Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study

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.

Keywords: chronotype; metabolomics; sleep duration; sleep timing.

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.

Figures

Figure 1.

Manhattan plot of metabolite associations with physical activity energy expenditure (MET-h/day).

Figure 2.

Metabolites associated with physical activity energy expenditure (MET-h/day). Effect estimates are expressed as changes in physical activity energy expenditure per 1 standard deviation increase in metabolite (log scale). Results are adjusted for age (continuous), body mass index (continuous) and smoking status (non-smoker, smoker). Statistical significance is defined as false discovery rate

Figure 3.

This heat map shows the correlation among the known metabolites significantlya associated with physical activity energy expenditure. The colours represent Pearson correlation coefficient: very dark red, ≥0.8; dark red, 0.6–< 0.8; medium red, 0.4–< 0.6; light red, 0–<0.4; light green, -0.4–<0. aStatistical significance was determined using a false discovery rate threshold of 0.2.

Figure 4.

Metabolites associated with physical activity energy expenditure (MET-h/day), by sex. Effect estimates are expressed as changes in physical activity energy expenditure per 1 standard deviation increase in metabolite (log scale). Results are adjusted for age (continuous), body mass index (continuous) and smoking status (non-smoker, smoker). Statistical significance is defined as false discovery rate

Figure 5.

This heat map shows the associations between different measures of physical activity and metabolites. Metabolites that are included in this figure have at least one association with a P-value < 0.05. The eight colour codes represent different z-scores: very dark green, <-3.29 (P-value < 0.001); dark green, -3.29–<-2.58 (0.001–<0.01); medium green, -2.58–<-1.96 (0.01–<0.05); light green, -1.96–<0 (0.05–1.0); light red, >0-1.96 (0.05–1.0); medium red, >1.96–2.58 (0.01–<0.05); dark red, >2.58–3.29 (0.001–<0.01); very dark red, >3.29 (<0.001). All models were adjusted for age (continuous), gender (male, female), body mass index (continuous) and smoking status (non-smoker, smoker). aDefined as the amount of wear time per day with a recording of <100 ct/min, expressed as proportion of total wear time; bdefined as the amount of wear time per day with a recording of 100–759 ct/min, expressed as proportion of total wear time; cdefined as the amount of wear time per day with a recording of ≥760 ct/min, expressed as proportion of total wear time; ddefined as the amount of wear time spent in MVPA or light PA; edefined as PAEE/PA duration; fPA duration and PA intensity are mutually adjusted in the same model.