Introduction
General overview of the gut microbiota
The gut microbiome comprises approximately 40 trillion microorganisms, including
bacteria, viruses, archaea and fungi. Of these, bacteria are the most abundant,
residing throughout the gastrointestinal tract, with the highest density in the
large intestine and lowest in the stomach.[1]
[2] This distribution
reflects compositional differences between sections of the gastrointestinal
tract.[3] The human gut
microbiota plays essential roles in immunomodulation, food digestion, nutrient
absorption, metabolite production and vitamin metabolism, and the maintenance of
intestinal epithelium integrity.[4]
[5]
[6] Most gut bacteria in humans belong
to four dominant phyla: Bacillota (previously Firmicutes), Bacteroidota
(previously Bacteroidetes), Actinomycetota (previously Actinobacteria) and
Pseudomonadota (previously Proteobacteria),[6] according to the International Code of Nomenclature of
Prokaryotes (ICNP).
Athlete-specific microbial profiles
This bacterial distribution is similarly observed in athletes, with Bacillota
(62–74%), Bacteroidota (10–34%), Actinomycetota (1–3%) and Pseudomonadota (1–9%)
consistently reported as predominant phyla. At the family level, Lachnospiraceae
(25–33%), Ruminococcaceae (20–34%), Bacteroidaceae (5–20%) and Prevotellaceae
(5–10%) are the most prevalent.[7]
[8]
[9]
[10] However, considerable variation exists at the genus and
potentially species levels (e.g., Bacteroides, Alistipes,
Blautia, Agathobacter, Faecalibacterium,
Phascolarctobacterium, Prevotella, Roseburia,
Subdoligranulum), with limited consistency across studies despite
comparable experimental designs and attempts to control for key
confounders.[7]
[8]
[9]
[10] This variability
complicates the interpretation of microbial profiles in athletic cohorts.
Influences on gut microbiota and rationale for interventions
The human gut microbiota, defined as the collective genomes of microorganisms
residing within the gut microbiome, is influenced by numerous factors, including
age, medication, early-life exposures, diet, supplementation, pharmaceuticals,
psychological stress, exercise and disease or illness.[11] Although relatively stable over
time,[12] it remains responsive
to external factors, with nutrient supplementation, diet and exercise recognised
as primary modulators.[13]
[14]
[15]
[16]
[17] These alterations may, in turn,
influence host metabolic and immune functions, potentially contributing to
enhanced exercise performance, as suggested by Di Dio et al.,[18] Jarrett et al.,[19] Patel et al.,[20] and Santibanez-Gutirrrez et
al.[21] Despite growing interest
in this area, a gap remains in the scientific literature regarding the effects
of intervention-induced changes in gut microbiota on direct exercise performance
outcomes. These outcomes include time to exhaustion (TTE), time-trial (TT),
Cooper’s test, strength and anaerobic performance, as well as functional
performance measures, such as balance, flexibility and muscular endurance, as
well as physiological indicators like V̇O2max or
V̇O2peak. These outcomes are variably assessed across
studies included in this review.[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
Short-chain fatty acids and proposed mechanistic links
Growing interest in the role of the gut microbiota in physical performance has
prompted exploration of both its compositional and functional attributes.[41] Research has proposed that
characteristics of the gut microbiota, such as microbial diversity (e.g.,
α-diversity) and the abundance of short-chain fatty acid (SCFA) producing
commensal bacterial species, differ between physically active individuals,
including athletes, and their sedentary or non-athlete counterparts.[42] Exploratory studies have reported
that SCFA-producing commensal bacteria (e.g., Faecalibacterium
prausnitzii, Roseburia hominis, Akkermansia muciniphila),
microbial metabolic pathways (e.g., amino acid and carbohydrate metabolism) and
faecal metabolites (e.g., SCFAs such as acetate, butyrate and propionate) are
positively correlated with physical activity. In some studies, physical activity
was objectively measured using accelerometer-derived activity counts to
distinguish sedentary, light and moderate-to-vigorous intensities,[43] whereas others classified
participants by training status or activity level using self-reported
questionnaires.[44]
[45] Additionally, total exercise
volume has been positively associated with a greater abundance of
Prevotella, which in turn correlates with amino acid and carbohydrate
metabolic pathways.[46]
Sport-specific differences have also been reported; for example, genera such as
Faecalibacterium, Sutterella, Clostridium,
Haemophilus and Eisenbergiella have been observed in higher
abundance in bodybuilders compared with sedentary controls and distance
runners.[47]
Animal studies have proposed mechanistic links between specific bacterial taxa
and performance outcomes.[48]
[49] For instance, provision of
Veillonella atypica improved treadmill performance in mice,
potentially due to its capacity to metabolise lactate into propionate, a
SCFA-linked hypothesis to influence performance. However, the precise mechanisms
remain unclear.[12] While
Veillonella has been identified in the faecal samples of highly
trained endurance athletes compared with sedentary controls,[12] its abundance has not been
consistently observed across other exploratory studies in endurance
athletes.[7]
[8]
[9]
[44]
[45]
[46] The proposal that
Veillonella increases exercise performance has been contested.
Notably, the control group in the Veillonella study received L.
delbrueckii subsp. bulgaricus, a lactate-producing bacterium,
which may have confounded the performance outcomes.[50]
More broadly, the hypothesis that propionate, or SCFAs more generally, enhances
exercise performance in humans is not strongly supported. For instance,
increases in faecal or plasma SCFAs (i.e., acetate, butyrate and/or propionate)
following high FODMAP intakes in endurance-trained individuals have not
translated to improved performance outcomes, though they may offer some
protection against disturbances to gastrointestinal integrity associated with
exercise-induced gastrointestinal syndrome.[8]
[51]
[52] SCFAs are critical to host energy
metabolism and may influence skeletal muscle activity[53] via enhanced carbohydrate uptake,
lipid metabolism and fatty acid oxidation,[54] processes associated with improved exercise performance.[15] Nonetheless, it remains uncertain
whether performance improvements observed in animal or human studies are
directly attributable to changes in gut microbiota composition, SCFA production
or the intervention itself.
Biotics and microbiota-targeted strategies
Prebiotics, probiotics and synbiotics, collectively referred to as biotics, are
well-studied functional food strategies for modifying the gut microbiota.[55] Prebiotics are non-digestible
substrates that are fermented by gut bacteria [56]; probiotics are live
microorganisms that, when consumed in sufficient quantities, confer health
benefits to the host [57]; and
synbiotics are combinations of the two, proposed to confer beneficial effects on
gastrointestinal health [58].
Following the 2019 consensus definition, postbiotics, preparations containing
inactivated microorganisms and/or their components that confer health benefits
to the host, have also gained attention in scientific research and commercial
applications.[59] In sport and
exercise contexts, biotics are of interest both for managing gastrointestinal
issues among athletes and for potentially enhancing performance.[60] Proposed mechanisms include
improved nutrient absorption and gastrointestinal integrity, modulation of
immune function via strengthening of the gut barrier, increased production of
antimicrobial proteins (e.g., β-defensin, IgA) and regulation of cytokine
secretion via NFκB and MAPK pathways.[55]
[56]
[57]
[61]
[62] Collectively, these mechanisms
may contribute to enhanced physiological resilience and nutrient
availability.[7]
[53]
[63]
[64]
[65]
[66] However, there remains a lack of
systematic reviews assessing whether observed performance benefits are
specifically linked to microbiota changes and whether methodological confounders
such as diet, training load and study design are adequately addressed.
Other dietary and exercise interventions
In addition to biotics, other strategies have been explored for their
microbiota-modulating potential and possible effects on exercise performance.
These include α-cyclodextrin (αCD), a non-digestible carbohydrate with selective
microbial effects and links to relevant metabolic pathways.[67]
[68]
[69]
[70] High-carbohydrate diets,
particularly those rich in fermentable substrates like FODMAPs, have also been
shown to modulate bacterial composition and increase SCFA production,
potentially supporting gastrointestinal integrity and fuel availability during
exercise.[8]
[51] Physical activity itself,
including both aerobic and resistance training, can independently modulate gut
microbial diversity and metabolite profiles, with possible implications for host
physiology and training adaptations.[20]
[44]
[45]
[55]
Aim and objectives
Despite growing interest and emerging evidence that both support and challenge
the performance benefits of gut microbiota-targeted interventions, a systematic
and comprehensive synthesis of the literature is lacking. This systematic
literature review aims to critically evaluate current evidence to determine
whether exercise performance can be improved through microbiota modulation via
nutritional supplementation, dietary modification or exercise interventions. The
review will also highlight methodological limitations and propose directions for
future research in this evolving area.
Materials and methods
This systematic review was conducted following the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines[71] and registered with PROSPERO
(CRD42024551751) (http://www.crd.york.ac.uk/PROSPERO).
Search strategy
A three-step search strategy was developed in consultation with an academic
librarian to identify relevant English-language studies. Searches were conducted
across five electronic databases, Ovid MEDLINE, EMBASE, CINAHL Complete, Web of
Science and Scopus from inception to February 2025. Reference lists of included
studies and additional sources known to the authors were screened to identify
missed publications. The keywords applied in the literature search are shown in
[Table 1]. Search terms were
tailored to each database’s structure (e.g., CINAHL complete, Scopus and Web of
Science) to ensure comprehensive retrieval of relevant literature. Keyword
selection was guided by an a priori gold set of known eligible articles
and refined iteratively to maximise sensitivity and specificity. Athlete-related
terms (e.g., triathlete, cyclist, badminton player) were included based on terms
found in this gold set, while broader terms such as athlete, sports and exercise
ensured inclusion of other athletic populations not explicitly named.
Table 1 General search strategy for the systematic literature
review on the effect of nutritional supplements, diet and/or
exercise interventions on inducing alterations of the gut microbiota
and its effects on exercise performance in a healthy active adult
population
|
Field one (combine with OR): population
|
|
Field two (combine with OR): intervention and comparison
|
|
Field three (combine with OR): outcome
|
|
Keywords: athletea, athletes, walking, sports,
triathletea, runa, race
walkera, sportspeople, exercisea,
cyclist, cycling, ((badminton or basketball) adj2
(playera), ((physical or recreational or
elite or competitive or endurance) adj2 (exercise or
activity or performance))
|
AND
|
Keywords: short-chain fatty acids, SCFA, microbial
composition, microbiome, microbiota, microflora, intestine
flora, gastrointestinal microbiome, Lactobacillus
plantarum, Bacillus coagulan,
Lactobacillus paracasei PS23, Bacillus
subtilis, Bifidobacterium longum,
((Lacticaseribacillus or Lactobacillus)
adj2 (casei))
|
AND
|
Keywords: athletic performance, training, aerobic, fitness,
physical fitness, skeletal muscle, isokinetic, isometric,
neuromuscular, force, torque, power, strength,
maxa voluntary contraction, ((exercise or
athletic or physical or endurance or sport or muscular) adj2
(performance)), ((time) adj2 (trial or exhaustion)),
((aerobic or anaerobic) adj2 (capacity))
|
aUsed to retrieve unlimited suffix variations.
Eligibility criteria
Eligibility criteria were established using the Participant Intervention
Comparator Outcomes Study design (PICOS) framework ([Table 2]). Studies were included if
they met the following PICOS criteria: population: healthy, active adults;
intervention: exercise/physical training, and/or diet, and/or nutrient
supplementation; comparator: control or placebo groups; outcomes: quantified gut
microbiome measures and exercise performance metrics; and study design: original
human research studies. Exclusion criteria included sedentary individuals, those
with disease states or established gastrointestinal disorders, populations
undergoing uncontrolled dietary, exercise or supplementation modifications and
studies lacking a control or placebo group.
Table 2 PICOS table, showing the inclusion and exclusion
criteria for the study population, intervention, comparator,
outcome/s and study design
|
PICOS
|
Inclusion
|
Exclusion
|
|
Population
|
Human.
|
Animals and in vitro studies.
|
|
Recreational and competitive active adults (≥18 yr).
|
Infants or children.
|
|
Male and female biological sex.
|
Pregnancy or lactating.
|
|
Sedentary individuals (i.e., no adherence to exercise or
structured physical activity programs). Diagnosed disease or
syndrome states (i.e., all clinical populations;
gastrointestinal disease/disorders (e.g., irritable bowel
syndrome, inflammatory bowel disease, coeliac disease or any
other functional gastrointestinal disorder/infections).
Population adhering to dietary modifications and/or dietary
supplementation, including pre-/pro-/syn-biotics in the 3 mo
before experimental protocols. Antibiotic and/or other drugs
intake (e.g., non-steroidal anti-inflammatory or
stool-altering medications) within 1 mo of experimental
protocols.
|
|
Intervention
|
Exercise and/or physical training OR diet OR nutrient
supplementation OR prebiotic/s, probiotic/s and symbiotic/s
blends (i.e., prebiotic + probiotic, with or without other
nutrient inclusion) (e.g., vitamins, minerals, lipids,
phytochemicals and/or volatiles).
|
No exercise and/or physical training OR dietary modification
OR nutrient supplementation OR provisions of prebiotic/s,
probiotic/s and symbiotic blends.
|
|
Comparator
|
Placebo group or control group.
|
No placebo or control.
|
|
Outcome
|
Gastrointestinal microbiota: e.g., bacterial taxonomy (ASV or
OTU) including α-diversity and relative abundance, bacterial
functional markers including SCFA concentration (e.g.,
butyrate, propionate and/or acetate).
|
Failure to meet outcome criteria.
|
|
AND
|
|
Exercise performance: e.g., time to exhaustion, time trial,
maximal strength.
|
|
Study design
|
RCT or randomised crossover trial.
|
All other study designs.
|
ASV, amplicon sequence variant; OTU, operational taxonomic units; RCT,
randomised control trial; SCFA, short-chain fatty acid.
Study selection
Search results were imported into Endnote for deduplication before being uploaded
to Covidence for study selection. Two independent reviewers (SKG and IGM)
screened titles and abstract for eligibility, with full-text assessment
conducted for studies meeting inclusion criteria. A third reviewer (RC) resolved
conflicts when consensus was not reached.
Data extraction
Two reviewers (SKG and IGM) independently extracted study data using a
standardised data extraction table, with verification by a third reviewer (RC).
Extracted variables included study characteristics, such as sample size, age,
biological sex and training status, as well as details of the intervention,
including duration, type, dose, and bacterial species or strain.
Microbiome-related outcomes, such as microbial relative abundance, α-diversity
and SCFA concentrations, were recorded alongside exercise performance outcomes,
including time-trial, time to exhaustion and maximal strength measures. Data
extraction focused on interpretable numerical results, with graphical data
digitised using WebPlotDigitizer,[72]
where applicable. Studies that presented results in non-extractable formats,
such as heat mapping or unclear data visualisations, were excluded from
analysis. Any discrepancies in data extraction were resolved by discussion and
consensus, in accordance with the third reviewer (RC). Given the heterogeneity
of interventions, methodologies, outcome measures and varying degrees of meeting
best practice guidelines and recommendations checklist in exercise
gastroenterology research that included experimental control of confounding
factors for primary variables explored in this systematic literature
review,[73] a meta-analysis was
not feasible and results were synthesised descriptively.
Risk of bias assessment
Risk of bias was independently assessed by two reviewers (SKG and IGM) using the
Cochrane Risk-of-Bias Tool (RoB 2),[74] following established criteria. Discrepancies were resolved
through discussion and consensus. The overall risk of bias rating for each study
reflects the highest level of risk across any domain, in accordance with RoB 2
guidance. No studies were excluded based on their risk of ratings; however,
these assessments were integral to our interpretation of findings. Studies with
a higher risk of bias, particularly due to inadequate blinding and important
methodological limitations such as insufficient dietary or exercise control,
were interpreted with caution when considering their reported performance
outcomes. While industry funding and author affiliations were noted as part of
the study context, they were not used as direct indicators of study reliability.
Throughout the results and discussion, risk of bias concerns and methodological
limitations were explicitly highlighted to provide a nuanced understanding of
the evidence, acknowledging that these factors contributed to heterogeneity and
uncertainty in the overall conclusions.
Results
Search results
Results of the literature search are shown in [Fig. 1]. A total of 3,779
non-duplicate studies, including those identified via citation searching, were
screened. After title and abstract screening, 3,750 were excluded. Of the 29
studies sought for retrieval (26 from databases and registers and 3 from other
sources), 27 full-text articles were successfully retrieved and assessed for
eligibility. Studies were excluded due to wrong study design (n=2), wrong
study population (n=3) and wrong outcomes (n=4), resulting in 18
studies being included in the final review ([Fig. 1]).
Fig. 1 PRISMA diagram illustrating the systematic review process
and the inclusion and exclusion of research papers. [71]
Study characteristics
Outcomes are reported from a total of 588 participants. The majority were male
(67%), with participant ages ranging from 19 to 69 years. The populations
studied included n=5 active adults,[22]
[33]
[38 ]
[39]
[40]
n=1 active elderly,[26]
n=2 football players
(semi-professional and professional soccer; Tier 2–3),[29]
[32 ]
n=1 triathletes (described
as elite but lacking sufficient detail to classify by Tier),[23 ]
n=1 national level
cross-country skiers (Tier 3),[28
]
n=1 cyclists (Tier 2),[37
]
n=1 well trained MMA (Tier 2),[34]
n=4 runners (Tier 2–3)[25]
[27]
[30]
[36] and n=1 race walkers (Tier
4).[31]
Interventions included n=11 probiotic and/or postbiotic trials (n=8
single strain, n=3 multi-strain);[22]
[23]
[25]
[28]
[30]
[32]
[34]
[36]
[37]
[38]
[40] and n=2 using other
nutritional supplements containing α-Cyclodextrin.[33]
[39] Other interventions included
n=3 dietary modifications: (1) a high protein diet (40% protein/30%
carbohydrate/ 30% fat) or a high carbohydrate diet (60% carbohydrate/10%
protein/30% fat)[27]; (2) a ketogenic
Mediterranean diet with phytoextracts (<30 g/d carbohydrate, comprising
<10% of total energy intake, 25–30 % of energy from protein, with fat
consumed ad libitum and three herbal extracts)[29]; and (3) high-carbohydrate (HCHO)
diet (60% carbohydrate/16% protein/20% fat) and periodised carbohydrate (PCHO)
diet (same macronutrient composition as HCHO, but periodised across
days/sessions).[31] Additionally,
a low-carbohydrate, high-fat (LCHF) diet was employed, consisting of 78% fat,
17% protein and <50 g/d carbohydrate, comprising approximately 3.5% of total
energy intake. Furthermore, n=1 exercise intervention was included, which
involved a 60-minute exercise program performed four times per week. The program
comprised a 10-minute warm-up, 20 minutes of aerobic exercise, 25 minutes of
resistance exercise and a 5-minute cool-down.[26]
Among the probiotic and/or postbiotic interventions, delivery formats included
capsules (n=7),[22]
[23]
[25]
[30]
[37]
[38]
[40] tablets (n=1),[34] sachets (n=1),[36] yoghurt (n=1)[28] and kefir (n=1).[32] Intervention durations ranged from
2 to 11 weeks. Outcomes assessed across studies included direct exercise
performance measures such as TTE, TT, distance test, vertical jump height,
strength assessments (i.e., knee extensor/flexor, grip and isometric
quadriceps), peak power output, fatigue index, single leg standing with eyes
closed and the 2-minute step test. In addition, physiological indicators of
aerobic capacity, such as V̇O2max, were also reported in
several studies.
Due to the heterogeneity of study designs, interventions and outcome measures,
results are presented descriptively in [Table 3] (study characteristics) and [Table 4] (study outcomes); expanded
outcome data corresponding to [Table
4] are provided in Supplementary Table S1 (available in the
online version only). Wu et al.[38]
conducted a post hoc analysis of data from Lee et al.,[24] and Murtaza et al.[31] performed a post-hoc analysis of
data originally published by Burke et al.[75] Przewłócka et al.[34]
[35] reported
different performance outcomes from the same trial in two separate
publications.
Table 3 Systematic literature review search results and study
characteristics of included studies investigating the impact of
nutritional supplement-, dietary- and exercise-associated changes to
the gut microbiota and their impact on athletic performance
|
Author, country, study design
|
Population characteristics
|
Intervention protocol (vs. placebo or control)
|
Dietary control (DC)
|
Faecal collection technique
|
Faecal analysis techniqueb
|
Funding source and conflict(s) of interest
|
|
Physical activity (PA)
|
|
Probiotic and postbiotic studies—single strain
|
|
Gross et al.,[22
]USA, RXT
|
N=7 (3 males and 4 females), age: 31±8 yr, physically
active, Tier 1, V̇O2 peak (running)
(mL/kg/min): 49.2±8.4.
|
Veillonella atypica FB0054 (VA), 1×1010
CFU/cap, 1× capsule consumed with 8–12 ounces water, Pla:
corn starch, duration: 14 d with 21 d washout period.
|
DCa: Completed 2-d food/fluid log, instructed to
replicate diet before each study visit. Fasted overnight
(e.g., avoiding food, caffeine, nicotine). Dietary records
analysed for nutritional composition (energy, carbohydrate,
protein, fat).
|
Participants self-collected stool samples using provided
kits, froze them at −20°C with preservation reagents (RNA
Later and OMNIgene) and transported them on ice to the lab
for storage at −80°C.
|
Shotgun metagenomics analysis, α-diversity, Shannon entropy,
SCFA not measured.
|
Funded by FitBiomics (NY) and Increnovo, LLC (WI). One author
is a scientific advisor to FitBiomics and others have
ownership stakes and patents on the probiotic strain used.
These authors had no role in data collection or
analysis.
|
|
PA: Avoided vigorous exercise at least 24 h before each
visit. Required to engage in aerobic exercise at least
2×/wk; typically exercised 5.4±1.5 d/wk, with 6.7±0.8 d of
overall PA.
|
|
Huang et al.,[23
]Taiwan, RCT
|
N=20 (male), age: Int: 21.6±1.3; Pla: 21.9±1.4 yr,
Triathletes, V̇O2max (running) (mL/kg/min)
Int: 55.5±8.6; Pla: 56.6±9.0.
|
Lactiplantibacillus plantarum PS128,
1.5×1010 CFU/cap + 100 mg microcrystalline
cellulose, 2 capsules after training/before sleeping, Pla:
400 mg microcrystalline cellulose, duration: 4 wk.
|
DCa: Instructed to avoid fermented foods,
probiotics, prebiotics and antibiotics. Dietary records
analysed for nutritional composition and caloric intake
(data not reported).
|
Fresh stool samples collected; collection procedure and
handling prior to lab processing not specified.
|
16S rRNA gene amplicon sequencing targeting V1–V3,
α-diversity, Shannon index, GC–MS analysis.
|
Supported by Ministry of Science and Technology in Taiwan
(grant nos. MOST 107-2321-B-158-001 and MOST
108-2410-H-038-017).
|
|
PAa: Daily training for 4 wk, not
monitored/reported. Maintain a regular lifestyle, avoiding
any strenuous exercise.
|
|
Lee et al.,[40
]Taiwan, RCT
|
N=53 (26 males and 27 females), age: control: 21.6
±1.6; TWK-10 21.3±1.7; TWK10-hk 21.6±2.5 yr, physically
active, Tier 0–1, V̇O2max (running)
(mL/kg/min): Pla: 47.3±8.3; TWK10: 46.8±9.3; TWK10-hk:
47.5±10.2.
|
TWK10: viable Lactiplantibacillus plantarum TWK10-hk:
heat-killed Lactiplantibacillus plantarum,
1.0×1010 CFU/cap, 3 capsules, Pla:
maltodextrin, microcrystalline cellulose, duration: 6
wk.
|
DCa: Instructed to maintain usual diet, to cease
supplements (e.g., probiotics, prebiotics, antibiotics) 2 wk
pre-intervention. Baseline energy intake recorded.
|
Fresh stool samples collected; collection procedure not
specified prior to lab processing.
|
16S rRNA gene amplicon sequencing targeting V3–V4,
α-diversity, Shannon index, HPLC.
|
Some authors employed by Synbio Tech Inc.
|
|
PAa: Instructed to avoid any strenuous physical
activity for 3 d before V̇O2max and
exercise performance tests. No activity monitoring
stated.
|
|
Li et al.,[28
]China, RCT
|
N=16 (male), age control: 19.3±0.7; Int: 19.6±1.1 yr,
national top-level cross-country skiers, Tier 3,
V̇O2max (running)
(mL/kg/min): control: 55.9±4.4; Int: 55.8±5.4.
|
Yogurt with Bifidobacterium animalis subsp.
lactis BL-99, 1×109 CFU, control:
ordinary yoghurt, yoghurt with each of 3 meals and at 21:00,
4× d, duration: 8 wk.
|
DCa: Instructed to maintain usual diet; no dairy,
yogurt-containing foods and supplements 1 wk pre-trial.
Completed 2-d weighed food/fluid diary. NS
differences in energy or macronutrient intake between
groups.
|
Faecal samples collected by researchers; participant
collection procedure not described.
|
DNA Nanoball Sequencing (DNB-seq) using combinatorial
probe-anchor synthesis (cPAS), α-diversity, Shannon index,
plasma-targeted metabolomic analysis via LC-MS.
|
Funded by the National Key R&D Program of China (grant
no. 2019YFF0301700).
|
|
PA: Firstbeat Sport Sensor and Bodyguard 2 monitored training
load and energy expenditure. NS changes in TRIMP
or energy expenditure between groups.
|
|
Lin et al.,[25
]Taiwan, RCT
|
N=21 (14 males and 7 females), age: Pla (mean±SEM):
21.2±0.4; Int: 21.6±0.7 yr, healthy, well-trained middle and
long distance runners, Tier 2, fitness status not
reported.
|
OLP-01, human strain probiotic Bifidobacterium longum
subsp. longum, 5×109 CFU/cap, after each
meal, 3× d, Pla: Maltodextrin, duration: 5 wk.
|
DCa: Instructed to avoid nutritional supplements,
yogurt, Yakult, probiotic products, antibiotics. Team
dietitian ‘specified the diet’ and ‘provided the same meal’
to ensure consistency of the diet. Nutrition composition
data not reported, monitoring of adherence not reported.
|
Faecal samples were self-collected using DNA/RNA
Shield-preserved tubes with attached spoons and stored at
−80°C for subsequent DNA extraction and sequencing; further
collection details not provided.
|
16S rRNA gene amplicon sequencing targeting V3–V4, SCFA not
measured.
|
Funded by projects from the university-industry cooperation
fund (NTSU No. 1091038), National Taiwan Sport University,
Taoyuan, Taiwan. Glac Biotech Co., Ltd. (Tainan City,
Taiwan) provided probiotics.
|
|
PA: Experiment included 3 wk regular training and 2 wk of
de-training. All subjects followed team’s work-rest schedule
(data not reported).
|
|
McDermott et al.[30
]USA RCT
|
N=28 (13 males and 15 females), age: Pla: 25.6±4.9;
Int: 24.6±5.1 yr, Healthy, adult runners, Tier 2,
V̇O2max (running)
(mL/kg/min): Pla 48.8±6.5; Int: 49.4±6.1.
|
Lactobacillus helveticus Lafti L10 with same
excipients as Pla capsule, 5×109 CFU/cap, 1× d
with a meal, Pla: potato starch, ascorbic acid, and
magnesium stearate, duration: 6 wk.
|
DCa: Instructed to consume standardised low-fat
breakfast. Dietary habits assessed at baseline and
post-intervention. NS between groups for fibre
intake and total diet quality scores.
|
Stool samples collected using a commode specimen collection
system (Thermo Fisher Scientific) and a nucleic acid
preservation tube (Norgen Biotek). Pea-sized samples were
taken from three different stool sites and mixed with
preservative. If a sample was not provided during the first
visit, participants were instructed to collect it at their
next bowel movement. Samples stored at −80°C until
analysis.
|
Real-Time qPCR, SCFA not measured.
|
Funded by Lallemand Health Solutions Inc. and USDA National
Institute of Food and Agriculture Hatch project (FLA-FOS-
510 006391.
|
|
PA: PA self-reported (questionnaires) and tracked
(accelerometer). Int: performed fewer aerobic sessions
(p=0.02) and less vigorous exercise
(p=0.007) than Pla. NS difference in
strength, combination training or moderate/very vigorous
exercise minutes.
|
|
West et al.[37
]Australia RCT
|
N=88 (64 male and 35 female recruited), Competitive
cyclists, Tier 2, Age: Int: M: 35.2±10.3, F: 36.5±8.6; Pla:
M: 36.4±8.9, F: 35.6±10.2 yr,
V̇O2max (mL /kg/min): Int: M:
56.5±6.2, F: 53.0±5.0; Pla: M: 55.8±5.6, F: 51.6±7.4.
|
Limosilactobacillus fermentum VRI-003 PCC,
1×109 CFU/cap, 1× d, consume any time with or
without food. Pla: microcrystalline cellulose, duration: 11
wk.
|
DCa: 4-d food diary. Usual diet, without probiotic
foods. No substantial differences between groups for energy,
macronutrients, fibre.
|
Faecal samples collected in sealable plastic bags and
immediately frozen in a portable −20°C freezer for transport
to the laboratory, though the time between collection and
lab arrival is not specified.
|
16S rRNA gene diversity analysis using DGGE and qPCR, SCFA
not measured.
|
Funded by Christian Hansen A/S, Probiomics, and the
Australian Institute of Sport.
|
|
PA: Training log kept. No substantial differences between
groups.
|
Two of the authors held full-time positions with Christian
Hansen A/S and Probiomics Ltd, respectively.
|
|
Wu et al.,[38
]Taiwan, RCT
|
N=105 (75 males and 30 females), age: Pla: 21.6 ±2.0;
L-PS23 21.4±1.3; HT-PS23: 21.8±2.5 yr, physically active,
Tier 0–1, not well described.
|
Lacticaseibacillus paracasei PS23 (L-PS23) (DSM 32322)
previously isolated from healthy human faeces, HT-PS23:
heat-treated L-PS23, 1×10 billion CFU/cap, 2× d, Pla:
microcrystalline cellulose, duration: 6 wk.
|
DCa: Instructed to maintain usual diet, to cease
supplements (e.g., probiotics, prebiotics, antibiotics). 3-d
food diary with meal photos including scale completed at
baseline and end. NS differences in energy,
macronutrient or fibre, non-starch polysaccharides, intake
between or within groups.
|
Fresh stool samples were collected and immediately placed in
containers with 95% ethanol for DNA preservation, then
transported to the laboratory and stored at −80°C until DNA
extraction. The time between collection and transportation
to the lab is not specified.
|
16S rRNA gene amplicon sequencing targeting V3–V4,
α-diversity, Shannon index and Chao1, UPLC–MS/MS.
|
Bened Biomedical Co., Ltd provided the capsules. The study
was supported by the University-Industry Cooperation Fund,
National Taiwan Sport University (NTSU No. 1111040) and
partial funding to SIW from the Department of Medical
Research, Mackay Memorial Hospital (MMH-113-19, MMH-110-110,
MMH-109-112, MMH-109-14, MMH-108-121, MMH-108-146,
MMH-TT-10804, MMH-TH-10804).
|
|
PAa: Not monitored.
|
Funders had no role in study design, data collection,
analysis, interpretation, writing or publication.
|
|
Probiotic studies—multi-strain
|
|
Przewłócka et al.,[35
]Study 1 Poland, RCT
|
N=23 male well-trained MMA athletes, Tier 2, age:
control: 26.02±4.00; Int: 24.70±6.50 yr,
V̇O2max (cycling) (mL/kg/min):
control 52.33±5.06; Int: 56.92±0.83.
|
Bifidobacterium lactis W51, Levilactobacillus
brevis W63, Lactobacillus acidophilus W22,
Bifidobacterium bifidum W23 and Lactococcus
lactis W58 + 5 mL Vit D3 (0.5 mg
Cholecalciferol/mL), 5×108/tablet, 4× d with
meals, control: maltodextrin and plant proteins + 5 mL Vit.
D3, duration: 4 wk.
|
DCa: Participants were instructed to maintain
their usual diet. Pre-exercise breakfast was standardised.
Dietary intake was assessed via a 3-d food interview and FFQ
by a qualified sports nutritionist. Nutrient intake (energy,
carbohydrate, fat, protein) was analysed using professional
software; nutritional composition data were not
reported.
|
This was assessed in Przewłócka et al.[34]
|
This was assessed in Przewłócka et al.[34]
|
Partially funded by project from Ministry of Education and
Science: IDUB 664/306/63/73-3326.
|
|
PA: Instructed to maintain regular training (at least 5× wk
MMA sessions, 60–90 min, including combat, grappling,
striking, strength and endurance). No exercise 24 h before
testing. Training log kept and qualified sports nutritionist
conducted interview before and after intervention to assess
training load.
|
|
Przewłócka et al.,[34
]Study 2 Poland, RCT
|
As per Przewłócka et al.[35]
|
As per Przewłócka et al.[35]
|
As per Przewłócka et al.[35]
|
Faecal samples collected by participants into a standardised
container before and after the intervention period,
following instructions provided by the researchers. Samples
were immediately frozen and stored at −80°C for
analysis.
|
Shallow shotgun sequencing, α-diversity, Inverted
Simpson, Chao1, ACE, Shannon, gas chromatography with flame
ionisation detection.
|
As per Przewłócka et al.[37]
|
|
Wang et al.,[36
]China, RCT
|
N=19 (15 males and 4 females), active amateur marathon
runners, Tier 2, age: Int: 28.50±12.18; Pla: 29.78±12.39 yr,
V̇O2max (cycling)
(mL/kg/min): not reported, training hx: Int: 5.10±2.02 yr;
188±41.31 km/mo; Pla: 5.00±2.35 yr; 183±40.93 km/mo.
|
Lactobacillus acidophilus and Bifidobacterium
longum, 1.5 g/d (1.5×109 CFU: 1.02×10⁹
CFU, Lactobacillus acidophilus and 4.95×10⁸ CFU
Bifidobacterium longum), 1× d sachet, Pla: Sachet
containing maltodextrin, duration: 5 wk.
|
DCa: Received ‘assigned diet’—no details
about this diet. Participants were instructed to avoid
supplements, probiotics, prebiotics, yogurt and antibiotics.
Alcohol prohibited.
|
Stool samples collected in freeze-dried tubes containing a
freeze-dried solution. Samples immediately frozen at −80°C
for analysis.
|
16S rRNA gene amplicon sequencing targeting V4, SCFA not
measured.
|
Zhejiang Provincial Natural Science Foundation (Grant Number:
TGY24H180038); Zhejiang Medical and Health Science and
Technology (Grant Numbers: 2022KY258 and 2024KY198);
Hangzhou Health, Science and Technology Plan (Grant Numbers:
A20210057 and A20230654); Hangzhou Biomedical and Health
Industry Development Support Science and Technology (Grant
Number: 2021WJCY052).
|
|
PA: Instructed to maintain regular training, logged
intensity, duration and distance of aerobic sessions.
|
|
Önes et al.,[32
]Turkey, RCT
|
N=21 females, professional soccer players, Tier 3,
age: Int: 24.42±2.52; control: 22.14±3.61 yr,
V̇O2max measured, not
reported.
|
Kefir from a single, commercially available, standardised
brand. The product, with quality control measures, provided
140 kcal, 7.3 g fat, 4.2 g CHO, 9.2 g protein/250 mL.
|
DCa: Nutritional status assessed via 3-d food
records (single, double, off days). Dietitian-guided
record-keeping, analysed via Nutrition Information Systems.
Players instructed to maintain usual diet. Nutritional
composition not reported.
|
Stool collection method not reported.
|
16S rRNA gene amplicon sequencing targeting V3–V4,
α-Diversity, Shannon index and Chao1, SCFA not measured.
|
Supported by Acibadem University Scientific Research Projects
Coordination Unit (ABAPKO) (Grant: TDK-2023-91).
|
|
200 mL/d, after training or any time on rest days, control:
continue daily diet routine, duration: 28 d.
|
PAa: Players’ actual physical activity not
recorded.
|
|
Nutrient
|
|
Morita et al.,[39
]Japan, RCT
|
N=31 (males), healthy, non-athletes, exercise
regularly (1–2× wk), Tier 0–1, age: Pla: 36.3±9.6; FL:
33.9±10.0; αCD: 34.5±10.9 yr,
V̇O2max (mL/kg/min): Pla:
46.40±6.45; FL: 43.20±6.83; αCD: 45.67±8.66.
|
FL: 200 mg/d, NIPPN flaxseed lignans, ~40% (w/w) SDG and ~40%
(w/w) αCD, αCD: 200 mg/d, Dexypearl-α (Ensuiko Sugar
Refining, Tokyo, Japan), αCD (>98%), 3× tablets/d,
ingested with water at the same time of day, Pla: maltitol,
duration: 9 wk.
|
DCa: Instructed to maintain normal diet and avoid
prohibited foods, including performance enhancing
foods/beverages, pharmaceuticals or quasi-drugs for
recovery/fatigue/strength and dietary supplements.
Nutritional composition of diets not reported.
|
Participants collected stool samples at home using Sarstedt
containers within 5 d prior to each visit, froze them at
approximately −30°C and transported them to the clinic.
|
16S rRNA gene amplicon sequencing targeting V4, complemented
by species-specific qPCR for Bacteroides uniformis
quantification, α-Diversity, Shannon, Chao1, Phylogenetic
distance, SCFA not measured.
|
Partially supported by JSPS KAKENHI (22H03541), JST ERATO
(JPMJER1902), AMED-CREST (JP22gm1010009), the Food Science
Institute Foundation and Asahi Quality & Innovations
Ltd. Some authors are employees of Asahi Quality &
Innovations Ltd. and filed patents related to antifatigue
and strength-enhancing substances using B. uniformis,
its derivatives, FL or αCD. One author is the founder and
CEO of Metagen Inc., which had no role in the study’s
interpretation, writing or publication.
|
|
PAa: Instructed to maintain their normal physical
activity, defined as 1–2 sessions (≥30 min each) per wk of
exercise ≥5 METs, excluding resistance training. Physical
activity was not monitored or reported in this study.
|
|
Onishi et al.,[33
]Japan, RCT
|
N=81 (males), healthy, non-athletes, with regular
exercise habits, Tier 0–1, age: median [first quartile–third
quartile] Pla: 40.00 [29.50–44.00]; αCD: 40.00 [30.50–44.50]
yr, V̇O2max (mL/kg/min): Pla: 44.05
[39.63–50.10]; αCD: 42.30 [38.15–51.60].
|
αCD: 4× tablets d, 1 g/d, Pla: maltitol, duration: 8 wk.
|
DCa: Participants received a prescribed dinner the
day before and a prescribed breakfast on the day of the
clinic visit; otherwise asked to maintain their normal diet.
Daily meal intake recorded, showing no change during the
supplementation period for both groups. [10]
|
Participants collected stool samples at home in faecal
collection containers within 5 d before each visit, froze
them at ~−30°C and delivered them to the clinic; samples
were thawed at room temperature before analysis.
|
16S rRNA gene amplicon sequencing targeting V4, complemented
by species-specific qPCR for Bacteroides uniformis
quantification, SCFA not measured.
|
Funded by Asahi Quality & Innovations, Ltd.
|
|
PAa: Recorded daily exercise habits during
supplementation period. NS differences in
exercise frequency between groups.
|
Some authors are employees of Asahi Quality & Innovations
Ltd. and have filed a patent for sport performance-improving
agents containing αCD.
|
|
Diet
|
|
Furber et al.,[27
]UK, RCT
|
N=16 (males) highly trained endurance runners, Tier 3,
age: HPD: 25±3.6; HCD: 27±5.0 yr,
V̇O2max (mL/kg/min): HPD:
63.1±4.8; HCD: 65.3±6.4.
|
Phases: 7-d habitual diet → 7-d isocaloric HPD (protein
40/CHO 30/fat 30%EI) or HCD (CHO 60/protein 10/30% Fat EI) →
7-d habitual diet, duration: 22 d.
|
DC: Rigorously controlled diets (specific macronutrient
ratios) were prescribed to match energy expenditure,
supplemented by a daily 500 kcal meal. Participant
compliance monitored using a 3-d food record for each diet
phase, which was analysed.
|
Participants collected the first stool of the day, which was
transferred to −80°C storage within 2 h of passage and
stored there until analysis.
|
16S rRNA gene amplicon sequencing targeting V4, ITS1–ITS2
amplicon sequencing for fungal taxa and viral metagenomics,
Fisher-alpha diversity, SCFA not measured.
|
The authors declare a conflict of interest. Two authors are
former employees of GlaxoSmithKline and two have received
funding for gut microbiota research. Previous employers and
funders had no role in the study’s design, participant
recruitment, results or conclusions.
|
|
PA: Training volume controlled; instructed to maintain the
same weekly program, replicating sessions on the same day
and time, logging all sessions with the provided GPS watch,
automatically uploaded to Garmin Connect.
|
|
Mancin et al.,[29
]Italy, RCT
|
N=16 (males), semi-professional soccer players, Tier
2, age: KD: 25.5±2.5; WD: 25.5±3.1 yr.
|
Diets: KEMEPHY ketogenic Mediterranean diet (KD) or Western
diet (WD), both isocaloric and isoproteic (1.8 g/kg BW/d
protein), KD: <30 g/d carbs (<10 %EI), 25–30 %EI
protein, fat ad libitum, 3 herbal extracts,
nutritional counselling, meal plans, WD: 50–55 %EI carbs, 30
%EI protein, 20–25 %EI fats (<10% SF, <300 mg
cholesterol), whole cereals, legumes, moderate wine,
Compliance: Weighed food records, urine ketone testing,
duration: 30 d.
|
DC: Participants received nutritional counselling, meal plans
and recipes and were provided with specific items like
ready-to-eat ketogenic products, herbal extracts and MCT oil
(KEMEPHY group) and pre-sleep protein (both groups). Dietary
adherence was rigorously monitored using food diaries,
ketone testing (KEMEPHY group) and regular follow-ups with a
nutritionist.
|
Faecal samples (100–150 mg) collected using sterile swab
tubes with preservative buffer on the morning of day 0 and
day 30, sent to the lab within 2 d and stored at −20°C until
DNA extraction.
|
16S rRNA gene amplicon sequencing targeting V3–V4,
α-diversity, OTU and Shannon’s ENS, SCFA not measured.
|
Funded by Department of Biomedical Sciences, University of
Padua Institutional Grant. One author received, and another
is supported by a research grant from Gianluca Mech S.p.A.,
a company specialising in herbal products and dietary
keto-foods, which had no role in study design, data
collection, analysis, interpretation or writing. Two authors
were employed by BMR Genomics srl.
|
|
PA: Players asked to keep their normal training schedule (8 h
of training/wk), managed by team structure and explicit
instructions to players.
|
|
Murtaza et al.,[31
]Australia, RCT
|
N=29 (males), highly competitive race walkers, Tier 4,
age: HCHO 25.4±4.0; PCHO: 27.4±4.6; LCHO 28.3±3.5 yr,
V̇O2 peak (mL/kg/min):
HCHO: 61.6±6.8; PCHO: 64.6±5.3; LCHO: 66.3±4.8.
|
3-wk training camp, diets assigned based on performance
beliefs, HCHO: 60 %EI CHO (~8.5 g/kg BM/d), 16 %EI protein
(~2.1 g/kg BM/d), 20 %EI fat, PCHO: same macronutrient
composition as HCHO but periodised across d/sessions, LCHF:
78 %EI fat, 17 %EI protein (~2.2 g/kg BM/d), <50 g/d CHO
(~3.5 %EI).
|
DC: All food and fluids were provided and prepared by
professional staff. Individualised meal plans were served in
a controlled setting, with intake weighed, monitored and
cross-checked daily for compliance.
|
Stool samples were collected from athletes at start and end
of training-diet intervention using the OMNIgene stool
collection and preservative kit.
|
16S rRNA gene amplicon sequencing targeting V6–V8,
α-Diversity, Shannon and Simpson, SCFA not measured.
|
This research was supported by CRN AESS, an Australian
Catholic University Research Fund Program Grant (No.
201300800), and the Australian Institute of Sport’s
High-Performance Sport Research Fund. One of the authors is
supported by a University of Queensland International
Postgraduate Fellowship.
|
|
PA: 3-wk intensified training block with race walking,
resistance and cross-training (running, cycling, swimming).
6 mandatory sessions under standardised conditions with
external monitoring; additional sessions based on athlete
preference, logged.
|
|
Exercise
|
|
Zhong et al.,[26
]China, RCT
|
N=14 (females), physically active elderly, Tier 1,
age: Int: 66.38±4.07; control: 68.50±3.78 yr, Physical
Activity Scale for Elderly (PASE) scores: Int: 117.11±24.49;
control: 105.36±49.90.
|
Int: exercise program: 4× wk, 60 min, including warm-up (10
min), aerobic exercise (20 min), resistance exercise (25
min) and cool down (5 min), control: maintain daily life, 8
wk.
|
DCa: Diet not monitored.
|
Stool samples collected before baseline test and after
intervention, no further details on collection or storage
method provided.
|
16S rRNA gene amplicon sequencing targeting V3–V4,
α-diversity, Sobs, Chao, Ace, Shannon, Simpson, SCFA not
measured.
|
Supported by Hangzhou Philosophy and Social Science Project
(Z20JC074), China; The Fundamental Research Funds for the
Central Universities.
|
|
PAa: Maintain daily life activities along with
intervention. No specific methods for monitoring or
assessing physical activity during intervention period were
described.
|
Abbreviations: ax, assessment; BM, body mass; CFU, colony forming units;
CHO, carbohydrate; DGGE, denaturing gradient gel electrophoresis; EI,
energy intake; GC–MS, gas chromatography–mass spectrometry; HCD, high
carbohydrate diet; HCHO, high carbohydrate; HPD, high protein diet;
HPLC, high-performance liquid chromatography; ITS1, internal transcribed
spacer 1; ITS2, internal transcribed spacer 2; LC_MS, liquid
chromatography–mass spectrometry; LCHF, low carbohydrate, high fat;
LCHO, low carbohydrate; MMA, mixed martial arts; no., number;
NS, not significant; PCHO, periodised carbohydrate; Pla,
placebo; QPCR, quantitative polymerase chain reaction; RCT, randomised
control trial; RXT, randomised crossover trial; SDG,
secoisolariciresinol diglucoside; UPLC–MS/MC, ultra-performance liquid
chromatography–tandem mass spectrometry; αCD, α-cyclodextrin. Mean age
is±SD unless otherwise stated.
aNot in accordance with best practice guidelines and
recommendations in exercise gastroenterology research- did not follow
minimal confounder control for diet and/or physical activity Costa et
al.[73]
bFaecal analysis technique- bacterial taxa, α-diversity and
short-chain fatty acids.
Table 4 Systematic literature review search results and study
characteristics of included studies investigating the impact of
nutritional supplement-, dietary- and exercise-associated changes to
the gut microbiota and their impact on athletic performance
|
Author, study design
|
N_Intervention protocol
|
Microbiota changes (∆)
|
SCFA changes (∆)
|
Performance changes (∆)
|
Key takeaway
|
|
Probiotic and postbiotic studies—single strain
|
|
Gross et al.,[22]
RXT
|
N=7 (3 males and 4 females), physically active
|
NS changes in α-diversity (Shannon entropy)
between baseline, Pla, washout and Int
(p>0.05).
|
Not reported
|
NS difference in treadmill TTE between Int and
Pla
|
Veillonella supplementation did not significantly
alter gut microbiota diversity or improve exercise
performance, though individual microbiota changes were
noted.
|
|
Probiotic vs. placebo
|
β-diversity (Bray Curtis) showed no group-level
changes, some individual variability observed.
|
Mean TTE ∆
|
|
Veillonella atypica FB0054, 14 d.
|
No changes in specific taxa or microbial functions after
intervention.
|
Int: 13.29±100.13 s, (p=0.738)
|
|
Pla: 61.14±72.04 s (p=0.066)
|
|
No correlation between changes in β-diversity and TTE
(r=0.09).
|
|
Huang et al.,[23
]RCT
|
N=20 males, triathletes
|
α-Diversity (Shannon index) ↓ with Int vs. Pla (Int
4.4, Pla 4.7; p < 0.05)
|
Measured only at post-intervention time points.
|
Treadmill TTE was significantly greater in Int compared to
Pla (Int: 1,679 Pla: 1,083 s, p < 0.05)
|
Post-intervention, probiotic group showed higher beneficial
genera, SCFAs, and TTE vs. Pla; no baseline data reported,
limiting interpretation.
|
|
Probiotic vs. placebo
|
No baseline data reported.
|
Significant ↑ observed in acetic (4.7 vs. 3.8 ng/mL),
propionic (1.18 vs. 0.5 ng/mL), and butyric acid (0.5 vs.
0.3 ng/mL) in the Int compared to Pla (p <
0.05).
|
NS difference observed in
V̇O2max between groups
(Int: 59.2, Pla: 57.2 mL/kg/min)
|
|
Lactiplantibacillus plantarum PS128, 4 wk.
|
NS difference in phylum-level RA between
groups.
|
NS differences for decanoic, heptanoic, hexanoic,
isobutyric, isovaleric, octanoic and valeric acids.
|
No correlation analysis between performance and microbiota
reported.
|
|
Significant ↓ in RA of multiple genera in Int vs. Pla
(e.g., Anaerotruncus,
Caproiciproducens, Coprobacillus,
Desulfovibrio, Dielma,
Family_XIII_UCG_001, Holdemani, Oxalobacter)
(p < 0.05).
|
|
Significant ↑ in RA of beneficial genera (Akkermansia,
Bifidobacterium, Butyricimonas,
Lactobacillus) in Int vs. Pla (p <
0.05).
|
|
Lee et al.,[40
]RCT
|
N=53 (26 males and 27 females), physically active
|
NS change in α-diversity (Shannon index) within or
between groups
|
Acetate ↑ post-intervention in both TWK10 and TWK10-hk
(p<0.05); NS between group
differences.
|
Postintervention treadmill, TTE significantly ↑ in both TWK10
(17.55±3.98 min) and TWK10-hk (16.72±5.91 min) vs. control
(12.23±2.08 min) (p<0.001)
|
Both TWK10 and TWK10-hk increased endurance and acetate (no
between-group difference).
|
|
Probiotic vs. heat-killed probiotic vs. placebo
|
β-Diversity (UniFrac): Post-intervention differences observed
between TWK10-hk vs. control (p=0.036) and TWK10 vs.
TWK10-hk (p < 0.0001); no difference between TWK10
vs. control.
|
Propionate ↑ trend only in TWK10-hk (p=0.0857)
|
Within-group TTE: ↑ 1.38-fold (TWK10) and 1.33-fold
(TWK10-hk) (both p < 0.001)
|
TWK10-hk altered β-diversity and showed a trend for
propionate ↑.
|
|
TWK10: viable Lactiplantibacillus plantarum
|
Within-group shifts significant in TWK10-hk and control
only.
|
Butyrate ↑ trend in TWK10 (p=0.0744)
|
TTE positively correlated with Coriobacteriaceae family
(TWK10; r=~0.5, p < 0.01) and
Veillonellaceae family (TWK10-hk; r=0.65, p
< 0.01); negative correlation observed in TWK10-hk with
Eubacterium coprostanoligenes,
Erysipelatoclostridiaceae and Lachnospiraceae (correlation
coefficient represented on heatmap, difficult to extract
data, p < 0.05).
|
TWK10 had within-group microbiota shifts and a trend for
butyrate ↑.
|
|
TWK10-hk: heat-killed Lactiplantibacillus plantarum, 6
wk.
|
No between-group differences in phylum-, family- or
genus-level RA.
|
Performance correlated with specific taxa.
|
|
Within-group changes (post vs. baseline): TWK10-hk ↑
Pseudomonadota (phylum, p=0.030), ↑
Lactococcus, Escherichia-Shigella (genus,
p < 0.05), ↓ Peptostreptococcaceae
(family, p=0.030), ↑ trend
Enterobacteriaceae (family, p=0.072).
|
|
TWK10 ↑ Lactobacillaceae (family, p=0.039),
↓ Lachnospira (genus, p=0.022), ↓ trend
Faecalibacterium (genus, p=0.077)
|
|
Li et al.,[28
]RCT
|
N=16 males, National top-level cross-country
skiers
|
NS between group differences in α-diversity
(Shannon index) or β-diversity (data not shown).
|
Acetic acid
|
Treadmill, V̇O2max
(mL/kg/min):↑ in both groups (p < 0.01);
post-intervention Int>control (p < 0.02)
|
B. animalis abundance ↑ in Int (40-fold vs. 2-fold);
acetic acid ↑ significantly vs. control (p <
0.05).
|
|
Probiotic vs. control
|
Within group: RA of Bifidobacterium animalis ↑
~40-fold in intervention vs. ~2-fold in control.
|
↑ 4.5 fold (Int), ↑ 3.4 fold (control); Int>control
(p < 0.05)
|
Knee strength (Nm/kg)
|
Improvements in V̇O₂max and knee extensor
strength observed in the intervention group, with positive
correlations between B. animalis and multiple
SCFAs.
|
|
Yogurt with Bifidobacterium animalis subsp.
lactis BL-99, 8 wk.
|
Statistical significance between groups for this change not
reported.
|
Propanoic, butyric, valeric acids: ↑ in both groups (2–5
fold); NS difference between groups
|
60°/s extensors: ↑ in Int (p < 0.01), NS
in control; Int>control at post (p < 0.05)
|
Improvements in V̇O₂max and knee extensor
strength observed in the intervention group, with positive
correlations between B. animalis and multiple
SCFAs.
|
|
Positive correlations (r>0.5, p < 0.05)
between Bifidobacterium animalis and acetic,
propanoic, butyric and valeric acids
|
60°/s flexors: ↑ in Int (p < 0.05); NS
in control
|
|
180°/s extensors: ↑ in both groups (p < 0.05)
|
|
180°/s flexors: NS in both groups
|
|
Lin et al.,[25
]RCT
|
N=21 (14 males and 7 females), well-trained middle and
long-distance runners
|
RA
|
Not measured
|
12 min Cooper’s test (running distance by minute):
|
Although there were NS between-group differences
in running distance at any time point during the 12-min
Cooper’s test, the Int demonstrated significantly greater
pre-to-post improvements at 6, 9 and 12 min compared to Pla
(all p < 0.01).
|
|
Probiotic vs. placebo
|
Phylum (post only):
|
No between-group differences at any time point
(p>0.05)
|
|
Bifidobacterium longum subsp. longum, 5 wk.
|
↑ Actinomycetota and Bacillota
|
∆ Pre- to postintervention
|
|
↓Pseudomonadota in Int vs. Pla (no p-values
reported)
|
6th minute: Int 72±14 m Pla 4±9 m (p=0.0014)
|
|
Genus: ↑ Bifidobacterium in Int vs. Pla
(p=0.0027); ninefold ↑ in Lactobacillus
count (no p-value provided)
|
9th minute: Int 116±17 m Pla −27±21 m (p=0.0001)
|
|
Species: Bifidobacterium longum subsp. Longum ↑
8.63-fold in Int to 0.95% (p=0.0178); Pla
post-intervention was 0.11%
|
12th minute: Int 105±16 m Pla −56±29 m (p=0.0001)
|
|
NS differences in the abundance of common and
pathogenic strains between groups
|
Within group (post vs. pre):
|
|
Int: ↑ at 3rd (p=0.0051), ↑ at 6th (p=0.0004),
↑ at 9th (p < 0.0001) and ↑ at 12th min (p
< 0.0001)
|
|
Pla: ↑ at 3rd min (p=0.0051)
|
|
McDermott et al.,[30] RCT
|
N=28 (13 males and 15 females), runners
|
RA, baseline
|
Not measured
|
Treadmill, TTE (s):
|
Despite recovery of Lactobacillus helveticus Lafti L10
in over half of Int, no performance benefit was
observed.
|
|
Probiotic vs. placebo
|
No detectable levels of Lactobacillus helveticus Lafti
L10 in either group.
|
Int: ↓ (1,655±230–1,547±215; p=0.23)
|
Treadmill TTE decreased in Int and increased in Pla, with
post-intervention TTE significantly greater in Pla vs. Int
(p=0.01).
|
|
Lactobacillus helveticus Lafti L10, 6 wk.
|
Post intervention:
|
Pla: ↑ (1,344±188–1,565±219; p=0.01)
|
SCFAs were not measured, and no statistical comparison of
microbiota changes between groups was reported.
|
|
Lactobacillus helveticus Lafti L10 detected in 8/14
participants in Int (median: 104.37 bacteria/mL);
not detected in Pla
|
Post intervention:
|
|
No statistical analysis of between-group differences
reported
|
Int < Pla (p=0.01)
|
|
West et al.,[37]
RCT
|
N=88 (64 males and 35 females recruited), competitive
cyclists
|
NS differences in diversity (16S rRNA, data not
shown)
|
Not measured
|
Incremental cycling ergometer test
(V̇O2max): No substantial
difference between groups (data not shown). Training
patterns unaffected.
|
NS changes in microbiota diversity or
V̇O2max were observed between
groups following Limosilactobacillus fermentum
supplementation.
|
|
Probiotic vs. placebo
|
No statistical testing performed; only raw bacterial count
data reported by sex.
|
Bacterial counts were reported descriptively (by sex), with
no statistical analysis of between-group changes.
|
|
Limosilactobacillus fermentum VRI-003 PCC, 11 wk.
|
Total bacteria:
|
SCFAs were not measured.
|
|
Males: ↓ in both Int and Pla (0.5×1010)
|
|
Females: ↑ in Int (0.7×1010), ↓ in Pla
(1.0×1010)
|
|
C. coccoides
|
|
Males: ↓ in both groups
|
|
Females: ↔ in Int, ↓ in Pla
|
|
E. coli
|
|
↑ in both sexes, greater increase in Int
|
|
Bifibacteria
|
|
Males: ↓ in both groups
|
|
Females: ↑ in Int, ↓ in Pla
|
|
Bacteroides
|
|
Males: ↑ in both groups
|
|
Females: ↑ in Int, ↓ in Pla
|
|
Lactobacillus
|
|
Males and females: ↑ in Int
|
|
Males: ↓ in Pla
Females: ↑ in Pla
|
|
Wu et al.,[38]
RCT
|
N=105 (75 males and 30 females), physically active
|
NS change in α-diversity (Shannon index and Chao1)
within or between groups
|
NS differences in acetic, propionic, butyric,
isobutyric, valeric or isovaleric acid within or between
groups
|
CMJa
|
L-PS23 and HT-PS23 improved post-EIMD performance recovery
(CMJ, RFD, WAnT); post-intervention microbiota differences
observed (e.g., ↑ Lacticaseibacillus, ↓
Prevotella) but baseline data not reported; no
SCFA changes.
|
|
Probiotic vs. heat-treated probiotic vs. placebo
|
β-Diversity, post only: L-PS23 significantly different from
Pla (p=0.006); HT-PS23: not different from Pla
(p=0.868)
|
Post-EIMD Recovery:
|
|
Lacticaseibacillus paracasei PS23 (L-PS23)
|
No baseline data reported.
|
All groups showed reduced RFD and relative force peak post
EIMD (3, 24, 48 h) (p < 0.05)
|
|
HT-PS23, 6 wk.
|
RA (post-intervention, log10)
|
L-PS23 and HT-PS23 less reduction in RFD and relative force
peak vs. Pla at multiple time points (p < 0.05 to
p < 0.0001)
|
|
Genus: L-PS23>Pla for Lacticaseibacillus,
Streptococcus, Blauti,
Lactobacillus
|
HT-PS23 showed less reduction in CMJ vs. Pla at 24 and 48 h
(p < 0.05)
|
|
Time effects significant for RFD, relative peak force and CMJ
(p < 0.0001)
|
|
L-PS23 < Pla for Prevotella (p <
0.05)
|
Isotonic Muscle Strength (IMTP)
|
|
HT-PS23>Pla for Lacticaseibacillus and
Collinsella (p < 0.05)
|
L-PS23 and HT-PS23 showed significantly less reduction in
relative force peak vs. Pla at various time points post-EIMD
(p < 0.05)
|
|
RFD:
|
|
L-PS23 and HT-PS23 had less loss of peak RFD at all post-EIMD
time points vs. Pla (p < 0.05)
|
|
Wingate anaerobic test (WAnT):
|
|
L-PS23 and HT-PS23 less reduction of relative mean and peak
power vs. Pla at 3, 24 and 48 h post-EIMD (p <
0.05)
|
|
Fatigue index:
|
|
All groups increased fatigue index post-EIMD (p <
0.0001)
|
|
HT-PS23 and L-PS23 had smaller increases vs. Pla at 24 and 48
h (p < 0.05).
|
|
Correlation: Data difficult to extract (heat map).
|
|
Positive correlations: Lacticaseibacillus,
Streptococcus, Blautia,
Lactobacillus: with improved performance in CMJ,
IMTP, and WAnT, weak to moderate (r=0.1–0.3).
|
|
Negative correlations: Prevotella with exercise
performance including RFD % 24 h (r=−~0.3), Wingate
relative peak power (%) 48 h and Wingate Fatigue Index (%)
24 h (r=~−0.2)
|
|
Probiotic studies—multi-strain
|
|
Przewłócka et al.[35] Study 1 RCT
|
N=23 males, well-trained MMA athletes
|
This was assessed in Przewłócka et al.[45]
|
This was assessed in Przewłócka et al.[45]
|
Cycle ergometer, supramaximal sprints, triple WAnT
|
↑ W
tot and MP within intervention group
(p=0.04); no between-group differences in
performance.
|
|
Probiotic vs. control
|
↑ within-group W
tot (J/kg) and MP (W/kg) in
Int; no between-group differences in WAnT performance
outcomes (i.e., W
tot,
P
max, MP or FI)
|
|
Bifidobacterium lactis W51, Levilactobacillus
brevis W63, Lactobacillus acidophilus W22,
Bifidobacterium bifidum W23 and Lactococcus
lactis W58
|
|
+ 5 mL Vit D3, 4 wk.
|
|
Przewłócka et al.,[34] Study 2 RCT
|
N=23 males, well-trained MMA athletes
|
NS difference between groups in α-diversity
(Inverted Simpson, p=0.086)
|
NS differences between groups for acetic,
propionic, butyric and valeric acid
|
Graded cycle ergometry test
|
NS changes in α- or between-group β-diversity.
|
|
Probiotic vs. control
|
β-Diversity (Bray–Curtis), significant within group ∆ in Int
(p=0.0005); NS in control
(p=0.145)
|
Post-intervention: ↓ propionate in both groups, statistically
significant in control (p=0.004)
|
NS within or between group differences in
V̇O2max or MAP (W)
|
Int altered specific gut taxa (↑ Negativicutes,
Faecalibacterium; ↓ Firmicutes, Lachnospiraceae).
|
|
Bifidobacterium lactis W51, Levilactobacillus
brevis W63, Lactobacillus acidophilus W22,
Bifidobacterium bifidum W23 and Lactococcus
lactis W58
|
No between-group differences reported
|
Int propionate tended to be lower than control
(p=0.061)
|
TTE (s) significantly improved in Int (p=0.023) but
not in control (p=0.685); between-group difference at
post-intervention not reported.
|
No group differences in SCFAs; propionate decreased in both
groups.
|
|
+ 5 mL Vit D3, 4 wk.
|
Total abundance
|
No effect on V̇O2max or power; TTE
improved only in Int (between-group difference not
reported).
|
|
Between group (post-intervention, Int vs. control)
|
|
Class: ↑ Negativicutes (Est pairwise=2.23, p=0.032), ↓
Firmicutes (Est pairwise=−0.93, p=0.038)
|
|
Family: ↓ Lachnospiraceae (Est pairwise=−1.27, p <
0.001), ↓ Peptostreptococcaceae (Est pairwise=−2.69,
p=0.004), ↓ Lactobacillaceae (Est pairwise=−2.04,
p=0.018)
|
|
Genus: ↑ Faecalibacterium (Est pairwise=1.21,
p=0.013), ↑ Prevotella (Est pairwise=−1.01,
p=0.002), ↓ Collinsella (Est
pairwise=−0.93, p=0.038), ↓ Bacteroides (Est
pairwise=−0.041, missing p-value)
|
|
Species: ↑ Bacteroides fluxus (Est pairwise=2.02,
p=0.002), ↑ Roseburia inulinivorans (Est
pairwise=1.40, p=0.005)
|
|
Within group changes (Int)
|
|
Class: ↑ Negativicutes (Est=1.98, p=0.006), ↓
Firmicutes (Est=−1.68, p < 0.001).
|
|
Family: ↓ Lachnospiraceae bacterium (Est=−1.55, p
<0.001), ↓ Peptostreptococcaceae bacterium (Est=−1.86,
p=0.005), genus: Int: ↑ Bacteroides
(Est=0.259, p < 0.001), ↑ Faecalibacterium
(Est=1.03, p=0.002), ↑ Prevotella (Est=3.62,
p < 0.001), ↓ Collinsella (Est=−1.28,
p=0 .003)
|
|
Species: Int: ↑ Bacteroides fluxus (Est=2.11, p
< 0.001), ↑ Roseburia inulinivorans (Est=0.74,
p=0.030)
|
|
Within-group changes (control)
|
|
Class: ↓ Firmicutes (Est=−0.75, p=0.021).
|
|
Wang et al.,[36]
RCT
|
N=19 (15 males and 4 females), active amateur marathon
runners
|
RA (Phylum)
|
Not measured
|
Cooper’s test
|
Intervention increased abundance of several genera (e.g.,
Lacticaseibacillus, Olsenella) and
decreased others (Cloacibacillus) vs. placebo.
|
|
Probiotic vs. placebo
|
Bacteroidota, Bacillota, Pseudomonadota
and Actinomycetota more abundant in Int vs. Pla
(qualitative, no p-values shown).
|
Total distance (km) improved within Int (p < 0.05),
but not in Pla; NS between-group difference
(p=0.323)
|
B. longum increased (NS).
|
|
Lactobacillus acidophilus and Bifidobacterium
longum, 5 wk.
|
Genus: ↑ Lacticaseibacillus (p=0.001),
Olsenella, Weissella, Anaerostipes
(p < 0.05) in Int vs. Pla; ↓
Cloacibacillus and
Alphaproteobacteria_unclassified in Int vs. Pla
(p < 0.01).
|
Running distance improved within intervention only; no
between-group difference.
|
|
Species: Bifidobacterium longum increased from pre- to
post-intervention in Int (NS)
|
|
Önes et al.,[32]
RCT
|
N=21 females, professional soccer players
|
NS between-group differences in α-diversity
(Shannon index, Chao1)
|
Not measured
|
30–15 intermittent fitness test
|
No change in α-diversity; β-diversity differed between
groups.
|
|
Probiotic vs. control
|
β-Diversity:
|
V̇O2max and finishing speed data not
reported numerically
|
Kefir ↑ Akkermansia, Bifidobacterium and A.
muciniphila.
|
|
Kefir, 28 d.
|
Significant difference between groups (Bray–Curtis,
p=0.018; Jaccard, p=0.016).
|
Subgroup analysis:
|
No numerical performance data; exploratory findings linked
certain taxa (e.g., F. prausnitzii, P. copri)
with higher fitness.
|
|
NS within group changes in Int
|
High performers: ↑ Faecalibacterium and P.
copri
|
|
RA
|
Low performers: ↑ Dorea formicigenerans and
Oxalobacter formigenes
|
|
Phylum: dominant: Bacillota and Bacteroidota in
all groups.
|
No statistical comparisons between groups reported
|
|
Int: ↓ Pseudomonadota, ↑ Verrucomicrobiota and
Euryarchaeota (no p-values reported)
|
|
Genus: dominant: Prevotella, Bacteroides and
Faecalibacterium in all groups.
|
|
Int: ↓ Bacteroides and Faecalibacterium
|
|
↑ Akkermansia and Bifidobacterium (no
p-values reported)
|
|
Species:
|
|
Int: ↑ A. muciniphila and Roseburia faecis (no
p-values reported)
|
|
Nutrient
|
|
Morita et al.,[39] RCT
|
N=31 males, physically active
|
α-Diversity and β-diversity: NS difference between
groups.
|
Not measured
|
10 km TT exercise bike
|
No changes in α- or β-diversity.
|
|
Nutrient vs. nutrient vs. placebo
|
RA
|
FL or Pla: NS at any time point
|
B. uniformis increased within FL and αCD groups over
time, but between-group differences were NS.
|
|
FL
|
Between groups: Non-significant increase in B.
uniformis in FL and αCD vs. Pla at wks 4 and 8
|
αCD: ↓ time to complete at 4 wk (p=0.004) and 8 wk
(p=0.014) vs. baseline
|
αCD group showed improved 10 km TT at 4 and 8 wk compared to
baseline; significantly faster than Pla at 8 wk.
|
|
αCD, 9 wk.
|
Within groups:
|
Between groups at 8 wk: αCD fasters than Pla
(p=0.010)
|
|
FL: ↑ B. uniformis at wks 4 (p=0.038) and 8
(p=0.011)
|
|
αCD: ↑ B. uniformis at wk 8 (p=0.036)
|
|
Pla: NS ↑ B. uniformis at wks 4 and 8
|
|
∆ B. uniformis abundance, (0–8 wk): median
copies/g:
|
|
αCD: 3.99×1010
|
|
FL: 1.46×1010
|
|
Pla: 3.74×108
|
|
NS between groups
|
|
Onishi et al.,[33] RCT
|
N=81 males, physically active
|
NS difference between groups in B.
uniformis abundance post-intervention.
|
Not measured
|
10 km TT (s) exercise bike
|
No between-group difference in B. uniformis
abundance.
|
|
Nutrient vs. placebo
|
Both αCD and Pla showed significant within-group increases
over time.
|
10 km TT completion times improved significantly from
baseline in both groups. At 8 wk, αCD group times were
significantly faster than Pla (p=0.016).
|
Significant within-group increases observed in both αCD and
Pla
|
|
αCD, 8 wk.
|
NS
changes within or between groups for
V̇O2max or PWC 75%
HRmax
|
10 km TT improved from baseline in both groups; αCD group was
significantly faster than Pla at 8 wk, although
between-group change over time was not assessed.
|
|
NS changes in V̇O2max or
PWC 75% HRmax
|
|
Diet
|
|
Furber et al.,[27] RCT
|
N=16 males, highly trained endurance runners
|
HPD: ↓ Fisher-alpha diversity of inducible viruses
(IV) (p=0.04), altered viral composition
(R²=0.15–0.16), ↑ Sk1virus, correlated with ↑
|
Not measured
|
Treadmill, 10 km TTE (s)
|
HPD impaired performance and altered viral composition (↑
Sk1virus, ↓ Bifidobacterium spp.); HCD mostly
improved performance, linked to microbial stability. All
effects reversed after washout.
|
|
Diet vs. diet vs. diet
|
Leuconostoc and ↓Bifidobacterium spp.
|
HPD: ↓ 23.3% (p < 0.001); linked with greater FVP
shifts, performance returned to baseline after washout
|
|
Habitual diet vs. HPD vs. HCD, 22 d.
|
HCD: Bacterial shifts included
|
HCD: ↑ 6.5% (p=0.05); linked with microbial stability
and subject-specific profiles, performance returned to
baseline after washout
|
|
↑ Leuconostoc, Lactococcus, Collinsella.
Ruminococcus; ↓ Streptococcus; viral changes
included ↑ Cc31virus
|
|
Mancin et al.,[29] RCT
|
N=16 males, semi-professional soccer players
|
No change in α-diversity (OTU count, Shannon’s ENS)
|
Not measured
|
Maximal strength, isometric,
quadriceps
|
KDP reduced Actinobacterota vs. WD (p=0.021);
no changes in α-diversity. Performance improved in both
diets with no between-group differences.
|
|
Diet vs. diet
|
RA
|
NS within or between groups
|
|
KEMEPHY ketogenic Mediterranean diet (KDP) vs. Western diet,
30 d.
|
Phylum: Actinobacterota ↓ in KDP and ↑ in WD;
KDP < WD (p=0.021)
|
Yo-yo Intermittent Test (m) and CMJ
(cm) ↑ within both groups (p < 0.001);
NS between-group differences
|
|
Post intervention:
|
|
KDP: ↑ Clostridia UCG-014, Butyricimonas,
Odoribacterter and Ruminococcus
|
|
WD: ↑ Bifidobacterium, Butyricicoccus and
Acidaminococcus
|
|
CHO intake negatively correlated with
∆’s
of
Odoribacter genus (r=−0.59); fat intake
negatively with Fusicatenibacter genus
(r=−0.53)
|
|
Murtaza et al.[31] RCT
|
N=29 male, highly competitive race walkers
|
At baseline, participants clustered into gut microbiota
enterotypes: 7/29 were Prevotella dominant, 20/29
Bacteroides dominant and 2 Bacillota
dominant. These enterotypes remained relatively stable
post-intervention.
|
Not reported.
|
V̇O2 peak ↑ in all
groupsb (p < 0.001):
|
LCHF diet altered β-diversity and increased
Bacteroides and Dorea, with a reduction in
Faecalibacterium. These changes were associated
with impaired performance.
|
|
Diet vs. diet vs. diet
|
No changes in α-diversity
|
Submaximal O
2 costb ↓ in
|
Enterotypes (mainly Bacteroides or Prevotella)
remained stable and may influence responsiveness to dietary
interventions.
|
|
HCHO vs. PCHO vs. LCHO, 3 wk.
|
HCHO and PCHO: subtle, non-significant microbiota shifts.
|
HCHO/PCHO; unchanged in LCHF
|
|
LCHF: Significant shifts in β-diversity (RDA p=0.02;
anosim p=0.029).
|
10-km walk timeb improved in HCHO (↓ 6.6%) and
PCHO (↓5.3%), not in LCHF (↑1.6%)
|
|
↓ Faecalibacterium spp. (p=0.0003), ↑
Dorea spp. (p=0.007) and ↑
Bacteroides (p=0.002).
|
Significant negative correlations observed between
Bacteroides and fat oxidation and Dorea
spp. and exercise economy following LCHF. NS
baseline correlations were reported.
|
|
Exercise
|
|
Zhong et al.,[26]
RCT
|
N=14 females, physically active
|
α-Diversity
|
Not reported.
|
NS differences in 2-min step-test, Grip strength,
30-s chair stand test, Timed up and go test.
|
Exercise increased microbial richness within group, with no
between-group differences in α-diversity. Some taxa differed
between groups post-intervention.
|
|
Exercise vs. control
|
NS differences between groups.
|
Chair sit-and-reach (cm) and single-leg standing with eyes
closed (s) significantly improved in Int vs. control
|
No between-group differences in most functional measures.
Chair sit-and-reach and balance improved in Int vs. control;
both outcomes negatively correlated with specific taxa.
|
|
4× wk, 60 min, including warm-up (10 min), aerobic exercise
(20 min), resistance exercise (25 min) and cool down (5
min), 8 wk.
|
Within Int, richness indices (Sobs, Chao, Ace index)
increased significantly, but diversity indices (Shannon,
Simpson) did not change significantly.
|
Chair sit-and-reach: negatively correlated with
Betaproteobacteria (r=− 0.704,
p=0.005).
|
|
Post intervention, Int showed significantly lower RA (vs.
control) of:
|
Single-leg standing with eyes closed: negatively correlated
with Holdemania (r=− 0.553,
p=0.040).
|
|
Class: Betaproteobacteria (p=0.008)
|
Grip strength: negatively correlated with
Betaproteobacteria (r=− 0.551,
p=0.041).
|
|
Order: Burkholderiales (p=0.008)
|
|
Family: Sutterellaceae (p=0.008), Bacteroidaceae
(p=0.009)
|
|
Genus: Holdemania (p=0.030)
|
|
Anaerostipes (p=0.008)
|
|
Bacteroides (p=0.009)
|
|
Bacilli and Lactobacillales were higher in Int
(both p=0.027)
|
|
Within-group increases in Int vs. control:
|
|
Order: Coriobacteriales (p=0.012)
|
|
Family: Coriobacteriaceae (p=0.012)
|
|
Genus: Asaccharobacter (p=0.028),
Collinsella (p=0.028),
Fusicatenibacter (p=0.049)
|
Abbreviations: CHO, carbohydrate; EIMD, exercise-induced muscle damage;
FI, fatigue index; HCD, high carbohydrate diet; HCHO, high carbohydrate;
HPD, high protein diet; IMTP, isometric mid-thigh pull; Int,
Intervention; LCHF, low carbohydrate; LCHO, low carbohydrate; MMA, mixed
martial arts; MP, mean power; NS, not significant; PCHO,
periodised carbohydrate, high fat; Pla, Placebo; Pre, Prebiotic
intervention; RA, relative abundance; RCT, Randomised control trial;
RFD, rate of force development; RXT, randomised crossover trial; WAnT,
Wingate anaerobic test based.
Notes: ∆ Indicates the change in mean or median from pre- to
post-intervention, unless otherwise specified.
aPerformance data from Lee et al.[24]
bPerformance data from Burke et al.[75]
Risk of bias assessment
Results of the risk of bias assessment are presented in [Table 5]. Several studies did not
explicitly state their blinding or randomisation procedures, or blinding was not
possible due to the nature of the intervention. These studies were therefore
assessed as having ‘some concerns’ or ‘high risk’ in certain domains.[23]
[25]
[31]
[32]
[40] Most included studies, however,
had an overall low risk of bias. More than half were either directly funded by,
received supplementation and/or placebo products from or included authors
affiliated with, commercial product manufacturers (i.e., probiotic, herbal or
dietary products) ([Table 3]).[22]
[25]
[27]
[29]
[30]
[33]
[34]
[35]
[37]
[38]
[39]
[40] Most studies included in this
review did not adhere to best practice guidelines for exercise-gastroenterology
research,[73] which emphasises
rigorous methodology and control of confounding variables, especially those
related to diet and exercise ([Table
3]).
Table 5
Risk of bias assessment
 
|
Dietary monitoring and control
Dietary monitoring and control strategies varied widely, with inconsistent
implementation across studies. The majority (n=13) did not provide
participants with food; three studies partially provided meals,[25]
[29]
[33] and only two implemented fully
controlled dietary interventions.[31]
[36] Most trials
monitored dietary intake to some extent rather than supplying all meals. In
studies investigating single-strain probiotics or postbiotics, dietary
monitoring was often reported, but comprehensive control and detailed reporting
were uncommon. For instance, Gross et al.[22] used 2-day food and fluid logs and required participants to
replicate intake before each study visit and fast overnight. Despite this
dietary control method within the experimental procedures being
inappropriate,[73] macronutrient
intake was reported, but fibre and FODMAPs were not. Huang et al.[23] asked participants to avoid
fermented foods, prebiotics, probiotics and antibiotics and analysed dietary
records; yet, detailed results were not reported. Lee et al.[40] required cessation of supplements
two weeks prior to intervention and recorded baseline energy intake, but did not
monitor fibre or FODMAPs. Similarly, Li et al.[28] reported no between-group
differences in energy or macronutrients using 2-day weighed food diaries, but
did not include fibre or FODMAP data. Lin et al.[25] employed a dietitian to prescribe
a diet and provide the same meal, while restricting probiotic- and
antibiotic-containing foods; however, no dietary intake data were reported. West
et al.[37] used 4-d food diaries and
instructed participants to maintain habitual diets while avoiding probiotic
foods, reporting no between-group differences in energy, macronutrients or fibre
intake. Wu et al.[38] collected 3-day
food diaries with photographic records and reported no significant differences
between or within groups, although diets were not otherwise controlled.
McDermott et al.[30] standardised
pre-trial breakfasts and evaluated dietary habits at baseline and
post-intervention, reporting no changes in fibre or diet quality.
Among multi-strain probiotic studies, dietary control and reporting were
similarly limited. Przewłócka et al.[34]
[35] assessed dietary
intake using a 3-day interview and food frequency questionnaire and standardised
the pre-exercise breakfast, but did not report nutrient data. Wang et al.[36] prescribed an ‘assigned diet’ and
restricted supplements, fermented foods and alcohol, yet provided no dietary
details. Önes et al.[32] used
dietitian-guided 3-day food records across training phases but again did not
report nutritional composition. In nutrient-based intervention studies, dietary
control varied. Morita et al.[39]
asked participants to maintain their usual diets while avoiding functional foods
and supplements but did not report dietary composition. Onishi et al.[33] provided participants with a
prescribed dinner the day prior and a prescribed breakfast on the day of the
clinic visit. Outside of this, participants were instructed to maintain their
usual diet. Daily meal intake was recorded, though nutrient composition was not
analysed. In contrast, Furber et al.,[27] Murtaza et al.[31]
and Mancin et al.[29] implemented
tightly controlled diets with detailed dietary analysis, more closely aligning
with methodological best practice.[73] In the sole exercise-only study, Zhong et al.[26] did not monitor or report dietary
intake, representing a significant limitation given the influence of diet on gut
microbiota outcomes. Overall, while many studies incorporated some level of
dietary monitoring, such as food diaries, exclusion criteria or standardised
meals, few provided detailed nutrient data, especially on fibre and FODMAP
intake, which are key confounding nutrients to gut microbiota change. Only a
minority provided food directly to participants. These inconsistencies limit the
interpretability of intervention effects (e.g., any positive outcome could
simply be due to artefact subsequent to the experimental design) and underscore
the need for improved methodological rigour, as recommended by Costa et al.[73]
Exercise monitoring and control
Exercise control and reporting were similarly variable ([Table 3]). Most studies provided only
general instructions, such as maintaining a regular lifestyle and avoiding
strenuous exercise, with some specifying a restriction period of 1–3 days before
testing. However, n=3 studies[23]
[38]
[40] did not monitor or record
participants’ physical activity levels at all. Eleven studies[22]
[23]
[25]
[27]
[29]
[32]
[36]
[37]
[38]
[39]
[40] lacked detailed exercise data,
while five studies[28]
[30]
[31]
[33]
[35] provided overall comprehensive
information. Given the influence of physical activity on gut microbiota, this
lack of standardisation limits result interpretation (e.g., any positive outcome
could simply be due to an artefact subsequent to the experimental design).
Stool sample collection and storage
Seventeen studies assessed microbial composition in stool samples during the
intervention period, reflecting changes in luminal microbiota. However,
collection protocols varied considerably, complicating direct comparison across
studies ([Table 3]). In probiotic and
postbiotic interventions, Gross et al.,[22] participants used kits containing preservation reagents (RNA
Later and OMNIgene); samples were initially stored at −20°C, transported on ice
and then stored at −80°C. Lin et al.[25] used DNA/RNA Shield tubes for self-collection, with storage at
−80°C. McDermott et al.[30] employed
a commode system and nucleic acid preservation tubes, storing samples at −80°C.
In contrast, West et al.[37] used
sealable plastic bags frozen at −20°C, although the interval between collection
and lab delivery was not reported. Wu et al.[38] preserved fresh samples in 95% ethanol prior to transport and
stored them at −80°C. Li et al.,[28]
Huang et al.[23] and Lee et al.[40] collected fresh stool samples but
did not describe collection and handling protocols. In multi-strain probiotic
studies, Przewłócka et al.[34] used
standardised containers with immediate freezing at −80°C, while Wang et al.[36] used freeze-dried collection tubes
with similar freezing procedures. Önes et al.[32] did not report their collection or
storage methods. Of the two other nutritional supplement studies, Morita et
al.[39] and Onishi et al.[33] provided Sarstedt containers for
home collection, instructing participants to freeze samples at approximately
−30°C before transport to the clinic. Dietary intervention protocols also
employed varied approaches. Furber et al.[27] collected the first stool of the day, freezing samples at −80°C
within 2 hours. Mancin et al.[29]
used sterile swab tubes containing preservative, stored at −20°C. Murtaza et
al.[31] used OMNIgene kits for
samples collected before and after the training-diet intervention in athletes.
In the sole exercise-only intervention, Zhong et al.,[26] stool samples were pre- and
post-intervention, but the collection and storage protocols were not
described.
Microbial, α-diversity, and SCFA techniques
Most probiotic and postbiotic interventions used 16S rRNA gene amplicon
sequencing, targeting different hypervariable regions: V1–V3,[23] V3–V4[25]
[32]
[38]
[40] and V4.[36] Shannon index was commonly used to
assess α-diversity,[23]
[28]
[32]
[38]
[40] sometimes alongside Chao1,[32]
[38] Inverted Simpson, ACE and
others.[34] Two studies employed
alternative sequencing: Gross et al.[22] used shotgun metagenomic (Shannon entropy) and Li et al.[28] used DNB-seq with combinatorial
probe-anchor synthesis. West et al.[37] used denaturing gradient gel electrophoresis (DGGE) and qPCR and
McDermott et al.[30] used real-time
qPCR, though α-diversity was not reported in either. Of the two other
nutritional supplement studies, Morita et al.[39] and Onishi et al.[33] used 16S rRNA gene amplicon
sequencing, targeting the V4 region and employed species-specific qPCR to
quantify Bacteroides uniformis. Morita et al.[39] also used Shannon, Chao1 and
phylogenetic distance indices, but SCFA concentrations were not measured in
either study.
SCFA quantification methods varied and were inconsistently reported. They
included gas chromatography–mass spectrometry (GC-MS),[2] high-performance liquid
chromatography (HPLC),[40]
ultra-performance liquid chromatography tandem mass spectrometry
(UPLC-MS/MS),[38] and gas
chromatography with flame ionisation detection (GC-FID),[34] while several studies did not
report SCFA analysis.[22]
[25]
[30]
[32]
[36]
[37]
Diet-based interventions applied 16S rRNA gene amplicon sequencing, targeting
V3–V4,[29] V6–V8,[31] and V4.[27] Furber et al.[27] also included ITS1–ITS2 sequencing
for fungal taxa and viral metagenomics. Reported diversity metrics included
Fisher’s alpha,[27] Shannon’s
effective number of species (ENS) and operational taxonomic units (OTUs)[29] and Shannon and Simpson
indices.[27] None of the dietary
studies assessed SCFA concentrations. In the sole exercise-only intervention,
Zhong et al.[26] employed 16S rRNA
gene amplicon sequencing (V3–V4), with multiple α-diversity metrics reported:
Sobs, Chao1, ACE, Shannon and Simpson. SCFA concentrations were not
reported.
Faecal microbial composition and diversity
Two probiotic studies showed increased levels of supplemented bacterial species:
B. longum subsp. longum
[25] and Limosilactobacillus fermentum VRI-003 PCC.[37] Przewłócka et al.[34] found increases in Bacteroides
fluxus and Roseburia inulinivorans after 4 weeks of a
multi-strain probiotic with vitamin D3. No other significant species-level
changes were found.[22]
[25]
[28]
[32]
[36]
[38]
[40] McDermott et al.[30] detected Lactobacillus
helveticus Lafti L10 in 57.1% of participants post-intervention, though
without statistical group comparisons.
At the phylum level, Lin et al.[25]
noted an increase in Actinomycetota and Bacillota and a reduction in
Pseudomonadota following Bifidobacterium longum subsp. longum
supplementation, although significance was not stated. Gross et al.[22] found no changes in
Veillonella abundance after Veillonella atypica FB0054
supplementation. Huang et al.[23]
reported reductions in several bacterial groups (e.g., Anaerotruncus,
Caproiciproducens, Coprobacillus, Desulfovibrio,
Dielma, Family_XIII_UCG_001, Holdemania and
Oxalobacter) and increases in Akkermansia, Bifidobacterium,
Butyricimonas and Lactobacillus, though no baseline data were
reported. Wang et al.[36] noted
increases in Bacteroidota, Bacillota, Pseudomonadota, and
Actinomycetota, after 5 weeks of Lactobacillus acidophilus and
Bifidobacterium longum supplementation but statistical significance
was not reported.
At the family and class levels, Przewłócka et al.[34] found reductions in the families
Lachnospiraceae, Peptostreptococcaceae and Lactobacillaceae, all within the
phylum Bacillota, alongside an increase in the class Negativicutes and an
overall reduction in Bacillota abundance. No further faecal sample bacterial
taxa changes were observed with probiotic supplementation. At the genus level,
Lin et al.[25] showed significant
increases in Bifidobacterium and members of the Lactobacillaceae family,
including genera formerly classified under Lactobacillus. Wu et al.[38] found Lacticaseibacillus
paracasei PS23 supplementation led to increases in the genera
Lacticaseibacillus, Streptococcus, Blautia and other
members of the Lactobacillaceae family (formerly grouped under
Lactobacillus), alongside a decrease in Prevotella. Postbiotic
supplementation with heat-killed Lacticaseibacillus paracasei PS23 led to
an increase in the genera Lacticaseibacillus and Collinsella;
however, baseline microbiota data were not reported.[38] Przewłócka et al.[34] reported an increase in the genera
Faecalibacterium and Prevotella and a decrease in
Collinsella and Bacteroides after supplementation with a
multi-bacterial formulation. Wang et al.[36] noted increases in the genera Lacticaseibacillus,
Olsenella, Weissella and Anaerostipes and decreases in
Cloacibacillus and Alphaproteobacteria_unclassified.
Seven studies reported no significant changes in α-diversity following probiotic
supplementation [22]
[28]
[32]
[34]
[37]
[38]
[40] and two reported no significant
β-diversity shifts.[22]
[28] However, three
studies observed significant β-diversity shifts following probiotic
use.[32]
[38]
[40] Lee et al.[40] reported differences in
β-diversity between groups receiving heat-killed Lactiplantibacillus
plantarum TWK10 and controls and between live and heat-killed TWK10. Wu
et al.[38] found significant
β-diversity changes after 6 weeks of Lacticaseibacillus paracasei PS23
supplementation compared to placebo. Finally, Önes et al.[32] observed significant β-diversity
differences following kefir supplementation over four weeks. Of the two other
nutritional supplement studies, one examined both α- and β-diversity and
reported no significant differences between groups following 9 weeks of
supplementation with flaxseed lignans, αCD or a placebo.[39] Additionally, no significant
changes in species-level relative abundance were reported.[33]
[39]
Among the three dietary studies, two assessed α-diversity and found no
significant differences between dietary intervention groups.[29]
[31] At the phylum level, Mancin et
al.[29] reported a decrease in
the abundance of Actinomycetota following adherence to the KEMEPHY ketogenic
Mediterranean diet (KDP) compared to a Western diet over 30 days. Carbohydrate
intake was negatively correlated with the genus, Odoribacter, and fat
intake was negatively associated with Fusicatenibacter. Murtaza et
al.[31] identified three dominant
gut microbiota profiles at baseline: Prevotella predominant,
Bacteroides dominant and a profile dominated by members of the phylum
Bacillota in one participant. These profiles remained relatively
stable after participants adhered to one of three prescribed diets (high
carbohydrate, protein carbohydrate and low carbohydrate).
Furber et al.[27] did not compare α-
or β-diversity between intervention groups (high protein diet vs. high
carbohydrate diet), instead focusing on within-group changes over time. The high
protein diet was associated with a significant reduction in Fisher-α diversity
of inducible viruses, which did not return to baseline levels post-intervention.
Significant shifts were observed in both free viral particles and inducible
virus communities. The high-carbohydrate diet appeared to have a greater impact
on bacterial community composition than on the viral community; however, these
shifts were not statistically significant. Notably, the high carbohydrate diet
was associated with increased relative abundance in Leuconostoc,
Lactococcus and Collinsella, while Streptococcus
decreased.
In the sole exercise-only intervention, Zhong et al.[26] reported no significant changes in
α-diversity between intervention and control groups. At the class level, the
exercise intervention was associated with an increase in Bacilli and a
decrease in Betaproteobacteria. At the order level,
Lactobacillales increased while Burkholderiales decreased. At
the family level, reductions were noted in Sutterellaceae and Bacteroidaceae. At
the genus level, the intervention resulted in a decreased abundance of
Holdemania, Anaerostipes and Bacteroides.
Faecal SCFA concentration
Faecal SCFA concentrations were assessed pre- and post-intervention in n=4
studies ([Table 4]), all of which
investigated probiotic and/or postbiotic supplementation.[28]
[34]
[38]
[40] One additional study[23] reported only post-intervention
SCFA values. Significant increases in faecal acetate concentrations were
observed following 6 weeks of supplementation with L. plantarum or
heat-killed L. plantarum (formerly Lactobacillus plantarum). No
significant changes were detected in the control group, and no significant
between-group differences were reported.[40] Huang et al.[23]
observed higher post-intervention concentrations of acetic acid, propionic acid
and butyric acid in the probiotic group (L. plantarum PS128) compared to
placebo; however, the absence of baseline data limits the ability to attribute
these changes to the intervention. Li et al.[28] reported a 4.5-fold increase in faecal acetate following 8 weeks
of yoghurt supplementation containing Bifidobacterium animalis subsp.
lactis BL-99 compared to control. In contrast, two studies[34]
[38] found no significant
post-intervention changes in faecal SCFA concentrations.
None of the studies assessing SCFA outcomes adhered to best practice guidelines
for the control of experimental confounders in exercise–gastroenterology
research, as outlined by Costa et al.[73] As described previously, dietary control across studies varied
with those measuring SCFA mostly relying on simplistic strategies, such as
requesting participants to maintain usual intake or record dietary logs, which
are insufficient for standardising pre-trial diet. Several studies failed to
report quantitative dietary intake or key microbiota-modulating components.
Exercise performance outcomes in response to interventions
Eighteen studies assessed exercise performance outcomes across various domains,
including TTE, TT, Cooper’s test, strength and anaerobic performance and
functional capacity. Several also evaluated physiological parameters, such as
aerobic capacity, and examined correlations against faecal microbiota
composition ([Table 4]). Six studies
examined TTE outcomes, n=5 probiotics and/or postbiotics [22]
[23]
[30]
[34]
[40] and n=1 dietary
study.[27] Results from
single-strain probiotic trials were mixed: Veillonella atypica
FB0054[22] and Lactobacillus
helveticus Lafti L10 had no effect on TTE;[30] whereas L. plantarum PS128
[23] increased post-intervention
TTE compared to placebo. However, baseline values were not reported, limiting
the interpretation of the magnitude of change. In that study, TTE was
significantly greater in the intervention group than placebo (1,679 s vs.
1,083 s, p < 0.05). Both viable and heat-killed forms of L.
plantarum TWK10 improved TTE at 85% V̇O2max.[40] A multi-strain probiotic
containing B. lactis W51, L. brevis W63, L. acidophilus
W22, B. bifidum W23, Lc. lactis W58 and vitamin D₃ improved TTE by
approximately 12.6% in mixed martial arts athletes.[34] TTE increased significantly in the
intervention group (496.30±89.98 to 559.00±68.99 s, p=0.023), while no
significant change occurred in the control group (489.91±72.02 to
468.55±102.03 s, p=0.685). However, as only within-group comparisons were
conducted and no between-group statistical analyses were reported, the true
effect of the intervention remains unclear. In dietary studies, a high-protein
diet led to a significant decrease in TTE, while a high-carbohydrate diet
resulted in a modest increase in TTE.[27]
Three studies assessed TT, n=2 focused on nutritional
supplementation,[33]
[39] and n=1 on a dietary
intervention.[31] Both
supplementation trials demonstrated improved 10 km performance with αCD
supplementation in physically active males.[33]
[39] A dietary
intervention comparing HCHO, PCHO and LCHF diets found that only HCHO and PCHO
improved 10 km race walking time (average of 4.2%), despite all groups showing
increased aerobic capacity.[31]
Two studies employed Cooper’s test both were probiotic studies. Lin et al.[25] found that in well-trained middle
and long-distance runners, while no significant differences were observed at any
time point, the intervention group showed significantly greater improvements in
distance covered at the 6th, 9th, and 12th minutes, compared to the placebo
group. Wang et al.,[36] found
increased distance in amateur marathon runners post-supplementation, though
changes were not significantly different between groups.
Four studies investigated strength or anaerobic performance, n=3 probiotic
and/or postbiotics[28]
[35]
[38] and n=1 dietary
intervention.[29] Probiotic
supplementation significantly increased 60°/s knee joint extensor and flexor
strength in skiers compared to control.[28]
Lactobacillus plantarum PS23 (both viable and postbiotic,
i.e., heat-killed), helped mitigate neuromuscular fatigue following
exercise-induced muscle damage in physically active adults,[38] showing improved performance
countermovement jump height, rate of force development and Wingate anaerobic
performance. A multi-strain probiotic did not outperform placebo for anaerobic
capacity in MMA athletes.[35] One
dietary study comparing a ketogenic Mediterranean diet and a Western diet in
semi-professional soccer players found no significant differences between groups
in Yo–Yo intermittent test performance, countermovement jump height or maximal
isometric quadriceps strength, despite suggesting potential improvements in
agility, sprinting and power output.[29] In the sole exercise-only intervention and which involved older
adults, Zhong et al.[26] reported
improvements in chair sit-and-reach flexibility and single-leg standing balance
(eyes closed) following an 8-week aerobic and resistance training program.
However, no significant changes were observed in strength or timed functional
tests.
Six studies evaluated aerobic capacity, n=4 were probiotics,[23]
[28]
[34]
[37 ]
n=1 nutritional
supplementation[33] and
n=1 dietary intervention.[42]
Lactobacillus plantarum PS128 [23] and Limosilactobacillus
fermentum VRI-003 PCC[37] had
no effect on V̇O2max, whereas Bifidobacterium animalis
subsp. lactis BL-99 significantly improved V̇O2max in
national-level cross-country skiers compared to control.[28] A multi-strain probiotic had no
impact on MMA athletes.[34]
α-Cyclodextrin supplementation did not change V̇O2max.[33] Murtaza et al.[31] reported a significant
post-intervention increase in V̇O2peak across all dietary
intervention groups (HCHO, PCHO and LCHF diet) (p < 0.001, 90%
confidence interval [CI]: 2.55, 5.20%). However, V̇O2 uptake,
at a speed approximating 20 km race pace, decreased in both the high
carbohydrate (90% CI: −7.05, −0.244%) and periodised carbohydrate (90% CI:
−5.18, −0.86%) groups, whereas it remained at pre-intervention levels in the low
carbohydrate high fat group. Furthermore, mean 10 km race walk time improved in
the high carbohydrate (6.6%, 90% CI: 4.1, 9.1%) and periodised carbohydrate
(5.3%, 90% CI: 3.4, 7.2%) but showed no clear improvement in the low
carbohydrate high fat group (−1.6, 90% CI: −8.5, 5.3%).
Correlations between microbiota alterations and exercise performance
Seven studies, n=4 probiotic studies,[22]
[32]
[38]
[40]
n=2 dietary studies,[27]
[31] and n=1 exercise
study,[26] explored correlations
between changes in the gut microbiota and performance outcomes ([Table 4]). Among the probiotic
interventions, Gross et al.[22]
reported no correlation between changes in β-diversity and TTE. In contrast, Lee
et al.[40] reported a moderate
positive correlation between TTE and the Coriobacteriaceae family following
TWK10 probiotic supplementation, with an approximate correlation coefficient of
r=~0.5 (p < 0.05), which was inferred from the heat map
visualisation. They also observed a strong positive correlation between TTE and
the Veillonellaceae family following the TWK10-hk postbiotic intervention
(r=0.65, p < 0.01). Other microbial families demonstrated
negative correlations with TTE. Wu et al.[38] found weak to moderate positive correlations between the
abundance of Lacticaseibacillus, Streptococcus, Blautia and
Lactobacillus with improved performance across various exercise
tests, while Prevotella abundance was weakly negatively correlated with
performance. Önes et al.[32] reported
that high-performance athletes exhibited greater relative abundance of
Faecalibacterium prausnitzii and P. copri, whereas Dorea
formicigenerans and Oxalobacter formigenes were more abundant in
lower-performing athletes; however, most correlations between microbiota
profiles and performance outcomes were not statistically significant.
Among the dietary studies, Furber et al.[27] found that individuals whose performance declined during a
high-protein diet exhibited greater shifts in overall faecal microbial community
composition, whereas those on a high-carbohydrate diet showed improved
performance and greater microbial stability. Murtaza et al.[31] reported no significant
associations between baseline faecal bacterial profiles and performance;
however, following a low-carbohydrate high-fat dietary intervention, strong
negative correlations were found between Bacteroides and fat oxidation
and between Dorea and exercise economy.
The exercise-only study by Zhong et al.[26] identified negative associations between specific faecal
bacterial taxa and physical performance measures. A strong negative correlation
was found between Betaproteobacteria and chair sit-and-reach performance
(r=−0.704, p=0.005), while moderate negative associations were
reported between Holdemania and single-leg standing with eyes closed
(r=−0.553, p=0.040) and Betaproteobacteria and grip
strength (r=−0.551, p=0.041).
Discussion
This systematic literature review represents, to our knowledge, the first
comprehensive synthesis examining whether nutritional supplement, dietary and
exercise interventions can modulate gut microbiota composition and subsequently
enhance exercise performance in healthy active adult populations. Previous narrative
and systematic reviews in this topic area have typically focused on subsets of these
interventions, often overlooking key studies or methodological limitations. Many
have not consistently included concurrent assessments of gut microbial composition
alongside exercise performance outcomes, nor examined correlations with SCFAs in
plasma or faeces. By including only studies that assessed both gut microbiota and
exercise performance, this review addresses these critical gaps and provides a more
integrated perspective on the potential interactions between gut microbiota and
exercise outcomes.
From a methodological perspective, considerable variability was observed in how
studies assessed the impact of interventions on faecal bacterial composition. While
differences in stool collection and management procedures were noted, a greater
source of inconsistency arose from the analytical techniques employed, most notably,
quantitative polymerase chain reaction (qPCR) and next-generation sequencing
approaches such as 16S rRNA and shotgun metagenomics. These techniques yield
fundamentally different outputs: qPCR provides absolute values (e.g., CFU/g),
whereas sequencing typically reports relative abundance (e.g., % of total reads).
Compounding this, inconsistency in taxonomic levels, bacterial targets and reporting
units hindered direct comparisons across studies. As a result, interpreting
intervention-induced microbiota shifts, and, by extension, their effects on exercise
performance, requires considerable caution. These methodological discrepancies
underscore the urgent need for standardised protocols to enhance comparability and
improve the validity of conclusions drawn from this growing body of literature.
Despite methodological limitations, some studies did report changes in bacterial
composition following probiotic supplementation. Three probiotic studies reported
significant increases in the supplemented strains’ relative abundance[25]
[30]
[37]; however, effects
on broader microbiota taxa were negligible or inconsistent. This aligns with
previous findings from a systematic review showing limited microbial shifts beyond
the administered strains and no substantial effects on gastrointestinal status
(i.e., integrity and function) markers or SCFA concentrations.[76] While a subset of studies in the
current review reported taxonomic shifts beyond the supplemented strains, such as
increases in saccharolytic genera (Blautia, Faecalibacterium,
Anaerostipes)[34]
[36]
[38] and decreases in potentially pro-inflammatory taxa
(Desulfovibrio, Dielma),[23] these findings were inconsistent, often lacked baseline comparisons
and did not consistently align with improvements in α- or β-diversity. Notably, only
three probiotic studies found significant changes in β-diversity,[32]
[38]
[40] while three others
(two probiotic and one supplement) found no significant shifts.[22]
[28]
[39] Similar variability
was observed in dietary and exercise interventions. For example, a high-carbohydrate
diet was associated with increased relative abundance of Leuconostoc,
Lactococcus and Collinsella, while high-protein and ketogenic
diets were associated with reductions in microbial diversity or shifts in
phylum-level taxa.[27] The sole exercise
study included in this review reported modest taxonomic changes but no effects on
α-diversity.[26] Collectively, while
some interventions appeared to modulate saccharolytic or butyrate-producing taxa,
the broader impact on faecal bacterial diversity and composition appears limited and
inconsistent. A further limitation is that many studies did not adequately control
for key confounders known to affect faecal microbial profiles, such as diet
composition, faecal water content, physical activity and gastrointestinal transit
time.[77]
[78]
[79]
[80] The absence of such
controls compromises the ability to attribute microbiota shifts to the interventions
themselves, raising the possibility that reported improvements in exercise
performance may instead reflect suboptimal research design. This aligns with
previous findings by Kristensen et al.,[81] who reported no significant probiotic-induced changes in α-diversity
among healthy populations. Although probiotic-induced microbial shifts are often
small and transient, they may influence bacterial metabolic activity, particularly
the production of SCFAs like acetate and butyrate,[82] which have been proposed to support
both health[83] and exercise
performance.[84] However, it remains
unclear whether these changes are biologically meaningful in athletic contexts or
if
their significance has been overstated.
This variability in intervention outcomes contrasts with the inherent stability
observed in gut microbiota profiles among physically trained individuals under
controlled conditions. From a historical perspective, repeated assessments of faecal
bacterial profiles in endurance-trained individuals, using best practice
experimental controls,[73] with
test–re-test reliability assessments, have demonstrated consistent and stable
microbiota compositions.[9] Specifically,
predominant phyla include Bacillota (69% relative abundance),
Bacteroidota (24%), Actinomycetota (2%), Pseudomonadota
(2%) and Verrucomicrobiota (2%). At the family level, Ruminococcaceae and
Lachnospiraceae each comprise 27%, followed by Bacteriodaceae (13%),
Acidaminococcaceae (6%) and Prevotellaceae (5%). Dominant genera include
Bacteroides (13%), Faecalibacterium (11%), Agathobacter
(5.7%), Phascolarctobacterium (5.3%) and Prevotella (4.3%). Measures
of α-diversity indices, assessed using the Shannon Equitability Index (SEI), also
showed minimal variation: phyla SEI=0.188 (95% CI: 0.166–0.211), family SEI=0.245
(95% CI: 0.234–0.256) and genus SEI=0.282 (95% CI: 0.269–0.296).[9] These findings highlight the underlying
stability of the gut microbiota in physically trained individuals, reinforcing the
need for rigorous control of methodological and biological variability in
intervention trials. Within this context, the lack of consistent changes in
microbiota composition across studies included in this review, particularly at lower
taxonomic levels (i.e., more specific classifications such as genus and species),
is
unsurprising and likely reflects heterogeneity in study protocols, population
characteristics, analytical approaches and control of key confounders, rather than
genuine intervention effects.
Despite mechanisms by which changes in the gut microbiota may enhance exercise
performance being linked to the increased presence of SCFAs, these key microbial
metabolites were assessed in only five studies.[23]
[28]
[34]
[38]
[40] Of these, just one
study[28] reported a significant
increase in acetate, while the remainder found no meaningful changes. One study[23] reported post-intervention means only,
preventing evaluation of within-group change. Collectively, these findings suggest
that the interventions in this review did not reliably modulate microbial
fermentation end-products, which are proposed to mediate gut–exercise performance
relationships. Given the proposed role of SCFAs in exercise physiology, the limited
and inconsistent data further diminish the plausibility of a microbiota-mediated
performance benefit. Furthermore, only seven studies directly examined associations
between microbiota alterations and performance outcomes, with results varying
considerably in both the direction and strength of reported correlations. While a
small number of studies reported statistically significant associations between
specific bacterial taxa and improved or impaired performance,[26]
[31]
[32]
[38]
[40] substantial heterogeneity in study designs, microbial endpoints and
performance measures limits the ability to draw firm conclusions. Notably, one study
reported strong positive correlations between TTE and the relative abundance of
Coriobacteriaceae and Veillonellaceae following probiotic or postbiotic TWK10
interventions,[40] suggesting a
potential mechanistic role of these taxa in energy metabolism. In contrast, several
studies reported no significant associations, or only weak correlations between gut
microbial changes and performance outcomes.[22]
[32]
[38] Taken together, there appear to be no
considerable links between intervention-induced changes to the gut microbiota and
exercise performance. The dietary intervention by Furber et al.[27] adds complexity to these findings,
with a high-protein diet being associated with larger bacterial shifts in
participants whose performance declined; whereas HCHO was linked to improved
performance and microbial stability. Similarly, the exercise only study by Zhong et
al.[26] reported predominantly
negative correlations between microbial taxa and physical function measures.
However, the physiological relevance of these findings remains uncertain due to
small effect sizes and diverse outcome measures. As a whole, it would therefore be
premature from a translational practice perspective, to conclude that changes in gut
microbiota composition, whether induced through nutritional supplementation, dietary
or exercise interventions, directly improve exercise performance. Current evidence
does not support such claims, and doing so risks overstating the practical
significance of largely speculative or inconsistently observed findings. Given the
absence of consistent improvements in performance outcomes across the review, these
findings align with the broader conclusion that meaningful microbiota-mediated
exercise performance enhancements remain unsubstantiated.
Study limitations
Despite most included studies being rated as low risk of bias, several
methodological limitations were identified that constrain the strength of
conclusions in this systematic review. Common issues across studies included
inadequate blinding, unclear randomisation procedures, inconsistent adherence to
exercise-gastroenterology best-practice guidelines,[73] and potential conflicts of
interest, with more than half receiving industry funding or product support. A
key limitation was the limited and inconsistent investigation of associations
between gut microbiota and exercise performance, with only seven studies
exploring these relationships and considerable heterogeneity in study design,
intervention type, microbial analysis methods and performance assessments,
precluding direct comparisons. Substantial variability in faecal sample
collection and storage procedures raised concerns. While some studies used
validated collection and preservation methods, others provided minimal
procedural detail,[23]
[28]
[40] raising concerns about
cross-contamination, microbial degradation and/or compositional shifts due to
variable storage conditions. For example, some used freeze-dried collection
tubes[36] or home freezing at
approximately −30°C,[33]
[39] which may offer some stability.
However, inconsistency across studies hampers data comparability. Notably, two
studies[26]
[32] failed to report collection and
storage protocols entirely, limiting both reproducibility and interpretability.
Likewise, heterogeneity in microbiota and SCFA assessment methods, including
variation in sequencing platforms, depth and bioinformatic pipelines, as well as
inconsistent use of SCFA quantification techniques, limited comparability and
likely contributed to divergent findings. Inconsistent data reporting, such as
the use of fold-changes without absolute values[23] or reliance on heat maps, further
impeded interpretation. Additional study design concerns included
post-randomisation group switching,[32] unclear performance categorisation and inappropriate group
comparisons. Significant heterogeneity in probiotic strain, dose and delivery
mode, along with the predominant inclusion of biological sex male participants,
also limits generalisability. Finally, although rigorous search strategies were
employed across five databases and citation searches, it remains possible that
some relevant studies were missed. Collectively, these methodological
inconsistencies and data gaps precluded meta-analysis and reduced the overall
strength of conclusions regarding the impact of nutritional supplementation,
dietary or exercise interventions on gut microbiota and performance in healthy
active adults.
