Abstract
Body mass index (BMI) has been related to risk of infections. The aim of this study was to assess the shape of the association between BMI and risk of infections and to evaluate whether such associations represent causality. We included 101,447 individuals from The Copenhagen General Population Study who had BMI measured. Outcome was hospital contacts related to infections. The shape of the association between BMI and risk of infections was examined using restricted cubic spline Cox regression. To evaluate causality, we used Mendelian randomization, an epidemiological method that counteracts confounding and reverse causality by using genetic variation as instrumental variables. We created a genetic risk score based on five genetic variants causing lifelong higher BMI and used this score in instrumental variable analysis. During median follow-up of 8.8 years, 10,263 hospital contacts related to infections were recorded. We found a U-shaped association between BMI and risk of any infection and pneumonia, and a linear association between BMI and risk of skin infection, urinary tract infection, and sepsis. In instrumental variable analyses, higher BMI was associated with increased risk of skin infection: odds ratio 1.12 (95% CI 1.03–1.22) for a genetically induced 1 unit increase in BMI. Observationally, low as well as high BMI was associated with increased risk of any infection and pneumonia, whereas only high BMI was associated with increased risk of skin infection, urinary tract infection, and sepsis. High BMI was causally associated with increased risk of skin infection.
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Bjerregaard LG, Jensen BW, Ängquist L, Osler M, Sørensen TIA, Baker JL. Change in overweight from childhood to early adulthood and risk of type 2 diabetes. NEJM. 2018;378(14):1302–12.
Millard LAC, Davies NM, Tilling K, Gaunt TR, Davey Smith G. Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization. PLoS Genet. 2019;15(2):e1007951.
Ärnlöv J, Ingelsson E, Sundström J, Lind L. Impact of body mass index and the metabolic syndrome on the risk of cardiovascular disease and death in middle-aged men. Circulation. 2010;121:230–6.
Friedemann C, Heneghan C, Mahtani K, Thompson M, Perera R, Ward AM. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta-analysis. BMJ. 2012;345:129–32.
Rinella M. Nonalcoholic fatty liver disease: a systematic review. JAMA. 2015;313:2263–73.
Hagström H, Tynelius P, Rasmussen F. High BMI in late adolescence predicts future severe liver disease and hepatocellular carcinoma : a national, population-based cohort study in 1.2 million men. Gut. 2018;67:1536–42.
Secretan BL, Scoccianti C, Loomis D. Body fatness and cancer—viewpoint of the IARC working group. NEJM. 2016;375:794–8.
Bhaskaran K, Douglas I, Forbes H, Dos-Santos-Silva I, Leon DA, Smeeth L. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5.24 million UK adults. Lancet. 2014;384:755–65.
Benn M, Tybjærg-Hansen A, Smith GD, Nordestgaard BG. High body mass index and cancer risk—a Mendelian randomisation study. Eur J Epidemiol. 2016;31:879–92.
Andersen CJ, Murphy KE, Fernandez ML. Impact of obesity and metabolic syndrome on immunity. Adv Nutr Int Rev J. 2016;7:66–75.
Kanneganti T, Dixit VD. Immunological complications of obesity. Nat Immunol. 2012;13:707–12.
Kaspersen KA, Pedersen OB, Petersen MS, Hjalgrim H, Rostgaard K, Møller BK, et al. Obesity and risk of infection: results from the Danish Blood Donor Study. Epidemiology. 2015;26:580–9.
Harpsøe MC, Nielsen NM, Friis-Møller N, Andersson M, Wohlfahrt J, Linneberg A, et al. Body mass index and risk of infections among women in the Danish National Birth Cohort. Am J Epidemiol. 2016;183:1008–17.
Ghilotti F, Bellocco R, Ye W, Adami H-O, Lagerros YT. Obesity and risk of infections: results from men and women in the Swedish National March Cohort. Int J Epidemiol. 2019;48:1783–94.
Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22.
Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Smith GD. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:1133–63.
Benn M, Nordestgaard BG. From genome-wide association studies to Mendelian randomization: novel opportunities for understanding cardiovascular disease causality, pathogenesis, prevention, and treatment. Cardivasc Res. 2018;114:1192–208.
Nordestgaard BG, Palmer TM, Benn M, Zacho J, Tybjærg-Hansen A, Smith GD, et al. The effect of elevated body mass index on ischemic heart disease risk: causal estimates from a mendelian randomisation approach. PLoS Med. 2012;9(5):e1001212.
Lynge E, Sandegaard JL, Rebolj M. The Danish National Patient Register. Scand J Public Health. 2011;39:30–3.
Helby J, Nordestgaard BG, Benfield T, Bojesen SE. Shorter leukocyte telomere length is associated with higher risk of infections: a prospective study of 75,309 individuals from the general population. Haematologica. 2017;102:1457–65.
Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal eighteen new loci associated with body mass index. Nat Genet. 2010;42:937–48.
Renström F, Shungin D, Johansson I, Florez JC, Hallmans G, et al. Genetic predisposition to long-term nondiabetic deteriorations in glucose homeostasis. Diabetes. 2011;60:345–54.
R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017.
Staley JR, Burgess S. Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization. Genet Epidemiol. 2017;41:341–52.
Staley JR. nlmr: Non-linear Mendelian randomisation. 2018. https://github.com/jrs95/nlmr.
Sjolander A, Dahlqwist E, Martinussen T. ivtools: instrumental variables. 2019. https://cran.r-project.org/package=ivtools.
Bowden J, Smith GD, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.
Yosipovitch G, DeVore A, Dawn A. Obesity and the skin: skin physiology and skin manifestations of obesity. J Am Acad Dermatol. 2007;56:901–16.
Garcia Hidalgo L. Dermatological complications of obesity. Am J Clin Dermatol. 2002;3:497–506.
Hägg S, Fall T, Ploner A, Mägi R, Fischer K, Draisma HHM, et al. Adiposity as a cause of cardiovascular disease: a Mendelian randomization study. Int J Epidemiol. 2015;44:578–86.
Smith GD, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23:89–98.
Funding
This work was supported by the Novo Nordisk Foundation [NNF15OC0017740] and the Danish Council for Independent Research | Medical Sciences [DFF – 6110-00075]. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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Winter-Jensen, M., Afzal, S., Jess, T. et al. Body mass index and risk of infections: a Mendelian randomization study of 101,447 individuals. Eur J Epidemiol 35, 347–354 (2020). https://doi.org/10.1007/s10654-020-00630-7
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DOI: https://doi.org/10.1007/s10654-020-00630-7