Cited by 237Gut microbiota and healthOral microbiology and periodontitis researchAdipokines, Inflammation, and Metabolic Diseases

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Six-Week Endurance Exercise Alters Gut Metagenome That Is not Reflected in Systemic Metabolism in Over-weight Women

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Abstract

Previously sedentary overweight women (n=19) underwent a six-week endurance exercise intervention, but two were excluded due to antibiotic therapy. The gut microbiota composition and functions were analyzed by 16S rRNA gene amplicon sequencing and metagenomics. Body composition was analyzed with DXA X-ray densitometer and serum metabolomics with NMR metabolomics. Total energy and energy-yielding nutrient intakes were analyzed from food records using Micro-Nutrica software. Serum clinical variables were determined with KONELAB instrument. Soluble Vascular Adhesion Protein 1 (VAP-1) was measured with ELISA and its’ enzymatic activity as produced hydrogen peroxide. The exercise intervention was effective, as maximal power and maximum rate of oxygen consumption increased while android fat mass decreased. No changes in diet were observed. Metagenomic analysis revealed taxonomic shifts including an increase in Akkermansia and a decrease in Proteobacteria. These changes were independent of age, weight, fat % as well as energy and fibre intake. Training slightly increased Jaccard distance of genus level β-diversity. Training did not alter the enriched metagenomic pathways, which, according to Bray Curtis dissimilarity analysis, may have been due to that only half of the subjects’ microbiomes responded considerably to exercise. Nevertheless, training decreased the abundance of several genes including those related to fructose and amino acid metabolism. These metagenomic changes, however, were not translated into major systemic metabolic changes as only two metabolites, phospholipids and cholesterol in large VLDL particles, decreased after exercise. Training also decreased the amine oxidase activity of pro-inflammatory VAP-1, whereas no changes in CRP were detected. All clinical blood variables were within normal range, yet exercise slightly increased glucose and decreased LDL and HDL. In conclusion, exercise training modified the gut microbiome without greatly affecting systemic metabolites or body composition. Based on our data and existing literature, we propose that especially Akkermansia and Proteobacteria are exercise-responsive taxa. Our results warrant the need for further studies in larger cohorts to determine whether exercise types other than endurance exercise also modify the gut metagenome.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Six weeks of supervised endurance exercise in previously sedentary overweight women improved fitness and modestly shifted gut metagenome composition, including increased *Akkermansia* and decreased Proteobacteria, without major changes in systemic metabolism or body composition. The authors propose *Akkermansia* and Proteobacteria as exercise-responsive taxa but call for larger/longer trials.

Key findings

  • Exercise was effective: VO2max increased from 29.8 ± 5.2 to 32.5 ± 5.4 ml·min⁻¹·kg⁻¹ and maximal power from 182.9 ± 36.0 to 202.6 ± 36.4 W (both p < 0.001); submaximal blood lactate decreased from 3.0 ± 0.8 to 2.3 ± 0.7 mmol·L⁻¹ (p < 0.001). Android fat mass decreased from 3.87 ± 1.42 to 3.75 ± 1.39 kg (p = 0.005), with no significant weight, BMI, total fat, or diet changes except a slight increase in starch energy percentage (20.7 ± 6.3 to 22.8 ± 3.4, p = 0.033).
  • Metagenomic analysis showed increased genus-level Jaccard β-diversity (0.449 to 0.465, p = 0.014), increased Verrucomicrobia (p = 0.038), Verrucomicrobiaceae (p = 0.038), Bifidobacteriaceae (p = 0.015), *Akkermansia* (p = 0.038), *Dorea* (p = 0.028), and *Anaerofilum* (p = 0.037), and decreased Proteobacteria (p = 0.039), unidentified Porphyromonadaceae (p = 0.016), *Odoribacter* (p = 0.033), unidentified Desulfovibrionaceae (p = 0.037), and unidentified Enterobacteriaceae (p = 0.036). Only Proteobacteria, Verrucomicrobia, Verrucomicrobiaceae, and *Akkermansia* changed independently of age, weight, fat %, android fat %, energy, sucrose, or fiber intake.
  • No major KEGG pathway enrichment changes were detected; several individual genes decreased, including those related to fructose/mannose metabolism, amino acid metabolism, and B-vitamin synthesis. Systemic metabolomics showed only decreased phospholipids and cholesterol in large VLDL particles (p < 0.05 for both). VAP-1 enzymatic activity decreased (p = 0.011) without change in VAP-1 protein or CRP, and TLR5 mRNA increased (p = 0.029).

Methods

  • Design: six-week supervised endurance cycling intervention in previously sedentary overweight women (BMI >27.5 kg/m²), with a preceding six-week non-training control period; subjects served as their own controls. Three sessions/week progressed from 40 to 60 min, with low-intensity and moderate-intensity interval cycling.
  • Sample: 19 women underwent the intervention; 2 were excluded from final analyses due to antibiotic therapy, leaving n = 17 for physical performance, body composition, and clinical variables; n = 15 for metagenome analyses after 2 additional samples clustered differently; n = 14 completed food diaries at all time points.
  • Measurements/analysis: 16S rRNA gene V4 amplicon sequencing and shotgun metagenomics; DXA for body composition; NMR metabolomics; KONELAB clinical assays; VAP-1 ELISA and amine oxidase activity; whole-blood TLR4/TLR5 qPCR. Statistics included repeated-measures ANOVA with Bonferroni post-hoc, Wilcoxon signed-rank tests with Benjamini-Hochberg correction, QIIME, Microbiome Analyst, Spearman correlations, and general linear models.

Limitations

  • Stated: the cohort was small and the authors state that larger and longer exercise trials are needed; only approximately half of subjects’ microbiomes responded considerably to exercise, and inter-individual variation was large; food records are prone to under-reporting and social-desirability/social-approval bias; the mechanism by which exercise regulates VAP-1 activity without changing protein concentration could not be determined.
  • Evident from the text: the study included only overweight women and only endurance exercise, so findings may not generalize to men, other BMI groups, or other exercise types; metagenome analyses were reduced to n = 15, and diet analyses to n = 14, further limiting power; the absence of major systemic metabolic changes may partly reflect heterogeneous individual responses rather than true lack of effect.

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Integrity signals

Registry

Crossref · 10.3389/fmicb.2018.02323

Cites

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Figures

Figure 1 The study design, measurements and exercise protocol.
Figure 1 The study design, measurements and exercise protocol.CC BY · PubMed Central
Figure 2 The 16S rRNA gene amplicon sequencing failed to show differences in the microbiota composition between the samples collected before the control period (Pre), after the control period/before exercise (Mid) and after the exercise (Post) period. (A) The genus level abundance analyzed with Illumina MiSeq 16S rRNA gene amplicon sequencing show great inter-individual variation that may have caused the lack of significant differences between the samples collected before the control period (Pre, n = 17), after the control period/before exercise (Mid, n = 17) and after the exercise period (Post, n = 17). (B) PCoA plot of the 16S rRNA gene amplicon sequenced samples before the control period (n = 17), after the control/before the exercise period (n = 17) and after the exercise period (n = 17) shows an important inter-individual variation in the exercise responsiveness.
Figure 2 The 16S rRNA gene amplicon sequencing failed to show differences in the microbiota composition between the samples collected before the control period (Pre), after the control period/before exercise (Mid) and after the exercise (Post) period. (A) The genus level abundance analyzed with Illumina MiSeq 16S rRNA gene amplicon sequencing show great inter-individual variation that may have caused the lack of significant differences between the samples collected before the control period (Pre, n = 17), after the control period/before exercise (Mid, n = 17) and after the exercise period (Post, n = 17). (B) PCoA plot of the 16S rRNA gene amplicon sequenced samples before the control period (n = 17), after the control/before the exercise period (n = 17) and after the exercise period (n = 17) shows an important inter-individual variation in the exercise responsiveness.CC BY · PubMed Central
Figure 3 The fecal metagenome analysis reveals that exercise causes shifts in the microbiome at taxonomic while no changes in the major functional pathways were detected. (A) The box plots in the figure show phylum, family and genus level abundances before (n = 15) and after (n = 15) the exercise period of the significantly changing gut microbiota according to the metagenome analysis. An increase was detected in Verrucomicrobia, Verrucomicrobiaceae, Bifidobacteriaceae, Akkermansia, Anaerofilum and Dorea while Proteobacteria, Odoribacter and unidentified Desulfovibrionaceae, Porphyromonadaceae and Enterobacteriaceae decreased. (B) In the metagenomes no significant differences in the major KEGG metabolic pathways were detected between the samples before and after exercise according to the analysis performed with Microbiome Analyst. When assessing the metagenome data the samples of two subjects clustered differently and were removed from the final analyses. Therefore, 15 subjects were included in the metagenome analyses.
Figure 3 The fecal metagenome analysis reveals that exercise causes shifts in the microbiome at taxonomic while no changes in the major functional pathways were detected. (A) The box plots in the figure show phylum, family and genus level abundances before (n = 15) and after (n = 15) the exercise period of the significantly changing gut microbiota according to the metagenome analysis. An increase was detected in Verrucomicrobia, Verrucomicrobiaceae, Bifidobacteriaceae, Akkermansia, Anaerofilum and Dorea while Proteobacteria, Odoribacter and unidentified Desulfovibrionaceae, Porphyromonadaceae and Enterobacteriaceae decreased. (B) In the metagenomes no significant differences in the major KEGG metabolic pathways were detected between the samples before and after exercise according to the analysis performed with Microbiome Analyst. When assessing the metagenome data the samples of two subjects clustered differently and were removed from the final analyses. Therefore, 15 subjects were included in the metagenome analyses.CC BY · PubMed Central
Figure 4 Exercise training increases the variability of the functional genes but only half of the subjects microbiomes respond to exercise. (A) PCoA plot of the functional genes shows that the samples before (n = 15) and after (n = 15) the exercise cluster closely together indicating their similarity. The first component explains 39.3% and the second 11.5% of the variability. (B) The Correspondence analysis of the functional genes shows that the samples before the exercise are less variable (more close to the centroid) than the samples after the exercise training indicating that the exercise has caused shifts in the gene abundances. (C) The Bray Curtis dissimilarity analysis shows that only approximately half of the subjects microbiomes have considerably responded to the exercise training, i.e., have Bray Curtis index close to 0.8.
Figure 4 Exercise training increases the variability of the functional genes but only half of the subjects microbiomes respond to exercise. (A) PCoA plot of the functional genes shows that the samples before (n = 15) and after (n = 15) the exercise cluster closely together indicating their similarity. The first component explains 39.3% and the second 11.5% of the variability. (B) The Correspondence analysis of the functional genes shows that the samples before the exercise are less variable (more close to the centroid) than the samples after the exercise training indicating that the exercise has caused shifts in the gene abundances. (C) The Bray Curtis dissimilarity analysis shows that only approximately half of the subjects microbiomes have considerably responded to the exercise training, i.e., have Bray Curtis index close to 0.8.CC BY · PubMed Central
Figure 5 The cardiovascular and inflammatory changes in response to exercise. (A) After the exercise period (n = 17) the phospholipids (PL) and cholesterol (C) in large (L) VLDL particles were decreased compared to the samples before exercise (n = 17) as determined with NMR metabolomics. (B) The Correspondence analysis of the metabolites shows inter-individual variation in the metabolites and different clustering of the samples before (n = 17) and after (n = 17) the exercise (on left), and moreover, the samples of each subject before and after the exercise tend to cluster together (on right) indicating no gross changes in metabolites in response to exercise. (C) No differences in serum CRP levels were detected between the time points. After the exercise period (n = 17) VAP-1 enzyme activity (SSAO) decreased but no changes in VAP-1 protein concentration were observed. (D) After the exercise period (Post, n = 17) TLR5 mRNA increased but no changes in TLR4 were observed compared to the samples before the control period (Pre, n = 17) and after the control/before exercise (Mid, n = 17).
Figure 5 The cardiovascular and inflammatory changes in response to exercise. (A) After the exercise period (n = 17) the phospholipids (PL) and cholesterol (C) in large (L) VLDL particles were decreased compared to the samples before exercise (n = 17) as determined with NMR metabolomics. (B) The Correspondence analysis of the metabolites shows inter-individual variation in the metabolites and different clustering of the samples before (n = 17) and after (n = 17) the exercise (on left), and moreover, the samples of each subject before and after the exercise tend to cluster together (on right) indicating no gross changes in metabolites in response to exercise. (C) No differences in serum CRP levels were detected between the time points. After the exercise period (n = 17) VAP-1 enzyme activity (SSAO) decreased but no changes in VAP-1 protein concentration were observed. (D) After the exercise period (Post, n = 17) TLR5 mRNA increased but no changes in TLR4 were observed compared to the samples before the control period (Pre, n = 17) and after the control/before exercise (Mid, n = 17).CC BY · PubMed Central
Figure 6 The associations of the gut microbiota with body composition before the exercise period. The significant associations are marked with asterisks (*). The numeric colored scale bar represents the Spearman correlation coefficients.
Figure 6 The associations of the gut microbiota with body composition before the exercise period. The significant associations are marked with asterisks (*). The numeric colored scale bar represents the Spearman correlation coefficients.CC BY · PubMed Central

Verification

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  • The paper relies on this without support.

    Dietary composition affects the gut microbiota.

    “As dietary composition affects the gut microbiota, the total daily energy and the intake of energy-yielding nutrients, were analyzed from the 3-days food records.”
  • The paper relies on this without support.

    Starch is broken down into glucose moieties.

    “as starch is broken down into glucose moieties”
  • The paper relies on this without support.

    Fructose absorption mainly occurs in the small intestine.

    “that mainly occurs in the small intestine”
  • The paper relies on this without support.

    No studies in humans have reported associations between Proteobacteria and exercise or physical fitness that would support the responsiveness of this taxon to exercise training.

    “However, to our knowledge, no studies in humans have reported associations between Proteobacteria and exercise or physical fitness that would support the responsiveness of this taxon to exercise training.”
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Metadata

Authors
Munukka, E., Ahtiainen, J. P., Puigbó, P., Jalkanen, S., Pahkala, K., Keskitalo, A., Kujala, U. M., Pietilä, S., Hollmén, M., Elo, L., Huovinen, P., D'Auria, G., & Pekkala, S.
Affiliations
  • University of Turku (FI)
  • University of Jyväskylä (FI)
Corresponding author
Pekkala, Satu
Venue
Frontiers in Microbiology
Published
2018-10-03
Volume
9
Pages
2323-2323
ISSN
1664-302X (ISSN-L)
OpenAlex
W2895626130
Cited by
237
References
55
Citations by year
2026: 31 · 2025: 30 · 2024: 33 · 2023: 26 · 2022: 43 · 2021: 33 · 2020: 26 · 2019: 14
FWCI
6.49
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Microbiology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Jyväskylä University Digital Archive (University of Jyväskylä) (submittedVersion, cc-by)
  • UTUPub (University of Turku) (submittedVersion)
  • UTUPub (University of Turku) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Juho Vainion Säätiö
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
Keywords
endurance exercise, gut microbiota, gut metagenome, overweight women, Akkermansia, Proteobacteria, serum metabolomics, VAP-1, amine oxidase activity, hydrogen peroxide, body composition, fat mass, fructose metabolism, amino acid metabolism, lipid metabolism, VLDL cholesterol, phospholipids

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