1. Polygenic and Socioeconomic Contributions to Nicotine Use and Cardiometabolic Health in Early Mid-Life.
- Author
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Lippert AM, Corsi DJ, Kim R, Wedow R, Kim J, Taddess B, and Subramanian SV
- Subjects
- Humans, Male, Female, Adult, Longitudinal Studies, Adolescent, Body Mass Index, Socioeconomic Factors, United States epidemiology, Waist Circumference, Young Adult, Glycated Hemoglobin metabolism, White People genetics, White People statistics & numerical data, Life Style, Middle Aged, Tobacco Use epidemiology, Tobacco Use genetics, Vaping epidemiology, Cardiovascular Diseases genetics, Cardiovascular Diseases epidemiology, Nicotine, Black or African American genetics, Black or African American statistics & numerical data, White, Multifactorial Inheritance genetics
- Abstract
Introduction: Early mid-life is marked by accumulating risks for cardiometabolic illness linked to health-risk behaviors like nicotine use. Identifying polygenic indices (PGI) has enriched scientific understanding of the cumulative genetic contributions to behavioral and cardiometabolic health, though few studies have assessed these associations alongside socioeconomic (SES) and lifestyle factors., Aims and Methods: Drawing on data from 2337 individuals from the United States participating in the National Longitudinal Study of Adolescent to Adult Health, the current study assesses the fraction of variance in five related outcomes-use of conventional and electronic cigarettes, body mass index (BMI), waist circumference, and glycosylated hemoglobin (A1c)-explained by PGI, SES, and lifestyle., Results: Regression models on African ancestry (AA) and European ancestry (EA) subsamples reveal that the fraction of variance explained by PGI ranges across outcomes. While adjusting for sex and age, PGI explained 3.5%, 2.2%, and 0% in the AA subsample of variability in BMI, waist circumference, and A1c, respectively (in the EA subsample these figures were 7.7%, 9.4%, and 1.3%). The proportion of variance explained by PGI in nicotine-use outcomes is also variable. Results further indicate that PGI and SES are generally complementary, accounting for more variance in the outcomes when modeled together versus separately., Conclusions: PGI are gaining attention in population health surveillance, but polygenic variability might not align clearly with health differences in populations or surpass SES as a fundamental cause of health disparities. We discuss future steps in integrating PGI and SES to refine population health prediction rules., Implications: Study findings point to the complementary relationship of PGI and socioeconomic indicators in explaining population variance in nicotine outcomes and cardiometabolic wellness. Population health surveillance and prediction rules would benefit from the combination of information from both polygenic and socioeconomic risks. Additionally, the risk for electronic cigarette use among users of conventional cigarettes may have a genetic component tied to the cumulative genetic propensity for heavy smoking. Further research on PGI for vaping is needed., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
- Published
- 2024
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