Research Paper|Volume 12, Issue 21|pp 21747—21757

Detecting potential causal relationship between multiple risk factors and Alzheimer’s disease using multivariable Mendelian randomization

Qiang Zhang^1,², Fei Xu², Lianke Wang², Wei-Dong Zhang², Chang-Qing Sun^1,², Hong-Wen Deng^2,³

¹School of Nursing and Health, Zhengzhou University, High-Tech Development Zone of States, Zhengzhou 450001, P.R. China
²Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, High-Tech Development Zone of States, Zhengzhou 450001, P.R. China
³Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA

Received: May 10, 2020Accepted: August 14, 2020Published: November 7, 2020

Copyright: © 2020 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Alzheimer’s disease (AD) is a progressive brain disorder characterized by cognitive skills deterioration that affects many elderly individuals. The identified genetic loci for AD failed to explain the large variability in AD and very few causal factors have been identified so far.

Results: mvMR showed that increasing years of schooling (OR=0.674, 95%CI: 0.571-0.796, P=3.337E-06) and genetically elevated HDL cholesterol (OR ranging from 0.697 to 0.830, P=6.940E-10) were inversely associated with AD risk, genetically predicted total cholesterol (OR=1.300, 1.196 to 1.412; P=6.223E-10) and LDL cholesterol (OR=1.193, 1.097 to 1.296, P=3.564E-05) were associated with increasing AD risk. Genetically predicted FG was suggestively associated with increased AD risk. Furthermore, MR-BMA analysis also confirmed FG and years of schooling as two of the top five causal risk factors for AD.

Conclusions: Our findings might provide us novel insights for treatment and intervention into the causal risk factors for AD or AD-related complex diseases.

Methods: By using extension methods of Mendelian randomization (MR)--multivariable MR (mvMR) and MR based on Bayesian model averaging (MR-BMA), we intend to estimate the potential causal relationship between nine risk factors and AD outcome and try to prioritize the most causal risk factors for AD.