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Environmental factors shaping the gut microbiome in a Dutch population

Nature volume 604, pages 732–739 (2022)Cite this article

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Abstract

The gut microbiome is associated with diverse diseases1,2,3, but a universal signature of a healthy or unhealthy microbiome has not been identified, and there is a need to understand how genetics, exposome, lifestyle and diet shape the microbiome in health and disease. Here we profiled bacterial composition, function, antibiotic resistance and virulence factors in the gut microbiomes of 8,208 Dutch individuals from a three-generational cohort comprising 2,756 families. We correlated these to 241 host and environmental factors, including physical and mental health, use of medication, diet, socioeconomic factors and childhood and current exposome. We identify that the microbiome is shaped primarily by the environment and cohabitation. Only around 6.6% of taxa are heritable, whereas the variance of around 48.6% of taxa is significantly explained by cohabitation. By identifying 2,856 associations between the microbiome and health, we find that seemingly unrelated diseases share a common microbiome signature that is independent of comorbidities. Furthermore, we identify 7,519 associations between microbiome features and diet, socioeconomics and early life and current exposome, with numerous early-life and current factors being significantly associated with microbiome function and composition. Overall, this study provides a comprehensive overview of gut microbiome and the underlying impact of heritability and exposures that will facilitate future development of microbiome-targeted therapies.

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Fig. 1: Summary of the Dutch Microbiome Project.
Fig. 2: Heritability and effect of cohabitation on the gut microbiome.
Fig. 3: Microbiome–phenotype associations.
Fig. 4: Microbiome signatures of health and diseases.

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Data availability

The raw microbiome sequencing data, processed microbiome data (including taxonomy, pathway, virulence factor and antibiotic resistance gene profiles) and basic phenotypes (including age, sex and BMI) used in this study are available at the European Genome-Phenome Archive under accession EGAS00001005027. These datasets can be accessed from https://forms.gle/eHeBdXJMXbVvCJRc8 or by email from the corresponding author (R.K.W.) at the address listed at the EGA data access committee EGAC00001001996. The phenotype data can be requested, for a fee, by filling the application form at https://www.lifelines.nl/researcher/how-to-apply/apply-here. Lifelines will not charge an access fee for controlled access to the full dataset used in the manuscript (including phenotype and sequencing data), for the specific purpose of replication of the results presented in this Article or for further assessment by the reviewers, for a period of three months. Researchers interested in such a replication study or review assessment can contact Lifelines at research@lifelines.nl. Source data are provided with this paper.

Code availability

Open source codes and scripts used for the analyses or figures are available at the GitHub repository (https://github.com/GRONINGEN-MICROBIOME-CENTRE/DMP) and Zenodo (https://doi.org/10.5281/zenodo.5910709). To facilitate the re-use of the codes, the repository also includes example datasets that enable users to test the codes without the need to apply for access to phenotypes.

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Acknowledgements

We acknowledge and thank the late M. Hofker who initiated the Lifelines DAG3/Dutch Microbiome Project. We acknowledge the services of Lifelines Cohort Study, the contributing research centres delivering data to Lifelines and all the study participants. The Lifelines Biobank initiative has been made possible by subsidies from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG the Netherlands), the University of Groningen and the Northern Provinces of the Netherlands. We thank the Center for Information Technology of the University of Groningen (RUG) for their support and for providing access to the Peregrine high performance computing cluster, the Genomics Coordination Center (UMCG and RUG) for their support and for providing access to Calculon and Boxy high-performance computing clusters and the MOLGENIS team for data management and analysis support. Metagenomics library preparation and sequencing was done at Novogene. We also thank K. Mc Intyre for English and content editing and Tania Ballve Fernandez for illustration of Fig. 1a. Sequencing of the cohort was funded by a grant from CardioVasculair Onderzoek Nederland (CVON 2012-03) to M.H., J.F. and A.Z. R.G., H.J.M.H. and R.K.W. are supported by the collaborative TIMID project (LSHM18057-SGF) financed by the PPP allowance made available by Top Sector Life Sciences & Health to Samenwerkende Gezondheidsfondsen (SGF) to stimulate public–private partnerships and co-financed by health foundations that are part of the SGF. R.K.W. is supported by the Seerave Foundation and the Dutch Digestive Foundation (16-14). A.Z. is supported by European Research Council (ERC) Starting Grant 715772, Netherlands Organization for Scientific Research (NWO) VIDI grant 016.178.056, CVON grant 2018-27 and NWO Gravitation grant ExposomeNL 024.004.017. JF is supported by the Dutch Heart Foundation IN-CONTROL (CVON2018-27), the ERC Consolidator grant (grant agreement No. 101001678), NWO-VICI grant VI.C.202.022, and the Netherlands Organ-on-Chip Initiative, an NWO Gravitation project (024.003.001) funded by the Ministry of Education, Culture and Science of the government of The Netherlands. C.W. is further supported by an ERC advanced grant (ERC-671274) and an NWO Spinoza award (NWO SPI 92-266). L.C. is supported by a joint fellowship from the University Medical Center Groningen and China Scholarship Council (CSC201708320268) and a Foundation De Cock-Hadders grant (20:20-13). M.A.S. is supported by NWO VIDI grant 016 and EUCAN-connect, a project funded by European Commission H2020 grant 824989.

Author information

Author notes

  1. These authors contributed equally: R. Gacesa, A. Kurilshikov

  2. These authors jointly supervised this work: H. J. M. Harmsen, C. Wijmenga, J. Fu, A. Zhernakova & R. K. Weersma

  3. Deceased: M. Hofker

Authors and Affiliations

  1. University of Groningen and University Medical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, The Netherlands

    R. Gacesa, A. Vich Vila, M. A. Y. Klaassen, L. A. Bolte, V. Collij, S. Hu, J. R. Björk, J. C. Swarte, B. H. Jansen & R. K. Weersma

  2. University of Groningen and University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands

    R. Gacesa, A. Kurilshikov, A. Vich Vila, T. Sinha, M. A. Y. Klaassen, L. A. Bolte, S. Andreu-Sánchez, L. Chen, V. Collij, S. Hu, J. A. M. Dekens, J. R. Björk, J. C. Swarte, M. A. Swertz, B. H. Jansen, J. Gelderloos-Arends, S. Jankipersadsing, S. Sanna, C. Wijmenga, J. Fu & A. Zhernakova

  3. Department of Pediatrics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands

    S. Andreu-Sánchez, L. Chen, M. Hofker & J. Fu

  4. Center of Development and Innovation, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    J. A. M. Dekens

  5. University Medical Centre Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands

    V. C. Lenters & R. C. H. Vermeulen

  6. University of Groningen and University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands

    M. A. Swertz

  7. Utrecht University, Institute for Risk Assessment Sciences (IRAS), Department of Population Health Sciences, Utrecht, The Netherlands

    R. C. H. Vermeulen

  8. Institute for Genetic and Biomedical Research (IRGB), National Research Council (CNR), Cagliari, Italy

    S. Sanna

  9. Department of Medical Microbiology and Infection prevention, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands

    H. J. M. Harmsen

Authors
  1. R. Gacesa
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  2. A. Kurilshikov
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  8. L. Chen
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  9. V. Collij
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  11. J. A. M. Dekens
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  19. M. Hofker
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  25. A. Zhernakova
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  26. R. K. Weersma
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Contributions

R.G. designed and implemented the metagenomic data analysis pipelines, analysed metagenomic data, performed heritability analysis and drafted the manuscript. A.K. designed the prediction models and implemented statistical methods for association analyses and assisted in drafting of the manuscript. A.V.V., L.C., V.C., S.H., M.A.Y.K., S.A.-S., J.R.B., L.A.B., V.C.L., T.S., M.H., J.C.S. and S.S. assisted in other statistical analyses, interpretation of data and drafting of the manuscript. M.A.S. provided data stewardship and analysis infrastructure. B.H.J., J.A.M.D., S.J. and J.G.-A. collected data, assisted in study planning and critically reviewed the manuscript. S.S. supervised and coordinated heritability analysis. R.C.H.V. provided the air pollution data and supervised the air pollution analysis. H.J.M.H., A.Z., R.K.W., J.F. and C.W. conceived, coordinated and supported the study. All authors critically revised and approved the manuscript.

Corresponding authors

Correspondence to J. Fu, A. Zhernakova or R. K. Weersma.

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Competing interests

The authors declare no competing interests.

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Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Estimation of total number of species and genera in the DMP population.

Figure shows rarefaction and extrapolation sampling curve for a, genera and b, species richness calculated using Hill numbers implemented in the iNEXT package for R. The extrapolated part of rarefaction curve is shown dotted. The SD of the estimate is shaded and the asymptotic richness estimate is shown.

Extended Data Fig. 2 Overview of DMP microbiome composition and function.

a, First two principal coordinates of the Bray-Curtis distance matrix calculated on microbial species of the DMP cohort, coloured by the relative abundance of Prevotella copri bacterium. b, Average relative abundances of bacterial phyla present in > 0.1% of the DMP cohort. Red vertical line indicates rare phyla (abundance < 0.1%). c, Phylum-level composition of all samples in the cohort, sorted by abundance of phylum Bacteroidetes. Each vertical line indicates one sample. * phylum has significantly higher variance when compared to each of pathway classes (one-sided F test of variances FDRs < 0.05, Supplementary Table 1G) d, Relative abundances of the top 10 MetaCyc pathways of all samples (sorted to match panel c). Each vertical line indicates one sample. The means of standard deviations of taxa and pathways were found to be significantly different (mean(sd(tax1),...,sd(taxn)) - mean(sd(pwy1),...,sd(pwnm) > 0, two-sided permutation test (1,000 permutations) P < 1.0 x 10−3). All panels show results generated from n = 8,208 independent samples.

Extended Data Fig. 3 Clusters determined by bi-modally distributed Prevotella copri.

a, Density plots of log2-transformed relative abundances of the 10 most abundant bacterial species. b, Log2-transformed relative abundance of Prevotella copri per microbiome cluster (n (cluster1, red) = 6,346, n (cluster2, blue) = 1,862; boxplot: centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers; outer line, distribution of data). The clusters were determined using the partitioning around the medoid method on the relative abundances of microbial species. c. Association of P. copri with metadata (n = 8,208 independent samples; dot, mean; lines, 95% confidence intervals).

Extended Data Fig. 4 Bray-Curtis distances of microbiome features of cohabiting and non-cohabiting participants.

Pairwise microbiome Bray-Curtis dissimilarity comparisons of groups of random, non-cohabiting pairs (RND.PAIR, n = 2,000) compared to cohabitating partners (PARTNERS, n = 1,710); cohabiting parent–child pairs (PAR_CH, n = 285) and cohabiting siblings (SIBL, n = 144); and random pairs (n = 2,000) compared to non-cohabiting 1st-degree relatives (1stDEG.SEP, n = 600) and cohabiting 1st-degree relatives (1stDEG.COH, n = 429). a, MetaCyc pathways. b, Virulence factor gene families. c, antibiotic resistance gene families (centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers; outer line: distribution of data). Significantly different groups are marked with ** for FDR < 1.0e–5 or * for FDR < 0.05 (two-sided Wilcoxon test).

Extended Data Fig. 5 Overview of microbiome–phenotype associations.

Figure shows the number of study-wide significant associations (FDR < 0.05) per phenotype group, clustered by taxonomy. Bar heights represent the number of associations relative to the maximal number of associations for the phenotype group.

Extended Data Fig. 6 Gut Microbiome Health Index calculated for DMP cohort.

Box-plots of the Gut Microbiome Health Index (GMHI) for healthy participants of the DMP cohort samples (Y, n = 1,876 independent participants) vs participants who reported one or more diseases (N, n = 6,332 independent participants) (centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers; outer line, distribution of data). P-value is shown for two-sided Wilcoxon rank-sum test.

Extended Data Fig. 7 Microbiome associations with diseases and medication use.

Heatmap of microbiome–phenotype associations, with microbial species clustered by Z scores (multivariate linear regression of CLR-transformed relative abundance of taxa, correcting for age, Sex, BMI, Bristol stool scale of the faecal sample and technical factors (DNA concentration, sequencing read depth, sequencing batch and sampling season)) using hierarchical clustering and coloured by the direction of association. Study-wide significant associations (Benjamini-Hochberg corrected p-value < 0.05) are marked with +/−. Coloured associations without a label indicate nominally significant associations (Benjamini-Hochberg corrected p-value < 0.05).

Extended Data Fig. 8 Microbiome association with early-life exposures.

Heatmap of microbiome–phenotype associations, with microbial species clustered by Z scores (multivariate linear regression of CLR-transformed relative abundance of taxa, correcting for age, Sex, BMI, Bristol stool scale of the faecal sample and technical factors (DNA concentration, sequencing read depth, sequencing batch and sampling season)) using hierarchical clustering and coloured by the direction of association. Study-wide significant associations (Benjamini-Hochberg corrected p-value < 0.05) are marked with +/−. Coloured associations without a label indicate nominally significant associations (Benjamini-Hochberg corrected p-value < 0.05).

Extended Data Fig. 9 Microbiome association with smoking, pollutants and greenspace.

Heatmap of microbiome–phenotype associations, with microbial species clustered by Z scores (multivariate linear regression of CLR-transformed relative abundance of taxa, correcting for age, Sex, BMI, Bristol stool scale of the faecal sample and technical factors (DNA concentration, sequencing read depth, sequencing batch and sampling season)) using hierarchical clustering and coloured by the direction of association. Study-wide significant associations (Benjamini-Hochberg corrected p-value < 0.05) are marked with +/−. Coloured associations without a label indicate nominally significant associations (Benjamini-Hochberg corrected p-value < 0.05).

Extended Data Fig. 10 Microbiome association with diet.

Heatmap of microbiome–phenotype associations, with microbial species clustered by Z scores (multivariate linear regression of CLR-transformed relative abundance of taxa, correcting for age, Sex, BMI, Bristol stool scale of the faecal sample and technical factors (DNA concentration, sequencing read depth, sequencing batch and sampling season)) using hierarchical clustering and coloured by the direction of association. Study-wide significant associations (Benjamini-Hochberg corrected p-value < 0.05) are marked with +/−. Coloured associations without a label indicate nominally significant associations (Benjamini-Hochberg corrected p-value < 0.05).

Supplementary information

Supplementary Discussion

Elaborating on the results of microbiome models for prediction of general health and specific diseases in our study; elaborating on the associations of gut microbiome with diseases and medication use in our study; elaborating on diet stability over 5-year time period

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Supplementary Table 1

Excel file containing Descriptions of Supplementary Tables (Data_description sheet) and Supplementary Tables 1–10 (in separate excel sheets).

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Gacesa, R., Kurilshikov, A., Vich Vila, A. et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature 604, 732–739 (2022). https://doi.org/10.1038/s41586-022-04567-7

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