Socioeconomic modifiers of genetic and environmental influences on body mass index in adult twins

Abstract

Objective: Individual measures of socioeconomic status (SES) suppress genetic variance in body mass index (BMI). Our objective was to examine the influence of both individual-level (i.e., educational attainment, household income) and macrolevel (i.e., neighborhood socioeconomic advantage) SES indicators on genetic contributions to BMI.

Method: The study used education level data from 4,162 monozygotic (MZ) and 1,900 dizygotic (DZ) same-sex twin pairs (64% female), income level data from 3,498 MZ and 1,534 DZ pairs (65% female), and neighborhood-level socioeconomic deprivation data from 2,327 MZ and 948 DZ pairs (65% female). Covariates included age (M = 40.4 ± 17.5 years), sex, and ethnicity. The cotwin control model was used to evaluate the mechanisms through which SES influences BMI (e.g., through genetic vs. environmental pathways), and a gene-by-environment interaction model was used to test whether residual variance in BMI, after controlling for the main effects of SES, was moderated by socioeconomic measures.

Results: SES significantly predicted BMI. The association was noncausal, however, and instead was driven primarily through a common underlying genetic background that tended to grow less influential as SES increased. After controlling for the main effect of SES, both genetic and nonshared environmental variance decreased with increasing SES.

Conclusions: The impact of individual and macrolevel SES on BMI extends beyond its main effects. The influence of genes on BMI is moderated by individual and macrolevel measures of SES, such that when SES is higher, genetic factors become less influential.

Figure 1

Path diagram of the fully saturated model fit to the data (Model 3; only one twin shown for clarity). Successive models were fit by fixing parameters to zero and conducting likelihood ratio tests whether adding parameters resulted in a significant improvement in model fit. The A, C, and E latent variables (represented with circles) are the additive genetic, shared environmental and non-shared environmental variance components of SES. The Au and Eu latent variables represent residual additive genetic and non-shared environmental variance in BMI. In this model, the main effect of SES on BMI (captured in the dotted single-headed paths from the A and E components of SES to BMI) is permitted to vary with level of SES. Similarly, the variance in BMI that remains after controlling for the main effect of SES (double-headed paths from Au, Cu, and Eu to BMI) also varies as a function of SES.

Figure 2

Illustrative analyses of the main effects of education level (a), household income (b), and area deprivation (c) on body mass index. Figure 2a and 2c show pair differences in BMI as a function of pair differences in SES. The phenotypic effect of SES on BMI, equivalent to a population regression, is represented by the dashed line. The solid line represents the same relation within pairs of MZ twins, and shows the nonshared environmental effect of SES on BMI. Figure 2b shows mean BMI as a function of family income in various pair types. Comparison of the outermost bars (mean BMI of twins concordant for higher income versus that of twins concordant for lower income) shows the phenotypic effect of income on BMI. The inner bars show this same comparison within MZ and DZ pairs discordant for household income level.

Figure 3

Residual variance in BMI as a function of individual- and neighborhood-level socioeconomic status. The stacked variance plots illustrate how the A, E, and total residual variance in BMI decreases with increasing education (a), income (b), and area deprivation (c). The dotted white lines represent the 95% confidence intervals around the change in variance.

Figure 4

Box plots overlaid with violin plots, body mass index as a function of quartile of education level (a), household income (b), and area deprivation (c). Normal weight (18.5 kg/m³ to 25 kg/m³) falls between the two dotted red lines. The sample was divided according to quartiles of individual- and neighborhood-level SES. The boxplots of the distribution of BMI grow narrower as a function of increasing SES, demonstrating that the 25^th and 75^th percentiles cover a narrower range of BMI at greater levels of socioeconomic advantage. Similarly, the violin plots (which provide information about the probability density of the subsamples) grow shorter and wider with increasing SES, further reflecting decreased variance in BMI. The percentage next to each violin plot is the proportion of the subsample that is overweight or obese (BMI ≥ 25 kg/m³), and tends to decrease as SES increases.