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Proteome-wide Mendelian randomization implicates nephronectin as an actionable mediator of the effect of obesity on COVID-19 severity

Nature Metabolism volume 5, pages 248–264 (2023)Cite this article

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Abstract

Obesity is a major risk factor for Coronavirus disease (COVID-19) severity; however, the mechanisms underlying this relationship are not fully understood. As obesity influences the plasma proteome, we sought to identify circulating proteins mediating the effects of obesity on COVID-19 severity in humans. Here, we screened 4,907 plasma proteins to identify proteins influenced by body mass index using Mendelian randomization. This yielded 1,216 proteins, whose effect on COVID-19 severity was assessed, again using Mendelian randomization. We found that an s.d. increase in nephronectin (NPNT) was associated with increased odds of critically ill COVID-19 (OR = 1.71, P = 1.63 × 10-10). The effect was driven by an NPNT splice isoform. Mediation analyses supported NPNT as a mediator. In single-cell RNA-sequencing, NPNT was expressed in alveolar cells and fibroblasts of the lung in individuals who died of COVID-19. Finally, decreasing body fat mass and increasing fat-free mass were found to lower NPNT levels. These findings provide actionable insights into how obesity influences COVID-19 severity.

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Fig. 1: Study overview and summary.
Fig. 2: MR analyses for the effect of BMI on plasma protein levels.
Fig. 3: MR analyses of BMI-driven proteins on COVID-19 outcomes.
Fig. 4: Colocalization analyses of cis-pQTL for NPNT or HSD17B14 with COVID outcomes in the 1-Mb region around rs34712979.
Fig. 5: Colocalization analyses of cis-pQTL with sQTL and eQTL for NPNT.
Fig. 6: NPNT expression levels in lung cell types from COVID-19 lung autopsy samples at single-cell resolution.
Fig. 7: MR mediation analysis illustrated by the directed acyclic graph.
Fig. 8: Multivariable MR analysis for evaluating independent effects of body fat and fat-free mass on plasma NPNT levels.

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Data availability

GWAS summary statistics for each trait are available as follows:

BMI (https://portals.broadinstitute.org/collaboration/giant/).

Plasma proteome from the deCODE study (https://www.decode.com/summarydata/).

COVID-19 outcomes (https://www.covid19hg.org/results/r7/).

GTEx Portal v.8 (https://gtexportal.org/home/datasets/).

Plasma proteome from BQC19 (https://www.mcgill.ca/genepi/mcg-covid-19-biobank). Access to the data of BQC19 can be obtained upon approval of requests via bqc19.ca.

Cis-pQTLs of each study are available in the corresponding publications’ supplementary materials26,27,28.

Body fat percentage, body fat mass and fat-free mass GWASs are available at IEU OpenGWAS project with accession IDs ukb-b-8909, ukb-b-19393 and ukb-b-13354, respectively (https://gwas.mrcieu.ac.uk/).

scRNA-seq data of COVID-19 lung autopsy samples are available at the Single-Cell Portal under accession ID SCP1052 (https://singlecell.broadinstitute.org/single_cell/). CADD-scores v.1.6 can be accessed at https://cadd.gs.washington.edu/score.

Genotype data from the 1000 Genomes Project is available at https://www.internationalgenome.org/data.

Source data are provided with this paper.

Code availability

We used R v.4.1.2 (https://www.r-project.org/), TwoSampleMR v.0.5.6 (https://mrcieu.github.io/TwoSampleMR/), snappy v.1.0 (https://gitlab.com/richards-lab/vince.forgetta/snappy), coloc v.5.1.0 (https://chr1swallace.github.io/coloc/), FINEMAP R package v.1.4, Seurat v.4.0.6 (https://satijalab.org/seurat/), PLINK v.1.9 (http://pngu.mgh.harvard.edu/purcell/plink/) and GCTA fastGWA v.1.93.3 (https://yanglab.westlake.edu.cn/software/gcta/). Custom codes are available on GitHub (https://github.com/satoshi-yoshiji/TwostepMR_obesity_COVID/).

References

  1. Johns Hopkins University. COVID-19 Global Map https://coronavirus.jhu.edu/map.html (2022).

  2. COVID-19 Host Genetics Initiative. Mapping the human genetic architecture of COVID-19. Nature 600, 472–477 (2021).

  3. Stefan, N., Birkenfeld, A. L., Schulze, M. B. & Ludwig, D. S. Obesity and impaired metabolic health in patients with COVID-19. Nat. Rev. Endocrinol. 16, 341–342 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Foulkes, A. S. et al. Understanding the link between obesity and severe COVID-19 outcomes: causal mediation by systemic inflammatory response. J. Clin. Endocrinol. Metab. 107, e698–e707 (2021).

    Article  PubMed Central  Google Scholar 

  5. Zickler, M. et al. Replication of SARS-CoV-2 in adipose tissue determines organ and systemic lipid metabolism in hamsters and humans. Cell. Metab. 34, 1–2 (2022).

    Article  CAS  PubMed  Google Scholar 

  6. Goudswaard, L. J. et al. Effects of adiposity on the human plasma proteome: observational and Mendelian randomisation estimates. Int. J. Obes. 45, 2221–2229 (2021).

    Article  CAS  Google Scholar 

  7. Zaghlool, S. B. et al. Revealing the role of the human blood plasma proteome in obesity using genetic drivers. Nat. Commun. 12, 1279 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Filbin, M. R. et al. Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell–cell interactions. Cell. Rep. Med. 2, 100287 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Skrivankova, V. W. et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization. JAMA 326, 1614 (2021).

    Article  PubMed  Google Scholar 

  10. Skrivankova, V. W. et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ 375, n2233 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ponsford, M. J. et al. Cardiometabolic traits, sepsis, and severe COVID-19: a Mendelian randomization investigation. Circulation 142, 1791–1793 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Luo, S., Liang, Y., Wong, T. H. T., Schooling, C. M. & Au Yeung, S. L. Identifying factors contributing to increased susceptibility to COVID-19 risk: a systematic review of Mendelian randomization studies. Int. J. Epidemiol. 51, 1088–1105 (2022).

    Article  PubMed  Google Scholar 

  13. Zhou, S. et al. A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat. Med. 27, 659–667 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Gaziano, L. et al. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat. Med. 27, 668–676 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bovijn, J., Lindgren, C. M. & Holmes, M. V. Genetic variants mimicking therapeutic inhibition of IL-6 receptor signaling and risk of COVID-19. Lancet Rheumatol. 2, e658–e659 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Klaric, L. et al. Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19. Preprint at medRxiv https://doi.org/10.1101/2021.04.01.21254789 (2021).

  17. Niemi, M. E. K., Daly, M. J. & Ganna, A. The human genetic epidemiology of COVID-19. Nat. Rev. Genet. 23, 533–546 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Pietzner, M. et al. ELF5 is a potential respiratory epithelial cell-specific risk gene for severe COVID-19. Nat. Commun. 13, 4484 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. RECOVERY Collaborative Group. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. Lancet 397, 1637–1645 (2021).

  20. Holmes, M. V., Richardson, T. G., Ference, B. A., Davies, N. M. & Davey Smith, G. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development. Nat. Rev. Cardiol. 18, 435–453 (2021).

    Article  PubMed  Google Scholar 

  21. Manousaki, D., Mokry, L. E., Ross, S., Goltzman, D. & Richards, J. B. Mendelian Randomization Studies Do Not Support a Role for Vitamin D in Coronary Artery Disease. Circ. Cardiovasc. Genet. 9, 349–356 (2016).

    Article  CAS  PubMed  Google Scholar 

  22. Jiang, X. et al. Circulating vitamin D concentrations and risk of breast and prostate cancer: a Mendelian randomization study. Int. J. Epidemiol. 48, 1416–1424 (2019).

    Article  PubMed  Google Scholar 

  23. Meng, X. et al. Phenome-wide Mendelian-randomization study of genetically determined vitamin D on multiple health outcomes using the UK Biobank study. Int. J. Epidemiol. 48, 1425–1434 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Manson, J. E. et al. Vitamin D Supplements and Prevention of Cancer and Cardiovascular Disease. N. Engl. J. Med. 380, 33–44 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ferkingstad, E. et al. DECODE: Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712–1721 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Pietzner, M. et al. Synergistic insights into human health from aptamer- and antibody-based proteomic profiling. Nat. Commun. 12, 6822 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N. & Davey Smith, G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).

    Article  PubMed  Google Scholar 

  30. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Swerdlow, D. I. et al. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. Int. J. Epidemiol. 45, 1600–1616 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zhang, J. et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nat. Genet. 54, 593–602 (2022).

  33. Staley, J. R. et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32, 3207–3209 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tsukui, T. et al. Collagen-producing lung cell atlas identifies multiple subsets with distinct localization and relevance to fibrosis. Nat. Commun. 11, 1920 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Xie, T. et al. Single-cell deconvolution of fibroblast heterogeneity in mouse pulmonary fibrosis. Cell Rep. 22, 3625–3640 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Obeidat, M. E. et al. Molecular mechanisms underlying variations in lung function: a systems genetics analysis. Lancet Respir. Med. 3, 782–795 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bhatt, S. P. et al. Discriminative accuracy of FEV1:FVC thresholds for COPD-related hospitalization and mortality. JAMA 321, 2438–2447 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Davies, N. M., Holmes, M. V. & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 362, k601 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Holmes, M. V., Ala-Korpela, M. & Smith, G. D. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat. Rev. Cardiol. 14, 577–590 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Au Yeung, S. L., Li, A. M., He, B., Kwok, K. O. & Schooling, C. M. Association of smoking, lung function and COPD in COVID-19 risk: a two-step Mendelian randomization study. Addiction 117, 2027–2036 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45, 1866–1886 (2016).

    PubMed  Google Scholar 

  42. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Saferali, A. et al. Characterization of a COPD-associated NPNT functional splicing genetic variant in human lung tissue via long-read sequencing. Preprint at medRxiv https://doi.org/10.1101/2020.10.20.20203927 (2020).

  44. Shrine, N. et al. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat. Genet. 51, 481–493 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sakornsakolpat, P. et al. Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations. Nat. Genet. 51, 494–505 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

  47. Delorey, T. M. et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 595, 107–113 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Woolf, B., Zagkos, L. & Gill, D. TwoStepCisMR: a novel method and R package for attenuating bias in cis-Mendelian randomization analyses. Genes 13, 1541 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Burgess, S., Daniel, R. M., Butterworth, A. S., Thompson, S. G. & Consortium, E. P.-I. Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. Int. J. Epidemiol. 44, 484–495 (2015).

    Article  PubMed  Google Scholar 

  50. Relton, C. L. & Davey Smith, G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int. J. Epidemiol. 41, 161–176 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Sanderson, E., Spiller, W. & Bowden, J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Stat. Med. 40, 5434–5452 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Brandenberger, R. et al. Identification and characterization of a novel extracellular matrix protein nephronectin that is associated with integrin α8β1 in the embryonic kidney. J. Cell Biol. 154, 447–458 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Morimura, N. et al. Molecular cloning of POEM: a novel adhesion molecule that interacts with α8β1 integrin. J. Biol. Chem. 276, 42172–42181 (2001).

    Article  CAS  PubMed  Google Scholar 

  54. Inagaki, F. F. et al. Nephronectin is upregulated in acute and chronic hepatitis and aggravates liver injury by recruiting CD4-positive cells. Biochem. Biophys. Res. Commun. 430, 751–756 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Kon, S., Honda, M., Ishikawa, K., Maeda, M. & Segawa, T. Antibodies against nephronectin ameliorate anti-type II collagen-induced arthritis in mice. FEBS Open Bio. 10, 107–117 (2020).

    Article  CAS  PubMed  Google Scholar 

  56. O’Rourke, R. W. & Lumeng, C. N. Pathways to severe COVID-19 for people with obesity. Obesity 29, 645–653 (2021).

    Article  PubMed  Google Scholar 

  57. Strunz, M. et al. Alveolar regeneration through a Krt8+ transitional stem cell state that persists in human lung fibrosis. Nat. Commun. 11, 3559 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Xie, T. et al. Mesenchymal growth hormone receptor deficiency leads to failure of alveolar progenitor cell function and severe pulmonary fibrosis. Sci. Adv. 7, eabg6005 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Stiegler, P. & Cunliffe, A. The role of diet and exercise for the maintenance of fat-free mass and resting metabolic rate during weight loss. Sports Med. 36, 239–262 (2006).

    Article  PubMed  Google Scholar 

  60. Jastreboff, A. M. et al. Tirzepatide once weekly for the treatment of obesity. N. Engl. J. Med. 387, 205–216 (2022).

    Article  CAS  PubMed  Google Scholar 

  61. Heise, T. et al. Effects of subcutaneous tirzepatide versus placebo or semaglutide on pancreatic islet function and insulin sensitivity in adults with type 2 diabetes: a multicentre, randomised, double-blind, parallel-arm, phase 1 clinical trial. Lancet Diabetes Endocrinol. 10, 418–429 (2022).

    Article  CAS  PubMed  Google Scholar 

  62. Garvey, W. T. et al. Two-year effects of semaglutide in adults with overweight or obesity: the STEP 5 trial. Nat. Med. 28, 2083–2091 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Blundell, J. et al. Effects of once-weekly semaglutide on appetite, energy intake, control of eating, food preference and body weight in subjects with obesity. Diabetes Obes. Metab. 19, 1242–1251 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Pi-Sunyer, X. et al. A randomized, controlled trial of 3.0 mg of liraglutide in weight management. N. Engl. J. Med. 373, 11–22 (2015).

    Article  PubMed  Google Scholar 

  65. Grannell, A. et al. Liraglutide does not adversely impact fat-free mass loss. Obesity 29, 529–534 (2021).

    Article  CAS  PubMed  Google Scholar 

  66. Richardson, T. G., Fang, S., Mitchell, R. E., Holmes, M. V. & Davey Smith, G. Evaluating the effects of cardiometabolic exposures on circulating proteins which may contribute to severe SARS-CoV-2. eBioMedicine 64, 103228 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Pihlajamaki, J. et al. Expression of the splicing factor gene SFRS10 is reduced in human obesity and contributes to enhanced lipogenesis. Cell Metab. 14, 208–218 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Zhao, X. et al. FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis. Cell Res. 24, 1403–1419 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Roundtree, I. A., Evans, M. E., Pan, T. & He, C. Dynamic RNA modifications in gene expression regulation. Cell 169, 1187–1200 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Kim, M., Lee, C. & Park, J. Extracellular matrix remodeling facilitates obesity-associated cancer progression. Trends Cell Biol. 32, 825–834 (2022).

    Article  CAS  PubMed  Google Scholar 

  72. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  75. Deeks, J. J. et al. in Cochrane Handbook for Systematic Reviews of Interventions 2nd edn (eds Higgins, J. P. T. et al.) Ch. 10 (The Cochrane Collaboration, 2019).

  76. Hemani, G., Bowden, J. & Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. 27, R195–R208 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Burgess, S., Bowden, J., Fall, T., Ingelsson, E. & Thompson, S. G. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology 28, 30–42 (2017).

    Article  PubMed  Google Scholar 

  78. Pierce, B. L., Ahsan, H. & Vanderweele, T. J. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int. J. Epidemiol. 40, 740–752 (2011).

    Article  PubMed  Google Scholar 

  79. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Huffman, J. E. et al. Multi-ancestry fine mapping implicates OAS1 splicing in risk of severe COVID-19. Nat. Genet. 54, 125–127 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Carter, A. R. et al. Understanding the consequences of education inequality on cardiovascular disease: Mendelian randomisation study. BMJ 365, l1855 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Grant, A. J. & Burgess, S. Pleiotropy robust methods for multivariable Mendelian randomization. Stat. Med. 40, 5813–5830 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Vabistsevits, M. et al. Deciphering how early life adiposity influences breast cancer risk using Mendelian randomization. Commun. Biol. 5, 337 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Yoshiji, S. et al. Causal associations between body fat accumulation and COVID-19 severity: a Mendelian randomization study. Front. Endocrinol. 13, 899625 (2022).

    Article  Google Scholar 

  88. Cichonska, A. et al. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics 32, 1981–1989 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the COVID-19 Host Genetics Initiative for providing the latest summary statistics for COVID-19 outcomes. We acknowledge S. Wang, T. Khosroheidari, L. Cuddeback, W. Schwarzmann and D. Denmark at SomaLogic for constructive discussions. We acknowledge Biorender (biorender.com) for providing materials used to create the illustrative diagram. The Richards research group is supported by the Canadian Institutes of Health Research (grants 365825, 409511, 100558 and 169303), the McGill Interdisciplinary Initiative in Infection and Immunity (MI4), the Lady Davis Institute of the Jewish General Hospital, the Jewish General Hospital Foundation, the Canadian Foundation for Innovation, the National Institutes of Health Foundation, Genome Quebec, the Public Health Agency of Canada, McGill University, Cancer Research UK (grant no. C18281/A29019) and the Fonds de Recherche Quebec Santé (FRQS). J.B.R. is supported by an FRQS Mérite Clinical Research Scholarship. The support from Calcul Quebec and Compute Canada is acknowledged. TwinsUK is funded by the Welcome Trust, the Medical Research Council, the European Union, the National Institute for Health Research-funded BioResource and the Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. S.Y. is supported by the Japan Society for the Promotion of Science. T.L. is supported by a Vanier Canada Graduate Scholarship, an FRQS doctoral training fellowship and a McGill University Faculty of Medicine Studentship. G.B.-L. is supported by scholarships from the FRQS, the Canadian Institutes of Health Research and Quebec’s Ministry of Health and Social Services. Y.C. is supported by an FRQS doctoral training fellowship and the Lady Davis Institute/TD Bank Studentship Award. M.H. is supported by grants from the SciLifeLab/Knut and Alice Wallenberg national COVID-19 research program (KAW 2020.0182 and KAW 2020.0241), the Swedish Heart-Lung Foundation (20210089, 20190639 and 20190637) and the Swedish Society of Medicine (SLS-938101). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada

    Satoshi Yoshiji, Guillaume Butler-Laporte, Tianyuan Lu, Julian Daniel Sunday Willett, Chen-Yang Su, Tomoko Nakanishi, David R. Morrison, Yiheng Chen, Kevin Liang, Michael Hultström, Yann Ilboudo, Zaman Afrasiabi, Shanshan Lan, Naomi Duggan, Chantal DeLuca, Mitra Vaezi, Chris Tselios, Xiaoqing Xue, Meriem Bouab, Fangyi Shi, Laetitia Laurent, Marc Afilalo, Jonathan Afilalo, Sirui Zhou, Vincenzo Forgetta, Yossi Farjoun & J. Brent Richards

  2. Department of Human Genetics, McGill University, Montréal, Quebec, Canada

    Satoshi Yoshiji, Tomoko Nakanishi, Yiheng Chen, Vincent Mooser, Sirui Zhou & J. Brent Richards

  3. Kyoto-McGill International Collaborative Program in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Satoshi Yoshiji & Tomoko Nakanishi

  4. Japan Society for the Promotion of Science, Tokyo, Japan

    Satoshi Yoshiji & Tomoko Nakanishi

  5. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada

    Guillaume Butler-Laporte, Michael Hultström, Jonathan Afilalo & J. Brent Richards

  6. Quantitative Life Sciences Program, McGill University, Montréal, Quebec, Canada

    Tianyuan Lu, Julian Daniel Sunday Willett & Kevin Liang

  7. 5 Prime Sciences, Montréal, Quebec, Canada

    Tianyuan Lu, Vincenzo Forgetta & J. Brent Richards

  8. Department of Computer Science, McGill University, Montréal, Quebec, Canada

    Chen-Yang Su

  9. Anaesthesiology and Intensive Care Medicine, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden

    Michael Hultström

  10. Integrative Physiology, Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden

    Michael Hultström

  11. McGill Genome Centre, McGill University, Montréal, Quebec, Canada

    Hans Markus Münter, Vincent Mooser & Sirui Zhou

  12. Department of Emergency Medicine, Jewish General Hospital, McGill University, Montréal, Quebec, Canada

    Marc Afilalo

  13. Division of Cardiology, Jewish General Hospital, McGill University, Montréal, Quebec, Canada

    Jonathan Afilalo

  14. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    Nicholas J. Timpson

  15. Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden

    Hugo Zeberg

  16. Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

    Hugo Zeberg

  17. Department of Twin Research, King’s College London, London, UK

    J. Brent Richards

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Contributions

Conception and design was the responsibility of S.Y. and J.B.R. Methodology was overseen by S.Y., T.L. and J.B.R. Data analysis was performed by S.Y., T.L., J.D.S.W., C.-Y.S. and J.B.R. Visualization was conducted by S.Y. and T.L. Writing of the original draft was carried out by S.Y. Review and editing was carried out by S.Y., G.B.-L., T.L., J.D.S.W., C.-Y.S., T.N., D.R.M., Y.C., K.L., M.H., Y.I., Z.A., S.L., N.D., C.D., M.V., C.T., X.X., M.B., F.S., L.L., H.M.M., M.A., J.A., V.M., N.J.T., H.Z., S.Z., V.F., Y.F. and J.B.R.

Corresponding author

Correspondence to J. Brent Richards.

Ethics declarations

Competing interests

J.B.R. has served as an advisor to GlaxoSmithKline and Deerfield Capital. The institution of J.B.R. has received investigator-initiated grant funding from Eli Lilly, GlaxoSmithKline and Biogen for projects unrelated to this research. J.B.R. is the CEO of 5 Prime Sciences (www.5primesciences.com), which provides research services for biotech, pharma and venture capital companies for projects unrelated to this research. T.L. and V.F. are employees of 5 Prime Sciences. T.N. has received speaking fees from Boehringer Ingelheim and AstraZeneca regarding the projects unrelated to this research. The other authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Shiu Lun Ryan Au Yeung and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

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Yoshiji, S., Butler-Laporte, G., Lu, T. et al. Proteome-wide Mendelian randomization implicates nephronectin as an actionable mediator of the effect of obesity on COVID-19 severity. Nat Metab 5, 248–264 (2023). https://doi.org/10.1038/s42255-023-00742-w

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