International Journal of Epidemiology, 2016, 1433–1444 doi: 10.1093/ije/dyw033 Advance Access Publication Date: 12 April 2016 Original article
Metabolomics
Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study Qian Xiao,1* Steven C Moore,1 Sarah K Keadle,1 Yong-Bing Xiang,2 Wei Zheng,3 Tricia M Peters,4 Michael F Leitzmann,5 Bu-Tian Ji,6 Joshua N Sampson,7 Xiao-Ou Shu3 and Charles E Matthews1 1
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA, 2Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China, 3Vanderbilt Epidemiology Center and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 4Department of Internal Medicine, McGill University Health Center, Montreal, QC, Canada, 5Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany, 6Occupational and Environmental Epidemiology Branch, and 7 Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA *Corresponding author. 9609 Medical Center Dr Rockville, MD 20850. E-mail: [email protected] Accepted 22 January 2016
Abstract Background: Physical activity is associated with a variety of health benefits, but the biological mechanisms that explain these associations remain unclear. Metabolomics is a powerful tool to comprehensively evaluate global metabolic signature associated with physical activity and helps to pinpoint the pathways that mediate the health effects of physical activity. There has been limited research on metabolomics and habitual physical activity, and no metabolomics study has examined sedentary behaviour and physical activity of different intensities. Methods: In a group of Chinese adults (N ¼ 277), we used an untargeted approach to examine 328 plasma metabolites in relation to accelerometer-measured physical activity, including overall volume of physical activity (physical activity energy expenditure (PAEE) and duration of physically active time) and sedentary time, and measures related to different intensities of physical activity (moderate-to-vigorous activity (MVPA), light activity, average physical activity intensity). Results: We identified 11 metabolites that were associated with total activity, with a false discovery rate of 0.2 or lower. Notably, we observed generally lower levels of amino acids in the valine, leucine and isoleucine metabolism pathway and of carbohydrates in sugar metabolism among participants with higher activity levels. Moreover, we found that PAEE, time spent in light activity and duration of physically active time were associated with a similar metabolic pattern, whereas the metabolic signature associated with
Published by Oxford University Press on behalf of the International Epidemiological Association 2016. This work is written by US Government employees and is in the public domain in the United States.
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International Journal of Epidemiology, 2016, Vol. 45, No. 5
sedentary time mirrored this pattern. In contrast, average activity intensity and time spent in MVPA appeared to be associated with somewhat different metabolic patterns. Conclusions: Overall, the metabolomics patterns support a beneficial role of higher volume of physical activity in cardiometabolic health. Our findings identified candidate pathways and provide insight into the mechanisms underlying the health effects of physical activity. Key words: sleep timing, chronotype, sleep duration, metabolomics
Key Messages • Higher overall volume of habitual physical activity is associated with human metabolomics signatures that are
consistent with better cardiometabolic health. • In particular, a higher level of physical activity is associated with lower levels of branched chain amino acids and
monosaccharides. • Total physical activity, time spent in light activity and duration of physically active time were associated with a similar
metabolic pattern, and the metabolic signature associated with sedentary time mirrored this pattern. • Average activity intensity and time spent in MVPA appeared to be associated with somewhat different metabolic
patterns.
Introduction Physical activity confers a wide range of health benefits, with higher activity levels associated with reduced risks of: cardiovascular1 and neurodegenerative diseases;2 diabetes;3 hypertension;4 osteoporosis;5 and colon, breast and endometrial cancers.6–8 This wide array of health benefits may be partially explained by a variety of metabolic and physiological responses to physical activity, although these complex systemic changes are not well understood. Global profiling of the human metabolome presents a comprehensive approach to methodically study changes associated with physical activity, in order to better understand the molecular pathways linking physical activity to chronic disease. There have been limited metabolomics investigations that focused on physical activity. Previous studies examined changes in metabolomic patterns after exercise training sessions,9,10 but only two recent studies, by Kujala et al. 11 and by Floegel et al.12 have explored metabolomic signatures in relation to habitual physical activity. The study by Floegel et al. used a targeted approach to examine 127 metabolites in relation to objectively measured physical activity, and reported differential correlations with physical activity across multiple metabolite networks. Using nuclear magnetic resonance spectroscopy, Kujala et al. measured over 100 metabolites, mainly compounds in the lipid metabolism pathways, as well as amino acids
and ketone bodies. They found systemic differences in metabolomic profiles associated with self-reported leisuretime physical activity levels, and reported lower levels of isoleucine, a1-acid glycoprotein and glucose among more active participants. These findings are intriguing, and new studies are needed to evaluate alterations in a more expansive set of metabolites and their pathways. One gap in the literature on metabolomics and physical activity is an absence of information on the metabolomics profiles associated with sedentary behaviour and physical activity of different intensities. Numerous population studies have documented the adverse effects of sedentary behaviours, and a recent meta-analysis concluded that prolonged sedentary time is associated with mortality and various diseases, independently of physical activity.13 In addition, it remains unclear whether the total amount (or volume) of physical activity or the amount of higherintensity moderate-to-vigorous physical activity (MVPA), has a stronger impact on health and disease prevention.14 Interestingly, an earlier study suggested that even lowerintensity activities convey health benefits.15 These findings have potentially important public health implications and it is crucial to understand the biological adaptations elicited by different characteristics of physical activity behaviour. In a group of Chinese men and women, we used an untargeted approach to investigate over 300 plasma
International Journal of Epidemiology, 2016, Vol. 45, No. 5
metabolites in relation to objectively measured physical activity. We examined metabolite associations in relation to the overall volume of physical activity [physical activity energy expenditure (PAEE) and duration of physically active time] and sedentary time, as well as measures related to different intensities of physical activity (i.e. MVPA, light activity and average physical activity intensities). Our study aimed at identifying novel circulating metabolite markers and providing insight into potential biological mechanisms that may mediate the health effects of physical activity.
Materials and methods Study population The study population included men and women from the Shanghai Physical Activity Study. Details of this study have been previously reported.16 Briefly, the Shanghai Physical Activity Study enrolled randomly selected participants from two population-based prospective studies, the Shanghai Women’s Health Study17 and the Shanghai Men’s Health Study.18 A total of 619 participants were enrolled in two waves, in 2005 and 2007, respectively. To estimate habitual physical activity levels, participants were asked to wear an accelerometer for 7 consecutive days on four separate occasions during a 1-year study period (roughly one administration in each season). They also donated blood samples at the beginning and at the end of the 1-year period. Of the 339 men and women with sufficient accelerometer data and blood samples (details in sample selection are presented in Supplementary material, available as Supplementary data at IJE online), we excluded those with non-fasting samples (N ¼ 62). The analytical sample contained 130 men and 147 women.
Accelerometer-measured physical activity For each accelerometer measurement week, participants were instructed to wear an ActiGraph accelerometer (model 7164) on the left hip except when sleeping, swimming and showering. Dates of monitor distribution and collection were recorded by study staff. Monitors were checked for their calibration status and were recalibrated as required. The ActiGraph measures vertical acceleration, which is integrated over a pre-specified epoch (1 minu for the present study) and converted to activity counts per minute (ct/min). Monitor wear time was estimated using the automated scoring algorithm adapted from Troiano et al.,19 which employed a ct/min threshold of 60 min to determine non-wear periods, and a minimum threshold
1435
of 50 ct/min to terminate the non-wear period. We excluded monitored days (d) that were outside the target wearing dates, days of observation with evidence of monitor malfunction (e.g. activity counts > 20 000) and days with
Metabolomics
Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study Qian Xiao,1* Steven C Moore,1 Sarah K Keadle,1 Yong-Bing Xiang,2 Wei Zheng,3 Tricia M Peters,4 Michael F Leitzmann,5 Bu-Tian Ji,6 Joshua N Sampson,7 Xiao-Ou Shu3 and Charles E Matthews1 1
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA, 2Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China, 3Vanderbilt Epidemiology Center and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 4Department of Internal Medicine, McGill University Health Center, Montreal, QC, Canada, 5Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany, 6Occupational and Environmental Epidemiology Branch, and 7 Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA *Corresponding author. 9609 Medical Center Dr Rockville, MD 20850. E-mail: [email protected] Accepted 22 January 2016
Abstract Background: Physical activity is associated with a variety of health benefits, but the biological mechanisms that explain these associations remain unclear. Metabolomics is a powerful tool to comprehensively evaluate global metabolic signature associated with physical activity and helps to pinpoint the pathways that mediate the health effects of physical activity. There has been limited research on metabolomics and habitual physical activity, and no metabolomics study has examined sedentary behaviour and physical activity of different intensities. Methods: In a group of Chinese adults (N ¼ 277), we used an untargeted approach to examine 328 plasma metabolites in relation to accelerometer-measured physical activity, including overall volume of physical activity (physical activity energy expenditure (PAEE) and duration of physically active time) and sedentary time, and measures related to different intensities of physical activity (moderate-to-vigorous activity (MVPA), light activity, average physical activity intensity). Results: We identified 11 metabolites that were associated with total activity, with a false discovery rate of 0.2 or lower. Notably, we observed generally lower levels of amino acids in the valine, leucine and isoleucine metabolism pathway and of carbohydrates in sugar metabolism among participants with higher activity levels. Moreover, we found that PAEE, time spent in light activity and duration of physically active time were associated with a similar metabolic pattern, whereas the metabolic signature associated with
Published by Oxford University Press on behalf of the International Epidemiological Association 2016. This work is written by US Government employees and is in the public domain in the United States.
1433
1434
International Journal of Epidemiology, 2016, Vol. 45, No. 5
sedentary time mirrored this pattern. In contrast, average activity intensity and time spent in MVPA appeared to be associated with somewhat different metabolic patterns. Conclusions: Overall, the metabolomics patterns support a beneficial role of higher volume of physical activity in cardiometabolic health. Our findings identified candidate pathways and provide insight into the mechanisms underlying the health effects of physical activity. Key words: sleep timing, chronotype, sleep duration, metabolomics
Key Messages • Higher overall volume of habitual physical activity is associated with human metabolomics signatures that are
consistent with better cardiometabolic health. • In particular, a higher level of physical activity is associated with lower levels of branched chain amino acids and
monosaccharides. • Total physical activity, time spent in light activity and duration of physically active time were associated with a similar
metabolic pattern, and the metabolic signature associated with sedentary time mirrored this pattern. • Average activity intensity and time spent in MVPA appeared to be associated with somewhat different metabolic
patterns.
Introduction Physical activity confers a wide range of health benefits, with higher activity levels associated with reduced risks of: cardiovascular1 and neurodegenerative diseases;2 diabetes;3 hypertension;4 osteoporosis;5 and colon, breast and endometrial cancers.6–8 This wide array of health benefits may be partially explained by a variety of metabolic and physiological responses to physical activity, although these complex systemic changes are not well understood. Global profiling of the human metabolome presents a comprehensive approach to methodically study changes associated with physical activity, in order to better understand the molecular pathways linking physical activity to chronic disease. There have been limited metabolomics investigations that focused on physical activity. Previous studies examined changes in metabolomic patterns after exercise training sessions,9,10 but only two recent studies, by Kujala et al. 11 and by Floegel et al.12 have explored metabolomic signatures in relation to habitual physical activity. The study by Floegel et al. used a targeted approach to examine 127 metabolites in relation to objectively measured physical activity, and reported differential correlations with physical activity across multiple metabolite networks. Using nuclear magnetic resonance spectroscopy, Kujala et al. measured over 100 metabolites, mainly compounds in the lipid metabolism pathways, as well as amino acids
and ketone bodies. They found systemic differences in metabolomic profiles associated with self-reported leisuretime physical activity levels, and reported lower levels of isoleucine, a1-acid glycoprotein and glucose among more active participants. These findings are intriguing, and new studies are needed to evaluate alterations in a more expansive set of metabolites and their pathways. One gap in the literature on metabolomics and physical activity is an absence of information on the metabolomics profiles associated with sedentary behaviour and physical activity of different intensities. Numerous population studies have documented the adverse effects of sedentary behaviours, and a recent meta-analysis concluded that prolonged sedentary time is associated with mortality and various diseases, independently of physical activity.13 In addition, it remains unclear whether the total amount (or volume) of physical activity or the amount of higherintensity moderate-to-vigorous physical activity (MVPA), has a stronger impact on health and disease prevention.14 Interestingly, an earlier study suggested that even lowerintensity activities convey health benefits.15 These findings have potentially important public health implications and it is crucial to understand the biological adaptations elicited by different characteristics of physical activity behaviour. In a group of Chinese men and women, we used an untargeted approach to investigate over 300 plasma
International Journal of Epidemiology, 2016, Vol. 45, No. 5
metabolites in relation to objectively measured physical activity. We examined metabolite associations in relation to the overall volume of physical activity [physical activity energy expenditure (PAEE) and duration of physically active time] and sedentary time, as well as measures related to different intensities of physical activity (i.e. MVPA, light activity and average physical activity intensities). Our study aimed at identifying novel circulating metabolite markers and providing insight into potential biological mechanisms that may mediate the health effects of physical activity.
Materials and methods Study population The study population included men and women from the Shanghai Physical Activity Study. Details of this study have been previously reported.16 Briefly, the Shanghai Physical Activity Study enrolled randomly selected participants from two population-based prospective studies, the Shanghai Women’s Health Study17 and the Shanghai Men’s Health Study.18 A total of 619 participants were enrolled in two waves, in 2005 and 2007, respectively. To estimate habitual physical activity levels, participants were asked to wear an accelerometer for 7 consecutive days on four separate occasions during a 1-year study period (roughly one administration in each season). They also donated blood samples at the beginning and at the end of the 1-year period. Of the 339 men and women with sufficient accelerometer data and blood samples (details in sample selection are presented in Supplementary material, available as Supplementary data at IJE online), we excluded those with non-fasting samples (N ¼ 62). The analytical sample contained 130 men and 147 women.
Accelerometer-measured physical activity For each accelerometer measurement week, participants were instructed to wear an ActiGraph accelerometer (model 7164) on the left hip except when sleeping, swimming and showering. Dates of monitor distribution and collection were recorded by study staff. Monitors were checked for their calibration status and were recalibrated as required. The ActiGraph measures vertical acceleration, which is integrated over a pre-specified epoch (1 minu for the present study) and converted to activity counts per minute (ct/min). Monitor wear time was estimated using the automated scoring algorithm adapted from Troiano et al.,19 which employed a ct/min threshold of 60 min to determine non-wear periods, and a minimum threshold
1435
of 50 ct/min to terminate the non-wear period. We excluded monitored days (d) that were outside the target wearing dates, days of observation with evidence of monitor malfunction (e.g. activity counts > 20 000) and days with
