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Cited by 601Gut microbiota and healthNutritional Studies and DietInflammatory Bowel Disease

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Long-term dietary patterns are associated with pro-inflammatory and anti-inflammatory features of the gut microbiome

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Bolte, Laura A, Vich Vila, Arnau, Imhann, Floris, Collij, Valerie, Gacesa, Ranko, Peters, Vera, Wijmenga, Cisca, Kurilshikov, Alexander, Campmans-Kuijpers, Marjo J E, Fu, Jingyuan, Dijkstra, Gerard, Zhernakova, Alexandra, Weersma, Rinse K·2021·Gut
DOI 10.1136/gutjnl-2020-322670PMC PMC8223641
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Abstract

Objective: The microbiome directly affects the balance of pro-inflammatory and anti-inflammatory responses in the gut. As microbes thrive on dietary substrates, the question arises whether we can nourish an anti-inflammatory gut ecosystem. We aim to unravel interactions between diet, gut microbiota and their functional ability to induce intestinal inflammation. Design: We investigated the relation between 173 dietary factors and the microbiome of 1425 individuals spanning four cohorts: Crohn’s disease, ulcerative colitis, irritable bowel syndrome and the general population. Shotgun metagenomic sequencing was performed to profile gut microbial composition and function. Dietary intake was assessed through food frequency questionnaires. We performed unsupervised clustering to identify dietary patterns and microbial clusters. Associations between diet and microbial features were explored per cohort, followed by a meta-analysis and heterogeneity estimation. Results: We identified 38 associations between dietary patterns and microbial clusters. Moreover, 61 individual foods and nutrients were associated with 61 species and 249 metabolic pathways in the meta-analysis across healthy individuals and patients with IBS, Crohn’s disease and UC (false discovery rate<0.05). Processed foods and animal-derived foods were consistently associated with higher abundances of Firmicutes, Ruminococcus species of the Blautia genus and endotoxin synthesis pathways. The opposite was found for plant foods and fish, which were positively associated with short-chain fatty acid-producing commensals and pathways of nutrient metabolism. Conclusion: We identified dietary patterns that consistently correlate with groups of bacteria with shared functional roles in both, health and disease. Moreover, specific foods and nutrients were associated with species known to infer mucosal protection and anti-inflammatory effects. We propose microbial mechanisms through which the diet affects inflammatory responses in the gut as a rationale for future intervention studies.

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Figure 1 Unsupervised dietary cluster analysis reveals common food patterns. Cladogram showing clustering of the dietary intake into 25 patterns. Food frequency questionnaires were used to assess the diet of 1425 individuals comprising healthy controls (n=871), individuals with irritable bowel syndrome (n=223), Crohn’s disease (n=205) and ulcerative colitis (n=126). Unsupervised hierarchical clustering was performed using squared Euclidean distances.
Figure 1 Unsupervised dietary cluster analysis reveals common food patterns. Cladogram showing clustering of the dietary intake into 25 patterns. Food frequency questionnaires were used to assess the diet of 1425 individuals comprising healthy controls (n=871), individuals with irritable bowel syndrome (n=223), Crohn’s disease (n=205) and ulcerative colitis (n=126). Unsupervised hierarchical clustering was performed using squared Euclidean distances.CC BY · PubMed Central
Figure 2 Consistent associations of dietary patterns with clusters of pathways (A) and species (B) in the cross-disease meta-analysis. Forest plot showing consistent results between dietary patterns and microbial clusters in a cross-disease meta-analysis of 1425 individuals spanning four cohorts (FDRMeta<0.05, p-Cochran’s-Q>0.05). Dots indicate pooled results of the meta-analysis; black lines indicate CIs. Dot size indicates the significance of the association (FDR-corrected p value). X-axis represents coefficients. Unsupervised hierarchical clustering was performed on dietary intake, species and pathway abundance, using squared Euclidean and Bray-Curtis distance. In each cohort, a multivariate linear model of food clusters versus microbial clusters was constructed, adding age, sex, sequencing depth and caloric intake as covariates. An inverse-variance meta-analysis was conducted on results obtained per cohort, followed by multiple testing correction and a Cochran’s Q test. AA, amino acid; ECA, enterobacterial common antigen; FA, fatty acid; FDR, false discovery rate; ferment, fermentation; LPS, lipopolysaccharides; spp, species.
Figure 2 Consistent associations of dietary patterns with clusters of pathways (A) and species (B) in the cross-disease meta-analysis. Forest plot showing consistent results between dietary patterns and microbial clusters in a cross-disease meta-analysis of 1425 individuals spanning four cohorts (FDRMeta<0.05, p-Cochran’s-Q>0.05). Dots indicate pooled results of the meta-analysis; black lines indicate CIs. Dot size indicates the significance of the association (FDR-corrected p value). X-axis represents coefficients. Unsupervised hierarchical clustering was performed on dietary intake, species and pathway abundance, using squared Euclidean and Bray-Curtis distance. In each cohort, a multivariate linear model of food clusters versus microbial clusters was constructed, adding age, sex, sequencing depth and caloric intake as covariates. An inverse-variance meta-analysis was conducted on results obtained per cohort, followed by multiple testing correction and a Cochran’s Q test. AA, amino acid; ECA, enterobacterial common antigen; FA, fatty acid; FDR, false discovery rate; ferment, fermentation; LPS, lipopolysaccharides; spp, species.CC BY · PubMed Central
Figure 3 Dietary factors associated with Faecalibacterium prausnitzii (A) and Roseburia (B) relative abundance in the meta-analysis. Heatmap showing significant and consistent results of the cross-disease meta-analysis between individual foods and relative abundance of (A) Faecalibacterium prausnitzii and (B) Roseburia sp (FDR<0.05, p-Cochran’s-Q>0.05). Dietary intake was assessed by Food Frequency Questionnaires. Energy adjustment was performed by the nutrient density method. For each food item, we constructed a multivariate linear model of the food intake versus taxa and pathways, adding age, sex and sequencing depth as covariates. Association analyses were performed per cohort, followed by an inverse-variance meta-analysis, multiple testing correction and a Cochran’s Q test. carb; carbohydrates; CD, Crohn’s disease; en-%, energy-per cent; FDR, false discovery rate; g/d, gram per day; IBS, irritable bowel syndrome; nut_d, nuts added to dinner; sp, species; UC, ulcerative colitis. Red, positive association; blue, negative association. Colour density indicates significance of the association (FDR-corrected p value).
Figure 3 Dietary factors associated with Faecalibacterium prausnitzii (A) and Roseburia (B) relative abundance in the meta-analysis. Heatmap showing significant and consistent results of the cross-disease meta-analysis between individual foods and relative abundance of (A) Faecalibacterium prausnitzii and (B) Roseburia sp (FDR<0.05, p-Cochran’s-Q>0.05). Dietary intake was assessed by Food Frequency Questionnaires. Energy adjustment was performed by the nutrient density method. For each food item, we constructed a multivariate linear model of the food intake versus taxa and pathways, adding age, sex and sequencing depth as covariates. Association analyses were performed per cohort, followed by an inverse-variance meta-analysis, multiple testing correction and a Cochran’s Q test. carb; carbohydrates; CD, Crohn’s disease; en-%, energy-per cent; FDR, false discovery rate; g/d, gram per day; IBS, irritable bowel syndrome; nut_d, nuts added to dinner; sp, species; UC, ulcerative colitis. Red, positive association; blue, negative association. Colour density indicates significance of the association (FDR-corrected p value).CC BY · PubMed Central
Figure 4 Microbial metabolic pathways (A) and taxa (B) associated with plant protein intake in the meta-analysis. Heatmap showing significant and consistent results of the cross-disease meta-analysis between plant protein intake and the relative abundance of (A) metabolic pathways and (B) taxonomical abundance of the gut microbiome (FDR<0.05, p-Cochran’s-Q>0.05). Dietary intake was assessed by Food Frequency Questionnaires. For each food item, we constructed a multivariate linear model of the food intake versus taxa and pathways, adding age, sex and sequencing depth as covariates. Association analyses were performed per cohort, followed by an inverse-variance meta-analysis, multiple testing correction and a Cochran’s Q test. CD, Crohn’s disease; FDR, false discovery rate; IBS, irritable bowel syndrome; UC, ulcerative colitis. Red, positive association; blue, negative association. Colour density indicates significance of the association (FDR-corrected p value).
Figure 4 Microbial metabolic pathways (A) and taxa (B) associated with plant protein intake in the meta-analysis. Heatmap showing significant and consistent results of the cross-disease meta-analysis between plant protein intake and the relative abundance of (A) metabolic pathways and (B) taxonomical abundance of the gut microbiome (FDR<0.05, p-Cochran’s-Q>0.05). Dietary intake was assessed by Food Frequency Questionnaires. For each food item, we constructed a multivariate linear model of the food intake versus taxa and pathways, adding age, sex and sequencing depth as covariates. Association analyses were performed per cohort, followed by an inverse-variance meta-analysis, multiple testing correction and a Cochran’s Q test. CD, Crohn’s disease; FDR, false discovery rate; IBS, irritable bowel syndrome; UC, ulcerative colitis. Red, positive association; blue, negative association. Colour density indicates significance of the association (FDR-corrected p value).CC BY · PubMed Central

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Authors
Bolte, L. A., Vich Vila, A., Imhann, F., Collij, V., Gacesa, R., Peters, V., Wijmenga, C., Kurilshikov, A., Campmans-Kuijpers, M. J. E., Fu, J., Dijkstra, G., Zhernakova, A., & Weersma, R. K.
Affiliations
  • University Medical Center Groningen (NL)
  • Department of Gastroenterology and Hepatology, University of Groningen and University Medical Centre Groningen, Groningen, The Netherlands
Corresponding author
Weersma, Rinse K
ORCID
Venue
Gut
Publisher
BMJ
Published
2021-04-02
Volume
70
Issue
7
Pages
1287-1298
ISSN
  • 0017-5749 (ISSN-L)
  • 1468-3288
DOI
10.1136/gutjnl-2020-322670
PMC
PMC8223641
OpenAlex
W3151963551
Cited by
595
References
60
Citations by year
2026: 87 · 2025: 151 · 2024: 121 · 2023: 117 · 2022: 92 · 2021: 26
FWCI
28.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Gut (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
  • CardioVasculair Onderzoek Nederland (CVON 2012-03)
  • Netherlands’ Top Institute Food and Nutrition (GH001)
  • Top Sector Life Sciences & Health, Samenwerkende Gezondheidsfondsen (LSHM18057-SGF)
  • European Research Council (ERC Consolidator grant 101001678, ERC-715772)
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO Gravitation grant ExposomeNL 024.004.017, NWO Gravitation grant Organ-on-Chip 024.003.001, NWO SPI 92-266, NWO VIDI grant 016.178.056)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
  • Health Sciences › Medicine › Gastroenterology › Inflammatory Bowel Disease
Keywords
dietary habits, gut microbiota, intestinal inflammation, Crohn's disease, ulcerative colitis, irritable bowel syndrome, processed foods, animal products, plant-based foods, fish consumption, Firmicutes, Ruminococcus, Blautia, short-chain fatty acids, mucosal protection, anti-inflammatory bacteria, microbial metabolic pathways
MeSH terms
Gastrointestinal Microbiome, Adult, Bacteria, Beverages, Colitis, Ulcerative, Crohn Disease, Diet, Feces, Food, Humans, Inflammation, Middle Aged, Surveys and Questionnaires, Time Factors, Irritable Bowel Syndrome, Metagenomics

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