Deciphering the influence of socioeconomic status on brain structure: insights from Mendelian randomization

Abstract

Socioeconomic status (SES) influences physical and mental health, however its relation with brain structure is less well documented. Here, we examine the role of SES on brain structure using Mendelian randomisation. First, we conduct a multivariate genome-wide association study of SES using educational attainment, household income, occupational prestige, and area-based social deprivation, with an effective sample size of N = 947,466. We identify 554 loci associated with SES and distil these loci into those that are common across those four traits. Second, using an independent sample of ~35,000 we provide evidence to suggest that SES is protective against white matter hyperintensities as a proportion of intracranial volume (WMHicv). Third, we find that differences in SES still afford a protective effect against WMHicv, independent of that made by cognitive ability. Our results suggest that SES is a modifiable risk factor, causal in the maintenance of cognitive ability in older-age.

Fig. 1. Shows GWAS sample size, relationship among samples, and analytic plan.

Blue arrow refers to meta-analysis in METAL. Orange arrow refers to common factor GWAS in GenomicSEM. N refers to sample size. In common factor GWAS, N refers to effective sample size. TBV total brain volume, TBVicv total brain volume as a proportion of intracranial volume, GM total grey matter volume, GMicv total grey matter volume as a proportion of intracranial volume, WMH white matter hyperintensity volume WMHicv white matter hyperintensity volume as a proportion of intracranial volume NAWM normal-appearing white matter volume, WMicv white matter volume as a proportion of intracranial volume. gFA The first unrotated component of fractional anisotropy properties. gMD The first unrotated component of mean diffusivity properties. gICVF The first unrotated component of intra-cellular volume fraction properties. gISOVF The first unrotated component of isotropic volume fraction properties. gOD The first unrotated component of orientation dispersion properties.

Fig. 2. Genetic relationship between the indicators of SES and with MRI measures.

A Showing the phenotypic and genetic correlations between the variables used in UK Biobank. The lower diagonal shows the genetic correlations whereas the upper diagonal shows the phenotypic correlations. The diagonal shows the heritability estimates. Colour and size are used to illustrate the magnitude and directions of the correlations. Both heritability and genetic correlations were derived using LDSC implemented in Genomic SEM. Tabulated values are shown in Supplementary Tables 1–3. Social deprivation scores were reversed to facilitate a comparison with the other measures of SES. B Showing the standardised phenotypic (upper UK Biobank) and genetic (lower total sample) factor solutions for the covariance structure across the four indices of SES examined. Social deprivation scores were again reversed. Squares represent observed variables (i.e. those that were directly measured) whereas circles represent latent variables (i.e. those that were statistically inferred). C A miami plot of the general factor of SES in the total sample (effective N = 947,466). The X axis indicates chromosome and the y axis shows the –log(10) p value of each SNP with the upper section describing its association with the general factor of SES where the lower shows the p value for the heterogeneity Q statistics. TBV total brain volume, GM grey matter volume, WMH white matter hyperintensity volume, TBVicv TBV as a proportion of intracranial volume, GMicv GM as a proportion of intracranial volume, WMicv white matter volume as a proportion of intracranial volume, WMHicv WMH as a proportion of intracranial volume, gFA a general factor of brain white matter tract fractional anisotropy, gMD a general factor of brain white matter tract mean diffusivity, gIVCF a general factor of brain white matter tract intracellular volume fraction, gISOVF a general factor of brain white matter tract isotropic volume fraction, NAWM normal appearing white matter, gOD a general factor of brain white matter tract orientation dispersion.

Fig. 3. Genetic relationship between SES, cognitive ability and white matter hyperintensities.

A–G shows a selection of models that may underlie the univariable MR effects of gSES and cognitive ability on brain structure. SES indicates gSES, CA indicates cognitive ability, and brain indicates brain structure (WMHicv). SNP indicates a set of SNPs used to derive instrumental variables illustrated with a single box for ease of plotting. A–C indicates models where SES has an effect on brain structure. A shows that the univariate effects of CA are due to confounding. B shows a model whereby the effects of SES on brain structure are mediated through CA, and C shows a model where the effect of CA on brain structure is best explained by confounding due to horizontal pleiotropy between SES and CA. Models D–F show the same pattern of effects but with CA, not SES, as the likely causal variable. Model G shows a scenario where neither CA nor SES has an effect on brain structure but SNP influences brain structure through a separate path. H shows a model where both CA and SES have independent effects on brain structure. I shows a Venn diagram of cognitive ability and gSES showing the unique and shared genetic components at the causal level. Grey illustrates the polygenic overlap between trait pairs, orange shows the SES specific components, and blue the unique contributors to cognitive ability. Numbers indicate the estimated quantity of causal variants in thousands with the standard error in brackets. The size of the circle indicates the degree of polygenicity for each trait pair. J Illustrating the total and direct effects of gSES, and cognitive ability. Colour represents trait and solid shapes indicate a statistically significant causal estimate. Error bars show ± one standard error.