Disadvantaged social status contributed to sleep disorders: An observational and genome-wide gene-environment interaction analysis

Background: Sleep is a natural and essential physiological need for individuals. Our study aimed to research the associations between accumulated social risks and sleep disorders. Methods: In this study, we came up with a polysocial risk score (PsRS), which is a cumulative social risk index composed of 13 social determinants of health. This research includes 239,165 individuals with sleep disorders and social determinants of health data from the UK Biobank cohort. First, logistic regression models were performed to examine the associations of social determinants of health and sleep disorders, including chronotype, narcolepsy, insomnia, snoring, short and long sleep duration. Then, PsRS was calculated based on statistically significant social determinants of health for each sleep disorder. Third, a genome-wide gene-environment interaction study was conducted to explore the interactions between single-nucleotide polymorphisms and PsRS in relation to sleep disorders. Results: Higher PsRS scores were associated with worse sleep status, with the adjusted odds ratio (OR) ranging from 1.10 (95% Confidence interval [CI]: 1.09-1.11) to 1.29 (95% CI: 1.27-1.30) for sleep disorders. Emotional stress (OR = 1.36, 95% CI: 1.28-1.43) and not in paid employment (OR = 2.62, 95% CI: 2.51-2.74) were found to have significant contributions for sleep disorders. Moreover, multiple single-nucleotide polymorphisms were discovered to have interactions with PsRS, such as FRAS1 (P P = 2.57 x 10-14) -14 ) and CACNA1A (P P = 8.62 x 10-14) -14 ) for narcolepsy, and ACKR3 (P P = 1.24 x 10-8) -8 ) for long sleep. Conclusions: Our findings suggested that cumulative social risks was associated with sleep disorders, while the interactions between genetic susceptibility and disadvantaged social status are risk factors for the development of sleep disorders. (c) 2024 National Sleep Foundation. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.