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Statin therapy is associated with lower prevalence of gut microbiota dysbiosis - Nature

  • ️Raes, Jeroen
  • ️Wed May 06 2020

Data availability

Raw amplicon sequencing data used in this study have been deposited in the EMBL-EBI European Nucleotide Archive (ENA) under accession number PRJEB37249. The metadata and processed microbiome data required for the reanalysis of results presented in the manuscript are respectively provided as Supplementary Table 2 and available for download at http://raeslab.org/software/BMIS/. For clinical cohort-related questions, contact K.C.

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Acknowledgements

We thank the study participants and nurses for their contributions to the project. MetaCardis was funded by European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement HEALTH-F4-2012-305312 (MetaCardis project) and the French National Agency of Research (ANR; ‘Investissement d’Avenir’ FORCE, Metagenopolis grant ANR-11-DPBS-0001 and ICAN ANR-10-IAHU-05). The promotor of the clinical study was the Assistance Publique Hôpitaux de Paris (APHP). S.V.-S. was supported by a post-doctoral fellowship from the Research Foundation Flanders (FWO Vlaanderen). The Raes laboratory is supported by the VIB Grand Challenges programme, KU Leuven, the Rega Institute for Medical Research, and the FWO EOS program (30770923). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research institution at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation. M.-E.D. was funded by the NIHR Imperial Biomedical Research Centre.

Author information

Author notes

  1. These authors contributed equally: Sara Vieira-Silva, Gwen Falony, Eugeni Belda

  2. These authors jointly supervised this work: Karine Clément, Jeroen Raes

Authors and Affiliations

  1. Laboratory of Molecular Bacteriology, Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium

    Sara Vieira-Silva, Gwen Falony, Mireia Valles-Colomer, Thi Thuy Duyen Nguyen, Sebastian Proost & Jeroen Raes

  2. Center for Microbiology, VIB, Leuven, Belgium

    Sara Vieira-Silva, Gwen Falony, Mireia Valles-Colomer, Thi Thuy Duyen Nguyen, Sebastian Proost & Jeroen Raes

  3. Nutrition and Obesities: Systemic Approaches Research Unit (NutriOmics), INSERM, Sorbonne Université, Paris, France

    Eugeni Belda, Judith Aron-Wisnewsky, Karen Assmann, Edi Prifti, Fabrizio Andreelli, Christine Rouault, Sothea Touch, Chloe Amouyal, Maria-Carlota Dao, Jean Debedat, Aurélie Lampuré, Christine Poitou-Bernert, Timothy Swartz, Eric Verger, Jean-Daniel Zucker & Karine Clément

  4. Institute of Cardiometabolism and Nutrition, Integromics Unit, Paris, France

    Eugeni Belda, Edi Prifti, Marianne Graine, Timothy Swartz & Jean-Daniel Zucker

  5. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Trine Nielsen, Tue H. Hansen, Helle K. Pedersen, Nadja B. Søndertoft, Ehm Astrid Andersson Galijatovic, Line Engelbrechtsen, Bolette Hartmann, Malene Hornbak, Johanne Justesen, Nikolaj Krarup, Mathilde Svendstrup, Torben Hansen, Jens J. Holst, Henrik Vestergaard, Oluf Pedersen & Fredrik Bäckhed

  6. Nutrition Department, Pitie-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

    Judith Aron-Wisnewsky, Dominique Cassuto, Cecile Ciangura, Jean Khemis, Lea Lucas-Martini, Jonathan Medina-Stamminger, Sandrine Moutel, Christine Poitou-Bernert, Camille Vatier, Jean-Michel Oppert & Karine Clément

  7. Medical Department III – Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany

    Rima Chakaroun, Judith Kammer, Stefanie Walther & Michael Stumvoll

  8. Experimental and Clinical Research Center, Charité-Universitätsmedizin and Max-Delbrück Center, Berlin, Germany

    Sofia K. Forslund

  9. Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany

    Sofia K. Forslund, Luis Pedro Coelho, Renato Alves, Michael Kuhn, Lucas Moitinho-Silva, Sebastian Schmidt & Peer Bork

  10. Max Delbruck Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

    Sofia K. Forslund

  11. DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany

    Sofia K. Forslund

  12. Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, Sorbonne Université, IRD, Bondy, France

    Edi Prifti & Jean-Daniel Zucker

  13. Wallenberg Laboratory, Department of Molecular and Clinical Medicine and Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Valentina Tremaroli & Fredrik Bäckhed

  14. Université Paris-Saclay, INRAE, Metagenopolis, Jouy en Josas, France

    Nicolas Pons, Emmanuelle Le Chatelier, Nathalie Galleron, Benoit Quinquis, Hugo Roume, Magalie Berland, Hervé Blottière, Mickael Camus, Angélique Doré, Sebastien Fromentin, Hanna Julienne, Véronique Lejard, Florence Levenez, Robin Massey, Nicolas Maziers, Laetitia Pasero Le Pavin, Thierry Vanduyvenboden & Stanislav Dusko Ehrlich

  15. Diabetes Department, Pitie-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

    Fabrizio Andreelli, Chloe Amouyal, Frederic Bosquet, Olivier Bourron, Cecile Ciangura, Agnes Hartemann & Sophie Jaqueminet

  16. UF Biomarqueurs Inflammatoires et Métaboliques, Biochemistry and Hormonology Department, Tenon Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

    Jean-Phillippe Bastard & Soraya Fellahi

  17. Centre de Recherche Saint-Antoine, Sorbonne Université-INSERM UMR-S 938, IHU ICAN, Paris, France

    Jean-Phillippe Bastard & Soraya Fellahi

  18. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Luis Pedro Coelho

  19. NICO Cardio-oncology Program, CIC-1421, Department of Pharmacology, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, INSERM, Sorbonne Université, Paris, France

    Jean-Sébastien Hulot & Joe-Elie Salem

  20. Université de Paris, PARCC, INSERM, Paris, France

    Jean-Sébastien Hulot

  21. CIC1418 and DMU CARTE, Assistance Publique–Hôpitaux de Paris, Hôpital Européen Georges-Pompidou, Paris, France

    Jean-Sébastien Hulot

  22. Department of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

    Christian Lewinter & Lars Køber

  23. Computational and Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK

    Marc-Emmanuel Dumas

  24. Genomic and Environmental Medicine, National Heart & Lung Institute, Faculty of Medicine, Imperial College London, London, UK

    Marc-Emmanuel Dumas

  25. Sorbonne Paris Cité Epidemiology and Statistics Research Centre (CRESS), U1153 INSERM, U1125, INRA, CNAM, University of Paris, Nutritional Epidemiology Research Team (EREN), Bobigny, France

    Serge Hercberg & Pilar Galan

  26. Department of Clinical Biochemistry, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

    Jens P. Gøtze

  27. Biobyte Solutions, Heidelberg, Germany

    Ivica Letunic

  28. Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Jens Nielsen

  29. Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig, Leipzig, Germany

    Michael Stumvoll

  30. Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, Heidelberg, Germany

    Peer Bork

  31. Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany

    Peer Bork

  32. Cardiology Department, Pitie-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

    Olivier Barthelemy, Jean-Paul Batisse, Rachid Boubrit, Jean-Philippe Collet, Morad Djebbar, Gerard Helft, Richard Isnard, Mathieu Kerneis, Gilles Montalescot, Francoise Pousset & Johanne Silvain

  33. Biochemistry Department, Pitie-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

    Randa Bittar

  34. Endocrinology Department, Pitie-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris, Paris, France

    Philippe Giral & Laurence Pouzoulet

  35. Department of Clinical Biochemistry, Rigshospitalet, Glostrup University of Copenhagen, Copenhagen, Denmark

    Niklas Rye Jørgensen

Authors

  1. Sara Vieira-Silva

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  2. Gwen Falony

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  3. Eugeni Belda

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  4. Trine Nielsen

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  5. Judith Aron-Wisnewsky

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  6. Rima Chakaroun

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  7. Sofia K. Forslund

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  8. Karen Assmann

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  9. Mireia Valles-Colomer

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  10. Thi Thuy Duyen Nguyen

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  11. Sebastian Proost

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  12. Edi Prifti

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  13. Valentina Tremaroli

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  14. Nicolas Pons

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  15. Emmanuelle Le Chatelier

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  16. Fabrizio Andreelli

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  17. Jean-Phillippe Bastard

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  18. Luis Pedro Coelho

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  19. Nathalie Galleron

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  20. Tue H. Hansen

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  21. Jean-Sébastien Hulot

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  22. Christian Lewinter

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  23. Helle K. Pedersen

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  24. Benoit Quinquis

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  25. Christine Rouault

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  26. Hugo Roume

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  27. Joe-Elie Salem

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  28. Nadja B. Søndertoft

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  29. Sothea Touch

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  30. Marc-Emmanuel Dumas

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  31. Stanislav Dusko Ehrlich

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  32. Pilar Galan

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  33. Jens P. Gøtze

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  34. Torben Hansen

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  35. Jens J. Holst

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  36. Lars Køber

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  37. Ivica Letunic

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  38. Jens Nielsen

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  39. Jean-Michel Oppert

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  40. Michael Stumvoll

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  41. Henrik Vestergaard

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  42. Jean-Daniel Zucker

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  43. Peer Bork

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  44. Oluf Pedersen

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  45. Fredrik Bäckhed

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  46. Karine Clément

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  47. Jeroen Raes

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Consortia

MetaCardis Consortium

  • Renato Alves
  • , Chloe Amouyal
  • , Ehm Astrid Andersson Galijatovic
  • , Olivier Barthelemy
  • , Jean-Paul Batisse
  • , Magalie Berland
  • , Randa Bittar
  • , Hervé Blottière
  • , Frederic Bosquet
  • , Rachid Boubrit
  • , Olivier Bourron
  • , Mickael Camus
  • , Dominique Cassuto
  • , Cecile Ciangura
  • , Jean-Philippe Collet
  • , Maria-Carlota Dao
  • , Jean Debedat
  • , Morad Djebbar
  • , Angélique Doré
  • , Line Engelbrechtsen
  • , Soraya Fellahi
  • , Sebastien Fromentin
  • , Philippe Giral
  • , Marianne Graine
  • , Agnes Hartemann
  • , Bolette Hartmann
  • , Gerard Helft
  • , Serge Hercberg
  • , Malene Hornbak
  • , Richard Isnard
  • , Sophie Jaqueminet
  • , Niklas Rye Jørgensen
  • , Hanna Julienne
  • , Johanne Justesen
  • , Judith Kammer
  • , Mathieu Kerneis
  • , Jean Khemis
  • , Nikolaj Krarup
  • , Michael Kuhn
  • , Aurélie Lampuré
  • , Véronique Lejard
  • , Florence Levenez
  • , Lea Lucas-Martini
  • , Robin Massey
  • , Nicolas Maziers
  • , Jonathan Medina-Stamminger
  • , Lucas Moitinho-Silva
  • , Gilles Montalescot
  • , Sandrine Moutel
  • , Laetitia Pasero Le Pavin
  • , Christine Poitou-Bernert
  • , Francoise Pousset
  • , Laurence Pouzoulet
  • , Sebastian Schmidt
  • , Johanne Silvain
  • , Mathilde Svendstrup
  • , Timothy Swartz
  • , Thierry Vanduyvenboden
  • , Camille Vatier
  • , Eric Verger
  •  & Stefanie Walther

Contributions

M.-E.D., S.D.E., P.G., J.P.G., T.H., J.J.H., L.K., I.L., J.N., J.-M.O., M.S., H.V., J.-D.Z., P.B., O.P., F.B., K.C. (the MetaCardis Consortium coordinator) and J.R. conceived the MetaCardis study protocol, including clinical standard operating procedures, study objectives and study design. T.N., J.A.-W. and R.C. coordinated recruitment and sample collection efforts over the different cohorts. T.N., J.A.-W., R.C. and K.A. curated and harmonized the clinical metadata. S.V.-S., G.F., E.B., T.N., J.A.-W., S.K.F., K.A., R.C., M.V.-C., S.P., E.P., V.T., N.P., E.L.C., F.A., J.-P.B., L.P.C., N.G., T.H.H., J.-S.H., C.L., H.K.P., B.Q., C.R., H.R., J.-E.S., N.B.S., S.T. and the MetaCardis Consortium assisted in sample collection, analyses, and/or data pre-processing and exploration. Faecal microbial DNA extraction and shotgun sequencing was performed by N.P., E.L.C. and S.F. Flow-cytometry-based faecal microbial load estimations were performed by T.T.D.N. Statistical analyses were designed and executed by S.V.-S., G.F., E.B., K.A., S.K.F. and M.V.-C. The manuscript was drafted by G.F., S.V.-S., K.C. and J.R. All authors revised the article and approved the final version for publication.

Corresponding authors

Correspondence to Karine Clément or Jeroen Raes.

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Competing interests

J.R., S.V.-S., G.F. and M.V.-C. are listed as inventors on patent application PCT/EP2018/084920, in the name of VIB VZW, Katholieke Universiteit Leuven, KU Leuven R&D and Vrije Universiteit Brussel, covering the features of the microbiome associated with inflammation described in ref. 2.

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Extended data figures and tables

Extended Data Fig. 1 Microbiome variation in the BMIS cohort (n = 888 participants).

a, Percentage of subjects in the BMIS cohort taking medication of the stated drug classes. ACE inhibitors, angiotensin converting enzyme inhibitors; ARB, angiotensin II receptor blockers; ASA, acetylsalicylic acid; PPI, proton-pump inhibitors. b, Best model explaining inter-individual microbiome variation based on obesity-defining and metabolic-syndrome-defining variables as well as on most frequently disclosed therapeutics (taken by more than 10% of participants; Supplementary Table 4). Explanatory power of the included variables are reported for the variables taken individually (black bars; n = 888 biologically independent samples, univariate dbRDA)) or in a multivariate model (grey bars; n = 888 biologically independent samples, multivariate dbRDA). c, Principal coordinates analysis of inter-individual differences (genus level Bray–Curtis dissimilarity) in the microbiome profiles of the BMIS cohort (n = 888 biologically independent samples, data points coloured by enterotypes (Extended Data Fig. 4)) with the rest of the MetaCardis dataset in the background (n = 1,134, grey dots). Full and open circles corresponding to statin-medicated (Stat(+)) and non-statin-medicated participants (Stat(−)), respectively. Arrows represent the effect sizes of a post hoc fit of significant microbiome covariates identified in the multivariate model in b. d, Same principal coordinates analysis as in c, with the statin intake variable split into the separate statin classes (n = 888 biologically independent samples, simvastatin (n = 51), atorvastatin (n = 33) and other statins (n = 22); Supplementary Table 4). In c, d, the body of the box plot represents the first and third quartiles of the distribution, the line represents the median, and the whiskers extend from the quartiles to the last data point within 1.5× the interquartile range (IQR), with outliers beyond.

Extended Data Fig. 2 The association of BMI, fat mass percentage and serum fasting triglyceride levels with faecal microbial gene richness and faecal microbial load in the non-statin-medicated BMIS cohort (n = 782 participants).

All three covariates were found to be associated with both microbiome gene richness (n = 711 biologically independent samples, Spearman’s ρ = −0.45 to −0.26, Padj = 4.0 × 10−39 to 1.6 × 10−13), a proxy for microbial biodiversity previously suggested as a marker of metabolic health in obese individuals8, and faecal microbial load (n = 711 biologically independent samples, Spearman’s ρ = −0.17 to −0.13, Padj = 4.1 × 10−6 to 3.1 × 10−4; Supplementary Table 7). Adjustment for multiple testing (Padj) was performed using the Benjamini–Hochberg method. Least square linear regression lines (dashed line) with 95% confidence interval (grey shading) are provided for visual representation of the non-parametric testing provided in Supplementary Table 7. Data points are coloured by enterotype classification.

Extended Data Fig. 3 Association between the variation in quantitative butyrate production potential and the BMI, fat mass percentage and triglycerides levels of participants, or the enterotype classification of the samples, in the non-statin-medicated BMIS cohort (n = 782 participants).

Quantitative functional microbiome profiles were constructed by multiplication of relative proportions to an indexing factor proportional to the microbial load of the samples. The module ‘butyrate production II’ describes butyrate production from the butyryl-CoA–acetate CoA-transferase pathway—the most common among colon bacteria. a–d, The abundance of the butyrate production II module was negatively correlated with BMI (n = 771 biologically independent samples, Spearman’s ρ = −0.27, Padj = 3.1 × 10−13) (a), fat mass percentage (n = 771 biologically independent samples, Spearman’s ρ = −0.21, Padj = 6.0 × 10−8) (b) and tryglyceride levels (n = 771 biologically independent samples, Spearman’s ρ = −0.20, Padj = 6.4 × 10−8) (c), and significantly decreased in the Bact2 enterotype compared with the others (Bact2 <  Prev < Bact1 = Rum; n = 771 biologically independent samples, Kruskal–Wallis Padj = 4.71 × 10−35; different letters denote enterotypes with a significant pairwise difference (post hoc Dunn tests provided in Supplementary Table 10) (d). The body of the box plot represents the first and third quartiles of the distribution, the line represents the median, and the whiskers extend from the quartiles to the last data point within 1.5× IQR, with outliers beyond. In a–d, adjustment for multiple testing (Padj) was performed using the Benjamini–Hochberg method.

Extended Data Fig. 4 Enterotyping of the MetaCardis dataset (n = 2,022 biologically independent samples).

a, Principal coordinates visualization of the four enterotypes resulting from community typing was performed using DMM52 on genus-level faecal microbiome profiles. b, Information criteria (minimum Laplace) used to determine the optimal number of clusters (enterotypes) for the MetaCardis dataset (n = 2,022 biologically independent samples) DMM-based community typing. c, Average relative composition of the enterotypes for key genera, used to label the enterotypes Bacteroides1 (Bact1; high percentages of Bacteroides and Faecalibacterium), Bacteroides2 (Bact2; high percentages of Bacteroides and low percentages of Faecalibacterium), Prevotella (Prev; high percentages of Prevotella) and Ruminococcaceae (Rum; low percentages of Bacteroides and Prevotella), on the basis of their respective genus-level proportional abundance profiles.

Extended Data Fig. 5 Increased quantitative abundance of Eggerthella in the Bact2 enterotype of the non-statin-medicated BMIS cohort.

a, Difference in quantitative Eggerthella abundances between enterotypes (Prev = Rum < Bact1 < Bact2; n = 771 biologically independent samples, Kruskal–Wallis Padj = 4.10 × 10−47; different letters denote enterotypes with a significant pairwise difference (post hoc Dunn tests provided in Supplementary Table 10)). Adjustment for multiple testing (Padj) was performed using the Benjamini–Hochberg method. b, Difference in the proportion of Eggerthella (normalized by the sample total microbial load) between enterotypes, showing a comparable trend to that seen in a (n = 771 biologically independent samples). The body of the box plot represents the first and third quartiles of the distribution, the line represents the median, and the whiskers extend from the quartiles to the last data point within 1.5× IQR, with outliers beyond.

Extended Data Fig. 6 Species dominating the Bacteroides fraction in the different enterotypes of the non-statin-medicated BMIS cohort.

The top associations with the Bact2 enterotype—with the proportions they contribute to the total fraction shown in the ring chart—were the depletion in B. caccae (n = 768 biologically independent samples, Kruskal–Wallis, Padj = 1.3 × 10−15) and B. cellulosilyticus (n = 768 biologically independent samples, Kruskal–Wallis, Padj = 5.3 × 10−13) when compared with the Rum, Prev and Bact1 enterotypes, and the enrichment in B. fragilis (n = 768 biologically independent samples, Kruskal–Wallis, Padj = 3.5 × 10−11; Supplementary Table 11). Species were defined by species-level annotation of metagenomic species, and their proportional abundances were defined relative to the genus abundance. Samples for which the genus had a low total abundance (below the 20% quantile for all species belonging to the top 10 genera) were excluded from the analysis (n = 768 biologically independent samples were included). Adjustment for multiple testing (Padj) was performed using the Benjamini–Hochberg method.

Extended Data Fig. 7 Systemic inflammation and its relation to enterotypes and to BMI in the non-statin-medicated BMIS cohort.

a, Individuals with faecal samples enterotyped as Bact2 displayed more pronounced systemic inflammation levels as assessed through fasting serum hsCRP concentrations when compared with participants classified as Rum, Prev and Bact1 (n = 763 biologically independent samples, Kruskal–Wallis P = 1.37 × 10−10; Rum = Bact1 < Prev < Bact2; different letters denote enterotypes with a significant pairwise difference (post hoc Dunn tests provided in Supplementary Table 13)). The body of the box plot represents the first and third quartiles of the distribution, the line represents the median, and the whiskers extend from the quartiles to the last data point within 1.5× IQR, with outliers beyond. b, Linear model of the correlation between host systemic inflammation (hsCRP concentration, log10-transformed) and BMI, fitted by least squares regression (n = 763 biologically independent samples; estimated intercept = −0.8681, estimated slope = 0.0379, R2 = 0.47, P = 1.5 × 10−108).

Extended Data Fig. 8 Control for the effect of additional medication taken by obese statin-medicated or non-statin-medicated individuals of the BMIS cohort (n = 888 participants) on the association between reduced Bact2 prevalence and statin intake.

a, List of drugs taken by non-statin-medicated and statin-medicated obese BMIS participants separated into 5 groups: those reporting no (co-)medication (beyond statin intake) (+0), and those reporting one (+1), two (+2), three (+3) and more than three (more) (co-)medications. The size and colour of the dots represent the fraction of the non-statin-medicated or statin-medicated obese BMIS participants falling within that group. b, Difference in prevalence of the Bact2 enterotype in statin-medicated compared with non-statin-medicated obese BMIS participants, with decreasing co-medication threshold for inclusion of participants. For ‘all’, the total number of statin-medicated and non-statin-medicated obese BMIS participants were included (n = 474 biologically independent samples); then only subjects reporting three or fewer (≤3; n = 419), two or fewer (≤2; n = 369), one or fewer (≤1; n = 296) or no (0; n = 226) (co-)medications were included. The relative risk and respective significance level associated with the prevalence of the Bact2 enterotype given statin intake is provided above the bar plots (Fisher’s exact test, two-sided, *P < 0.05, relative risk = P(Bact2|Statin = Yes)/P(Bact2|Statin = No)).

Extended Data Fig. 9 Variation in prevalence of the Bact2 enterotype with BMI and statin intake in the BMIS discovery cohort, and in the FGFP and CVD validation cohorts.

a–c, Variation in the prevalence of the Bact2 enterotype with BMI for statin-medicated and non-statin-medicated individuals, showing the significant effect (represented by the range bar with an asterisk; Supplementary Table 16) of statin intake given individuals’ BMI, in the BMIS obese participants (n = 474 biologically independent samples, multivariate binomial logistic regression, Statin | BMI, relative risk = 0.34, *Padj = 0.025) (a); the FGFP cohort, a population-level recruitment with a much narrower BMI range than the BMIS cohort (n = 2,345 biologically independent samples, multivariate binomial logistic regression, Statin | BMI, relative risk = 0.72, *Padj = 0.045) (b) and the MetaCardis CVD cohort (n = 271 biologically independent samples, excluding 11 individuals for which BMI was not known, multivariate binomial logistic regression, Statin | BMI, relative risk = 0.29, *Padj = 0.021) (c). In a–c, the fit lines were obtained by multinomial logistic regression of enterotypes as predicted by BMI, for statin-medicated and non-statin-medicated individuals separately, with the shaded area corresponding to the 95% confidence intervals for the Bact2 regression. Adjustment for multiple testing (Padj) was performed using the Benjamini–Hochberg method.

Extended Data Fig. 10 Probability of carrying a Bact2 enterotype microbiota as a function of CRP levels and statin intake in the obese BMIS cohort.

Association between systemic inflammation (measured by hsCRP levels) and having a faecal microbiota of the Bact2 enterotype, according to statin medication status. Binomial logistic regression (lines with 95% confidence intervals as shaded area) was performed for statin-medicated and non-statin-medicated individuals separately (n = 462 biologically independent samples).

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Vieira-Silva, S., Falony, G., Belda, E. et al. Statin therapy is associated with lower prevalence of gut microbiota dysbiosis. Nature 581, 310–315 (2020). https://doi.org/10.1038/s41586-020-2269-x

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  • Received: 05 August 2019

  • Accepted: 03 April 2020

  • Published: 06 May 2020

  • Issue Date: 21 May 2020

  • DOI: https://doi.org/10.1038/s41586-020-2269-x