Effect of High-intensity Training and Probiotics on Gut Microbiota Diversity in Competitive Swimmers: Randomized Controlled Trial

Abstract

Background: Physical exercise has favorable effects on the structure of gut microbiota and metabolite production in sedentary subjects. However, little is known whether adjustments in an athletic program impact overall changes of gut microbiome in high-level athletes. We therefore characterized fecal microbiota and serum metabolites in response to a 7-week, high-intensity training program and consumption of probiotic Bryndza cheese.

Methods: Fecal and blood samples and training logs were collected from young competitive male (n = 17) and female (n = 7) swimmers. Fecal microbiota were categorized using specific primers targeting the V1-V3 region of 16S rDNA, and serum metabolites were characterized by NMR-spectroscopic analysis and by multivariate statistical analysis, Spearman rank correlations, and Random Forest models.

Results: We found higher α-diversity, represented by the Shannon index value (HITB-pre 5.9 [± 0.4]; HITB-post 6.4 [± 0.4], p = 0.007), (HIT-pre 5.5 [± 0.6]; HIT-post 5.9 [± 0.6], p = 0.015), after the end of the training program in both groups independently of Bryndza cheese consumption. However, Lactococcus spp. increased in both groups, with a higher effect in the Bryndza cheese consumers (HITB-pre 0.0021 [± 0.0055]; HITB-post 0.0268 [± 0.0542], p = 0.008), (HIT-pre 0.0014 [± 0.0036]; HIT-post 0.0068 [± 0.0095], p = 0.046). Concomitant with the increase of high-intensity exercise and the resulting increase of anaerobic metabolism proportion, pyruvate (p[HITB] = 0.003; p[HIT] = 0.000) and lactate (p[HITB] = 0.000; p[HIT] = 0.030) increased, whereas acetate (p[HITB] = 0.000; p[HIT] = 0.002) and butyrate (p[HITB] = 0.091; p[HIT] = 0.019) significantly decreased.

Conclusions: Together, these data demonstrate a significant effect of high-intensity training (HIT) on both gut microbiota composition and serum energy metabolites. Thus, the combination of intensive athletic training with the use of natural probiotics is beneficial because of the increase in the relative abundance of lactic acid bacteria.

Keywords: Athletes; Butyrate; Gut microbiome; Physical exercise; Probiotics.

Fig. 1

α-diversity by Shannon index before and after intervention. a High-intensity training and use of probiotic cheese (HITB); b High-intensity training only (HIT). Floating bars are the minimum to maximum values, the line shows the mean. *p < 0.05

Fig. 2

β-diversity of analyzed samples represented by significantly altered (p < 0.05) bacterial taxa, selected by Random Forest machine learning analysis, before and after 7 weeks of the high-intensity training phase (HIT and HITB) as visualized by PCA. SVD with imputation was used to calculate principal components. The X and Y axis show principal component 1 and principal component 2 that explain 21% and 17.4% of the total variance, respectively. Prediction ellipses are such that, with a probability of 0.95, a new observation from the same group will fall inside the ellipse (n = 48 data points). HIT-pre variables before high-intensity training, HIT-post variables after high-intensity training

Fig. 3

ROC (receiver operating characteristic) curves with an area under the ROC curve (AUC) for the RFM-L algorithm acetate, pyruvate, Butyricimonas, butyrate, Bacteroidetes, Alistipes, and α-diversity (Shannon index) as joint predictors/discriminators between pre- and post-intervention in HIT. FPR, false-positive rate; HIT, high-intensity training group; RFM-L, Random Forest machine-learning; TPR, true positive rate

Fig. 4

ROC (receiver operating characteristic) curves with an area under the ROC curve (AUC) for the RFM-L algorithm pyruvate, lactate, acetate, α-diversity (Shannon index), and butyrate, as excellent joint predictors/discriminators between pre- and post-intervention in HITB. FPR, false-positive rate; HITB, high-intensity training and use of probiotic cheese group; RFM-L, Random Forest machine-learning; TPR, true positive rate