Abstract
AbstractThis study evaluates the potential role of multiple correlated risk factors upon coronary heart disease (CHD) and ischemic stroke, and the extent to which using GWAS summary data including prevalent cases of stroke, as opposed to incident cases, can influence Mendelian randomization (MR) analyses. Initially, thirteen candidate risk factors were identified through a literature review, including age of menarche, adiposity, blood pressure, lipid fractions, physical activity, type-II diabetes, smoking, sleep duration, alcohol consumption, and kidney function. Using publicly available summary data from genome-wide association studies (GWAS), the total effect of each exposure on CHD, ischemic, and cardioembolic stroke was estimated using univariable summary MR. Multivariable MR (MVMR) analyses were then used to estimate the conditional effects of low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides and systolic blood pressure (SBP) on each outcome. To select the MVMR model a novel forward selection algorithm was applied to include the greatest number of exposures while maintaining sufficient conditional instrument strength for estimation. To examine potential bias from using GWAS summary data derived from prevalent cases of ischemic stroke a GWAS of incident ischemic stroke was conducted using data from the UK Biobank. In univariable MR analyses negative effects of blood pressure were observed across all outcomes, while the effects of remaining exposures differed markedly. HDL was also estimated to have a protective effect on all outcomes except cardioembolic stroke. Univariable and MVMR estimates were directionally consistent, though MVMR estimates were attenuated. Finally, repeating analyses using incident stroke cases yielded results in agreement with prevalent stroke data, suggesting the use of prevalent outcome data did not bias our initial analysis.
Publisher
Cold Spring Harbor Laboratory