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Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug–metabolite atlas

Nature Medicine volume 26, pages 110–117 (2020)Cite this article

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Abstract

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug–metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug–metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

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Fig. 1: Drug–metabolite associations in model 1 versus model 2.
Fig. 2: Drug–metabolite associations in model 2 versus model 3.
Fig. 3: Drug–metabolite associations in model 3 versus significance after disentangling the indicated disease/endophenotype effect.
Fig. 4: Comparison of statin–metabolite associations between cross-sectional, longitudinal and genetic studies.
Fig. 5: Integrated data of PPIs, metabolites, liver function measurements and gut microbiome.

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Data availability

All summary statistics of the meta-analysis, and those utilized in compilation of the figures, are made available through the Supplementary tables. In regard to the availability of the raw data, the analyses are based on a meta-analysis of multiple Dutch studies. The raw metabolomics data of the studies are pooled in a single database. The quantified metabolic biomarker datasets included in this study are available through the BBMRI-NL website http://www.bbmri.nl/omics-metabolomics/, where details of how to access the data through centralized computational facilities are described. To request data, researchers are required to fill out and sign the data access request and code-of-conduct forms. Applications compliant with ethical and legal legislations will be reviewed by the BBMRI-NL board in regard to overlap with other ongoing projects before access is granted. Data on medication used in the current study are available through the individual studies on reasonable request. To obtain these, the principal investigator of the cohorts can be contacted through http://www.bbmri.nl/omics-metabolomics/. No custom code or mathematical algorithm was used in the current study.

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Acknowledgements

We acknowledge all participants included in the cohorts. We also acknowledge the BBMRI Metabolomics Consortium (see Supplementary Information, http://www.bbmri.nl/omics-metabolomics/) funded by BBMRI-NL, a research infrastructure financed by the Dutch government through the Netherlands Organisation for Scientific Research (NWO) (grant nos. 184.021.007 and 184033111). This work is part of the CardioVasculair Onderzoek Nederland (CVON 2012-03), the Common mechanisms and pathways in Stroke and Alzheimer’s disease (CoSTREAM) project (www.costream.eu, grant agreement no. 667375), the Memorabel program (project no. 733050814), Netherlands X-omics Research Infrastructure and U01-AG061359 NIA. The full list of funding information for each cohort can be found in the cohort acknowledgements below. J.L., C.M.v.D. and A.D. benefitted from exchange grants from the Personalized pREvention of Chronic DIseases consortium (no. H2020-MSCA-RISE-2014). A.D. is supported by the Dutch Science Organization (ZonMW-VENI, grant no. 2015). L.C. is supported by a joint PhD fellowship from China Scholarship Council (no. 201708320268) and University of Groningen. M.G.N. is supported by Royal Netherlands Academy of Science Professor Award (no. PAH/6635) to D.I.B. D.O.M.-K. is supported by the ZonMW-VENI (grant no. 916.14.023). B.H.C.S. received a grant from TransQST (no. 116030-2; IMI2). D.R. is funded by an Erasmus MC mRACE grant (Profiling of the human gut microbiome). Cohort acknowledgements, ERF: we are grateful to all study participants and their relatives, general practitioners and neurologists for their contributions and to P. Veraart for her help in genealogy, J. Vergeer for supervision of the laboratory work and P. Snijders for his help in data collection. ERF was supported by the Consortium for Systems Biology (NCSB), both within the framework of the Netherlands Genomics Initiative (NGI)/NWO). The ERF study as a part of European Special Populations Research Network (EUROSPAN) was supported by European Commission FP6 STRP, grant no. 018947 (LSHG-CT-2006-01947) and also received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013)/grant agreement no. HEALTH-F4-2007-201413 by the European Commission under the program ‘Quality of Life and Management of the Living Resources’ of the 5th Framework Programme (no. QLG2-CT-2002-01254), as well as the FP7 project EUROHEADPAIN (no. 602633). High-throughput analysis of ERF data was supported by a joint grant from NWO and the Russian Foundation for Basic Research (NWO-RFBR no. 047.017.043). High-throughput metabolomics measurements in the ERF study were supported by BBMRI-NL. Rotterdam Study: we thank the study subjects, the staff from Rotterdam Study and the participating pharmacists and general practitioners. The Rotterdam Study is supported by Erasmus MC and Erasmus University Rotterdam; by NWO, the Netherlands Organisation for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Netherlands Genomics Initiative (NGI), the Ministry of Education, Culture and Science, the Ministry of HealthWelfare and Sports, the European Commission (DG XII) and the Municipality of Rotterdam. L.L. reports expert consultation from Boehringer Ingelheim and Novartis, and unrestricted grants from AstraZeneca and Chiesi. We are also grateful to nurse ultrasonographist, Mrs. van Wijngaarden for performing abdominal ultrasonography and liver stiffness measurements. The generation and management of stool microbiome data for Rotterdam Study (Rotterdam Study III-2) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam. We thank N. El Faquir and J. Verkroost-Van Heemst for their help in sample collection and registration, and P. van der Wal, K. Arabe, H. Razawy and K. Singh Asra for their help in DNA isolation and sequencing. Furthermore, we thank J. Raes and J. Wang (KU Leuven, Belgium) for their guidance in 16 S rRNA profiling and dataset generation. NTR: we are grateful to all twins and their relatives for their continued participation. Funding was obtained from NWO and MagW/ZonMW (grant nos. 904-61-090, 985-10-002, 904-61-193,480-04-004, 400-05-717, Addiction-31160008, Middelgroot-911-09-032 and Spinozapremie 56-464-14192); BBMRI-NL (no. 184.021.007); VU University’s Institute for Health and Care Research (no. EMGO1); Neuroscience Campus Amsterdam (NCA); the European Community’s Seventh Framework Program (no. FP7/2007-2013); ENGAGE (no. HEALTH-F4-2007-201413); and the European Science Council (ERCAdvanced, no. 230374). M.G.N. is supported by the ZonMw grant, ‘Genetics as a research tool: a natural experiment to elucidate the causal effects of social mobility on health’ (pnr:531003014), ZonMw project: ‘Can sex- and gender-specific gene expression and epigenetics explain sex-differences in disease prevalence and etiology?’ (pnr:849200011) and grant no. R01AG054628 02 S. NESDA: the infrastructure for the NESDA study (www.nesda.nl) was funded through the Geestkracht program of ZonMw (grant no. 10-000-1002) and through financial contributions of participating universities and mental health care organizations (VU University Medical Center, GGZ inGeest, Leiden University Medical Center, Leiden University, GGZ Rivierduinen, University Medical Center Groningen, University of Groningen, Lentis, GGZ Friesland, GGZ Drenthe and Rob Giel Onderzoekscentrum). LLS: we thank all participants. This study was supported by a grant from the Innovation-Oriented Research Program on Genomics (SenterNovem, no. IGE05007), the Centre for Medical Systems Biology and the Netherlands Consortium for Healthy Ageing (grant no. 050-060-810), all within the framework of NWO by the BBMRI Metabolomics Consortium funded by BBMRI-NL (NWO, grant nos. 184.021.007 and 184033111). LifeLines DEEP: we thank participants and staff of the LifeLines DEEP cohort for their collaboration. We thank J. Dekens, M. Platteel, A. Maatman and J. Arends for management and technical support. This project was funded by the Netherlands Heart Foundation (IN-CONTROL CVON, grant no. 2012-03 to A.Z. and J.F.; by NWO (nos. NWO-VIDI 864.13.013 to J.F. and NWO-VIDI 016.178.056 to A.Z.; and by the European Research Council Starting Grant no. 715772 to A.Z., who also holds a Rosalind Franklin Fellowship from the University of Groningen. Hoorn DCS: we thank participants of this study and research staff of the Diabetes Care System West-Friesland. High-throughput metabolomics measurements in the DCS study were supported by BBMRI-NL and the Parelsnoer Initiative which is part of, and is funded by, the Dutch Federation of University Medical Centres and, from 2007 to 2011, received initial funding from the Dutch Government. To perform additional research (in subsamples of the DCS cohort), funding was received from several sources including the Dutch Federation of University Medical Centres, health insurers, NWO, ZonMw, the Dutch Diabetes Foundation, the European Foundation for the Study of Diabetes, International Diabetes Federation, the European Innovative Medicine Initiative and the European Union. Alpha Omega Cohort: the Alpha Omega Cohort is registered with ClinicalTrials.gov. (identifier: NCT03192410). It was funded by the Netherlands Heart Foundation (grant no. 200T401) and the National Institutes of Health (NIH, grant no. R01HL076200). J.M.G. received funding from Unilever for analyses of dietary and circulating fatty acids in the Alpha Omega Cohort. High-throughput metabolomics measurements for the Alpha Omega Cohort were supported by BBMRI-NL. TMS: this study was supported by the European Regional Development Fund via OP-Zuid, the Province of Limburg, the Dutch Ministry of Economic Affairs (grant no. 31 O.041), Stichting De Weijerhorst (Maastricht, the Netherlands), the Pearl String Initiative Diabetes (Amsterdam, the Netherlands), CARIM School for Cardiovascular Diseases (Maastricht, the Netherlands), Stichting Annadal (Maastricht, the Netherlands), Health Foundation Limburg (Maastricht, the Netherlands) and by unrestricted grants from Janssen-Cilag B.V. (Tilburg, the Netherlands), Novo Nordisk Farma B.V. (Alphen aan den Rijn, the Netherlands) and Sanofi-Aventis Netherlands B.V. (Gouda, the Netherlands). LUMINA: the LUMINA study is funded by grants obtained from ZonMw (no. 90700217) and VIDI (ZonMw, no. 91711319) (to G.M.T.); by NCSB and the Centre for Medical System Biology (CMSB), both within the framework of the Netherlands Genomics Initiative (NGI)/NWO (to A.M.J.M.v.d.M.) and by the FP7 EU project EUROHEADPAIN (grant no. 602633) (to A.M.J.M.v.d.M. and G.M.T.). NEO: the authors of the NEO study thank all individuals who participated, all participating general practitioners for inviting eligible participants and all research nurses for collection of the data. We thank the NEO study group—P. van Beelen, P. Noordijk and I. de Jonge—for coordination, laboratory and data management of the study. Genotyping in the NEO study was supported by Centre National de Génotypage (Paris, France), headed by J.-F. Deleuze. This study was supported by the participating departments, the Division and the Board of Directors of Leiden University Medical Center and by Leiden University, Research Profile Area Vascular and Regenerative Medicine. The study was also supported by the Netherlands Cardiovascular Research Initiative: an initiative with the support of the Dutch Heart Foundation (no. CVON2014-02 ENERGISE).

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Authors and Affiliations

  1. Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands

    Jun Liu, Lies Lahousse, Dina Vojinovic, Najaf Amin, André G. Uitterlinden, Ayşe Demirkan, Bruno H. C. Stricker & Cornelia M. van Duijn

  2. Nuffield Department of Population Health, University of Oxford, Oxford, UK

    Jun Liu & Cornelia M. van Duijn

  3. Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium

    Lies Lahousse

  4. Department of Biological Psychology, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands

    Michel G. Nivard, Mariska Bot, Carisha S. Thesing, Gonneke Willemsen, Rene Pool, Yuri Milaneschi, Brenda W. J. H. Penninx & Dorret I. Boomsma

  5. Amsterdam Public Health Research Institute, Amsterdam, the Netherlands

    Michel G. Nivard, Mariska Bot, Carisha S. Thesing, Roderick C. Slieker, Gonneke Willemsen, Rene Pool, Yuri Milaneschi, Femke Rutters, Petra J. M. Elders, Joline W. J. Beulens, Amber A. W. A. van der Heijden, Leen M. ‘t Hart, Brenda W. J. H. Penninx & Dorret I. Boomsma

  6. Department of Genetics, University Medical Center Groningen, Groningen, the Netherlands

    Lianmin Chen, Alexandra Zhernakova, Jingyuan Fu & Ayşe Demirkan

  7. Department of Pediatrics, University Medical Center Groningen, Groningen, the Netherlands

    Lianmin Chen & Jingyuan Fu

  8. Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands

    Jan Bert van Klinken, Arn M. J. M. van den Maagdenberg & Ko Willems van Dijk

  9. Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands

    Jan Bert van Klinken & Ko Willems van Dijk

  10. Department of Clinical Chemistry, Laboratory Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, the Netherlands

    Jan Bert van Klinken

  11. Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands

    Marian Beekman, Erik Ben van den Akker, H. Eka D. Suchiman, Leen M. ‘t Hart & P. Eline Slagboom

  12. Department of Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, the Netherlands

    Erik Ben van den Akker

  13. Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands

    Erik Ben van den Akker

  14. Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands

    Roderick C. Slieker, Femke Rutters, Joline W. J. Beulens & Leen M. ‘t Hart

  15. Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands

    Roderick C. Slieker & Leen M. ‘t Hart

  16. Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands

    Eveline Waterham & Johanna M. Geleijnse

  17. Department of Internal Medicine, Maastricht University, Maastricht, the Netherlands

    Carla J. H. van der Kallen, Marleen M. J. van Greevenbroek & Coen D. A. Stehouwer

  18. School for Cardiovascular Diseases, Maastricht University, Maastricht, the Netherlands

    Carla J. H. van der Kallen, Marleen M. J. van Greevenbroek, Ilja C. W. Arts & Coen D. A. Stehouwer

  19. Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands

    Irene de Boer, Gerrit L. J. Onderwater, Arn M. J. M. van den Maagdenberg & Gisela M. Terwindt

  20. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands

    Ruifang Li-Gao & Dennis O. Mook-Kanamori

  21. Department of Internal Medicine, Erasmus MC, University Medical Center, Rotterdam, the Netherlands

    Djawad Radjabzadeh, Robert Kraaij, André G. Uitterlinden & Bruno H. C. Stricker

  22. Department of Gastroenterology and Hepatology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands

    Louise J. M. Alferink & Sarwa Darwish Murad

  23. Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands

    Diana van Heemst

  24. Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands

    Petra J. M. Elders & Amber A. W. A. van der Heijden

  25. Department of Epidemiology, Maastricht University, Maastricht, the Netherlands

    Ilja C. W. Arts

  26. Maastricht Center for Systems Biology, Maastricht University, Maastricht, the Netherlands

    Ilja C. W. Arts

  27. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands

    Dennis O. Mook-Kanamori

  28. Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands

    Thomas Hankemeier & Cornelia M. van Duijn

  29. Netherlands Metabolomics Center, Leiden, the Netherlands

    Thomas Hankemeier

  30. Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands

    Ko Willems van Dijk

  31. Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK

    Ayşe Demirkan

  32. Inspectorate of Healthcare, The Hague, the Netherlands

    Bruno H. C. Stricker

Authors
  1. Jun Liu
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  2. Lies Lahousse
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  3. Michel G. Nivard
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  5. Lianmin Chen
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  7. Carisha S. Thesing
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  8. Marian Beekman
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  10. Roderick C. Slieker
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  23. Rene Pool
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  26. H. Eka D. Suchiman
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  28. Petra J. M. Elders
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  29. Joline W. J. Beulens
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  30. Amber A. W. A. van der Heijden
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  31. Marleen M. J. van Greevenbroek
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  32. Ilja C. W. Arts
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  33. Gerrit L. J. Onderwater
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  34. Arn M. J. M. van den Maagdenberg
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  35. Dennis O. Mook-Kanamori
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  36. Thomas Hankemeier
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  37. Gisela M. Terwindt
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  38. Coen D. A. Stehouwer
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  39. Johanna M. Geleijnse
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  40. Leen M. ‘t Hart
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  41. P. Eline Slagboom
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  42. Ko Willems van Dijk
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  43. Alexandra Zhernakova
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  44. Jingyuan Fu
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Contributions

A.D., J.F., J.M.G., L.L., J.L., M.N., L.M.t’H, C.M.v.D. and A.Z. contributed to study design. M. Beekman, J.W.J.B., D.I.B., I.d.B., P.J.M.E., J.F., J.M.G., D.O.M.-K., G.L.J.O., B.W.J.H.P., F.R., P.E.S., C.D.A.S., B.H.C.S., L.M.t’H., G.M.T., A.M.J.M.v.d.M., A.A.W.A.v.d.H., G.J.H.v.d.K., K.W.v.D., C.M.v.D., M.M.J.v.G., G.W., R.K., S.D.M., A.G.U. and A.Z. contributed to cohort design and management. I.C.W.A., M. Beekman, D.I.B., I.d.B., J.F., J.M.G., T.H., G.L.J.O., R.P., P.E.S., C.D.A.S., B.H.S., H.E.D.S., L.M.t’H, G.M.T., A.M.J.M.v.d.M., A.A.W.A.v.d.H., G.J.H.v.d.K., C.M.v.D., M.M.J.v.G., D.v.H., G.W., D.V., N.A., D.R., R.K., S.D.M., A.G.U. and A.Z. contributed to cohort data collection. M. Beekman, M. Bot, L.C., I.d.B., J.M.G., L.L., R.L.-G., J.L., M.G.N., R.C.S., L.M.H., C.S.T., E.B.v.d.A., D.V., D.R., L.J.M.A. and E.W. contributed to data analysis. J.B.v.K. contributed to web development. A.D., J.M.G., L.L., J.L. and C.M.v.D. contributed to writing of the manuscript. I.C.W.A., M. Beekman, J.W.J.B., D.I.B., M. Bot, L.C., I.d.B., A.D., P.J.M.E., J.F., J.M.G., L.L., R.L.-G., J.L., Y.M., D.O.M.K., M.N., G.L.J.O., B.P., R.P., F.R., R.C.S., B.H.S., L.M.t’H, G.M.T., C.T., E.B.v.d.A., A.M.J.M.v.d.M., A.A.W.A.v.d.H., G.J.H.v.d.K., K.W.v.D., C.M.v.D., M.M.J.v.G., G.W., D.V., N.A., R.K., L.J.M.A., S.D.M. and A.Z. contributed to critical review of manuscript.

Corresponding authors

Correspondence to Jun Liu or Cornelia M. van Duijn.

Ethics declarations

Competing interests

D.O.M.-K. is a part-time clinical research consultant for Metabolon, Inc. All other authors have nothing to disclose. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Additional information

Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Correlation between metabolites in Rotterdam Study.

The correlation matrix of metabolites were performed by Pearson’s correlation (n = 5,191). The hierarchical cluster analysis was used in the clustering. Color in the boxes, correlation coefficient.

Extended Data Fig. 2 Drug–metabolite associations in model 1 versus model 2.

The drugs with at least one significant metabolite association in baseline model (model 1) by linear regression are shown. The first letter of the ATC code precedes the drug name, to identify different categories. Sample sizes of the drug users and non-users in model 1 (age and sex adjusted) and model 2 (age, sex, BMI and smoking adjusted) are shown following drug names, respectively. Dark red, positive significant associations in model 1 (P-value < 1.9 × 10−5); light red, positive nonsignificant associations in model 1 (P-value ≥ 1.9 × 10−5); dark blue, negatively significant associations in model 1 (P-value < 1.9 × 10−5); light blue, negatively nonsignificant associations in model 1 (P-value ≥ 1.9 × 10−5). Asterisks in boxes denote that neither direction nor significance status were different between model 1 and model 2 (P-value < 1.9 × 10−5). Two-tailed tests were used.

Extended Data Fig. 3 Drug–metabolite associations in model 2 versus model 3.

The drugs with at least one significant metabolite association in model 2 (age, sex, BMI and smoking adjusted) by linear regression are shown. The first letter of the ATC code is shown preceding the drug name, to identify different categories. Sample sizes of the drug users and non-users in model 2 and model 3 (age, sex, BMI, smoking and co-treatment adjusted) are shown following drug names, respectively. Dark red, positive significant associations in model 2 (P-value < 1.9 × 10−5); light red, positive nonsignificant associations in model 2 (P-value ≥ 1.9 × 10−5) dark blue, negatively significant associations in in model 2 (P-value < 1.9 × 10−5); light blue, negatively nonsignificant associations in in model 2 (P-value ≥ 1.9 × 10−5). Asterisks in boxes denote that neither direction nor significance status was different between model 2 and model 3 (P-value threshold is multiple testing-corrected per drug; See Supplementary Table 4). Two-tailed tests were used.

Extended Data Fig. 4 Correlation between drugs.

The correlation matrix of metabolites were performed by Spearman’s correlation (n = 6,631). The first letter of the ATC code is shown preceding the drug name, to identify different categories. Sample size of the drug users and non-users is shown following drug names. The depth of the color refers to the correlation coefficients. Asterisks in boxes denote the positively significant correlations (P-value < 5.9 × 10−4). Two-tailed tests were used.

Extended Data Fig. 5 Drug–metabolite Associations in model 3 versus single drug test.

The first letter of the ATC code is shown preceding the drug name, to identify different categories. Single drug test: Association analysis (linear regression) in the sub-samples of patients who use one drug only (one-drug-users) and all-treatment-naive controls. Sample size of the drug users and non-users in model 3 (age, sex, BMI, smoking and co-treatment adjusted) and the single drug test are shown following drug names, respectively. Dark red, positive significant associations in model 3 which are available for the single drug test; light red, positive non-significant associations in model 3 or not available for the single drug test; dark blue, negatively significant associations in model 3 which are available for the single drug test; light blue, negatively non-significant associations in model 3 or not available for the single drug test. Asterisks in boxes denote that the significant associations confirmed in the single drug test (P threshold is multiple testing-corrected per drug; see Supplementary Table 4). Two-tailed tests were used.

Extended Data Fig. 6 Association of PPI/dosage and the PPI-related metabolites.

The association of dosage of PPI and metabolites were tested by linear regression in Rotterdam Study (n = 700). The PPI-related metabolites were selected in model 3. DDD, defined daily dose of PPI. (/), sample size of user/non-user. Red, positive association, blue, negative association. The depth of the color refers to the association estimates. Asterisks in boxes denote significance after correcting for multiple test (P-value < 0.004). Two-tailed tests were used.

Extended Data Fig. 7 Association of specific PPI drugs and the PPI-related metabolites.

The association of PPI drugs (A02BC) and metabolites were tested by linear regression in Rotterdam Study. The PPI-related metabolites were selected in model 3. A02BC01, omeprazole; A02BC02, pantoprazole; A02BC03, lansoprazole; A02BC04, rabeprazole; A02BC05, esomeprazole. (/), sample size of user/non-user. Red, positive association; blue: negative association. The depth of the color refers to the association estimates. Asterisks in boxes denote significance after correcting for multiple test (P-value < 0.004). Two-tailed tests were used.

Extended Data Fig. 8 The effect of population structure on metabolite clustering across datasets.

Principal component (PC) analysis was performed using joint metabolite data from the cohorts (AlphaOmega, n = 877; ERF, n = 778; RS1, RS Dataset 1, n = 2,975; RS2, RS Dataset 2, n = 729; RS3, RS Dataset 3, n = 1,487; TMS, n = 854). Two-tailed tests were used.

Supplementary information

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Supplementary Tables

Supplementary Tables 1–15.

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Liu, J., Lahousse, L., Nivard, M.G. et al. Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug–metabolite atlas. Nat Med 26, 110–117 (2020). https://doi.org/10.1038/s41591-019-0722-x

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