WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/ Accessed 13 Sept 2021.
Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430–6.
Gupta S, Hayek SS, Wang W, Chan L, Mathews KS, Melamed ML, et al. Factors associated with death in critically ill patients with coronavirus disease 2019 in the US. JAMA Intern Med. 2020;180:1436–47.
Juonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, Sabin MA, et al. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365:1876–85.
Ripatti P, Rämö JT, Mars NJ, Fu Y, Lin J, Söderlund S, et al. Polygenic hyperlipidemias and coronary artery disease risk. Circ Genom Precis Med. 2020;13:e002725.
Mooradian AD. Dyslipidemia in type 2 diabetes mellitus. Nat Clin Pract Endocrinol Metab. 2009;5:150–9.
Scalsky RJ, Chen Y-J, Desai K, O’Connell JR, Perry JA, Hong CC. Baseline cardiometabolic profiles and SARS-CoV-2 infection in the UK Biobank. PLoS ONE. 2021;16:e0248602.
Zinellu A, Paliogiannis P, Fois AG, Solidoro P, Carru C, Mangoni AA. Cholesterol and triglyceride concentrations, COVID-19 severity, and mortality: a systematic review and meta-analysis with meta-regression. Front Public Health. 2021;9:705916.
Aparisi Á, Iglesias-Echeverría C, Ybarra-Falcón C, Cusácovich I, Uribarri A, García-Gómez M, et al. Low-density lipoprotein cholesterol levels are associated with poor clinical outcomes in COVID-19. Nutr Metab Cardiovasc Dis. 2021;31:2619–27.
Tanaka S, De Tymowski C, Assadi M, Zappella N, Jean-Baptiste S, Robert T, et al. Lipoprotein concentrations over time in the intensive care unit COVID-19 patients: results from the ApoCOVID study. PLoS ONE. 2020;15:e0239573.
Dai W, Lund H, Chen Y, Zhang J, Osinski K, Jones SZ, et al. Hypertriglyceridemia during hospitalization independently associates with mortality in patients with COVID-19. J Clin Lipidol. 2021. https://doi.org/10.1016/j.jacl.2021.08.002.
Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:R89-98.
Richardson TG, Sanderson E, Palmer TM, Ala-Korpela M, Ference BA, Davey Smith G, et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: a multivariable Mendelian randomisation analysis. PLoS Med. 2020;17:e1003062.
Leong A, Cole JB, Brenner LN, Meigs JB, Florez JC, Mercader JM. Cardiometabolic risk factors for COVID-19 susceptibility and severity: a Mendelian randomization analysis. PLoS Med. 2021;18:e1003553.
Aung N, Khanji MY, Munroe PB, Petersen SE. causal inference for genetic obesity, cardiometabolic profile and COVID-19 susceptibility: a mendelian randomization study. Front Genet. 2020;11:586308.
Ponsford MJ, Gkatzionis A, Walker VM, Grant AJ, Wootton RE, Moore LSP, et al. Cardiometabolic traits, sepsis, and severe COVID-19: a mendelian randomization investigation. Circulation. 2020;142:1791–3.
Zhang K, Dong SS, Guo Y, Tang SH, Wu H, Yao S, et al. Causal associations between blood lipids and COVID-19 risk: a two-sample Mendelian randomization study. Arterioscler Thromb Vasc Biol. 2021. https://doi.org/10.1161/ATVBAHA.121.316324.
R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2020.
UK Biobank: Data-Field 30890. https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=30870. Accessed 6 Sept 2021.
MRC IEU Open GWAS Project. https://gwas.mrcieu.ac.uk/. Accessed 1 Sept 2021.
COVID-19 Host Genetics Initiative. Mapping the human genetic architecture of COVID-19. Nature; 2021. https://doi.org/10.1038/s41586-021-03767-x.
The COVID-19 Host Genetics Initiative. https://www.covid19hg.org/results/r5/ Accessed 3 Feb 2021.
Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27:3641–9.
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408.
Xiuyun W, Qian WX, Weidong L, Lizhen L. Education and stroke: evidence from epidemiology and Mendelian randomization study. Sci Rep. 2020;10:21208.
Gill D, Karhunen V, Malik R, Dichgans M, Sofat N. Cardiometabolic traits mediating the effect of education on osteoarthritis risk: a Mendelian randomization study. Osteoarthritis Cartilage. 2021;29:365–71.
Liu HM, Hu Q, Zhang Q, Su GY, Xiao HM, Li BY, et al. Causal effects of genetically predicted cardiovascular risk factors on chronic kidney disease: a two-sample Mendelian randomization study. Front Genet. 2019;10:415.
Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016;35:1880–906.
Burgess S, Small D, Thompson S. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26:2333–55.
Zhao JV, Schooling CM. Effect of linoleic acid on ischemic heart disease and its risk factors: a Mendelian randomization study. BMC Med. 2019;17:61.
Burgess S, O’Donnell CJ, Gill D. Expressing results from a mendelian randomization analysis: separating results from inferences. JAMA Cardiol. 2021;6:7–8.
Walker VM, Davies NM, Hemani G, Zheng J, Haycock PC, Gaunt TR, et al. Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes. Wellcome Open Res. 2019;4:113.
Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. 2020;4:186.
Greco MFD, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34:2926–40.
PhenoScanner V2. A database of human genotype-phenotype associations. http://www.phenoscanner.medschl.cam.ac.uk/. Accessed 6 Sept 2021.
Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB, et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics. 2016;32:3207–9.
Freuer D, Linseisen J, Meisinger C. Impact of body composition on COVID-19 susceptibility and severity: a two-sample multivariable Mendelian randomization study. Metabolism. 2021;118:154732.
Yuan S, Michaëlsson K, Wan Z, Larsson SC. Associations of smoking and alcohol and coffee intake with fracture and bone mineral density: a Mendelian randomization study. Calcif Tissue Int. 2019;105:582–8.
Xu H, Jin C, Guan Q. Causal effects of overall and abdominal obesity on insulin resistance and the risk of type 2 diabetes mellitus: a two-sample Mendelian randomization study. Front Genet. 2020;2(11):603.
Adams CD, Boutwell BB. Can increasing years of schooling reduce type 2 diabetes (T2D)?: Evidence from a Mendelian randomization of T2D and 10 of its risk factors. Sci Rep. 2020;10:12908.
Lu Y, Guo Y, Lin H, Wang Z, Zheng L. Genetically determined tobacco and alcohol use and risk of atrial fibrillation. BMC Med Genomics. 2021;14:73.
mRnd: Power calculations for Mendelian Randomization. https://shiny.cnsgenomics.com/mRnd/ Accessed 18 Oct 2021.
Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42:1497–501.
Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83.
Severe Covid-19 GWAS Group, Ellinghaus D, Degenhardt F, Bujanda L, Buti M, Albillos A, et al. Genomewide association study of severe Covid-19 with respiratory failure. N Engl J Med. 2020;383:1522–34.
Sun Y, Zhou J, Ye K. Prioritizing causal risk factors for severe COVID-19: an exhaustive Mendelian randomization study. 25 Jan 2021 (Version 1). https://doi.org/10.21203/rs.3.rs-149087/v1
Sun Y, Zhou J, Ye K. White blood cells and severe COVID-19: a Mendelian randomization study. J Pers Med. 2021;11:195.
Alipour A, van Oostrom AJ, Izraeljan A, Verseyden C, Collins JM, Frayn KN, et al. Leukocyte activation by triglyceride-rich lipoproteins. Arterioscler Thromb Vasc Biol. 2008;28:792–7.
Saja MF, Baudino L, Jackson WD, Cook HT, Malik TH, Fossati-Jimack L, et al. Triglyceride-rich lipoproteins modulate the distribution and extravasation of Ly6C/Gr1(low) monocytes. Cell Rep. 2015;12:1802–15.
Ting HJ, Stice JP, Schaff UY, Hui DY, Rutledge JC, Knowlton AA, et al. Triglyceride-rich lipoproteins prime aortic endothelium for an enhanced inflammatory response to tumor necrosis factor-alpha. Circ Res. 2007;100:381–90.
Richardson TG, Fang S, Mitchell RE, Holmes MV, Davey SG. Evaluating the effects of cardiometabolic exposures on circulating proteins which may contribute to severe SARS-CoV-2. EBioMedicine. 2021. https://doi.org/10.1016/j.ebiom.2021.103228.
Li S, Hua X. Modifiable lifestyle factors and severe COVID-19 risk: a Mendelian randomisation study. BMC Med Genomics. 2021;14:38.
Au Yeung SL, Zhao JV, Schooling CM. Evaluation of glycemic traits in susceptibility to COVID-19 risk: a Mendelian randomization study. BMC Med. 2021;19:72.
Gong J, Chen Y, Jie Y, Tan M, Jiang Z, Yuan L, et al. U-shaped relationship of low-density lipoprotein cholesterol with risk of severe COVID-19 from a multicenter pooled analysis. Front Cardiovasc Med. 2021;8:604736.
Yuan S, Tang B, Zheng J, Larsson SC. Circulating lipoprotein lipids, apolipoproteins and ischemic stroke. Ann Neurol. 2020;88:1229–36.