A sociodemographic index identifies non-biological sex-related effects on insomnia in the Hispanic Community Health Study/Study of Latinos

Abstract Background Sex differences are related to both biological factors and the gendered environment. To untangle sex-related effects on health and disease it is important to model sex-related differences better. Methods Data came from the baseline visit of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a longitudinal cohort study following 16,415 individuals recruited at baseline from four study sites: Bronx NY, Miami FL, San Diego CA, and Chicago IL. We applied LASSO penalized logistic regression of male versus female sex over sociodemographic, acculturation, and psychological factors jointly. Two “gendered indices”, GISE and GIPSE, summarizing the sociodemographic environment (GISE, primary) and psychosocial and sociodemographic environment (GIPSE, secondary) associated with sex, were calculated by summing these variables, weighted by their regression coefficients. We examined the association of these indices with insomnia derived from self-reported symptoms assessed via the Women Health Initiative Insomnia Rating Scale (WHIIRS), a phenotype with strong sex differences, in sex-adjusted and sex-stratified analyses. All analyses were adjusted for age, Hispanic/Latino background, and study center. Results The distribution of GISE and GIPSE differed by sex with higher values in male individuals, even when constructing and validating them on separate, independent, subsets of HCHS/SOL individuals. In an association model with insomnia, male sex was associated with lower likelihood of insomnia (odds ratio (OR)=0.60, 95% CI (0.53, 0.67)). Including GISE in the model, the association was slightly weaker (OR=0.63, 95% CI (0.56, 0.70)), and weaker when including instead GIPSE in the association model (OR=0.78, 95% CI (0.69, 0.88)). Higher values of GISE and of GIPSE, more common in male sex, were associated with lower likelihood of insomnia, in analyses adjusted for sex (per 1 standard deviation of the index, GISE OR= 0.92, 95% CI (0.87, 0.99), GIPSE OR=0.65, 95% CI (0.61, 0.70)). Conclusions New measures such as GISE and GIPSE capture sex-related differences beyond binary sex and have the potential to better model and inform research studies of health. However, such indices do not account for gender identity and may not well capture the environment experienced by intersex and non-binary persons.