Advanced Glycation End Products Are Associated With Physical Activity and Physical Functioning in the Older Population

Advanced Glycation End Products Are Associated With Physical Activity and Physical Functioning in... Abstract Background Decline in physical activity and functioning is commonly observed in the older population and might be associated with biomarkers such as advanced glycation end products (AGEs). AGEs contribute to age-related decline in the function of cells and tissues in normal aging and have been found to be associated with motor function decline. The aim of this study is to investigate the association between the levels of AGEs, as assessed by skin autofluorescence, and the amount of physical activity and loss of physical functioning in older participants. Methods Cross-sectional data of 5,624 participants aged 65 years and older from the LifeLines Cohort Study were used. Linear regression analyses were utilized to study the associations between skin autofluorescence/AGE levels (AGE Reader), the number of physically active days (SQUASH), and physical functioning (RAND-36). A logistic regression analysis was used to study the associations between AGE levels and the compliance with the Dutch physical activity guidelines (SQUASH). Results A statistical significant association between AGE levels and the number of physically active days (β = −0.21, 95% confidence interval: −0.35 to −0.07, p = .004), physical functioning (β = −1.60, 95% confidence interval: −2.64 to −0.54, p = .003), and compliance with the Dutch physical activity guidelines (odds ratio = 0.76, 95% confidence interval: 0.62 to 0.94, p = .010) was revealed. Conclusions This study indicates that high AGE levels may be a contributing factor as well as a biomarker for lower levels of physical activity and functioning in the older population. Biomarker, Skin autofluorescence, Motor function, Disablement process In the aging population, decline in physical activity and functioning is commonly observed. Physical activity is generally defined as any skeletal muscle effort resulting in more energy being used than when at rest; physical functioning is defined as being able to perform activities of daily life (1,2). With aging, most human physiological systems regress independently from substantial disease effects at an average linear loss rate of 0.34%–1.28% per year between the age of 30 and 70 years (3). Regular moderate physical activity has an advantageous influence on health status and can reduce the risk on and improve the prognosis of chronic diseases such as diabetes mellitus (DM) and cardiovascular (CV) disease (4,5). A lower level and accelerated decline of physical functioning, such as gait, has been determined to predict the subsequent development of mild cognitive impairment and Alzheimer’s disease and can precede cognitive impairment by several years (6). A lack of physical activity is a known precipitating factor for the age-related loss of muscle mass (sarcopenia) leading to strength losses and physical disability (7). A decline in motor function, such as decreased muscle properties, declined walking abilities and declined activities of daily living have been found to be associated with advanced glycation end products (AGEs) in the aging population (8). AGEs accumulate in hyperglycemic environments and contribute to the age-related decline of the functioning of cells and tissues in normal aging (9,10). In many age-related diseases, the accumulation of AGEs is a significant contributing factor in degenerative processes, especially in renal failure, CV diseases, DM, and Alzheimer’s disease (9,11). The formation of AGEs is mediated by nonenzymatic condensation of a reducing sugar with proteins and is accelerated during not only glycemic but also oxidative stress (9,10,12). It is suggested that AGEs alter organ properties including the biomechanical properties of muscle tissue, which leads to impaired muscle function through collagen cross-linking and/or upregulated inflammation by the binding of AGEs to their receptor (13–15). Increasing levels of AGEs are also determined by the exogenous intake of AGEs that are spontaneously generated in standard diets (16). AGEs are removed from the body through enzymatic clearance and renal excretion. It has been proposed that, with aging, there is an imbalance between the formation and natural clearance of AGEs, which results in an incremental accumulation in tissues with slow turnover such as muscles, cartilage, tendons, eye lens, vascular media, and the dermis of the skin, whereas blood levels have fewer changes (17,18). AGEs can be biochemically quantified in blood or tissue biopsies but, due to their fluorescent properties, their presence in the dermis of the skin can be noninvasively assessed using skin autofluorescence (SAF). The AGE-induced tissue damage negatively affects motor function (eg, muscle function, walking impairment) and may influence the amount of physical activity. Although improvements of glycemic control by regular physical activity or exercise are suggested to attenuate the formation and accumulation of AGEs, it is currently unclear if the accumulation of AGEs is also a contributor to a loss of physical activity (10,19). Although AGEs have been found to be associated with declined motor function, these studies are few in number, and none studied the association between AGEs and physical activity in a large sample. The aim of this study is to investigate the association between AGE levels, as assessed by SAF, and the amount of physical activity and loss of physical functioning in older participants. Methods Design and Study Population The cross-sectional data from the LifeLines Cohort Study were used. In brief, the LifeLines Cohort Study is a large population-based cohort study and biobank that was established as a resource for research on phenotypic, genomic, and environmental factors interacting between the development of chronic diseases and healthy aging (20). Between 2006 and 2013, inhabitants of the northern part of the Netherlands were invited to participate. Eligible participants were invited to participate in the LifeLines Cohort Study through their general practitioner, unless the participating general practitioner considered the patient not eligible based on the following criteria: severe psychiatric or physical illness, limited life expectancy (<5 years), and insufficient knowledge of the Dutch language to complete a Dutch questionnaire. Participants visited one of the LifeLines research centers for a physical examination and additional measurements such as AGE assessment and cognition tests. They also completed extensive questionnaires. Baseline data were collected for 167,729 participants ranging in ages from 6 months to 93 years, with 7.6% being 65 years and older (21). For this study, we utilized the data of the LifeLines participants who were 65 years and older and who had completed SAF-AGE-level measurements. All of the participants provided written informed consent. The LifeLines Cohort Study is conducted according to the principles of the Declaration of Helsinki and is approved by the medical ethical committee of the University Medical Center Groningen, The Netherlands (M12.113965). Additional details on the LifeLines study were described previously (20). Outcome Measures Physical activity Data on physical activity were extracted from the LifeLines database, which was assessed with the Short Questionnaire to Assess Health-Enhancing Physical Activity (SQUASH) (22). The SQUASH is a valid and reliable instrument and contains questions about the amount of time a participant has spent on physical activity at work, housework, leisure activities, and sports activities. Each of the 11 physical activity items consists of three main questions: the number of days spent per week, average time per day, and intensity. The total scores from the SQUASH are used to calculate the average number of physically active days per week and to estimate whether a participant complies with the Dutch Physical Activity (DPA) guidelines, meaning a desired moderately intensive activity for 30 minutes at least 5 days a week. Physical functioning Data on physical functioning were extracted from the LifeLines database, which was assessed with the physical functioning section of the RAND-36 questionnaire (23) that comprises 10 questions regarding daily activities such as walking, stair climbing, lifting groceries, washing, and dressing. End scores are established by transforming the raw scores into a scale ranging from 0 to 100. A high score represents that the participant can perform strenuous activities (such as sports). Participants with low scores are severely restricted in performing all activities including washing and dressing. The RAND-36 is a valid and reliable questionnaire with a high internal consistency (23). AGE levels AGE levels were assessed by measuring SAF using an AGE Reader device (Diagnoptics, Groningen, The Netherlands). The AGE Reader measures fluorescent skin tissue AGEs and is reported as being a reliable and valid instrument for the quantification of AGEs accumulation (24). The AGE Reader is a desktop device that has a light source that illuminates the skin of the forearm and uses the fluorescent properties of AGEs to measure tissue accumulation of AGEs (24). The AGE Reader software calculates the SAF as the ratio between the emission light and the excitation light, multiplied by 100, and expressed in arbitrary units (AU). An elevated SAF score corresponds to a high tissue AGE level (24). All AGE Reader measurements were performed with the participants in a seated position and the volar side of the forearm placed on top of the AGE Reader. The measurements were performed on the skin without sweat, skin lotions, or visible skin abnormalities. The mean of three consecutive measurements was used. SAF values were not used in this study when skin reflection was below 10% because pigmentation influences SAF measurement thereby excluding people with a skin type of IV–VI on the Fitzpatrick scale (25). Other variables Gender, age, history of DM, CV disease, chronic pulmonary disease, kidney disease, cancer, smoking status, and alcohol consumption (5,6,9,10,13,19,24,26–29) were assessed by questionnaire. Participants were regarded as having a history of CV disease if they reported having had a history of stroke, heart attack, thrombosis, hypertension, heart failure, or atherosclerosis. Pulmonary disease was defined as a history of asthma or chronic obstructive pulmonary disease. If the participants had stopped smoking, were currently smoking, or had smoked in the past month, they were considered smokers. Alcohol consumption was classified as drinking alcoholic beverages less than 1 day per week or 1 day or more days per week. Cognitive function was measured with the Mini–Mental State Examination (MMSE) (30), which is an 11-item questionnaire with a score of 0–30 (with higher scores representing better cognitive function). Glucose levels and body mass index (BMI) were determined as described in the LifeLines protocol (20). Statistical Analysis Study population characteristics are categorized into tertile groups of SAF-AGE levels. Differences between SAF-AGEs tertiles (low SAF ≤ 2.19 AU, middle SAF: 2.19 > < 2.56 AU, and high SAF ≥ 2.56 AU) were tested using the analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. To estimate the association of AGEs with physical activity (active days per week) and physical functioning (total score), multiple linear regression analysis was used. To estimate the association of AGEs with the binary outcome on compliance with the DPA guidelines, multiple logistic regression analysis was used. Each analysis started with several known potential confounders, gender, age, DM, CV disease, pulmonary disease, kidney disease, cancer, MMSE, BMI, glucose level, smoking status, and alcohol consumption (5,6,9,10,13,19,24,26–29), as well as with physical activity in models with physical functioning as the response variable and vice versa. Because of the growing evidence of gender differences in factors associated with physical activity and functioning (31) and gender-based differences in the effects of AGEs (8), additional gender–AGE interaction analyses were performed on all models. Backward manual selection was utilized to identify statistically significant explanatory variables. During this process, the variables AGE levels, gender, and age were always retained. Missing data were handled through pairwise deletion. Testing for inflation factors indicated that multicollinearity was not of concern. Analyses were conducted using the SPSS software, version 22 for Windows, and a p value of less than .05 was considered statistically significant in two-sided tests. Results Study Population Characteristics Out of a number of 167,729 participants in the LifeLines study, 12,685 (7.6%) were 65 years and older. A SAF-AGE-level measurement was performed for 5,925 participants (46.7%) of the older subpopulation in the LifeLines study. Unfortunately, 301 persons (5.1%) had to be excluded due to skin reflection value less than 10%, which resulted in 5,624 participants with a mean (SD) SAF-AGE level of 2.41 (0.48) for analysis. The number of participants with complete data on the response variables SQUASH and RAND-36 was 4,202 and 4,641 with both a mean age (SD) of 69.4 (4.2) years and a mean (SD) SAF-AGE level of 2.39 (0.47) and 2.40 (0.47), respectively. Missingness on the SQUASH and RAND-36 were 25% and 17%, respectively. These participants had a mean age (SD) of 71.0 (5.1) and 71.5 (5.3) years and a mean (SD) SAF-AGE level of 2.47 (0.52) and 2.49 (0.53), respectively (see Supplementary Appendix for more details). Table 1 provides the characteristics of the participants categorized into groups according to the tertiles of SAF-AGE levels; 54.3% females and 45.7% males with a mean (SD) age of 69.8 (4.5) years. One-way ANOVA and chi-square tests revealed significant differences in means for all covariates among the SAF-AGE-level tertile groups with the exception of some specific cancer subgroups. Table 1. Characteristics of the Participants According to the Tertiles of AGE Levels Tertiles of AGE Levels n Low Middle High p Value SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AU SAF ≥ 2.56 AU Participants, n 5,624 1,874 1,874 1,876 AGE levels (SAF) 5,624 1.94 (0.19) 2.37 (0.10) 2.94 (0.37) Female, n (%) 3,054 1,197 (63.9) 997 (53.2) 860 (45.8) <.001 Age, y 5,624 69 (3.9) 69.6 (4.3) 70.7 (4.9) <.001 Medical history (yes)  Diabetes, n (%) 498 95 (5.1) 149 (8.0) 254 (13.5) <.001  CV disease, n (%) 2,445 746 (39.8) 787 (42.0) 912 (48.6) <.001  Kidney disease, n (%) 48 13 (0.7) 10 (0.5) 25 (1.3) .012  Pulmonary disease, n (%) 653 174 (9.3) 189 (10.1) 290 (15.5) <.001  Cancer, n (%) 815 254 (13.6) 271 (14.5) 290 (15.5) .222 Glucose, mmol/L 5,566 5.31 (0.85) 5.42 (1.08) 5.61 (1.28) <.001 BMI 5,620 26.76 (3.71) 27.16 (3.79) 27.72 (4.05) <.001 Smoking, n (%) 3,227 946 (50.5) 1,076 (57.4) 1,205 (64.2) <.001 Alcohol, ≥1 d/wk, n (%) 3,041 1,092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0–30a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001 SQUASH  Physical active days, score 0–7a 4,202 4.94 (2.10) 4.71 (2.26) 4.53 (2.35) <.001  DPA guidelines (yes), n (%) 3,743 1,325 (70.7) 1,253 (66.9) 1,165 (62.1) <.001 RAND-36, score 0–100a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001 Tertiles of AGE Levels n Low Middle High p Value SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AU SAF ≥ 2.56 AU Participants, n 5,624 1,874 1,874 1,876 AGE levels (SAF) 5,624 1.94 (0.19) 2.37 (0.10) 2.94 (0.37) Female, n (%) 3,054 1,197 (63.9) 997 (53.2) 860 (45.8) <.001 Age, y 5,624 69 (3.9) 69.6 (4.3) 70.7 (4.9) <.001 Medical history (yes)  Diabetes, n (%) 498 95 (5.1) 149 (8.0) 254 (13.5) <.001  CV disease, n (%) 2,445 746 (39.8) 787 (42.0) 912 (48.6) <.001  Kidney disease, n (%) 48 13 (0.7) 10 (0.5) 25 (1.3) .012  Pulmonary disease, n (%) 653 174 (9.3) 189 (10.1) 290 (15.5) <.001  Cancer, n (%) 815 254 (13.6) 271 (14.5) 290 (15.5) .222 Glucose, mmol/L 5,566 5.31 (0.85) 5.42 (1.08) 5.61 (1.28) <.001 BMI 5,620 26.76 (3.71) 27.16 (3.79) 27.72 (4.05) <.001 Smoking, n (%) 3,227 946 (50.5) 1,076 (57.4) 1,205 (64.2) <.001 Alcohol, ≥1 d/wk, n (%) 3,041 1,092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0–30a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001 SQUASH  Physical active days, score 0–7a 4,202 4.94 (2.10) 4.71 (2.26) 4.53 (2.35) <.001  DPA guidelines (yes), n (%) 3,743 1,325 (70.7) 1,253 (66.9) 1,165 (62.1) <.001 RAND-36, score 0–100a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001 Note: AGE = advanced glycation end product; AU = arbitrary units (ie, the output units of the AGE Reader); BMI = body mass index; CV = cardiovascular; DPA: Dutch Physical Activity; MMSE = Mini–Mental State Examination; SAF = skin autofluorescence (AGE Reader). Data represent mean values (SD) unless indicated otherwise. aHigh score indicates better performance. View Large Table 1. Characteristics of the Participants According to the Tertiles of AGE Levels Tertiles of AGE Levels n Low Middle High p Value SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AU SAF ≥ 2.56 AU Participants, n 5,624 1,874 1,874 1,876 AGE levels (SAF) 5,624 1.94 (0.19) 2.37 (0.10) 2.94 (0.37) Female, n (%) 3,054 1,197 (63.9) 997 (53.2) 860 (45.8) <.001 Age, y 5,624 69 (3.9) 69.6 (4.3) 70.7 (4.9) <.001 Medical history (yes)  Diabetes, n (%) 498 95 (5.1) 149 (8.0) 254 (13.5) <.001  CV disease, n (%) 2,445 746 (39.8) 787 (42.0) 912 (48.6) <.001  Kidney disease, n (%) 48 13 (0.7) 10 (0.5) 25 (1.3) .012  Pulmonary disease, n (%) 653 174 (9.3) 189 (10.1) 290 (15.5) <.001  Cancer, n (%) 815 254 (13.6) 271 (14.5) 290 (15.5) .222 Glucose, mmol/L 5,566 5.31 (0.85) 5.42 (1.08) 5.61 (1.28) <.001 BMI 5,620 26.76 (3.71) 27.16 (3.79) 27.72 (4.05) <.001 Smoking, n (%) 3,227 946 (50.5) 1,076 (57.4) 1,205 (64.2) <.001 Alcohol, ≥1 d/wk, n (%) 3,041 1,092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0–30a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001 SQUASH  Physical active days, score 0–7a 4,202 4.94 (2.10) 4.71 (2.26) 4.53 (2.35) <.001  DPA guidelines (yes), n (%) 3,743 1,325 (70.7) 1,253 (66.9) 1,165 (62.1) <.001 RAND-36, score 0–100a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001 Tertiles of AGE Levels n Low Middle High p Value SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AU SAF ≥ 2.56 AU Participants, n 5,624 1,874 1,874 1,876 AGE levels (SAF) 5,624 1.94 (0.19) 2.37 (0.10) 2.94 (0.37) Female, n (%) 3,054 1,197 (63.9) 997 (53.2) 860 (45.8) <.001 Age, y 5,624 69 (3.9) 69.6 (4.3) 70.7 (4.9) <.001 Medical history (yes)  Diabetes, n (%) 498 95 (5.1) 149 (8.0) 254 (13.5) <.001  CV disease, n (%) 2,445 746 (39.8) 787 (42.0) 912 (48.6) <.001  Kidney disease, n (%) 48 13 (0.7) 10 (0.5) 25 (1.3) .012  Pulmonary disease, n (%) 653 174 (9.3) 189 (10.1) 290 (15.5) <.001  Cancer, n (%) 815 254 (13.6) 271 (14.5) 290 (15.5) .222 Glucose, mmol/L 5,566 5.31 (0.85) 5.42 (1.08) 5.61 (1.28) <.001 BMI 5,620 26.76 (3.71) 27.16 (3.79) 27.72 (4.05) <.001 Smoking, n (%) 3,227 946 (50.5) 1,076 (57.4) 1,205 (64.2) <.001 Alcohol, ≥1 d/wk, n (%) 3,041 1,092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0–30a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001 SQUASH  Physical active days, score 0–7a 4,202 4.94 (2.10) 4.71 (2.26) 4.53 (2.35) <.001  DPA guidelines (yes), n (%) 3,743 1,325 (70.7) 1,253 (66.9) 1,165 (62.1) <.001 RAND-36, score 0–100a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001 Note: AGE = advanced glycation end product; AU = arbitrary units (ie, the output units of the AGE Reader); BMI = body mass index; CV = cardiovascular; DPA: Dutch Physical Activity; MMSE = Mini–Mental State Examination; SAF = skin autofluorescence (AGE Reader). Data represent mean values (SD) unless indicated otherwise. aHigh score indicates better performance. View Large Association Between AGE Levels and Physical Activity (Number of Physical Active Days) Participants, on average, were physically active for (SD) 4.73 (2.24) days per week. The high AGE-level group showed less active days per week compared with the low-level group. The linear regression model showed that after correcting for all potentially confounding variables the number of physically active days was significantly associated with higher AGE levels (β = −0.19, 95% confidence interval [CI]: −0.34 to −0.05, p = .009). Table 2 shows, after adding gender–AGE interaction to the model, that the number of physically active days was significantly associated with higher AGE levels (β = −0.30, 95% CI: −0.50 to −0.10, p = .003). The interaction term between AGEs and gender was not found significant, suggesting insufficient evidence for difference in the relationship of AGEs by gender on the number of physical active days. Backward selection on linear regression indicated that after correcting for gender, age, CV disease, BMI, cognition (MMSE), and physical functioning (RAND-36), the number of active days was lower for participants with higher AGE levels (β = −0.21, 95% CI: −0.35 to −0.07, p = .004). Table 2. Association Between AGE Levels and Physical Activity (SQUASH) Number of Physical Active Days (SQUASH)a Compliance With the DPA Guidelines (SQUASH)b Unstandardized β 95% CI p Value Odds Ratio 95% CI p Value Lower Limit Upper Limit Lower Limit Upper Limit Model 1 (n = 4,177) Model 1 (n = 4,104)  (Constant) 4.54 2.79 6.30 <.001 38.18 .021  AGE levels −0.30 −0.50 −0.10 .003 0.74 0.57 0.96 .025  Gender (male) −0.18 −0.87 0.51 .608 1.27 0.42 3.85 .675  Age (y) −0.01 −0.02 0.01 .554 0.97 0.95 0.99 .008  CV disease (yes) −0.25 −0.39 −0.11 <.001 0.93 0.75 1.15 .494  DM (yes) −0.02 −0.31 0.26 .877 0.95 0.64 1.41 .800  Pulmonary disease (yes) 0.16 −0.05 0.37 .134 1.29 0.93 1.78 .129  Kidney disease (yes) −0.14 −0.79 0.51 .676 1.35 0.45 4.07 .597  Cancer (yes) −0.13 −0.32 0.05 .162 1.07 0.79 1.43 .675  MMSE (0–30) 0.06 0.03 0.09 <.001 1.05 0.98 1.13 .191  Smoking (yes) −0.07 −0.21 0.07 .325 0.97 0.78 1.21 .776  Alcohol (yes) 0.13 −0.02 0.28 .090 1.04 0.83 1.31 .711  BMI −0.05 −0.07 −0.03 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) −0.04 −0.11 0.04 .363 0.90 0.81 1.00 .049  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001  Gender × AGE level 0.22 −0.06 0.50 .122 1.09 0.71 1.68 .699 Model 2 (n = 4,190) Model 2 (n = 4,172)  (Constant) 4.43 2.73 6.14 <.001 181.43 <.001  AGE levels −0.21 −0.35 −0.07 .004 0.76 0.62 0.94 .010  Gender (male) 0.36 0.22 0.50 <.001 1.61 1.29 2.00 <.001  Age (y) −0.01 −0.02 0.01 .414 0.97 0.95 0.99 .007  CV disease (yes) −0.26 −0.40 −0.12 <.001 — — — —  MMSE (0–30) 0.06 0.03 0.09 <.001 — — — —  BMI −0.06 −0.08 −0.04 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) — — — — 0.89 0.82 0.97 .006  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001 Number of Physical Active Days (SQUASH)a Compliance With the DPA Guidelines (SQUASH)b Unstandardized β 95% CI p Value Odds Ratio 95% CI p Value Lower Limit Upper Limit Lower Limit Upper Limit Model 1 (n = 4,177) Model 1 (n = 4,104)  (Constant) 4.54 2.79 6.30 <.001 38.18 .021  AGE levels −0.30 −0.50 −0.10 .003 0.74 0.57 0.96 .025  Gender (male) −0.18 −0.87 0.51 .608 1.27 0.42 3.85 .675  Age (y) −0.01 −0.02 0.01 .554 0.97 0.95 0.99 .008  CV disease (yes) −0.25 −0.39 −0.11 <.001 0.93 0.75 1.15 .494  DM (yes) −0.02 −0.31 0.26 .877 0.95 0.64 1.41 .800  Pulmonary disease (yes) 0.16 −0.05 0.37 .134 1.29 0.93 1.78 .129  Kidney disease (yes) −0.14 −0.79 0.51 .676 1.35 0.45 4.07 .597  Cancer (yes) −0.13 −0.32 0.05 .162 1.07 0.79 1.43 .675  MMSE (0–30) 0.06 0.03 0.09 <.001 1.05 0.98 1.13 .191  Smoking (yes) −0.07 −0.21 0.07 .325 0.97 0.78 1.21 .776  Alcohol (yes) 0.13 −0.02 0.28 .090 1.04 0.83 1.31 .711  BMI −0.05 −0.07 −0.03 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) −0.04 −0.11 0.04 .363 0.90 0.81 1.00 .049  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001  Gender × AGE level 0.22 −0.06 0.50 .122 1.09 0.71 1.68 .699 Model 2 (n = 4,190) Model 2 (n = 4,172)  (Constant) 4.43 2.73 6.14 <.001 181.43 <.001  AGE levels −0.21 −0.35 −0.07 .004 0.76 0.62 0.94 .010  Gender (male) 0.36 0.22 0.50 <.001 1.61 1.29 2.00 <.001  Age (y) −0.01 −0.02 0.01 .414 0.97 0.95 0.99 .007  CV disease (yes) −0.26 −0.40 −0.12 <.001 — — — —  MMSE (0–30) 0.06 0.03 0.09 <.001 — — — —  BMI −0.06 −0.08 −0.04 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) — — — — 0.89 0.82 0.97 .006  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001 Note: AGE = advanced glycation end product; BMI = body mass index; CI = confidence interval; CV = cardiovascular; DM = diabetes mellitus; DPA = Dutch Physical Activity; MMSE = Mini–Mental State Examination. Model 1: Full model with gender × AGE level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGE level and biological variables; age and gender). Missing response variables: SQUASH: 25%; RAND-36: 17%; missing predictors: kidney disease: 18%; alcohol: 18%; other (range): 0%–1%. aLinear regression analysis. bLogistic regression analysis. View Large Table 2. Association Between AGE Levels and Physical Activity (SQUASH) Number of Physical Active Days (SQUASH)a Compliance With the DPA Guidelines (SQUASH)b Unstandardized β 95% CI p Value Odds Ratio 95% CI p Value Lower Limit Upper Limit Lower Limit Upper Limit Model 1 (n = 4,177) Model 1 (n = 4,104)  (Constant) 4.54 2.79 6.30 <.001 38.18 .021  AGE levels −0.30 −0.50 −0.10 .003 0.74 0.57 0.96 .025  Gender (male) −0.18 −0.87 0.51 .608 1.27 0.42 3.85 .675  Age (y) −0.01 −0.02 0.01 .554 0.97 0.95 0.99 .008  CV disease (yes) −0.25 −0.39 −0.11 <.001 0.93 0.75 1.15 .494  DM (yes) −0.02 −0.31 0.26 .877 0.95 0.64 1.41 .800  Pulmonary disease (yes) 0.16 −0.05 0.37 .134 1.29 0.93 1.78 .129  Kidney disease (yes) −0.14 −0.79 0.51 .676 1.35 0.45 4.07 .597  Cancer (yes) −0.13 −0.32 0.05 .162 1.07 0.79 1.43 .675  MMSE (0–30) 0.06 0.03 0.09 <.001 1.05 0.98 1.13 .191  Smoking (yes) −0.07 −0.21 0.07 .325 0.97 0.78 1.21 .776  Alcohol (yes) 0.13 −0.02 0.28 .090 1.04 0.83 1.31 .711  BMI −0.05 −0.07 −0.03 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) −0.04 −0.11 0.04 .363 0.90 0.81 1.00 .049  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001  Gender × AGE level 0.22 −0.06 0.50 .122 1.09 0.71 1.68 .699 Model 2 (n = 4,190) Model 2 (n = 4,172)  (Constant) 4.43 2.73 6.14 <.001 181.43 <.001  AGE levels −0.21 −0.35 −0.07 .004 0.76 0.62 0.94 .010  Gender (male) 0.36 0.22 0.50 <.001 1.61 1.29 2.00 <.001  Age (y) −0.01 −0.02 0.01 .414 0.97 0.95 0.99 .007  CV disease (yes) −0.26 −0.40 −0.12 <.001 — — — —  MMSE (0–30) 0.06 0.03 0.09 <.001 — — — —  BMI −0.06 −0.08 −0.04 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) — — — — 0.89 0.82 0.97 .006  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001 Number of Physical Active Days (SQUASH)a Compliance With the DPA Guidelines (SQUASH)b Unstandardized β 95% CI p Value Odds Ratio 95% CI p Value Lower Limit Upper Limit Lower Limit Upper Limit Model 1 (n = 4,177) Model 1 (n = 4,104)  (Constant) 4.54 2.79 6.30 <.001 38.18 .021  AGE levels −0.30 −0.50 −0.10 .003 0.74 0.57 0.96 .025  Gender (male) −0.18 −0.87 0.51 .608 1.27 0.42 3.85 .675  Age (y) −0.01 −0.02 0.01 .554 0.97 0.95 0.99 .008  CV disease (yes) −0.25 −0.39 −0.11 <.001 0.93 0.75 1.15 .494  DM (yes) −0.02 −0.31 0.26 .877 0.95 0.64 1.41 .800  Pulmonary disease (yes) 0.16 −0.05 0.37 .134 1.29 0.93 1.78 .129  Kidney disease (yes) −0.14 −0.79 0.51 .676 1.35 0.45 4.07 .597  Cancer (yes) −0.13 −0.32 0.05 .162 1.07 0.79 1.43 .675  MMSE (0–30) 0.06 0.03 0.09 <.001 1.05 0.98 1.13 .191  Smoking (yes) −0.07 −0.21 0.07 .325 0.97 0.78 1.21 .776  Alcohol (yes) 0.13 −0.02 0.28 .090 1.04 0.83 1.31 .711  BMI −0.05 −0.07 −0.03 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) −0.04 −0.11 0.04 .363 0.90 0.81 1.00 .049  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001  Gender × AGE level 0.22 −0.06 0.50 .122 1.09 0.71 1.68 .699 Model 2 (n = 4,190) Model 2 (n = 4,172)  (Constant) 4.43 2.73 6.14 <.001 181.43 <.001  AGE levels −0.21 −0.35 −0.07 .004 0.76 0.62 0.94 .010  Gender (male) 0.36 0.22 0.50 <.001 1.61 1.29 2.00 <.001  Age (y) −0.01 −0.02 0.01 .414 0.97 0.95 0.99 .007  CV disease (yes) −0.26 −0.40 −0.12 <.001 — — — —  MMSE (0–30) 0.06 0.03 0.09 <.001 — — — —  BMI −0.06 −0.08 −0.04 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) — — — — 0.89 0.82 0.97 .006  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001 Note: AGE = advanced glycation end product; BMI = body mass index; CI = confidence interval; CV = cardiovascular; DM = diabetes mellitus; DPA = Dutch Physical Activity; MMSE = Mini–Mental State Examination. Model 1: Full model with gender × AGE level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGE level and biological variables; age and gender). Missing response variables: SQUASH: 25%; RAND-36: 17%; missing predictors: kidney disease: 18%; alcohol: 18%; other (range): 0%–1%. aLinear regression analysis. bLogistic regression analysis. View Large Association Between AGE Levels and Physical Activity (Compliance With the DPA Guidelines) The percentage of participants who complied with the DPA guidelines was 66.6%. The mean (SD) AGE levels of the group who did and did not comply with the DPA guidelines were 2.38 (0.48) and 2.48 (0.53) AU, respectively. The logistic regression model showed that after correcting for all potentially confounding variables, compliance with DPA guidelines was lower in participants with higher AGE levels (odds ratio = 0.76, 95% CI: 0.62 to 0.94, p = .013). Table 2 shows, after adding gender–AGE interaction to the model, that the compliance with DPA guidelines was lower in participants with higher AGE levels (odds ratio = 0.74, 95% CI: 0.57 to 0.96, p = .025). The interaction term between AGEs and gender was not found significant, suggesting insufficient evidence for difference in the relationship of AGEs by gender on the compliance with the DPA guidelines. Backward selection on logistic regression indicated that, after correcting for gender, age, glucose, BMI, and physical functioning (RAND-36), compliance with DPA guidelines was lower in participants with higher AGE levels (odds ratio = 0.76, 95% CI: 0.62 to 0.94, p = .010). Association Between AGE Levels and Physical Functioning Physical functioning, measured by the RAND-36, was lower for participants with higher AGE levels. The linear regression model showed that after correcting for all potentially confounding variables that physical functioning was lower in participants with higher AGE levels (β = −1.46, 95% CI: −2.51 to −0.40, p = .007). Table 3 shows that when adding the gender–AGE interaction variable to the model, its size appeared not to be significant. Adding this interaction term into the model resulted also in becoming not statistically significant of the association between AGEs and physical functioning (β = −1.20, 95% CI: −2.63 to 2.29, p = .100). However, without the interaction term, after the backward selection on linear regression correcting for age, gender, DM, CV disease, pulmonary disease, alcohol status, BMI, and the number of physically active days (SQUASH), the association between AGEs and physical functioning was statistically significant, indicating that physical functioning was lower in participants with higher AGE levels (β = −1.60, 95% CI: −2.64 to −0.54, p = .003). Table 3. Association Between AGE Levels and Physical Functioning (RAND-36) Physical Functioning (RAND-36) Unstandardized β 95% CI p Value Lower Limit Upper Limit Model 1 (n = 4,177)  (Constant) 160.99 149.16 172.82 <.001  AGE levels −1.20 −2.63 2.29 .100  Gender (male) 8.00 2.96 13.04 .002  Age −0.73 −0.85 −0.62 <.001  CV disease −3.69 −4.71 −2.67 <.001  DM −3.47 −5.55 −1.40 .001  Pulmonary disease −9.18 −10.70 −7.68 <.001  Kidney disease (yes) −4.26 −9.01 0.50 .079  Cancer (yes) −0.05 −1.42 1.32 .941  MMSE (0–30) 0.10 −0.11 0.31 .352  Smoking (yes) −0.94 −2.00 1.14 .80  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −1.19 −1.32 −1.06 <.001  Glucose (mmol/L) 0.43 −0.51 0.60 .878  Number of active days 0.74 0.52 0.96 <.001  Gender × AGE level −0.54 −2.57 1.50 .604 Model 2 (n = 4,182)  (Constant) 165.01 156.31 173.87 <.001  AGE levels −1.60 −2.64 −0.54 .003  Gender (male) 6.46 5.45 7.48 <.001  Age −0.74 −0.85 −0.63 <.001  CV disease −3.64 −4.63 −2.65 <.001  DM −3.42 −5.16 −1.68 <.001  Pulmonary disease −9.24 −10.75 −7.73 <.001  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −−1.19 −1.32 −1.06 <.001  Number of active days 0.74 0.52 0.96 <.001 Physical Functioning (RAND-36) Unstandardized β 95% CI p Value Lower Limit Upper Limit Model 1 (n = 4,177)  (Constant) 160.99 149.16 172.82 <.001  AGE levels −1.20 −2.63 2.29 .100  Gender (male) 8.00 2.96 13.04 .002  Age −0.73 −0.85 −0.62 <.001  CV disease −3.69 −4.71 −2.67 <.001  DM −3.47 −5.55 −1.40 .001  Pulmonary disease −9.18 −10.70 −7.68 <.001  Kidney disease (yes) −4.26 −9.01 0.50 .079  Cancer (yes) −0.05 −1.42 1.32 .941  MMSE (0–30) 0.10 −0.11 0.31 .352  Smoking (yes) −0.94 −2.00 1.14 .80  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −1.19 −1.32 −1.06 <.001  Glucose (mmol/L) 0.43 −0.51 0.60 .878  Number of active days 0.74 0.52 0.96 <.001  Gender × AGE level −0.54 −2.57 1.50 .604 Model 2 (n = 4,182)  (Constant) 165.01 156.31 173.87 <.001  AGE levels −1.60 −2.64 −0.54 .003  Gender (male) 6.46 5.45 7.48 <.001  Age −0.74 −0.85 −0.63 <.001  CV disease −3.64 −4.63 −2.65 <.001  DM −3.42 −5.16 −1.68 <.001  Pulmonary disease −9.24 −10.75 −7.73 <.001  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −−1.19 −1.32 −1.06 <.001  Number of active days 0.74 0.52 0.96 <.001 Note: AGE = advanced glycation end product, BMI = body mass index; CI = confidence interval; CV = cardiovascular; DM = diabetes mellitus; MMSE = Mini–Mental State Examination. Linear regression analysis. Model 1: Full model with gender × AGE level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGE level and biological variables; age and gender). Missing response variables: SQUASH: 25%, RAND-36: 17%; missing predictors: kidney disease: 18%, alcohol: 18%, other (range): 0%–1%. View Large Table 3. Association Between AGE Levels and Physical Functioning (RAND-36) Physical Functioning (RAND-36) Unstandardized β 95% CI p Value Lower Limit Upper Limit Model 1 (n = 4,177)  (Constant) 160.99 149.16 172.82 <.001  AGE levels −1.20 −2.63 2.29 .100  Gender (male) 8.00 2.96 13.04 .002  Age −0.73 −0.85 −0.62 <.001  CV disease −3.69 −4.71 −2.67 <.001  DM −3.47 −5.55 −1.40 .001  Pulmonary disease −9.18 −10.70 −7.68 <.001  Kidney disease (yes) −4.26 −9.01 0.50 .079  Cancer (yes) −0.05 −1.42 1.32 .941  MMSE (0–30) 0.10 −0.11 0.31 .352  Smoking (yes) −0.94 −2.00 1.14 .80  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −1.19 −1.32 −1.06 <.001  Glucose (mmol/L) 0.43 −0.51 0.60 .878  Number of active days 0.74 0.52 0.96 <.001  Gender × AGE level −0.54 −2.57 1.50 .604 Model 2 (n = 4,182)  (Constant) 165.01 156.31 173.87 <.001  AGE levels −1.60 −2.64 −0.54 .003  Gender (male) 6.46 5.45 7.48 <.001  Age −0.74 −0.85 −0.63 <.001  CV disease −3.64 −4.63 −2.65 <.001  DM −3.42 −5.16 −1.68 <.001  Pulmonary disease −9.24 −10.75 −7.73 <.001  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −−1.19 −1.32 −1.06 <.001  Number of active days 0.74 0.52 0.96 <.001 Physical Functioning (RAND-36) Unstandardized β 95% CI p Value Lower Limit Upper Limit Model 1 (n = 4,177)  (Constant) 160.99 149.16 172.82 <.001  AGE levels −1.20 −2.63 2.29 .100  Gender (male) 8.00 2.96 13.04 .002  Age −0.73 −0.85 −0.62 <.001  CV disease −3.69 −4.71 −2.67 <.001  DM −3.47 −5.55 −1.40 .001  Pulmonary disease −9.18 −10.70 −7.68 <.001  Kidney disease (yes) −4.26 −9.01 0.50 .079  Cancer (yes) −0.05 −1.42 1.32 .941  MMSE (0–30) 0.10 −0.11 0.31 .352  Smoking (yes) −0.94 −2.00 1.14 .80  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −1.19 −1.32 −1.06 <.001  Glucose (mmol/L) 0.43 −0.51 0.60 .878  Number of active days 0.74 0.52 0.96 <.001  Gender × AGE level −0.54 −2.57 1.50 .604 Model 2 (n = 4,182)  (Constant) 165.01 156.31 173.87 <.001  AGE levels −1.60 −2.64 −0.54 .003  Gender (male) 6.46 5.45 7.48 <.001  Age −0.74 −0.85 −0.63 <.001  CV disease −3.64 −4.63 −2.65 <.001  DM −3.42 −5.16 −1.68 <.001  Pulmonary disease −9.24 −10.75 −7.73 <.001  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −−1.19 −1.32 −1.06 <.001  Number of active days 0.74 0.52 0.96 <.001 Note: AGE = advanced glycation end product, BMI = body mass index; CI = confidence interval; CV = cardiovascular; DM = diabetes mellitus; MMSE = Mini–Mental State Examination. Linear regression analysis. Model 1: Full model with gender × AGE level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGE level and biological variables; age and gender). Missing response variables: SQUASH: 25%, RAND-36: 17%; missing predictors: kidney disease: 18%, alcohol: 18%, other (range): 0%–1%. View Large Discussion We found evidence that AGEs, as assessed by SAF, are associated with lower physical activity and physical functioning in older individuals. The revealed associations were consistently determined considering the presence of various independent variables for several measurements of physical activity and physical functioning. This study indicated that, in those individuals with one unit of AGE increase, the number of active days per week was 21% of a day less, and the risk of not complying with the DPA guidelines increased by 24%. Reference values for AGE levels in healthy people can be described as 0.024 × person’s age + 0.83 (R2 = 60%) (32). For those more than 70 years, reference values are unknown, but an enhanced increase in AGE levels may be expected because they may develop age-related diseases (32). In individuals with early-stage dementia, an AGE-level increase of 10 times as much as normal has been found after 1 year (33). AGE formation from a reversible to an irreversible end product usually takes weeks to months, but for AGE levels to increase by 0.3 AU is a process that generally takes 10 years in normal aging (10,32,34). Considering this, the revealed β coefficients on physical activity and functioning appear low, but as AGE formation is accelerated in age-related diseases such as DM and Alzheimer’s disease, they may become relevant. Notwithstanding that this study provides evidence for a relationship between higher AGE levels and lower physical activity and functioning, other factors, such as intrinsic motivation, access to activities/exercise, or pain may play a role. Further research over several years is necessary to improve insight into the long-term effects of AGEs on physical activity and physical function in older people. The results of this study provide partial evidence that AGE formation and accumulation contributes to motor function decline and consequently to decline in the amount of physical activity. Physical activity is in turn considered to be effective for maintaining health or preventing functional decline and disability; however, the exact underlying physiological pathways remain unclear (35,36). Physical activity can be an intervention for reducing AGE formation to improve physical functioning and thus essential to healthy aging. Previous studies have shown that individuals who are regularly physically active have, on average, lower AGE levels than those that are hardly or not physically active (37–39). On the other hand, as mentioned previously, AGE accumulation is known to affect lung, CV, and musculoskeletal tissue, which could have a negative impact on physical activity. Therefore, whether the association between high AGE levels and a decline in physical activity exists because of AGEs induced damage of relevant tissues, whether loss of physical activity influences AGEs accumulation, or both, remains to be determined. Due to the temporal bivariate relationship, we could not show this in the cross-sectional analysis because physical activity is also limited by declined physical functioning. Future research with follow-up assessments is necessary to study the size of effect of physical activity on AGE accumulation, adjusted for physical functioning impairment, as a proof of concept underpinning the causal relationship of AGEs and physical activity. The relationship between AGEs and poorer physical functioning corresponds with studies describing the effect of AGEs on walking abilities and activities of daily life and contributes to the increasing evidence that AGE accumulation is associated with functional decline (8). Although it has been suggested that AGE-induced impaired musculoskeletal function is a contributor to functional decline (8,13), it must also be considered that AGE accumulation in the central nervous system may hamper physical functioning. AGEs have been shown to be associated with less gray matter volume and to accumulate in brain tissue (26). Also, elevated levels of SAF were associated with a decline in cognitive performance (26). AGE accumulation in specific relevant motor-related brain regions may possibly affect the complex interrelationship between the motor networks within the central nervous system as well as with the musculoskeletal structures. Future research is required to determine the contribution of AGE accumulation on the central nervous system with a direct relationship on the decline in physical functioning. Our analyses show a statistically significant gender effect on physical activity and physical functioning, which is in line with the scientific literature on this topic (31,40). Although it is suggested that the effect of AGEs could be gender specific (8), we could not confirm this in our study. Future longitudinal research is necessary to study gender-based differences in the effects of AGEs on physical activity and physical functioning in depth. Strength and Limitations This study is one of the few to investigate the association between AGEs and physical activity and physical functioning in a large sample of older individuals. This study also has a number of limitations. First, this is a cross-sectional study; therefore, a causal relationship cannot be inferred. Second, outcome measures on physical activity and physical functioning were established by valid and reliable questionnaires and not with physical measurements in the LifeLines study. This may have resulted in an underestimation of our results. Third, although the response to the invitation to participate was comparable in size to other large-scale population cohort studies (20), it cannot be completely ruled out that healthy, active, older people were preferentially included because of their capabilities to visit the research site. Expanding the population under study with participants who are less active would broaden the range of outcome and probably result in a larger effect. Future studies should take that into account and include frail and/or cognitively impaired people. Finally, a critical point on the current study pertains to the missing cases not completely ad random potentially causing some bias in the findings. The various analysis of the data which were performed based upon pairwise as well as listwise deletion, the representativeness of the sample judged by the proportions and age range sustains the generalizability of our conclusions. Also, those missing SQUASH and RAND-36 were older than those not missing data, and also had a higher AGE level. This might suggest that our results underestimate the true effect of AGE levels due to missing data. It seems, however, clear that the final word is to future research of similar or larger size cohorts to confirm our findings from settings in e.g. other countries. In conclusion, this study indicates that high AGE levels may be a contributing factor as well as a biomarker for lower physical activity and functioning in older adults. Further longitudinal observational and controlled intervention studies with physical activities are necessary to investigate a causal relationship. Supplementary Material Supplementary data is available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online. Funding This study was supported by regular funds of the Research Group Healthy Ageing, Allied Healthcare and Nursing, Hanze University Groningen; the Department of General Practice and Elderly Care Medicine, University Medical Center Groningen; the Frailty in Ageing Research Group and Gerontology Department, Vrije Universiteit Brussels; and ZuidOostZorg, Organisation for Elderly Care, Drachten. Acknowledgments The authors acknowledge and thank Jaron Brinkhuizen for his assistance in organizing the databases. We also kindly thank the participants for their participation in the LifeLines study. Conflict of Interest A.J. Smit is founder and shareholder of Diagnoptics Technologies, the company that develops the AGE Reader. Diagnoptics Technologies had no role in the design of the LifeLines project, or in the analyses of this study, provided no funding, and exerted no restrictions of any sort on publications concerning the AGE Reader. References 1. 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For permissions, please e-mail: journals.permissions@oup.com. 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) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences Oxford University Press

Advanced Glycation End Products Are Associated With Physical Activity and Physical Functioning in the Older Population

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Abstract

Abstract Background Decline in physical activity and functioning is commonly observed in the older population and might be associated with biomarkers such as advanced glycation end products (AGEs). AGEs contribute to age-related decline in the function of cells and tissues in normal aging and have been found to be associated with motor function decline. The aim of this study is to investigate the association between the levels of AGEs, as assessed by skin autofluorescence, and the amount of physical activity and loss of physical functioning in older participants. Methods Cross-sectional data of 5,624 participants aged 65 years and older from the LifeLines Cohort Study were used. Linear regression analyses were utilized to study the associations between skin autofluorescence/AGE levels (AGE Reader), the number of physically active days (SQUASH), and physical functioning (RAND-36). A logistic regression analysis was used to study the associations between AGE levels and the compliance with the Dutch physical activity guidelines (SQUASH). Results A statistical significant association between AGE levels and the number of physically active days (β = −0.21, 95% confidence interval: −0.35 to −0.07, p = .004), physical functioning (β = −1.60, 95% confidence interval: −2.64 to −0.54, p = .003), and compliance with the Dutch physical activity guidelines (odds ratio = 0.76, 95% confidence interval: 0.62 to 0.94, p = .010) was revealed. Conclusions This study indicates that high AGE levels may be a contributing factor as well as a biomarker for lower levels of physical activity and functioning in the older population. Biomarker, Skin autofluorescence, Motor function, Disablement process In the aging population, decline in physical activity and functioning is commonly observed. Physical activity is generally defined as any skeletal muscle effort resulting in more energy being used than when at rest; physical functioning is defined as being able to perform activities of daily life (1,2). With aging, most human physiological systems regress independently from substantial disease effects at an average linear loss rate of 0.34%–1.28% per year between the age of 30 and 70 years (3). Regular moderate physical activity has an advantageous influence on health status and can reduce the risk on and improve the prognosis of chronic diseases such as diabetes mellitus (DM) and cardiovascular (CV) disease (4,5). A lower level and accelerated decline of physical functioning, such as gait, has been determined to predict the subsequent development of mild cognitive impairment and Alzheimer’s disease and can precede cognitive impairment by several years (6). A lack of physical activity is a known precipitating factor for the age-related loss of muscle mass (sarcopenia) leading to strength losses and physical disability (7). A decline in motor function, such as decreased muscle properties, declined walking abilities and declined activities of daily living have been found to be associated with advanced glycation end products (AGEs) in the aging population (8). AGEs accumulate in hyperglycemic environments and contribute to the age-related decline of the functioning of cells and tissues in normal aging (9,10). In many age-related diseases, the accumulation of AGEs is a significant contributing factor in degenerative processes, especially in renal failure, CV diseases, DM, and Alzheimer’s disease (9,11). The formation of AGEs is mediated by nonenzymatic condensation of a reducing sugar with proteins and is accelerated during not only glycemic but also oxidative stress (9,10,12). It is suggested that AGEs alter organ properties including the biomechanical properties of muscle tissue, which leads to impaired muscle function through collagen cross-linking and/or upregulated inflammation by the binding of AGEs to their receptor (13–15). Increasing levels of AGEs are also determined by the exogenous intake of AGEs that are spontaneously generated in standard diets (16). AGEs are removed from the body through enzymatic clearance and renal excretion. It has been proposed that, with aging, there is an imbalance between the formation and natural clearance of AGEs, which results in an incremental accumulation in tissues with slow turnover such as muscles, cartilage, tendons, eye lens, vascular media, and the dermis of the skin, whereas blood levels have fewer changes (17,18). AGEs can be biochemically quantified in blood or tissue biopsies but, due to their fluorescent properties, their presence in the dermis of the skin can be noninvasively assessed using skin autofluorescence (SAF). The AGE-induced tissue damage negatively affects motor function (eg, muscle function, walking impairment) and may influence the amount of physical activity. Although improvements of glycemic control by regular physical activity or exercise are suggested to attenuate the formation and accumulation of AGEs, it is currently unclear if the accumulation of AGEs is also a contributor to a loss of physical activity (10,19). Although AGEs have been found to be associated with declined motor function, these studies are few in number, and none studied the association between AGEs and physical activity in a large sample. The aim of this study is to investigate the association between AGE levels, as assessed by SAF, and the amount of physical activity and loss of physical functioning in older participants. Methods Design and Study Population The cross-sectional data from the LifeLines Cohort Study were used. In brief, the LifeLines Cohort Study is a large population-based cohort study and biobank that was established as a resource for research on phenotypic, genomic, and environmental factors interacting between the development of chronic diseases and healthy aging (20). Between 2006 and 2013, inhabitants of the northern part of the Netherlands were invited to participate. Eligible participants were invited to participate in the LifeLines Cohort Study through their general practitioner, unless the participating general practitioner considered the patient not eligible based on the following criteria: severe psychiatric or physical illness, limited life expectancy (<5 years), and insufficient knowledge of the Dutch language to complete a Dutch questionnaire. Participants visited one of the LifeLines research centers for a physical examination and additional measurements such as AGE assessment and cognition tests. They also completed extensive questionnaires. Baseline data were collected for 167,729 participants ranging in ages from 6 months to 93 years, with 7.6% being 65 years and older (21). For this study, we utilized the data of the LifeLines participants who were 65 years and older and who had completed SAF-AGE-level measurements. All of the participants provided written informed consent. The LifeLines Cohort Study is conducted according to the principles of the Declaration of Helsinki and is approved by the medical ethical committee of the University Medical Center Groningen, The Netherlands (M12.113965). Additional details on the LifeLines study were described previously (20). Outcome Measures Physical activity Data on physical activity were extracted from the LifeLines database, which was assessed with the Short Questionnaire to Assess Health-Enhancing Physical Activity (SQUASH) (22). The SQUASH is a valid and reliable instrument and contains questions about the amount of time a participant has spent on physical activity at work, housework, leisure activities, and sports activities. Each of the 11 physical activity items consists of three main questions: the number of days spent per week, average time per day, and intensity. The total scores from the SQUASH are used to calculate the average number of physically active days per week and to estimate whether a participant complies with the Dutch Physical Activity (DPA) guidelines, meaning a desired moderately intensive activity for 30 minutes at least 5 days a week. Physical functioning Data on physical functioning were extracted from the LifeLines database, which was assessed with the physical functioning section of the RAND-36 questionnaire (23) that comprises 10 questions regarding daily activities such as walking, stair climbing, lifting groceries, washing, and dressing. End scores are established by transforming the raw scores into a scale ranging from 0 to 100. A high score represents that the participant can perform strenuous activities (such as sports). Participants with low scores are severely restricted in performing all activities including washing and dressing. The RAND-36 is a valid and reliable questionnaire with a high internal consistency (23). AGE levels AGE levels were assessed by measuring SAF using an AGE Reader device (Diagnoptics, Groningen, The Netherlands). The AGE Reader measures fluorescent skin tissue AGEs and is reported as being a reliable and valid instrument for the quantification of AGEs accumulation (24). The AGE Reader is a desktop device that has a light source that illuminates the skin of the forearm and uses the fluorescent properties of AGEs to measure tissue accumulation of AGEs (24). The AGE Reader software calculates the SAF as the ratio between the emission light and the excitation light, multiplied by 100, and expressed in arbitrary units (AU). An elevated SAF score corresponds to a high tissue AGE level (24). All AGE Reader measurements were performed with the participants in a seated position and the volar side of the forearm placed on top of the AGE Reader. The measurements were performed on the skin without sweat, skin lotions, or visible skin abnormalities. The mean of three consecutive measurements was used. SAF values were not used in this study when skin reflection was below 10% because pigmentation influences SAF measurement thereby excluding people with a skin type of IV–VI on the Fitzpatrick scale (25). Other variables Gender, age, history of DM, CV disease, chronic pulmonary disease, kidney disease, cancer, smoking status, and alcohol consumption (5,6,9,10,13,19,24,26–29) were assessed by questionnaire. Participants were regarded as having a history of CV disease if they reported having had a history of stroke, heart attack, thrombosis, hypertension, heart failure, or atherosclerosis. Pulmonary disease was defined as a history of asthma or chronic obstructive pulmonary disease. If the participants had stopped smoking, were currently smoking, or had smoked in the past month, they were considered smokers. Alcohol consumption was classified as drinking alcoholic beverages less than 1 day per week or 1 day or more days per week. Cognitive function was measured with the Mini–Mental State Examination (MMSE) (30), which is an 11-item questionnaire with a score of 0–30 (with higher scores representing better cognitive function). Glucose levels and body mass index (BMI) were determined as described in the LifeLines protocol (20). Statistical Analysis Study population characteristics are categorized into tertile groups of SAF-AGE levels. Differences between SAF-AGEs tertiles (low SAF ≤ 2.19 AU, middle SAF: 2.19 > < 2.56 AU, and high SAF ≥ 2.56 AU) were tested using the analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. To estimate the association of AGEs with physical activity (active days per week) and physical functioning (total score), multiple linear regression analysis was used. To estimate the association of AGEs with the binary outcome on compliance with the DPA guidelines, multiple logistic regression analysis was used. Each analysis started with several known potential confounders, gender, age, DM, CV disease, pulmonary disease, kidney disease, cancer, MMSE, BMI, glucose level, smoking status, and alcohol consumption (5,6,9,10,13,19,24,26–29), as well as with physical activity in models with physical functioning as the response variable and vice versa. Because of the growing evidence of gender differences in factors associated with physical activity and functioning (31) and gender-based differences in the effects of AGEs (8), additional gender–AGE interaction analyses were performed on all models. Backward manual selection was utilized to identify statistically significant explanatory variables. During this process, the variables AGE levels, gender, and age were always retained. Missing data were handled through pairwise deletion. Testing for inflation factors indicated that multicollinearity was not of concern. Analyses were conducted using the SPSS software, version 22 for Windows, and a p value of less than .05 was considered statistically significant in two-sided tests. Results Study Population Characteristics Out of a number of 167,729 participants in the LifeLines study, 12,685 (7.6%) were 65 years and older. A SAF-AGE-level measurement was performed for 5,925 participants (46.7%) of the older subpopulation in the LifeLines study. Unfortunately, 301 persons (5.1%) had to be excluded due to skin reflection value less than 10%, which resulted in 5,624 participants with a mean (SD) SAF-AGE level of 2.41 (0.48) for analysis. The number of participants with complete data on the response variables SQUASH and RAND-36 was 4,202 and 4,641 with both a mean age (SD) of 69.4 (4.2) years and a mean (SD) SAF-AGE level of 2.39 (0.47) and 2.40 (0.47), respectively. Missingness on the SQUASH and RAND-36 were 25% and 17%, respectively. These participants had a mean age (SD) of 71.0 (5.1) and 71.5 (5.3) years and a mean (SD) SAF-AGE level of 2.47 (0.52) and 2.49 (0.53), respectively (see Supplementary Appendix for more details). Table 1 provides the characteristics of the participants categorized into groups according to the tertiles of SAF-AGE levels; 54.3% females and 45.7% males with a mean (SD) age of 69.8 (4.5) years. One-way ANOVA and chi-square tests revealed significant differences in means for all covariates among the SAF-AGE-level tertile groups with the exception of some specific cancer subgroups. Table 1. Characteristics of the Participants According to the Tertiles of AGE Levels Tertiles of AGE Levels n Low Middle High p Value SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AU SAF ≥ 2.56 AU Participants, n 5,624 1,874 1,874 1,876 AGE levels (SAF) 5,624 1.94 (0.19) 2.37 (0.10) 2.94 (0.37) Female, n (%) 3,054 1,197 (63.9) 997 (53.2) 860 (45.8) <.001 Age, y 5,624 69 (3.9) 69.6 (4.3) 70.7 (4.9) <.001 Medical history (yes)  Diabetes, n (%) 498 95 (5.1) 149 (8.0) 254 (13.5) <.001  CV disease, n (%) 2,445 746 (39.8) 787 (42.0) 912 (48.6) <.001  Kidney disease, n (%) 48 13 (0.7) 10 (0.5) 25 (1.3) .012  Pulmonary disease, n (%) 653 174 (9.3) 189 (10.1) 290 (15.5) <.001  Cancer, n (%) 815 254 (13.6) 271 (14.5) 290 (15.5) .222 Glucose, mmol/L 5,566 5.31 (0.85) 5.42 (1.08) 5.61 (1.28) <.001 BMI 5,620 26.76 (3.71) 27.16 (3.79) 27.72 (4.05) <.001 Smoking, n (%) 3,227 946 (50.5) 1,076 (57.4) 1,205 (64.2) <.001 Alcohol, ≥1 d/wk, n (%) 3,041 1,092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0–30a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001 SQUASH  Physical active days, score 0–7a 4,202 4.94 (2.10) 4.71 (2.26) 4.53 (2.35) <.001  DPA guidelines (yes), n (%) 3,743 1,325 (70.7) 1,253 (66.9) 1,165 (62.1) <.001 RAND-36, score 0–100a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001 Tertiles of AGE Levels n Low Middle High p Value SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AU SAF ≥ 2.56 AU Participants, n 5,624 1,874 1,874 1,876 AGE levels (SAF) 5,624 1.94 (0.19) 2.37 (0.10) 2.94 (0.37) Female, n (%) 3,054 1,197 (63.9) 997 (53.2) 860 (45.8) <.001 Age, y 5,624 69 (3.9) 69.6 (4.3) 70.7 (4.9) <.001 Medical history (yes)  Diabetes, n (%) 498 95 (5.1) 149 (8.0) 254 (13.5) <.001  CV disease, n (%) 2,445 746 (39.8) 787 (42.0) 912 (48.6) <.001  Kidney disease, n (%) 48 13 (0.7) 10 (0.5) 25 (1.3) .012  Pulmonary disease, n (%) 653 174 (9.3) 189 (10.1) 290 (15.5) <.001  Cancer, n (%) 815 254 (13.6) 271 (14.5) 290 (15.5) .222 Glucose, mmol/L 5,566 5.31 (0.85) 5.42 (1.08) 5.61 (1.28) <.001 BMI 5,620 26.76 (3.71) 27.16 (3.79) 27.72 (4.05) <.001 Smoking, n (%) 3,227 946 (50.5) 1,076 (57.4) 1,205 (64.2) <.001 Alcohol, ≥1 d/wk, n (%) 3,041 1,092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0–30a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001 SQUASH  Physical active days, score 0–7a 4,202 4.94 (2.10) 4.71 (2.26) 4.53 (2.35) <.001  DPA guidelines (yes), n (%) 3,743 1,325 (70.7) 1,253 (66.9) 1,165 (62.1) <.001 RAND-36, score 0–100a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001 Note: AGE = advanced glycation end product; AU = arbitrary units (ie, the output units of the AGE Reader); BMI = body mass index; CV = cardiovascular; DPA: Dutch Physical Activity; MMSE = Mini–Mental State Examination; SAF = skin autofluorescence (AGE Reader). Data represent mean values (SD) unless indicated otherwise. aHigh score indicates better performance. View Large Table 1. Characteristics of the Participants According to the Tertiles of AGE Levels Tertiles of AGE Levels n Low Middle High p Value SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AU SAF ≥ 2.56 AU Participants, n 5,624 1,874 1,874 1,876 AGE levels (SAF) 5,624 1.94 (0.19) 2.37 (0.10) 2.94 (0.37) Female, n (%) 3,054 1,197 (63.9) 997 (53.2) 860 (45.8) <.001 Age, y 5,624 69 (3.9) 69.6 (4.3) 70.7 (4.9) <.001 Medical history (yes)  Diabetes, n (%) 498 95 (5.1) 149 (8.0) 254 (13.5) <.001  CV disease, n (%) 2,445 746 (39.8) 787 (42.0) 912 (48.6) <.001  Kidney disease, n (%) 48 13 (0.7) 10 (0.5) 25 (1.3) .012  Pulmonary disease, n (%) 653 174 (9.3) 189 (10.1) 290 (15.5) <.001  Cancer, n (%) 815 254 (13.6) 271 (14.5) 290 (15.5) .222 Glucose, mmol/L 5,566 5.31 (0.85) 5.42 (1.08) 5.61 (1.28) <.001 BMI 5,620 26.76 (3.71) 27.16 (3.79) 27.72 (4.05) <.001 Smoking, n (%) 3,227 946 (50.5) 1,076 (57.4) 1,205 (64.2) <.001 Alcohol, ≥1 d/wk, n (%) 3,041 1,092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0–30a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001 SQUASH  Physical active days, score 0–7a 4,202 4.94 (2.10) 4.71 (2.26) 4.53 (2.35) <.001  DPA guidelines (yes), n (%) 3,743 1,325 (70.7) 1,253 (66.9) 1,165 (62.1) <.001 RAND-36, score 0–100a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001 Tertiles of AGE Levels n Low Middle High p Value SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AU SAF ≥ 2.56 AU Participants, n 5,624 1,874 1,874 1,876 AGE levels (SAF) 5,624 1.94 (0.19) 2.37 (0.10) 2.94 (0.37) Female, n (%) 3,054 1,197 (63.9) 997 (53.2) 860 (45.8) <.001 Age, y 5,624 69 (3.9) 69.6 (4.3) 70.7 (4.9) <.001 Medical history (yes)  Diabetes, n (%) 498 95 (5.1) 149 (8.0) 254 (13.5) <.001  CV disease, n (%) 2,445 746 (39.8) 787 (42.0) 912 (48.6) <.001  Kidney disease, n (%) 48 13 (0.7) 10 (0.5) 25 (1.3) .012  Pulmonary disease, n (%) 653 174 (9.3) 189 (10.1) 290 (15.5) <.001  Cancer, n (%) 815 254 (13.6) 271 (14.5) 290 (15.5) .222 Glucose, mmol/L 5,566 5.31 (0.85) 5.42 (1.08) 5.61 (1.28) <.001 BMI 5,620 26.76 (3.71) 27.16 (3.79) 27.72 (4.05) <.001 Smoking, n (%) 3,227 946 (50.5) 1,076 (57.4) 1,205 (64.2) <.001 Alcohol, ≥1 d/wk, n (%) 3,041 1,092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0–30a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001 SQUASH  Physical active days, score 0–7a 4,202 4.94 (2.10) 4.71 (2.26) 4.53 (2.35) <.001  DPA guidelines (yes), n (%) 3,743 1,325 (70.7) 1,253 (66.9) 1,165 (62.1) <.001 RAND-36, score 0–100a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001 Note: AGE = advanced glycation end product; AU = arbitrary units (ie, the output units of the AGE Reader); BMI = body mass index; CV = cardiovascular; DPA: Dutch Physical Activity; MMSE = Mini–Mental State Examination; SAF = skin autofluorescence (AGE Reader). Data represent mean values (SD) unless indicated otherwise. aHigh score indicates better performance. View Large Association Between AGE Levels and Physical Activity (Number of Physical Active Days) Participants, on average, were physically active for (SD) 4.73 (2.24) days per week. The high AGE-level group showed less active days per week compared with the low-level group. The linear regression model showed that after correcting for all potentially confounding variables the number of physically active days was significantly associated with higher AGE levels (β = −0.19, 95% confidence interval [CI]: −0.34 to −0.05, p = .009). Table 2 shows, after adding gender–AGE interaction to the model, that the number of physically active days was significantly associated with higher AGE levels (β = −0.30, 95% CI: −0.50 to −0.10, p = .003). The interaction term between AGEs and gender was not found significant, suggesting insufficient evidence for difference in the relationship of AGEs by gender on the number of physical active days. Backward selection on linear regression indicated that after correcting for gender, age, CV disease, BMI, cognition (MMSE), and physical functioning (RAND-36), the number of active days was lower for participants with higher AGE levels (β = −0.21, 95% CI: −0.35 to −0.07, p = .004). Table 2. Association Between AGE Levels and Physical Activity (SQUASH) Number of Physical Active Days (SQUASH)a Compliance With the DPA Guidelines (SQUASH)b Unstandardized β 95% CI p Value Odds Ratio 95% CI p Value Lower Limit Upper Limit Lower Limit Upper Limit Model 1 (n = 4,177) Model 1 (n = 4,104)  (Constant) 4.54 2.79 6.30 <.001 38.18 .021  AGE levels −0.30 −0.50 −0.10 .003 0.74 0.57 0.96 .025  Gender (male) −0.18 −0.87 0.51 .608 1.27 0.42 3.85 .675  Age (y) −0.01 −0.02 0.01 .554 0.97 0.95 0.99 .008  CV disease (yes) −0.25 −0.39 −0.11 <.001 0.93 0.75 1.15 .494  DM (yes) −0.02 −0.31 0.26 .877 0.95 0.64 1.41 .800  Pulmonary disease (yes) 0.16 −0.05 0.37 .134 1.29 0.93 1.78 .129  Kidney disease (yes) −0.14 −0.79 0.51 .676 1.35 0.45 4.07 .597  Cancer (yes) −0.13 −0.32 0.05 .162 1.07 0.79 1.43 .675  MMSE (0–30) 0.06 0.03 0.09 <.001 1.05 0.98 1.13 .191  Smoking (yes) −0.07 −0.21 0.07 .325 0.97 0.78 1.21 .776  Alcohol (yes) 0.13 −0.02 0.28 .090 1.04 0.83 1.31 .711  BMI −0.05 −0.07 −0.03 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) −0.04 −0.11 0.04 .363 0.90 0.81 1.00 .049  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001  Gender × AGE level 0.22 −0.06 0.50 .122 1.09 0.71 1.68 .699 Model 2 (n = 4,190) Model 2 (n = 4,172)  (Constant) 4.43 2.73 6.14 <.001 181.43 <.001  AGE levels −0.21 −0.35 −0.07 .004 0.76 0.62 0.94 .010  Gender (male) 0.36 0.22 0.50 <.001 1.61 1.29 2.00 <.001  Age (y) −0.01 −0.02 0.01 .414 0.97 0.95 0.99 .007  CV disease (yes) −0.26 −0.40 −0.12 <.001 — — — —  MMSE (0–30) 0.06 0.03 0.09 <.001 — — — —  BMI −0.06 −0.08 −0.04 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) — — — — 0.89 0.82 0.97 .006  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001 Number of Physical Active Days (SQUASH)a Compliance With the DPA Guidelines (SQUASH)b Unstandardized β 95% CI p Value Odds Ratio 95% CI p Value Lower Limit Upper Limit Lower Limit Upper Limit Model 1 (n = 4,177) Model 1 (n = 4,104)  (Constant) 4.54 2.79 6.30 <.001 38.18 .021  AGE levels −0.30 −0.50 −0.10 .003 0.74 0.57 0.96 .025  Gender (male) −0.18 −0.87 0.51 .608 1.27 0.42 3.85 .675  Age (y) −0.01 −0.02 0.01 .554 0.97 0.95 0.99 .008  CV disease (yes) −0.25 −0.39 −0.11 <.001 0.93 0.75 1.15 .494  DM (yes) −0.02 −0.31 0.26 .877 0.95 0.64 1.41 .800  Pulmonary disease (yes) 0.16 −0.05 0.37 .134 1.29 0.93 1.78 .129  Kidney disease (yes) −0.14 −0.79 0.51 .676 1.35 0.45 4.07 .597  Cancer (yes) −0.13 −0.32 0.05 .162 1.07 0.79 1.43 .675  MMSE (0–30) 0.06 0.03 0.09 <.001 1.05 0.98 1.13 .191  Smoking (yes) −0.07 −0.21 0.07 .325 0.97 0.78 1.21 .776  Alcohol (yes) 0.13 −0.02 0.28 .090 1.04 0.83 1.31 .711  BMI −0.05 −0.07 −0.03 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) −0.04 −0.11 0.04 .363 0.90 0.81 1.00 .049  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001  Gender × AGE level 0.22 −0.06 0.50 .122 1.09 0.71 1.68 .699 Model 2 (n = 4,190) Model 2 (n = 4,172)  (Constant) 4.43 2.73 6.14 <.001 181.43 <.001  AGE levels −0.21 −0.35 −0.07 .004 0.76 0.62 0.94 .010  Gender (male) 0.36 0.22 0.50 <.001 1.61 1.29 2.00 <.001  Age (y) −0.01 −0.02 0.01 .414 0.97 0.95 0.99 .007  CV disease (yes) −0.26 −0.40 −0.12 <.001 — — — —  MMSE (0–30) 0.06 0.03 0.09 <.001 — — — —  BMI −0.06 −0.08 −0.04 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) — — — — 0.89 0.82 0.97 .006  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001 Note: AGE = advanced glycation end product; BMI = body mass index; CI = confidence interval; CV = cardiovascular; DM = diabetes mellitus; DPA = Dutch Physical Activity; MMSE = Mini–Mental State Examination. Model 1: Full model with gender × AGE level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGE level and biological variables; age and gender). Missing response variables: SQUASH: 25%; RAND-36: 17%; missing predictors: kidney disease: 18%; alcohol: 18%; other (range): 0%–1%. aLinear regression analysis. bLogistic regression analysis. View Large Table 2. Association Between AGE Levels and Physical Activity (SQUASH) Number of Physical Active Days (SQUASH)a Compliance With the DPA Guidelines (SQUASH)b Unstandardized β 95% CI p Value Odds Ratio 95% CI p Value Lower Limit Upper Limit Lower Limit Upper Limit Model 1 (n = 4,177) Model 1 (n = 4,104)  (Constant) 4.54 2.79 6.30 <.001 38.18 .021  AGE levels −0.30 −0.50 −0.10 .003 0.74 0.57 0.96 .025  Gender (male) −0.18 −0.87 0.51 .608 1.27 0.42 3.85 .675  Age (y) −0.01 −0.02 0.01 .554 0.97 0.95 0.99 .008  CV disease (yes) −0.25 −0.39 −0.11 <.001 0.93 0.75 1.15 .494  DM (yes) −0.02 −0.31 0.26 .877 0.95 0.64 1.41 .800  Pulmonary disease (yes) 0.16 −0.05 0.37 .134 1.29 0.93 1.78 .129  Kidney disease (yes) −0.14 −0.79 0.51 .676 1.35 0.45 4.07 .597  Cancer (yes) −0.13 −0.32 0.05 .162 1.07 0.79 1.43 .675  MMSE (0–30) 0.06 0.03 0.09 <.001 1.05 0.98 1.13 .191  Smoking (yes) −0.07 −0.21 0.07 .325 0.97 0.78 1.21 .776  Alcohol (yes) 0.13 −0.02 0.28 .090 1.04 0.83 1.31 .711  BMI −0.05 −0.07 −0.03 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) −0.04 −0.11 0.04 .363 0.90 0.81 1.00 .049  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001  Gender × AGE level 0.22 −0.06 0.50 .122 1.09 0.71 1.68 .699 Model 2 (n = 4,190) Model 2 (n = 4,172)  (Constant) 4.43 2.73 6.14 <.001 181.43 <.001  AGE levels −0.21 −0.35 −0.07 .004 0.76 0.62 0.94 .010  Gender (male) 0.36 0.22 0.50 <.001 1.61 1.29 2.00 <.001  Age (y) −0.01 −0.02 0.01 .414 0.97 0.95 0.99 .007  CV disease (yes) −0.26 −0.40 −0.12 <.001 — — — —  MMSE (0–30) 0.06 0.03 0.09 <.001 — — — —  BMI −0.06 −0.08 −0.04 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) — — — — 0.89 0.82 0.97 .006  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001 Number of Physical Active Days (SQUASH)a Compliance With the DPA Guidelines (SQUASH)b Unstandardized β 95% CI p Value Odds Ratio 95% CI p Value Lower Limit Upper Limit Lower Limit Upper Limit Model 1 (n = 4,177) Model 1 (n = 4,104)  (Constant) 4.54 2.79 6.30 <.001 38.18 .021  AGE levels −0.30 −0.50 −0.10 .003 0.74 0.57 0.96 .025  Gender (male) −0.18 −0.87 0.51 .608 1.27 0.42 3.85 .675  Age (y) −0.01 −0.02 0.01 .554 0.97 0.95 0.99 .008  CV disease (yes) −0.25 −0.39 −0.11 <.001 0.93 0.75 1.15 .494  DM (yes) −0.02 −0.31 0.26 .877 0.95 0.64 1.41 .800  Pulmonary disease (yes) 0.16 −0.05 0.37 .134 1.29 0.93 1.78 .129  Kidney disease (yes) −0.14 −0.79 0.51 .676 1.35 0.45 4.07 .597  Cancer (yes) −0.13 −0.32 0.05 .162 1.07 0.79 1.43 .675  MMSE (0–30) 0.06 0.03 0.09 <.001 1.05 0.98 1.13 .191  Smoking (yes) −0.07 −0.21 0.07 .325 0.97 0.78 1.21 .776  Alcohol (yes) 0.13 −0.02 0.28 .090 1.04 0.83 1.31 .711  BMI −0.05 −0.07 −0.03 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) −0.04 −0.11 0.04 .363 0.90 0.81 1.00 .049  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001  Gender × AGE level 0.22 −0.06 0.50 .122 1.09 0.71 1.68 .699 Model 2 (n = 4,190) Model 2 (n = 4,172)  (Constant) 4.43 2.73 6.14 <.001 181.43 <.001  AGE levels −0.21 −0.35 −0.07 .004 0.76 0.62 0.94 .010  Gender (male) 0.36 0.22 0.50 <.001 1.61 1.29 2.00 <.001  Age (y) −0.01 −0.02 0.01 .414 0.97 0.95 0.99 .007  CV disease (yes) −0.26 −0.40 −0.12 <.001 — — — —  MMSE (0–30) 0.06 0.03 0.09 <.001 — — — —  BMI −0.06 −0.08 −0.04 <.001 0.95 0.93 0.98 <.001  Glucose (mmol/L) — — — — 0.89 0.82 0.97 .006  Physical functioning (RAND-36, 0–100) 0.01 0.01 0.02 <.001 1.02 1.02 1.03 <.001 Note: AGE = advanced glycation end product; BMI = body mass index; CI = confidence interval; CV = cardiovascular; DM = diabetes mellitus; DPA = Dutch Physical Activity; MMSE = Mini–Mental State Examination. Model 1: Full model with gender × AGE level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGE level and biological variables; age and gender). Missing response variables: SQUASH: 25%; RAND-36: 17%; missing predictors: kidney disease: 18%; alcohol: 18%; other (range): 0%–1%. aLinear regression analysis. bLogistic regression analysis. View Large Association Between AGE Levels and Physical Activity (Compliance With the DPA Guidelines) The percentage of participants who complied with the DPA guidelines was 66.6%. The mean (SD) AGE levels of the group who did and did not comply with the DPA guidelines were 2.38 (0.48) and 2.48 (0.53) AU, respectively. The logistic regression model showed that after correcting for all potentially confounding variables, compliance with DPA guidelines was lower in participants with higher AGE levels (odds ratio = 0.76, 95% CI: 0.62 to 0.94, p = .013). Table 2 shows, after adding gender–AGE interaction to the model, that the compliance with DPA guidelines was lower in participants with higher AGE levels (odds ratio = 0.74, 95% CI: 0.57 to 0.96, p = .025). The interaction term between AGEs and gender was not found significant, suggesting insufficient evidence for difference in the relationship of AGEs by gender on the compliance with the DPA guidelines. Backward selection on logistic regression indicated that, after correcting for gender, age, glucose, BMI, and physical functioning (RAND-36), compliance with DPA guidelines was lower in participants with higher AGE levels (odds ratio = 0.76, 95% CI: 0.62 to 0.94, p = .010). Association Between AGE Levels and Physical Functioning Physical functioning, measured by the RAND-36, was lower for participants with higher AGE levels. The linear regression model showed that after correcting for all potentially confounding variables that physical functioning was lower in participants with higher AGE levels (β = −1.46, 95% CI: −2.51 to −0.40, p = .007). Table 3 shows that when adding the gender–AGE interaction variable to the model, its size appeared not to be significant. Adding this interaction term into the model resulted also in becoming not statistically significant of the association between AGEs and physical functioning (β = −1.20, 95% CI: −2.63 to 2.29, p = .100). However, without the interaction term, after the backward selection on linear regression correcting for age, gender, DM, CV disease, pulmonary disease, alcohol status, BMI, and the number of physically active days (SQUASH), the association between AGEs and physical functioning was statistically significant, indicating that physical functioning was lower in participants with higher AGE levels (β = −1.60, 95% CI: −2.64 to −0.54, p = .003). Table 3. Association Between AGE Levels and Physical Functioning (RAND-36) Physical Functioning (RAND-36) Unstandardized β 95% CI p Value Lower Limit Upper Limit Model 1 (n = 4,177)  (Constant) 160.99 149.16 172.82 <.001  AGE levels −1.20 −2.63 2.29 .100  Gender (male) 8.00 2.96 13.04 .002  Age −0.73 −0.85 −0.62 <.001  CV disease −3.69 −4.71 −2.67 <.001  DM −3.47 −5.55 −1.40 .001  Pulmonary disease −9.18 −10.70 −7.68 <.001  Kidney disease (yes) −4.26 −9.01 0.50 .079  Cancer (yes) −0.05 −1.42 1.32 .941  MMSE (0–30) 0.10 −0.11 0.31 .352  Smoking (yes) −0.94 −2.00 1.14 .80  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −1.19 −1.32 −1.06 <.001  Glucose (mmol/L) 0.43 −0.51 0.60 .878  Number of active days 0.74 0.52 0.96 <.001  Gender × AGE level −0.54 −2.57 1.50 .604 Model 2 (n = 4,182)  (Constant) 165.01 156.31 173.87 <.001  AGE levels −1.60 −2.64 −0.54 .003  Gender (male) 6.46 5.45 7.48 <.001  Age −0.74 −0.85 −0.63 <.001  CV disease −3.64 −4.63 −2.65 <.001  DM −3.42 −5.16 −1.68 <.001  Pulmonary disease −9.24 −10.75 −7.73 <.001  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −−1.19 −1.32 −1.06 <.001  Number of active days 0.74 0.52 0.96 <.001 Physical Functioning (RAND-36) Unstandardized β 95% CI p Value Lower Limit Upper Limit Model 1 (n = 4,177)  (Constant) 160.99 149.16 172.82 <.001  AGE levels −1.20 −2.63 2.29 .100  Gender (male) 8.00 2.96 13.04 .002  Age −0.73 −0.85 −0.62 <.001  CV disease −3.69 −4.71 −2.67 <.001  DM −3.47 −5.55 −1.40 .001  Pulmonary disease −9.18 −10.70 −7.68 <.001  Kidney disease (yes) −4.26 −9.01 0.50 .079  Cancer (yes) −0.05 −1.42 1.32 .941  MMSE (0–30) 0.10 −0.11 0.31 .352  Smoking (yes) −0.94 −2.00 1.14 .80  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −1.19 −1.32 −1.06 <.001  Glucose (mmol/L) 0.43 −0.51 0.60 .878  Number of active days 0.74 0.52 0.96 <.001  Gender × AGE level −0.54 −2.57 1.50 .604 Model 2 (n = 4,182)  (Constant) 165.01 156.31 173.87 <.001  AGE levels −1.60 −2.64 −0.54 .003  Gender (male) 6.46 5.45 7.48 <.001  Age −0.74 −0.85 −0.63 <.001  CV disease −3.64 −4.63 −2.65 <.001  DM −3.42 −5.16 −1.68 <.001  Pulmonary disease −9.24 −10.75 −7.73 <.001  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −−1.19 −1.32 −1.06 <.001  Number of active days 0.74 0.52 0.96 <.001 Note: AGE = advanced glycation end product, BMI = body mass index; CI = confidence interval; CV = cardiovascular; DM = diabetes mellitus; MMSE = Mini–Mental State Examination. Linear regression analysis. Model 1: Full model with gender × AGE level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGE level and biological variables; age and gender). Missing response variables: SQUASH: 25%, RAND-36: 17%; missing predictors: kidney disease: 18%, alcohol: 18%, other (range): 0%–1%. View Large Table 3. Association Between AGE Levels and Physical Functioning (RAND-36) Physical Functioning (RAND-36) Unstandardized β 95% CI p Value Lower Limit Upper Limit Model 1 (n = 4,177)  (Constant) 160.99 149.16 172.82 <.001  AGE levels −1.20 −2.63 2.29 .100  Gender (male) 8.00 2.96 13.04 .002  Age −0.73 −0.85 −0.62 <.001  CV disease −3.69 −4.71 −2.67 <.001  DM −3.47 −5.55 −1.40 .001  Pulmonary disease −9.18 −10.70 −7.68 <.001  Kidney disease (yes) −4.26 −9.01 0.50 .079  Cancer (yes) −0.05 −1.42 1.32 .941  MMSE (0–30) 0.10 −0.11 0.31 .352  Smoking (yes) −0.94 −2.00 1.14 .80  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −1.19 −1.32 −1.06 <.001  Glucose (mmol/L) 0.43 −0.51 0.60 .878  Number of active days 0.74 0.52 0.96 <.001  Gender × AGE level −0.54 −2.57 1.50 .604 Model 2 (n = 4,182)  (Constant) 165.01 156.31 173.87 <.001  AGE levels −1.60 −2.64 −0.54 .003  Gender (male) 6.46 5.45 7.48 <.001  Age −0.74 −0.85 −0.63 <.001  CV disease −3.64 −4.63 −2.65 <.001  DM −3.42 −5.16 −1.68 <.001  Pulmonary disease −9.24 −10.75 −7.73 <.001  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −−1.19 −1.32 −1.06 <.001  Number of active days 0.74 0.52 0.96 <.001 Physical Functioning (RAND-36) Unstandardized β 95% CI p Value Lower Limit Upper Limit Model 1 (n = 4,177)  (Constant) 160.99 149.16 172.82 <.001  AGE levels −1.20 −2.63 2.29 .100  Gender (male) 8.00 2.96 13.04 .002  Age −0.73 −0.85 −0.62 <.001  CV disease −3.69 −4.71 −2.67 <.001  DM −3.47 −5.55 −1.40 .001  Pulmonary disease −9.18 −10.70 −7.68 <.001  Kidney disease (yes) −4.26 −9.01 0.50 .079  Cancer (yes) −0.05 −1.42 1.32 .941  MMSE (0–30) 0.10 −0.11 0.31 .352  Smoking (yes) −0.94 −2.00 1.14 .80  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −1.19 −1.32 −1.06 <.001  Glucose (mmol/L) 0.43 −0.51 0.60 .878  Number of active days 0.74 0.52 0.96 <.001  Gender × AGE level −0.54 −2.57 1.50 .604 Model 2 (n = 4,182)  (Constant) 165.01 156.31 173.87 <.001  AGE levels −1.60 −2.64 −0.54 .003  Gender (male) 6.46 5.45 7.48 <.001  Age −0.74 −0.85 −0.63 <.001  CV disease −3.64 −4.63 −2.65 <.001  DM −3.42 −5.16 −1.68 <.001  Pulmonary disease −9.24 −10.75 −7.73 <.001  Alcohol (yes) 3.32 2.25 4.38 <.001  BMI −−1.19 −1.32 −1.06 <.001  Number of active days 0.74 0.52 0.96 <.001 Note: AGE = advanced glycation end product, BMI = body mass index; CI = confidence interval; CV = cardiovascular; DM = diabetes mellitus; MMSE = Mini–Mental State Examination. Linear regression analysis. Model 1: Full model with gender × AGE level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGE level and biological variables; age and gender). Missing response variables: SQUASH: 25%, RAND-36: 17%; missing predictors: kidney disease: 18%, alcohol: 18%, other (range): 0%–1%. View Large Discussion We found evidence that AGEs, as assessed by SAF, are associated with lower physical activity and physical functioning in older individuals. The revealed associations were consistently determined considering the presence of various independent variables for several measurements of physical activity and physical functioning. This study indicated that, in those individuals with one unit of AGE increase, the number of active days per week was 21% of a day less, and the risk of not complying with the DPA guidelines increased by 24%. Reference values for AGE levels in healthy people can be described as 0.024 × person’s age + 0.83 (R2 = 60%) (32). For those more than 70 years, reference values are unknown, but an enhanced increase in AGE levels may be expected because they may develop age-related diseases (32). In individuals with early-stage dementia, an AGE-level increase of 10 times as much as normal has been found after 1 year (33). AGE formation from a reversible to an irreversible end product usually takes weeks to months, but for AGE levels to increase by 0.3 AU is a process that generally takes 10 years in normal aging (10,32,34). Considering this, the revealed β coefficients on physical activity and functioning appear low, but as AGE formation is accelerated in age-related diseases such as DM and Alzheimer’s disease, they may become relevant. Notwithstanding that this study provides evidence for a relationship between higher AGE levels and lower physical activity and functioning, other factors, such as intrinsic motivation, access to activities/exercise, or pain may play a role. Further research over several years is necessary to improve insight into the long-term effects of AGEs on physical activity and physical function in older people. The results of this study provide partial evidence that AGE formation and accumulation contributes to motor function decline and consequently to decline in the amount of physical activity. Physical activity is in turn considered to be effective for maintaining health or preventing functional decline and disability; however, the exact underlying physiological pathways remain unclear (35,36). Physical activity can be an intervention for reducing AGE formation to improve physical functioning and thus essential to healthy aging. Previous studies have shown that individuals who are regularly physically active have, on average, lower AGE levels than those that are hardly or not physically active (37–39). On the other hand, as mentioned previously, AGE accumulation is known to affect lung, CV, and musculoskeletal tissue, which could have a negative impact on physical activity. Therefore, whether the association between high AGE levels and a decline in physical activity exists because of AGEs induced damage of relevant tissues, whether loss of physical activity influences AGEs accumulation, or both, remains to be determined. Due to the temporal bivariate relationship, we could not show this in the cross-sectional analysis because physical activity is also limited by declined physical functioning. Future research with follow-up assessments is necessary to study the size of effect of physical activity on AGE accumulation, adjusted for physical functioning impairment, as a proof of concept underpinning the causal relationship of AGEs and physical activity. The relationship between AGEs and poorer physical functioning corresponds with studies describing the effect of AGEs on walking abilities and activities of daily life and contributes to the increasing evidence that AGE accumulation is associated with functional decline (8). Although it has been suggested that AGE-induced impaired musculoskeletal function is a contributor to functional decline (8,13), it must also be considered that AGE accumulation in the central nervous system may hamper physical functioning. AGEs have been shown to be associated with less gray matter volume and to accumulate in brain tissue (26). Also, elevated levels of SAF were associated with a decline in cognitive performance (26). AGE accumulation in specific relevant motor-related brain regions may possibly affect the complex interrelationship between the motor networks within the central nervous system as well as with the musculoskeletal structures. Future research is required to determine the contribution of AGE accumulation on the central nervous system with a direct relationship on the decline in physical functioning. Our analyses show a statistically significant gender effect on physical activity and physical functioning, which is in line with the scientific literature on this topic (31,40). Although it is suggested that the effect of AGEs could be gender specific (8), we could not confirm this in our study. Future longitudinal research is necessary to study gender-based differences in the effects of AGEs on physical activity and physical functioning in depth. Strength and Limitations This study is one of the few to investigate the association between AGEs and physical activity and physical functioning in a large sample of older individuals. This study also has a number of limitations. First, this is a cross-sectional study; therefore, a causal relationship cannot be inferred. Second, outcome measures on physical activity and physical functioning were established by valid and reliable questionnaires and not with physical measurements in the LifeLines study. This may have resulted in an underestimation of our results. Third, although the response to the invitation to participate was comparable in size to other large-scale population cohort studies (20), it cannot be completely ruled out that healthy, active, older people were preferentially included because of their capabilities to visit the research site. Expanding the population under study with participants who are less active would broaden the range of outcome and probably result in a larger effect. Future studies should take that into account and include frail and/or cognitively impaired people. Finally, a critical point on the current study pertains to the missing cases not completely ad random potentially causing some bias in the findings. The various analysis of the data which were performed based upon pairwise as well as listwise deletion, the representativeness of the sample judged by the proportions and age range sustains the generalizability of our conclusions. Also, those missing SQUASH and RAND-36 were older than those not missing data, and also had a higher AGE level. This might suggest that our results underestimate the true effect of AGE levels due to missing data. It seems, however, clear that the final word is to future research of similar or larger size cohorts to confirm our findings from settings in e.g. other countries. In conclusion, this study indicates that high AGE levels may be a contributing factor as well as a biomarker for lower physical activity and functioning in older adults. Further longitudinal observational and controlled intervention studies with physical activities are necessary to investigate a causal relationship. Supplementary Material Supplementary data is available at The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences online. Funding This study was supported by regular funds of the Research Group Healthy Ageing, Allied Healthcare and Nursing, Hanze University Groningen; the Department of General Practice and Elderly Care Medicine, University Medical Center Groningen; the Frailty in Ageing Research Group and Gerontology Department, Vrije Universiteit Brussels; and ZuidOostZorg, Organisation for Elderly Care, Drachten. Acknowledgments The authors acknowledge and thank Jaron Brinkhuizen for his assistance in organizing the databases. We also kindly thank the participants for their participation in the LifeLines study. Conflict of Interest A.J. Smit is founder and shareholder of Diagnoptics Technologies, the company that develops the AGE Reader. Diagnoptics Technologies had no role in the design of the LifeLines project, or in the analyses of this study, provided no funding, and exerted no restrictions of any sort on publications concerning the AGE Reader. References 1. 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For permissions, please e-mail: journals.permissions@oup.com. 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)

Journal

The Journals of Gerontology Series A: Biomedical Sciences and Medical SciencesOxford University Press

Published: Oct 8, 2018

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