Body Shape and Alzheimer's Disease: A Mendelian Randomization Analysis

Abstract

Obesity has been reported to be related to memory impairment and decline in cognitive function, possibly further leading to the development of Alzheimer's disease (AD). However, observational studies revealed both negative and positive associations between body shape (BS) and AD, thereby making it difficult to confirm causality due to residual confounds and reverse causation. Thus, using genome-wide association study summary data, two-sample Mendelian randomization (MR) analyses were applied to identify whether there exists a causal association between BS and AD. BS was measured using anthropometric traits (ATs) in this study, including body mass index (BMI), waist-to-hip ratio (WHR), waist-to-hip ratio adjusted by body mass index (WHRadjBMI), and waist circumference (WC). The associations of single nucleotide polymorphisms (SNP) with each AT and AD were obtained separately from aggregated data from the Genetic Investigation of Anthropometric Traits (GIANT) consortium and International Genomics of Alzheimer's Project (IGAP) summary data (17,008 cases with AD and 37,154 controls). An inverse-variance weighted method was applied to obtain the overall causal estimate for multiple instrumental SNPs. The odds ratio (OR) [95% confidence interval (CI)] for AD risk per 1-SD difference in BMI was 1.04 (0.88, 1.23), in WHR was 1.01 (0.77, 1.33), in WHRadjBMI was 1.12 (0.89, 1.41), and in WC was 1.02 (0.82, 1.27). Furthermore, simulation analyses of survivor bias indicated the overall causal effect of BMI on risk of AD was not biased. In conclusion, the evidence from MR analyses showed no casual effect of BS on AD risk, which is inconsistent with the results from previous observational studies. The biological mechanism underlying the findings warrants further study.

Keywords: Alzheimer’s disease; Mendelian randomization; body shape; simulation analysis; survivor bias.

FIGURE 1

The explanation of Mendelian randomization analysis by a directed acyclic graph. The accuracy of estimating causality using Mendelian randomization (MR) analyses is based on the following three assumptions: (1) The instrumental variable (IV) associate robustly with the exposure (IV assumption 1). This assumption can be satisfied by ensuring F statistic > 10 and that SNPs are selected using genome-wide significance levels (P < 5 × 10^–8), which suggests that potential bias from weak IV should not be substantial (Nordestgaard et al., 2017). (2) The IV is independent of combined influence of all confounders (IV assumption 2). For the same population and reference, we assess correlation of linkage disequilibrium between SNPs associated robustly with exposure and SNPs linked to possible known confounders. If the correlation coefficient is higher (i.e., r² ≥ 0.5), the corresponding selected SNPs will be discarded (Geng et al., 2018). (3) The IV is independent of the outcome given the exposure and confounders (IV assumption 3). Horizontal pleiotropy, that IVs influence the outcome through alternative pathways other than the exposure, could violate this assumption. It can be checked by using MR-Egger regression and MR-PRESSO method (Noyce et al., 2017; Verbanck et al., 2018).

FIGURE 2

Study design of two-sample Mendelian randomization analysis. In this MR analysis, exposure and outcome refer to body shape and Alzheimer’s disease separately. MR, Mendelian randomization.

FIGURE 3

The overall causal effect of body shape on the risk of Alzheimer’s disease from each of four different methods (inverse-variance weighted method, MR-egger regression method, weighted-median method and weighted-mode method). Results were reported as the odd ratio (OR) of AD per 1-SD increase in each anthropometric trait. The results of additional MR analyses were the more reliable causal estimates of ATs on AD, and the causal effect of WHRadjBMI on AD was displayed in this column was just for easy reading. MR, Mendelian randomization; AD, Alzheimer’s disease; AT, anthropometric traits; BMI, body mass index; OR, odds ratio; WC, waist circumference; WHR, waist-to-hip ratio, WHRadjBMI, waist-to-hip ratio adjusted for body mass index.

FIGURE 4

Survivor bias. The causal effects of these simulation analyses were obtained separately from base survivor model and full survivor model using observational study, inverse-variance weighted method and MR-egger method. Each point and horizontal line denote the mean estimated causal effect and 95% confidence interval (CI) from corresponding model and method. AD, Alzheimer’s disease; BMI, body mass index; baseline, baseline survivor model without survivor bias; full, full survivor model with selected mortality.