Study: Intersection of Diet and Exercise with the Gut Microbiome and Circulating Metabolites in Male Bodybuilders: A Pilot Study. Image Credit: Goami/Shutterstock

How do bodybuilders’ diet and exercise relate to gut microbes and their metabolites?

A recent study was published in metabolites Explore the dynamics of the diet and exercise microbiome of male bodybuilders.

Stady: The intersection of diet and exercise with the gut microbiome and circulating metabolites in male bodybuilders: a pilot study. Image Credit: Goami / Shutterstock


Optimal exercise and diet regimens are considered elusive as exercise/diet interventions have achieved varying results between individuals. This is observed in sports where diets are designed to supplement athletic performance and improve energy availability. However, individual factors determine sports results. Recently, there has been increasing interest in the role of the gut microbiota in individual sports results.

about studying

In this study, researchers tested whether specific changes in diet and exercise in bodybuilders are related to changes in the gut microbiota and its metabolites. Male participants 18 years of age or older, preparing for a bodybuilding competition, were eligible for inclusion.

Five bodybuilders were selected with longitudinal blood and stool samples matched with exercise history and diet. They were, on average, 28 years old, 177 cm tall, 77.7 kg in weight with 4.2 years of bodybuilding experience. Samples were obtained eight weeks (PRE8), one week (PRE1) before competition, and four weeks (POST4) after competition.

Participants abstained from alcohol, caffeine, and exercise 12 hours before blood samples were taken. Hydrophilic metabolites were measured in a targeted metabolic analysis using a liquid chromatography-mass spectrometry (LC-MS) system. Participants completed a food and training diary for a week prior to each assessment point.

Consumption of food, fluids, and supplements was documented in the food diary, while resistance and aerobic exercise were documented in the training diary. Body composition was estimated using a dual-energy X-ray absorptiometry (DXA) scanner. Stool samples were collected by participants within a week prior to each evaluation time point. Total DNA was isolated from stool samples. The V4 region of 16S rRNA was used to characterize the microbiome.

Changes in exercise and body composition at the PRE1 and POST4 time points were compared with PRE8 as a baseline. Differences in metabolite concentrations between time points were analyzed using the Kruskal-Wallis test. One sample from the PRE1 time point was excluded from the analysis due to a failure of the LC-MS quality control check.

the findings

All participants reached the intended changes in body composition during the preparation period (PRE8 – PRE1). There was a greater decrease in fat mass compared to lean mass. Two of the participants were more successful in maintaining lean mass than the others. One participant had the lowest reduction in fat mass (6.4%). There was an increase in fat and lean mass after competition among all participants.

Hands-on training was reduced at the PRE8 and POST4 time points compared to baseline, but the training regimens differed across individuals. An increase in aerobic and resistance training from the PRE8 to PRE1 time point in only one participant reflected better lean mass preservation but did not correspond to a reduction in fat mass. Next, participants were assessed in terms of dietary intake in – food items, macronutrients, and energy levels.

Energy intake was similar among participants and higher post-competition in four participants. A greater pre-competition (PRE8 to PRE1) decrease in energy intake corresponded to a better decrease in fat mass but not changes in lean mass. The pre-competition protein contribution to energy was above the upper limit of the acceptable macronutrient distribution range (AMDR), while the carbohydrate contribution was lower than the lower limit. Energy intake as fat was within AMDR limits.

The minimum recommended daily intake (MRDI) of protein was exceeded in participants at all time points. Besides, there was inter-individual variability in the consumption of protein/amino acid supplements. Each participant presented a unique and dynamic gut microbiota. There was no significant correlation between samples from one time point from different participants.

After the competition, a temporal shift was observed in the microbial diversity between and within the sample(s). Microbial communities at each individual’s PRE8 and PRE1 time points were more similar than that of the POST4 sample. Furthermore, four individuals had low intra-sample diversity at the time point POST4.

In all participants, most microbes (55% to 85%) were packet. Serum metabolic profiles of participants in fasting and abstinence states were evaluated. Among the 127 metabolites, nine were found to be significant with a time point. Participants had unique metabolite profiles throughout the evaluation period.

POST4 metabolite profiles were different from PRE8 or PRE1 profiles. Prior to competition, metabolite profiles were characterized by higher levels of malonate, guanidinoacetic acid, acetylcarnitine, α-ketobutyrate, and -hydroxybutyrate. In contrast, post-competition profiles were characterized by increased polysaccharides, choline, and NAD+ levels.


Overall, all participants succeeded in reducing fat mass and maintaining lean mass during the pre-competition period. Participants with the highest pre-competition reduction in dietary energy intake showed a greater reduction in fat mass. Microbial composition differed significantly between individuals. Despite interindividual differences in gut microbiome composition, microbial diversity within and between sample(s) can be predicted by diet.

The results suggest that predicting the dynamics between the gut microbiome, metabolites, diet, and exercise would be successful at the individual level rather than between individuals. Thus, personal training and diets would be more beneficial than diet/exercise regimens based on generalized population patterns.

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