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Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome

An Author Correction to this article was published on 19 October 2018

This article has been updated


Despite a growing body of evidence, the role of the gut microbiome in cardiovascular diseases is still unclear. Here, we present a systems-genome-wide and metagenome-wide association study on plasma concentrations of 92 cardiovascular-disease-related proteins in the population cohort LifeLines-DEEP. We identified genetic components for 73 proteins and microbial associations for 41 proteins, of which 31 were associated to both. The genetic and microbial factors identified mostly exert additive effects and collectively explain up to 76.6% of inter-individual variation (17.5% on average). Genetics contribute most to concentrations of immune-related proteins, while the gut microbiome contributes most to proteins involved in metabolism and intestinal health. We found several host–microbe interactions that impact proteins involved in epithelial function, lipid metabolism, and central nervous system function. This study provides important evidence for a joint genetic and microbial effect in cardiovascular disease and provides directions for future applications in personalized medicine.

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Fig. 1: Study analysis workflow.
Fig. 2: Proportion of inter-individual variation explained by genetic and microbial factors.
Fig. 3: Association of FUT2, Ep-CAM, and Blautia.
Fig. 4: Network of protein co-abundance and PPIs.
Fig. 5: Genetic–microbiome interaction for CNTN1.

Data availability

The LifeLines-DEEP metagenomics sequencing data are available at the European Genome-phenome Archive under accession EGAS00001001704.

Change history

  • 19 October 2018

    In the version of this paper originally published, there was a typographical error. In the Discussion, the sentence “In line with this, Ep-CAM-deficient mice exhibited increased intestinal permeability and decreased ion transport60, which may contribute to CVD susceptibility risk59” originally read iron instead of ion transport. This error has been corrected in the HTML, PDF and print versions of the article.


  1. 1.

    Tang, W. H. W. & Hazen, S. L. The contributory role of gut microbiota in cardiovascular disease. J. Clin. Invest. 124, 4204–4211 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

    CAS  Google Scholar 

  3. 3.

    Knights, D. et al. Complex host genetics influence the microbiome in inflammatory bowel disease. Genome Med. 6, 107 (2014).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Imhann, F. et al. Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut 67, 108–119 (2016).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Nakatsu, G. et al. Gut mucosal microbiome across stages of colorectal carcinogenesis. Nat. Commun. 6, 8727 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Nelson, C. P. et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat. Genet. 49, 1385–1391 (2017).

    CAS  PubMed  Google Scholar 

  8. 8.

    Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2016).

    Google Scholar 

  10. 10.

    Sun, W. et al. Common genetic polymorphisms influence blood biomarker measurements in COPD. PLoS Genet. 12, e1006011 (2016).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Folkersen, L. et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet. 13, 1–21 (2017).

    Google Scholar 

  12. 12.

    Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Google Scholar 

  13. 13.

    Kenny, S. et al. Increased expression of the urokinase plasminogen activator system by Helicobacter pylori in gastric epithelial cells. Am. J. Physiol. Gastrointest. Liver Physiol. 295, G431–G441 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Keates, S. et al. cag+ Helicobacter pylori induce transactivation of the epidermal growth factor receptor in AGS gastric epithelial cells. J. Biol. Chem. 276, 48127–48134 (2001).

    CAS  PubMed  Google Scholar 

  15. 15.

    Pezzulo, A. A. et al. Expression of human paraoxonase 1 decreases superoxide levels and alters bacterial colonization in the gut of Drosophila melanogaster. PLoS One 7, e43777 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Stoltz, D. A. et al. Drosophila are protected from Pseudomonas aeruginosa lethality by transgenic expression of paraoxonase-1. J. Clin. Invest. 118, 3123–3131 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Miller, P. G., Bonn, M. B., Franklin, C. L., Ericsson, A. C. & McKarns, S. C. TNFR2 deficiency acts in concert with gut microbiota to precipitate spontaneous sex-biased central nervous system demyelinating autoimmune disease. J. Immunol. 195, 4668–4684 (2015).

    CAS  PubMed  Google Scholar 

  18. 18.

    Yan, J. et al. Gut microbiota induce IGF-1 and promote bone formation and growth. Proc. Natl Acad. Sci. USA 113, E7554–E7563 (2016).

    CAS  PubMed  Google Scholar 

  19. 19.

    Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412 (2016).

    CAS  PubMed  Google Scholar 

  20. 20.

    The Gtex Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    PubMed Central  Google Scholar 

  21. 21.

    Trzpis, M., McLaughlin, P. M. J., de Leij, L. M. F. H. & Harmsen, M. C. Epithelial cell adhesion molecule: more than a carcinoma marker and adhesion molecule. Am. J. Pathol. 171, 386–395 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Baeuerle, P. A. & Gires, O. EpCAM (CD326) finding its role in cancer. Br. J. Cancer 96, 417–423 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Gires, O. & Bauerle, P. A. EpCAM as a target in cancer therapy. J. Clin. Oncol. 28, e239–e240 (2010).

    PubMed  Google Scholar 

  24. 24.

    Kurtz, J.-E. & Dufour, P. Adecatumumab: an anti-EpCAM monoclonal antibody, from the bench to the bedside. Expert Opin. Biol. Ther. 10, 951–958 (2010).

    CAS  PubMed  Google Scholar 

  25. 25.

    Andersson, Y. et al. Phase I trial of EpCAM-targeting immunotoxin MOC31PE, alone and in combination with cyclosporin. Br. J. Cancer 113, 1548–1555 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Lindesmith, L. et al. Human susceptibility and resistance to Norwalk virus infection. Nat. Med. 9, 548–553 (2003).

    CAS  PubMed  Google Scholar 

  27. 27.

    Magalhães, A. et al. Fut2-null mice display an altered glycosylation profile and impaired BabA-mediated Helicobacter pylori adhesion to gastric mucosa. Glycobiology 19, 1525–1536 (2009).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    McGovern, D. P. B. et al. Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn’s disease. Hum. Mol. Genet. 19, 3468–3476 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    He, M. et al. A genome wide association study of genetic loci that influence tumour biomarkers cancer antigen 19-9, carcinoembryonic antigen and α fetoprotein and their associations with cancer risk. Gut 63, 143–151 (2014).

    CAS  PubMed  Google Scholar 

  30. 30.

    Hazra, A. et al. Common variants of FUT2 are associated with plasma vitamin B12 levels. Nat. Genet. 40, 1160–1162 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Rausch, P. et al. Colonic mucosa-associated microbiota is influenced by an interaction of Crohn disease and FUT2 (Secretor) genotype. Proc. Natl Acad. Sci. USA 108, 19030–19035 (2011).

    CAS  PubMed  Google Scholar 

  32. 32.

    Tong, M. et al. Reprograming of gut microbiome energy metabolism by the FUT2 Crohn’s disease risk polymorphism. ISME J. 8, 2193–2206 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ríos-Covián, D. et al. Intestinal short chain fatty acids and their link with diet and human health. Front. Microbiol. 7, 185 (2016).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Tanaka, S., Yamamoto, K., Yamada, K., Furuya, K. & Uyeno, Y. Relationship of enhanced butyrate production by colonic butyrate-producing bacteria to immunomodulatory effects in normal mice fed an insoluble fraction of Brassica rapa L. Appl. Environ. Microbiol. 82, 2693–2699 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Canani, R. B. et al. Potential beneficial effects of butyrate in intestinal and extraintestinal diseases. World J. Gastroenterol. 17, 1519–1528 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Lopetuso, L. R., Scaldaferri, F., Petito, V. & Gasbarrini, A. Commensal Clostridia: leading players in the maintenance of gut homeostasis. Gut Pathog. 5, 23 (2013).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Xu, D. et al. PreQ0 Base, an unusual metabolite with anti-cancer activity from streptomyces qinglanensis 172205. Anticancer. Agents Med. Chem. 15, 285–290 (2015).

    CAS  PubMed  Google Scholar 

  38. 38.

    Goralski, K. B. et al. Chemerin, a novel adipokine that regulates adipogenesis and adipocyte metabolism. J. Biol. Chem. 282, 28175–28188 (2007).

    CAS  PubMed  Google Scholar 

  39. 39.

    Bae, J.-H., Song, D.-K. & Im, S.-S. Regulation of IGFBP-1 in metabolic diseases. J. Lifestyle Med. 3, 73–79 (2013).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Witte, I., Foerstermann, U., Devarajan, A., Reddy, S. T. & Horke, S. Protectors or traitors: the roles of PON2 and PON3 in atherosclerosis and cancer. J. Lipids 2012, 1–12 (2012).

    Google Scholar 

  41. 41.

    Kowalska, K., Socha, E. & Milnerowicz, H. Review: the role of paraoxonase in cardiovascular diseases. Ann. Clin. Lab. Sci. 45, 226–233 (2015).

    CAS  PubMed  Google Scholar 

  42. 42.

    Everard, A. et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl Acad. Sci. USA 110, 9066–9071 (2013).

    CAS  Google Scholar 

  43. 43.

    Schneeberger, M. et al. Akkermansia muciniphila inversely correlates with the onset of inflammation, altered adipose tissue metabolism and metabolic disorders during obesity in mice. Sci. Rep. 5, 16643 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Tamanai-Shacoori, Z. et al. Roseburia spp.: a marker of health? Future Microbiol. 12, 157–170 (2017).

    CAS  PubMed  Google Scholar 

  45. 45.

    Gottlieb, K., Wacher, V., Sliman, J. & Pimentel, M. Review article: inhibition of methanogenic archaea by statins as a targeted management strategy for constipation and related disorders. Aliment. Pharmacol. Ther. 43, 197–212 (2016).

    CAS  PubMed  Google Scholar 

  46. 46.

    Mbakwa, C. A. et al. Gut colonization with Methanobrevibacter smithii is associated with childhood weight development. Obesity 23, 2508–2516 (2015).

    PubMed  Google Scholar 

  47. 47.

    McCully, K. S. Homocysteine and vascular disease. Nat. Med. 2, 386–389 (1996).

    CAS  PubMed  Google Scholar 

  48. 48.

    Wierzbicki, A. S. Homocysteine and cardiovascular disease: a review of the evidence. Diabetes Vasc. Dis. Res. 4, 143–150 (2007).

    Google Scholar 

  49. 49.

    Chi, Y. S., Bong, C. K., Hye, K. H. & Hyun, S. L. Oxidized LDL activates PAI-1 transcription through autocrine activation of TGF-beta signaling in mesangial cells. Kidney Int. 67, 1743–1752 (2005).

    Google Scholar 

  50. 50.

    Hong, H. K., Song, C. Y., Kim, B. C. & Lee, H. S. ERK contributes to the effects of Smad signaling on oxidized LDL-induced PAI-1 expression in human mesangial cells. Transl. Res. 148, 171–179 (2006).

    CAS  PubMed  Google Scholar 

  51. 51.

    Précourt, L. P. et al. The three-gene paraoxonase family: physiologic roles, actions and regulation. Atherosclerosis 214, 20–36 (2011).

    PubMed  Google Scholar 

  52. 52.

    Rothem, L. et al. Paraoxonases are associated with intestinal inflammatory diseases and intracellularly localized to the endoplasmic reticulum. Free Radic. Biol. Med. 43, 730–739 (2007).

    CAS  PubMed  Google Scholar 

  53. 53.

    Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Ruddick, J. P. et al. Tryptophan metabolism in the central nervous system: medical implications. Expert Rev. Mol. Med. 8, 1–27 (2006).

    PubMed  Google Scholar 

  55. 55.

    Philonenko, E. S. et al. TMEM8—A non-globin gene entrapped in the globin web. Nucleic Acids Res. 37, 7394–7406 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2016).

    PubMed  Google Scholar 

  57. 57.

    Jin, D. et al. Vitamin D receptor is a novel transcriptional regulator for Axin1. J. Steroid Biochem. Mol. Biol. 165, 430–437 (2017).

    CAS  PubMed  Google Scholar 

  58. 58.

    Zhang, Y. et al. Axin1 prevents salmonella invasiveness and inflammatory response in intestinal epithelial cells. PLoS One 7, e34942 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Bischoff, S. C. et al. Intestinal permeability—A new target for disease prevention and therapy. BMC Gastroenterol. 14, 189 (2014).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Kozan, P. A. et al. Mutation of EpCAM leads to intestinal barrier and ion transport dysfunction. J. Mol. Med. 93, 535–545 (2015).

    CAS  PubMed  Google Scholar 

  61. 61.

    Yin, X. et al. Protein biomarkers of new-onset cardiovascular disease: prospective study from the systems approach to biomarker research in cardiovascular disease initiative. Arterioscler. Thromb. Vasc. Biol. 34, 939–945 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Rodriguez-Nunez, I. et al. Nod2 and Nod2-regulated microbiota protect BALB/c mice from diet-induced obesity and metabolic dysfunction. Sci. Rep. 7, 548 (2017).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Soran, H., Schofield, J. D. & Durrington, P. N. Antioxidant properties of HDL. Front. Pharmacol. 6, 222 (2015).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Meijers, W. C. et al. The failing heart stimulates tumor growth by circulating factors. Circulation 138, 678–691 (2018).

    CAS  PubMed  Google Scholar 

  65. 65.

    Zhang, C. et al. Studies on protective effects of human paraoxonases 1 and 3 on atherosclerosis in apolipoprotein E knockout mice. Gene Ther. 17, 626–633 (2010).

    CAS  PubMed  Google Scholar 

  66. 66.

    Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Scholtens, S. et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 44, 1172–1180 (2015).

    PubMed  Google Scholar 

  68. 68.

    Assarsson, E. et al. Homogenous 96-Plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One 9, e95192 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).

    CAS  Google Scholar 

  70. 70.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—A Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Google Scholar 

  72. 72.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    The Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat. Genet. 46, 818–825 (2014).

    Google Scholar 

  76. 76.

    Fehrmann, R. S. N. et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 7, e1002197 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429.e19 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Lawrence, M., Gentleman, R. & Carey, V. rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25, 1841–1842 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Magurran, A. E. Measuring Biological Diversity. (Blackwell Publishing, Oxford, 2004).

    Google Scholar 

  81. 81.

    Dunn, O. J. Multiple comparisons among means. J. Am. Stat. Assoc. 56, 52–64 (1961).

    Google Scholar 

  82. 82.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).

    Google Scholar 

  83. 83.

    Tibshirani, R. Regression shrinkage and selection via the lasso: a retrospective. J. R. Stat. Soc. Ser. B 73, 273–282 (2011).

    Google Scholar 

  84. 84.

    Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412 (2016).

    CAS  PubMed  Google Scholar 

  85. 85.

    Szklarczyk, D. et al. STRINGv10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    CAS  Google Scholar 

  86. 86.

    Scutari, M. Learning Bayesian networks with the bnlearn R package. J. Stat. Softw. 35, 1–22 (2010).

    Google Scholar 

  87. 87.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Kim, S. ppcor: an r package for a fast calculation to semi-partial correlation coefficients. Commun. Stat. Appl. Methods 22, 665–674 (2015).

    PubMed  PubMed Central  Google Scholar 

  89. 89.

    Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. mediation: R package for causal mediation analysis. J. Stat. Softw. 59, 1–38 (2014).

    Google Scholar 

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We dedicate this paper to the memory of Marten H. Hofker, who was a key person in developing the concept of this study. We thank participants and staff of the LifeLines-DEEP cohort for their collaboration. The study was approved by the UMCG review board, ref. M12.113965. We thank J. Dekens, M. Platteel, A. Maatman, and J. Arends for management and technical support and Jackie Senior and Kate Mc Intyre for English editing. We thank A. V. Vila and R. K. Weersma for helpful discussions. This project was funded by the Netherlands Heart Foundation (IN-CONTROL CVON grant 2012-03 to M.H.H., M.G.N., F.K., A.Z., and J.F. and CVON-DOSIS grant 2014-40 to R.A.D.B.); by Top Institute Food and Nutrition, Wageningen, The Netherlands (TiFN GH001 to C.W.); by the Netherlands Organization for Scientific Research (NWO) (NWO-VIDI 864.13.013 to J.F., NWO VIDI 917.13.350 to R.A.D.B., NWO-VIDI 016.178.056 to A.Z., NWO-VIDI 917.14.374 to L.F., NWO Spinoza Prize SPI 94-212 to M.G.N., NWO Spinoza Prize SPI 92-266 to C.W., and NWO Gravitation Netherlands Organ-on-Chip Initiative (024.003.001) to C.W.); by the European Research Council (ERC) (FP7/2007-2013/ERC Advanced Grant Agreement 2012-322698 to C.W., ERC Consolidator Grant 310372 to M.G.N., ERC Starting Grant 715772 to A.Z., and ERC Starting Grant 637640 to L.F.); by the Stiftelsen Kristian Gerhard Jebsen Foundation (Norway) to C.W.; and by the RuG Investment Agenda Grant Personalized Health to C.W. A.Z. also holds a Rosalind Franklin Fellowship from the University of Groningen. D.V.Z. was supported by St. Petersburg State University (Genome Russia Grant no. 1.52.1647.2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information





M.H.H., C.W., A.Z., and J.F. conceptualized the study. D.V.Z., T.L., A.K., M.J.B., A.Z., and J.F. designed the methodology. D.V.Z., T.L., M.J.B., and J.F. were responsible for the software. D.V.Z., T.L., A.K., B.A., M.J.B., A.C., U.V., P.D., A.Z., and J.F. carried out the formal analysis. D.V.Z., T.L., A.K., A.Z., and J.F. wrote the original draft of the manuscript. B.A., M.J.B., S.S., A.C., U.V., P.D., L.F., R.A.B., F.K., M.G.N., C.W., A.Z., and J.F. reviewed and edited the manuscript. D.V.Z., T.L., A.K., and J.F. were responsible for visualization. A.Z. and J.F. supervised the project. M.H.H., A.Z., and J.F. were responsible for project administration. L.F., R.A.D.B., F.K., M.G.N., M.H.H., C.W., A.Z., and J.F. were responsible for funding acquisition.

Corresponding authors

Correspondence to Alexandra Zhernakova or Jingyuan Fu.

Ethics declarations

Competing interests

Dr. de Boer has received research grants and/or fees from AstraZeneca, Abbott, Bristol-Myers Squibb, Novartis, Roche, Trevena, and ThermoFisher GmbH. Dr. de Boer is a minority shareholder of scPharmaceuticals, Inc. Dr. de Boer received personal fees from MandalMed Inc, Novartis, and Servier. All other authors declare no competing interests.

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Integrated supplementary information

Supplementary Figure 1 Protein tissue specificity and association with diseases: CVD III panel protein characteristics according to the OLINK panel description.

(a), Forty-four proteins were widely expressed in different tissue types (not shown), but some proteins were tissue specific. (b), In addition to association with CVD, proteins were found to be associated with many other diseases, including cancer, neurological, metabolic, inflammatory and infectious diseases. (c), Correlation of protein levels with Framingham risk score calculated for the LifeLines-DEEP cohort. Significant correlations at Bonferroni-corrected P < 0.05 are shown.

Supplementary Figure 2

Manhattan plot of 129 cis-pQTLs for 66 proteins.

Supplementary Figure 3 Circos plot of 85 trans-pQTLs for 36 proteins.

The chromosome locations of the associated trans-SNPs and proteins are highlighted. SNPs are marked as red bars, and the mapped proteins are labeled. Each curved arrow indicates the trans-pQTL effect from a SNP to a protein.

Supplementary Figure 4 Pleiotropic effect of the KLKB1 locus.

(a), Trans-pQTL effect detected at four independent SNPs at the KLKB1 locus with multiple trans-regulated proteins associated with a missense SNP (rs3733402) and neighboring SNPs. The assessed allele is given in brackets. The associations of the SNP and proteins are presented as a bar plot. The y axis refers to the association strength in terms of –log P value. Association direction is shown. A blue positive bar indicates that the allele assessed is associated with a higher level of the protein. A red negative bar indicates association to a lower level. (b), Inter-correlation structure of nine proteins trans-regulated by rs3733402. Colored circles represent Spearman correlation coefficients: positive correlation (blue) and negative correlation (red). Color intensity and circle size indicate correlation strength. (c), Association of the KLKB1 locus in cis and in trans: trans-pQTL SNPs located at the KLKB1 locus are depicted as red diamonds; the corresponding trans-regulated proteins are depicted as blue ellipses; cis-pQTL SNPs regulating these proteins are shown as green diamonds; complex diseases and traits associated with SNPs and genes are shown as gray rectangles. We also show expression QTLs (eQTLs) on the same plot: genes whose expression is regulated in cis by trans-pQTL SNPs are depicted as pink ellipses. Blue edges correspond to trans-pQTLs, green edges to cis-pQTLs, red edges to cis-eQTLs and gray edges to trait associations.

Supplementary Figure 5 Comparison of QTL effect between plasma cis-pQTL and blood cis-eQTL from the same individuals.

x axis, Z score of cis-eQTL; y axis: Z score of cis-pQTL effect. Each circle represents a SNP–gene/protein pair. Circle size and color represent the strength and direction of the pQTL effect, respectively. The gray area indicates non-significant eQTL effects.

Supplementary Figure 6

Protein associations with microbial diversity with Bonferroni-corrected P < 0.05.

Supplementary Figure 7

Overlap of proteins significantly affected by genetics and microbiome.

Supplementary Figure 8 Comparison of protein and microbiome associations before and after correcting for genetic effects.

(a), The association between proteins and taxonomies. (b), The association between proteins and pathways. For all reported protein–microbiome associations, we compared the association strength, in terms of Spearman correlation coefficients and the corresponding P values, before and after correcting for 224 pQTLs and 26 mbQTLs.

Supplementary Figure 9

Comparison of the explained variance of circulating protein levels between a combined model of cis-pQTL, trans-pQTL and microbiome and an additive model that sums the explained variation by genetic factors and microbial factors separately.

Supplementary Figure 10 FUT2 and TMIGD1 gene expression by tissue type.

RPKM (reads per kilobase per million mapped reads) values are given according to ‘HPA RNA-seq normal tissues’ data from Fagerberg et al. (Mol. Cell. Proteomics 20141) displayed at NCBI Gene.

Supplementary Figure 11 PON3-PAI cluster microbiome associations.

(a), Pairwise Spearman correlation between PON3-associated taxa and pathways. (b), The number of pathways (left) and species (right) associated with PON3, PAI and both proteins is given before and after (in brackets) correction for BMI.

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Zhernakova, D.V., Le, T.H., Kurilshikov, A. et al. Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome. Nat Genet 50, 1524–1532 (2018).

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