The role of lipid species in Alzheimer’s disease onset: A comprehensive Mendelian randomization analysis

Background

Alzheimer’s disease (AD) remains a significant global health challenge, with its etiology intricately linked to a variety of genetic and environmental factors. Among these, lipid metabolism has been hypothesized to play a crucial role, though the causal pathways remain inadequately elucidated. This study aims to employ Mendelian Randomization (MR) to unravel the potential causal relationships between a comprehensive array of lipid species and the risk of developing AD.

Methods

Utilizing a two-sample MR framework, we analyzed data from genome-wide association studies (GWAS) encompassing 487,511 individuals of European descent. A total of 179 lipid species across 13 lipid categories were investigated for their causal association with AD. Genetic variants serving as instrumental variables (IVs) were carefully selected based on stringent criteria to ensure validity. The statistical analyses, including inverse variance weighting (IVW), weighted median-based estimation, and sensitivity analyses, were conducted using the R software environment.

Results

Our findings reveal a significant causal relationship between ten specific lipid species and the risk of AD. Notably, certain lipids such as Sterol ester (27:1/15:0) and Phosphatidylcholine (16:0_22:4) exhibited a protective effect against AD, as evidenced by their inverse correlation with the disease’s risk. Additionally, a reciprocal analysis suggested a negative causal impact of AD on the levels of certain Triacylglycerol species. The integrity of our results was reinforced by sensitivity analyses, including the MR Egger intercept test, indicating the absence of horizontal pleiotropy and confirming the reliability of our findings.

Conclusions

This study substantiates the causal link between specific lipid species and Alzheimer’s disease, highlighting the complex interplay between lipid metabolism and AD pathogenesis. The identified lipid biomarkers offer new insights into the disease’s etiology and potential therapeutic targets. Furthermore, our rigorous methodological approach demonstrates the utility of MR in disentangling the causal relationships in complex diseases.