CoChat
Sign Up
Cited by 225Gut microbiota and healthProbiotics and Fermented FoodsGastrointestinal motility and disorders

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

The combination of sport and sport-specific diet is associated with characteristics of gut microbiota: an observational study

Open access
Jang, Lae-Guen, Choi, Geunhoon, Kim, Sung-Woo, Kim, Byung-Yong, Lee, Sunghee, Park, Hyon·2019·Journal of the International Society of Sports Nutrition
DOI 10.1186/s12970-019-0290-yPMC PMC6500072
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Recently, gut microbiota have been studied extensively for health promotion, disease prevention, disease treatment, and exercise performance. It is recommended that athletes avoid dietary fiber and resistant starch to promote gastric emptying and reduce gastrointestinal distress during exercise, but this diet may reduce microbial diversity and compromise the health of the athlete's gut microbiota. OBJECTIVE: This study compared fecal microbiota characteristics using high-throughput sequencing among healthy sedentary men (as controls), bodybuilders, and distance runners, as well as the relationships between microbiota characteristics, body composition, and nutritional status. METHODS: Body composition was measured using DXA, and physical activity level was assessed using IPAQ. Dietary intake was analyzed with the computerized nutritional evaluation program. The DNA of fecal samples was extracted and it was sequenced for the analysis of gut microbial diversity through bioinformatics cloud platform. RESULTS: We showed that exercise type was associated with athlete diet patterns (bodybuilders: high protein, high fat, low carbohydrate, and low dietary fiber diet; distance runners: low carbohydrate and low dietary fiber diet). However, athlete type did not differ in regard to gut microbiota alpha and beta diversity. Athlete type was significantly associated with the relative abundance of gut microbiota at the genus and species level: Faecalibacterium, Sutterella, Clostridium, Haemophilus, and Eisenbergiella were the highest (p < 0.05) in bodybuilders, while Bifidobacterium and Parasutterella were the lowest (p < 0.05). At the species level, intestinal beneficial bacteria widely used as probiotics (Bifidobacterium adolescentis group, Bifidobacterium longum group, Lactobacillus sakei group) and those producing short chain fatty acids (Blautia wexlerae, Eubacterium hallii) were the lowest in bodybuilders and the highest in controls. In addition, aerobic or resistance exercise training with an unbalanced intake of macronutrients and low intake of dietary fiber led to similar diversity of gut microbiota. Specifically, daily protein intake was negatively correlated with operation taxonomic unit (r = - 0.53, p < 0.05), ACE (r = - 0.51, p < 0.05), and Shannon index (r = - 0.64, p < 0.01) in distance runners.. CONCLUSION: Results suggest that high-protein diets may have a negative impact on gut microbiota diversity for athletes, while athletes in resistance sports that carry out the high protein low carbohydrates diet demonstrate a decrease in short chain fatty acid-producing commensal bacteria.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12970-019-0290-y

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Comparison of the percentage of energy from each macronutrient category. The percentages were calculated by dividing the available energy from the macronutrients by the total energy. a The percentage of the energy obtained from carbohydrates; b The percentage of the energy obtained from protein; c The percentage of the energy obtained from total fat. The red line represents acceptable macronutrient distribution ranges (AMDR). The AMDR for carbohydrates is 55 to 65%, for total fat is 15 to 30%, and for protein is 7 to 20% of the energy intake for Korean adults. Controls versus bodybuilders: +p < 0.05 or +++p < 0.001, bodybuilders versus distance runners: **p < 0.01 or ***p < 0.001
Fig. 1 Comparison of the percentage of energy from each macronutrient category. The percentages were calculated by dividing the available energy from the macronutrients by the total energy. a The percentage of the energy obtained from carbohydrates; b The percentage of the energy obtained from protein; c The percentage of the energy obtained from total fat. The red line represents acceptable macronutrient distribution ranges (AMDR). The AMDR for carbohydrates is 55 to 65%, for total fat is 15 to 30%, and for protein is 7 to 20% of the energy intake for Korean adults. Controls versus bodybuilders: +p < 0.05 or +++p < 0.001, bodybuilders versus distance runners: **p < 0.01 or ***p < 0.001CC BY · PubMed Central
Fig. 2 Certain types of exercise training and athlete diet affected the relative abundance of some microorganisms. a Comparison of gut microbiota relative abundance at the genus level in the three groups. Relative abundance represent log of percentage as whole microbiota. For example, when the relative abundance of a particular genus is 1, it means 10% of whole microbiota, and when it is 0, it means 1% and when it is −1, it means 0.1%. Controls versus bodybuilders: +p < 0.05 or ++p < 0.01, bodybuilders versus distance runners: *p < 0.05 or **p < 0.01, distance runners versus controls: ¥p < 0.05 or ¥¥p < 0.01. b Total fat intake negatively correlated with relative abundance of Bifidobacterium in bodybuilders (Correlation coefficient: −0.52, p-value: 0.048). c Total fat intake positively correlated with relative abundance of Sutterella in bodybuilders (Correlation coefficient: 0.58, p-value: 0.023)
Fig. 2 Certain types of exercise training and athlete diet affected the relative abundance of some microorganisms. a Comparison of gut microbiota relative abundance at the genus level in the three groups. Relative abundance represent log of percentage as whole microbiota. For example, when the relative abundance of a particular genus is 1, it means 10% of whole microbiota, and when it is 0, it means 1% and when it is −1, it means 0.1%. Controls versus bodybuilders: +p < 0.05 or ++p < 0.01, bodybuilders versus distance runners: *p < 0.05 or **p < 0.01, distance runners versus controls: ¥p < 0.05 or ¥¥p < 0.01. b Total fat intake negatively correlated with relative abundance of Bifidobacterium in bodybuilders (Correlation coefficient: −0.52, p-value: 0.048). c Total fat intake positively correlated with relative abundance of Sutterella in bodybuilders (Correlation coefficient: 0.58, p-value: 0.023)CC BY · PubMed Central
Fig. 3 There was no difference in the gut microbiota diversity between the controls, bodybuilders, and distance runners. a Comparison of observed species of controls, bodybuilders, and distance runners obtained from 30,000 sequences per sample. b Estimation of the abundance of unique operational taxonomic units (OTUs) using Chao1. Phylogenetic diversity was estimated using the average values for the Chao1 plot of the gut microbiota in the controls, bodybuilders, and distance runners. Data are based on 30,000 sequences per sample from the study subjects
Fig. 3 There was no difference in the gut microbiota diversity between the controls, bodybuilders, and distance runners. a Comparison of observed species of controls, bodybuilders, and distance runners obtained from 30,000 sequences per sample. b Estimation of the abundance of unique operational taxonomic units (OTUs) using Chao1. Phylogenetic diversity was estimated using the average values for the Chao1 plot of the gut microbiota in the controls, bodybuilders, and distance runners. Data are based on 30,000 sequences per sample from the study subjectsCC BY · PubMed Central
Fig. 4 Protein intake negatively correlated with alpha diversity in distance runners. a Protein intake negatively correlated with OTUs (Correlation coefficient: − 0.53, p value: 0.04). b Protein intake negatively correlated with ACE (Correlation coefficient: − 0.51, p-value: 0.05). c Protein intake negatively correlated with Shannon index (Correlation coefficient: − 0.63, p-value: 0.012)
Fig. 4 Protein intake negatively correlated with alpha diversity in distance runners. a Protein intake negatively correlated with OTUs (Correlation coefficient: − 0.53, p value: 0.04). b Protein intake negatively correlated with ACE (Correlation coefficient: − 0.51, p-value: 0.05). c Protein intake negatively correlated with Shannon index (Correlation coefficient: − 0.63, p-value: 0.012)CC BY · PubMed Central
Fig. 5 The plot was generated using a generalized UniFrac principal coordinate analysis (PCoA) of fecal microbiota from 45 subjects. Generalized UniFrac PCoA analyzed genus rank level and included unclassified OTUs. Subject color coding: green, controls; blue, bodybuilders; and yellow, distance runners
Fig. 5 The plot was generated using a generalized UniFrac principal coordinate analysis (PCoA) of fecal microbiota from 45 subjects. Generalized UniFrac PCoA analyzed genus rank level and included unclassified OTUs. Subject color coding: green, controls; blue, bodybuilders; and yellow, distance runnersCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 59 references

Research works printed but not linked: 3

  1. Barton W, 2018, Gut., 67, 625
  2. Hughes R, 2000, Curr Issues Intestinal Microbiol, 1, 51
  3. Altschul SF, 1990, J Mol Biol, 215, 403, 10.1016/S0022-2836(05)80360-2 · DOI · Paper page

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Jang, L.-G., Choi, G., Kim, S.-W., Kim, B.-Y., Lee, S., & Park, H.
Affiliations
  • Kyung Hee University (KR)
  • ChunLab, Inc. Seocho-gu Seoul Republic of Korea
  • Ildong (South Korea) (KR)
Corresponding author
Park, Hyon
ORCID
Venue
Journal of the International Society of Sports Nutrition
Publisher
Informa UK Limited
Published
2019-01-15
Volume
16
Issue
1
Pages
21-21
ISSN
1550-2783 (ISSN-L)
DOI
10.1186/s12970-019-0290-y
PMC
PMC6500072
OpenAlex
W2947024351
Cited by
224
References
59
Citations by year
2026: 19 · 2025: 26 · 2024: 47 · 2023: 32 · 2022: 44 · 2021: 38 · 2020: 14 · 2019: 3
FWCI
8.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of the International Society of Sports Nutrition (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Research Foundation of Kore (NRF-2016S1A5A2A01023587)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
  • Health Sciences › Medicine › Gastroenterology › Gastrointestinal motility and disorders
Keywords
gut microbiota, sports nutrition, bodybuilders, long-distance runners, high-protein diet, dietary fiber, gut microbiota diversity, short-chain fatty acids, Bifidobacterium, Faecalibacterium, Clostridium, exercise type, body composition, macronutrient intake, probiotics, sedentary men, Shannon diversity index, physical activity level, nutritional status
MeSH terms
Gastrointestinal Microbiome, Adult, Bacteria, Body Composition, Diet, Dietary Fiber, Dietary Proteins, Fatty Acids, Volatile, Humans, Male, Sports, Case-Control Studies, Young Adult, Sports Nutritional Physiological Phenomena

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review