Motor function, paratonia and glycation cross-linked in older people
Drenth, Hans
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Drenth, H. (2018). Motor function, paratonia and glycation cross-linked in older people: Motor function decline and paratonia and their relation with Advanced Glycation End-products. University of Groningen.
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Chapter3
Advanced Glycation End-Products are
Associated with Physical Activity and
Physical Functioning in the Older Population
Hans Drenth, Sytse U. Zuidema, Wim P. Krijnen,
Ivan Bautmans, Andries J. Smit, Cees
van der Schans, Hans Hobbelen
Journals of Gerontology;
Series A Biological Sciences and
Medical Sciences 2018, Apr 28.
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 was used. Linear regression analyses were utilized to study associations between skin autofluorescence/AGE-levels (AGE reader), the number of physically active days (SQUASH), and physical functioning (RAND-36), respectively. A logistic regression analysis was used to study 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 (OR = 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
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INTRODUCTION
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 (ADL) 1,2. With aging, most human physiologic 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 hyperglycaemic 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 non-enzymatic condensation of a reducing sugar with proteins and is accelerated during not only glycaemic 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 while 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 AGEs induced tissue damage negatively affects motor function (e.g., muscle function, walking impairment) and may influence the amount of physical activity. While improvements of glycaemic 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), 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 complete 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.
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Outcome MeasuresPhysical activity
Data on physical activity were extracted from the LifeLines database, which was assessed with the Short Questionnaire to Asses 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 which 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 AGEs 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 cardiovascular 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 chronical obstructive pulmonary disease. If the participants had stopped smoking, were currently smoking, or 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 to 30 (with higher scores representing better cognitive function). Glucose levels and body mass index (BMI) were determined as described in the LifeLines protocol20.
Statistical Analysis
Study population characteristics are categorized in 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 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, pulmonary and kidney disease, cancer, MMSE, BMI, glucose level, smoking status, and alcohol consumption 5,6,9,10,13,19,24,26–29 , as well 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 was 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 <0.05 was considered statistically significant in two-sided tests.
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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 Appendix
Table 1. Characteristics of the participants according to the tertiles of AGE levels
Tertiles of AGE levels
n Low
SAF ≤ 2.19 AU SAF 2.19 > < 2.56 AUMiddle SAF ≥ 2.56 AUHigh P Value 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 1197 (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) 1076 (57.4) 1205 (64.2) <.001 Alcohol, ≥ 1 day a week, n (%) 3,041 1092 (58.3) 984 (52.5) 965 (51.4) .003 MMSE, score 0-30 a 5,585 27.80 (2.32) 27.62 (2.42) 27.41 (2.60) <.001
SQUASH
Physical active days, score 0-7 a 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 0-100 a 4,641 84.70 (16.49) 83.34 (17.93) 80.73 (20.27) <.001
AGE = advanced glycation end-product; AU = arbitrary units i.e., 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.
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.
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 to 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-AGEs 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).
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 that 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, 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-AGEs 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).
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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 95% CI P value Odds Ratio 95% CI P value
Beta 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 (years) -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*AGEs 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 (years) -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 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*AGEs level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGEs 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%.
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-AGEs 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
Table 3. Association between AGE levels and physical functioning (RAND-36)
Physical functioning (RAND-36)
Unstandardized
Beta lower limit upper limit 95% CI P value
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 .800 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*AGEs 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 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*AGEs level interaction. Model 2: Obtained after removing statistically insignificant variables (retaining AGEs level and biological variables; age and gender). Missing response variables; SQUASH: 25%, RAND-36: 17%, missing predictors; kidney disease: 18%, alcohol:18%,
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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- and 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).
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 x person’s age + 0.83 (R2 = 60%) 32. For those over 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 AD, 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 in 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 in order 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, cardio-vascular, 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 this cross-sectional analysis since 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 ADL 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(CNS) may hamper physical functioning. AGEs have been shown to be associated with less grey 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 inter-relationship between the motor networks within the CNS as well as with the musculoskeletal structures. Future research is required to determine the contribution of AGEs accumulation on the CNS with a direct relationship on the decline in physical functioning.
Our analyses show a statistical significant gender effect on physical activity and physical functioning, which is in line with the scientific literature on this topic 31,40. Although is it 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
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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 cognitive 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 based upon pair-wise as well as list-wise 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 AGEs 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.
Appendix. Characteristics of the participants according to non-missing vs. missing data on physical
activity (SQUASH) and functioning (RAND-36), respectively.
SQUASH RAND-36
non-missing missing P Value non-missing missing P Value
Participants, n 4,202 1,422 4,641 983
AGE levels (SAF) 2.39 (0.47) 2.47 (0.52) <0.001 2.40 (0.47) 2.49 (0.53) <0.001 Female, n (%) 2,306 (54.9) 748 (52.6) 0.136 2,493 (53.7) 561 (57.1) 0.056 Age, years 69.4 (4.2) 71.0 (5.1) <0.001 69.4 (4.2) 71.5 (5.3) <0.001 Medical history (yes, %)
Diabetes, n (%) 346 (8.2) 152 (10.7) 0.003 388 (8.4) 110 (11.2) 0.002 CV disease, n (%) 2,102 (50.0) 343 (24.1) <0.001 2,328 (50.2) 117 (11.9) <0.001 Kidney disease, n (%) 37 (0.9) 11 (0.8) 0.001 48 (1.0) Pulmonary disease, n (%) 465 (11.1) 188 (13.5) 0.015 518 (11.2) 135 (13.7) 0.008 Cancer, n (%) 613 (14.6) 202 (14.2) 0.901 680 (14.7) 135 (13.7) 0.678 Glucose, mmol/L 5.41 (1.06) 5.55 (1.17) <0.001 5.42 (1.07) 5.71 (1.17) <0.001 BMI 27.06 (3.84) 27.66 (3.94) <0.001 27.11 (3.85) 27.71 (3.94) <0.001 Smoking, n (%) 2,507 (59.7) 720 (50.6) <0.001 2,757 (59.4) 470 (47.8) <0.001 Alcohol, ≥ 1 day a week, n (%) 2,764 (65.8) 277 (19.5) 0.990 3,039 (65.6) 2 (0.2) 0.308 MMSE, score 0-30 28.49 (1.33) 24.95 (3.06) <0.001 28.47 (1.34) 23.43 (2.31) <0.001 AGE = advanced glycation end-product; AU = arbitrary units i.e., the output units of the AGE reader; BMI = Body Mass Index; CV = cardiovascular; MMSE = Mini Mental State Examination; SAF = skin autofluorescence (AGE reader)
3
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