Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention.

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.

Association of fatal myocardial infarction with past level of physical activity: a pooled analysis of cohort studies.

AIMS: To assess the association between past level of physical activity (PA) and risk for death during the acute phase of myocardial infarction (MI) in a pooled analysis of cohort studies. METHODS AND RESULTS: European cohorts including participants with a baseline assessment of PA, conventional cardiovascular (CV) risk factors, and available follow-up on MI and death were eligible. Patients with an incident MI were included. Leisure-time PA was grouped as sedentary (<7 MET-hours), low (7-16 MET-hours), moderate (16.1-32 MET-hours), or high (>32 MET-hours) based on calculated net weekly energy expenditure. The main outcome measures were instant and 28-day case fatality of MI. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using multivariate random-effects models. Adjustments for age, sex, CV risk factors, alcohol consumption, and socioeconomic status were made. From 10 cohorts including a total of 1 495 254 participants, 28 140 patients with an incident MI comprised the study population. A total of 4976 (17.7%) died within 28 days-of these 3101 (62.3%) were classified as instant fatal MI. Compared with sedentary individuals, those with a higher level of PA had lower adjusted odds of instant fatal MI: low PA [OR, 0.79 (95% CI, 0.60-1.04)], moderate PA [0.67 (0.51-0.89)], and high PA [0.55 (0.40-0.76)]. Similar results were found for 28-day fatal MI: low PA [0.85 (0.71-1.03)], moderate PA [0.64 (0.51-0.80)], and high PA [0.72 (0.51-1.00)]. A low-to-moderate degree of heterogeneity was detected in the analysis of instant fatal MI (I2 = 47.3%), but not in that of 28-day fatal MI (I2 = 0.0%). CONCLUSION: A moderate-to-high level of PA was associated with a lower risk of instant and 28-day death in relation to a MI.

Associations of Activity and Sleep With Quality of Life: A Compositional Data Analysis.

INTRODUCTION: Associations between time spent on physical activity, sedentary behavior, and sleep and quality of life are usually studied without considering that their combined time is fixed. This study investigates the reallocation of time spent on physical activity, sedentary behavior, and sleep during the 24-hour day and their associations with quality of life. METHODS: Data from the 2011-2016 Rotterdam Study were used to perform this cross-sectional analysis among 1,934 participants aged 51-94 years. Time spent in activity levels (sedentary, light-intensity physical activity, moderate-to-vigorous physical activity, and sleep) were objectively measured with a wrist-worn accelerometer combined with a sleep diary. Quality of life was measured using the EuroQoL 5D-3L questionnaire. The compositional isotemporal substitution method was used in 2018 to examine the association between the distribution of time spent in different activity behaviors and quality of life. RESULTS: Reallocation of 30 minutes from sedentary behavior, light-intensity physical activity, or sleep to moderate-to-vigorous physical activity was associated with a higher quality of life, whereas reallocation from moderate-to-vigorous physical activity to sedentary behavior, light-intensity physical activity, or sleep was associated with lower quality of life. To illustrate this, a reallocation of 30 minutes from sedentary behavior to moderate-to-vigorous physical activity was associated with a 3% (95% CI=2, 4) higher quality of life score. By contrast, a reallocation of 30 minutes from moderate-to-vigorous physical activity to sedentary behavior was associated with a 4% (95% CI=2, 6) lower quality of life score. CONCLUSIONS: Moderate-to-vigorous physical activity is important with regard to the quality of life of middle-aged and elderly individuals. The benefits of preventing less time spent in moderate-to-vigorous physical activity were greater than the benefits of more time spent in moderate-to-vigorous physical activity. These results could shift the attention to interventions focused on preventing reductions in moderate-to-vigorous physical activity levels. Further longitudinal studies are needed to confirm these findings and explore causality.

Physical Activity as Moderator of the Association Between APOE and Cognitive Decline in Older Adults: Results from Three Longitudinal Cohort Studies.

BACKGROUND: Previous studies have suggested that the association between APOE ɛ 4 and dementia is moderated by physical activity (PA), but the results remain inconclusive and longitudinal data on cognitive decline are missing. In this study, we examine whether there is a gene-environment interaction between APOE and PA on cognitive decline in older adults using 9-year follow-up data of three cohort studies. METHODS: We followed 7,176 participants from three longitudinal cohort studies: Longitudinal Aging Study Amsterdam (LASA), InCHIANTI, and Rotterdam Study for 9 years. PA was assessed with self-reported questionnaires and was categorized in low, moderate, and high PA. Cognitive function was assessed with the Mini-Mental State Examination (MMSE) and cognitive decline was defined as a decrease of three points or more on the MMSE during 3 years follow-up. We fitted logistic regression models using generalized estimating equations adjusting for age, sex, education, depressive symptoms, and number of chronic disease. Interaction between APOE and PA was tested on multiplicative and additive scale. RESULTS: Cohorts were similar in most aspects but InCHIANTI participants were on average older and had lower education. APOE ɛ 4 carriers had higher odds of cognitive decline (odds ratio [OR] = 1.46, 95% confidence interval [CI]: 1.29-1.64) while PA was not significantly associated with cognitive decline overall (moderate PA: OR = 0.87, 0.67-1.13; high PA: OR = 0.71, 0.36-1.40). There was no evidence for an interaction effect between PA and APOE ɛ 4 in cognitive decline in older adults (APOE × moderate PA: p = .83; APOE × high PA: p = .90). CONCLUSIONS: Previous claims of a gene-environment interaction between APOE ɛ 4 and PA in cognitive decline are not supported by our results.

Objectively measured sleep and body mass index: a prospective bidirectional study in middle-aged and older adults.

BACKGROUND: In recent years, short sleep has been increasingly recognized as a risk factor for obesity. However, current evidence has so far been limited to cross-sectional studies or longitudinal studies using self-reported sleep. Therefore, we explored the directionality of the association between objectively measured sleep and body mass index (BMI). METHODS: The study consists of 1031 participants from the general population (52% women, 45-91 years at baseline). Sleep, BMI and waist circumference (WC) were measured twice across a follow-up of six years. BMI and WC were measured at the research center. Total sleep time (TST, hrs), sleep onset latency (SOL, min), sleep efficiency (SE, %) and wake after sleep onset (WASO, min) were estimated by a wrist-worn actigraph. In addition, cross-sectional and longitudinal associations in both directions were explored. RESULTS: An hour shorter TST was cross-sectionally associated with approximately 0.5 kg/m2 higher BMI. Longitudinally, longer TST and higher SE were associated with lower BMI (ßTST = -0.75, 95% CI: -1.08, -0.42; ßSE = -0.04, 95% CI: -0.08, -0.01). Conversely, one kg/m2 higher BMI was prospectively associated with 0.02 h shorter TST (95% CI: -0.03, -0.01), and this association was more pronounced over time. Results from analyses with WC were in line with those of BMI. CONCLUSIONS: This is the first study to explore bidirectionality in the association between objectively measured sleep and BMI in a large population of middle-aged and older adults. Indices of poor sleep were associated with higher and less stable BMI across time. Conversely, a high BMI was associated with a decrease in sleep duration. This confirms that the relation between sleep and body size is bidirectional, and changes in either sleep or BMI are likely to co-occur with changes in health through multiple pathways.

Physical activity types and atrial fibrillation risk in the middle-aged and elderly: The Rotterdam Study.

Background The association between physical activity and atrial fibrillation remains controversial. Physical activity has been associated with a higher and lower atrial fibrillation risk. These inconsistent results might be related to the type of physical activity. We aimed to investigate the association of total and types of physical activity, including walking, cycling, domestic work, gardening and sports, with atrial fibrillation. Design Prospective cohort study. Methods Our study was performed in the Rotterdam Study, a prospective population-based cohort. We included 7018 participants aged 55 years and older with information on physical activity between 1997-2001. Cox proportional hazards models were used to examine the association of physical activity with atrial fibrillation risk. Models were adjusted for biological and behavioural risk factors and the remaining physical activity types. Physical activity was categorised in tertiles and the low group was used as reference. Results During 16.8 years of follow-up (median: 12.3 years, interquartile range: 8.7-15.9 years), 800 atrial fibrillation events occurred (11.4% of the study population). We observed no association between total physical activity and atrial fibrillation risk in any model. After adjustment for confounders, the hazard ratio and 95% confidence interval for the high physical activity category compared to the low physical activity category was: 0.71 (0.80-1.14) for total physical activity. We did not observe a significant association between any of the physical activity types with atrial fibrillation risk. Conclusion Our results suggest that physical activity is not associated with higher or lower risk of atrial fibrillation in older adults. Neither total physical activity nor any of the included physical activity types was associated with atrial fibrillation risk.

Physical activity and cause-specific mortality: the Rotterdam Study.

Background: Physical activity (PA) is associated with lower risk for all-cause mortality. However, in elderly people, it remains unknown which types of PA are associated with mortality and whether the association between PA and mortality differs by cause of death. Methods: We assessed the association of total PA, walking, cycling, domestic work, sports and gardening with all-cause mortality, and the association of total PA with cause-specific mortality, using Cox proportional hazard models among 7225 older adults (mean age: 70 years) from the prospective population-based Rotterdam Study. Deaths were classified as due to cardiovascular diseases (CVDs), cancer, infections, external causes, dementia, chronic lung diseases or other causes. Activities were categorized into tertiles (lowest tertile as reference). To account for the possibility of reverse causation, we excluded the first 5 and 10 years of follow-up. Results: Over a median of 13.1 years of follow-up (interquartile range: 8.4-14.6 years), 3261 participants died. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) associated with high total PA compared with low were 0.69 (0.63, 0.75), 0.69 (0.58, 0.81), 0.44 (0.27, 0.71), 0.47 (0.32, 0.71) and 0.56 (0.46, 0.69) for mortality from all causes, CVDs, chronic lung diseases, infections and other causes, respectively. With longer exclusion times, the strength of these associations was attenuated. All PA types were associated with lower all-cause mortality risk. Conclusions: Engagement in higher PA levels was associated with lower risk of mortality from CVDs, chronic lung diseases, infections and other causes. Participating in any PA might reduce mortality risk in older adults.

Seasonality of physical activity, sedentary behavior, and sleep in a middle-aged and elderly population: The Rotterdam study.

INTRODUCTION: Physical activity (PA) and sedentary behavior (SB) have seasonal patterns. It remains unclear how these patterns are associated with sleep, meteorological factors, and health. METHODS: Activity levels were continuously measured with an accelerometer for seven days between July 2011 and May 2016, among middle-aged (50-64 years), young-elderly (65-74 years) and old-elderly (≥75 years) participants of a population-based Dutch cohort study (n = 1116). Meteorological factors (ambient temperature, wind speed, sunlight hours, precipitation, and minimum visibility) were locally recorded. We first examined the seasonality of PA, SB, and nighttime sleep, stratified by age group. Second, we examined the influence of meteorological factors. Third, we modeled the potential seasonality of the all-cause mortality risk due to the seasonality of PA and SB, by using previously published relative risks. RESULTS: Levels of light and moderate-to-vigorous PA were higher in summer than in winter among middle-aged (seasonal variation = 18.1 and 14.8 min/day) and young-elderly adults (12.8 and 8.6 min/day). The pattern was explained by ambient temperature and sunlight hours. Nighttime sleep was 31.8 min/day longer in winter among middle-aged adults. SB did not show a seasonal pattern. No seasonality in activity levels was observed among old-elderly adults. The all-cause mortality risk may be higher in winter than in summer due to the accumulation of low levels of moderate to vigorous PA and high levels of SB. CONCLUSION: PA has a larger degree of seasonality than SB and nighttime sleep among middle-aged and young-elderly adults. SB appears strongly ingrained in daily routine. Recommending the interruption of SB with light PA might be a good starting point for public health institutions.

Physical activity derived from questionnaires and wrist-worn accelerometers: comparability and the role of demographic, lifestyle, and health factors among a population-based sample of older adults.

BACKGROUND: Agreement between questionnaires and accelerometers to measure physical activity (PA) differs between studies and might be related to demographic, lifestyle, and health characteristics, including disability and depressive symptoms. METHODS: We included 1,410 individuals aged 51-94 years from the population-based Rotterdam Study. Participants completed the LASA Physical Activity Questionnaire and wore a wrist-worn accelerometer on the nondominant wrist for 1 week thereafter. We compared the Spearman correlation and disagreement (level and direction) for total PA across levels of demographic, lifestyle, and health variables. The level of disagreement was defined as the absolute difference between questionnaire- and accelerometer-derived PA, whereas the direction of disagreement was defined as questionnaire PA minus accelerometer PA. We used linear regression analyses with the level and direction of disagreement as outcome, including all demographic, lifestyle, and health variables in the model. RESULTS: We observed a Spearman correlation of 0.30 between questionnaire- and accelerometer-derived PA in the total population. The level of disagreement (ie, absolute difference) was 941.9 (standard deviation [SD] 747.0) minutes/week, and the PA reported by questionnaire was on average 529.4 (SD 1,079.5) minutes/week lower than PA obtained by the accelerometer. The level of disagreement decreased with higher educational levels. Additionally, participants with obesity, higher disability scores, and more depressive symptoms underestimated their self-reported PA more than their healthier counterparts. CONCLUSION: We observed large differences in PA time derived from the LASA Physical Activity Questionnaire and the wrist-worn accelerometer. Differences between the methods were related to body-mass index, level of disability, and presence of depressive symptoms. Future studies using questionnaires and/or accelerometers should account for these differences.

Levels of ambient air pollution according to mode of transport: a systematic review.

BACKGROUND: Controversy exists about the differences in air pollution exposure and inhalation dose between mode of transport. We aimed to review air pollution exposure and inhaled dose according to mode of transport and pollutant and their effect in terms of years of life expectancy (YLE). METHODS: In this systematic review, we searched ten online databases from inception to April 13, 2016, without language or temporal restrictions, for cohort, cross-sectional, and experimental studies that compared exposure to carbon monoxide, black carbon, nitrogen dioxide, and fine and coarse particles in active commuters (pedestrian or cyclist) and commuters using motorised transport (car, motorcycle, bus, or massive motorised transport [MMT-ie, train, subway, or metro]). We excluded studies that measured air pollution exposure exclusively with biomarkers or on the basis of simulated data, reviews, comments, consensuses, editorials, guidelines, in-vitro studies, meta-analyses, ecological studies, and protocols. We extracted average exposure and commuting time per mode of transport and pollutant to calculate inhaled doses. We calculated exposure and inhaled dose ratios using active commuters as the reference and summarised them with medians and IQRs. We also calculated differences in YLE due to fine particle inhaled dose and physical activity. FINDINGS: We identified 4037 studies, of which 39 were included in the systematic review. Overall, car commuters had higher exposure to all pollutants than did active commuters in 30 (71%) of 42 comparisons (median ratio 1·22 [IQR 0·90-1·76]), followed by those who commuted by bus in 57 (52%) of 109 (1·0 [0·79-1·41]), by motorcycle in 16 (50%) of 32 (0·99 [0·86-1·38]), by a car with controlled ventilation settings in 39 (45%) of 86 (0·95 [0·66-1·54]), and by MMT in 21 (38%) of 55 (0·67 [0·49-1·13]). Overall, active commuters had higher inhalation doses than did commuters using motorised transport (median ratio car with controlled ventilation settings 0·16 [0·10-0·28]; car 0·22 [0·15-0·30]; motorcycle 0·38 [0·26-0·78]; MMT 0·49 [0·34-0·81]; bus 0·72 [IQR 0·50-0·99]). Commuters using motorised transport lost up to 1 year in YLE more than did cyclists. INTERPRETATION: Proximity to traffic and high air interchange increased the exposure to air pollution of commuters using motorised transport. Larger inhalation rates and commuting time increased inhaled dose among active commuters. Benefits of active commuting from physical activity are larger than the risk from an increased inhaled dose of fine particles. FUNDING: Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), National Health and Medical Research Council, Nestlé Nutrition (Nestec), Metagenics, and AXA.

Objective Measures of Activity in the Elderly: Distribution and Associations With Demographic and Health Factors.

BACKGROUND: Little is known about the distribution of activity over the full 24-hour spectrum in late old age and its association with demographic and health factors. Therefore, we aimed to evaluate the distribution of physical activity (PA), sedentary behavior, and sleep, and associated factors in the elderly population. METHODS: Our study included 1210 participants (51.9% women) aged 70-94 years [mean age 77.5 years, standard deviation (SD) 5.0] from the population-based Rotterdam Study. Participants wore a triaxial accelerometer (GENEActiv) around the wrist for 7 days between July 2014 and June 2016. We examined if PA, sedentary behavior, and sleep differed by age, sex, body mass index (BMI), smoking status, alcohol consumption, education, season, functional capacity, marital status, presence of chronic disease, and use of sleep medication. RESULTS: Mean total PA, expressed in milli-gravity (mg) units, was slightly higher for women (20.3, SD 5.6) than for men (19.3, SD 5.2, P < .01). Mean (SD) daily duration spent in sedentary behavior and light and moderate-to-vigorous PA was 13.3 (1.5) h/d, 147.5 (31.5) min/d, and 75.0 (25.5) min/d, respectively, among women; and 13.8 (1.6) h/d, 140.5 (31.1) min/d, and 71.5 (24.5) min/d, respectively, among men. Women spent on average 6.7 (SD 1.1) h/d sleeping and men 6.6 (1.4) h/d. Across increasing categories of age and BMI and in participants with chronic disease and disability, time spent in light and moderate-to-vigorous PA was decreased. Higher age and BMI were associated with more sedentary time. In addition, obese men spent slightly more time sleeping than their normal weight counterparts and women spent slightly less time sleeping in the summer than in spring. CONCLUSIONS: PA and sedentary behavior in the elderly differed by sex, age, BMI, prevalence of chronic disease, and disability, whereas there were no clear patterns for sleep. On average, our participants spent up to 79.5% of their time awake being sedentary and 7%-8% in moderate-to-vigorous PA. Replacing sedentary behavior with light PA would be a good starting point for those with the lowest level of PA. Older adults, those with high BMI and worse health could benefit from targeted interventions to increase PA.

Impact of physical activity on the association of overweight and obesity with cardiovascular disease: The Rotterdam Study.

Background Being overweight or obese is associated with an increased risk of cardiovascular disease (CVD). Physical activity might reduce the risk associated with overweight and obesity. We examined the association between overweight and obesity and CVD risk as a function of physical activity levels in a middle-aged and elderly population. Design The study was a prospective cohort study. Methods The study included 5344 participants aged 55 years or older from the population-based Rotterdam Study. Participants were classified as having high or low physical activity based on the median of the population. Normal weight (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2) and obese participants (≥30 kg/m2) were categorized as having high or low physical activity to form six categories. We assessed the association of the six categories with CVD risk using Cox proportional hazard models adjusted for confounders. High physical activity and normal weight was used as the reference group. Results During 15 years of follow-up (median 10.3 years, interquartile range 8.2-11.7 years), 866 (16.2%) participants experienced a CVD event. Overweight and obese participants with low physical activity had a higher CVD risk than normal weight participants with high physical activity. The HRs and 95% confidence intervals (CIs) were 1.33 (1.07-1.66) and 1.35 (1.04-1.75), respectively. Overweight and obese participants with high physical activity did not show a higher CVD risk (HRs (95%CIs) 1.03 (0.82-1.29) and 1.12 (0.83-1.52), respectively). Conclusions Our findings suggest that the beneficial impact of physical activity on CVD might outweigh the negative impact of body mass index among middle-aged and elderly people. This emphasizes the importance of physical activity for everyone across all body mass index strata, while highlighting the risk associated with inactivity even among normal weight people.

Physical activity types and life expectancy with and without cardiovascular disease: the Rotterdam Study.

Background: We aimed to determine the contribution of specific physical activity (PA) types (i.e. walking, cycling, domestic work, sports and gardening) on total life expectancy (LE) and LE with and without cardiovascular disease (CVD). Methods: We constructed multistate life tables to calculate the effects of total PA and PA types on LE, among individuals older than 55 years from the Rotterdam Study. For the life table calculations, we used sex-specific prevalences, incident rates and hazard ratios for three transitions (healthy-to-CVD, healthy-to-death and CVD-to-death) by levels of PA and adjusted for confounders. Results: High total PA was associated with gains in total and CVD-free LE. High cycling contributed to higher total LE in men (3.7 years) and women (2.1 years) and higher LE without CVD in men (3.1 years) and women (2.4 years). Total and CVD-free LE were increased by high domestic work in women (2.6 and 2.4 years, respectively) and high gardening in men (2.7 and 2.0 years, respectively). Conclusions: Higher PA levels are associated with increased LE and more years lived without CVD. Of the different PA types, cycling provided high effects in both men and women. Cycling could be more strongly encouraged in activity guidelines to maximize the population benefits of PA.

Sedentary time assessed by actigraphy and mortality: The Rotterdam Study.

Research suggests that sedentary behavior is a risk factor for mortality. However, most studies rely on questionnaires, which are prone to reporting error. We examined the association between sedentary time assessed by actigraphy and mortality among 1839 participants, aged 45-98years, from the prospective population-based Rotterdam Study, enrolled between 2004 and 2007. Participants wore an actigraph around the wrist for seven days. Sedentary time was evaluated continuously, per 1h/day increase, and categorically in three groups (<8, 8-11, ≥11h/day). The lowest category was used as reference. Mortality risks were examined using Cox proportional hazard models, adjusted for confounders and biological risk factors. We examined the association between sedentary behavior and mortality over and beyond other activity measures (including physical activity (PA) and activities of daily living (ADL)) in a final model. During 11years of follow-up (median: 7.5years, interquartile range: 6.6-8.3years), 212 participants (11.5%) died. In the multivariable model, the hazard ratio (HR) and 95% confidence interval (95% CI) per 1 more hour/day sedentary time was 1.09 (1.00, 1.18). The HR (95% CI) after adjustment for PA and ADL was 1.04 (0.96, 1.13). Participants sedentary for ≥11h/day had a higher mortality risk (HR: 1.80, 95% CI: 1.14, 2.84) than those sedentary <8h/day, in the multivariable model. After adjusting for PA and ADL, this association was clearly attenuated (HR: 1.50, 95% CI: 0.93, 2.41). In conclusion, our study suggests that sedentary behavior is a risk factor for mortality. Further investigation is needed to examine whether this association is distinct from the effect of other measures of activity.

Association of anthropometric measures with fat and fat-free mass in the elderly: The Rotterdam study.

OBJECTIVES: The decrease in fat-free mass (FFM) seen in many elderly people is usually associated with an increase in fat mass (FM), a state referred to as sarcopenic obesity. It is not clear which anthropometric measures are best used to identify sarcopenic obesity. We therefore evaluated which anthropometric measures are differentially associated with FM and FFM. MAIN OUTCOME AND MEASURES: The anthropometric measures tested were body mass index (BMI), waist circumference (WC), and a body shape index (ABSI = WC/(BMI(2/3)*Height(1/2))). FM and FFM were estimated by dual-energy X-ray absorptiometry. An index-score was calculated for both FM (FMI) and FFM (FFMI) by dividing FM and FFM by height. Multivariable linear regression models were used to assess the associations of BMI, WC and ABSI with FMI and FFMI among 3612 participants (2092 women) from the prospective population-based Rotterdam Study. RESULTS: In multivariate models adjusted for confounders, BMI and WC were positively associated with both FMI and FFMI in men and women. ABSI was positively associated with FMI (ß 1.01, 95% confidence interval (95%CI) 0.85, 1.17) and negatively associated with FFMI (ß -0.28, 95%CI -0.38, -0.17) in men. In women, ABSI was not associated with FMI and was positively associated with FFMI (ß 0.18, 95%CI 0.10, 0.26). CONCLUSIONS: While BMI and WC were both positively associated with FM and FFM, ABSI showed a differential association with FM and FFM in men, but not in women. Since sarcopenic obesity is associated with decreased FFM and increased FM, ABSI could be a useful tool for identifying men at higher risk of sarcopenic obesity.

Metabolically Healthy Obesity and the Risk of Cardiovascular Disease in the Elderly Population.

BACKGROUND: Whether being metabolically healthy obese (MHO)-defined by the presence of obesity in the absence of metabolic syndrome-is associated with subsequent cardiovascular disease (CVD) remains unclear and may depend on the participants' age. We examined the association of being MHO with CVD risk in the elderly. METHODS AND FINDINGS: This study included 5,314 individuals (mean age 68 years) from the prospective population-based Rotterdam Study. We categorized our population in groups according to body mass index (BMI) and presence and absence of metabolic syndrome, and estimated the hazard ratio (HR) and 95% confidence interval (95%CI) for every group by using Cox proportional hazard models. Among 1048 (19.7%) obese individuals we identified 260 (24.8%) MHO subjects. Over 14 years of follow-up there were 861 incident CVD cases. In the multivariable adjusted analysis, we did not observe an increased CVD risk in MHO individuals (HR 1.07, 95%CI 0.75-1.53), compared to normal weight individuals without metabolic syndrome. CVD risk was increased by the presence of metabolic syndrome in normal weight (HR 1.35, 95%CI 1.02-1.80), overweight (HR 1.32, 95%CI 1.09-1.60) and obese (HR 1.33, 95%CI 1.07-1.66) individuals, compared to those with normal weight without metabolic syndrome. In a mediation analysis, 71.3% of the association between BMI and CVD was explained by the presence of metabolic syndrome. CONCLUSIONS: In our elderly population, we found that the presence of obesity without metabolic syndrome did not confer a higher CVD risk. However, metabolic syndrome was strongly associated with CVD risk, and was associated with an increased risk in all BMI categories. Therefore, preventive interventions targeting cardiometabolic risk factors could be considered in elderly, regardless of weight status.

Physical Activity Types and Coronary Heart Disease Risk in Middle-Aged and Elderly Persons: The Rotterdam Study.

Physical activity is associated with decreased risk of coronary heart disease (CHD). The specific physical activity types that provide beneficial effects in an older population remain unclear. We assessed the association of total physical activity, walking, cycling, domestic work, sports, and gardening with CHD by using Cox proportional hazard models among 5,901 participants aged >55 (median age, 67) years from the prospective population-based Rotterdam Study, enrolled between 1997 and 2001. Activities were categorized into tertiles, and the lowest tertiles were used as reference. In the multivariable model, we adjusted for age, sex, smoking, alcohol consumption, education, diet, and other physical activity types. During 15 years of follow-up (median, 10.3 (interquartile range, 8.0-11.8) years), 642 participants (10.9%) experienced a CHD event. In the multivariable model, the respective hazard ratios for the medium and high categories compared with the low category were 0.79 (95% confidence interval CI): 0.66, 0.96) and 0.71 (95% CI: 0.58, 0.87) for total physical activity, 0.76 (95% CI: 0.63, 0.92) and 0.70 (95% CI: 0.57, 0.88) for cycling, and 0.81 (95% CI: 0.66, 0.98) and 0.71 (95% CI: 0.56, 0.90) for domestic work. Walking, sports, and gardening were not associated with CHD. In conclusion, in this long-term follow-up study of older adults, domestic work and cycling were associated with reduced CHD risk. Physical activity should be promoted in this population with the aim to prevent CHD.

More children more active: Tailored playgrounds positively affect physical activity levels amongst youth.

OBJECTIVES: Overall physical activity (PA) of children is low, and the physical inactivity problem is highest in deprived neighborhoods. The overall goal of the Richard Krajicek Foundation is to provide children in deprived neighborhoods with safe public playgrounds that stimulate daily PA. This study investigates whether Krajicek playgrounds are associated with higher usage and intensity of PA compared to control playgrounds during playground use amongst children. DESIGN: Cross-sectional observational study. METHODS: Ten Krajicek and ten control playgrounds in The Netherlands were matched for neighborhood and playground characteristics. Usage and intensity of PA at the playgrounds were measured using direct observation (SOPLAY). Trained observers collected PA data after-school time on weekdays and weekend days. Multilevel regression analyses were performed to analyze the difference in usage and intensity of PA between control and Krajicek playgrounds. RESULTS: Krajicek playgrounds were significantly less often empty compared to control playgrounds (12% vs. 29%). In addition, there was a statistically significant difference in the number of boys observed on the Krajicek playgrounds (14 vs. 9, OR 1.8). Across all categories differences in playground PA were found with an average of 13% of the children on Krajicek playgrounds engaged in moderate-to-vigorous physical activity (MVPA) compared to 10% on control playgrounds. Energy-expenditure (EE) per child was higher on Krajicek playgrounds across all groups (B.006). CONCLUSIONS: Krajicek playgrounds are positively associated with higher usage and PA intensity compared to control playgrounds. Our results indicate that Krajicek playgrounds can benefit PA of children living in deprived neighborhoods during playground play.