Abstract
ackground/Objectives:This exploratory study aimed to evaluate the impact of an80 km ultra-marathon trail running event on changes in faecal bacterial composition, and to inves- tigate whether any correlations exist between exercise-associated gastrointestinal symptoms (Ex-GIS) with faecal bacterial profiles. Such events represent a unique physiological stressor and may impact the composition of the gut microbiota. Studying this impact may provide insights into acute (i.e., <24 h) gut microbiota changes under extreme conditions.Methods: Thirteen endurance athletes (n= 7 males,n= 6 females) aged 41±8 years completed the 80 km Margaret River (Australia) ultra-marathon race in 2022. Faecal samples were collected pre- and post-race. Faecal bacterial profile, as per relative abundance (RA) of operational taxonomic units and the determination ofα-diversity (Shannon Equitability Index (SEI)), was achieved by 16S rRNA amplicon gene sequencing. Changes in RA% and SEI pre- to post-race were assessed by the Wilcoxon signed-rank test. Correlations between Ex-GIS with bacterial profile and changes pre-, during, and post-ultra-marathon race
collected pre- and post-race. Faecal bacterial profile, as per relative abundance (RA) of operational taxonomic units and the determination ofα-diversity (Shannon Equitability Index (SEI)), was achieved by 16S rRNA amplicon gene sequencing. Changes in RA% and SEI pre- to post-race were assessed by the Wilcoxon signed-rank test. Correlations between Ex-GIS with bacterial profile and changes pre-, during, and post-ultra-marathon race were determined by Spearman’s rank correlation coefficients.Results:Bacterial calculations of phyla (n= 5), family (n= 23), and genus (n= 41) were detected for RA (≥0.5%). A significant decrease pre- to post-race ofActinobacteriota(p= 0.035) phyla,Bifidobacteriaceae(p= 0.007), andClostridiaceae(p= 0.010) family, andBlautia(p= 0.039) andSubdoligranulum(p= 0.023) genus was determined; meanwhile,Oscillospiraceae(p= 0.016) andMonoglobaceae(p= 0.039) family significantly increased pre- to post-race. No other bacterial group changes were observed. No correlations were observed between pre- to post-ultra-marathon RA change and Ex-GIS.Conclusions:The completion of an 80 km ultra-marathon did not invoke substantial changes in the gut microbiota as determined by faecal bacterial profiling. Very strong and strong correlations were observed between certain bacterial groups and Ex-GIS; however, no significant correlations were observed between pre- to post-ultra-marathon changes in RA≥0.5% and Ex-GIS. Keywords:ultra-endurance; microbiota; EIGS; exercise-associated gastrointestinal symptoms Nutrients2025,17, 3275 https://doi.org/10.3390/nu17203275
Nutrients2025,17, 3275 2 of 17 1. Introduction Ultra-endurance sport is growing in popularity, with increasing participation across events such as single- and multi-stage ultra-marathons, ultra-distance triathlons, adven- ture racing, open-water swimming, and other activities lasting≥4 h [1,2]. These events impose significant physiological demands due to prolonged exertion, often compounded by environmental stressors like extreme temperatures (≤0 ◦ C to≥30 ◦ C), high altitudes (>3000 m), and rugged terrain [1]. Among these, ultra-marathon running (defined as any race exceeding 42.195 km) has seen notable growth in participation and adherence [3–5]. A key clinical concern with rising ultra-marathon participation is increased susceptibil- ity to exercise-induced gastrointestinal syndrome (EIGS), which arises from two primary mechanisms [6]. First, prolonged exertion redirects blood flow away from the gastroin- testinal tract, causing splanchnic hypoperfusion and intestinal epithelial damage, leading to increased permeability and impaired absorption [6–11]. Second, neuroendocrine dis- turbances, including elevated cortisol and sympathetic drive, suppress gut motility via altered enteric nervous system activity [7]. These pathways may facilitate translocation of luminal pathogens into systemic circulation, potentially triggering systemic inflammation and severe outcomes such as sepsis or multi-organ failure [7,12–21]. Various intrinsic and extrinsic factors may exacerbate EIGS onset [7,22,23]. One intrinsic factor warranting further investigation is the role of the gut microbiota in modulating EIGS and the severity of exercise-associated gastrointestinal symptoms (Ex-GIS) [7]. The gut bacterial profile may exert protective or harmful effects depending on its composition [19,22,24–28]. Increasedα-diversity and relative abundance (RA) of commensal bacteria (e.g.,Lachnospiraceae,Ruminococcus, and/orBacteroides) are associated with production of short-chain fatty acids (SCFAs) and other metabolites that enhance gut immunity, epithelial integrity, and motility [19]. Conversely, prolonged exertion may alter gut pH during hypoperfusion, disrupting bacterial function and potentially converting commensal bacteria into opportunistic pathogens [19,23]. These microbiota-mediated changes may contribute to the onset and severity of Ex-GIS, which frequently accompany EIGS. Over 60% of ultra-endurance athletes have reported symptoms such as nausea, bloating, and diarrhoea [6,12,29]. Upper-GIS symptoms are more common in dehydrated athletes or those exercising in heat, while lower-GIS symptoms like flatulence and bloating are often linked to prolonged activity (>4 h) [12,29]. Severe systemic EIGS
may contribute to the onset and severity of Ex-GIS, which frequently accompany EIGS. Over 60% of ultra-endurance athletes have reported symptoms such as nausea, bloating, and diarrhoea [6,12,29]. Upper-GIS symptoms are more common in dehydrated athletes or those exercising in heat, while lower-GIS symptoms like flatulence and bloating are often linked to prolonged activity (>4 h) [12,29]. Severe systemic EIGS has been associated with symptoms including vomiting and/or bloody defecation, likely due to compromised gut integrity and immune responses [1,9,10,12,30–33]. Currently, there is no standardised profile of the athlete’s gut microbiome, with significant intra- and inter-individual variability reported [22,24,25,32]. Literature on acute (<24 h) microbiome changes in response to exercise remains limited. Given the extremes of ultra-endurance events, it is plausible that changes to the bacterial profile of the gut microbiota would be substantially pronounced with ultra-endurance event participation. It also remains unclear whether these changes correlate with Ex-GIS severity. Accordingly, the aims of this study were to (1) determine whether completing an 80 km ultra- marathon trail event alters gut bacterial composition via faecal profiling; and (3) evaluate whether these changes correlate with Ex-GIS onset. We hypothesised that participation in an 80 km ultra-marathon trail running event would result in measurable alterations to gut bacterial composition, specifically (1) a reduction in Shannon Equitability Index (SEI) and relative abundance (RA) of short-chain fatty acid (SCFA)-producing commensal bacteria (e.g.,Lachnospiraceae,Ruminococcus, and/orBacteroides); and (2) an increase in RA of potentially pathogenic or opportunistic bacteria. Furthermore, we hypothesised that these microbiota changes would be positively correlated with the incidence and severity
Nutrients2025,17, 3275 3 of 17 of exercise-associated gastrointestinal symptoms (Ex-GIS), as reported by the participants post-race. 2. Materials and Methods 2.1. Participants Thirteen endurance-trained athletes [mean±SD (n= 7 males,n= 6 females): age41±8 years, height 174±9 cm, body mass 66.5±7.7 kg, weekly training load 456±158 min/week ] provided written informed consent to volunteer to participate in the study. The study protocol received approval from the local ethics committee (Monash University Human Research Ethics Committee (MUHREC: 29429) and conformed to the 2024 Helsinki Declaration for Human Research Ethics. The inclusion criteria for this study were endurance-trained athletes aged between 18 and 50 years who were free of illness and/or disease. All participants freely and voluntarily entered the Margaret River ultra- marathon. Participants were excluded from the study if they had gastrointestinal infections, diseases and/or disorders (e.g., coeliac disease, inflammatory bowel disease, irritable bowel syndrome, diverticular disease, gastro-esophageal reflux disease, history of gastrointestinal surgery, and/or other self-reported gastrointestinal ailments), consumed potential modi- fiers of gastrointestinal integrity (e.g., prebiotics, probiotics, and/or antibiotics), adhered to gastrointestinal-focused dietary regimes (e.g., low fermentable oligo, di-, mono-saccharide, and polyol (FODMAP) and/or fibre modified diets) within the previous 3 months, or consumed nonsteroidal anti-inflammatory medications and/or stool altering medications (e.g., laxatives and antidiarrheal) within 1 month before the ultra-marathon race. 2.2. Experimental Procedures Experimental procedures are demonstrated in Figure. To minimise participant burden on race day, baseline characteristics and resting biomarker data were obtained during the afternoon prior to the ultra-marathon race. All participants completed the 80 km ultra-marathon race, with Ex-GIS data recorded via recall of the entirety of the race by the athletes. Ex-GIS was determined via an exercise-specific, validated, and reliability-checked modified visual analogue scale (mVAS) gastrointestinal symptoms assessment tool [31]. The rating scale of the mVAS GIS tool is as follows: 0 indicates no GIS experienced,1–4 indicates mild GIS (i.e., the athlete is aware of the symptoms; however, the symptoms are not severe enough to impact exercise workload and/or cessation from the activity),5–9 indicates severe GIS (i.e., GIS is severe enough to noticeably impact exercise workload, but not to the point of cessation of the
mVAS GIS tool is as follows: 0 indicates no GIS experienced,1–4 indicates mild GIS (i.e., the athlete is aware of the symptoms; however, the symptoms are not severe enough to impact exercise workload and/or cessation from the activity),5–9 indicates severe GIS (i.e., GIS is severe enough to noticeably impact exercise workload, but not to the point of cessation of the activity), and 10 indicates extreme GIS and cessation from the activity [31]. A comprehensive dietary assessment and analysis was collected 72 h prior to the ultra-marathon, in accordance with the method described by Costa et al., 2014 [34]. Total energy (kCal), protein, carbohydrate, fat, water, and dietary fibre consumption were quantified via FoodWorks dietary analysis software (FoodWorks 10 Professional, v10.0. Brisbane, Australia: Xyris Pty Ltd., 2019). 2.3. Faecal Sample Collection and Bacterial Profiling All participants (n= 13) were instructed to provide an approximately 30 g mid-flow faecal sample into a sterile collection container (SARSTEDT Australia Pty Ltd., Mawson Lakes, South Australia, Australia), which was promptly stored at−20 ◦ C and subsequently transferred to−80 ◦ C for long-term preservation until further processing and analysis. All post-race measures were repeated in the same sequence within 15–30 min of race comple- tion to ensure procedural consistency. Competitors were given a 24-hour window to submit their initial post-race faecal sample. Faecal samples were thawed to room temperature and homogenised, allowing for 0.20–0.30 g of each sample to be transferred into a 2 mL dry
Nutrients2025,17, 3275 4 of 17 garnet bead microtube, in addition to bead solution. Mechanical and chemical cell lysis, sample purification, and DNA extraction were then completed according to the manufac- turer’s instructions (PowerFecal DNA isolation kit, Qiagen, Germantown, TN, USA), with pyrogen/DNAse/RNAse-free consumables within a biohazard ventilation cabinet to avoid artefact contamination (Safemate 1.2 ECO, AF Technologies Pty Ltd., Baywater North, Victo- ria, Australia). Blank control samples (pyrogen/DNAse/RNAse-free water) were run simul- taneously in duplicate and demonstrated minimal presence of microbial DNA concentration after analysis (11,730 and 11,065). Similarly, positive control samples (ZymoBIOMICS Fae- cal Reference with TruMatrixTM Technology) were also run simultaneously in duplicate and demonstrated vast amounts of microbial DNA concentration (5,458,933 and 5,099,250). An aliquot of 50µL of the purified extracted DNA sample was immediately frozen at −20 ◦ C prior to bacterial gene sequencing. The extracted genomic DNA was delivered to Micromon Genomics (Monash University, Clayton, Australia) for PCR amplification of the V3-V4 region of the 16S rRNA gene and sequencing. Reactions contained 1µM forward (TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTAC GGGNGGCWGCAG) and reverse (GTCTCGTGGGCTCGGAGATGTGTATA AGAGACAGGACTACHVGGGTATC- TAATCC) primers; 5µL of purified concentration-standardised genomic DNA; 25µL of 2×HiFi HotStart ReadyMix (Kapa Biosystems, Wilmington, MA, USA). Amplified DNA, derived from the triplicate reactions, was then pooled for each sample and purified using Ampure Axygen AxyPrep PCR Clean Mag Beads XP (0.6 V) according to the manufacturer’s instructions. The libraries were then pooled in equimolar concentrations and sequenced using a MiSeq V3 600c Reagent Kit (Illumina, San Diego, CA, USA). The assembled reads were then analysed via QIIME2 (v.2019.1) software, and underwent quality assessment, filtering, barcode trimming, and chimaera detection via the DADA2 pipeline [22]. Taxo- nomic evaluation was achieved with a 98% identity and confidence value ofp≤0.05% with the SILVA 138.1 release. Sequence variance counts for phyla, family, and genus were determined by dividing the number of reads of each taxon by the total number of reads from each faecal sample. 16S rRNA sequences per faecal sample ranged from 7924 to 20,140,332, and a rarefaction sampling depth of 197,298 was utilised for analysis due to the high sequencing yield observed across the majority
variance counts for phyla, family, and genus were determined by dividing the number of reads of each taxon by the total number of reads from each faecal sample. 16S rRNA sequences per faecal sample ranged from 7924 to 20,140,332, and a rarefaction sampling depth of 197,298 was utilised for analysis due to the high sequencing yield observed across the majority of samples, allowing for consistent comparison of diversity metrics without excluding any samples from analysis. For ampli- con sequence variants (AVS), only bacterial groups with≥0.5% RA across the combined dataset (i.e., pooled pre- and post-ultra-marathon samples) were included for data analysis to avoid the risk of the inclusion of artefact values in data analysis, resulting from potential contamination during sample handling (i.e., sample collection, processing, and analysis). Bacterial calculations of phyla (5 taxa), family (23 taxa), and genus (41 taxa) were adequately detected for RA (≥0.5%) and SEI determination. 2.4. Statistical Analysis Given the exploratory nature of the present study, the statistical power was deemed suf- ficient based on prior laboratory research demonstrating exercise-induced disturbances in gastrointestinal integrity, systemic responses, and changes in faecal bacterialprofiles [12,29] ; nevertheless, post hoc statistical power analysis was performed on G*Power (version 3.1), and relevant effect sizes were reported. Statistical analysis was completed with the use of SPSS statistical software (V.29.0, Chicago, IL, USA) with significance accepted atp≤0.05. Descriptive data within the text are demonstrated as mean±SD. Data within the tables are demonstrated as SEI, RA (%) of total identified bacteria, or mean±SD. Comparison data of faecal bacterial composition pre- to post-ultra-marathon was examined by using the Wilcoxon signed-rank test. Spearman’s Bivariate Correlation coefficient was used to
Nutrients2025,17, 3275 5 of 17 determine potential correlations between Ex-GIS and bacterial group RA changes pre- to post-ultra-marathon. Figure 1.Schematic illustration of the experimental, sample processing, and analysis procedures. 3. Results 3.1. Dietary Intake Inter-individual variability of total energy (1420–3773 kCal/day), protein(52–193 g/day), carbohydrate (23–543 g/day), fat (34–181 g/day), water (698–4218 mL/day), and dietary fibre (11–60 g/day) consumption for 72 h prior to the ultra-marathon event was demon- strated among the athletes that recorded their dietary intake. Given the sample size and the descriptive nature of the dietary intake data, these findings should be interpreted with cau- tion and not overemphasised; they primarily serve to illustrate the extent of inter-individual variability in nutritional strategies prior to the ultra-marathon. 3.2. Faecal Microbial Taxa The faecal bacterial taxa SEI and RA of predominant phyla (5 taxa), family (23 taxa), and genus (41 taxa) groups are demonstrated in Table. Before the ultra-marathon race, the identification of RA of bacterial phyla taxa in faecal samples includedFirmicutes,Bacteroidota, Actinobacteroita,Verrucomicrobiota, andProteobacteria(Supplementary Figure S1). The identi- fication of the relative abundance of bacterial family taxa was also achieved. The predomi- nant bacterial family taxa wereLachnospiraceae,Ruminococcaceae,Bacteroidaceae,Bifidobac- teriaceae,Peptostreptococcaceae,Akkermansiaceae,Prevotellaceae,Coriobacteriaceae,Erysipelato- clostridiaceae,Oscillospiraceae,Streptococcaceae,Clostridiaceae,Christensenellaceae,Rikenellaceae, Eggerthellaceae,Tannerellaceae,Barnesiellaceae,Erysipelotrichaceae,Coprostanoligenes,Enterobac- teriaceae,Monoglobaceae,Butyricicoccaceae, andVeillonellaceae(Supplementary Figure S2). Similarly, the identification of the relative abundance of bacterial genera was also sufficient. The predominant bacterial genera wereBlautia,Bacteroides,Faecalibacterium,Bifidobacterium, Agathobacter,Subdoligranulum,Ruminococcus,Anaerostipes,Akkermansia,Fusicatenibacter, Eubacterium hallii group,Collinsella,Coprococcus,Dorea,Prevotella,Erysipelotrichaceae UCG- 003,Clostridium,Streptococcus,Christensenellaceae,Romboutsia,Alistipes,Parabacteroides,Ru- minococcus torques group,Barnesiella,Intestinibacter,CAG-352,Eubacterium Coprostanoligenes,
Nutrients2025,17, 3275 6 of 17 Escherichia-Shigella,UCG-002,Roseburia,Monoglobus,Holdemanella,Ruminococcus gauvreauii group,Incertae Sedis,NK4A214 group,Lachnospiraceae,Butyricicoccus,Lachnospiraceae ND3007, Adlercreutzia,Phascolarctobacterium, andDialister(Supplementary Figure S3). As demon- strated in Table, there was no significant difference ( p> 0.05) in phyla, family, and/or genus SEI pre- to post-ultra-marathon. However, a significant alteration in bacterial RA for phyla, family, and genus groups pre- to post-ultra-marathon did occur (e.g.,Actinobac- teriotaphyla (−1.372%),Bifidobacteriaceaefamily (−0.258%), andSubdoligranulumgenus (−0.959%)). Table 1.Shannon Equitability Index (SEI) and relative abundance (RA%) of faecal bacterial phylum, family, and genus AVS at rest prior to and post an 80 km ultra-marathon trail running event. Pre-Ultra-Marathon Post-Ultra-Marathon ∆ Faecal phylum SEI 0.171 (0.035) 0.179 (0.032) 0.008 (0.034) Actinobacteriota 8.808 (4.432) 7.436 (5.896) −1.372 (4.210) * Bacteroidota 17.119 (10.753) 21.234 (9.285) −4.115 (9.533) Firmicutes 70.364 (8.152) 67.523 (7.607) −2.841 (7.895) Proteobacteria 0.878 (2.231) 1.604 (2.532) 0.726 (2.244) Verrucomicrobia 2.827 (3.312) 2.200 (2.754) −0.627 (2.765) Faecal family SEI 0.234 (0.032) 0.233 (0.024) −0.001 (0.039) Bifidobacteriaceae 5.073 (3.862) 4.815 (4.708) −0.258 (1.699) * Coriobacteriaceae 2.592 (2.409) 1.994 (1.663) −0.597 (0.763) Bacteroidaceae 10.490 (8.573) 11.933 (7.631) 1.443 (3.171) Prevotellaceae 2.696 (4.307) 5.122 (8.546) 2.425 (2.653) Erysipelotrichaceae 1.021 (1.901) 1.113 (2.513) 0.091 (0.849) Lachnospiraceae 36.141 (10.366) 34.510 (9.168) −5.8543 (3.405) Oscillospiraceae 2.356 (1.860) 2.771 (2.196) 0.414 (0.789) * Ruminococcaceae 18.790 (5.770) 19.943 (7.182) 1.152 (2.589) Peptostreptococcaceae 2.937 (4.154) 1.352 (1.619) −1.584 (0.900) Akkermansiaceae 2.927 (3.404) 2.294 (2.897) −0.633 (1.264) Eggerthellaceae 1.072 (1.020) 0.684 (0.585) −0.388 (0.290) Barnesiellaceae 1.035 (1.124) 1.144 (1.066) 0.109 (0.431) Rikenellaceae 1.264 (1.319) 1.494 (1.390) 0.229 (0.541) Tannerellaceae 1.067 (1.210) 1.324 (1.072) 0.257 (0.452) Erysipelatoclostridiaceae 2.495 (2.828) 2.225 (2.965) −0.270 (1.158) Streptococcaceae 1.635 (3.402) 1.166 (1.226) −0.468 (1.032) Christensenellaceae 1.552 (1.881) 1.262 (1.496) −0.289 (0.655) Clostridiaceae 1.581 (4.236) 0.577 (1.270) −1.003 (0.417) * Monoglobaceae 0.736 (0.612) 0.741 (0.728) 0.005 (0.265) * Butyricicoccaceae 0.642 (0.805) 0.526 (0.416) −0.115 (0.258) Coprostanoligenes 0.797 (0.674) 0.717 (0.476) −0.080 (0.207) Veillonellaceae 0.312 (0.547) 0.860 (1.557) 0.548 (0.446) Enterobacteriaceae 0.776 (2.351) 1.419 (2.647) 0.642 (1.001) Faecal genus SEI 0.265 (0.021) 0.264 (0.015) 0.001 (0.017) Bifidobacterium 5.406 (4.039) 5.144 (4.993) −0.262 (1.781) Bacteroides 11.422 (9.543) 12.852 (8.296) 1.430 (3.507) Agathobacter 4.718 (3.835) 5.431 (4.568) 0.713 (1.654) Anaerostipes 3.188 (2.514) 3.492 (3.210) 0.304 (1.131)
−0.115 (0.258) Coprostanoligenes 0.797 (0.674) 0.717 (0.476) −0.080 (0.207) Veillonellaceae 0.312 (0.547) 0.860 (1.557) 0.548 (0.446) Enterobacteriaceae 0.776 (2.351) 1.419 (2.647) 0.642 (1.001) Faecal genus SEI 0.265 (0.021) 0.264 (0.015) 0.001 (0.017) Bifidobacterium 5.406 (4.039) 5.144 (4.993) −0.262 (1.781) Bacteroides 11.422 (9.543) 12.852 (8.296) 1.430 (3.507) Agathobacter 4.718 (3.835) 5.431 (4.568) 0.713 (1.654) Anaerostipes 3.188 (2.514) 3.492 (3.210) 0.304 (1.131) Blautia 13.810 (7.621) 10.543 (4.801) −3.267 (2.498) *
Nutrients2025,17, 3275 7 of 17 Table 1.Cont. Pre-Ultra-Marathon Post-Ultra-Marathon ∆ Fusicatenibacter 2.923 (1.908) 3.088 (2.641) 0.164 (0.903) Eubacterium hallii group 2.890 (1.808) 3.465 (2.054) 0.575 (0.759) Faecalibacterium 11.224 (7.208) 12.114 (5.194) 0.890 (2.464) Ruminococcus 3.206 (2.626) 3.574 (2.915) 0.368 (1.088) Subdoligranulum 3.720 (1.434) 22.761 (1.427) −0.959 (0.561) * Collinsella 2.807 (2.621) 2.120 (1.731) −0.686 (0.871) Adlercreutzia 0.351 (0.565) 0.230 (0.298) −0.121 (0.177) Barnesiella 1.090 (1.194) 1.204 (1.150) 0.113 (0.113) Prevotella 1.909 (3.724) 4.681 (9.559) 2.772 (2.845) Alistipes 1.251 (1.363) 1.487 (1.545) 0.235 (0.571) Parabacteroides 1.160 (1.336) 1.413 (1.134) 0.253 (0.486) Erysipelotrichaceae UCG-003 1.798 (2.650) 1.561 (3.024) −0.236 (1.115) Holdemanella 0.782 (1.936) 0.862 (2.655) 0.079 (0.911) Streptococcus 1.719 (3.549) 1.218 (1.262) −0.501 (1.044) Christensenellaceae 1.704 (2.121) 1.385 (1.701) −0.318 (0.754) Clostridium 1.780 (4.856) 0.650 (1.479) −1.129 (1.408) Lachnospiraceae 0.715 (0.605) 0.877 (0.666) 0.162 (0.249) Coprococcus 2.129 (1.676) 2.002 (1.420) −0.126 (0.609) Dorea 2.094 (0.966) 2.143 (1.035) 0.048 (0.392) Lachnospiraceae ND3007 0.606 (1.066) 0.700 (1.019) 0.093 (0.409) Roseburia 0.786 (0.723) 0.979 (0.969) 0.192 (0.335) Ruminococcus gauvreauii group 0.754 (0.909) 0.682 (0.882) −0.072 (0.351) Ruminococcus torques group 1.140 (0.930) 1.017 (1.035) −0.122 (0.386) Monoglobus 0.784 (0.636) 0.791 (0.762) 0.007 (0.275) Butyricicoccus 0.667 (0.863) 0.529 (0.437) −0.139 (0.268) NK4A214 group 0.740 (0.944) 0.868 (1.132) 0.128 (0.409) UCG-002 0.801 (0.899) 0.916 (0.922) 0.115 (0.357) CAG-352 0.906 (2.785) 0.752 (2.620) −0.154 (1.060) Incertae Sedis 0.753 (0.900) 1.792 (5.083) 1.039 (1.431) Eubacterium Coprostanoligenes 0.865 (0.735) 0.777 (0.522) −0.088 (0.250) Intestinibacter 1.071 (2.035) 0.523 (0.705) −0.548 (0.597) Romboutsia 1.665 (3.400) 0.666 (0.848) −0.999 (0.972) Phascolarctobacterium 0.347 (0.421) 0.502 (0.498) 0.154 (0.181) Dialister 0.322 (0.591) 0.715 (1.333) 0.393 (0.404) Escherichia-Shigella 0.811 (2.446) 0.954 (1.975) 0.143 (0.872) Akkermansia 3.155 (3.662) 2.514 (3.286) −0.641 (1.364) Mean (SD) of≥0.5% RA (n= 13): *p< 0.05 pre- to post-ultra-marathon. AVS: amplicon sequence variant, RA: relative abundance, SEI: Shannon Equitability Index. 3.3. Exercise-Associated Gastrointestinal Symptoms (Ex-GIS) Ex-GIS reported pre-, during, and immediately post-ultra-marathon race is presented in Table. All participants reported some form of Ex-GIS along the ultra-marathon. Ex-GIS types reported pre-, during, and post-ultra-marathon were, however, mild in severity (mVAS < 5). The most commonly reported upper-GIS reported was belching, whilst flatu- lence was
Description
The study investigates gut microbiota changes in endurance athletes post ultra-marathon.