Assessing the causal role of hypertension on left atrial and left ventricular structure and function: A two-sample Mendelian randomization study

Abstract

AimThe aim of this study was to investigate whether hypertension may be causally linked to left atrial (LA) and left ventricular (LV) structure and function.Methods and resultsWe performed a two-Mendelian randomization (MR) analysis implementing the results from the FinnGen large-scale, genome-wide association study for hypertension (N = 218,754), and LV (N = 16,923) and LA studies (N = 35,648) by the UK Biobank to identify genetic instruments. The MR analysis was implemented using an inverse-variance weighted (IVW) approach. We identified a positive potential causal relationship between hypertension and indices for the LA maximum (LAmax with causal estimates of 0.126 [95% CI, (0.093 to 0.160)]); LA minimum (LAmin with causal estimates of 0.122 [95% CI, (0.089 to 0.156)]); LV function (causal estimates are LV end-diastolic volume (LVEDV), 0.078 [95% CI, (0.003 to 0.153)]; LV end-systolic volume (LVESV), 0.102 [95% CI, (0.030 to 0.173)]; LV mass (LVM), 0.171 [95% CI, (0.108 to 0.233)]; and LV mass to end-diastolic volume ratio (LVMVR at 0.098 [95% CI, (0.048 to 0.149)], respectively), which was directionally concordant with other robust MR methods. Other than this, we observed a significantly negative causal relationship between hypertension and the LA active emptying fraction (LAAEF), the LA passive emptying fraction (LAPEF), and the LA total emptying fraction (LATEF).ConclusionOur genetic analyses demonstrated a potential causal relationship between hypertension and the left atrium and left ventricle’s structures and functions.

Associations of the circulating levels of cytokines with the risk of myeloproliferative neoplasms: a bidirectional mendelian-randomization study

“…As a genetic instrumental variable analysis, MR Uses single nucleotide polymorphisms (SNPs) as instrumental variables for the risk factor of interest. SNP S are randomly assigned at meiosis and are not subject to reverse causality bias, so the Mendelian randomization approach can overcome unmeasured confounders and lead to more reliable causal inferences [20].…”