Abstract

Numerous observational studies have reported an association between frailty and atherosclerosis. However, the causal relationship between frailty and the occurrence of atherosclerosis in different anatomical sites remains unclear. we conducted a bidirectional Mendelian randomization (MR) study to evaluate the causal relationship between the frailty index (FI), and both systemic atherosclerosis and lipids. We obtained summary statistics from large-scale genome-wide association studies (GWAS) of various phenotypes, including frailty (n = 175,226), coronary atherosclerosis (n = 56,685), cerebral atherosclerosis (n = 150,765), peripheral arterial disease (PAD) (n = 361,194), atherosclerosis at other sites (n = 17,832), LDL-C (n = 201,678), HDL-C (n = 77,409), and triglycerides (n = 78,700). The primary MR analysis employed the inverse variance weighted (IVW) method. Furthermore, to assess reverse causality, we employed inverse MR and multivariate MR analysis. Genetically predicted FI showed positive associations with the risk of coronary atherosclerosis (OR = 1.47, 95% CI 1.12-1.93) and cerebral atherosclerosis (OR = 1.99, 95% CI 1.05-3.78), with no significant association (p >0.05) applied to peripheral arterial disease and atherosclerosis at other sites. Genetically predicted FI was positively associated with the risk of triglycerides (OR = 1.31, 95% CI 1.08-1.59), negatively associated with the risk of LDL-C (OR = 0.87, 95% CI 0.78-0.97), and showed no significant association with the risk of HDL-C (p >0.05). Furthermore, both reverse MR and multivariate MR analyses demonstrated a correlation between systemic atherosclerosis, lipids, and increased FI. Our study elucidated that genetically predicted FI is associated with the risk of coronary atherosclerosis and cerebral atherosclerosis by the MR analysis method, and they have a bidirectional causal relationship. Moreover, genetically predicted FI was causally associated with triglyceride and LDL-C levels. Further understanding of this association is crucial for optimizing medical practice and care models specifically tailored to frail populations.

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