Contextual and Health Behavior Effects on Epigenetic Aging Among African Americans

摘要：有相当多的证据表明早期应激与加速衰老有关，但潜在的机制 (调解人)对这种影响的解释知之甚少。同样，持续的压力对年轻人的影响 成年期对加速衰老的影响在很大程度上是未知的。更严重的是缺乏关于机制的信息 和影响的时间，可用的研究是有限的，因为它主要集中在白色样本上-尽管 黑人的比例比几乎所有类型的其他种族/民族都高得多 慢性病，以及大多数炎症和表观遗传衰老(epi-A)的标志--即差异 根据DNA甲基化的表观遗传学变化，在时间年龄和生物年龄之间。建议数 研究旨在检查：a)影响黑人加速衰老的因素，特别是经济和种族因素- 相关压力；以及b)调解人和调解人的作用，这可能是预防的有用目标 干预措施，如药物使用和不良饮食，以及保护性因素。 目前调查的一个焦点是测试可归因于早期衰老的替代途径 以及后来的压力源。一种途径侧重于应激源对潜在不健康行为(例如，物质)的影响 使用，饮食)，这反过来可能会导致慢性衰老疾病。这种可能性得到了以下证据的支持 有压力的生活事件，尤其是那些与歧视有关的事件，会预测生活方式的选择。相反，压力效应 也可能通过改变糖皮质激素系统的功能来加速衰老。或者，这两条路径可能是 是相互关联的。此外，我们专注于在给定的压力范围内扩大已知的压力源范围 最关键的发育时期，以及保护性因素对epi-A的影响程度。寻址 先前研究的一个重要局限性是，我们将拥有可靠、敏感的吸烟、大麻、 酒精使用，两个时间点的饮食，以及人口普查跟踪和父母报告压力源指数 在多个时间点，允许和我们补充自我报告措施并更严格地检查模型 而且精度比以前更高。 拟议的研究将集中在470名年轻人，他们是黑人更大(N=889)小组研究的一部分 家庭、家庭和社区健康研究。FACHS跟踪了一群来自 年龄在10岁到28岁之间，以及他们的父母和家庭成员。通过使用Illumina来表征甲基化 EPIC阵列在青年时期的两个时间点(28和33)，我们将能够确定EPI-A的变化；即， 加速衰老在青壮年继续保持可塑性的程度。使用现有的自我和 父母对各种应激源的报告--来自8波数据，以及经过充分验证的表观遗传生物标记物 对于EPI-A、吸烟和饮酒，我们将能够利用现有的极其丰富的未来面孔 数据集，包括关于压力、保护因素、药物使用和应对的信息，目标是 为预防干预研究提供信息，以解决健康老龄化中差异的来源。

ABSTRACT: Considerable evidence exists linking early stress to accelerated aging, but potential mechanisms (mediators) accounting for this effect are poorly understood. Likewise, the impact of continuing stress in young adulthood on accelerated aging is largely unknown. Compounding the lack of information regarding mechanisms and timing of effects, available research is limited because it has focused mostly on White samples -- in spite of the fact that Blacks have significantly higher rates than other racial/ethnic groups of almost every type of chronic illness, as well as most markers of inflammation, and epigenetic aging (epi-A) —i.e., the difference between chronological age, and biological age, based on epigenetic changes in DNA methylation. The proposed research is designed to examine: a) factors that affect accelerated aging in Blacks, especially economic and race- related stress; and b) the role of mediators and moderators that may be useful targets for preventive interventions, such as substance use and poor diet, and also protective factors. A focus of the current investigation is testing alternative pathways to accelerated aging attributable to early and later stressors. One pathway focuses on stressor effects on potentially unhealthy behavior (e.g., substance use, diet) that may, in turn, give rise to chronic diseases of aging. This possibility is supported by evidence that stressful life events, especially those linked to discrimination, predict life style choices. Conversely, stress effects may also accelerate aging by altering functioning of the glucocorticoid system. Or, the two pathways may be interrelated. In addition, we focus on expanding what is known about the range of stressors within a given developmental period that are most critical, and the extent to which protective factors affect epi-A. Addressing an important limitation of prior research, we will have reliable, sensitive biomarkers of smoking, marijuana, alcohol use, and diet available at two time points as well as census track and parent report indices of stressors at multiple time points, allowing and us to supplement self-report measures and examine models more rigorously and with greater precision than has previously been possible. The proposed research will focus on 470 young adults who are part of a larger (N=889) panel study of Black families, the Family and Community Health Study. FACHS has followed a cohort of “target” participants from ages 10 to 28, along with their parents and family members. By characterizing methylation using the Illumina Epic array for two time points in young adulthood (28 and 33), we will be able to determine change in epi-A; i.e., the extent to which accelerated aging continues to be malleable in young adulthood. Using existing self- and parental reports of a variety of stressors-- from 8 waves of data, along with well-validated epigenetic biomarkers of epi-A, smoking, and drinking, we will be able to leverage the exceedingly rich existing prospective FACHS dataset, including its information on stress, protective factors, substance use, and coping, with the goal of informing preventive intervention research that can address the sources of disparities in healthy aging.