression of morbidity at the approximate limit of life span . J Gerontol A Biol Sci Med Sci. 2012 ; 67 : 395 – 405 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 18. Fries JF . Aging, natural death, and the compression of morbidity . N Engl J Med. 1980 ; 303 : 130 – 135 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 19. Vauthier LC . The Sanatorium Universitaire of Leysin, Switzerland . Brit J Tuberculosis. 1929 ; 23 : 30 – 34 . doi:10.1016/S0366-0850(29)80007-5 Google Scholar Crossref Search ADS WorldCat 20. Morabia A , Khatchatrian N . Major causes of deaths between 1901 and 1990 in Geneva, Switzerland . Soz Praventivmed. 1996 ; 41 : 315 – 21 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 21. Levraud C. Jeanne Calment, 118 ans, Prototype de Longévité. Mémoire pour la Capacité de Gérontologie, Années 1991/1992 et 1992/1993 . Dissertation. Faculté de Médecine de Marseille. Marseille, France: University of Marseille. Google Preview WorldCat COPAC 22. Le Bourg E . Jeanne Calment’s daughter has usurped her mother’s identity”: an amateur work or scientific research? Médecine/Sciences. 2019 ; 35 : 375 – 380 . doi:. https://www.medecinesciences.org/en/articles/medsci/full_html/2019/05/msc190028/msc190028.html. Accessed July 23, 2019. Google Scholar Crossref Search ADS WorldCat Crossref 23. Lenart A , Aburto JM , Stockmarr A , Vaupel JW . The Human Longevity Record may Hold for Decades . https://arxiv.org/pdf/1809.03732.pdf. Accessed July 23, 2019. arXiv 2018 :1809.03732 [q-bio.PE]. https://arxiv.org/ftp/arxiv/papers/1809/1809.pdf. Accessed July 23, 2019. 24. Medford A , Vaupel JW . Human lifespan records are not remarkable but their durations are . PLoS One . 2019 ; 14 : e0212345 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 25. Robine JM , Cubaynes S . Worldwide demography of centenarians . Mech Ageing Dev. 2017 ; 165 ( Pt B ): 59 – 67 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 26. Gondo Y , Hirose N , Yasumoto S , et al. Age verification of three Japanese supercentenarians who reach Age 115+ . In: Maier H , Jeune B , Vaupel JW , eds. Execeptional Lifespans . Heidelberg, Germany : Springer Verlag ; 2020 . Google Preview WorldCat COPAC 27. Jeune B , Poulain M . Emma Morano – 117 years and 137 days . In: Maier H , Jeune B , Vaupel JW , eds. Exeptional Lifespans . Heidelberg, Germany : Springer Verlag ; 2020 . Google Preview WorldCat COPAC © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Gurinovich,, Anastasia;Andersen, Stacy, L;Puca,, Annibale;Atzmon,, Gil;Barzilai,, Nir;Sebastiani,, Paola

doi: 10.1093/gerona/glz179pmid: 31724059

Abstract APOE is a well-studied gene with multiple effects on aging and longevity. The gene has three alleles: e2, e3, and e4, whose frequencies vary by ethnicity. While the e2 is associated with healthy cognitive aging, the e4 allele is associated with Alzheimer’s disease and early mortality and therefore its prevalence among people with extreme longevity (EL) is low. Using the PopCluster algorithm, we identified several ethnically different clusters in which the effect of the e2 and e4 alleles on EL changed substantially. For example, PopCluster discovered a large group of 1,309 subjects enriched of Southern Italian genetic ancestry with weaker protective effect of e2 (odds ratio [OR] = 1.27, p = .14) and weaker damaging effect of e4 (OR = 0.82, p = .31) on the phenotype of EL compared to other European ethnicities. Further analysis of this cluster suggests that the odds for EL in carriers of the e4 allele with Southern Italian genetic ancestry differ depending on whether they live in the United States (OR = 0.29, p = .009) or Italy (OR = 1.21, p = .38). PopCluster also found clusters enriched of subjects with Danish ancestry with varying effect of e2 on EL. The country of residence (Denmark or United States) appears to change the odds for EL in the e2 carriers. APOE, Bioinformatics, Human genetics, Longevity Apolipoprotein E (APOE) is a class of proteins involved in lipid metabolism with functions determined by alleles of the gene APOE. The gene has three alleles e2, e3, and e4 defined by combinations of genotypes of the single-nucleotide polymorphisms (SNPs) rs7412 and rs429358 (1,2). APOE is a well-studied gene with multiple effects on aging and longevity. The e4 allele is a well-established risk factor for late onset of Alzheimer’s disease (3–5). We and others have demonstrated that having an APOE e4 allele has a deleterious effect on longevity that decreases the odds to reach extreme human life span (1,6) independently of e2. The e3 allele is the “neutral allele” in many ethnicities, while e2 is the allele that promotes longevity and healthy aging, independently of e4 (1,6–8). The frequency of the APOE alleles varies among human populations (9). For example, the most common e3 allele frequency varies from 54% in African Pygmies to 90% in Southern Italians and Sardinians. The frequency of the e4 allele varies from 5% in Sardinians to 41% in African Pygmies. It has been reported that the frequency of the e4 allele increases with latitude due to the natural selection to protect against low-cholesterol levels (10). Additionally, the e4 allele is associated with better resistance to adverse nonindustrialized environments, specifically to parasites and infections in children (11,12). Studies have suggested that APOE e4 has different effects on the risk of Alzheimer’s disease in Europeans, African Americans, and Hispanics (5,13,14), while the role of ethnicity on the effect of APOE e2 on longevity and neuroprotection is unknown. To investigate the ethnic-specific effect of APOE e2 and e4 on extreme longevity (EL), we used our new algorithm PopCluster (15) to search for ethnically different clusters of Europeans in which the effect of APOE e2 and e4 on EL change. Methods Study Populations We used genome-wide genotype data from a consortium of four studies of EL and healthy aging: the Southern Italian Centenarian Study (SICS) (16), the Longevity Gene Project (LGP) (17), the Long Life Family Study (LLFS) (18), and the New England Centenarian Study (NECS) (19) (Table 1). The SICS is a study of longevity that focused enrollment of long-lived individuals in the South of Italy (16). The LGP is a study of longevity that enrolled long-lived individuals who were of Ashkenazy Jewish descent, survived to at least age 95 years old, and were dementia free at the time of enrollment (20). Some siblings, offspring and spouses of offspring were also enrolled and additional unrelated population controls were selected based on lack of familial longevity. The LLFS is a family-based study of healthy aging and longevity that recruited approximately 550 families and 5,000 family members selected for familial longevity (18,21). Participants were enrolled at three American field centers (Boston, Pittsburgh and New York), and a European field center in Denmark. The NECS is a study of centenarians, some of the long-lived siblings, offspring, offspring spouses, and additional unrelated controls selected because their parents died before reaching the median age survival of their birth year cohort (19). The study recruits centenarians in worldwide. Table 1. Study Characteristics Study Cases (median age, range) Controls SICS 174 (100, 96–109) 540 LLFS 574 (100, 96–111) 2,561 LGP 308 (102, 96–113) 621 NECS 1,087 (103, 96–119) 3,103 Total 2,143 6,825 Study Cases (median age, range) Controls SICS 174 (100, 96–109) 540 LLFS 574 (100, 96–111) 2,561 LGP 308 (102, 96–113) 621 NECS 1,087 (103, 96–119) 3,103 Total 2,143 6,825 Note: SICS = Southern Italian Centenarian Study; LLFS = Long Life Family Study; LGP = Longevity Genes Project; NECS = New England Centenarian Study. Cases were defined as individuals who lived past the 1 percentile survival age from the 1900 birth year cohort based on U.S. Social Security Administration cohort life tables (26). Controls were defined either as individuals who died before reaching the threshold age, or as random subjects from the general population. Open in new tab Table 1. Study Characteristics Study Cases (median age, range) Controls SICS 174 (100, 96–109) 540 LLFS 574 (100, 96–111) 2,561 LGP 308 (102, 96–113) 621 NECS 1,087 (103, 96–119) 3,103 Total 2,143 6,825 Study Cases (median age, range) Controls SICS 174 (100, 96–109) 540 LLFS 574 (100, 96–111) 2,561 LGP 308 (102, 96–113) 621 NECS 1,087 (103, 96–119) 3,103 Total 2,143 6,825 Note: SICS = Southern Italian Centenarian Study; LLFS = Long Life Family Study; LGP = Longevity Genes Project; NECS = New England Centenarian Study. Cases were defined as individuals who lived past the 1 percentile survival age from the 1900 birth year cohort based on U.S. Social Security Administration cohort life tables (26). Controls were defined either as individuals who died before reaching the threshold age, or as random subjects from the general population. Open in new tab Genotype Data Genome-wide genotype data for all studies were generated using Illumina SNP arrays (22) and imputed to the 1,000 genomes haplotypes Phase 1 using IMPUTE2 and standard protocol l (23). Imputation was preceded by prephasing with ShapeIT (24). More details on the datasets can be found in in (25). APOE alleles were inferred from SNPs rs7412 and rs429358 that were either genotyped using real time PCR (LLFS samples) or imputed using IMPUTE2 (SICS, LGP, and NECS samples; Supplementary Table S1). The imputation quality scores of the two SNPs when imputed were > 0.9 (6). Cases were defined as individuals who lived past the 1 percentile survival age from the 1900 birth year cohort based on U.S. Social Security Administration cohort life tables (26), that is, age 96 years and greater for males, and 100 years and greater for females. Controls were defined either as individuals who died before reaching the threshold age, or as random subjects from the general population. The combined datasets contain several European ethnicities and information about place of birth and mother/father tongue. Statistical Analysis We used PopCluster (15) to find ethnic-specific clusters of subjects with varying effect of APOE e2 and e4 on EL. PopCluster discovers subsets of individuals characterized by significant effects of a genetic variant. The algorithm uses hierarchical clustering of the first four principal components calculated from the genome-wide genotype data to generate the smallest clusters with at least 100 subjects and uses logistic regression to estimate adjusted OR for EL in each cluster. Then, a recursive procedure is used to test whether the ORs in connected clusters are significantly different using Z-tests. By design, the algorithm finds nonoverlapping clusters; thus, no cluster contains subjects from another cluster. We used logistic regression adjusted by sex and cluster-specific principal components calculated from the genome-wide genotype data to estimate cluster-specific associations between APOE alleles and EL. To adjust p values for multiple testing, we used p < .05/(number of clusters returned by the algorithm). Our evaluation showed that this correction maintains a family-wide error rate < 5% (15). To evaluate the effect of APOE e2 and e4 on EL independently of each other, we conducted two analyses. In one analysis, we removed all carriers of e4, and used an additive genetic model with e3e3 coded as 0 (5,901 subjects), e2e3 as 1 (1,362 subjects), and e2e2 as 2 (56 subjects). Similarly, to evaluate the effect of e4 on EL, we removed all carriers of e2, and used an additive genetic model with e3e3 coded as 0 (5,901 subjects), e3e4 as 1 (1,497 subjects), and e4e4 as 2 (126 subjects). Total genotype counts are presented in Supplementary Table S2. All power analyses were done using the G*Power software (27). Annotation of Clusters by Ethnicity Ancestry information was available for 68% of the subjects, specifically: 3,717 NECS subjects had information on either their birth places or native languages of their grandparents, or both (28), all SICS subjects were assumed to be South Italians, all LGP subjects were assumed to be Ashkenazi Jews, and 735 subjects from the LLFS were recruited in Denmark and assumed to be of Danish descent. Additionally, when the data set is limited to Caucasians (as is the case here), subjects’ ethnicities can be inferred from their location on the scatter plots of first four genome-wide principal components (22,28–30). We labeled each cluster with ethnicity based on the following rules followed in order: (i) If more than 50% of subjects in a cluster are enriched of a certain ethnicity, the cluster was labeled by that ethnicity. (ii) If there is no known ethnicity with more than 50% subjects represented in a cluster, but the scatter plots of genome-wide principal components (Figures 1 and 2 and Supplementary Figures S1–S3) of a cluster are localized, the cluster was labeled based on the location on the scatter plots (31). (iii) If neither of two previous conditions hold, a cluster was labeled as mixed. Figure 1. Open in new tabDownload slide Scatter plots of principal components PC1-PC2 and PC3-PC4 from genome-wide genotype data of all subjects in the study of extreme longevity. Grey (red in the online version) points are subjects in clusters with a significant effect of APOE e2: (A) cluster with 977 subjects enriched of Ashkenazi Jews; (B) cluster with 437 subjects enriched of Danish subjects. Figure 1. Open in new tabDownload slide Scatter plots of principal components PC1-PC2 and PC3-PC4 from genome-wide genotype data of all subjects in the study of extreme longevity. Grey (red in the online version) points are subjects in clusters with a significant effect of APOE e2: (A) cluster with 977 subjects enriched of Ashkenazi Jews; (B) cluster with 437 subjects enriched of Danish subjects. Figure 2. Open in new tabDownload slide Scatter plots of principal components PC1-PC2 and PC3-PC4 from genome-wide genotype data of all subjects in the study of extreme longevity. Grey (red in the online version) points are subjects in clusters with a significant effect of APOE e4: (A) cluster with 1416 subjects enriched of British ancestry; (B) cluster with 559 subjects enriched of Danish ancestry; (C) cluster with 974 subjects with mixed ancestry; (D) cluster with 977 subjects enriched of Ashkenazi Jews; (E) cluster with 635 subjects enriched of Danish subjects. Figure 2. Open in new tabDownload slide Scatter plots of principal components PC1-PC2 and PC3-PC4 from genome-wide genotype data of all subjects in the study of extreme longevity. Grey (red in the online version) points are subjects in clusters with a significant effect of APOE e4: (A) cluster with 1416 subjects enriched of British ancestry; (B) cluster with 559 subjects enriched of Danish ancestry; (C) cluster with 974 subjects with mixed ancestry; (D) cluster with 977 subjects enriched of Ashkenazi Jews; (E) cluster with 635 subjects enriched of Danish subjects. Results We analyzed the ethnic-specific association of EL with APOE alleles in 2,143 cases of EL, and 6,825 controls summarized in Table 1. Ethnic-Specific Effect of APOE e2 PopCluster identified two clusters with a significant, positive effect of APOE e2 on EL after correction for multiple comparisons (p < .05/13 = .0038; Table 2), and an additional cluster with nominally significant effect of APOE e2 on EL (Supplementary Table S3). In all other clusters, the genetic association between APOE e2 and EL did not reach statistical significance although several clusters had sample size > 180 that is the minimum size required to have 80% power to detect an odds ratio (OR) of 2, assuming a level of significance of 0.0038. Two pairs of these 13 clusters (Ashkenazi Jews_1 and British_2, Danish_1 and British_2 from Supplementary Table S3) had significantly different effects of APOE e2 on EL using a false discovery rate (FDR) < 7% as level of significance to correct for multiple testing. Figure 1 displays the scatter plots of the first four genome-wide principal components for the two clusters in Table 2. In both clusters, carriers of e2 have increased odds for EL compared to carriers of e3e3: OR = 2.24, 95% confidence interval (CI): 1.35, 3.73 in the cluster enriched of Danish ancestry, and OR = 2.12, 95% CI: 1.44, 3.13 in the cluster enriched of Ashkenazi Jewish ancestry. The effects of e2 on EL in the two clusters were not significantly different (p = .87), although sample and effect sizes provided only 6% power to detect whether the difference between these two clusters was significant. Table 2. Significant Positive Associations Between APOE e2 and EL in Ethnic-Specific Clusters Ethnicity Label No. Subjects (Enriched ethnicity) No. Enriched Ethnicity/No. Subjects with Labels OR (e2 vs e3) 95% CI p Ashkenazi Jews_1 (*) 977 (Ashkenazi Jews) 411/646 ≈ 0.64 2.12 1.44, 3.13 .0002 Danish_1 (*) 437 (Danish) 319/344 ≈ 0.93 2.24 1.35, 3.73 .002 Ethnicity Label No. Subjects (Enriched ethnicity) No. Enriched Ethnicity/No. Subjects with Labels OR (e2 vs e3) 95% CI p Ashkenazi Jews_1 (*) 977 (Ashkenazi Jews) 411/646 ≈ 0.64 2.12 1.44, 3.13 .0002 Danish_1 (*) 437 (Danish) 319/344 ≈ 0.93 2.24 1.35, 3.73 .002 Note:Ethnicity label: This was inferred by either the enriched ethnicity of subjects (>50% in the cluster) (*) or based on the PCA plots for this cluster (**) (underscore and number mean that there are more than 1 distinct cluster that are labeled the same). No. subjects: total number of subjects in the cluster; (Enriched ethnicity) No. enriched ethnicity / No. subjects with labels: proportion of subjects with enriched ethnicity from the subjects with known information on their ancestry; OR (e2 vs e3): odds ratio for EL comparing carriers of one copy of APOE e2 to e3e3 carriers; 95% CI: 95% confidence interval. p: p value (association is significant if p < .05/13 = .004). Given sample and effect sizes, there was not enough power (6%) to detect whether the difference between these two clusters was significant. CI = Confidence interval; OR = Odds ratio. Open in new tab Table 2. Significant Positive Associations Between APOE e2 and EL in Ethnic-Specific Clusters Ethnicity Label No. Subjects (Enriched ethnicity) No. Enriched Ethnicity/No. Subjects with Labels OR (e2 vs e3) 95% CI p Ashkenazi Jews_1 (*) 977 (Ashkenazi Jews) 411/646 ≈ 0.64 2.12 1.44, 3.13 .0002 Danish_1 (*) 437 (Danish) 319/344 ≈ 0.93 2.24 1.35, 3.73 .002 Ethnicity Label No. Subjects (Enriched ethnicity) No. Enriched Ethnicity/No. Subjects with Labels OR (e2 vs e3) 95% CI p Ashkenazi Jews_1 (*) 977 (Ashkenazi Jews) 411/646 ≈ 0.64 2.12 1.44, 3.13 .0002 Danish_1 (*) 437 (Danish) 319/344 ≈ 0.93 2.24 1.35, 3.73 .002 Note:Ethnicity label: This was inferred by either the enriched ethnicity of subjects (>50% in the cluster) (*) or based on the PCA plots for this cluster (**) (underscore and number mean that there are more than 1 distinct cluster that are labeled the same). No. subjects: total number of subjects in the cluster; (Enriched ethnicity) No. enriched ethnicity / No. subjects with labels: proportion of subjects with enriched ethnicity from the subjects with known information on their ancestry; OR (e2 vs e3): odds ratio for EL comparing carriers of one copy of APOE e2 to e3e3 carriers; 95% CI: 95% confidence interval. p: p value (association is significant if p < .05/13 = .004). Given sample and effect sizes, there was not enough power (6%) to detect whether the difference between these two clusters was significant. CI = Confidence interval; OR = Odds ratio. Open in new tab Ethnic-Specific Effect of APOE e4 Similarly, PopCluster identified four ethnic-specific clusters with a nominally significant effect of APOE e4 on EL (Supplementary Table S4), and five clusters in which APOE e4 was significantly and negatively associated with EL after correcting for multiple comparisons (p < .05/12 = .004; Table 3). In the remaining three clusters (South Italians, Polish, British_4), the genetic association did not reach statistical significance and it is noticeable that in the cluster enriched of Southern Italians with N = 1,309, the effect of e4 on EL was substantially smaller than in other ethnic groups (OR = 0.82, p = .31; Supplementary Table S4). Two pairs of these 12 clusters (British_3 and South Italians, British_3 and British_4 from Supplementary Table S4) had significantly different effects of APOE e4 on EL using an FDR < 7% as level of significance to correct for multiple testing. Figure 2 displays the scatter plots of the first four genome-wide principal components for the five clusters in which the association between APOE e4 and EL is statistically significant, after correction for multiple testing. In all five clusters, the effect of APOE e4 is deleterious on longevity with worst effect in subjects of British ancestry (OR = 0.3, 95% CI: 0.21, 0.44) and slightly less severe effect in subjects with North Eastern Europeans ethnicities. None of the pairwise difference of genetic effects between the five clusters was statistically significant. However, when the effect of the cluster enriched of British ancestry was compared to the effect of the other four clusters combined, the difference was borderline significant (p = .06). Given sample and effect sizes, there was not enough power (5%–43%) to detect whether the difference between each pair of the five significant clusters was significant (27). Table 3. Significant Negative Associations Between APOE e4 and EL in Ethnic-Specific Clusters Ethnicity Label No. Subjects (Enriched ethnicity) No. Enriched Ethnicity/No. Subjects with Labels OR (e4 vs e3) 95% CI p British_3 (**) 1,416 (British) 101/995 ≈ 0.10 0.3 0.21, 0.44 4.42E−10 Danish_4 (**) 559 (Danish) 57/175 ≈ 0.33 0.44 0.26, 0.72 .001 Mixed_1 (**) 974 (Danish) 134/286 ≈ 0.47 0.49 0.32, 0.78 .002 Ashkenazi Jews_1 (*) 977 (Ashkenazi Jews) 411/646 ≈ 0.64 0.48 0.30, 0.77 .003 Danish_5 (*) 635 (Danish) 444/484 ≈ 0.92 0.47 0.28, 0.78 .004 Ethnicity Label No. Subjects (Enriched ethnicity) No. Enriched Ethnicity/No. Subjects with Labels OR (e4 vs e3) 95% CI p British_3 (**) 1,416 (British) 101/995 ≈ 0.10 0.3 0.21, 0.44 4.42E−10 Danish_4 (**) 559 (Danish) 57/175 ≈ 0.33 0.44 0.26, 0.72 .001 Mixed_1 (**) 974 (Danish) 134/286 ≈ 0.47 0.49 0.32, 0.78 .002 Ashkenazi Jews_1 (*) 977 (Ashkenazi Jews) 411/646 ≈ 0.64 0.48 0.30, 0.77 .003 Danish_5 (*) 635 (Danish) 444/484 ≈ 0.92 0.47 0.28, 0.78 .004 Note:Ethnicity label: This was inferred by either the enriched ethnicity of subjects (>50% in the cluster) (*) or based on the PCA plots for this cluster (**) (underscore and number mean that there are more than 1 distinct cluster that are labeled the same). No. subjects: total number of subjects in the cluster; (Enriched ethnicity) No. enriched ethnicity / No. subjects with labels: proportion of subjects with enriched ethnicity from the subjects with known information on their ancestry; OR (e4 vs e3): odds ratio for EL comparing carriers of one copy of APOE e4 to e3e3 carriers. 95% CI: 95% confidence interval. p: p value (association is significant if p < .05/12 = .004). Given sample and effect sizes, there was not enough power (5%–43%) to detect whether the difference between each pair of these five clusters was significant. CI = Confidence interval; OR = Odds ratio. Open in new tab Table 3. Significant Negative Associations Between APOE e4 and EL in Ethnic-Specific Clusters Ethnicity Label No. Subjects (Enriched ethnicity) No. Enriched Ethnicity/No. Subjects with Labels OR (e4 vs e3) 95% CI p British_3 (**) 1,416 (British) 101/995 ≈ 0.10 0.3 0.21, 0.44 4.42E−10 Danish_4 (**) 559 (Danish) 57/175 ≈ 0.33 0.44 0.26, 0.72 .001 Mixed_1 (**) 974 (Danish) 134/286 ≈ 0.47 0.49 0.32, 0.78 .002 Ashkenazi Jews_1 (*) 977 (Ashkenazi Jews) 411/646 ≈ 0.64 0.48 0.30, 0.77 .003 Danish_5 (*) 635 (Danish) 444/484 ≈ 0.92 0.47 0.28, 0.78 .004 Ethnicity Label No. Subjects (Enriched ethnicity) No. Enriched Ethnicity/No. Subjects with Labels OR (e4 vs e3) 95% CI p British_3 (**) 1,416 (British) 101/995 ≈ 0.10 0.3 0.21, 0.44 4.42E−10 Danish_4 (**) 559 (Danish) 57/175 ≈ 0.33 0.44 0.26, 0.72 .001 Mixed_1 (**) 974 (Danish) 134/286 ≈ 0.47 0.49 0.32, 0.78 .002 Ashkenazi Jews_1 (*) 977 (Ashkenazi Jews) 411/646 ≈ 0.64 0.48 0.30, 0.77 .003 Danish_5 (*) 635 (Danish) 444/484 ≈ 0.92 0.47 0.28, 0.78 .004 Note:Ethnicity label: This was inferred by either the enriched ethnicity of subjects (>50% in the cluster) (*) or based on the PCA plots for this cluster (**) (underscore and number mean that there are more than 1 distinct cluster that are labeled the same). No. subjects: total number of subjects in the cluster; (Enriched ethnicity) No. enriched ethnicity / No. subjects with labels: proportion of subjects with enriched ethnicity from the subjects with known information on their ancestry; OR (e4 vs e3): odds ratio for EL comparing carriers of one copy of APOE e4 to e3e3 carriers. 95% CI: 95% confidence interval. p: p value (association is significant if p < .05/12 = .004). Given sample and effect sizes, there was not enough power (5%–43%) to detect whether the difference between each pair of these five clusters was significant. CI = Confidence interval; OR = Odds ratio. Open in new tab Effect of APOE in Italians The cluster enriched of Southern Italians with N = 1,309 includes 77% of subjects of South Italian ancestry (Supplementary Tables S3 and S4 and Supplementary Figure S2A) with 805 subjects from the Southern Italian Centenarian Study (SICS; Table 1) who live in South Italy, and 504 subjects who live in the United States (Figure 3). In this cluster, neither the effect of APOE e2 nor e4 were statistically significant when the data were analyzed without adjustment to the country of residence (carriers of e2: OR for EL = 1.27, 95% CI: 0.92, 1.76; carriers of e4: OR for EL = 0.82, 95% CI: 0.56, 1.20). To investigate the interaction between APOE alleles and country of residence, we analyzed the data in this cluster using a logistic regression model that included the APOE effect, sex, country of residence indicator variable coded as 0 for subjects living in United States, and 1 for subjects living in Italy, and the interaction term between the indicator variable and the genetic effect. We did not detect a statistical significant interaction between residence and the effect of APOE e2. However, the model with the APOE e4 allele had a significant interaction between residence and the e4 effect (Supplementary Table S5). EL was 71% less likely in Italians with one copy of e4 versus e3e3 who live in the United States (OR = 0.29, 95% CI: 0.11, 0.73). However, there was no significant association between e4 and EL in Italians who live in Italy (OR = 1.21, 95% CI: 0.79, 1.86), although a sample size of 805 provides more than 75% power to detect an OR > 1.2. To reduce genetic heterogeneity between the two groups of Italian subjects shown by significant differences in the principal components (Supplementary Table S6), we also reanalyzed the data after removing 157 subjects who mostly live in the United States, but whose ethnicity is more consistent with Northern and Central Europeans based on the PC1-PC2 plot (Supplementary Figure S2A, Figure 3, and Supplementary Table S6). Removing these subjects reduced the variability and differences between PC1-PC4 of the two groups of subjects, and the difference between the statistical estimates slightly increased. Specifically, EL was 81% less likely in Italians with one copy of e4 versus e3e3 who live in the United States (OR = 0.19, 95% CI: 0.04, 0.80), and there was no significant association between e4 and EL in Italians who live in Italy (OR = 1.21, 95% CI: 0.79, 1.85). Figure 3. Open in new tabDownload slide Scatter plots of principal components PC1-PC2 and PC3-PC4 from genome-wide genotype data of all subjects in the extreme longevity (EL) study. Subjects are colored in lighter grey (blue in the online version) if they are in the cluster of 1,309 subjects of Southern Italian descent and live in South Italy. Subjects in darker grey (red in the online version) denote individuals in this cluster who live in the United States. On the scatter plot in the left panel, subjects with PC1 ≥ −0.005 are more consistent with Northern Italian ethnicity, rather than Southern Italian ethnicity. Figure 3. Open in new tabDownload slide Scatter plots of principal components PC1-PC2 and PC3-PC4 from genome-wide genotype data of all subjects in the extreme longevity (EL) study. Subjects are colored in lighter grey (blue in the online version) if they are in the cluster of 1,309 subjects of Southern Italian descent and live in South Italy. Subjects in darker grey (red in the online version) denote individuals in this cluster who live in the United States. On the scatter plot in the left panel, subjects with PC1 ≥ −0.005 are more consistent with Northern Italian ethnicity, rather than Southern Italian ethnicity. Effect of APOE in Danish There were five distinct clusters enriched of subjects of Danish ancestry found to have ethnic-specific effects of APOE alleles on EL (Supplementary Tables S3 and S4), that is, clusters with 437 (Danish_1), 198 (Danish_2), and 766 (Danish_3) subjects for APOE e2, and clusters with 559 (Danish_4) and 635 (Danish_5) subjects for APOE e4. LLFS enrolled participants in both the United States and Denmark, and therefore, we could examine how country of residence modifies the effects of APOE on EL. Table 4 summarizes the distribution of Danish subjects in the five clusters by residence (Denmark or United States). Interestingly, while e2 was significantly positively associated with EL in the Danish_1 cluster that includes 72% of Danish living in Denmark (OR = 2.24, 95% CI: 1.35, 3.73), this association in the Danish_3 cluster that includes only 7% of Danish living in Denmark was not significant (OR = 1.13, 95% CI: 0.75, 1.69), and the ORs were significantly different (p = .04;Supplementary Table S7). The OR for EL in carriers of e4 in the Danish_4 cluster that includes 90% of Danish living in the United States (OR = 0.44, 95% CI: 0.26, 0.72) was slightly smaller than the OR for EL in carriers of e4 in the Danish_5 cluster that includes only 29% of Danish living in the United States (OR = 0.47, 95% CI: 0.28, 0.78), and the ORs were not significantly different (p = .86). Table 4. Distribution of Countries Where Subjects Live for Clusters Enriched of Danish Ethnicity Cluster, No. Subjects Live in the United States Live in Denmark Danish_1, 437 28% 72% Danish_2, 198 40% 60% Danish_3, 766 93% 7% Danish_4, 559 90% 10% Danish_5, 635 29% 71% Cluster, No. Subjects Live in the United States Live in Denmark Danish_1, 437 28% 72% Danish_2, 198 40% 60% Danish_3, 766 93% 7% Danish_4, 559 90% 10% Danish_5, 635 29% 71% Note:Detailed information for each cluster can be found inSupplementary Tables S3 and S4. We refer to the clusters in which large majority (>65%) of subjects live in the United States or Denmark as clusters with Danish living in the United States (clusters Danish_3 and Danish_4) versus Danish living in Denmark (clusters Danish_1 and Danish_5), respectively. Open in new tab Table 4. Distribution of Countries Where Subjects Live for Clusters Enriched of Danish Ethnicity Cluster, No. Subjects Live in the United States Live in Denmark Danish_1, 437 28% 72% Danish_2, 198 40% 60% Danish_3, 766 93% 7% Danish_4, 559 90% 10% Danish_5, 635 29% 71% Cluster, No. Subjects Live in the United States Live in Denmark Danish_1, 437 28% 72% Danish_2, 198 40% 60% Danish_3, 766 93% 7% Danish_4, 559 90% 10% Danish_5, 635 29% 71% Note:Detailed information for each cluster can be found inSupplementary Tables S3 and S4. We refer to the clusters in which large majority (>65%) of subjects live in the United States or Denmark as clusters with Danish living in the United States (clusters Danish_3 and Danish_4) versus Danish living in Denmark (clusters Danish_1 and Danish_5), respectively. Open in new tab Discussion APOE e2 and e4 alleles are known to have an effect on EL (1,6,7) but the analyses in this manuscript suggest that the magnitude of these associations is ethnic-specific among Europeans. We used a novel algorithm to search for clusters of individuals characterized by specific genetic ancestry and varying genetic effects. Our analysis discovered multiple ethnicities with no significant effect of APOE e2 and e4 alleles on EL, one group of North, Eastern European ancestry with a strong protective effect of APOE e2 on EL, and two groups of North European ancestry with different, deleterious effects of APOE e4 on EL. While with larger sample sizes the genetic association between APOE e2 and EL could become statistically significant in more European ethnicities, our analysis suggests that the protective effect of APOE e2 on EL in most European ethnicities is smaller than the effect in Ashkenazi Jewish/Northern European and Danish subjects living in Denmark. Conomos et al. (32) have shown that the PCA of genetic data might capture family relatedness instead of population structure when applied to the datasets with relatedness. Even though our combined dataset contains ~14% related individuals (Table 1), the evaluation of PopCluster on this very dataset with and without the related subjects performed similarly well when false-positive rate was evaluated. More details of this and other evaluations of PopCluster can be found in ref. (15). We also provided evidence that the genetic effect of APOE alleles changes based on country of residence in addition to genetic ancestry, suggesting the presence of environmental risk factors of a place of residence that modify the genetic effects of APOE after controlling for genetic ancestry. For example, our analysis showed that there was no deleterious effect of APOE e4 in subjects with Southern Italian ancestry living in the South of Italy. These results suggest that factors related to living in the South of Italy may mitigate the deleterious effect of APOE e4. Our hypothesis, which needs to be further investigated, is that the Mediterranean diet that is followed in Italy contributes to the difference. The results are consistent with previous findings that the Mediterranean diet reduces the risk of Alzheimer’s (33), and APOE e4 carriers versus noncarriers might have an exaggerated or different response to nutrition and other factors in relation to Alzheimer’s and cognitive function (34–36).Similarly, our analyses showed that the protective effect of the e2 allele in subjects with Danish ancestry is stronger in those individuals who live in Denmark and becomes much weaker in individuals of Danish ancestry who live in the United States. The overall diet composition (energy/protein/fat/carbohydrate amounts) in Denmark and the United States is comparable (37,38). A major difference between two countries is that Denmark is one of the world’s happiest countries to live in—Denmark was ranked second in the United Nations’ 2019 World Happiness Report as compared with the United States being ranked 19th (39). These differences are suggestive of complex gene–environment interaction of APOE and nutrition on EL that could lead to the development of natural interventions for healthy aging. The APOE protein is essential for healthy cholesterol metabolism and central nervous system cholesterol transport. Total APOE levels in plasma in very old individuals were found to be associated with lower total cholesterol and LDL cholesterol levels, which in turn were associated with the APOE e2 allele (40). The APOE e4 allele has been associated with abnormal lipid metabolism in cerebrospinal fluid, and reduced capacity to deliver neuronal cholesterol (5). Detrimental effects of APOE e4 may be alleviated through diet interventions (41), specifically Mediterranean diet (increased omega-3 fatty acids) (36,42). Additionally, APOE e4 carriers may be more sensitive to cholesterol and saturated fatty acids (43). APOE e2 carriers with metabolic syndrome might benefit from diet interventions as well (44). Our results are consistent with the hypothesis of an interaction between APOE and nutrition that differs by European ethnicity. Future investigations into diet and APOE genotype interaction might point at viable nutrition interventions to reduce the deleterious effect of APOE e4 allele. Additionally, accounting for ethnic-specific differences in the drug development process would contribute to higher drug efficacy for more populations (45). Acknowledgments Authors’ contribution: A.G. and P.S. designed the study, contributed to data analysis, and wrote the manuscript; S.A., A.P., G.A., and N.B. designed centenarian studies and enrolled study subjects; all authors reviewed the manuscript. Funding This paper was published as part of a supplement sponsored and funded by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP Conflict of Interest None reported. References 1. Schächter F , Faure-Delanef L , Guénot F , et al. Genetic associations with human longevity at the APOE and ACE loci . Nat Genet. 1994 ; 6 : 29 – 32 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 2. Weisgraber KH , Rall SC Jr , Mahley RW . Human E apoprotein heterogeneity. Cysteine-arginine interchanges in the amino acid sequence of the apo-E isoforms . J Biol Chem . 1981 ; 256 : 9077 – 9083 . Google Scholar PubMed WorldCat 3. Corder EH , Saunders AM , Strittmatter WJ , et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families . Science . 1993 ; 261 : 921 – 923 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 4. Yip AG , McKee AC , Green RC , et al. APOE, vascular pathology, and the AD brain . Neurology . 2005 ; 65 : 259 – 265 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 5. Liu CC , Liu CC , Kanekiyo T , Xu H , Bu G . Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy . Nat Rev Neurol . 2013 ; 9 : 106 – 118 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 6. Sebastiani P , Gurinovich A , Nygaard M , et al. APOE alleles and extreme human longevity . J Gerontol A Biol Sci Med Sci . 2018 ; 74 : 44 – 51 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 7. Schupf N , Barral S , Perls T , et al. Apolipoprotein E and familial longevity . Neurobiol Aging . 2013 ; 34 : 1287 – 1291 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 8. Wu L , Zhao L . ApoE2 and Alzheimer’s disease: time to take a closer look . Neural Regen Res. 2016 ; 11 : 412 – 413 . doi:10.4103/1673–5374.179044 Google Scholar Crossref Search ADS PubMed WorldCat 9. Corbo RM , Scacchi R . Apolipoprotein E (APOE) allele distribution in the world. Is APOE*4 a ‘thrifty’ allele? Ann Hum Genet . 1999 ; 63 ( Pt 4 ): 301 – 310 . Google Scholar Crossref Search ADS PubMed WorldCat 10. Eisenberg DT , Kuzawa CW , Hayes MG . Worldwide allele frequencies of the human apolipoprotein E gene: climate, local adaptations, and evolutionary history . Am J Phys Anthropol . 2010 ; 143 : 100 – 111 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 11. van Exel E , Koopman JJE , Bodegom DV , et al. Effect of APOE ε4 allele on survival and fertility in an adverse environment . PLoS One . 2017 ; 12 : e0179497 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 12. Trumble BC , Stieglitz J , Blackwell AD , et al. Apolipoprotein E4 is associated with improved cognitive function in Amazonian forager-horticulturalists with a high parasite burden . FASEB J . 2017 ; 31 : 1508 – 1515 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 13. Hendrie HC , Murrell J , Baiyewu O , et al. APOE ε4 and the risk for Alzheimer disease and cognitive decline in African Americans and Yoruba . Int Psychogeriatr. 2014 ; 26 : 977 – 985 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 14. Campos M , Edland SD , Peavy GM . Exploratory study of apolipoprotein E ε4 genotype and risk of Alzheimer’s disease in Mexican Hispanics . J Am Geriatr Soc. 2013 ; 61 : 1038 – 1040 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 15. Gurinovich A , Bae H , Farrell J , et al. PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects . Bioinformatics . 2019 . doi:. WorldCat Crossref 16. Malovini A , Illario M , Iaccarino G , et al. Association study on long-living individuals from Southern Italy identifies rs10491334 in the CAMKIV gene that regulates survival proteins . Rejuvenation Res . 2011 ; 14 : 283 – 291 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 17. Atzmon G , Schechter C , Greiner W , Davidson D , Rennert G , Barzilai N . Clinical phenotype of families with longevity . J Am Geriatr Soc. 2004 ; 52 : 274 – 277 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 18. Newman AB , Glynn NW , Taylor CA , et al. Health and function of participants in the long life family study: a comparison with other cohorts . Aging (Albany NY) . 2011 ; 3 : 63 – 76 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 19. Sebastiani P , Perls TT . The genetics of extreme longevity: lessons from the new England centenarian study . Front Genet . 2012 ; 3 : 277 . doi: Google Scholar PubMed WorldCat Crossref 20. Barzilai N , Atzmon G , Schechter C , et al. Unique lipoprotein phenotype and genotype associated with exceptional longevity . JAMA . 2003 ; 290 : 2030 – 2040 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 21. Sebastiani P , Hadley EC , Province M , et al. A family longevity selection score: ranking sibships by their longevity, size, and availability for study . Am J Epidemiol . 2009 ; 170 : 1555 – 1562 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 22. Sebastiani P , Solovieff N , Dewan AT , et al. Genetic signatures of exceptional longevity in humans . PLoS One . 2012 ; 7 : e29848 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 23. Howie B , Fuchsberger C , Stephens M , Marchini J , Abecasis GR . Fast and accurate genotype imputation in genome-wide association studies through pre-phasing . Nat Genet . 2012 ; 44 : 955 – 959 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 24. Delaneau O , Marchini J , Zagury JF . A linear complexity phasing method for thousands of genomes . Nat Methods . 2012 ; 9 : 179 – 181 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 25. Sebastiani P , Gurinovich A , Bae H , et al. Four Genome-Wide association studies identify new extreme longevity variants . J Gerontol A Biol Sci Med Sci . 2017 ; 72 : 1453 – 1464 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 26. Bell FC , Miller ML Life tables for the United States social security area 1900–2100 . In: Office of the Chief Actuary Social Securty Administration , ed. Washington, DC : SSA Pub. No. 11-11536 . 2005 . Google Preview WorldCat COPAC 27. Faul F , Erdfelder E , Buchner A , Lang AG . Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses . Behav Res Methods . 2009 ; 41 : 1149 – 1160 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 28. Solovieff N , Hartley SW , Baldwin CT , Perls TT , Steinberg MH , Sebastiani P . Clustering by genetic ancestry using genome-wide SNP data . BMC Genet . 2010 ; 11 : 108 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 29. Reich D , Price AL , Patterson N . Principal component analysis of genetic data . Nat Genet . 2008 ; 40 : 491 – 492 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 30. Tian C , Kosoy R , Nassir R , et al. European population genetic substructure: further definition of ancestry informative markers for distinguishing among diverse European ethnic groups . Mol Med . 2009 ; 15 : 371 – 383 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 31. Sebastiani P , Gurinovich A , Bae H , Andersen SL , Perls TT . Assortative mating by ethnicity in longevous families . Front Genet . 2017 ; 8 : 186 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 32. Conomos MP , Miller MB , Thornton TA . Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness . Genet Epidemiol . 2015 ; 39 : 276 – 293 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 33. Sindi S , Mangialasche F , Kivipelto M . Advances in the prevention of Alzheimer’s Disease . F1000Prime Rep . 2015 ; 7 : 50 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 34. Kivipelto M , Rovio S , Ngandu T , et al. Apolipoprotein E epsilon4 magnifies lifestyle risks for dementia: a population-based study . J Cell Mol Med . 2008 ; 12 ( 6B ): 2762 – 2771 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 35. Hanson AJ , Bayer JL , Baker LD , et al. Differential effects of meal challenges on cognition, metabolism, and biomarkers for apolipoprotein E ɛ4 carriers and adults with mild cognitive impairment . J Alzheimers Dis . 2015 ; 48 : 205 – 218 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 36. Bos MM , Noordam R , Blauw GJ , Slagboom PE , Rensen PCN , van Heemst D . The ApoE epsilon4 Isoform: can the risk of diseases be reduced by environmental factors? J Gerontol A Biol Sci Med Sci. 2018 . doi: WorldCat Crossref 37. Auestad N , Hurley JS , Fulgoni VL 3rd , Schweitzer CM . Contribution of food groups to energy and nutrient intakes in five developed countries . Nutrients . 2015 ; 7 : 4593 – 4618 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 38. Harika RK , Eilander A , Alssema M , Osendarp SJ , Zock PL . Intake of fatty acids in general populations worldwide does not meet dietary recommendations to prevent coronary heart disease: a systematic review of data from 40 countries . Ann Nutr Metab . 2013 ; 63 : 229 – 238 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 39. Helliwell J , Layard , R. , & Sachs , J. World Happiness Report 2019 . New York, NY : Sustainable Development Solutions Network ; 2019 . Google Preview WorldCat COPAC 40. Muenchhoff J , Song F , Poljak A , et al. Plasma apolipoproteins and physical and cognitive health in very old individuals . Neurobiol Aging . 2017 ; 55 : 49 – 60 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 41. Ordovas JM , Lopez-Miranda J , Mata P , Perez-Jimenez F , Lichtenstein AH , Schaefer EJ . Gene-diet interaction in determining plasma lipid response to dietary intervention . Atherosclerosis . 1995 ; 118 ( Suppl ): S11 – S27 . Google Scholar Crossref Search ADS PubMed WorldCat 42. Grimm MOW , Michaelson DM , Hartmann T . Omega-3 fatty acids, lipids, and apoE lipidation in Alzheimer’s disease: a rationale for multi-nutrient dementia prevention . J Lipid Res . 2017 ; 58 : 2083 – 2101 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 43. Carvalho-Wells AL , Jackson KG , Lockyer S , Lovegrove JA , Minihane AM . APOE genotype influences triglyceride and C-reactive protein responses to altered dietary fat intake in UK adults . Am J Clin Nutr . 2012 ; 96 : 1447 – 1453 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 44. Fallaize R , Carvalho-Wells AL , Tierney AC , et al. APOE genotype influences insulin resistance, apolipoprotein CII and CIII according to plasma fatty acid profile in the metabolic syndrome . Sci Rep . 2017 ; 7 : 6274 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 45. Schork NJ . Personalized medicine: time for one-person trials . Nature . 2015 ; 520 : 609 – 611 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Crimmins, Eileen, M;Zhang, Yuan, S;Kim, Jung, Ki;Levine, Morgan, E

doi: 10.1093/gerona/glz075pmid: 31724057

Abstract Background This article investigates changes in disease prevalence, incidence, and mortality among four cohorts of older persons in the Health and Retirement Study. Methods We examine two cohorts initially aged 51 to 61, whom we call younger cohorts, and two older cohorts aged 70 to 80 at the start of observation. Each of the paired cohorts was born about 10 years apart. We follow the cohorts for approximately 10 years. Results The prevalence of cancer, stroke, and diabetes increased in later-born cohorts; while the prevalence of myocardial infarction decreased markedly in both later-born cohorts. The incidence of heart disease, myocardial infarction, and stroke decreased among those in the later-born older cohort; while only the incidence of myocardial infarction decreased in the later-born younger cohort. On the other hand, diabetes incidence increased among those in both later-born cohorts. Death rates among those with heart disease, cancer, and diabetes decreased in the later-born cohorts. The declining incidence of three cardiovascular conditions among those who are over age 70 reflects improving population health and has resulted in stemming the increase in prevalence of people with heart disease and stroke. Discussion While these results provide some important signs of improving population health, especially among those over 70; trends for those less than 70 in the United States are not as positive. Cardiovascular disease, Cancer, Diabetes Research reporting disease trends for older populations has been fairly consistent in showing an increase over recent decades in the prevalence of major diseases such as heart disease, cancer, stroke, and diabetes in older populations (1–5). Because increased prevalence stems from either an increase in the incidence of disease or an increase in the length of time lived with disease, it is important to determine how incidence and mortality among people with diseases have changed, in order to identify the determinants of changes in observed prevalence as well as the implications of the trends for future health change (6,7). Increases in incidence would imply worsening health of the population. Conversely, decreases in incidence may reflect the success of interventions to prevent and delay disease onset; while increases in the length of time lived with disease would imply better survival among those with disease resulting from increasing effectiveness of medical treatment; however, this could also have the seemingly negative effect of increasing disease prevalence in a population. Prevalence of diseases in an older population can reflect the onset of conditions occurring at any time in the life cycle; incidence of diseases reflects onset in a given time period among those who do not have the condition and thus can better characterize recent conditions. Recent studies of hospitalizations examining heart disease and stroke incidence among selected samples of older persons have indicated that incidence of these conditions may have begun to decline after the year 2000 (8–14). The incidence of myocardial infarction has been reported to be declining since the 1990s in a multi-site sample (15). Surveillance, Epidemiology, and End Results (SEER) data also suggest some decline in overall cancer incidence (16). This would suggest that after a long period of increase in the prevalence of many conditions, we may be seeing changes in the process of disease onset that may reflect improvement in the health of older persons. On the other hand, numerous studies have shown an increase in diabetes incidence over time and project further increase in the future (17,18). Due to advances in early detection and treatment as well as improvements in modifiable risk factors, reductions in mortality from major chronic diseases have been observed in the United States over past decades. Cardiovascular mortality has been decreasing since the late 1960s and has been the major contributor to increasing life expectancy among older Americans (19). From 2000 to 2010, age-adjusted mortality decreased by about 30% for heart disease and almost 40% for stroke (20,21). Deaths attributed to acute myocardial infarction also showed a substantial downward trend over recent decades, reflecting improvements in the quality of care (22). Cancer mortality has also declined in recent decades, but at a slower rate than cardiovascular mortality (20). From 1980 to 2014, mortality from all cancers combined declined by 20%, and the overall decline has been driven by decreasing death rates from specific cancers (eg, lung cancer and breast cancer) (23). Falling death rates among individuals with diabetes have also been observed. National data from 1988 to 2015 showed death rates declined by 20% every 10 years among U.S. adults with diabetes, a faster decline than among those without diabetes (24). It is important to note that there is evidence that the very significant declines in heart disease, stroke, and diabetes mortality have slowed, or been eliminated in the last couple of years (19–21). For instance, the annual decline in heart disease mortality slowed from 3.9% during the 2000–2010 period to 1.4% during the 2010–2013 period (19). In fact, very recently the national vital statistics showed significant increases in age-adjusted rates for many leading causes of death, with the exception of cancer (25). We examine changes over about a decade in disease onset and survival across cohorts of the Health and Retirement Study (HRS): two cohorts representing change at the ages 51 through 71 of this nationally representative panel and two representing change at ages 70 through 90 or 91. We examine a set of major chronic diseases in this analysis as disease is the focus of medical intervention and of prevention. A cohort rather than a period formulation of the data is used as this most appropriately examines health change over the lifecycle of a set of individuals born at different times and is the best way to link time changes in prevalence, incidence, and mortality to the aging process within individuals. It is better to examine changes in the lives of real, rather than synthetic cohorts as is often done when age differences are examined within cross-sectional samples; earlier health change experienced by individuals affects the current health state and the likelihood of later change within cohorts. Our analysis allows us to examine change over about a 10-year span in disease prevalence across cohorts along with changes in disease incidence and mortality among those with diseases within these cohorts. Methods and Data Data Our source of data is the HRS, a longitudinal panel study of Americans over age 50 and their spouses, which was begun in 1992 and has collected data approximately every 2 years up to the present. When the study began in 1992, it included sample members 51–61 years of age; in 1993, a sample of persons 70+ was added; in the ensuing years sample members were added from missing cohorts and the sample was refreshed with 51- to 56-year-olds every 6 years. This structure of the data is central to how we select our comparison cohorts. To represent persons as they approach old age and in the earlier older ages, we use data for two cohorts aged 51 to 61 from the HRS, the 1931 to 1941 birth cohorts who were first interviewed in 1992 (N = 10,645) and the 1943 through 1953 cohorts who were first interviewed at age 51 to 61 in 2004 (N = 7,896). Each of these cohorts is then followed for 10 years or until they were aged 61 to 71: Cohort 1, 1992/1993, 1994/1995, 1996, 1998, 2000, and 2002; Cohort 2, 2004, 2006, 2008, 2010, 2012, and 2014. We refer to these as younger cohorts. Figure 1 shows the age of the cohort at each observation and the year of observation. Figure 1. Open in new tabDownload slide Cohorts in Health and Retirement Study: cohort years of birth, years of observation, age at observation. Figure 1. Open in new tabDownload slide Cohorts in Health and Retirement Study: cohort years of birth, years of observation, age at observation. To represent older persons, we use cohorts aged 70 to 80 at the start of observation. The first cohort was born in 1913–1923 and first interviewed in 1993; a second cohort represents those born in 1924–1934, and we began their observation in 2004. These cohorts are followed for 11 and 10 years: Cohort 1, 1993, 1995, 1998, 2000, 2002, 2004; Cohort 2: 2004, 2006, 2008, 2010, 2012, 2014. The first cohort is followed until they are aged 81 to 91 and the second cohort is observed until age 80 to 90. We refer to these as older cohorts. At the first interview, and at each later interview, respondents report the presence of diseases diagnosed by a doctor. We examine five conditions that represent leading causes of death and important causes of disability for older persons: cancer (excluding non-melanoma skin cancer), heart disease, myocardial infarction (heart attack), stroke, and diabetes. We assume that once respondents report a disease, they always have it and it is included in the prevalence estimate until they die. This is shown in Figure 2. Figure 2. Open in new tabDownload slide Open in new tabDownload slide Prevalence of diseases (%) by age in two younger cohorts (age 51–71) and two older cohorts (age 70–90). Figure 2. Open in new tabDownload slide Open in new tabDownload slide Prevalence of diseases (%) by age in two younger cohorts (age 51–71) and two older cohorts (age 70–90). Disease incidence reflects the reporting of a disease onset after the baseline observation among those who did not have the condition at the beginning of the interval. People who already had a disease at baseline are excluded from the incidence analysis. We assume that disease onset occurs at a person’s age at the midpoint of the survey interval. People who do not have a disease are at risk for incidence and once they report a condition they are no longer at risk. In our figures, we show 2-year moving averages for incidence in order to make the pattern clearer. We examine mortality among people with a disease using mortality records collected by the HRS from the Social Security Administration, from designated survivors, and from interviewer reports. Mortality reporting in the HRS has been shown to be effectively complete (26). Statistical Methods We examine the cohort differences in disease prevalence at the first time of observation using logistic regressions for each disease, where being in the later cohort is included as a dummy variable with age and sex. For incidence of disease, we use hazard models to examine interval age-specific incidence over 10 or 11 years among those who do not have a condition at the beginning of the interval in equations with age, sex, and cohort. Each equation includes a variable indicating the exposure time in the interval. For death, we examine death using a hazard model at each later wave over the 10 or 11 years among those who have a disease at the first wave. The equations include age, sex, and an indication of being in a later-born cohort. Results Before we describe our results in detail, we should note that the direction of the effect when comparing later-born and earlier-born cohorts indicated by all three parameters (ie, for prevalence, incidence, and mortality) was the same for the younger and older age groups. In looking at the detailed results, we first examine cohort differences in prevalence of major diseases; then we will examine incidence, and mortality among those with disease. Prevalence results are shown in Figure 2 and Table 1. Prevalence significantly increases in later-born cohorts for 3 out of 5 diseases in the younger age group and 4 out of 5 diseases in the older age group. The prevalence of MI decreases in the later-born cohorts for both the younger and older age groups (Table 1). The decline in the relative likelihood of having had a myocardial infarction was quite large in both the later-born cohorts, that is, the younger and older cohorts: 68% and 67%, respectively. On the other hand, cancer prevalence is higher in the later-born cohorts. The odds ratios indicate a 29% increase in cancer among those in their 50s and 60s and a 46% increase among those 70 to 80. The relative increases in diabetes are even larger in the later-born cohorts: 48% and 67%. There was also an increase in the likelihood of having had a stroke in the later-born cohorts: 29% among the younger and 15% among the older. There was no change in the prevalence of heart disease in the younger cohorts but an increase in the likelihood of 9% in the older group. Table 1. Odds Ratio or Hazard Ratio (with 95% Confidence Interval) for Effect of Being in Later-Born Cohort on Prevalence, Incidence, and Death Among Those with Disease—Older and Younger Cohorts Younger Cohorts N p Older Cohorts N p Effect of Being Born in 1943–1953 Compared to 1931–1941 Effect of Being Born in 1924–1934 Compared to 1913–1923 Prevalence Cancer 1.29 (1.12–1.48) 15,386 .0003 1.46 (1.33–1.60) 10,438 <.0001 Heart disease 1.02 (0.93–1.13) 15,386 .6892 1.09 (1.02–1.18) 10,409 .0161 MI 0.32 (0.26–0.38) 15,229 <.0001 0.33 (0.27–0.40) 10,265 <.0001 Stroke 1.29 (1.06–1.58) 15,387 .0106 1.15 (1.02–1.31) 10,324 .0264 Diabetes 1.48 (1.33–1.64) 15,385 <.0001 1.67 (1.52–1.83) 10,452 <.0001 Incidence Cancer 1.10 (0.98–1.23) 13,867 .1021 1.00 (0.90–1.11) 8,016 .9415 Heart disease 0.94 (0.85–1.03) 12,826 .1560 0.88 (0.81–0.95) 6,612 .0016 MI 0.42 (0.35–0.50) 13,937 <.0001 0.52 (0.45–0.59) 8,810 <.0001 Stroke 0.92 (0.79–1.07) 14,295 .2857 0.70 (0.63–0.78) 8,624 <.0001 Diabetes 1.54 (1.40–1.70) 12,979 <.0001 1.21(1.08–1.37) 7,785 .0012 Mortality Cancer 0.69 (0.51–0.94) 933 .0189 0.82 (0.73–0.92) 1,626 .0011 Heart disease 0.76 (0.62–0.93) 1,972 .0076 0.88 (0.81–0.96) 3,198 .0022 MI 0.92 (0.64–1.31) 656 .6267 0.80 (0.62–1.02) 459 .0722 Stroke 0.89 (0.64–1.23) 484 .4718 0.87 (0.76–1.01) 846 .0665 Diabetes 0.77 (0.63–0.94) 1,872 .0088 0.77 (0.69–0.85) 1,889 <.0001 None of the diseases 0.85 (0.73–0.98) 11,117 .0229 0.85 (0.79–0.93) 4,985 .0002 Younger Cohorts N p Older Cohorts N p Effect of Being Born in 1943–1953 Compared to 1931–1941 Effect of Being Born in 1924–1934 Compared to 1913–1923 Prevalence Cancer 1.29 (1.12–1.48) 15,386 .0003 1.46 (1.33–1.60) 10,438 <.0001 Heart disease 1.02 (0.93–1.13) 15,386 .6892 1.09 (1.02–1.18) 10,409 .0161 MI 0.32 (0.26–0.38) 15,229 <.0001 0.33 (0.27–0.40) 10,265 <.0001 Stroke 1.29 (1.06–1.58) 15,387 .0106 1.15 (1.02–1.31) 10,324 .0264 Diabetes 1.48 (1.33–1.64) 15,385 <.0001 1.67 (1.52–1.83) 10,452 <.0001 Incidence Cancer 1.10 (0.98–1.23) 13,867 .1021 1.00 (0.90–1.11) 8,016 .9415 Heart disease 0.94 (0.85–1.03) 12,826 .1560 0.88 (0.81–0.95) 6,612 .0016 MI 0.42 (0.35–0.50) 13,937 <.0001 0.52 (0.45–0.59) 8,810 <.0001 Stroke 0.92 (0.79–1.07) 14,295 .2857 0.70 (0.63–0.78) 8,624 <.0001 Diabetes 1.54 (1.40–1.70) 12,979 <.0001 1.21(1.08–1.37) 7,785 .0012 Mortality Cancer 0.69 (0.51–0.94) 933 .0189 0.82 (0.73–0.92) 1,626 .0011 Heart disease 0.76 (0.62–0.93) 1,972 .0076 0.88 (0.81–0.96) 3,198 .0022 MI 0.92 (0.64–1.31) 656 .6267 0.80 (0.62–1.02) 459 .0722 Stroke 0.89 (0.64–1.23) 484 .4718 0.87 (0.76–1.01) 846 .0665 Diabetes 0.77 (0.63–0.94) 1,872 .0088 0.77 (0.69–0.85) 1,889 <.0001 None of the diseases 0.85 (0.73–0.98) 11,117 .0229 0.85 (0.79–0.93) 4,985 .0002 Open in new tab Table 1. Odds Ratio or Hazard Ratio (with 95% Confidence Interval) for Effect of Being in Later-Born Cohort on Prevalence, Incidence, and Death Among Those with Disease—Older and Younger Cohorts Younger Cohorts N p Older Cohorts N p Effect of Being Born in 1943–1953 Compared to 1931–1941 Effect of Being Born in 1924–1934 Compared to 1913–1923 Prevalence Cancer 1.29 (1.12–1.48) 15,386 .0003 1.46 (1.33–1.60) 10,438 <.0001 Heart disease 1.02 (0.93–1.13) 15,386 .6892 1.09 (1.02–1.18) 10,409 .0161 MI 0.32 (0.26–0.38) 15,229 <.0001 0.33 (0.27–0.40) 10,265 <.0001 Stroke 1.29 (1.06–1.58) 15,387 .0106 1.15 (1.02–1.31) 10,324 .0264 Diabetes 1.48 (1.33–1.64) 15,385 <.0001 1.67 (1.52–1.83) 10,452 <.0001 Incidence Cancer 1.10 (0.98–1.23) 13,867 .1021 1.00 (0.90–1.11) 8,016 .9415 Heart disease 0.94 (0.85–1.03) 12,826 .1560 0.88 (0.81–0.95) 6,612 .0016 MI 0.42 (0.35–0.50) 13,937 <.0001 0.52 (0.45–0.59) 8,810 <.0001 Stroke 0.92 (0.79–1.07) 14,295 .2857 0.70 (0.63–0.78) 8,624 <.0001 Diabetes 1.54 (1.40–1.70) 12,979 <.0001 1.21(1.08–1.37) 7,785 .0012 Mortality Cancer 0.69 (0.51–0.94) 933 .0189 0.82 (0.73–0.92) 1,626 .0011 Heart disease 0.76 (0.62–0.93) 1,972 .0076 0.88 (0.81–0.96) 3,198 .0022 MI 0.92 (0.64–1.31) 656 .6267 0.80 (0.62–1.02) 459 .0722 Stroke 0.89 (0.64–1.23) 484 .4718 0.87 (0.76–1.01) 846 .0665 Diabetes 0.77 (0.63–0.94) 1,872 .0088 0.77 (0.69–0.85) 1,889 <.0001 None of the diseases 0.85 (0.73–0.98) 11,117 .0229 0.85 (0.79–0.93) 4,985 .0002 Younger Cohorts N p Older Cohorts N p Effect of Being Born in 1943–1953 Compared to 1931–1941 Effect of Being Born in 1924–1934 Compared to 1913–1923 Prevalence Cancer 1.29 (1.12–1.48) 15,386 .0003 1.46 (1.33–1.60) 10,438 <.0001 Heart disease 1.02 (0.93–1.13) 15,386 .6892 1.09 (1.02–1.18) 10,409 .0161 MI 0.32 (0.26–0.38) 15,229 <.0001 0.33 (0.27–0.40) 10,265 <.0001 Stroke 1.29 (1.06–1.58) 15,387 .0106 1.15 (1.02–1.31) 10,324 .0264 Diabetes 1.48 (1.33–1.64) 15,385 <.0001 1.67 (1.52–1.83) 10,452 <.0001 Incidence Cancer 1.10 (0.98–1.23) 13,867 .1021 1.00 (0.90–1.11) 8,016 .9415 Heart disease 0.94 (0.85–1.03) 12,826 .1560 0.88 (0.81–0.95) 6,612 .0016 MI 0.42 (0.35–0.50) 13,937 <.0001 0.52 (0.45–0.59) 8,810 <.0001 Stroke 0.92 (0.79–1.07) 14,295 .2857 0.70 (0.63–0.78) 8,624 <.0001 Diabetes 1.54 (1.40–1.70) 12,979 <.0001 1.21(1.08–1.37) 7,785 .0012 Mortality Cancer 0.69 (0.51–0.94) 933 .0189 0.82 (0.73–0.92) 1,626 .0011 Heart disease 0.76 (0.62–0.93) 1,972 .0076 0.88 (0.81–0.96) 3,198 .0022 MI 0.92 (0.64–1.31) 656 .6267 0.80 (0.62–1.02) 459 .0722 Stroke 0.89 (0.64–1.23) 484 .4718 0.87 (0.76–1.01) 846 .0665 Diabetes 0.77 (0.63–0.94) 1,872 .0088 0.77 (0.69–0.85) 1,889 <.0001 None of the diseases 0.85 (0.73–0.98) 11,117 .0229 0.85 (0.79–0.93) 4,985 .0002 Open in new tab Now we examine changes in incidence. Cancer incidence did not change significantly in either the younger or older cohorts (Figure 3 and Table 1). Diabetes incidence increased in both the younger and older cohorts: with the hazard increasing 54% and 21%, respectively. The incidence of the three cardiovascular-related diseases declined in the later-born cohort of older persons; the decrease in the hazard ratio for heart disease was 12%, 48% for myocardial infarction, and 30% for stroke. The hazard of stroke and heart disease did not change among the younger cohort while the hazard of having a myocardial infarction decreased by 58% among the later-born younger cohort. Figure 3. Open in new tabDownload slide Open in new tabDownload slide Incidence of disease (% with onset among those without condition at beginning of interval) by age (2-year moving averages) in two younger cohorts and two older cohorts. Figure 3. Open in new tabDownload slide Open in new tabDownload slide Incidence of disease (% with onset among those without condition at beginning of interval) by age (2-year moving averages) in two younger cohorts and two older cohorts. The hazard of dying among those with none of these diseases at baseline was reduced by 15% for the later-born cohorts in both age groups (Table 1). Among the younger old, the hazard of dying was reduced for those with cancer (31%), heart disease (24%), and diabetes (23%); older cohorts experienced significant reductions in the likelihood of dying among those with the same three conditions: cancer (18%), heart disease (12%), and diabetes (23%) (Figure 4 and Table 1). There was no significant cohort change in the hazard of dying among those with a stroke or myocardial infarction for the younger or older cohorts, although the decrease in both is close to significant among the older group. Figure 4. Open in new tabDownload slide Open in new tabDownload slide Mortality by years after interview among those with disease at baseline for two younger cohorts and two older cohorts. Figure 4. Open in new tabDownload slide Open in new tabDownload slide Mortality by years after interview among those with disease at baseline for two younger cohorts and two older cohorts. Discussion Understanding the meaning of trends in health in an older population characterized by the presence of chronic disease requires knowledge of several indicators of the process of health change. The routinely observed increase in the prevalence of disease among the older population has left researchers unable to clearly state whether health among the older population is improving, or not, over time. In this nationally representative data set where health change has been observed over more than a 20-year period, we observe increases in the prevalence of three diseases: cancer, diabetes, and stroke among people over age 50 and up to age 90. We also observe an increase in heart disease among those over 70. These increases in prevalence, however, result from a set of different processes across the diseases. Cancer incidence does not change significantly across these cohorts but cancer mortality is lower among the later-born cohorts. We can assume that the increase in cancer prevalence reflects the fact that people are living longer once they have been diagnosed with cancer; an indicator of improving treatments and possibly earlier diagnosis. The increase in diabetes prevalence reflects both an increase in incidence and a decrease in mortality. The increase in incidence is largely the result of increasing obesity, while the decrease in mortality probably reflects better diagnosis and treatment (27). Both of these trends work to increase the prevalence of diabetes in the population; one reflects improvement in health and one deterioration. Trends for stroke portray an even more complex pattern of change. The incidence decreases across the older cohorts, indicating health improvement; however, decreases in mortality among this age group, while not significant, tended to work to increase the prevalence in the population. At the younger ages, the changes in incidence and mortality both indicate lower levels in the more recent cohort, but neither change is significant. The overall heart disease picture is relatively positive with no significant prevalence increase among the young old and only a modest increase among the old; the major decreases in both prevalence and incidence of myocardial infarction indicate major health improvements for more recent cohorts. Finding this in a national population sample is an important addition to the findings from more localized studies. Declines in incidence of myocardial infarction may reflect emphasis on prevention including increasing treatment and effectiveness of treatment of cardiovascular risk factors such as hypertension and high cholesterol and reductions in smoking. Researchers examining trends in disability among the old have tended to find more positive trends among the older segments of the group and less improvement among the younger old, especially those born in the baby-boom (28). We find differentials by age in the pattern of significant change with more reduction in the incidence of all three cardiovascular conditions, heart disease, myocardial infarction and stroke, among the older cohorts. There are some limitations to our analysis. We use self-reports from respondents of their being told by a doctor that they have a condition. These may be sensitive to use of medical care and changes in medical care usage could affect self-reports. Reporting of diseases can also be sensitive to changes in diagnosis and reporting to patients. One potential result from our analysis, a large increase in stroke prevalence among the later-born younger cohort with small decreases in mortality and incidence, could reflect more reporting of stroke in this group. The increasing education of cohorts over time could result in more informed patients who are more likely to report diseases. Unfortunately, we cannot control for these changes. It would have been preferable to follow two individual cohorts from their 50s through their 90s, rather than having four cohorts. It is possible that the outcomes for earlier-born and later-born cohorts both represent the 20-year period of observation and we cannot assume change in the future for the younger cohort will be reproduced. While we recognize the limitations, we feel the results of our analysis of data from a unique nationally representative sample further our understanding of health trends in the older population. Trends are influenced by a set of complex processes. In general, researchers have not recognized the feedback role of declining mortality in affecting the subsequent health of older populations. The outcome we have sought to achieve, lower mortality, can feed back into the population and produce indicators which appear to indicate worsening health. Adding incidence of disease to our analysis of a national population database has allowed us to see some very positive trends in cardiovascular health and to understand why cancer might be increasing in prevalence. The study of individual diseases also adds to our understanding of health trends and needs for intervention. Some trends are clearly adverse, for example, the rise in diabetes and prevention needs to target this condition. Prevention of myocardial infarction appears to be progressing, but cancer is not being prevented, and this will be a challenge for the future. Funding This paper was published as part of a supplement sponsored and funded by AARP. The statements and opinions expressed herein by the authors are for information, debate, and discussion, and do not necessarily represent official policies of AARP. Conflict of Interest Statement None reported. Acknowledgments E.C. conceived the study, designed the analysis, and drafted the article. Y.Z., M.L., and J.K.K. analyzed the data. All authors refined results and contributed to the final manuscript. References 1. Beltrán-Sánchez H , Jiménez MP , Subramanian SV . Assessing morbidity compression in two cohorts from the health and retirement study . J Epidemiol Community Health . 2016 ; 70 : 1011 – 1016 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 2. Crimmins EM , Beltrán-Sánchez H . Mortality and morbidity trends: is there compression of morbidity? J Gerontol B Psychol Sci Soc Sci . 2011 ; 66 : 75 – 86 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 3. Crimmins EM , Saito Y . Change in the prevalence of diseases among older Americans: 1984–1994 . Demographic Res . 2000 ; 3 : 1 – 20 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 4. Cutler DM , Richardson E , Keeler TE , Staiger D . Measuring the health of the US population. Brookings papers on economic activity . Microeconomics . 1997 ; 1997 : 217 – 282 . doi: WorldCat Crossref 5. Martin LG , Freedman VA , Schoeni RF , Andreski PM . Health and functioning among baby boomers approaching 60 . J Gerontol B Psychol Sci Soc Sci . 2009 ; 64 : 369 – 377 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 6. Beltrán-Sánchez H , Soneji S , Crimmins EM . Past, present, and future of healthy life expectancy . Cold Spring Harb Perspect Med . 2015 ; 5 : 1 – 11 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 7. Nusselder WJ . Compression of morbidity. In: Robine JM , Jagger C , Mathers CD , Crimmins EM , eds. Determining Health Expectancies . West Sussex, UK : John Wiley & Sons ; 2003 : 35 – 58 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC 8. Chen J , Normand SL , Wang Y , Drye EE , Schreiner GC , Krumholz HM . Recent declines in hospitalizations for acute myocardial infarction for medicare fee-for-service beneficiaries: progress and continuing challenges . Circulation . 2010 ; 121 : 1322 – 1328 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 9. Fang J , Alderman MH , Keenan NL , Croft JB . Declining US stroke hospitalization since 1997: national hospital discharge survey, 1988–2004 . Neuroepidemiology . 2006 ; 29 ( 3–4 ): 243 – 249 . doi: WorldCat Crossref 10. Koton S , Schneider AL , Rosamond WD , et al. Stroke incidence and mortality trends in US communities, 1987 to 2011 . JAMA . 2014 ; 312 ( 3 ): 259 – 268 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 11. Mozaffarian D , Benjamin EJ , Go AS , et al. Heart disease and stroke statistivs-2016 update: a report from the American heart association . Circulation . 2016 ; 133 ( 4 ): e38 – 360 . doi: Google Scholar PubMed WorldCat Crossref 12. Talbott EO , Rager JR , Brink LL , et al. Trends in acute myocardial infarction hospitalization rates for US States in the CDC tracking network . PLoS One . 2013 ; 8 : e64457 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 13. Wang OJ , Wang Y , Chen J , Krumholz HM . Recent trends in hospitalization for acute myocardial infarction . Am J Cardiol . 2012 ; 109 : 1589 – 1593 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 14. Yeh RW , Sidney S , Chandra M , Sorel M , Selby JV , Go AS . Population trends in the incidence and outcomes of acute myocardial infarction . N Engl J Med . 2010 ; 362 : 2155 – 2165 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 15. Rosamond WD , Chambless LE , Heiss G , et al. Twenty-two-year trends in incidence of myocardial infarction, coronary heart disease mortality, and case fatality in 4 US communities, 1987–2008 . Circulation . 2012 ; 125 ( 15 ): 1848 – 1857 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 16. National Cancer Institute . Online Summary of Trends in US Cancer Control Measures . https://progressreport.cancer.gov/diagnosis/incidence#field_most_recent_estimates. Accessed July 12, 2018 . 17. Boyle J , Thompson T , Gregg E , Barker L , Williamson D . Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence . Popul Health Metr . 2010 ; 8 : 29 – 40 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 18. Geiss LS , Wang J , Cheng YJ , et al. Prevalence and incidence for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980–2012 . JAMA . 2014 ; 312 ( 12 ): 1218 – 1226 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 19. Ma J , Ward EM , Siegel RL , Jemal A . Temporal trends in mortality in the United States, 1969–2013 . JAMA . 2015 ; 314 ( 16 ): 1731 – 1739 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 20. Sidney S , Quesenberry CP Jr , Jaffe MG , et al. Recent trends in cardiovascular mortality in the United States and public health goals . JAMA Cardiol . 2016 ; 1 : 594 – 599 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 21. Yang Q , Tong X , Schieb L , et al. Vital signs: recent trends in stroke death rates-United States, 2000–2015 . MMWR Morb Mortal Wkly Rep . 2017 ; 66 ( 35 ): 933 – 939 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 22. Krumholz HM , Wang Y , Chen J , et al. Reduction in acute myocardial infarction mortality in the United States: risk-standardized mortality rates from 1995–2006 . JAMA . 2009 ; 302 ( 7 ): 767 – 773 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 23. Mokdad AH , Dwyer-Lindgren L , Fitzmaurice C , et al. Trends and patterns of disparities in cancer mortality among US counties, 1980–2014 . JAMA . 2017 ; 317 ( 4 ): 388 – 406 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 24. Gregg EW , Cheng YJ , Srinivasan M , et al. Trends in cause-specific mortality among adults with and without diagnosed diabetes in the USA: an epidemiological analysis of linked national survey and vital statistics data . Lancet . 2018 ; 391 : 2430 – 2440 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 25. Murphy SL , Xu JQ , Kochanek KD , Curtin SC , Arias E . Deaths: final data for 2015 . National Vital Statistics Reports . 2017 ; 66 ( 6 ). Hyattsville, MD: National Center for Health Statistics. WorldCat 26. Weir D . Validating mortality ascertainment in the Health and Retirement Study . https://hrs.isr.umich.edu/sites/default/files/biblio/Weir_mortality_ascertainment.pdf. Accessed July 12, 2018 . 27. Mokdad AH , Ford ES , Bowman BA , et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001 . JAMA . 2003 ; 289 ( 1 ): 76 – 79 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 28. Martin LG , Freedman VA , Schoeni RF , Andreski PM . Health and functioning among baby boomers approaching 60 . J Gerontol B Psychol Sci Soc Sci . 2009 ; 64 : 369 – 377 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Dzau, Victor, J;Jenkins, Jo Ann, C

doi: 10.1093/gerona/glz226pmid: 31690929

We are all aging, every day—and, in fact, so is the global population. With life expectancies increasing and fertility rates declining in most of the world, the growth of the older population is steadily outpacing that of younger generations. In just 30 years, the number of people over age 65 is expected to almost double, reaching 1.6 billion—or 17% of the world’s population. A subset of that group, those known as the “oldest old,” or people over 80, will more than triple in size during the same period (1). Longer lives are a triumph of medicine, public health, technology, and international development. But whether the extra years will be good ones—and whether societies and economies will benefit as a result—depends on the actions we take now. To be sure, an unprecedented and exciting opportunity lies before us. We need to empower societies around the world to both embrace the opportunities of aging to the fullest extent possible and address the attendant challenges. Healthy longevity requires societies to focus more on physical and mental fitness rather than diminishment alone—on preventing disease and improving well-being rather than simply treating ailments. Furthermore, as we empower societies, we also need to empower people to become active partners in ensuring their own health and well-being. Imagine how our collective vision of the future might change if we could extend healthy longevity and, just as importantly, the number of years of good health and productivity. As people live longer, healthier lives, they could stay in the workforce longer, whether the motivation be out of desire or need. They could continue contributing to their families and maintain leadership positions in industry and communities alike. All the while, they could delay or prevent admissions to hospitals and other care facilities, enjoying active, independent, and fulfilling lives and enriching their environments in myriad ways. In fact, signs of such exciting trends are already appearing in different parts of the world (2). The catalyst, at least in part, may be the understanding that a healthy, engaged, and productive older population has the potential to be an economic boom, not an economic and social burden. As we extend healthy longevity, the growing number of older people is not a drain on society, but a key driver of economic growth, innovation, and new value creation. We are facing a fork in the road. In one direction lies a tremendous risk; in the other, a greater reward. We need to change the conversation about age and aging around the world. What if instead of seeing just dependent retirees, we begin to see a new type of experienced, accomplished work force? What if instead of seeing expensive costs, we see the exploding consumer market that here in the U.S. generates 7.6 trillion dollars in economic activity by people 50 and over? What if instead of seeing a growing pool of dependents, we see intergenerational communities with new and different strengths? And, what if we begin to realize that more often than not, what is good for the old is also good for the young? It is time to cast aside outdated assumptions and stereotypes and embrace the changes that are occurring. Aging is not about decline, but growth. It creates not only challenges but also new opportunities. Older people are not burdens; they are contributors. And each and every one of us should be valued not by how old we are but who we are. Achieving healthy longevity requires decisive, multisector action. In the past century, major breakthroughs have saved millions of lives that previously would have been lost to infectious diseases. However, not all of these gains have been shared equally. It is just as important to understand how individual biology, societal enablers, and science and technology can be harnessed to ensure that all people have the opportunity to live longer, healthier, and more fulfilling lives worldwide. We all have a role and a responsibility for this, from the personal to the private and public. A comprehensive global effort to guide the implementation of evidence-based strategies to advance healthy longevity among all people is urgently needed. That work is now underway. The Global Roadmap for Healthy Longevity initiative, launched this month by the U.S. National Academy of Medicine (NAM), will bring together international leaders in science, medicine, health, engineering, technology, economics, and policy to gather and assess evidence around strategies for extending the health span worldwide. By late 2020, the initiative will produce a report that can serve as a prioritized 10-year action plan adaptable to local contexts. This Roadmap is structured to ensure rigor, balance, and the incorporation of multidisciplinary and international perspectives. An independent International Commission will author the report with guidance from an International Oversight Board (IOB). The IOB, appointed by the NAM, is tasked with selecting members of the Commission, ensuring integrity, addressing conflicts of interest, assessing scientific credibility, providing strategic guidance, and supporting global dissemination (see Figure 1). Figure 1. Open in new tabDownload slide Organizational chart for the National Academy of Medicine’s Global Roadmap for Healthy Longevity. (SOURCE: National Academy of Medicine) Figure 1. Open in new tabDownload slide Organizational chart for the National Academy of Medicine’s Global Roadmap for Healthy Longevity. (SOURCE: National Academy of Medicine) The Commission, which met for the first time in September 2019, consists of 17 distinguished global experts in gerontology and geriatrics, demography, the social determinants of health, behavioral health, built environments, business and workforce, economics, health care delivery and financing, biomedical science and technology, and health and finance policy. To inform its final report, the Commission will host three public workshops to gather stakeholder input and foster global awareness and buy-in. The first workshop will be held at AARP in the United States, followed by workshops in Singapore and Japan. Information and insights from these workshops will supplement the Commission’s formal analysis and synthesis of published literature and best practice examples. The IOB and Commission have selected three priority focus areas in which they believe the opportunities for action are richest and the need is most urgent. The first of these workstreams focuses on the social, behavioral, and environmental enablers of healthy longevity. The Commission will assess approaches to enhance social structures and living environments that strengthen socioeconomic and community support. Special consideration will be given to education, training, employment/volunteer status and working conditions, income, social connectedness, culture, diversity, and ageism and other forms of discrimination. The second major focus area is the role of public health and health care systems. Within this workstream, the Commission will examine approaches and reforms across the entire spectrum of care systems and institutions, including primary, specialty, emergency, post-acute, rehabilitation, long-term, and palliative care. The Commission will also examine community and home health care, including the important role of family caregivers, as well as public health, health promotion, and prevention. In particular, the Commission will consider the management of chronic diseases, prevention across the life-course, social services, the eldercare workforce, workplace health, health insurance, and health care financing innovations. The third and final workstream will assess opportunities for research and development across basic, clinical, pharmaceutical, social, and behavioral sciences; bioengineering; information technology; and assistive technologies. In addition, it will explore strategies for expanding research funding and incentivizing research. Special consideration will be given to elucidation of the mechanisms of aging and regeneration, tissue destruction and repair, cellular death and survival, advances in information technologies including large databases, machine learning, and artificial intelligence. This workstream will also identify emerging engineering technologies that hold promise for monitoring health and activity as well as mobility and functionality; and options investment in R&D, regulation, commercialization, and scalability. Across all three areas of inquiry, the Commission will carefully consider implications for policy and practice, health equity and disparities, innovation, financing, and monitoring metrics. As much as possible, the Commission will coordinate with other related global initiatives to achieve an integrated and synergistic effort—including the World Health Organization’s Decade of Healthy Aging, universal health coverage, and sustainable development goals. Global population aging is an enormous challenge—arguably, one of the largest humanity has faced in modern times—and a great opportunity. It can spark new technologies, new industries, and new ways of thinking. One of those new ways of thinking involves the unprecedented opportunity to transform the traditional paradigms around aging (3). We know getting older has significant benefits—wisdom, resilience, and satisfaction among them (4). Already older adults make important contributions to the workforce, their communities, and their social networks (5). As the older population grows in proportion to other cohorts, maximizing health, productivity, and well-being later in the life span will pay off in dividends. The opportunity to live longer, healthier, more productive lives is one of humankind’s greatest accomplishments. Fully capitalizing on such an unprecedented opportunity will require the input and buy-in of public and private stakeholders worldwide. It will require commitment to innovation across all sectors of society, from the personal, private, and public. Most fundamentally, however, it will require the adoption of a new, more dynamic narrative around global population aging. We need to reject the status quo and envision a different future—one that is prosperous, sustainable, and rooted in healthy longevity. Funding This article was published as part of a supplement sponsored and funded by AARP. Acknowledgments V.J.D. is the president of the National Academy of Medicine and chair of the International Oversight Board (IOB) of the Global Roadmap for Healthy Longevity. J.A.J., vice co-chair of the IOB and CEO of AARP, which cosponsors the initiative alongside the California Health Care Foundation, Nathaniel (Ned) David, the Gary and Mary West Foundation, the Harvey V. Fineberg Impact Fund, the John A. Hartford Foundation, the Mehta Family Foundation, the Ministry of Health of Singapore, the National Research Foundation of Singapore, the National University of Singapore, Gil Omenn, the Robert Wood Johnson Foundation, the Rockefeller Foundation, and the Tsao Foundation. Conflict of Interest None reported. References 1. He W , Goodkind D , Kowal P. An Aging World: 2015 . U.S. Census Bureau, International Population Reports. Report Number P95/16-1. Washington, DC : U.S. Government Publishing Office ; 2016 . Google Preview WorldCat COPAC 2. Jenkins JC . From the CEO: celebrating 60 years of international engagement . AARP Int. 2019 ; 12 : 10 – 13 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 3. Jenkins JC . Disrupt aging: a call to action for gerontologists . Gerontologist. 2017 ; 57 ( suppl 2 ): S115 – S117 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 4. Charles ST , Carstensen LL . Social and emotional aging . Annu Rev Psychol . 2010 ; 61 : 383 – 409 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 5. Fried LP . Investing in health to create a third demographic dividend . Gerontologist. 2016 ; 56 ( suppl 2 ): S167 – S177 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Ciaglia,, Elena;Montella,, Francesco;Maciag,, Anna;Scala,, Pasqualina;Ferrario,, Anna;Banco,, Carlotta;Carrizzo,, Albino;Spinelli, Chiara, Carmela;Cattaneo,, Monica;, De Candia, Paola;Vecchione,, Carmine;Villa,, Francesco;Puca, Annibale, Alessandro

Olshansky, S, Jay;Carnes, Bruce, A

doi: 10.1093/gerona/glz098pmid: 31001621

Abstract The rise in human longevity is one of humanity’s crowning achievements. Although advances in public health beginning in the 19th century initiated the rise in life expectancy, recent gains have been achieved by reducing death rates at middle and older ages. A debate about the future course of life expectancy has been ongoing for the last quarter century. Some suggest that historical trends in longevity will continue and radical life extension is either visible on the near horizon or it has already arrived; whereas others suggest there are biologically based limits to duration of life, and those limits are being approached now. In “inconvenient truths about human longevity” we lay out the line of reasoning and evidence for why there are limits to human longevity; why predictions of radical life extension are unlikely to be forthcoming; why health extension should supplant life extension as the primary goal of medicine and public health; and why promoting advances in aging biology may allow humanity to break through biological barriers that influence both life span and health span, allowing for a welcome extension of the period of healthy life, a compression of morbidity, but only a marginal further increase in life expectancy. Longevity, Public Health, Life Expectancy One of the more spirited debates in science today involves questions of how long people can live, whether resources should be directed toward life extension versus health extension, and what is standing in the way of rapid progress in efforts to slow the biological process of aging? At the center of these debates are two fundamental questions: is there a limit to human longevity, and if there is one, are we close to it? This debate and a closely related effort to modify the basic biological processes of aging now has the eye of entrepreneurs and scientists that envision a breakthrough in aging biology in time to positively influence the health and longevity of most people alive today (1). A breakthrough of this kind would likely be the most impactful public health revolution in this century (2). The possible impact of such a breakthrough on life expectancy (a population-based metric) is likely to be small for reasons to be described later, but justification for research on aging biology as a new method of Primary Prevention (3) to enhance the health span of individuals should not be influenced by an answer to this question. Using aging biology as a preventive measure is hypothesized to compress morbidity and extend the period of healthy life; living longer in good health would be the bonus. Obstacles to breakthroughs in aging biology are both prevalent and challenging, but one obstacle is avoidable—assertions of radical increases in life expectancy and maximum life span that are supported primarily by hyperbole, exaggeration, misinformation, and secondary gain. Therein lies the problem. The “inconvenient truths about human longevity” described here yield insights into why there are limits to human longevity; why predictions of radical life extension are unlikely to be forthcoming; why health extension should supplant life extension as the primary goal of medicine and public health; and why recent efforts to promote aging biology based on exaggerated claims about the future of human longevity stand in the way of funding for aging science. Is There a Ceiling on Human Longevity? Let’s begin with a basic question—is there a limit to how long we can live? One might ordinarily think this would be an easy question to answer given that death has always called upon humanity with such consistency and regularity (4)—resembling a “law of mortality” that was first proposed by Benjamin Gompertz in 1825 (5). Nevertheless, there remain diametrically opposed answers to this question. One mathematical demographer suggested that “over sufficiently long time periods, it is not at all unusual for death rates to decline by half or more,” and therefore “there is simply no convincing evidence (demographic, biological or otherwise) of a lower bound on death rates other than zero” (6). Make no mistake about it—this is a declarative statement that because death rates declined in the past, they can and will continue to do so indefinitely into the future such that one more day of life can always be manufactured by medical technology. The result according to this line of reasoning is death rates of zero, and this of course translates into immortality. Although it may seem odd to use a purely mathematical line of reasoning to formulate a hypothesis about a fundamentally biological phenomenon such as human longevity, in the field of aging this is common because duration of life is often studied by scientists that work exclusively with mortality statistics, without considering the biology that drives the phenomenon being observed. This mathematical line of reasoning, suggesting that survival time can be manufactured indefinitely by hypothetical medical technologies that do not yet exist, is suspiciously close to a mathematical argument formulated by the Greek philosopher Zeno in approximately 450 bc—referred to as Zeno’s Paradox (7). In Zeno’s purely mathematical description of a problem in physics, an argument is made that an arrow shot at a tree will never reach its target, and a tortoise with a head start in a race with a hare will never be overtaken, because the distance between the two can, mathematically, be reduced by half indefinitely—never reaching zero. Of course, in the real world the arrow always reaches its target and the hare always surpasses the tortoise because the mathematical equation fails to comport with the reality of basic physics, just as mathematical arguments for immortality fail to consider limits or ceilings imposed by human biology—notwithstanding declarative statements about future radical increases in longevity without biological evidence to support them. A simple example reveals the problem with mathematically derived claims for immortality. Consider the current world record for the one-mile run. Charles Westhall from England first set the record in 1855 when he ran a mile in 4 minutes 28 seconds. The record declined linearly since then to the current record set by Hicham El Guerrouj in 1999 at 3 minutes and 43.13 seconds. The rate of improvement in world record running times for the mile in the last 150 years is every bit as linear as the rise in life expectancy at birth in humans over the same time period (8). It would be a simple matter to extend this historical trend linearly into the future, and forecast that a mile will be run instantaneously several centuries from now. This is a laughable exercise to even the casual observer, but virtually identical to the effect of deploying purely mathematical arguments to support radical life extension and immortality in the future, or a prediction that life expectancy in the past was zero based on back casting extrapolation. The inconvenient truth is, reality gets in the way. Improvements in world record running times for the mile have not changed in the last 19 years, and for decades prior to 1999 the rate of improvement decelerated. World records for other Olympic events have also decelerated to a snail’s pace in the modern era (9,10). This phenomenon may be referred to as “peak Olympics” or “peak longevity” when applied to the topic of this article; an age when it is no longer possible to push the functioning of the human body much beyond its current limits (11). Although there is no reason to believe that there are specific biologically based constraints on running the mile, skating 1,000 meters, or the distance a javelin can be thrown, the basic design of the human body nevertheless imposes indirectly determined limits on strength, speed, and duration of life. There is no reason to believe that natural selection favored such limits explicitly, but the limits exist nonetheless. However, this reality does not mean that humans should stop seeking ways to improve and extend our health span. A description of some of these biologically based limits on human longevity imposed by body design, including the Achilles heel of an aging brain, was described years ago by Olshansky and colleagues (12,13). The fact is, humans cannot run as fast as a cheetah, jump as high as a gazelle, or live as long as a Greenland shark (392 ± 120 years) because the body design of each species, which is based on a genetically determined set of life history attributes that evolved over millions of years, are not optimized with longevity as the end game. Aging as we know it is the unintended consequence of accumulated damage (coupled with imperfect repair mechanisms) to the same human biology that also gives us life. Human longevity should best be thought of as an inadvertent byproduct of fixed genetic programs that optimize for growth, development, reproduction, and ensuring the reproductive success of offspring (eg, grandparenthood) (14). The first inconvenient truth is that purely mathematical arguments used to support radical life extension are inherently flawed for the same reason that Zeno’s Paradox cannot be true—because just like Zeno who failed to invoke basic rules of physics, purveyors of mathematical arguments supporting radical life extension fail to take into account the biological reality that drives longevity determination in humans. There are two other mathematically based predictions of radical life extension that are similar to the one stated earlier. In one case, de Grey (15) contends that humans are approaching an “actuarial escape velocity”—a hypothetical world in which “mortality rates fall so fast that people’s remaining (not merely total) life expectancy increases with time.” For this to happen, medical technology would need to manufacture survival time faster than the rate of living is taking it away—a condition de Grey contends (without evidence) is forthcoming. de Grey (16) (p. 393) further contends that declines in death rates will soon accelerate dramatically at older ages (past age 105) until the probability of death will “ . . . fall to 5% or lower, and most to below 1% . . .” As a frame of reference, death rates at ages more than 105 years now are at 50 per cent or higher (17,18). The absence of empirical, biological, or even suggestive evidence to support any of these claims, especially those with specific mathematical predictions about future death rates attached to them, demonstrates that these estimates are derived from nonscientific methods. This exaggeration proves harmful for those seeking funding for aging science. The second inconvenient truth is that hyperbole about the impact of interventions in aging biology that do not yet exist, and the resulting hypothetical future course of human longevity, is unnecessary given that trends in population aging and life extension already experienced, is sufficient rationale for accelerated funding of aging science. Another mathematically based prediction for radical life extension is the simple suggestion that if declines in death rates observed in the past continue into the future, radical life extension has already arrived for cohorts born today (19). A claim similar to this was originally made by Vaupel and Gowen (20), but the argument then (more than 30 years ago) was that babies born in the modern era could, on average, live to 100 years or more. A more forceful statement has since been made by the same author ‘ . . . in countries with high life expectancies most children born since the year 2000 will [emphasis ours] celebrate their 100th birthday . . . ’ ((21); p.536). This is not just a prediction that period life expectancy at birth will rise to 100 years; rather, this latest assertion is orders of magnitude more audacious. The underlying and unstated assumptions behind this view might not be appreciated by non-demographers, so an explanation is provided later. The current use of “will” instead of “could” transforms this into a prognostication that is by now already 28 years in the making—which means its truthfulness can be measured today using national vital statistics data. The prediction that cohort life expectancy at birth for babies born today will be 100 years or more once the entire cohort dies out in the early 22nd century, required at the time it was made, that total mortality decline by a minimum of 2 per cent annually at all ages beginning in 1990, and extending through the 21st century. Furthermore, it also means that cohort life expectancy at birth for babies born after 1990 will be roughly 15–20 years higher than period life expectancy estimates based on death rates observed at all ages since then. How much greater is unclear as the term “most babies” was not defined by the authors, but it is certainly more than 50 per cent, and frankly it may not matter given the radical tenor of this prediction. To provide non-demographer readers with a sense of just how radical this prediction is, consider the fact that during the 20th century when life expectancy at birth rose by an unprecedented 30 years (faster than at any time in recorded history), cohort life expectancy for those born in 1900 was an astounding 9.3 years greater than period life expectancy in that same year (see Ref. (7), Figure 1). The primary reason why the difference of 9.3 years was so large was because infant and child mortality dropped precipitously in the first half of the century due to advances in public health that included rising living standards and improved socioeconomic status. When early age mortality declines, decades of life for each person saved are added back into the life table because saving a child from death enables most of them to live into their 60s, 70s, and beyond. This powerful force that brought forth the first longevity revolution and a rapid 30-year increase in life expectancy at birth, cannot happen again. The implication is that future large gains in life expectancy, should they occur, must result from declining middle and old age mortality. Therein lies both the dilemma and the barrier to such forecasts. To be clear, the underlying premise of extrapolation-based forecasts—that future trends in life expectancy will follow along a path drawn from the past—is invalid from the start because the gains in longevity must now come from a different part of the age structure, and for totally different reasons. It is worth noting that period life expectancy at birth is calculated from death rates observed at all ages in a given year. The underlying assumption is that this estimate is how long an average person in that year would live if death rates prevailing in that year, remain constant for the duration of life of the entire birth cohort. If death rates decline, as they did in the 20th century, then period life expectancy at birth will underestimate how long the cohort will live; if death rates rise, then period life expectancy overestimates duration of life. Cohort life expectancy, by contrast, is how long a birth cohort actually lived. The year 1900 is ideal for illustrating the difference between period and cohort life expectancy because everyone born in that year and earlier has already died. For extrapolation-based forecasts of cohort life expectancy to come true now, cohort life expectancy for babies born today would need to be greater than 20 years higher than period life expectancy at birth—more than double the magnitude of the difference observed during the last century. This view requires that medical technology in the future must manufacture far more survival time for the old than public health did a century ago for the young—we’ll leave it to the reader to decide on the plausibility of this assumption. This is not the only problem with this line of reasoning. Because mathematically based forecasts of linear increases in life expectancy at birth are projected to occur at a rate of 2 years per decade (0.2 year increase in life expectancy at birth annually) (19), this places a particularly onerous burden on the forces required to make it all come to pass. By way of illustration, a 0.2-year annual improvement in e(0) today requires that total mortality at all ages decline by 2.2 per cent annually. Fifty years from now the same 0.2-year improvement in e(0) requires a 3.7 per cent decline in total mortality at all ages ((7); p.6). To be clear, just like the prediction from de Grey, the rate of improvement in old age mortality mustacceleratefrom one year to the next to maintain linear increases in life expectancy—such acceleration rarely occurred in the past and is not occurring now. Given that the technological advances required for this to occur are hypothetical (ie, not yet invented), this position of advocacy is indefensible. The third inconvenient truth is that forecasts of linear increases in cohort life expectancy at birth and accelerating declines in death rates at older ages are not just sharp deviations from the past—they are radically different, and presented directly in the face of contradicting empirical evidence that life expectancy at birth is decelerating in many developed nations. According to Wilmoth ((22), p. 1127), “ . . . the burden of proof lies with those who predict sharp deviations from past trends.” As these forecasts of radical life extension were first made in 1990, it is possible to determine whether the last quarter century of mortality experience in the United States has followed the predicted pattern. Table 1 illustrates the annual average rate of improvement in the observed pattern of life expectancy at birth for males and females combined in the U.S. There isn’t a single decade since 1990 when life expectancy rose by two years; there were only 9 years of the 26 since 1990 that life expectancy rose by 0.2 years or more; during 3 of the last 26 years life expectancy actually declined; the annual rate of improvement in life expectancy since 1990 was only 0.17 years (not 0.2 as predicted by Vaupel); and during the last 6 years the annual rate of improvementdeceleratedprecipitously to 0.017 years (23). In fact, since the middle of the 20th century, the only decade that witnessed an increase in life expectancy at birth that exceeded two years was during the 1970s when cardiovascular disease began to decline precipitously (although the decade of change between 2000 and 2010 was close to 2 per cent). Observed mortality trends in the United States since 1990 indicate definitively that the rate of improvement in life expectancy in the United States has decelerated dramatically (24,25). Table 1. Annual Average Rate of Improvement in e(0), by Decade (United States, 1990–2016) Life Expectancy at Birth Annual Improvement 1990 75.40 — 2000 76.84 0.142 2010 78.81 0.197 2016 78.91 0.017 Life Expectancy at Birth Annual Improvement 1990 75.40 — 2000 76.84 0.142 2010 78.81 0.197 2016 78.91 0.017 Note: Human Mortality Database (1) (data accessed, January 2019). Open in new tab Table 1. Annual Average Rate of Improvement in e(0), by Decade (United States, 1990–2016) Life Expectancy at Birth Annual Improvement 1990 75.40 — 2000 76.84 0.142 2010 78.81 0.197 2016 78.91 0.017 Life Expectancy at Birth Annual Improvement 1990 75.40 — 2000 76.84 0.142 2010 78.81 0.197 2016 78.91 0.017 Note: Human Mortality Database (1) (data accessed, January 2019). Open in new tab Notions of limits to human longevity have been made before (26–28)—with a specific prediction that life expectancy at birth for any national population was unlikely to ever exceed 85 years without a breakthrough in aging biology. To date, and contrary to false claims that this limit has been broken (29), no national population has ever exceeded this “limit” proposed back in 1990. The fourth inconvenient truth is that the observed rise in life expectancy and observed declines in death rates have not in the past, and are not now, occurring at the pace predicted by those claiming radical life extension is forthcoming or is already happening. Linear forecasts of life expectancy increases should not be used by governments or organizations for forecasting purposes. Instead, three-dimensional forecasting models that rely on the observed health status of living cohorts as the basis for predicting death rates have been proposed as the best method of forecasting life expectancy. (30) Biodemographic Reasoning Behind Limits to Longevity The other side of the longevity debate suggesting that human life expectancy is limited, follows from both empirical evidence and several related lines of scientific inquiry. The demographic evidence supporting the limited life-span hypothesis is compelling. First, Olshansky and colleagues (28) demonstrated more than a quarter century ago a phenomenon known as entropy in the life table—this is a purely mathematical attribute of how life expectancy is calculated where it is illustrated that the higher life expectancy gets, the more difficult it becomes to raise it further. The reason is straightforward—when life expectancy at birth approaches 80 years, the vast majority of all deaths in a population are concentrated between ages 60 and 95 years. As death rates in this age window are so high with a doubling time of approximately 7–8 years; and they’re high at these ages because aging has become the dominant risk factor for diseases; and as aging is currently immutable; saving lives at older ages yields diminishing longevity returns relative to lives saved at younger ages. This is not a declaration that efforts to save lives at older ages should be abandoned as has been mistakenly suggested (31); rather, it is a purely mathematical argument illustrating that the metric of life expectancy becomes less sensitive to declining mortality as it approaches and exceeds 80 years. This is the reason why cures for major fatal diseases today will no longer produce large increases in life expectancy, and it is the primary reason why projected linear increases in life expectancy is unrealistic. We reaffirmed entropy in the life table using data from several developed nations in an article published 11 years after our original article (32). The overall conclusion then was that breaching the upper limit of 85 for a national population (men and women combined) would require a modification to the biological rate of aging. Nothing has happened since then to change that view. In a second line of inquiry we compared the mortality of humans with two other species, mice and dogs, where causes of death for all three species were verified to be aging related (33). This was done because it was earlier hypothesized that almost all sexually reproducing species possess an intrinsic mortality signature (eg, schedules of age-specific death rates) that is linked specifically to the reproductive schedule inherited by each species. Once normalized for time, these mortality schedules should, according to evolutionary theory (34), overlap—revealing a common mortality pattern and a predicted maximum life span for each species. We were not the first to hypothesize this phenomenon—it was intimated by Benjamin Gompertz in 1825 when he coined the term “Law of Mortality” (35); confirmed by Makeham (36), studied by Loeb and Northrop (37) and Brownlee (38); empirically evaluated by Greenwood (39) and Pearl (40,41); re-evaluated by Deevey (42); and then the Law of Mortality was finally solved by Carnes and colleagues (34). For a detailed history of these efforts to understand the dynamics of human mortality, see Ref. (5). Once the intrinsic mortality schedules for all three species were compared and scaled for time (for more on interspecies time scaling see Ref. (8)), it was concluded that the life expectancy limit for humans was approximately 85 years. Our third line of inquiry on limits was predicated on the evolutionary conclusion that bodies have biological warranty periods and that the expiration date of those warranty periods is linked to the time required to reach sexual maturity, reproduce, nurture young, and (for some species) provide grandparenting (43). Observed age-specific fertility patterns in mice and humans were used to infer the median age of death from intrinsic causes for humans on the basis of mouse data (33). The resulting maximum median age at death for humans (an approximation of life expectancy at birth) fell within the mid to upper 80s. The fifth inconvenient truth is that while there can be no genetically-driven program for aging or death, there are nevertheless biologically based limits on human longevity that are driven by fixed genetic programs that influence human body design. An inadvertent byproduct of these programs is limits on multiple functional attributes of the species—longevity is one among many. These three totally independent approaches (the last one not even involving mortality data for humans) produced nearly identical probabilistic limits for the life expectancy of human populations. Taken together, we contend this is compelling evidence that age 85 years represents an upper limit to life expectancy for humans. Keep in mind that this 85-year life expectancy limit is for a population, which means approximately 40 per cent of the original birth cohort must live at least to age 90 years; 5%–6% is likely to reach 100; and even a small percentage of the cohort is expected to reach the ages of 110–115 years. Exceeding 115 is likely to occur for only a handful of people—and this has proven to be so (44). It is therefore not surprising that the rise in life expectancy in developed nations has decelerated in recent decades, and that it has begun to level off just short of 85 in many of today’s developed nations (24,25). This is exactly what we predicted would happen more than a quarter century ago (28). Although there is still plenty of room for improvement to reach the 85 limit (remember that entropy in the life table implies that an increase in e(0) from 83 to 84, or 84 to 85, requires an extraordinary effort that is much more difficult to achieve than moving life expectancy from 80 to 81); going beyond that limit still requires, in our view, modifications to the underlying biology of aging. The Future The 30-year rise in life expectancy in the last 120 years was one of humanity’s greatest achievements. Public health played a critical role in the beginning when the easy gains in longevity were possible by saving the young, but these easy gains cannot happen again. Medical technology took over in the later part of the century to manufacture survival time for people that would have otherwise succumbed at younger ages to death’s consistent harvest. The rise of diseases of aging such as heart disease, cancer, stroke, and Alzheimer’s disease, to name a few, were not a consequence of humanity’s failure to live a healthy lifestyle or the consequence of increasingly more polluted environments—they were a product of success. In the modern era in long-lived populations we now live long enough for aging related diseases to impact human health. In other words, the longer we live, the more powerful the biological process of aging becomes as a risk factor for the diseases that kill us. These observations present humanity with a rather interesting dilemma today. If we continue to attack chronic fatal and disabling diseases in the future as we have in the past, we might very well succeed in postponing death, but the price of this success will likely be a rise in the prevalence and severity of aging related conditions. The trade-offs may no longer be favorable as increasingly larger segments of the population survive deeper into the “red zone”—a period in the life span when frailty and disability rise exponentially (45). The sixth inconvenient truth is that combating diseases of aging as if they are independent of each other is likely to lead to a rising prevalence and severity of aging related diseases. The solution is to challenge the conventional approach to disease and instead of attacking one disease at a time, enhance the effort to combat the processes of aging that give rise to these diseases. (46) This new form of Primary Prevention in an aging world has been referred to as the Longevity Dividend (3,47) or Geroscience (48,49). Evidence amassed in recent years indicates that aging science shows great promise as a method of extending health span (50–56). We are now witnessing the rise of a large number of companies that have taken on this challenge and the acceptance by the U.S. Food and Drug Administration that aging is a legitimate target for therapeutic interventions (1). The first to succeed in developing a documented safe and efficacious intervention that modulates aging will mark their place in public health history alongside John Snow, Edward Jenner, Jonas Salk, Louis Pasteur, Florence Nightingale, Sir Edwin Chadwick, and Sara Josephine Baker (among others). No one can know exactly how anticipated advances in aging biology will influence the future course of life expectancy, which is why we have fundamental disagreements with scientists that claim radical life-span extension is forthcoming in the absence of empirical evidence to support this view, and in the presence of global trends indicating that limits to longevity are being approached. Our view is that right now it doesn’t matter what the effect of such aging interventions might be on life expectancy. If the goal of aging science and modern medicine shifts from its historical emphasis on trying to make us live longer, to a new goal of extending the period of healthy life, we no longer have to fight the uphill battle against life table entropy. Indeed, the very aging interventions advocated by those claiming that radical life extension is forthcoming might very well come to pass; and we’re advocating here that they should be pursued aggressively. Where we differ from advocates of radical life extension is that we don’t attach unsubstantiated and/or exaggerated increases in life expectancy to them. Health-span extension can also be measured in the short term, which means public health will quickly know whether an aging intervention is having the desired outcome. Questions about upper limits to life expectancy should best be left to esoteric elements of mathematical demography that focus on mortality dynamics where few people survive, or science fiction. The latter appears to be the genre used by those now predicting life expectancies of more than 100 and life spans of more than 1,000, and even occasional forays into discussions of immortality. We prefer the focus shift exclusively to health-span extension. Funding Dr. Olshansky was funded by The Glenn Award from the Glenn Foundation for Medical Research. Conflict of interest statement None reported. References 1. Olshansky SJ , Martin GM , Kirkland JL , eds. Aging: The Longevity Dividend . Cold Spring Harbor Laboratory Press ; 2015 . Google Preview WorldCat COPAC 2. Goldman DP , Cutler D , Rowe JW , et al. Substantial health and economic returns from delayed aging may warrant a new focus for medical research . Health Aff (Millwood). 2013 ; 32 : 1698 – 1705 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 3. Olshansky SJ , Carnes BA . Primary prevention with a capital P . Perspect Biol Med. 2017 ; 60 : 478 – 496 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 4. Carnes BA , Olshansky SJ . Evolutionary perspectives on human senescence . Popul Dev Rev. 1993 ; 19 ( 4 ): 793 – 806 . Google Scholar Crossref Search ADS WorldCat 5. Olshansky SJ , Carnes BA . Ever since Gompertz . Demography. 1997 ; 34 : 1 – 15 . Google Scholar Crossref Search ADS PubMed WorldCat 6. Wilmoth JR . How long can we live? A review essay . Population and Development Review. 2001 ; 27 : 791 – 800 . http://www.sciencemag.org/content/ 291/5508/1491.summary/reply#sci_el_32. 7. Olshansky SJ , Carnes BA . Zeno’s paradox of immortality . Gerontology. 2013 ; 59 : 85 – 92 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 8. Olshansky SJ , Carnes BA . 2013 . A Measured Breath of Life . 27 . ISBN 0-9889895-0-4. Google Preview WorldCat COPAC 9. Berthelot G , Tafflet M , El Helou N , et al. Athlete atypicity on the edge of human achievement: performances stagnate after the last peak, in 1988 . PLoS One. 2010 ; 5 : e8800 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 10. Berthelot G , Sedeaud A , Marck A , et al. Has athletic performance reached its peak? Sports Med. 2015 ; 45 : 1263 – 1271 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 11. Marck A , Antero J , Berthelot G , et al. Are we reaching the limits of homo sapiens? Front Physiol. 2017 ; 8 : 812 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 12. Olshansky SJ , Butler RA , Carnes BA . If humans were built to last . Scientific American. 2001 ; 28 : 50 – 55 . Google Scholar Crossref Search ADS WorldCat 13. Olshansky SJ , Butler RN , Carnes BA . Re-engineering humans . The Scientist. 2007 ; 21 ( 3 ): 28 – 31 . WorldCat 14. Olshansky SJ , Hayflick LH , Carnes BA , et al. The truth about human aging . Scientific American. 2002 . https://www.scientificamerican.com/article/the-truth-about-human-agi/. WorldCat 15. de Grey ADN . Escape velocity: why the prospect of extreme human life extension matters now . PLoS Biol. 2004 ; 2 : e187 . Google Scholar Crossref Search ADS WorldCat 16. de Grey A . Foreseeable and more distant rejuvenation therapies . In: Rattan S , ed. Aging Interventions and Therapies . Singapore : World Scientific ; 2005 : 379 – 395 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC 17. Barbi E , Lagona F , Marsili M , Vaupel JW , Wachter KW . The plateau of human mortality: demography of longevity pioneers . Science. 2018 ; 360 : 1459 – 1461 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 18. Gavrilova N , Gavrilov L . Testing the limit to human lifespan hypothesis with data on supercentenarians . Innov Aging. 2018 ; 2 ( Suppl 1 ): 888 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 19. Christensen K , Doblhammer G , Rau R , Vaupel JW . Ageing populations: the challenges ahead . Lancet. 2009 ; 374 : 1196 – 1208 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 20. Vaupel JW , Gowan AE . Passage to Methuselah: some demographic consequences of continued progress against mortality . Am J Public Health. 1986 ; 76 : 430 – 433 . Google Scholar Crossref Search ADS PubMed WorldCat 21. Vaupel JW . Biodemography of human ageing . Nature. 2010 ; 464 : 536 – 542 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 22. Wilmoth JR . Demography of longevity: past, present, and future trends . Exp Gerontol. 2000 ; 35 : 1111 – 1129 . Google Scholar Crossref Search ADS PubMed WorldCat 23. Human Mortality Database . http://www.mortality.org/. 24. Case A , Deaton A . Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century . Proc Natl Acad Sci USA. 2015 ; 112 : 15078 – 15083 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 25. Crimmins EM . Lifespan and healthspan: past, present, and promise . Gerontologist. 2015 ; 55 : 901 – 911 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 26. Bourgeois-Pichat J . Future outlook for mortality decline in the world . Population Bulletin of the United Nations. 1978 ; 11 : 12 – 41 . WorldCat 27. Fries JF . Aging, natural death, and the compression of morbidity . N Engl J Med. 1980 ; 303 : 130 – 135 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 28. Olshansky SJ , Carnes BA , Cassel C . In search of Methuselah: estimating the upper limits to human longevity . Science. 1990 ; 250 : 634 – 640 . Google Scholar Crossref Search ADS PubMed WorldCat 29. Oeppen J , Vaupel JW . Demography. Broken limits to life expectancy . Science. 2002 ; 296 : 1029 – 1031 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 30. Reither EN , Olshansky SJ , Yang Y . New forecasting methodology indicates more disease and earlier mortality ahead for today’s younger Americans . Health Aff (Millwood). 2011 ; 30 : 1562 – 1568 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 31. Vaupel JW . The remarkable improvements in survival at older ages . Phil Trans R Soc Lond B Biol Sci. 1997 ; 352 : 1799 – 1804 . doi: Google Scholar Crossref Search ADS WorldCat Crossref 32. Olshansky SJ , Carnes BA , Désesquelles A . Prospect for human longevity. Science. 2001 ; 292 : 1654 . Google Scholar PubMed WorldCat 33. Carnes BA , Holden LR , Olshansky SJ , Witten MT , Siegel JS . Mortality partitions and their relevance to research on senescence . Biogerontology. 2006 ; 7 : 183 – 198 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 34. Carnes BA , Olshansky SJ , Grahn D . Continuing the search for a law of mortality . Population and Development Review. 1996 ; 22 ( 2 ): 231 – 264 . Google Scholar Crossref Search ADS WorldCat 35. Gompertz B . On the nature of the function expressive of the law of human mortality and on the new mode of determining life contingencies . Philos Trans R Soc London. 1825 ; 52 ( 1 ): 511 – 559 . WorldCat 36. Makeham WM . On the law of mortality . J Inst Actuar. 1867 ; 13 : 325 – 358 . Google Scholar Crossref Search ADS WorldCat 37. Loeb J , Northrop JH . Is there a temperature coefficient for the duration of life? Proc Natl Acad Sci USA. 1916 ; 2 : 456 – 457 . Google Scholar Crossref Search ADS WorldCat 38. Brownlee J . Notes on the biology of a life table . J R StatSoc. 1919 ; 82 : 34 – 77 . Google Scholar Crossref Search ADS WorldCat 39. Greenwood M . Laws of mortality from the biological point of view . Journal of Hygiene. 1928 ; 28 : 267 – 294 . Google Scholar Crossref Search ADS WorldCat 40. Pearl R . Experimental studies on the duration of life . Am Nat. 1921 ; 55 : 481 – 509 . Google Scholar Crossref Search ADS WorldCat 41. Pearl R . A comparison of the laws of mortality in Drosophila and in man . Am Nat. 1922 ; 56 : 398 – 405 . WorldCat 42. Deevey ES Jr . Life tables for natural populations of animals . Q Rev Biol. 1947 ; 22 : 283 – 314 . Google Scholar Crossref Search ADS PubMed WorldCat 43. Carnes BA , Olshansky SJ , Grahn D . Biological evidence for limits to the duration of life . Biogerontology. 2003 ; 4 : 31 – 45 . Google Scholar Crossref Search ADS PubMed WorldCat 44. Dong X , Milholland B , Vijg J . Evidence for a limit to human lifespan . Nature. 2016 ; 538 : 257 – 259 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 45. Olshansky SJ . From lifespan to healthspan . JAMA. 2018 ; 320 : 1323 – 1324 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 46. Butler RN , Miller RA , Perry D , et al. New model of health promotion and disease prevention for the 21st century . BMJ. 2008 ; 337 : a399 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 47. Olshansky SJ , Perry D , Miller RA , Butler RN . In pursuit of the longevity dividend . The Scientist. 2006 ; 20 ( 3 ): 28 – 36 . WorldCat 48. Sierra F , Kohanski R , eds. Advances in Geroscience . Dordrecht : Springer ; 2016 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC 49. Huffman DM , Justice JN , Stout MB , Kirkland JL , Barzilai N , Austad SN . Evaluating health span in preclinical models of aging and disease: guidelines, challenges, and opportunities for geroscience . J Gerontol A Biol Sci Med Sci. 2016 ; 71 : 1395 – 1406 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 50. Kennedy BK , Pennypacker JK . Drugs that modulate aging: the promising yet difficult path ahead . Transl Res. 2014 ; 163 : 456 – 465 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 51. Kennedy BK , Berger SL , Brunet A , et al. Geroscience: linking aging to chronic disease . Cell. 2014 ; 159 : 709 – 713 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 52. Kirkland JL . Translating advances from the basic biology of aging into clinical application . Exp Gerontol. 2013 ; 48 : 1 – 5 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 53. Mahmoudi S , Xu L , Brunet A . Turning back time with emerging rejuvenation strategies . Nat Cell Biol. 2019 ; 21 : 32 – 43 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 54. Miller RA . Extending life: scientific prospects and political obstacles . Milbank Q. 2002 ; 80 : 155 – 174 .doi:10.1111/1468–0009.00006 Google Scholar Crossref Search ADS PubMed WorldCat 55. Sierra F , Hadley E , Suzman R , Hodes R . Prospects for life span extension . Annu Rev Med. 2009 ; 60 : 457 – 469 . doi: Google Scholar Crossref Search ADS PubMed WorldCat Crossref 56. Kerrigan S . 12 Innovations that could make reverse aging a reality: could science really turn back the clock on aging? We might be closer to reverse aging than you think . 2018 . https://interestingengineering.com/12-innovations-that-could-make-reverse-aging-a-reality. © The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)