Associations of genetics, behaviors, and life course circumstances with a novel aging and healthspan measure: Evidence from the Health and Retirement Study

BackgroundAn individual’s rate of aging directly influences his/her susceptibility to morbidity and mortality. Thus, quantifying aging and disentangling how various factors coalesce to produce between-person differences in the rate of aging, have important implications for potential interventions. We recently developed and validated a novel multi-system-based aging measure, Phenotypic Age (PhenoAge), which has been shown to capture mortality and morbidity risk in the full US population and diverse subpopulations. The aim of this study was to evaluate associations between PhenoAge and a comprehensive set of factors, including genetic scores, childhood and adulthood circumstances, and health behaviors, to determine the relative contributions of these factors to variance in this aging measure.Methods and findingsBased on data from 2,339 adults (aged 51+ years, mean age 69.4 years, 56% female, and 93.9% non-Hispanic white) from the US Health and Retirement Study, we calculated PhenoAge and evaluated the multivariable associations for a comprehensive set of factors using 2 innovative approaches—Shapley value decomposition (the Shapley approach hereafter) and hierarchical clustering. The Shapley approach revealed that together all 11 study domains (4 childhood and adulthood circumstances domains, 5 polygenic score [PGS] domains, and 1 behavior domain, and 1 demographic domain) accounted for 29.2% (bootstrap standard error = 0.003) of variance in PhenoAge after adjustment for chronological age. Behaviors exhibited the greatest contribution to PhenoAge (9.2%), closely followed by adulthood adversity, which was suggested to contribute 9.0% of the variance in PhenoAge. Collectively, the PGSs contributed 3.8% of the variance in PhenoAge (after accounting for chronological age). Next, using hierarchical clustering, we identified 6 distinct subpopulations based on the 4 childhood and adulthood circumstances domains. Two of these subpopulations stood out as disadvantaged, exhibiting significantly higher PhenoAges on average. Finally, we observed a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the seemingly most disadvantaged subpopulation, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. The main limitations of this study were the retrospective nature of self-reported circumstances, leading to possible recall biases, and the unrepresentative racial/ethnic makeup of the population.ConclusionsIn a sample of US older adults, genetic, behavioral, and socioenvironmental circumstances during childhood and adulthood account for about 30% of differences in phenotypic aging. Our results also suggest that the detrimental effects of disadvantaged life course circumstances for health and aging may be further exacerbated among persons with genetic predisposition to coronary artery disease. Finally, our finding that behaviors had the largest contribution to PhenoAge highlights a potential policy target. Nevertheless, further validation of these findings and identification of causal links are greatly needed.

研究背景 个体的衰老速率直接影响其发病与死亡的易感性。因此，量化衰老进程并厘清各类因素如何共同作用，造成个体间衰老速率的差异，对于开发潜在的衰老干预手段具有重要意义。本团队近期开发并验证了一种基于多系统的新型衰老评估指标——表型年龄（Phenotypic Age，PhenoAge），已有研究证实该指标可反映全美国人群及各多样化亚群的死亡与发病风险。本研究旨在评估表型年龄与一系列全面的影响因素（包括遗传评分、童年与成年时期的生活环境、健康行为）之间的关联，以明确这些因素对该衰老评估指标变异度的相对贡献。

研究方法与结果 本研究基于美国健康与退休研究（Health and Retirement Study）中2339名成年受试者的数据（年龄≥51岁，平均年龄69.4岁，女性占比56%，非西班牙裔白人占比93.9%），计算了表型年龄，并采用两种创新分析方法——夏普利值分解（Shapley value decomposition，以下简称夏普利法）与层次聚类（hierarchical clustering），对一系列全面的影响因素开展多变量关联分析。夏普利法分析结果显示，在校正实际年龄后，本研究涵盖的11个研究领域（4个童年与成年生活环境领域、5个多基因评分（polygenic score, PGS）领域、1个行为领域与1个人口统计学领域）共解释了表型年龄变异度的29.2%（Bootstrap标准误=0.003）。其中，健康行为对表型年龄的贡献最大（9.2%），紧随其后的是成年时期的不良生活境遇，其对表型年龄变异度的贡献为9.0%。综合来看，多基因评分共解释了校正实际年龄后表型年龄变异度的3.8%。随后，本研究通过层次聚类，基于4个童年与成年生活环境领域划分出6个特征鲜明的亚群。其中2个亚群处于社会经济劣势地位，其平均表型年龄显著更高。最后，本研究观察到，此前已验证的冠状动脉疾病多基因评分与社会经济最劣势亚群之间存在显著的基因-环境交互作用，提示不良人生历程境遇与遗传风险对表型衰老存在相乘效应。本研究的主要局限性在于：自我报告的生活环境数据为回顾性收集，可能存在回忆偏倚；且研究人群的种族/族裔构成不具有全国代表性。

研究结论 在美国老年人群样本中，遗传、行为因素以及童年与成年时期的社会环境因素共解释了约30%的表型衰老个体差异。本研究结果还提示，对于携带冠状动脉疾病遗传易感倾向的人群，劣势人生历程境遇对健康与衰老的有害影响可能进一步加剧。此外，本研究发现健康行为对表型年龄的贡献最大，这一结果为相关公共卫生政策制定提供了潜在靶点。然而，仍需进一步验证本研究结果并明确其中的因果关联。