The Effect of BMI and Type 2 Diabetes on Socioeconomic Status: A Two-Sample Multivariable Mendelian Randomization Study

Previous evidence indicates that high BMI and type 2 diabetes (T2D) are associated with poorer labor market prospects, lower productivity, and higher absenteeism (旷课) (1–6). These disadvantages may accumulate over time and affect income and living circumstances, leading to a selection of individuals in more regionally deprived areas. However, identifying the causal effect of BMI or diabetes on socioeconomic outcomes is challenging, mainly due to intrinsic problems of unmeasured confounding and reverse causation (1–3). Earlier approaches focused on the use of instrumental variable (IV) methods, exploiting the disease status of biological parents as IV (1–3). Recent studies have used genetic characteristics in one-sample Mendelian randomization (MR) approaches and showed an effect of BMI on socioeconomic status (4–6), while no effect of diabetes could be revealed (5). This study aims at estimating the causal effect of BMI and T2D on household income and regional deprivation using a multivariable two-sample MR approach. This approach allows considering the shared genetic components of BMI and diabetes (7) to jointly estimate their causal effects on these socioeconomic outcomes (8).

RESEARCH DESIGN AND METHODS

MR

The principle of MR roots in Mendel’s laws of inheritance (i.e., the individual genotype is largely independent of external factors and therefore independent of potential confounders). In MR techniques, significant single nucleotide polymorphisms (SNPs) that are associated with the exposure are exploited (利用) as exogenous (外生的，外源的) genetic variation in the form of IVs (8,9). Genome-wide association studies (GWAS) have shown significant independent associations between several SNPs and BMI or T2D (10,11) but also the presence of distinct signals influencing both conditions (7). While the relevance assumption and exclusion criteria are satisfied for our data (see SupplementaryMaterial 1), this overlap could lead to horizontal pleiotropy that violates the exchangeability (可替换性) assumption (i.e., the same SNP independently influences multiple phenotypes) and could result in biased estimates (9). Horizontal pleiotropy can be overcome by using multivariable MR methods (i.e., by considering the overlapping instruments directly in the estimation) (8).

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