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Background: A large literature demonstrates associations between socioeconomic status (SES) and health, including physiological health and well-being. Moreover, gender differences are often observed among measures of both SES and health. However, relationships between SES and health are sometimes questioned given the lack of true experiments, and the potential biological and SES mechanisms explaining gender differences in health are rarely examined simultaneously.Purpose: To use a national sample of twins to investigate lifetime socioeconomic adversity and a measure of physiological dysregulation separately by sex.Methods: Using the twin sample in the second wave of the Midlife in the United States survey (MIDUS II), biometric regression analysis was conducted to determine whether the established SES-physiological health association is observed among twins both before and after adjusting for potential familial-level confounds (additive genetic and shared environmental influences that may underly the SES-health link), and whether this association differs among men and women.Results: Although individuals with less socioeconomic adversity over the lifespan exhibited less physiological dysregulation among this sample of twins, this association only persisted among male twins after adjusting for familial influences.Conclusions: Findings from the present study suggest that, particularly for men, links between socioeconomic adversity and health are not spurious or better explained by additive genetic or early shared environmental influences. Furthermore, gender-specific role demands may create differential associations between SES and health. [ABSTRACT FROM AUTHOR]

Numerous studies have found interactions between socioeconomic status (SES) and the heritability of cognitive ability in samples from the United States, with individuals from lower SES backgrounds showing decreased heritability compared to those reared in higher SES environments. However, nearly all published studies of the Scarr-Rowe interaction have been univariate and cross-sectional. In this study, we sought to maximize statistical power by fitting multivariate models of gene (G) x SES interaction, including longitudinal models. Cognitive ability data collected at up to five time points between ages 7 and 15 years were available for 566 twin pairs from the Louisville Twin Study. We used multilevel and latent factor models to pool intelligence subtest scores cross-sectionally. To examine interactions longitudinally, we fit latent growth curve models to IQ scores. Power analysis results indicated that the multivariate approach substantially boosted power to detect G x SES interaction. The predicted interaction effect was observed at most ages in cross-sectional multivariate analyses. In longitudinal analyses, we found significant G x SES interactions on mean-level (intercept) full scale IQ and performance IQ (p s <.001), but not verbal IQ intercept (p =.08). SES did not significantly moderate the heritability of change in IQ over time (slope). Interaction appeared to be driven by DZ twin correlations decreasing more substantially as a function of higher SES than MZ correlations. • G x SES interaction on intelligence is found across mid-childhood/early adolescence. • The interaction appears to be driven by divergence in DZ twins. • Multivariate methods boost power to detect G x SES interaction. [ABSTRACT FROM AUTHOR]