The modulating role of the gut microbiome in body weight responses to physical activity

Background: Although physical activity (PA) is a major approach to weight control, PA does not always result in expected weight loss and shows high individual variability in body weight responses. The gut microbiome plays an important role in host energy balance, but how the gut microbiome modulates the body weight response to PA remains unknown. The current study analyzed the gut microbiomes profiled by shotgun metagenomics and metatranscriptomics sequencing in modifying the association of recent and long-term PA with body weight measures and relevant plasma biomarkers.

Methods: We collected data on PA type and intensity and body weight using the validated biennial questionnaires since 1986 from 51,529 men enrolled in the Health Professionals Follow-up Study. In a subcohort of 307 healthy men, we collected up to 2 pairs of stool samples and 2 blood samples, 6 months apart in 2012-2013. We profiled 925 stool metagenomes, 340 stool metatranscriptomes, and 468 blood samples. One month before and after the stool collections, participants were asked to wear accelerometer for consecutive 7 days to monitor their PA and received Doubly Labeled Water (DLW) test for body weight and fat mass assessment. We assessed the overall gut microbiome configurations, microbial species abundances, microbial functional pathways and enzymes in relation to recent PA level measured by accelerometer, long-term PA level from questionnaires, body mass index and fat mass percentage measured by DLW, short-term body weight change in 6 months between the 1^st and 2^nd stool collection, long-term body weight change from age 21 to stool collection, and plasma high-sensitivity C-reactive protein (CRP) and hemoglobin A1c (HbA1c) levels at stool collection. We then examined how the microbial species might modify the associations of PA with the body weight measures and biomarkers.

Results: Among the 307 healthy men, mean age (standard deviation) at stool collection was 70 (4) years. Total PA level accounted for 0.64% of variation in the species community. Abundance of several species, such as Clostridium bolteae, was associated negatively with PA level and positively with BMI. PA was particularly associated with microbial pathways involved in glucose metabolism. The associations of total PA level with BMI, fat mass percentage, short-term body weight change in 6 months, long-term body weight change between age 21 and stool collection, and plasma HbA1c level, were modified by abundance of Alistipes putredinis. Individuals with a higher abundance of A. putredinis showed a more pronounced response to PA level in lowering BMI, fat mass percentage, short- and long-term body weight change, and plasma HbA1c level. A. putredinis was found to contribute in 75%-50% of the microbial enzymes within the five Glycolysis pathways.

Conclusions: Our findings suggest that individuals with a higher A. putredinis abundance may have a better body weight response to PA; the modulating role of A. putredinis may be partly attributed to its roles in Glycolysis. More studies are needed to elucidate the potential of A. putredinis as a probiotic in improving body weight response to PA.

Figure 1. Alistipes putredinis abundance modulates the associations of physical activity (PA) with body weight measures and plasma biomarkers. The interactions between PA and A. putredinis abundance (with median level as cutoff for low and high abundance) are significant in relation to all of body mass index (BMI), fat mass percentage, short-term and long-term body weight changes, and plasma hemoglobin A1c (HbA1c) level. a, The interaction between recent total PA and A. putredinis abundance in relation to BMI. b, Recent total PA levels in relation to BMI among participants with low and high A. putredinis abundance separately. Box plot centers show medians of the PA measures with boxes indicating their inter-quartile ranges (IQRs), upper and lower whiskers indicating 1.5 times the IQR from above the upper quartile and below the lower quartile, respectively. c, Association between recent total PA and BMI according to A. putredinis abundance. The dots in the plot indicate beta coefficients in the multivariable-adjusted generalized linear mixed-effects regression models, with error bars indicating upper and lower limits of their 95% confidence intervals. Beta coefficients and P_interaction were calculated from multivariable-adjusted generalized linear mixed-effects regression models while adjusting for age, smoking, total energy intake, probiotic use, antibiotic use, and Bristol stool scale. d, Associations between PA levels with other body weight measures, including fat mass percentage, short-term body weight change (6-month weight change), long-term body weight change (weight change between age 21 and stool collection), plasma HbA1c and high-sensitivity C-reactive protein (CRP) levels.