Causal relationship between lipid profile and muscle atrophy: A bi‐directional Mendelian randomization study

Abstract

BackgroundThe aim of this study was to analyze the bi‐directional causal relationship between lipid profile and characteristics related to muscle atrophy by using a bi‐directional Mendelian randomization (MR) analysis.MethodsThe appendicular lean mass (ALM), whole body fat‐free mass (WBFFM) and trunk fat‐free mass (TFFM) were used as genome‐wide association study (GWAS) data for evaluating muscle mass; the usual walking pace (UWP) and low grip strength (LGS) were used as GWAS data for evaluating muscle strength; and the triglycerides (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), apolipoprotein A‐1 (Apo A‐1), and apolipoprotein B (Apo B) were used as GWAS data for evaluating lipid profile. For specific investigations, we mainly employed inverse variance weighting for causal estimation and MR‐Egger for pleiotropy analysis.ResultsMR results showed that the lipid profile predicted by genetic variants was negatively correlated with muscle mass, positively correlated with UWP, and was not causally correlated with LGS. On the other hand, the muscle mass predicted by genetic variants was negatively correlated with lipid profile, the UWP predicted by genetic variants was mainly positively correlated with lipid profile, while the LGS predicted by genetic variants had no relevant causal relationship with lipid profile.ConclusionsFindings of this MR analysis suggest that hyperlipidemia may affect muscle mass and lead to muscle atrophy, but has no significant effect on muscle strength. On the other hand, increased muscle mass may reduce the incidence of dyslipidemia.

Unveiling potential therapeutic targets for diabetes-induced frozen shoulder through Mendelian randomization analysis of the human plasma proteome

“…MR analysis is not plagued by issues of reverse causal associations and confounders that are commonly seen in traditional observational studies. 12 For a randomized controlled study, MR analysis can not only circumvent conditions, difficulties, and ethical constraints, but also help generate design ideas. With the development of proteomic genome-wide association studies (GWAS) in recent years, MR analysis has been widely used to explore the relationship between plasma proteins and diseases.…”