Gut Microbiota Composition Positively Correlates with Sports Performance in Competitive Non-Professional Female and Male Runners

here is still a pressing need for further investigation to bridge the gap in understanding the differences in gut microbiota composition between female runners and their male counterparts. We aimed to determine the gut microbiota composition in competitive non-professional female and male runners and to correlate the gut bacteria to performance. Our study included 40 subjects, of which 22 were runners (13 males and 9 females) and 18 control subjects (9 males and 9 females, representing the general population who perform light physical activity with a weekly running volume of≤5 km per week). Fecal specimens were collected and analyzed for taxonomic profiling to compare species’ relative abundances between males and females based on the results of 16SrRNA analysis. Bacterial alpha and beta diversity were assessed to determine the differences in microbial

males and 9 females, representing the general population who perform light physical activity with a weekly running volume of≤5 km per week). Fecal specimens were collected and analyzed for taxonomic profiling to compare species’ relative abundances between males and females based on the results of 16SrRNA analysis. Bacterial alpha and beta diversity were assessed to determine the differences in microbial composition between runners and controls, and between sexes. Each participant underwent a maximal oxygen consumption test and a time-to-exhaustion test at 85% of the measured VO2max. Blood lactate was collected every 5 min during the tests. Bacterial alpha diversity showed a significant difference (p= 0.04) between runners and controls. Taxonomic analysis of gut microbiota composition showed a lower Enterobacteriaceaeabundance and a higherMethanosphaeraabundance in runners compared with the control group. Ten different bacteria (Methanosphaera,Mitsuokella,Prevotellaceae,Megamonas, Rothia,Oscillospira,Bacteroides,Odoribacter,Blautia massiliensis,Butyricicoccus_pullicaecorum) were positively correlated with exercise (VO2max, lactate blood levels, time to exhaustion, and weekly training volume). We found no significant differences in the gut microbiota composition between male and female runners. Gut microbiota composition positively correlates with sports performance in competitive non-professional female and male runners, and female runners show similar gut microbiome diversity to male runners. Keywords:microbiome; gut microbiota; endurance athletes; runners; males; females 1. Introduction Endurance exercise performance reflects a coordinated response of cardiovascular, pul- monary, and neural functions along with the action of exercising muscles. Exercise induces graded increases in heart rate, arterial pressure, cardiac output, myocardial contractility, and rate and depth of respiration. Exercise significantly contributes to the gut microbial population. Studies have shown that exercise training independently alters the composition and functional capacity of the gut microbiota [1]. Studies investigating the microbiome’s role in athletic performance Life2024,14, 1397.

Life2024,14, 1397 2 of 13 showed increased microbial diversity and increases in species or metabolites associated with muscle turnover, recovery, and protein breakdown [2,3]. A study [4] has shown that, in the bowel of marathon runners, there is a more sig- nificant amount ofVeillonella atypicabacterium than inactive counterparts. They isolated the strain from marathon runners, implanted it into the intestines of mice, and found a significant increase in running time compared with control animals. They hypothesized thatVeillonella atypicametabolizes the lactate produced by skeletal muscles during exercise. The metabolized lactate is converted into propionate, which acts as a substrate for the working muscles. As with other areas of research, the majority of microbiome studies are male-dominated studies. Studies examining the gut microbiomes of females are limited, and research com- paring the gut microbiomes of females and males in the general population is inconclu- sive [5]. Nevertheless, preliminary animal and human studies have shown sex differences in the microbiome’s composition, possibly due to differences in estrogen concentrations [5]. For example, in a human study conducted by four centers in France, Germany, Italy, and Sweden, a higher level of the Bacteroides-Prevotellagroup was observed in the males than in females [6]. In another study of Chinese family members, which was conducted using group-specific denaturing gradient gel electrophoresis (DGGE) profiling ofBacteroidesspp., a higher abundance of Bacteroidesthetaiotaomicronwas identified in the males than in the females [7]. Gastrointestinal physiology has also been gender-specific. Sadik et al. showed that gastric emptying, small-bowel transit, and colonic transit were significantly slower in healthy female subjects than in males [8]. In addition, an apparent bi-directional relationship between estrogen levels and the gut microbiota has been identified [9], an asso- ciation more pronounced in females than males when examined in animal studies [10]. This gender-based difference highlights the potentially central role of the gastrointestinal tract in runners’ performance and might explain sex-derived differences in other physiological and biological processes. To the best of our knowledge, there is a paucity of studies examining the differences between the microbiome composition of female and male runners under identical experimental settings. While physical activity has been

[10]. This gender-based difference highlights the potentially central role of the gastrointestinal tract in runners’ performance and might explain sex-derived differences in other physiological and biological processes. To the best of our knowledge, there is a paucity of studies examining the differences between the microbiome composition of female and male runners under identical experimental settings. While physical activity has been associated with positive effects on the gut microbiota, like greater diversity and abundance of specific probiotic species, competitive endurance sports have been shown to affect it negatively, such as increased I-FABP serum levels indicating more significant gut integrity loss, intestinal distress, an increase in intestinal permeability, and zonulin [11]. There is still a gap in understanding the difference between the gut microbiota com- position of competitive endurance athletes and trainees who engage in regular physical activity; the relationship between specific intestinal bacteria and sports performance needs to be better understood. This study aimed to determine the gut microbiota composition in competitive non- professional female and male runners in a sex-specific manner. We also aimed to examine whether the gut microbial composition expressed in runners correlated with endurance exercise performance. 2. Materials and Methods 2.1. Study Design and Participants For our study, we recruited runners from competitive sports groups, while participants in the control group were recruited from the general population. Forty subjects participated in the study (22 males and 18 females). They included 22 competitive non-professional runners (13 males and 9 females, with a mean age of 43±6.5 years). As controls, we used 18 subjects (9 males and 9 females, mean age of 41±7.4 years) representing the general population who perform light physical activity with a weekly running volume of≤5 km per week. The runners were competitive endurance athletes who ran at least 50 km per week.

Life2024,14, 1397 3 of 13 Each participant completed an online questionnaire describing their weekly training workout volume (days of training per week and total minutes per session), exercise intensity (expressed as % of heart rate maximum), and dietary habits (types of foods, amounts, frequency of eating) to learn about their diet, which can affect the gut microbiota population. To reduce dietary variability, we only included omnivorous participants. Subjects who consumed supplements, like probiotics, prebiotics, multivitamins, antacids (beta-alanine, sodium bicarbonate, others), and antibiotics in the three months before the study were excluded. Each subject received information about the study and signed an informed consent form after approval from the Tel Aviv University Ethics Committee, Israel (approval No. 0003766-1). All informed consent forms signed by the subjects are in the files of the principal researcher at Tel Aviv University, Israel. 2.2. Exercise Tests Each participant performed two exercise stress tests: (1) a maximal exercise stress test to evaluate the athletes’ aerobic fitness level and (2) a sub-maximal exercise stress test to de- termine ‘time to exhaustion’. For the maximal exercise test, maximal oxygen consumption (VO2max) and exercise performance were evaluated using a running test on a treadmill and gas exchange analyses using a COSMED Quark metabolic cart (COSMED S.r.l., Rome, Italy) [12,13]. Heart rate (HR) was monitored using either a POLAR watch (Polar Electro Oy, Kempele, Finland) or a GARMIN watch (Garmin Ltd., Olathe, KS, USA). We started the test with a running speed equal to 50% of the running economy (subjectively assessed). Running speed was increased every minute until reaching VO2max. For the sub-maximal exercise testing to exhaustion, a week later, each subject performed a sub-maximal exercise test at 85% of the measured VO2max until exhaustion [14]. Capillary blood lactate levels were measured from fingertip samples every 5 min during the exercise test using a Lactate Scout+ hand-held analyzer (EKF Diagnostics GmbH, Barleben, Germany). 2.3. Gut Microbiome Analysis 2.3.1. Stool Samples Collection The subjects received a sterile stomacher ® bag (Seward Ltd., Worthing, West Sussex, UK) for sample collection on their first visit to the Institute (when they performed the VO2max

were measured from fingertip samples every 5 min during the exercise test using a Lactate Scout+ hand-held analyzer (EKF Diagnostics GmbH, Barleben, Germany). 2.3. Gut Microbiome Analysis 2.3.1. Stool Samples Collection The subjects received a sterile stomacher ® bag (Seward Ltd., Worthing, West Sussex, UK) for sample collection on their first visit to the Institute (when they performed the VO2max test) and were requested to bring the fecal specimen at their subsequent visit a week later. Samples were aliquoted in collection tubes and stored within 4 h of sampling at −80 ◦ C pending analysis. The stool preparation was performed under anaerobic conditions. 2.3.2. DNA Extraction DNA was extracted from samples using 270µL GT lysis buffer and 30µL proteinase K (from MagCore Genomic DNA Tissue Kit, RBC Bioscience, Taipei, Taiwan) along with 200µL sample in bead beating tubes type C (GeneAid, Taipei, Taiwan). Bead beating was performed for 2 min using a Biospec machine (BioSpec Products, Bartlesville, OK, USA). Samples were then incubated at 60 ◦ C for 2 h and extracted on a MagCore machine (RBC Bioscience, Taipei, Taiwan) using MagCore Genomic DNA Tissue Kit cartridges and protocol. 2.3.3. PCR Protocol DNA was quantified by nanodrop from each tube, and ~20 ng was used as a template for initial PCR. Amplification was performed using Hot Start Ready Mix (PCR Biosystems Ltd., London, UK) using custom primers covering the V4 region primers from Earth Microbiome Project containing CS1/CS2 adaptors [15] for 25 cycles in a volume of 25 µL. From each sample, 2µL of PCR1 amplified sample containing CS1/CS2 adaptors was amplified for ten cycles in 10µL using the Fluidigm Access Array Barcode library according to the manufacturer’s protocol (2µL barcode per reaction) [16]. DNA was purified using Pure Beads (Roche Sequencing Solutions, Inc., Wilmington, DE, USA) at a ratio of 0.65× and quantified with qubit using dsDNA high sensitivity assay (DeNovix Inc., Wilmington,

Life2024,14, 1397 4 of 13 DE, USA). DNA size and integrity were quantified by TapeStation using Agilent DNA ScreenTape and reagents (Agilent Technologies Inc., Santa Clara, CA, USA). 2.3.4. Sequencing Samples were run on a MiSeq (Illumina Inc., San Diego, CA, USA) sequencer with 30% PhiX using MiSeq Reagent Kit v2 500PE (Illumina Inc., San Diego, CA, USA) [17]. Demultiplexing was performed using bcl2fastq (Illumina, Inc., San Diego, CA, USA, ver- sion 2.20.0.422) with default parameters, allowing for 0 mismatches. Data were then mapped to PhiX using bowtie2 (Johns Hopkins University, Baltimore, MD, USA, ver- sion 2.4.5) to remove PhiX control [18], and unmapped reads were quantified, collected, and examined using FastQC (Babraham Bioinformatics, Babraham Institute, Cambridge, UK, version 0.11.9). 2.3.5. Analysis Demultiplexed reads were analyzed using the QIIME2 pipeline (version qiime2-2020.8) on 16S rRNA gene sequences from microbial communities. The analysis workflow consisted of quality filtration of the sequence data and operational taxonomic unit (OTU) clustering performed using default parameter settings at 97% sequence similarity with the SILVA database (version V132). The adaptor sequences were removed, and read with a quality score lower than 25 or length < 150 bp were discarded. The maximum number of acceptable ambiguous nucleotides was set to two, and chimeric sequences and singletons were also detected and discarded. Subsequently, alpha diversity analysis (using the estimate_richness function along with Faith’s PD) was performed for different sample groups. Faith’s PD [19] is a popular and highly utilized phylogenetic alpha diversity metric that accounts for the phylogenetic relatedness of the community members, and it has been noted to be more sensitive in distinguishing disease factors in the human digestive system relative to other alpha diversity indices [20]. We employed a non-parametric statistical comparison method based on the assumption that the data are not normally distributed and that equal variance cannot be assumed. Specifically, the Kruskal–Wallis test was used to compare the multiple groups (i.e., male runners, female runners, male controls, and female controls). Additionally, beta diversity analysis (utilizing distance functions for unweighted and weighted UniFrac and Bray–Curtis dissimilarities) was conducted for various sample groups, and a principal

assumption that the data are not normally distributed and that equal variance cannot be assumed. Specifically, the Kruskal–Wallis test was used to compare the multiple groups (i.e., male runners, female runners, male controls, and female controls). Additionally, beta diversity analysis (utilizing distance functions for unweighted and weighted UniFrac and Bray–Curtis dissimilarities) was conducted for various sample groups, and a principal coordinate analysis (PCoA) plot was generated to present the beta diversity. For differential abundance analysis of gut microbiome, DESeq2 (version 1.36.0) from the R/bioconductor package (version 3.19) was employed. The laboratory with which we collaborated on bioinformatics [Koren Lab] employs the DESeq2 method for microbiome studies. We maintained the same approach and analysis as in previous works to ensure consistency in our methods. Rarified scaled OTUs were labeled by the lowest assigned taxa level possible and summarized per taxa. Differential abundance was assessed, with significant taxa determined by adjustedp-values < 0.05 and |log2FoldChange|≥0.58. For differential analysis of the different activities, sex was included as a blocking factor in the design formula. Boxplots were generated using ggplot2 (version 3.4.4). Correlations between the microbiome and clinical parameters were calculated using the corr.test function from the psych package (version 2.3.9) and visualized with the corrplot package (version 0.92). Comparison of BMI, weekly training volume, and cardiorespiratory measures between the groups was conducted using an independent samplest-test in IBM SPSS Statistics (version 29). 3. Results 3.1. Participants’ Characteristics and Cardiopulmonary Exercise Tests Each participant underwent exercise tests to evaluate their physical fitness. When comparing the two groups, runners vs. controls, the groups had no differences in body mass index (BMI). Table presents the characteristics of the female and male runners. As expected, the runners’

Life2024,14, 1397 5 of 13 cardiopulmonary exercise test results were higher than the controls (Table). Still, we found no statistically significant differences between female and male runners (Table). Table 1.Characteristics of the subjects. Runners (n= 22) Controls (n= 18) Overall (N = 40) pValue Runners vs. Controls Gender Females 9 (40.9%) 9 (50.0%) 18 (45%) Males 13 (59.1%) 9 (50.0%) 22 (55%) BMI Mean (SD) 23.2 (2.61) 23.9 (4.01) 23.5 (3.31) p= 0.47 Median (min, max) 22.9 (19.1, 28.7) 22.6 (18.9, 33.0) 22.75 (18.9, 33.0) Weekly training volume (km) Mean (SD) 67 (15.6) 5 (0) 36 (74.8) p< 0.001 Median (min, max) 60 (50.0, 90.0) 5 (5.0, 5.0) 30 (5.0, 90.0) Cardiopulmonary indices VT1 (mL/kg/min) 35.86 ±4.4 26.47 ±3.9 p< 0.001 VT2 (mL/kg/min) 43.2 ±5.7 31.7 ±4.5 p< 0.001 VO2max (mL/kg/min) 46 ±6.7 36.7 ±5.4 p< 0.001 TEE (min) 15.43 ±6.7 7.4 ±3.1 p< 0.001 Lactate max (mmol/L) 8 ±1.6 7.2 ±2.8 p= 0.2 BMI = body mass index. VT1 = first ventilatory threshold, also known as the aerobic threshold. VT2 = second ventilatory threshold, also known as the respiratory compensation threshold (RCT), and the onset of blood lactate accumulation (OBLA). VO2max = maximal consumption of oxygen. TEE = time to exhaustion. The analysis was performed using an independent samplest-test in SPSS. Table 2.Characteristics of the female and male runners. Female Runners (n= 9) Male Runners (n= 13) BMI Mean (SD) 21.06 (1.46) 24.61 (2.2) Weekly training volume (km) Mean (SD) 61.11 (12.6) 71.15 (16.6) Cardiopulmonary indices VT1 (mL/kg/min) 34.25 ±4.37 36.97 ±4.38 VT2 (mL/kg/min) 40.52 ±4.33 45.06 ±5.93 VO2max (mL/kg/min) 43.87 ±5.4 47.5 ±7.4 TEE (min) 15.20 ±7.83 15.59 ±6.22 Lactate max (mmol/L) 7.71 ±1.52 8.33 ±1.79 BMI = body mass index. VT1 = first ventilatory threshold, also known as the aerobic threshold. VT2 = second ventilatory threshold, also known as the respiratory compensation threshold (RCT), and the onset of blood lactate accumulation (OBLA). VO2max = maximal oxygen consumption. TEE = time to exhaustion. The analysis was performed using an independent samplest-test in SPSS. There was no observed statistically significant difference between female and male runners. 3.2. Microbiome Results

as the aerobic threshold. VT2 = second ventilatory threshold, also known as the respiratory compensation threshold (RCT), and the onset of blood lactate accumulation (OBLA). VO2max = maximal oxygen consumption. TEE = time to exhaustion. The analysis was performed using an independent samplest-test in SPSS. There was no observed statistically significant difference between female and male runners. 3.2. Microbiome Results Alpha diversity was significantly higher in runners than controls (p= 0.04) (Figure). No difference in alpha diversity was observed between males and females in any of the groups. Beta diversity measures the differences in microbial composition between different samples, providing insights into the variability and distribution of species across various environments. We examined the differences in beta diversity between runners and con- trols. Our results showed that principal coordinate analysis (PCoA) based on unweighted UniFrac distance revealed differential clustering between the groups, approaching statis- tical significance (p= 0.06). In contrast, analyses using weighted UniFrac distance and

Life2024,14, 1397 6 of 13 Bray–Curtis metrics showed no significant differences (p= 0.7 andp= 0.4, respectively). Additionally, we found no differences between females and males within each group. Figure 1.(A) Alpha diversity assessment between runners and controls, with runners exhibiting greater species richness than the controls (p= 0.04). (B) Alpha diversity comparison between male and female runners, male and female controls. No significant differences were observed between males and females within the groups. The alpha diversity assessment utilized the estimate_richness function along with Faith’s PD. Taxonomic analysis of gut microbiota composition revealed a quantitative difference in bacterial types between the study groups. At the family level, runners showed a signifi- cantly lower abundance ofEnterobacteriaceaethan control subjects (log2FoldChange =−3.9, p= 0.0001). At the genus level, runners showed a significantly higher abundance of Methanosphaera(log2FoldChange = 24.01,p= 5.64×10 −20 ) than the controls (Figure). Figure 2.The abundance of selected bacteria in runners compared to controls was identified by DESeq2. Log2FoldChange greater than zero indicated more abundant, whereas log2FoldChange less than zero indicated less abundant. Runners showed a significantly lower abundance ofEnterobacteri- aceaeand a significantly higher abundance ofMethanosphaerathan the controls. Male runners expressed a significantly lower abundance ofEnterobacteriaceae (log2FoldChange =−5.7 ,p= 1.27×10 −6 ) at the family level, a significantly higher abun- dance ofMethanosphaera(log2FoldChange = 18.1,p= 2.03×10 −6 ) at the genus level,

Life2024,14, 1397 7 of 13 and a significantly lower abundance ofLactobacillus_ruminis(log2FoldChange =−20.3, p= 8.99×10 −9 ) at the species level compared with control group (Figure runners showed a significantly higher abundance ofMethanosphaera(log2FoldChange = 29.1, p= 1.68×10 −8 ) and a significantly higher abundance ofMitsuokella(log2FoldChange =15.8, p= 2.20×10 −6 ) at the genus level (Figure) compared with controls. Compared to male runners, female runners showed a significantly lower abundance ofEnterococcus (log2FoldChange =−5.8,p= 0.0007) but a significantly higher abundance ofLactobacillus (log2FoldChange = 6,p= 0.0005), and a significantly higher abundance ofMethanosphaera (log2FoldChange = 11.7,p= 2.10×10 −9 ) (Figure). Figure 3.The abundance of bacteria in female and male runners was identified by DESeq2. Log2FoldChange greater than zero indicated more abundant, whereas log2FoldChange less than zero indicated less abundant. Microbiome composition is associated with exercise performance: we found a positive correlation between the presence ofMethanosphaeraand lactate blood levels (r = 0.51, p-adj= 0.05) and time to exhaustion (r = 0.41,p-adj = 0.007). Additionally, we found a positive correlation between the presence ofMitsuokellawith VO2max (r = 0.41,p-adj = 0.05) and time to exhaustion (r = 0.41,p-adj = 0.03). Table in runners between bacteria at the species or genus level and physiological measures in exercise. The table includes positive correlations (p-adj≤0.05) with a linear relationship to the Pearson correlation coefficient above 0.4 (r > 0.4). Table physiological measures during exercise in runners. The Pearson correlation coefficient (r) quantifies the strength and direction of a linear relationship between two continuous variables, with values ranging from−1 to +1. A weak correlation (0 to 0.3 or−0.3 to 0) indicates minimal association, while a moderate correlation (0.3 to 0.7 or−0.7 to−0.3) suggests a more substantial relationship. Strong correlations (0.7 to 1.0 or−1.0 to−0.7) reflect a robust linear association, where changes in one variable closely relate to changes in the other. Understanding these correlations is essential for interpreting the dynamics between the studied variables.

The study investigates gut microbiota composition in competitive runners and its correlation with performance.