Examining socioeconomic differences in sepsis risk and mediation by modifiable factors: a Mendelian randomization study | BMC Infectious Diseases

Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, Ikuta KS, Kissoon N, Finfer S, et al. Global, regional, and National sepsis incidence and mortality, 1990–2017: analysis for the global burden of disease study. Lancet. 2020;395(10219):200–11.
Google Scholar
World Health Assembly. Improving the prevention, diagnosis and clinical management of sepsis. In: WHA70.7. World Health Organization. 2017. Accessed 17 Jan 2025.
Bladon S, Ashiru-Oredope D, Cunningham N, Pate A, Martin GP, Zhong X, Gilham EL, Brown CS, Mirfenderesky M, Palin V, et al. Rapid systematic review on risks and outcomes of sepsis: the influence of risk factors associated with health inequalities. Int J Equity Health. 2024;23(1):34.
Google Scholar
Pepper GV, Nettle D. The behavioural constellation of deprivation: causes and consequences. Behav Brain Sci. 2017;40:e314.
Google Scholar
Cutler DM, Lleras-Muney A, Vogl T. Socioeconomic status and health: dimensions and mechanisms. In: Glied S, Smith PC, editors. The Oxford handbook of health economics. New York: Oxford University Press; 2012. pp. 124–63.
Koch K, Sogaard M, Norgaard M, Thomsen RW, Schonheyder HC. Socioeconomic inequalities in risk of hospitalization for Community-Acquired bacteremia: A Danish Population-Based Case-Control study. Am J Epidemiol. 2014;179(9):1096–106.
Google Scholar
Storm L, Schnegelsberg A, Mackenhauer J, Andersen LW, Jessen MK, Kirkegaard H. Socioeconomic status and risk of intensive care unit admission with sepsis. Acta Anaesthesiol Scand. 2018;62(7):983–92.
Google Scholar
Goodwin AJ, Nadig NR, McElligott JT, Simpson KN, Ford DW. Where you live matters the impact of place of residence on severe Sepsis incidence and mortality. Chest. 2016;150(4):829–36.
Google Scholar
Colon Hidalgo D, Tapaskar N, Rao S, Masic D, Su A, Portillo J. Rech M lower socioeconomic factors are associated with higher mortality in patients with septic shock. Heart Lung. 2021;50(4):477–80.
Google Scholar
Stensrud VH, Gustad LT, Damås JK, Solligård E, Krokstad S, Nilsen TIL. Direct and indirect effects of socioeconomic status on sepsis risk and mortality: a mediation analysis of the HUNT study. J Epidemiol Community Health. 2023;77(3):168–74.
Google Scholar
Lan Y, Chen L, Huang C, Wang X, Pu P. Associations of educational attainment with Sepsis mediated by metabolism traits and smoking: a Mendelian randomization study. Front Public Health. 2024;12:1330606.
Google Scholar
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.
Google Scholar
Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, Palmer T, Schooling CM, Wallace C, Zhao Q, et al. Mendelian randomization. Nat Reviews Methods Primers. 2022;2(1):6.
Google Scholar
Brumpton B, Sanderson E, Heilbron K, Hartwig FP, Harrison S, Vie G, Cho Y, Howe LD, Hughes A, Boomsma DI, et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun. 2020;11(1):3519.
Google Scholar
Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement. JAMA. 2021;326(16):1614–21.
Google Scholar
Okbay A, Wu Y, Wang N, Jayashankar H, Bennett M, Nehzati SM, Sidorenko J, Kweon H, Goldman G, Gjorgjieva T, et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat Genet. 2022;54(4):437–49.
Google Scholar
Howe LJ, Nivard MG, Morris TT, Hansen AF, Rasheed H, Cho Y, Chittoor G, Ahlskog R, Lind PA, Palviainen T, et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet. 2022;54(5):581–92.
Google Scholar
Saunders GRB, Wang X, Chen F, Jang SK, Liu M, Wang C, Gao S, Jiang Y, Khunsriraksakul C, Otto JM, et al. Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature. 2022;612(7941):720–4.
Google Scholar
Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, Frayling TM, Hirschhorn J, Yang J, Visscher PM. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27(20):3641–9.
Google Scholar
Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83.
Google Scholar
Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50(10):1412–25.
Google Scholar
Mahajan A, Spracklen CN, Zhang W, Ng MCY, Petty LE, Kitajima H, Yu GZ, Rüeger S, Speidel L, Kim YJ, et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet. 2022;54(5):560–72.
Google Scholar
Ponsford MJ, Gkatzionis A, Walker VM, Grant AJ, Wootton RE, Moore LSP, Fatumo S, Mason AM, Zuber V, Willer C, et al. Cardiometabolic traits, sepsis, and severe COVID-19: A Mendelian randomization investigation. Circulation. 2020;142(18):1791–3.
Google Scholar
Elsworth B, Lyon M, Alexander T, Liu Y, Matthews P, Hallett J, Bates P, Palmer T, Haberland V, Smith GD et al. The MRC IEU OpenGWAS data infrastructure. BioRxiv. 2020:2020.2008.2010.244293.
Chen H, Du Z, Zhang Y, Li M, Gao R, Qin L, Wang H. The association between vitamin C and cancer: A Two-Sample Mendelian randomization study. Front Genet. 2022;13:868408.
Google Scholar
Haycock PC, Borges MC, Burrows K, Lemaitre RN, Harrison S, Burgess S, Chang X, Westra J, Khankari NK, Tsilidis KK, et al. Design and quality control of large-scale two-sample Mendelian randomization studies. Int J Epidemiol. 2023;52(5):1498–521.
Google Scholar
Rogne T, Gill D, Liew Z, Shi X, Stensrud VH, Nilsen TIL, Burgess S. Mediating factors in the association of maternal educational level with pregnancy outcomes: A Mendelian randomization study. JAMA Netw Open. 2024;7(1):e2351166.
Google Scholar
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7.
Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–64.
Google Scholar
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(11):1880–906.
Google Scholar
Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of Pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195–208.
Google Scholar
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14.
Google Scholar
Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology. 2017;28(1):30–42.
Google Scholar
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal Pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98.
Google Scholar
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect Estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25.
Google Scholar
Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377–89.
Google Scholar
Sanderson E, Spiller W, Bowden J. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomization. Stat Med. 2021;40(25):5434–52.
Google Scholar
Burgess S, Thompson DJ, Rees JMB, Day FR, Perry JR, Ong KK. Dissecting causal pathways using Mendelian randomization with summarized genetic data: application to age at menarche and risk of breast Cancer. Genetics. 2017;207(2):481–7.
Google Scholar
Sanderson E. Multivariable Mendelian randomization and mediation. Cold Spring Harb Perspect Med. 2021;11(2).
Cheng C, Spiegelman D, Li F. Is the product method more efficient than the difference method for assessing mediation?? Am J Epidemiol. 2023;192(1):84–92.
Google Scholar
Hesterberg T, Bootstrap. WIRE Comput Stat. 2011;3(6):497–526.
Google Scholar
Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9.
Google Scholar
VanderWeele TJ, Vansteelandt S. Mediation analysis with multiple mediators. Epidemiol Methods. 2014;2(1):95–115.
Google Scholar
Øversveen E, Eikemo TA. Reducing social inequalities in health: moving from the ‘causes of the causes’ to the ‘causes of the structures’. Scand J Public Health. 2018;46(1):1–5.
Google Scholar
Fosse E. Norwegian policies to reduce social inequalities in health: developments from 1987 to 2021. Scand J Public Health. 2022;50(7):882–6.
Google Scholar
Khalatbari-Soltani S, Maccora J, Blyth FM, Joannès C. Kelly-Irving M measuring education in the context of health inequalities. Int J Epidemiol. 2022;51(3):701–8.
Google Scholar
Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40(7):597–608.
Google Scholar
Minelli C, Del Greco MF, Bowden J, Sheehan NA, Thompson J. The use of two-sample methods for Mendelian randomization analyses on single large datasets. Int J Epidemiol. 2021;50(5):1651–9.
Google Scholar
Mounier N, Kutalik Z. Bias correction for inverse variance weighting Mendelian randomization. Genet Epidemiol. 2023;47(4):314–31.
Google Scholar
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