Xiao Qian, Moore Steven C, Keadle Sarah K, Xiang Yong-Bing, Zheng Wei, Peters Tricia M, Leitzmann Michael F, Ji Bu-Tian, Sampson Joshua N, Shu Xiao-Ou, Matthews Charles E
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Int J Epidemiol. 2016 Oct;45(5):1433-1444. doi: 10.1093/ije/dyw033. Epub 2016 Apr 12.
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.
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).
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.
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.
身体活动与多种健康益处相关，但解释这些关联的生物学机制仍不清楚。代谢组学是一种强大的工具，可全面评估与身体活动相关的整体代谢特征，并有助于确定介导身体活动对健康影响的途径。关于代谢组学与习惯性身体活动的研究有限，且尚无代谢组学研究考察久坐行为和不同强度的身体活动。
在一组中国成年人（N = 277）中，我们采用非靶向方法检测了328种血浆代谢物与通过加速度计测量的身体活动的关系，包括身体活动总量（身体活动能量消耗（PAEE）和身体活动时间）以及久坐时间，以及与不同强度身体活动相关的指标（中等到剧烈活动（MVPA）、轻度活动、平均身体活动强度）。
我们鉴定出11种与总活动相关的代谢物，错误发现率为0.2或更低。值得注意的是，我们观察到活动水平较高的参与者中，缬氨酸、亮氨酸和异亮氨酸代谢途径中的氨基酸水平以及糖代谢中的碳水化合物水平普遍较低。此外，我们发现PAEE、轻度活动时间和身体活动时间与相似的代谢模式相关，而与久坐时间相关的代谢特征则与该模式相反。相比之下，平均活动强度和MVPA时间似乎与略有不同的代谢模式相关。
总体而言，代谢组学模式支持较高身体活动量对心脏代谢健康的有益作用。我们的研究结果确定了候选途径，并深入了解了身体活动对健康影响的潜在机制。
