Abstract
e recently have shown that the gut microbiota composition in female and male runners positively correlates with sports, and female runners show similar gut microbiome diversity to male runners. However, gut microbiota composition has not yet been fully investigated in other endurance athletes, such as cyclists. Therefore, in the current study, we investigated the gut microbiome profiles in competitive, non-professional female and male cyclists compared to what we have shown in runners. We aim to understand (1) whether the gut microbiome signature is sport-specific; (2) whether there is a microbiome difference between female and male cyclists and runners; and (3) whether the gut bacteria expressed in cyclists and runners correlates with exercise performance. Our study included 58 subjects: 18 cyclists (9 males), 22 runners (13 males), and 18 control subjects (9 males). Fecal samples were obtained and subjected to taxonomic analysis
signature is sport-specific; (2) whether there is a microbiome difference between female and male cyclists and runners; and (3) whether the gut bacteria expressed in cyclists and runners correlates with exercise performance. Our study included 58 subjects: 18 cyclists (9 males), 22 runners (13 males), and 18 control subjects (9 males). Fecal samples were obtained and subjected to taxonomic analysis to assess the relative abundances of species across subjects based on 16S rRNA sequencing results. Both alpha and beta diversity of the bacterial communities were evaluated to identify compositional variations between the groups. Each participant completed a maximal oxygen consumption test and a time-to-exhaustion test at 85% of the measured VO2max. Cyclists performed the test on an SRM ergometer, while runners used a motorized treadmill. Blood lactate levels were measured at 5 min intervals throughout the time-to-exhaustion trials. Alpha diversity demonstrated a significant difference (p-adj< 0.001) between cyclists and runners. Male cyclists showed significantly lower alpha diversity than runners (p-adj< 0.001). The taxonomic analysis of gut microbiota composition between cyclists, runners, and controls showed a lower or higher abundance of fifteen different bacteria. In cyclists, there was a significant positive correlation between six bacteria, and in runners, there was a significant positive correlation between eight bacteria, with weekly training volume, time-to-exhaustion, VO2max, and blood lactate levels. This study suggests potential sport-specific characteristics in long-distance cyclists’ and runners’ gut microbiome signatures. These findings emphasize the differences in gut microbiota between cyclists and runners, probably due to the difference in physiological and biomechanical conditions related to the activity mode during each sport. Keywords:gut microbiome profile; gut microbiota; long-distance endurance athletes; cyclists; runners 1. Introduction Recently, we have shown that the gut microbiota composition in competitive, non- professional female and male runners is positively correlated with sports performance [1]. These findings suggested that gut microbiota may be crucial in athletic performance, potentially influencing energy metabolism, inflammation, and recovery. In addition, studies Life2024,14, 1703.
positively correlated with sports performance [1]. These findings suggested that gut microbiota may be crucial in athletic performance, potentially influencing energy metabolism, inflammation, and recovery. In addition, studies Life2024,14, 1703.
Life2024,14, 1703 2 of 13 have shown that the microbiome of athletes is characterized by a higher amount of short- chain fatty acids, which can be energy substrates during exercise [2]. In studies examining the relationship between exercise and the microbiome, most data have focused on runners, with considerably less attention given to other endurance athletes. Research on the microbiome of competitive cyclists remains limited. For instance, Petersen et al. [3] investigated the gut microbiota of both professional and amateur cyclists, revealing that the proportion ofPrevotellabacteria in their microbiota increased with training intensity. These microorganisms metabolize carbohydrates and amino acids, including branched-chain amino acids [3,4]. Their pilot study offered the first insight into the gut microbiota of cyclists, uncovering significant correlations between the microbial taxa present in professional cyclists and those associated with high exercise intensity. The study of the gut microbiome profiles in endurance athletes from various sports disciplines, including cyclists and runners, and the conditions during exercise that may influence gut bacterial composition represent an intriguing area of research. This is particu- larly relevant because different sports impose distinct biomechanical forces on the athlete, which may, in turn, impact the gut. For example, while runners engage in dynamic move- ments involving jumping and landing during exercise, cyclists maintain a seated position with a fixed body posture during their activity. However, the potential influence of gut microbiota on athletic performance underscores the need for further research. Specifically, it is better to characterize the microbiota of athletes across different sports disciplines and investigate how these microbial communities vary between sports that involve distinct physical demands. Moreover, most gut microbiota studies are predominantly focused on male subjects. Research comparing the gut microbiome profiles of females and males in the general popu- lation is sparse and yields inconclusive results. However, human and animal studies have identified sex-based differences in the microbial composition [5–8]. Comparative studies between male and female athletes are even less common. Regarding the gastrointestinal microbiome, there appears to be a difference between females and males, which may be attributed to variations in estrogen levels [9]. In contrast, our previous
lation is sparse and yields inconclusive results. However, human and animal studies have identified sex-based differences in the microbial composition [5–8]. Comparative studies between male and female athletes are even less common. Regarding the gastrointestinal microbiome, there appears to be a difference between females and males, which may be attributed to variations in estrogen levels [9]. In contrast, our previous study found no difference in the overall microbiome composition between female and male competitive runners [1]. This raises important questions regarding the differences in microbiome com- position between male and female cyclists. Additionally, it is crucial to investigate whether there are distinctions in the microbiome profiles of male runners compared to cyclists and between female runners and cyclists. The current study aimed to determine the gut microbiota composition in long-distance endurance female and male cyclists and runners, to define whether the gut microbiome signature is sport-specific, and to examine the correlation between gut bacteria in cyclists and runners and exercise performance indicators, such as VO2max, blood lactate levels, and time-to-exhaustion. Additionally, the study explored potential microbiome differences between female and male cyclists and runners. 2. Materials and Methods 2.1. Study Design and Participants We recruited long-distance endurance cyclists and runners from competitive sports groups, while participants in the control group were recruited from the general population. Fifty-eight subjects participated in the study (31 males and 27 females). They included 18 cyclists (9 males), with a mean age of 46±7 years; 22 runners (13 males), with a mean age of 43±6.5 years; and 18 control subjects (9 males), with a mean age of 41±7.4 years. The cyclists and runners were amateur, competitive, non-professional athletes who competed at the domestic level, and their training was intense. The cyclists were defined as competitive endurance athletes who cycle at least 120 km per week, and the runners as competitive endurance athletes who run at least 50 km per week. The control group performed light physical activity with a weekly running volume of less than 5 km per week.
endurance athletes who cycle at least 120 km per week, and the runners as competitive endurance athletes who run at least 50 km per week. The control group performed light physical activity with a weekly running volume of less than 5 km per week.
Life2024,14, 1703 3 of 13 Each participant filled out an online survey detailing their weekly training regimen, including the number of training days per week, total duration per session, exercise inten- sity (expressed as a percentage of maximum heart rate), and eating habits (types of foods consumed, portion sizes, and meal frequency). This information was gathered to assess their dietary patterns, which may influence the composition of the gut microbiota. Based on this information, we included participants whose diet consisted of meat, fish, fruits, vegetables, and grains in more or less similar quantities throughout the week, without any dramatic deviations or significant differences in quantities between one participant and an- other. The ethnic origin of all the participants in this study was Ashkenazi Jews (originating from Europe and North America). Therefore, it can be assumed that no significant cultural differences among them would lead to substantial variations in the types of food consumed. To minimize variability in diet, only omnivorous participants were included. Individu- als who had taken supplements, such as probiotics, prebiotics, multivitamins, antacids (e.g., beta-alanine, sodium bicarbonate, among others), or antibiotics within 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 a maximal exercise stress test to evaluate their aerobic fitness level. Maximal oxygen consumption (VO2max) was measured using a cycling test on a stationary ergometry (SRM) for cyclists or a running test on a treadmill (H/P Cosmos) for the runners and the control group, with gas exchange analyses using a COSMED Quark metabolic cart (COSMED S.r.l., Rome, Italy) [10,11]. The cyclists were instructed to begin cycling at a workload of 80 watts, maintaining an average pedaling cadence of 80 revolutions per minute. The workload was increased by 20 watts every minute, while the average cadence was kept constant throughout the exercise until exhaustion. The runners and
exchange analyses using a COSMED Quark metabolic cart (COSMED S.r.l., Rome, Italy) [10,11]. The cyclists were instructed to begin cycling at a workload of 80 watts, maintaining an average pedaling cadence of 80 revolutions per minute. The workload was increased by 20 watts every minute, while the average cadence was kept constant throughout the exercise until exhaustion. The runners and controls were instructed to begin running at a speed corresponding to 50% of their assessed running economy, and the speed progressively increased every minute until reaching exhaustion. A week later, each subject performed a sub-maximal exercise test at 85% of the mea- sured VO2max until exhaustion to determine ‘time-to-exhaustion’ [12]. The runners per- form the test on the H/P Cosmos treadmill, and the cyclists on the SRM ergometer. Heart rate (HR) was monitored using a POLAR watch (Polar Electro Oy, Kempele, Finland). Capillary blood lactate levels were measured from fingertip sampled every 5 min during the time-to-exhaustion test using a Lactate Scout+ hand-held analyzer (EKF Diagnostics GmbH, Barleben, Germany). 2.3. Gut Microbiome Analysis The detailed description of stool sample collection, DNA extraction, PCR protocol, sequencing, and analysis is described in Shalmon et al. [1]. Briefly, fecal samples were collected using sterile stomacher ® bags, aliquoted, and stored at−80 ◦ C. DNA was extracted using the MagCore Genomic DNA Tissue Kit and amplified with custom primers targeting the V4 region of the 16S rRNA gene. Sequencing was performed on a MiSeq platform, and the resulting data were processed using the QIIME2 pipeline for quality filtering, OTU clustering, and diversity analyses. 2.4. Statistical Analyses A comparison of cardiorespiratory measures, weekly training volume, and body mass index (BMI) among the three groups (cyclists, runners, and controls) was performed utilizing One-Way ANOVA in IBM SPSS Statistics (version 29), supplemented by post hoc
Life2024,14, 1703 4 of 13 Bonferroni analysis. Furthermore, sex differences were assessed through independent- samplet-tests, also conducted in IBM SPSS Statistics (version 29). For the gut microbiome analysis, non-parametric statistical techniques were applied, assuming non-normal distribution and unequal variance. The Kruskal–Wallis test was used to compare the differences among groups (male vs. female, cyclists, runners, and controls). Alpha diversity was evaluated using the estimate_richness function and Faith’s Phylogenetic Diversity (Faith’s PD). For beta diversity, unweighted and weighted UniFrac distances and Bray–Curtis dissimilarities were computed, and principal coordinate analysis (PCoA) was employed to visualize these distances. Differential abundance in the gut mi- crobiome was determined using DESeq2 (version 1.36.0) from the R/Bioconductor package (version 3.19). Taxa were assigned to the lowest possible taxonomic classification, with differential abundance assessed based on adjustedp-values < 0.05 and |log2FoldChange| ≥0.58. Sex was incorporated as a blocking factor in the differential analysis. Boxplots were generated using ggplot2 (version 3.4.4), while correlations with clinical parameters were computed using the corr.test function from the psych package (version 2.3.9) and visualized using the corrplot package (version 0.92). Further details of the gut microbiome statistical analyses are provided in our previous publication [1]. 3. Results 3.1. Participants’ Characteristics and Cardiopulmonary Exercise Tests As expected, the cyclists’ and runners’ cardiopulmonary exercise test results were higher than the controls. A statistically significant difference was observed in the First Venti- latory Threshold (VT1), with the runners showing a significant difference from the controls (p-adj< 0.001) and cyclists (p-adj< 0.015). Similarly, for the Second Ventilatory Thresh- old (VT2), both the cyclists and runners exhibited significant differences compared to the controls (p-adj< 0.001). The VO2max was significantly higher in both the runners (p-adj< 0.001) and cyclists (p-adj= 0.007) compared to the controls. Finally, time-to- exhaustion was significantly higher in runners compared to controls (p-adj< 0.001), and cyclists showed a marginally significant difference (p-adj= 0.05) compared to controls. In addition to the cardiometabolic measures, blood lactate levels revealed a statistically significant difference only between the cyclists and the control group (p-adj= 0.018), with no significant difference found between the runners and controls (p-adj= 0.7). While there
was significantly higher in runners compared to controls (p-adj< 0.001), and cyclists showed a marginally significant difference (p-adj= 0.05) compared to controls. In addition to the cardiometabolic measures, blood lactate levels revealed a statistically significant difference only between the cyclists and the control group (p-adj= 0.018), with no significant difference found between the runners and controls (p-adj= 0.7). While there was a difference in the weekly training volume between the cyclists (174±54 km) and runners (67±15 km), no differences were found in the VT2 or VO2max. How- ever, a significant difference was found in the VT1 (p-adj= 0.015) and time-to-exhaustion (p-adj= 0.042). Table In our comparison of male and female cyclists and runners, we observed that male runners exhibited higher time-to-exhaustion than male cyclists (p= 0.03). Similarly, female runners had higher time-to-exhaustion than female cyclists (p= 0.05). However, when analyzing the VT1 and VO2max—two other critical metrics in exercise testing for assessing athletic performance—we found no statistically significant differences between female cyclists and female runners (VT1,p= 0.7; VO2max,p= 0.6) or between male cyclists and male runners (VT1,p= 0.3; VO2max,p= 0.8). The characteristics of the female and male runners and cyclists are presented in Table.
Life2024,14, 1703 5 of 13 Table 1.Characteristics of the participants. Cyclists (n= 18) Runners (n= 22) Controls (n= 18) Sex Females 9 (50.0%) 9 (40.9%) 9 (50.0%) Males 9 (50.0%) 13 (59.1%) 9 (50.0%) Age Mean (SD) 45 (5.12) 43.3 (7.56) 39.4 (5.6) BMI Mean (SD) 22.9 (3.25) 23.2 (2.61) 23.9 (4.01) Weekly training volume (km) Mean (SD) 174 (54) 67 (15.6) 5 (0) Cardiopulmonary indices VT1 (mL/kg/min) 31.36 ±5.9 35.86 ±4.4 26.47 ±3.9 VT2 (mL/kg/min) 40.6 ±9 43.2 ±5.7 31.7 ±4.5 VO2max (mL/kg/min) 44.62 ±9.6 46 ±6.7 36.7 ±5.4 Time-to-exhaustion (min) 11.3 ±3.9 15.43 ±6.7 7.4 ±3.1 Lactate max (mmol/L) 9.5 ±2.6 8 ±1.6 7.2 ±2.8 Table 2.Characteristics of the female and male cyclists and runners. Female Cyclists (n= 9) Male Cyclists (n= 9) Female Runners (n= 9) Male Runners (n= 13) BMI Mean (SD) 21.49 (3.04) 24.22 (2.9) 21.06 (1.46) 24.61 (2.2) Weekly training volume (km) Mean (SD) 170.55 (53.41) 177.77 (57.61) 61.11 (12.6) 71.15 (16.6) Cardiopulmonary indices VT1 (mL/kg/min) 31.22 ±5.99 31.51 ±6.19 34.25 ±4.37 36.97 ±4.38 VT2 (mL/kg/min) 39.35 ±8.02 41.86 ±10.27 40.52 ±4.33 45.06 ±5.93 VO2max (mL/kg/min) 42.44 ±8.3 46.8 ±10.7 43.87 ±5.4 47.5 ±7.4 Time-to-exhaustion (min) 10.38 ±4.11 12.21 ±3.67 15.20 ±7.83 15.59 ±6.22 Lactate max (mmol/L) 8.45 ±2.97 10.53 ±1.91 7.71 ±1.52 8.33 ±1.79 3.2. Microbiome Findings Alpha diversity was significantly lower in cyclists than in runners (p-adj< 0.001). While alpha diversity was significantly higher in runners than controls (p-adj= 0.04), no difference was observed between cyclists and controls (p-adj= 0.18) (FigureA). A comparison of alpha diversity between females and males within each group did not reveal any statistically significant differences. However, male runners exhibited significantly higher alpha diversity than male cyclists (p-adj< 0.001) (FigureB). Beta diversity is a key metric for characterizing the differences in microbial community composition between samples, reflecting the degree of dissimilarity in species distribution across ecological niches or experimental groups. We investigated beta diversity differences between cyclists and runners. Our findings revealed that Principal Coordinate Analysis (PCoA) based on unweighted UniFrac distance demonstrated distinct group clustering (p-adj= 0.002). However, no significant differences were observed in analyses using weighted
the differences in microbial community composition between samples, reflecting the degree of dissimilarity in species distribution across ecological niches or experimental groups. We investigated beta diversity differences between cyclists and runners. Our findings revealed that Principal Coordinate Analysis (PCoA) based on unweighted UniFrac distance demonstrated distinct group clustering (p-adj= 0.002). However, no significant differences were observed in analyses using weighted UniFrac distance or Bray–Curtis dissimilarity (p-adj= 0.8 andp-adj= 0.3, respectively). Furthermore, no differences in beta diversity were found between females and males within any of the groups.
Description
This study explores gut microbiome differences between cyclists and runners.