Abstract Age is the strongest risk factor for physical disability and Alzheimer’s disease (AD) and related dementias. As such, other aging-related risk factors are also shared by these two health conditions. However, clinical geriatrics and gerontology research has included cognition and depression in models of physical disability, with less attention to the pathophysiology of neurodegenerative disease. Similarly, AD research generally incorporates limited, if any, measures of physical function and mobility, and therefore often fails to consider the relevance of functional limitations in neurodegeneration. Accumulating evidence suggests that common pathways lead to physical disability and cognitive impairment, which jointly contribute to the aging phenotype. Collaborations between researchers focusing on the brain or body will be critical to developing, refining, and testing research paradigms emerging from a better understanding of the aging process and the interacting pathways contributing to both physical and cognitive disability. The National Institute of Aging sponsored a workshop to bring together the Claude D. Pepper Older Americans Independence Center and AD Center programs to explore areas of synergies between the research concerns of the two programs. This article summarizes the proceedings of the workshop and presents key gaps and research priorities at the intersection of AD and clinical aging research identified by the workshop participants. Cognitive aging, Dementia, Physical function, Multimorbidities, Disablement process Introduction Centers for Medicaid and Medicare Services data show that the age-adjusted proportion of older persons who are limited in activities of daily living (ADLs) and instrumental ADLs (IADLs) or reside in a long-term care facility has essentially remained constant since 1992. This stagnation has occurred despite increasing life expectancy. The need for long-term care represents the end-stage of a process of cognitive and physical decline. An emerging body of research clearly indicates that common risk factors and underlying pathologic processes contribute to declines in both cognitive and physical function, as depicted in Figure 1, and suggests opportunities to design interventions that simultaneously affect both domains, as well as clinical models of care that emphasize treatment of these shared pathways. Figure 1. View largeDownload slide Shared risk factors and overlapping pathways contributing to both physical disability and dementia. Figure 1. View largeDownload slide Shared risk factors and overlapping pathways contributing to both physical disability and dementia. On December 1 and 2 of 2016, the National Institute on Aging convened a workshop to explore the common ground between those studying the physical disablement process and those studying AD and related dementias, the proceedings of which are summarized here. Attendees included nationally represented researchers from both the Claude D. Pepper Older Americans Independence Center (OAIC) and the Alzheimer’s Disease Center (ADC) Programs, two NIH-funded programs devoted to enhancing independence in older adults and improving diagnosis and care for people with AD, respectively. A directory of workshop attendees can be found in Supplementary Appendix I. The primary goals of the workshop were to identify overlaps and synergies between the AD and Geriatrics/Gerontology research communities and discuss conceptual and operational challenges and opportunities. The workshop concluded by summarizing the current gaps in knowledge and future research priorities. The covered topics are reviewed in the following sections, along with key references; additional references for each section can be found in Supplementary Appendix II. The workshop agenda, participants, and slides are viewable at: https://www.peppercenter.org/public/ria/dspWorkshop_alz_ger.cfm. Overlapping Phenotypes in Dementia and Functional Decline: Common Neuropathologies Multiple lines of evidence indicate that decrements in physical health or frailty are risk factors for the development of cognitive impairment (1). In longitudinal analyses, baseline levels of physical frailty and change in physical frailty over time were associated with risk of incident AD (2) and incident mild cognitive impairment (MCI) (3). This study and most research referencing frailty employs scales derived from the five-factor Fried frailty index which includes muscle weakness, slow gait, fatigue, physical inactivity, and weight loss (4). A recent systematic review examined the relationship between physical frailty and cognition and found that 50% of studies reported a relationship with slower gait and 40% with muscle weakness; in contrast, 20% reported an association with exhaustion and 10% with weight loss (5). Moreover, changes in physical frailty and cognition are highly correlated, and the simultaneous decline in physical and cognitive function in late life likely reflects common underlying neuropathologies as evidenced by macroinfarcts, AD pathology, and nigral neuronal loss assessed in the brain at autopsy (6). The current literature suggests that AD and related dementias may be particularly associated with deficits in physical function. For example, Buchman et al. determined that the presence of the APOE ε4 allele (which is associated with greater risk for AD) is a risk factor for more rapid motor decline in the elderly and that this relationship was primarily driven by the association between the ε4 allele and change in muscle strength, in contrast to other components of the frailty syndrome (7). In addition, the evidence for an association between slower gait and cognitive impairment is robust. Brain amyloid burden is associated with impaired physical function (8), and it is clear that gait and cognition share common neurologic substrates as evidenced by the slowing of gait in persons also attempting a cognitive task (9). In studies of older adults, declines in gait performance appear closely linked in time with changes in cognitive performance, and gait may slow a decade before diagnosis of MCI (10). Such findings have prompted the identification and validation of a motoric cognitive risk syndrome (MCR), a recently described pre-dementia syndrome characterized by slow gait and cognitive complaints (11). In support, MCR is a better predictor of cognitive decline than either of its individual components (11,12). In addition to the direct relationship between failing physical function and the risk of MCI and AD, these processes share many common risk factors (3,13). Notably, cardiometabolic disease including type 2 diabetes is strongly related to both physical disability and AD risk (14). Clinical and subclinical cardiovascular diseases are strongly linked to physical impairments and physical frailty (15). These diseases are also linked to cerebral microinfarcts, white matter disease, and stroke which are related to both impaired gait and dementia. Furthermore, obesity in mid- and late life are strongly related to reduced gait speed and self-reported physical limitations; while mid-life obesity is also a strong risk factor for AD, lower body mass index or wasting, a subcomponent of frailty, is related to increased AD pathology in the elderly (16). The clinical consequences of these shared risk factors and common pathologies will be better understood in the context of prospective studies designed to investigate dual decline, whereby changes in physical and cognitive function over time, and potential underlying mechanisms, can be evaluated in association with long-term health trajectories. Biological Pathways and Markers for Physical Function Decline and AD Two largely independent bodies of research have examined biological pathways involved in, biomarkers that correlate with, and genetic polymorphisms that increase the risk for age-related physical decline/frailty/sarcopenia and AD/dementia, respectively. Examples of these risk markers for physical and cognitive decline are listed in Table 1; significant associations with specific deficits have been reported in previous studies (See supplemental references in Supplementary Appendix II). Investigators studying the biological basis of age-related frailty, sarcopenia, and other forms of physical decline have evaluated a large number of serum biomarkers associated with these conditions. In general, these biomarkers can be split into roughly three classes: inflammatory biomarkers (including interleukin-6 [IL-6]), hormones (including insulin growth factor-1 [IGF-1], testosterone, vitamin D, adiponectin, and dehydroepiandrosterone sulfate [DHEAS]), and other lipids and proteins (such as the serum protease inhibitor cystatin-C). Studies examining biomarkers and pathologic correlates of, and genetic polymorphisms that increase the risk for preclinical progression of AD pathology and the risk of developing dementia due to AD have largely identified increases in amyloid beta (Aβ)-containing plaques and intraneuronal tangles comprised of hyperphosphorylated tau protein (p-tau) within the brain; these central pathologic processes are accompanied by progressive decreases in Aβ and increases in tau and p-tau within the cerebrospinal fluid (CSF). Additional studies have shown that AD progression is associated with increased neuroinflammation as measured by increases in CSF monocyte chemoattractant protein-1 (MCP-1) levels and by increased inflammatory cell activation as seen on brain PET scans. Interestingly, modifying these biomarker levels in intervention trials, for example, to reduce Aβ or increase DHEAS or testosterone (17,18), have been unsuccessful in changing the progression of the clinical syndrome with which they are associated, highlighting the complex relationship between surrogate outcome measures and hard endpoints. Table 1. Examples of Risk Markers for Physical and Cognitive Decline Biomarker or Genetic Polymorphism P = Physical impairment; A = AD/MCI; C = Late-life cognitive decline (not AD/MCI) Comment Low IGF-1 P Trophic for muscle and brain Low Testosterone P Trophic for muscle Low 25(OH)D P, C Findings from supplementation trials are mixed Low/high adiponectin P, C Risk associations depend on age. High levels are desirable in middle age, but undesirable in old age Low DHEAS P, C High cystatin-C P, A, C High IL-6 P, A, C High TNF-a soluble receptor P High CSF MCP-1 A Associated with AD progression CSF AβPET P, A Associated with AD progression CSF Tau and P-tau A, C Associated with AD progression, also a brain injury biomarker APOE4 genotype A, C Associated with pulmonary function, Increases AD risk Neuron-specific exosomes A Biomarker or Genetic Polymorphism P = Physical impairment; A = AD/MCI; C = Late-life cognitive decline (not AD/MCI) Comment Low IGF-1 P Trophic for muscle and brain Low Testosterone P Trophic for muscle Low 25(OH)D P, C Findings from supplementation trials are mixed Low/high adiponectin P, C Risk associations depend on age. High levels are desirable in middle age, but undesirable in old age Low DHEAS P, C High cystatin-C P, A, C High IL-6 P, A, C High TNF-a soluble receptor P High CSF MCP-1 A Associated with AD progression CSF AβPET P, A Associated with AD progression CSF Tau and P-tau A, C Associated with AD progression, also a brain injury biomarker APOE4 genotype A, C Associated with pulmonary function, Increases AD risk Neuron-specific exosomes A Note: Listed biomarkers are in serum, unless otherwise specified. Aβ = amyloid beta; AβPET = amyloid beta brain PET imaging; CSF = cerebrospinal fluid; IL-6 = interleukin-6; IGF-1 = insulin-like growth factor 1; MCP-1 = monocyte chemoattractant protein 1; P-Tau = phosphorylated tau; 25(OH)D = 25-hydroxyvitamin D. View Large Table 1. Examples of Risk Markers for Physical and Cognitive Decline Biomarker or Genetic Polymorphism P = Physical impairment; A = AD/MCI; C = Late-life cognitive decline (not AD/MCI) Comment Low IGF-1 P Trophic for muscle and brain Low Testosterone P Trophic for muscle Low 25(OH)D P, C Findings from supplementation trials are mixed Low/high adiponectin P, C Risk associations depend on age. High levels are desirable in middle age, but undesirable in old age Low DHEAS P, C High cystatin-C P, A, C High IL-6 P, A, C High TNF-a soluble receptor P High CSF MCP-1 A Associated with AD progression CSF AβPET P, A Associated with AD progression CSF Tau and P-tau A, C Associated with AD progression, also a brain injury biomarker APOE4 genotype A, C Associated with pulmonary function, Increases AD risk Neuron-specific exosomes A Biomarker or Genetic Polymorphism P = Physical impairment; A = AD/MCI; C = Late-life cognitive decline (not AD/MCI) Comment Low IGF-1 P Trophic for muscle and brain Low Testosterone P Trophic for muscle Low 25(OH)D P, C Findings from supplementation trials are mixed Low/high adiponectin P, C Risk associations depend on age. High levels are desirable in middle age, but undesirable in old age Low DHEAS P, C High cystatin-C P, A, C High IL-6 P, A, C High TNF-a soluble receptor P High CSF MCP-1 A Associated with AD progression CSF AβPET P, A Associated with AD progression CSF Tau and P-tau A, C Associated with AD progression, also a brain injury biomarker APOE4 genotype A, C Associated with pulmonary function, Increases AD risk Neuron-specific exosomes A Note: Listed biomarkers are in serum, unless otherwise specified. Aβ = amyloid beta; AβPET = amyloid beta brain PET imaging; CSF = cerebrospinal fluid; IL-6 = interleukin-6; IGF-1 = insulin-like growth factor 1; MCP-1 = monocyte chemoattractant protein 1; P-Tau = phosphorylated tau; 25(OH)D = 25-hydroxyvitamin D. View Large Overall, although many pathologic processes that underlie physical frailty and other forms of age-related physical decline appear to be distinct from those that underlie AD, there are common pathways involved in both disorders. For example, inflammation appears to be a key correlate of age-related physical decline as well as AD pathology, though it is unclear to what extent inflammation plays a causal role in either of these processes. Further, the inflammatory biomarkers that have been best studied to date in physical functional decline and AD are different; IL-6 has been perhaps the best studied inflammatory biomarker that correlates with physical decline, while MCP-1 is perhaps the best studied inflammatory biomarker in AD. The idea that inflammation may be involved in both physical and cognitive decline, even if the specific inflammatory molecules and pathways differ, suggest the theoretical possibility that broad spectrum anti-inflammatory drugs or therapies may have efficacy in both disease states. More work is needed to fully understand which specific inflammatory molecules, cells, and pathways are involved and what role(s) they play in age-related physical versus cognitive decline. In addition to studying pathways involved in age-related physical and/or cognitive decline, it is also important to study the potential disparate role of these pathways in patients of different racial and ethnic backgrounds. There are clear racial/ethnic disparities in the incidence of AD, with economically disadvantaged minorities experiencing higher rates of disease (19). This may at least partly reflect more fundamental disparities in education, as well as contributing disease states (such as stroke, diabetes, and depression) among non-Hispanic African Americans and non-Hispanic White Americans. Understanding the basis for these racial disparities and the extent to which they reflect underlying differences in disease-specific biological pathways and biomarkers is a major goal for improving the cognitive and physical health of all older Americans. Although the systematic study of racial/ethnic differences in the development of frailty and physical decline is somewhat lacking, it does appear that there are important differences in the burden of frailty across racial/ethnic groups (20). The incidence of physical decline and dementia also differ by sex. Women live longer than men, but are more likely to experience physical disability and dementia than men (21). Workshop participants involved in multiethnic studies noted that interactions between race/ethnicity and serum biomarkers in the prediction of physical decline or physical disability are rarely observed, which suggests that disparities are secondary to differential exposure to environmental risk determinants. Similar trends are likely for other subgroups (ie, sex) and disease outcomes (ie, AD/dementia). New Directions from the Biology of Aging Chronologic age is the strongest risk factor for physical disability, frailty, AD, and associated comorbidities. Changes in the aging biology contributing to both disease processes may yield new intervention targets that could ameliorate both cognitive and physical decline. Senescence and Aging Senescence is a cellular phenotype characterized by growth arrest in response to age-associated stresses, including DNA damage, telomere attrition, reactive oxygen species, and protein aggregation (22). With advancing age, senescent cells accumulate at a greater frequency, and presumably, are less effectively cleared. They exhibit altered morphology, resistance to apoptosis, increased metabolic activity, and a senescence-associated secretory phenotype (SASP). The SASP consists of a broad array of cytokines and chemokines, matrix remodeling proteins, and growth factors. A growing body of evidence suggests senescent cells and the SASP contribute to age-related loss of tissue rejuvenation, inflammation, degeneration, and fibrosis. In mice, clearance of senescent cells and suppression of the SASP through inducible transgenes, exercise, and/or an emerging class of drugs termed “senolytics” has been shown to extend parameters of healthspan and counteract age-associated syndromes and diseases, including frailty (23), vascular dysfunction (24), diabetes (25), and atherosclerosis (26). However, there are not yet well-validated measures of senescent cell burden in humans. As these come available understanding its relationship with aging-related physical and cognitive decline is a priority. There are also on-going trials of senolytic drugs. These studies could be extended to older persons with physical impairments and those with MCI to understand the potential for these treatments to prevent disability. Mitochondria and Aging Three hypotheses/theories link mitochondrial function to the aging process, and each of them attributes metabolic rate either broadly (rate of living hypothesis) or succinctly (free radical theory and mitochondrial theory) to the mechanism of aging (27). The contribution of mitochondrial DNA (mtDNA) mutation burden to aging is an emerging research interest. Mice bearing a mutation in the mtDNA polymerase gene (PolgA) have increased mtDNA mutations and accumulate aging-associated phenotypes at an accelerated rate (28). In humans, mtDNA heteroplasmy is associated with both cognitive and neurosensory function and walking speed, and mitochondrial haplotypes are associated with dementia onset (29,30). In addition, mtDNA mutation burden is increased in the brains of aged and AD subjects (31). Furthermore, brain glucose utilization is decreased, mitochondrial number and mass are altered, and mitochondrial enzyme (eg, cytochrome oxidase) function is impaired in AD subjects (32–34). These findings prompted the mitochondrial cascade hypothesis for AD which links aging to sporadic neurodegenerative diseases while also providing important insight into disease etiology (32). Individuals who develop AD may develop mitochondrial dysfunction at an accelerated rate (compensated versus uncompensated aging) leading to bioenergetic dishomeostasis. Emerging literature also links both mtDNA mutation burden and ex vivo mitochondrial function to impaired physical function with some evidence that part of the variance in mitochondrial function in specific tissues represents a systemic organismal state (29,35). Future studies should focus on integrating the concept of brain aging and sporadic neurodegenerative diseases. Recently developed approaches to analyze mitochondrial function from peripheral blood mononuclear cells (PBMCs) indicates associations between reduced energetic capacity and physical function (35); mitochondrial energetic capacity in the blood is also associated with brain glucose utilization as assessed by fluorodeoxyglucose-positron emission tomography (FDG-PET) (36). This raises the possibility that improving systemic bioenergetics may simultaneously improve cognitive and physical function. Proteostasis and Aging Protein homeostasis (proteostasis) is a dynamic process regulated by chaperones, the unfolded protein response, and autophagy (37). Impaired proteostasis is a hallmark of aging, where species with more stable proteomes are longer lived (37,38). Autophagy, one mechanism of pathogenic protein clearance, is decreased with age and neurodegeneration (39), which drives further neurodegeneration and pathogenic protein build-up. Mice with genetic knockout of Atg5 or Atg7 (autophagy-related 5 and 7-genes essential for autophagy) in the nervous system have reduced macroautophagy (autophagy of proteins and full organelles, such as mitochondria) and increased neurodegeneration (40,41). Furthermore, pathogenic proteins such as those implicated in AD (tau and Aβ) can affect autophagy. Presenilin mutations associated with AD and increased Aβ production affect lysosome pH and ultimately prevent autophagosome degradation (42,43). These novel findings provide intervention opportunities, where autophagy can be increased to facilitate degradation of pathogenic proteins and restore proteostasis. Future research is required to develop methods for proteostasis network activity measurement from clinical specimens, such as autophagy, chaperones, and proteasomal systems, as well as identify biomarkers of disrupted proteostasis in humans. To further understand proteostasis and autophagy within the context of aging, research should focus on the molecular defect in these processes at different ages and disease stages while also determining if a predisposition to poor autophagy and proteostasis exists within various populations. Finally, therapeutic development requires focus on selective autophagy drugs and drugs targeting other components of the proteostasis networks, with the understanding that combinatory treatments may be required. Targeting AD and Physical Disability with Common Interventions The fact that dementia, cognitive decline, and physical decline occur in the context of an aging biology and share many risk factors and underlying pathologies suggests that theoretically some interventions would be expected to preserve physical and cognitive function simultaneously. Workshop presenters discussed potential interventions that target nutrition, inflammation, and exercise. Nutritional Interventions There is evidence that dietary components are important for healthy brain aging. Nutrients such as Vitamin E, docosahexaenoic acid (DHA), folate, and Vitamin B12 have been associated with beneficial effects (44–46), whereas dietary intake of other factors, such as saturated fats, have been associated with increased risk of cognitive decline (47). Vitamin B12 and D deficiencies are also linked to poor physical function, as is low dietary protein intake (48–50). The Mediterranean diet pattern has been linked to lower rates of physical frailty and the preservation of cognitive function (51,52). However, key research gaps remain; for instance, mechanisms for some dietary components (eg, folate and saturated fat) are not well defined, and the effect of nutrients on neurodegeneration is likely more complex than a single dietary component (53). There are few controlled trials of different types of diets in various populations (MCI, AD, Parkinson’s disease, stroke), and studies that have investigated a combination of diet and exercise are rare. A limitation of the randomized intervention trials of individual nutrients is that the trials have not targeted populations with marginal nutrient status (54). The evidence for individual nutrient associations with dementia outcomes is based primarily on the comparison of adequate intake versus low or marginal intake. Therefore, to test the importance of a nutrient on these outcomes in randomized trials it is important to restrict trial enrollment to (or stratify by) those individuals with low nutrient status. Few micronutrient trials focus on physical outcomes; including physical function phenotypes as secondary outcomes in such trials would provide new and important data in this domain. Anti-Inflammatory Interventions Chronic, low-grade inflammation is increased in older adults and is associated with a variety of age-related diseases. This is potentially due to a combination of genetic, endogenous, and exogenous factors that together result in molecular modifications, such as alterations in cell signaling (55), muscle regeneration (56), and mitochondrial function (57). The result may be indirect molecular changes involving platelet activation, insulin signaling, and other processes (58,59). These effects likely combine to elicit marked physiological consequences, such as sarcopenia and chronic disease (60). Markers of inflammation, such as C-reactive protein (CRP) and IL-6 are significantly related to incident mobility disability (61). Inflammatory pathways are also linked to cognitive decline and dementia risk. Studies suggest that caloric restriction results in attenuation of an aging-related increase in inflammatory markers [reviewed in (62)], and that an intensive weight loss intervention (exercise and energy-restricted diet) can improve inflammation and physical function (63). Yet, there is no consistent evidence that weight loss is beneficial for cognitive function. Trials are on-going to evaluate inflammation-reducing regimens to prevent mobility loss per se. These trials are designed to include persons at high risk of mobility disability without considering the risk for declining cognitive function. Designing trials with the appropriate inclusion criteria to allow the simultaneous examination of both cognitive and physical outcomes is a challenge that needs to be addressed. Exercise Interventions Some, but not all, studies indicate that exercise is an intervention that can target declines in physical function and cognition simultaneously. In nondemented elderly, exercise has been shown to improve both physical function (64) and cognition (65). In older adults at risk for mobility disability, hippocampal volume improved following a 2-year physical activity program (66), and aerobic exercise improved executive function in both non-demented and MCI subjects (67,68). On the other hand, a large trial of moderate intensity exercise lowered risk for major mobility disability (64,69), but failed to show an overall effect on continuous measures of cognitive function after two years of intervention, although executive function improved in those who were older (≥80 years) or had lower baseline physical function (70). Preclinical studies indicate several potential mechanisms for these improvements, including changes in inflammatory factors, neurotrophins, blood supply, and amyloid burden (71,72). Cardiorespiratory fitness decline tracks with brain atrophy and progression of dementia severity in AD (73), and change in cardiorespiratory fitness was positively correlated with memory change in an exercise trial of AD subjects (74). The possibility that cardiorespiratory fitness change is important in achieving optimal physical and cognitive effects of exercise is underscored by studies showing that change in cardiorespiratory fitness with exercise is positively related to markers of cortical thickness and brain volume in cognitively healthy, MCI, and AD subjects (75,76). The inclusion of physical outcomes in trials of exercise to prevent the progression of cognitive decline is quite feasible and could inform our understanding of how changes in one domain are linked to changes in the other. Similarly, the potential for mental “exercise” or cognitive training to improve physical function and related outcomes is a relatively new concept that remains largely unexplored (77); further study could reveal novel brain–body connections. Expanding Trial Endpoints Selecting the appropriate endpoint(s) for an intervention study is essential and may have significant clinical and public health implications. In the context of aging and aging-related diseases such as physical disability and dementia, consideration should be given to endpoints that adequately capture the relevant cognitive and physical domains (and the shared underlying pathways), that are affected. Universal Endpoints Nearly all Medicare beneficiaries have two or more chronic conditions (78). While most therapies for chronic diseases have been tested for their effect on disease-specific outcomes for a single condition, patients with multiple chronic conditions (MCC) typically receive multiple treatments that may affect coexisting conditions and interact with other interventions. Thus, commonly used disease-endpoints might be inappropriate for interventions that affect functional decline broadly. One solution may be to utilize universal health outcome measures such as general health, symptom burden, physical morbidity (functional status and disability), mental morbidity (depression, anxiety), and social and role functioning (79) that cross disease states and thus can be more universally applied to older adults with MCC, including those with AD and other dementias. Universal outcome measures assess treatment benefits and harms across conditions using a common metric to capture outcomes that are clinically meaningful to patients. These measures could be incorporated into electronic health records and used in clinical practice to help patients and providers prioritize treatment decisions and measure quality of care. Brief, validated, composite patient-reported outcome measures (PROMs), such as the SF-8, SF-36, or PROMIS 29-item health profile, along with gait speed, have been recommended for use in older adults with MCC (79). However, the reliability and validity of most PROMs have not been well established in persons with cognitive impairment or with proxy respondents. Currently available measures do not capture cognitive functioning or caregiver burden, two important domains for patients with dementia. In addition, PROMs do not incorporate patient preferences or capture the range of individual outcomes that may differ from universally applied outcomes (80,81). Goal attainment scaling (GAS) could be used to measure individualized health outcomes in clinical care or research (82,83). GAS entails the specification of a personal health goal by describing the expected level of goal achievement within a given follow-up period. GAS can accommodate diverse preferences (eg, physical function may be specified as climbing stairs or walking with a walker) and allows for modification if the clinical or social situation changes. Although GAS has been used in a variety of clinical settings and has good psychometric properties (84,85), it has not been well-validated in patients with cognitive impairment or with proxy respondents and has limited prior use in dementia cohorts (86). Longitudinal Endpoints The pathophysiological changes that occur in early AD can be asymptomatic making the detection of change in preclinical AD challenging. As cross-sectional relationships are not adequate to establish which pathophysiological changes are predictive of the onset of clinical AD, longitudinal cohorts of cognitively normal individuals are needed. Similarly, further development of endpoints that can be followed over the course of the disease and can detect early change in preclinical AD are needed. Two examples of such endpoint measures are (1) a latent variable combining cognitive and physical function to reflect the underlying severity of the AD pathophysiological process and (2) a composite outcome measure that captures several different cognitive domains. Findings from the longitudinal Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study suggest that development of an AD Severity Score reflective of underlying disease severity during preclinical AD is possible (87). This continuous composite measure was developed from a combination of cognitive, CSF, and MRI measures using latent trait methods. Similarly, a composite cognitive score could be used to detect change in preclinical AD and follow disease progression. There are several composite scores currently in use or under development. For example, Soldan et al. used a composite cognitive score, operationalized as the mean of the Z-scores from four different cognitive tests previously shown to be associated with time to onset of clinical symptoms to show that the combination of abnormal CSF Aβ and abnormal CSF tau or p-tau in cognitively normal persons was associated with greater decline in cognitive performance over time (88). This information could then be used in the design of clinical trial selection criteria to maximize the likelihood that substantial change in cognition will be observed during follow-up, thus making it more likely to observe an impact of therapy. Given the emerging understanding of the physical manifestations of AD, it is possible that monitoring physical function might also be informative and should be evaluated as a contributor to future composites. Uniform Endpoints: The NIH Toolbox Researchers who want to assess unfamiliar domains can find the identification of well-validated tools a challenge. The NIH Toolbox is a multi-dimensional set of brief measures to assess neurological and behavioral function that can be administered in 2 h or less across diverse study designs and settings (www.nihtoolbox.org). The NIH Toolbox was developed to address lack of uniformity in the use of measures, which hinders the ability to share, integrate, and interpret results. The cognitive, emotion, motor, and sensory batteries are available in English and Spanish with individual measures nationally normed for people from ages 3 to 85 years. The NIA is now seeking to establish measurement properties of the NIH Toolbox in persons with amnestic MCI, early AD, and in persons over age 85 (RFA-AG-17-052). Translating New Discoveries into Practice Translation of research findings to clinical care requires changing both the education and activation of patients, caregivers, and health professionals, as well as changes to health care delivery systems. The challenges of the current social/clinical context and the vision for best quality care share several key features between dementia and physical decline. Psychosocial factors and cultural beliefs about mobility disability (89) and “senility” (90), may affect older adults’ and caregivers’ understanding of dementia and immobility, as well as how symptoms are perceived and reported. Stigma affects patient acceptance of testing, diagnosis, and management plans. These, along with poor access to care and trust in the medical system (90,91), may impede uptake of prevention/treatment interventions, including best practices that emphasize behavioral interventions, especially physical activity (64,67) and counseling (92), over pharmacologic therapies. At the same time, practice change is slow and further retarded by a medical model that has valued disease over function and treatment over prevention, particularly for older persons (93). Until recently, reimbursement has focused on disease management and treatment, with limited time for prevention or care coordination. Few quality metrics reference physical or cognitive function (https://www.qualityindicators.ahrq.gov), and uptake of these metrics has been poor (94). Finally, the prevalence of ageism affects interest in systems change (95). Despite many challenges, the identified barriers may be ameliorable through community engagement (96,97), and there is growing attention towards translation and implementation (98). Academic-community partnerships have demonstrated efficacy in reducing stigma of dementia and aging, increasing community participation in research, and engendering a shared person-centered vision of medical care (99). Value-based care models, such as Medicare Shared Savings Plans and Accountable Care Organizations may inspire administrative investment in function, as reflected in the implementation of interventions to wrap services around vulnerable patients in an effort to reduce high-burden hospitalizations (100). Surgeons and proceduralists have shown keen interest in the prognostic value of geriatric assessments as a means to discern likelihood of benefit of potentially burdensome interventions (101,102). Ultimately, implementation of best practices will rely on community partnerships, differing research processes, and investment in building workforce skill and capacity. Future Directions and Priorities Common needs, parallels, and potential synergies emerged during the course of this workshop. There was a clear understanding that cognitive and physical decline are both best understood by taking a life-course approach, with a need to identify early phenotypes starting in at least mid-life in order to study how risk factors may change or evolve with aging and disease severity. There was also recognition that a dearth of data exists on the aging process in minority or low socioeconomic populations, even though these groups can have elevated rates of physical disability and AD. The lack of diversity in studies of both physical decline and dementia development can lead to errors in our understanding of the causes of declines and overestimation of the generalizability of the findings. This lack of information could also adversely affect the efficient targeting of intervention and prevention resources in an increasingly diverse society. The fact that there are shared pathways in the development of dementia and physical decline suggests that interventions targeting one domain may have benefits in the other. To deepen our understanding of the connection, measures in both domains should be collected routinely, with the choice of measures likely dependent on the study population and feasibility of assessments in a given situation. A process to help investigators integrate appropriate cognitive and physical assessments to a high standard would also be helpful in assuring data quality across domains. There was general agreement that exploring how aspects of cognition might be integrated into our understanding of frailty would be particularly fruitful. For example, a parallel and simple to operationalize “cognitive frailty” phenotype that identifies persons who can function adequately in typical situations but are at high risk for impairments emerging under stress or over time, would be useful. Ultimately, simple, concise measures have the potential to be deployed clinically to assist in screening and risk stratifying older populations. The understanding of physical decline developed with a deep appreciation for the contribution of concomitant comorbidities. On the other hand, the role and importance of age-related comorbidities in AD and related dementias deserves greater attention. This may be helpful in understanding why cognitive phenotypes differ in persons with similar burden of AD pathology and why the rates of cognitive impairments appear to have measurably declined over the past several decades. Both physical disability and AD occur in the context of an aging biology which is becoming increasingly well understood. The incorporation of measures that relate to the underlying biology of aging is likely to be broadly informative. Regardless of the outcome studied, there is likely much to be gained by comparing the experience of researchers who are incorporating new and emerging measures related to the biology of aging. The workshop advocated for a greater incorporation of functional assessments (both physical and cognitive) into clinical care and the development and promotion of systems of care that make the integrated attention to the functional needs and goals of older adults a priority. The development and use of clinical trial endpoints that reflect the broader functional concerns of older persons will be important to assess the success of interventions designed to prevent or treat both physical disability and AD and related dementias. Supplementary Material Supplementary data is available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online. Funding This workshop was supported by an Administrative Supplement to the Wake Forest Claude D. Pepper Older Americans Independence Center and Coordinating Center (P30 AG021332), with additional support from the American Federation of Aging Research, American Geriatrics Society, and Gerontological Society of America. Conflicts of Interest S.K.B. is on the editorial board at the Journal of Gerontology: Medical Sciences. No other authors have any conflicts to disclose. References 1. Gray SL, Anderson ML, Hubbard RAet al. Frailty and incident dementia. J Gerontol A Biol Sci Med Sci . 2013; 68: 1083– 1090. doi: 10.1093/gerona/glt013 Google Scholar CrossRef Search ADS PubMed 2. Buchman AS, Boyle PA, Wilson RS, Tang Y, Bennett DA. Frailty is associated with incident Alzheimer’s disease and cognitive decline in the elderly. Psychosom Med . 2007; 69: 483– 489. doi: 10.1097/psy.0b013e318068de1d Google Scholar CrossRef Search ADS PubMed 3. Boyle PA, Buchman AS, Wilson RS, Leurgans SE, Bennett DA. Physical frailty is associated with incident mild cognitive impairment in community-based older persons. J Am Geriatr Soc . 2010; 58: 248– 255. doi: 10.1111/j.1532-5415.2009.02671.x Google Scholar CrossRef Search ADS PubMed 4. Fried LP, Tangen CM, Walston Jet al. ; Cardiovascular Health Study Collaborative Research Group. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci . 2001; 56: M146– M156. Google Scholar CrossRef Search ADS PubMed 5. Brigola AG, Rossetti ES, Dos Santos BRet al. Relationship between cognition and frailty in elderly: A systematic review. Dement Neuropsychol . 2015; 9: 110– 119. doi: 10.1590/1980-57642015DN92000005 Google Scholar CrossRef Search ADS PubMed 6. Buchman AS, Yu L, Wilson RS, Boyle PA, Schneider JA, Bennett DA. Brain pathology contributes to simultaneous change in physical frailty and cognition in old age. J Gerontol A Biol Sci Med Sci . 2014; 69: 1536– 1544. doi: 10.1093/gerona/glu117 Google Scholar CrossRef Search ADS PubMed 7. Buchman AS, Boyle PA, Wilson RS, Beck TL, Kelly JF, Bennett DA. Apolipoprotein E e4 allele is associated with more rapid motor decline in older persons. Alzheimer Dis Assoc Disord . 2009; 23: 63– 69. Google Scholar CrossRef Search ADS PubMed 8. Tian Q, Resnick SM, Bilgel M, Wong DF, Ferrucci L, Studenski SA. Beta-amyloid burden predicts lower extremity performance decline in cognitively unimpaired older adults. J Gerontol A Biol Sci Med Sci . 2017; 72: 716– 723. doi: 10.1093/gerona/glw183 Google Scholar PubMed 9. Plummer P, Zukowski LA, Giuliani C, Hall AM, Zurakowski D. Effects of Physical exercise interventions on gait-related dual-task interference in older adults: A systematic review and meta-analysis. Gerontology . 2015; 62: 94– 117. doi: 10.1159/000371577 Google Scholar CrossRef Search ADS PubMed 10. Buracchio T, Dodge HH, Howieson D, Wasserman D, Kaye J. The trajectory of gait speed preceding mild cognitive impairment. Arch Neurol . 2010; 67: 980– 986. doi: 10.1001/archneurol.2010.159 Google Scholar CrossRef Search ADS PubMed 11. Verghese J, Wang C, Lipton RB, Holtzer R. Motoric cognitive risk syndrome and the risk of dementia. J Gerontol A Biol Sci Med Sci . 2013; 68: 412– 418. doi: 10.1093/gerona/gls191 Google Scholar CrossRef Search ADS PubMed 12. Verghese J, Ayers E, Barzilai Net al. Motoric cognitive risk syndrome: Multicenter incidence study. Neurology . 2014; 83: 2278– 2284. doi: 10.1212/WNL.0000000000001084 Google Scholar CrossRef Search ADS PubMed 13. Delrieu J, Andrieu S, Pahor Met al. Neuropsychological profile of “Cognitive Frailty” subjects in MAPT study. J Prev Alzheimers Dis . 2016; 3: 151– 159. doi: 10.14283/jpad.2016.94 Google Scholar PubMed 14. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer’s disease: An analysis of population-based data. Lancet Neurol . 2014; 13: 788– 794. doi: 10.1016/S1474-4422(14)70136-X Google Scholar CrossRef Search ADS PubMed 15. Newman AB, Gottdiener JS, Mcburnie MAet al. ; Cardiovascular Health Study Research Group. Associations of subclinical cardiovascular disease with frailty. J Gerontol A Biol Sci Med Sci . 2001; 56: M158– M166. Google Scholar CrossRef Search ADS PubMed 16. Buchman AS, Schneider JA, Wilson RS, Bienias JL, Bennett DA. Body mass index in older persons is associated with Alzheimer disease pathology. Neurology . 2006; 67: 1949– 1954. doi: 10.1212/01.wnl.0000247046.90574.0f Google Scholar CrossRef Search ADS PubMed 17. Baker WL, Karan S, Kenny AM. Effect of dehydroepiandrosterone on muscle strength and physical function in older adults: A systematic review. J Am Geriatr Soc . 2011; 59: 997– 1002. doi: 10.1111/j.1532-5415.2011.03410.x Google Scholar CrossRef Search ADS PubMed 18. Snyder PJ, Bhasin S, Cunningham GRet al. ; Testosterone Trials Investigators. Effects of testosterone treatment in older men. N Engl J Med . 2016; 374: 611– 624. doi: 10.1056/NEJMoa1506119 Google Scholar CrossRef Search ADS PubMed 19. Mehta KM, Yeo GW. Systematic review of dementia prevalence and incidence in United States race/ethnic populations. Alzheimers Dement . 2017; 13: 72– 83. doi: 10.1016/j.jalz.2016.06.2360 Google Scholar CrossRef Search ADS PubMed 20. Bandeen-Roche K, Seplaki CL, Huang Jet al. Frailty in older adults: A nationally representative profile in the United States. J Gerontol A Biol Sci Med Sci . 2015; 70: 1427– 1434. doi: 10.1093/gerona/glv133 Google Scholar CrossRef Search ADS PubMed 21. Winblad B, Amouyel P, Andrieu Set al. Defeating Alzheimer’s disease and other dementias: A priority for European science and society. Lancet Neurol . 2016; 15: 455– 532. doi: 10.1016/s1474-4422(16)00062-4 Google Scholar CrossRef Search ADS PubMed 22. Hayflick L, Moorhead PS. The serial cultivation of human diploid cell strains. Exp Cell Res . 1961; 25: 585– 621. Google Scholar CrossRef Search ADS PubMed 23. Xu M, Tchkonia T, Ding Het al. JAK inhibition alleviates the cellular senescence-associated secretory phenotype and frailty in old age. Proc Natl Acad Sci USA 2015; 112: E6301– E6310. doi: 10.1073/pnas.1515386112 Google Scholar CrossRef Search ADS PubMed 24. Roos CM, Zhang B, Palmer AKet al. Chronic senolytic treatment alleviates established vasomotor dysfunction in aged or atherosclerotic mice. Aging Cell . 2016; 15: 973– 977. doi: 10.1111/acel.12458 Google Scholar CrossRef Search ADS PubMed 25. Schafer MJ, White TA, Evans Get al. Exercise prevents diet-induced cellular senescence in adipose tissue. Diabetes . 2016; 65: 1606– 1615. doi: 10.2337/db15-0291 Google Scholar CrossRef Search ADS PubMed 26. Childs BG, Baker DJ, Wijshake T, Conover CA, Campisi J, van Deursen JM. Senescent initial foam cells are deleterious at all stages of atherosclerosis. Science . 2016; 354: 472– 477. doi: 10.1126/science.aaf6659 Google Scholar CrossRef Search ADS PubMed 27. Lipsky MS, King M. Biological theories of aging. Dis Mon . 2015; 61: 460– 466. doi: 10.1016/j.disamonth.2015.09.005 Google Scholar CrossRef Search ADS PubMed 28. Trifunovic A, Wredenberg A, Falkenberg Met al. Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature . 2004; 429: 417– 423. doi: 10.1038/nature02517 Google Scholar CrossRef Search ADS PubMed 29. Tranah GJ, Yaffe K, Katzman SMet al. ; Health, Aging and Body Composition Study. Mitochondrial DNA heteroplasmy associations with neurosensory and mobility function in elderly adults. J Gerontol A Biol Sci Med Sci . 2015; 70: 1418– 1424. doi: 10.1093/gerona/glv097 Google Scholar CrossRef Search ADS PubMed 30. Tranah GJ, Yokoyama JS, Katzman SMet al. ; Health, Aging and Body Composition Study. Mitochondrial DNA sequence associations with dementia and amyloid-β in elderly African Americans. Neurobiol Aging . 2014; 35: 442.e1– 442.e8. doi: 10.1016/j.neurobiolaging.2013.05.023 Google Scholar CrossRef Search ADS 31. Lin MT, Simon DK, Ahn CH, Kim LM, Beal MF. High aggregate burden of somatic mtDNA point mutations in aging and Alzheimer’s disease brain. Hum Mol Genet . 2002; 11: 133– 145. Google Scholar CrossRef Search ADS PubMed 32. Swerdlow RH, Burns JM, Khan SM. The Alzheimer’s disease mitochondrial cascade hypothesis: Progress and perspectives. Biochim Biophys Acta . 2014; 1842: 1219– 1231. doi: 10.1016/j.bbadis.2013.09.010 Google Scholar CrossRef Search ADS PubMed 33. Mosconi L, Berti V, Glodzik L, Pupi A, De Santi S, de Leon MJ. Pre-clinical detection of Alzheimer’s disease using FDG-PET, with or without amyloid imaging. J Alzheimers Dis . 2010; 20: 843– 854. doi: 10.3233/JAD-2010-091504 Google Scholar CrossRef Search ADS PubMed 34. Kish SJ, Bergeron C, Rajput Aet al. Brain cytochrome oxidase in Alzheimer’s disease. J Neurochem . 1992; 59: 776– 779. Google Scholar CrossRef Search ADS PubMed 35. Tyrrell DJ, Bharadwaj MS, Van Horn CG, Kritchevsky SB, Nicklas BJ, Molina AJ. Respirometric profiling of muscle mitochondria and blood cells are associated with differences in gait speed among community-dwelling older adults. J Gerontol A Biol Sci Med Sci . 2015; 70: 1394– 1399. doi: 10.1093/gerona/glu096 Google Scholar CrossRef Search ADS PubMed 36. Tyrrell DJ, Bharadwaj MS, Jorgensen MJet al. Blood-based bioenergetic profiling reflects differences in brain bioenergetics and metabolism. Oxid Med Cell Longev . 2017; 2017: 7317251. doi: 10.1155/2017/7317251 Google Scholar CrossRef Search ADS PubMed 37. Kaushik S, Cuervo AM. Proteostasis and aging. Nat Med . 2015; 21: 1406– 1415. doi: 10.1038/nm.4001 Google Scholar CrossRef Search ADS PubMed 38. Labbadia J, Morimoto RI. The biology of proteostasis in aging and disease. Annu Rev Biochem . 2015; 84: 435– 464. doi: 10.1146/annurev-biochem-060614-033955 Google Scholar CrossRef Search ADS PubMed 39. Menzies FM, Fleming A, Rubinsztein DC. Compromised autophagy and neurodegenerative diseases. Nat Rev Neurosci . 2015; 16: 345– 357. doi: 10.1038/nrn3961 Google Scholar CrossRef Search ADS PubMed 40. Hara T, Nakamura K, Matsui Met al. Suppression of basal autophagy in neural cells causes neurodegenerative disease in mice. Nature . 2006; 441: 885– 889. doi: 10.1038/nature04724 Google Scholar CrossRef Search ADS PubMed 41. Komatsu M, Waguri S, Chiba Tet al. Loss of autophagy in the central nervous system causes neurodegeneration in mice. Nature . 2006; 441: 880– 884. doi: 10.1038/nature04723 Google Scholar CrossRef Search ADS PubMed 42. Lee JH, Yu WH, Kumar Aet al. Lysosomal proteolysis and autophagy require presenilin 1 and are disrupted by Alzheimer-related PS1 mutations. Cell . 2010; 141: 1146– 1158. doi: 10.1016/j.cell.2010.05.008 Google Scholar CrossRef Search ADS PubMed 43. Yu WH, Cuervo AM, Kumar Aet al. Macroautophagy–a novel Beta-amyloid peptide-generating pathway activated in Alzheimer’s disease. J Cell Biol . 2005; 171: 87– 98. doi: 10.1083/jcb.200505082 Google Scholar CrossRef Search ADS PubMed 44. Devore EE, Grodstein F, van Rooij FJet al. Dietary antioxidants and long-term risk of dementia. Arch Neurol . 2010; 67: 819– 825. doi: 10.1001/archneurol.2010.144 Google Scholar CrossRef Search ADS PubMed 45. Lukiw WJ, Cui JG, Marcheselli VLet al. A role for docosahexaenoic acid-derived neuroprotectin D1 in neural cell survival and Alzheimer disease. J Clin Invest . 2005; 115: 2774– 2783. doi: 10.1172/JCI25420 Google Scholar CrossRef Search ADS PubMed 46. Durga J, van Boxtel MP, Schouten EGet al. Effect of 3-year folic acid supplementation on cognitive function in older adults in the FACIT trial: A randomised, double blind, controlled trial. Lancet . 2007; 369: 208– 216. doi: 10.1016/S0140-6736(07)60109-3 Google Scholar CrossRef Search ADS PubMed 47. Morris MC, Evans DA, Bienias JL, Tangney CC, Wilson RS. Dietary fat intake and 6-year cognitive change in an older biracial community population. Neurology . 2004; 62: 1573– 1579. Google Scholar CrossRef Search ADS PubMed 48. Houston DK, Tooze JA, Garcia Ket al. ; Health ABC Study. Protein intake and mobility limitation in community-dwelling older adults: The health ABC study. J Am Geriatr Soc . 2017; 65: 1705– 1711. doi: 10.1111/jgs.14856 Google Scholar CrossRef Search ADS PubMed 49. Oberlin BS, Tangney CC, Gustashaw KA, Rasmussen HE. Vitamin B12 deficiency in relation to functional disabilities. Nutrients . 2013; 5: 4462– 4475. doi: 10.3390/nu5114462 Google Scholar CrossRef Search ADS PubMed 50. Houston DK, Cesari M, Ferrucci Let al. Association between vitamin D status and physical performance: The InCHIANTI study. J Gerontol A Biol Sci Med Sci . 2007; 62: 440– 446. Google Scholar CrossRef Search ADS PubMed 51. Petersson SD, Philippou E. Mediterranean diet, cognitive function, and dementia: A systematic review of the evidence. Adv Nutr . 2016; 7: 889– 904. doi: 10.3945/an.116.012138 Google Scholar CrossRef Search ADS PubMed 52. Talegawkar SA, Bandinelli S, Bandeen-Roche Ket al. A higher adherence to a Mediterranean-style diet is inversely associated with the development of frailty in community-dwelling elderly men and women. J Nutr . 2012; 142: 2161– 2166. doi: 10.3945/jn.112.165498 Google Scholar CrossRef Search ADS PubMed 53. Smith PJ, Blumenthal JA. Dietary factors and cognitive decline. J Prev Alzheimers Dis . 2016; 3: 53– 64. doi: 10.14283/jpad.2015.71 Google Scholar PubMed 54. Morris MC, Tangney CC. A potential design flaw of randomized trials of vitamin supplements. JAMA . 2011; 305: 1348– 1349. doi: 10.1001/jama.2011.383 Google Scholar CrossRef Search ADS PubMed 55. Kamphuis W, Kooijman L, Schetters S, Orre M, Hol EM. Transcriptional profiling of CD11c-positive microglia accumulating around amyloid plaques in a mouse model for Alzheimer’s disease. Biochim Biophys Acta . 2016; 1862: 1847– 1860. doi: 10.1016/j.bbadis.2016.07.007 Google Scholar CrossRef Search ADS PubMed 56. Blau HM, Cosgrove BD, Ho AT. The central role of muscle stem cells in regenerative failure with aging. Nat Med . 2015; 21: 854– 862. doi: 10.1038/nm.3918 Google Scholar CrossRef Search ADS PubMed 57. Teng RJ, Wu TJ, Afolayan AJ, Konduri GG. Nitrotyrosine impairs mitochondrial function in fetal lamb pulmonary artery endothelial cells. Am J Physiol Cell Physiol . 2016; 310: C80– C88. doi: 10.1152/ajpcell.00073.2015 Google Scholar CrossRef Search ADS PubMed 58. Cesari M, Kritchevsky SB, Leeuwenburgh C, Pahor M. Oxidative damage and platelet activation as new predictors of mobility disability and mortality in elders. Antioxid Redox Signal . 2006; 8: 609– 619. doi: 10.1089/ars.2006.8.609 Google Scholar CrossRef Search ADS PubMed 59. Dagdeviren S, Jung DY, Friedline RHet al. IL-10 prevents aging-associated inflammation and insulin resistance in skeletal muscle. FASEB J . 2017; 31: 701– 710. doi: 10.1096/fj.201600832R Google Scholar CrossRef Search ADS PubMed 60. Chung HY, Cesari M, Anton Set al. Molecular inflammation: Underpinnings of aging and age-related diseases. Ageing Res Rev . 2009; 8: 18– 30. doi: 10.1016/j.arr.2008.07.002 Google Scholar CrossRef Search ADS PubMed 61. Cesari M, Penninx BW, Pahor Met al. Inflammatory markers and physical performance in older persons: The InCHIANTI study. J Gerontol A Biol Sci Med Sci . 2004; 59: 242– 248. Google Scholar CrossRef Search ADS PubMed 62. Chung HY, Kim HJ, Kim JW, Yu BP. The inflammation hypothesis of aging: Molecular modulation by calorie restriction. Ann N Y Acad Sci . 2001; 928: 327– 335. Google Scholar CrossRef Search ADS PubMed 63. Miller GD, Nicklas BJ, Loeser RF. Inflammatory biomarkers and physical function in older, obese adults with knee pain and self-reported osteoarthritis after intensive weight-loss therapy. J Am Geriatr Soc . 2008; 56: 644– 651. doi: 10.1111/j.1532-5415.2007.01636.x Google Scholar CrossRef Search ADS PubMed 64. Pahor M, Guralnik JM, Ambrosius WTet al. ; LIFE study investigators. Effect of structured physical activity on prevention of major mobility disability in older adults: The LIFE study randomized clinical trial. JAMA . 2014; 311: 2387– 2396. doi: 10.1001/jama.2014.5616 Google Scholar CrossRef Search ADS PubMed 65. Baker LD, Frank LL, Foster-Schubert Ket al. Aerobic exercise improves cognition for older adults with glucose intolerance, a risk factor for Alzheimer’s disease. J Alzheimers Dis . 2010; 22: 569– 579. doi: 10.3233/JAD-2010-100768 Google Scholar CrossRef Search ADS PubMed 66. Rosano C, Guralnik J, Pahor Met al. Hippocampal response to a 24-month physical activity intervention in sedentary older adults. Am J Geriatr Psychiatry . 2017; 25: 209– 217. doi: 10.1016/ j.jagp.2016.11.007 Google Scholar CrossRef Search ADS PubMed 67. Baker LD, Frank LL, Foster-Schubert Ket al. Effects of aerobic exercise on mild cognitive impairment: A controlled trial. Arch Neurol . 2010; 67: 71– 79. doi: 10.1001/archneurol.2009.307 Google Scholar PubMed 68. Kramer AF, Hahn S, Cohen NJet al. Ageing, fitness and neurocognitive function. Nature . 1999; 400: 418– 419. doi: 10.1038/22682 Google Scholar CrossRef Search ADS PubMed 69. de Labra C, Guimaraes-Pinheiro C, Maseda A, Lorenzo T, Millán-Calenti JC. Effects of physical exercise interventions in frail older adults: A systematic review of randomized controlled trials. BMC Geriatr . 2015; 15: 154. doi: 10.1186/s12877-015-0155-4 Google Scholar CrossRef Search ADS PubMed 70. Sink KM, Espeland MA, Castro CMet al. ; LIFE Study Investigators. Effect of a 24-month physical activity intervention vs health education on cognitive outcomes in sedentary older adults: The LIFE randomized trial. JAMA . 2015; 314: 781– 790. doi: 10.1001/jama.2015.9617 Google Scholar CrossRef Search ADS PubMed 71. Parachikova A, Nichol KE, Cotman CW. Short-term exercise in aged Tg2576 mice alters neuroinflammation and improves cognition. Neurobiol Dis . 2008; 30: 121– 129. doi: 10.1016/j.nbd.2007.12.008 Google Scholar CrossRef Search ADS PubMed 72. Adlard PA, Perreau VM, Pop V, Cotman CW. Voluntary exercise decreases amyloid load in a transgenic model of Alzheimer’s disease. J Neurosci . 2005; 25: 4217– 4221. doi: 10.1523/JNEUROSCI.0496-05.2005 Google Scholar CrossRef Search ADS PubMed 73. Vidoni ED, Honea RA, Billinger SA, Swerdlow RH, Burns JM. Cardiorespiratory fitness is associated with atrophy in Alzheimer’s and aging over 2 years. Neurobiol Aging . 2012; 33: 1624– 1632. doi: 10.1016/ j.neurobiolaging.2011.03.016 Google Scholar CrossRef Search ADS PubMed 74. Morris JK, Vidoni ED, Johnson DKet al. Aerobic exercise for Alzheimer’s disease: A randomized controlled pilot trial. PloS one . 2017; 12: e0170547. doi: 10.1371/journal.pone.0170547 Google Scholar CrossRef Search ADS PubMed 75. Reiter K, Nielson KA, Smith TJ, Weiss LR, Alfini AJ, Smith JC. Improved cardiorespiratory fitness is associated with increased cortical thickness in mild cognitive impairment. J Int Neuropsychol Soc . 2015; 21: 757– 767. doi: 10.1017/S135561771500079X Google Scholar CrossRef Search ADS PubMed 76. Colcombe SJ, Erickson KI, Scalf PEet al. Aerobic exercise training increases brain volume in aging humans. J Gerontol A Biol Sci Med Sci . 2006; 61: 1166– 1170. Google Scholar CrossRef Search ADS PubMed 77. Blackwood J, Shubert T, Fogarty K, Chase C. The impact of a home-based computerized cognitive training intervention on fall risk measure performance in community dwelling older adults, a pilot study. J Nutr Health Aging . 2016; 20: 138– 145. doi: 10.1007/s12603-015-0598-5 Google Scholar CrossRef Search ADS PubMed 78. Centers for Medicare and Medicaid Services. Chronic Conditions among Medicare Beneficiaries, Chartbook, 2012 Edition . Baltimore, MD. https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/chronic-conditions/downloads/2012chartbook.pdf PubMed PubMed 79. Universal health outcome measures for older persons with multiple chronic conditions. J Am Geriatr Soc . 2012; 60: 2333– 2341. doi: 10.1111/j.1532-5415.2012.04240.x CrossRef Search ADS PubMed 80. Fried TR, Tinetti M, Agostini J, Iannone L, Towle V. Health outcome prioritization to elicit preferences of older persons with multiple health conditions. Patient Educ Couns . 2011; 83: 278– 282. doi: 10.1016/j.pec.2010.04.032 Google Scholar CrossRef Search ADS PubMed 81. Tinetti ME, Naik AD, Dodson JA. Moving from disease-centered to patient goals-directed care for patients with multiple chronic conditions: Patient value-based care. JAMA Cardiol . 2016; 1: 9– 10. doi: 10.1001/jamacardio.2015.0248 Google Scholar CrossRef Search ADS PubMed 82. Krasny-Pacini A, Hiebel J, Pauly F, Godon S, Chevignard M. Goal attainment scaling in rehabilitation: A literature-based update. Ann Phys Rehabil Med . 2013; 56: 212– 230. doi: 10.1016/j.rehab.2013.02.002 Google Scholar CrossRef Search ADS PubMed 83. Ottenbacher KJ, Cusick A. Goal attainment scaling as a method of clinical service evaluation. Am J Occup Ther . 1990; 44: 519– 525. Google Scholar CrossRef Search ADS PubMed 84. Hurn J, Kneebone I, Cropley M. Goal setting as an outcome measure: A systematic review. Clin Rehabil . 2006; 20: 756– 772. doi: 10.1177/0269215506070793 Google Scholar CrossRef Search ADS PubMed 85. Rockwood K, Howlett S, Stadnyk K, Carver D, Powell C, Stolee P. Responsiveness of goal attainment scaling in a randomized controlled trial of comprehensive geriatric assessment. J Clin Epidemiol . 2003; 56: 736– 743. Google Scholar CrossRef Search ADS PubMed 86. Rockwood K, Fay S, Song X, MacKnight C, Gorman M. Attainment of treatment goals by people with Alzheimer’s disease receiving galantamine: A randomized controlled trial. CMAJ . 2006; 174: 1099– 1105. doi: 10.1503/cmaj.051432 Google Scholar CrossRef Search ADS PubMed 87. Sacktor N, Soldan A, Grega Met al. ; BIOCARD Research Team. The BIOCARD index: A summary measure to predict onset of mild cognitive impairment. Alzheimer Dis Assoc Disord . 2017; 31: 114– 119. doi: 10.1097/WAD.0000000000000194 Google Scholar CrossRef Search ADS PubMed 88. Soldan A, Pettigrew C, Cai Qet al. ; BIOCARD Research Team. Hypothetical preclinical Alzheimer disease groups and longitudinal cognitive change. JAMA Neurol . 2016; 73: 698– 705. doi: 10.1001/jamaneurol.2016.0194 Google Scholar CrossRef Search ADS PubMed 89. Sarkisian CA, Hays RD, Mangione CM. Do older adults expect to age successfully? The association between expectations regarding aging and beliefs regarding healthcare seeking among older adults. J Am Geriatr Soc . 2002; 50: 1837– 1843. Google Scholar CrossRef Search ADS PubMed 90. Gray HL, Jimenez DE, Cucciare MA, Tong HQ, Gallagher-Thompson D. Ethnic differences in beliefs regarding Alzheimer disease among dementia family caregivers. Am J Geriatr Psychiatry . 2009; 17: 925– 933. doi: 10.1097/JGP.0b013e3181ad4f3c Google Scholar CrossRef Search ADS PubMed 91. Corbie-Smith G, Thomas SB, St George DM. Distrust, race, and research. Arch Intern Med . 2002; 162: 2458– 2463. Google Scholar CrossRef Search ADS PubMed 92. Losada A, Márquez-González M, Romero-Moreno R. Mechanisms of action of a psychological intervention for dementia caregivers: Effects of behavioral activation and modification of dysfunctional thoughts. Int J Geriatr Psychiatry . 2011; 26: 1119– 1127. doi: 10.1002/gps.2648 Google Scholar PubMed 93. Kritchevsky SB, Williamson J. Putting function first. J Nutr Health Aging . 2014; 18: 467– 468. doi: 10.1007/s12603-014-0456-x Google Scholar CrossRef Search ADS PubMed 94. Tinetti M. Mainstream or extinction: Can defining who we are save geriatrics? J Am Geriatr Soc . 2016; 64: 1400– 1404. doi: 10.1111/jgs.14181 Google Scholar CrossRef Search ADS PubMed 95. Sarkisian CA, Hays RD, Berry SH, Mangione CM. Expectations regarding aging among older adults and physicians who care for older adults. Med Care . 2001; 39: 1025– 1036. Google Scholar CrossRef Search ADS PubMed 96. Dilworth-Anderson P. Introduction to the science of recruitment and retention among ethnically diverse populations. Gerontologist . 2011; 51( Suppl 1): S1– S4. doi: 10.1093/geront/gnr043 Google Scholar CrossRef Search ADS PubMed 97. Sood JR, Stahl SM. Community engagement and the resource centers for minority aging research. Gerontologist . 2011; 51( Suppl 1): S5– S7. doi: 10.1093/geront/gnr036 Google Scholar CrossRef Search ADS PubMed 98. Rubio DM, Schoenbaum EE, Lee LSet al. Defining translational research: Implications for training. Acad Med . 2010; 85: 470– 475. doi: 10.1097/ACM.0b013e3181ccd618 Google Scholar CrossRef Search ADS PubMed 99. Galvin JE, Tolea MI, George N, Wingbermuehle C. Public-private partnerships improve health outcomes in individuals with early stage Alzheimer’s disease. Clin Interv Aging . 2014; 9: 621– 630. doi: 10.2147/CIA.S60838 Google Scholar CrossRef Search ADS PubMed 100. Coleman EA. Falling through the cracks: Challenges and opportunities for improving transitional care for persons with continuous complex care needs. J Am Geriatr Soc . 2003; 51: 549– 555. Google Scholar CrossRef Search ADS PubMed 101. Green P, Woglom AE, Genereux Pet al. Gait speed and dependence in activities of daily living in older adults with severe aortic stenosis. Clin Cardiol . 2012; 35: 307– 314. doi: 10.1002/clc.21974 Google Scholar CrossRef Search ADS PubMed 102. Wilson CM, Kostsuca SR, Boura JA. Utilization of a 5-meter walk test in evaluating self-selected gait speed during preoperative screening of patients scheduled for cardiac surgery. Cardiopulm Phys Ther J . 2013; 24: 36– 43. Google Scholar PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: email@example.com.
The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences – Oxford University Press
Published: Mar 21, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera