Sociology Department, University of Colorado Denver, Denver, CO, USA.
Ottawa Hospital Research Institute, Clinical Epidemiology Program, Ottawa, ON, Canada.
Nicotine Tob Res. 2024 Nov 22;26(12):1616-1625. doi: 10.1093/ntr/ntae146.
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.
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.
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.
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.
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.
中年早期标志着与尼古丁使用等健康风险行为相关的心血管代谢疾病风险的积累。尽管很少有研究评估这些关联与社会经济地位 (SES) 和生活方式因素的相关性，但识别多基因指数 (PGI) 丰富了对行为和心血管代谢健康的累积遗传贡献的科学理解。
利用来自参与美国国家青少年至成人健康纵向研究的 2337 名个体的数据，本研究评估了五个相关结果（使用传统和电子烟、体重指数 (BMI)、腰围和糖化血红蛋白 (A1c)）的变异部分由 PGI、SES 和生活方式解释。
对非洲裔 (AA) 和欧洲裔 (EA) 亚组的回归模型显示，PGI 解释的变异部分因结果而异。在调整性别和年龄后，PGI 分别解释了 AA 亚组 BMI、腰围和 A1c 变异的 3.5%、2.2%和 0%（在 EA 亚组中，这些数字分别为 7.7%、9.4%和 1.3%）。PGI 在尼古丁使用结果中的变异解释比例也是可变的。结果进一步表明，PGI 和 SES 通常是互补的，当一起建模而不是分别建模时，它们可以解释更多的结果变异。
PGI 在人群健康监测中受到关注，但多基因变异性可能与人群中的健康差异不明确，或者超越 SES 成为健康差异的根本原因。我们讨论了整合 PGI 和 SES 以完善人群健康预测规则的未来步骤。
研究结果表明，PGI 和社会经济指标在解释尼古丁结果和心血管代谢健康的人群变异方面具有互补关系。人群健康监测和预测规则将受益于来自多基因和社会经济风险的信息的结合。此外，使用传统香烟的电子烟使用者的电子香烟使用风险可能与重度吸烟的累积遗传倾向有关。需要进一步研究用于蒸气的 PGI。
