RESUMO
STUDY OBJECTIVES: We used quantitative genetic models to assess whether area-level deprivation as indicated by the Singh Index predicts shorter sleep duration and modifies its underlying genetic and environmental contributions. METHODS: Participants were 4,218 adult twin pairs (2,377 monozygotic and 1,841 dizygotic) from the University of Washington Twin Registry. Participants self-reported habitual sleep duration. The Singh Index was determined by linking geocoding addresses to 17 indicators at the census-tract level using data from Census of Washington State and Census Tract Cartographic Boundary Files from 2000 and 2010. Data were analyzed using univariate and bivariate genetic decomposition and quantitative genetic interaction models that assessed A (additive genetics), C (common environment), and E (unique environment) main effects of the Singh Index on sleep duration and allowed the magnitude of residual ACE variance components in sleep duration to vary with the Index. RESULTS: The sample had a mean age of 38.2 y (standard deviation [SD] = 18), and was predominantly female (62%) and Caucasian (91%). Mean sleep duration was 7.38 h (SD = 1.20) and the mean Singh Index score was 0.00 (SD = 0.89). The heritability of sleep duration was 39% and the Singh Index was 12%. The uncontrolled phenotypic regression of sleep duration on the Singh Index showed a significant negative relationship between area-level deprivation and sleep length (b = -0.080, P < 0.001). Every 1 SD in Singh Index was associated with a â¼4.5 min change in sleep duration. For the quasi-causal bivariate model, there was a significant main effect of E (b(0E) = -0.063; standard error [SE] = 0.30; P < 0.05). Residual variance components unique to sleep duration were significant for both A (b(0Au) = 0.734; SE = 0.020; P < 0.001) and E (b(0Eu) = 0.934; SE = 0.013; P < 0.001). CONCLUSIONS: Area-level deprivation has a quasi-causal association with sleep duration, with greater deprivation being related to shorter sleep. As area-level deprivation increases, unique genetic and nonshared environmental residual variance in sleep duration increases.
Assuntos
Meio Ambiente , Sono/genética , Sono/fisiologia , Gêmeos Dizigóticos/genética , Gêmeos Dizigóticos/estatística & dados numéricos , Gêmeos Monozigóticos/genética , Gêmeos Monozigóticos/estatística & dados numéricos , Adulto , Censos , Feminino , Interação Gene-Ambiente , Humanos , Masculino , Modelos Genéticos , Sistema de Registros , Autorrelato , Fatores Socioeconômicos , Fatores de Tempo , Washington , População Branca/genéticaRESUMO
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.