Qian Xiao1, Steven C Moore2, Sarah K Keadle2, Yong-Bing Xiang3, Wei Zheng4, Tricia M Peters5, Michael F Leitzmann6, Bu-Tian Ji7, Joshua N Sampson8, Xiao-Ou Shu4, Charles E Matthews2. 1. Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA xiaoq2@mail.nih.gov. 2. Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA. 3. Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China. 4. Vanderbilt Epidemiology Center and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA. 5. Department of Internal Medicine, McGill University Health Center, Montreal, QC, Canada. 6. Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany. 7. Occupational and Environmental Epidemiology Branch. 8. Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
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 = 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 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. 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.
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 = 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 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. 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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