University of Groningen
A physically active lifestyle is related to a lower level of skin autofluorescence in a large
chronic disease population (Lifelines cohort)
Corine van de Zande, Saskia; de Vries, Jeroen Klaas; Akker-Scheek, Inge van den; Zwerver,
Johannes; Smit, Andries Jan
Published in:
Journal of sport and health science
DOI:
10.1016/j.jshs.2020.09.007
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Corine van de Zande, S., de Vries, J. K., Akker-Scheek, I. V. D., Zwerver, J., & Smit, A. J. (2020). A physically active lifestyle is related to a lower level of skin autofluorescence in a large chronic disease population (Lifelines cohort). Journal of sport and health science. https://doi.org/10.1016/j.jshs.2020.09.007
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Original article
A physically active lifestyle is related to a lower level of skin
autofluorescence in a large population with chronic-disease
(LifeLines cohort)
Q1
X X
D1
X XSaskia Corine van de Zande
D2
X X
a,*
,
X XJeroen Klaas de Vries
D3
D4
X X
b,
D5
X XInge van den Akker-Scheek
D6
X X
c,
D7
X XJohannes Zwerver
D8
X X
d,e,
D9
X XAndries Jan Smit
D10
X X
aaDepartment of Internal Medicine, Division of Vascular Medicine, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, the
Netherlands
b
Department of Internal Medicine, Antonius Hospital Sneek, 8601 ZK Sneek, the Netherlands
c
Department of Orthopaedics, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, the Netherlands
d
Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, the Netherlands
e
Sports Valley, Gelderse Vallei Hospital, 6716 RP Ede, the Netherlands Received 11 February 2020; revised 30 June 2020; accepted 30 July 2020
Available online xxx
2095-2546/Ó 2020 Published by Elsevier B.V. on behalf of Shanghai University of Sport. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract
Background: Physical activity (PA) has substantial health benefits and is important in combatting chronic diseases, which have been associated with elevated levels of advanced glycation endproducts (AGEs). AGEs play a role in the ageing process, and an association between PA and AGEs has been reported. We aimed to investigate the relationship between PA and AGE accumulation in a general population and in a popula-tion with chronic diseases.
Methods: This large cross-sectional population study used data from adult participants in the LifeLines project, with participant information drawn from the LifeLines database as well data from patients with diabetes mellitus or renal and/or cardiovascular diseases. Tissue AGE accumulation was assessed non-invasively by skin-autofluorescence (SAF) using an AGE reader. PA was assessed using the short questionnaire to assess health-enhanc-ing physical activity (SQUASH). Multivariate linear regression analyses were adjusted for age, body mass index, sex, and smokhealth-enhanc-ing status.
Results: Data from 63,452 participants (general population n = 59,177, chronic disease n = 4275) were analyzed. The general population was signifi-cantly younger (44§ 12 years, mean § SD) and had significantly lower SAF (1.90 § 0.42 arbitrary units (AU)) compared to the population with chronic disease (age: 56§ 12 years; SAF: 2.27 § 0.51 AU). In the group with chronic disease, more hours of moderate to vigorous physical activities per week were associated with lower SAF (b = 0.002, 95% confidence interval (CI): 0.002 to 0.001). For the general population, there was no association between hours of moderate to vigorous activity and SAF (b = 3.2 £ 105, 95%CI: 0.000 0.000, p = 0.742). However, there was an
asso-ciation in the general population between total hours of PA per week and SAF (b = 4.2 £ 104, 95%CI: 0.000 0.001, p < 0.001), but this association was not found in the chronic disease population (b = 3.2 £ 104, 95%CI:0.001 to 0.000, p = 0.347).
Conclusion: Our study demonstrates that an inverse relationship exists between PA and AGE accumulation in the population with chronic disease. More hours of moderate to vigorous activity is associated with a significantly decreased SAF. More PA is associated with a lower SAF, even after adjusting for the established predictors (age, body mass iindex, smoking status, and sex). Our findings could help to promote health and prolong longevity.
Keywords: Advanced glycation endproducts; Chronic disease population; General population; Physical activity; Skin autofluorescence
1. Introduction
Regular physical activity (PA) has substantial health ben-efits and is a component of chronic-disease prevention and
management.13Conversely, physical inactivity is compara-ble to smoking and obesity4in that it is associated with nega-tive effects on health. The amount of PA needed to convey health benefits ranges from> 15 min/day (or 90 min/week5) to 150 min/week spread out over 1 or 2 days/week.6The cur-rent Dutch Health Council recommendation, based on the international guidelines of the World Health Organization, is *Corresponding author.
E-mail address:s.c.van.de.zande@umcg.nl(S. Corine van de Zande).
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Available online atwww.sciencedirect.com
Journal of Sport and Health Science xxx (2020) xxx-xxx
to be active at a moderate or vigorous intensity level for at least 150 min/week spread out over several days.7
Accumulation of advanced glycation endproducts (AGEs) in long-lived tissues is partially a nonenzymatic, constitutional process, and the level of AGEs increases with age.8AGEs are also rapidly formed and accumulated in the human body dur-ing glycemic and oxidative stress.911Chronic diseases, such as diabetes mellitus (DM), chronic kidney disease (CKD), and cardiovascular diseases (CVDs), have been associated with elevated levels of AGEs8,1216and with higher levels of oxidative stress.1719 Accumulation of AGEs is associated with negative effects on health, as was first shown in relation to DM8,14,20and later extended to CKD.13
Skin autofluorescence (SAF) has been proposed as a non-invasive, simple way to assess tissue accumulation due to AGEs. In patients with DM, pre-existing CVD, or renal dis-ease, as well as in the general population, a raised SAF level is a predictor of disease and mortality.2023 Furthermore, a higher SAF level is associated with cardiovascular events and mortality in patients with peripheral artery disease.24 A positive association between SAF and smoking, body mass index (BMI), and glycated hemoglobin levels has been shown in a multi-disciplinary prospective population-based cohort study (the LifeLines cohort).25However, the effect of PA on SAF was not taken into account, despite the fact that physical inactivity is a risk factor for DM, CKD, and CVDs.4
So far, relatively few studies have analyzed the effect of regular PA on AGE accumulation or SAF and, likewise, few studies have analyzed the association between SAF and life-style-related diseases in a large population. Lower SAF was observed in a small subgroup of 226 healthy persons in a population study of Slovakians who performed physical exercise> 30 min/day more than 3 times a week.26Also, a decline in serum AGE level was shown for a population of healthy, sedentary, non-smoking, middle-aged women (n = 47) after they participated a 12-week lifestyle modifica-tion program that included an exercise component.27 Fur-thermore, an independent association was found between more training years and lower SAF values in healthy ath-letes (n = 182) compared to sedentary controls (n = 34),28 and life-long endurance training was also associated with lower SAF levels in athletes (n = 15) compared to older untrained persons (n = 12).29 A positive association was found between PA and SAF in people with type 1 DM (n = 119).30 However, another cross-sectional study was unable to demonstrate a relationship between PA level and sitting time and SAF in healthy adults (n = 256).31This find-ing was in line with the study of Sanchez et al.,32who also did not find an association between PA and SAF in middle-aged subjects (n = 2646). Because there are discrepancies in the findings of the few previously conducted studies, the association between PA and SAF remains unclear.
The aim of the current study was to investigate the asso-ciation between PA and SAF in a large population, adjusted for age, BMI, and smoking. We hypothesized that there is an inverse relationship between the level of PA and SAF.
2. Methods
LifeLines is a multi-disciplinary, prospective, population-based cohort study examining, in a unique 3-generation design, the health and health-related behaviors of 167,729 persons liv-ing in the north of the Netherlands.33It employs a broad range of investigative procedures in assessing the biomedical, socio-demographic, behavioral, physical, and psychological factors that contribute to the health and disease of the general popula-tion, with a special focus on multi-morbidity and complex genetics.34All LifeLines participants provided written informed consent before participating in the study. The Medical Ethical Review Committee of the University Medical Center Groningen approved the LifeLines study.
2.1. Participants
For the current study, we evaluated the data for all partic-ipants 18 years old, for whom the self-reported short questionnaire to assess health-enhancing physical activity (SQUASH) data on PA, SAF measurements were available, and information about DM, CKD, and CVD was available (n = 63,452). Sex, BMI (calculated as kg/m2), self-reported smoking behavior (yes or no) and packyears were retrieved from the LifeLines database. Height was measured to the nearest 0.5 cm and weight to the nearest 0.5 kg. We divided the cohort into 2 groups, 1 consisting of the general LifeLines population and the other consisting of participants with a chronic disease (we included DM, CKD, and/or CVD). We defined the presence of DM as self-reported and/or based on a fasting blood glucose level of 7.0 mmol/L, and/or a glycated hmoglobin level of 6.5% (48 mmol/L, and/or the use of glu-cose-lowering drugs). The presence of CKD was defined as an estimated glomerular filtration rate< 60 mL/min, as calculated by the Modification of Diet in Renal Disease formula.35 The presence of CVD was defined as a self-reported history of heart attack, stroke, aortic aneurysm, balloon angioplasty, and/or bypass surgery, heart failure, and narrowing in 1 or both carotid arteries. The response “I don’t know” to the question regarding heart failure was considered negative.
2.2. SQUASH questionnaire
In the LifeLines cohort, the SQUASH questionnaire was used to estimate the activity level of the participants. This Dutch questionnaire is considered a reliable and valid ques-tionnaire for measuring the level of PA in an adult popula-tion.36 The questionnaire consists of various domains of being physically active: commuting, work, household activ-ities, and leisure time (including walking, biking, gardening, doing odd jobs, and sports). When filling in the question-naire, participants were asked to keep in mind an average week during the past month. The results of the SQUASH questionnaire were processed as described previously.36For our analysis, the total hours per week of PA was used as the main outcome measure. Furthermore, the total hours of moderate to vigorous physical activity (MVPA) per week were used in the analysis.
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2.3. SAF analysis
SAF was assessed non-invasively by using the AGE Reader (DiagnOptics Technologies BV, Groningen, the Netherlands). A detailed description and reference values for this method have been reported earlier.37,38 In short, a skin surface of approximately 4 cm2is illuminated by the AGE Reader and guarded against surrounding light, with an excitation light source with a wavelength between 300 nm and 420 nm (peak intensity at 370 nm). An internal spectrometer in the range of 300600 nm measures the emission light and reflected excita-tion light from the skin. Measurements were performed by trained staff on the volar site of the left and right forearm, 10 cm below the elbow, at room temperature. To calculate the SAF, the average emitted light intensity per nm (range 420600 nm) is divided by the average excitation light inten-sity per nm (range 300420 nm) and multiplied by 100. SAF is expressed in arbitrary units (AU). An error of approximately 5%, when repeated SAF measurements were taken over 1 day, was found in previous studies.37A mean linear increase of 0.023 AU per year of aging was observed in men and women.38 2.4. Statistical analyses
Patients’ characteristics and outcome measures are shown as mean§ SD, number (%) or median (interquartile range). To assess differences between the general population group and the chronic disease group, an independent t test was per-formed for age and SAF. Ax2test was conducted for sex and smoking behavior, and a Mann-Whitney U test was performed for BMI, total hours of PA per week, and total hours of MVPA per week because of non-normality. A multivariate linear regression analysis was performed to examine the association between PA and SAF, corrected for age, BMI, smoking, and sex. These covariates were selected based on the relation with AGEs, as shown in earlier research.25We did not include waist and hip circumference as covariates because BMI was a stronger predictor for SAF. For statistical analysis, IBM SPSS (version 22.0; IBM, Armonk, NY, USA) was used.
3. Results
A total of 63,452 participants was included for analysis. The general population consisted of 59,177 participants. DM occurred in n = 2013, CKD in n = 696, and CVD in n = 1947 participants, for a total of 4656 diseased participants. More than 1 chronic disease condition could be present in an individ-ual, but participants were included only once in the chronic disease group, resulting in 4275 unique participants in the chronic disease group.Table 1provides the characteristics of the study’s population. The participants with chronic disease were significantly older compared to the general population (56 years old vs. 44 years old, p< 0.001), had higher BMIs (27.9 kg/m2vs. 25.2 kg/m2, p<0.001), and consisted of more men (52.3% vs. 40.3%, p < 0.001). The general population had a significantly lower SAF compared to the chronic disease population (1.90 AU vs. 2.27 AU, p < 0.001). The general population had significantly more total hours PA (48.3 h/week vs. 40.0 h/week, p < 0.001); however, the chronic disease
group had significantly more total hours of MVPA
(10.4 h/week vs. 6.3 h/week, p< 0.001). The chronic disease population had a significantly higher amount of packyears (6.5 packyears vs. 0.40 packyears, p< 0.001). There were no relevant differences in the characteristics between those with and without an available SQUASH questionnaire and SAF measurement.
The results of the linear regression analysis showed that SAF was independently associated with total hours of PA per week (b = 4.2 £ 104, 95%CI: 0.0000.001, p < 0.001) after adjusting for age, sex, BMI, and packyears (Table 2). In the chronic disease group, there was no independent asso-ciation between SAF and total hours of PA per week (b = 3.2 £ 104, 95%CI: 0.001 to 0.000, p = 0.347).
When looking at the MVPA (Table 3), it is clear that there was no independent association between SAF and MVPA hours per week for the general population (b = 3.2 £ 105,
95%CI: 0.0000.000, p = 0.742). However, for the chronic disease group there was an independent association between SAF and MVPA hours per week (b = 0.002, 95%CI: 0.002 Table 1
Characteristics of the study population.
Total General Disease
N 63,452 59,177 4275
Age (year) 44§ 12 44§ 12 56§ 12*
Sex (male, %) 41.1 40.3 52.3*
Body mass index (kg/m2)z 25.4 (23.128.1) 25.2 (2327.9) 27.9 (25.231.0)*
Packyearsz 0.60 (0.009.00) 0.40 (0.008.50) 6.5 (0.0019.50)*
Smoking statusz
Current smoker (packyears) 14.77§ 11.38 14.26§ 11.02 22.72§ 13.80
Former smoker (packyears) 9.81§ 9.98 9.10§ 9.05 16.34§ 14.70
Total PA (h/week) 48.0 (32.359.8) 48.3 (33.360.0) 40.0 (23.054.8)*
MVPA (h/week) 6.5 (2.518.8) 6.3 (2.518.0) 10.4 (3.824.8)*
Skin autofluorescence (AU) 1.92§ 0.44 1.90§ 0.42 2.27§ 0.51*
Note: Data are presented as mean§ SD, or median (interquartile range), or percentage (%).
z Missing values for body mass index (n = 12), smoking status (n = 2212), packyears (n = 2138).
* p< 0.001 for differences between general and disease groups.
Abbreviations: AU = arbitrary units; MVPA= moderate to vigorous physical activity; PA = physical activity.
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to0.001). Also, sex showed no independent association with SAF (p = 0.192).
4. Discussion
This is the first study to address the possible relationship between PA and SAF as an accepted marker of AGE accumu-lation in a very large and representative popuaccumu-lation cohort. This study shows an association between MVPA and SAF, after adjusting for the established predictors (age, BMI, pack-years, and sex) for the chronic disease group. A lower SAF is seen when people perform more hours of moderate to vigorous activities per week. This study also showed an association between total hours of PA per week and SAF for the general population; however, the contribution of total hours of PA was small (b = 4.2 £ 104). Because the mean SAF increases by
0.02 AU per year of aging,38an increase of 4.2£ 104cannot be considered clinically relevant.
Our findings in the general population confirm the earlier results of 2 smaller cross-sectional studies in adults (n = 256) and in people who are at risk for a CVD (n = 2636). Neither of these 2 studies found an association between SAF and PA.31,32 In these 2 studies, PA was assessed by a questionnaire and measured in metabolic equivalent of task-minutes per week, which is in line with the MVPA hours per week used in our
study. Another study of an elderly population (n = 4188) also did not find an association between PA energy expenditure and skin AGEs.39
Other studies, however, have found an association between PA and SAF. A smaller cross-sectional study of young healthy non-smokers observed a lower SAF in subjects who performed physical exercise for more than 30 min/day more than 3 times a week compared to people who exercised 12 days a week or not at all.26 Furthermore, there was a decline in serum AGEs levels among healthy sedentary non-smoking middle-aged women who participated in a 12-week lifestyle-modification program that had an exercise program as a component.27
These contradictory findings might be caused by the differ-ent measures of PA used in the studies. Our study used total hours of PA per week and total hours of MVPA per week. These variables do not provide information about the division of the active hours over a week. This might be essential because studies have shown that the so-called weekend war-rior, who is physically active for only 1 or 2 days a week, already has a reduced risk of disease and mortality.6,40 The SQUASH questionnaire we used contains a question about how many days per week a person is active for at least 30 min of moderate intensity; however, this question is harder to answer when individuals are asked to estimate accurately the number of hours spent per activity. More research is needed to Table 2
Multivariate linear regression with SAF as the dependent variable and total hours of PA as a covariate for PA.
General Group Chronic Disease Group
b SE 95%CI b SE 95%CI
Constant 1.002 0.011 0.9811.023 1.043 0.057 0.9301.155
Age 0.018* 0.000 0.0180.018 0.017* 0.001 0.0160.019
Sex
Male REF REF
Female 0.039* 0.003 0.044 to 0.033 0.007 0.014 0.034 to 0.020
BMI 0.003* 0.000 0.0020.003 0.007* 0.001 0.0040.009
Packyears 0.009* 0.000 0.0090.010 0.007* 0.000 0.0060.008
Total PA 4.2£ 104* 0.000 0.0000.001 3.2 £ 104 0.000 0.001 to 0.000
Notes: For the general population: adjusted R square = 0.358, p< 0.001. For the chronic disease population: adjusted R square = 0.255, p <0.001. * p< 0.001 a significant contribution to the regression model.
Abbreviations: CI = confidence interval; PA = physical activity; REF = reference; SAF = skin autofluorescence.
Table 3
Multivariate linear regression with SAF as the dependent variable and MVPA as a covariate for PA.
General Group Chronic Disease Group
b SE 95% CI b SE 95% CI
Constant 1.025 0.010 1.0051.044 1.030 0.051 0.9301.130
Age 0.018* 0.000 0.0180.018 0.018* 0.001 0.0170.019
Sex
Male REF REF
Female 0.039* 0.003 0.045 to 0.034 0.019 0.014 0.047 to 0.009
BMI 0.003* 0.000 0.0020.003 0.007* 0.001 0.0040.009
Packyears 0.009* 0.000 0.0090.010 0.007* 0.000 0.0060.008
MVPA 3.2£ 105 0.000 0.0000.000 0.002* 0.000 0.002 to 0.001
Notes: For the general population: adjusted R square = 0.357, p< 0.001. For the chronic disease population: adjusted R square = 0.257, p < 0.001. * p<0 .001 a significant contribution to the regression model.
Abbreviations: BMI = body mass index; CI = confidence interval; MVPA= moderate to vigorous physical activity; PA = physical activity; REF = reference; SAF = skin autofluorescence.
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provide detailed information about the hours of activities per day that individuals participate in to determine whether the division of active hours influences SAF.
The results of our study for the chronic disease group extend the findings of previous studies over a wider age range and across multiple chronic diseases (not only in type 1 DM, but also type 2 DM, renal disease, and CVD). One study found lower SAF in a small group (n = 119) of people with type 1 DM who performed more PA.30 In another study involving elderly participants ( 65 years old), higher SAF was found in those with lower PA.41In that study, the high-SAF group ( 2.56 AU) had fewer active days per week (4.53 days) of activ-ity compared to the low-SAF group ( 2.19 AU; 4.94 days). Accumulation of AGEs as measured by SAF has been shown to be an independent predictor of mortality and/or cardiovas-cular morbidity in persons with DM, renal failure, or pre-exist-ing CVD13,21,22,24,42 and also in the general population.23 Thus, reduction of AGE accumulation through PA, even if the effect on SAF is small, might be beneficial and contribute to a better health status. More research is needed to investigate whether reduction of AGE accumulation through PA could (partially) explain the demonstrated effects of exercise as med-icine in people with chronic diseases.43
5. Strengths and limitations
LifeLines is a large cohort, consisting of a general popula-tion as well as diseased people, from all age groups. The data collection is robust and extensive. PA was measured with the validated SQUASH questionnaire. To our knowledge, Life-Lines is the largest cohort in which AGE accumulation by SAF has been assessed. Therefore, this is an excellent cohort to test associations between AGE accumulation and health behaviors and the presence of chronic diseases. The LifeLines dataset contains reliable data on numerous confounders, of which the most relevant and well-known have been used in our study.
In our study, the general population consists of people with-out DM, CKD, or CVD. However, this does not mean that par-ticipants do not have any other diseases. DM, CKD, and CVD have been associated with elevated levels of AGEs,8,12,13but we cannot rule out the influence of other diseases. Another limitation in our study is that we cannot exclude the possibility that PA may be related to differences in dietary patterns and, thus, be a factor influencing SAF.
Self-reported PA itself is prone to the provision of socially acceptable answers and, therefore, may overestimate an indi-vidual’s level of activity. This possibility, however, would have led to an underestimation of the true effect of PA on SAF. Because the SQUASH questionnaire is short and easy to fill in, it is the most cost-effective and feasible way to measure PA in a large population and is reliable and valid,36we con-sider the results from the SQUASH questionnaire to be a good representation of the PA patterns among participants in the LifeLines project.
Our cross-sectional design has obvious limitations. It allows associations only between SAF and PA to be identified, as was
the case in previously mentioned literature.26,30,31,41The deca-des-long lag time between (un)healthy behaviors and AGE accumulation renders interventional research challenging, if not impossible. Because SAF measures the AGE accumulation in the skin from the past 15 years,44 previous behaviors that are not reflected in the LifeLines data could have influenced AGE accumulation significantly. The level of PA that was used for our model is a snapshot of the current activity level of participants, which has not necessarily been the same for the previous 15 years. Future research should incorporate the his-tory of PA and sitting time in order to further investigate the effect of PA on the accumulation of AGEs.
6. Conclusion
This study demonstrates, in a very large and representative population cohort, an inverse relation between PA and AGE accumulation in a population with chronic disease. More PA is associated with a lower SAF, even after adjusting for the estab-lished predictors (age, BMI, smoking status, and sex) and could help to promote health and prolong longevity. Our study shows that more hours of moderate to vigorous activity is asso-ciated with a significantly decreased SAF for the group with chronic disease.
Authors’ contributions
SCZ contributed to the study design with AJS and JZ, ana-lyzed the data and drafted the manuscript with JKV, and attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted; IAS helped in analyzing the data. All authors contributed to the interpretation of the results and revised the manuscript for important intellec-tual content. All authors have read and approved the final ver-sion of the manuscript and agree with the order of presentation of the authors.
Competing interests
AJS is founder and shareholder of Diagnoptics Technolo-gies, the company that develops the AGE Reader. Diagnoptics Technologies had no role in the design of the LifeLines project or in the analyses conducted for this study. It provided no funding and exerted no restrictions of any sort in the publica-tion of informapublica-tion concerning the AGE Reader.
Acknowledgments
The results of our study are presented clearly, honestly and without fabrication, falsification, or inappropriate data manip-ulation and do not constitute endorsement by the American College of Sports Medicine. This study was supported by the Samenwerkingsverband Noord-Nederland and the province of Groningen, the Netherlands (Innovative Action Program Groningen-4). The LifeLines biobank initiative has been made possible by subsidies from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen, the University Groningen, and the Northern Provinces of the Netherlands.
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