Biological Aging Across the Life Course: Harmonizing Cohort Biospecimen Archives

随着美国迅速过渡到老龄化社会，与衰老相关的疾病在这两个国家都在增加。 流行和成本。加剧这种担忧的是，有证据表明，今天进入成年和中年的人群 比前几代人在那个年龄段的健康状况要差。近年来， 这是一个令人关切的问题，需要更早地发现与衰老有关的疾病，往往早在中年之前。 确定与衰老和健康结果相关的个体生理变化提供了重要的 关于对衰老过程的影响的信息，以及确定潜在的干预领域， 增加健康寿命和长寿。各种各样的方法来测量生理老化使用集 近年来已经提出了生物标志物，但目前对它们如何与 这些关系在生命过程中如何变化，以及衰老的生物标志物在多大程度上 可推广到人口亚组（按性别、种族/民族和社会经济地位（SES））。早期生活 社会逆境与老龄化和与老龄化有关的疾病有着密切和持久的联系。然而，一把钥匙 一个尚未回答的问题是，在生命过程中，衰老的生物标志物在多大程度上将早期生活环境与 晚年健康。为了了解衰老的生物标志物如何在整个生命过程中相互关联，并与SES和 社会背景，我们利用调查和生物储存库的数据和样本，从三个大的国家 代表性小组研究：健康和退休研究（HRS;代表美国人口超过年龄 50;年龄51-110的生物标志物数据），国家青少年至成人健康纵向研究（添加 健康; 1994年7-12年级青少年的代表; 24-42岁的生物标志物数据）和脆性 家庭和儿童健康研究（FFCW; 1998-2000年美国大城市出生的代表;生物标志物 9-24岁的数据）。在这三个小组研究中，生物标志物和调查数据的协调提供了 这是一个前所未有的机会，可以发现衰老的生物标志物如何在生命过程中相互关联，以及它们如何在生命过程中相互作用。 与社会经济地位和其他与衰老相关的社会背景因素相关。目标1将产生协调一致的 三项国家小组研究为研究界提供的数据和衡量标准， 生物标志物：基于衰老血液的生物标志物（IL-6、TNFa、CRP、GDF 15、IGF-1、胱抑素C、NT-proBNP和 hbA 1c）、DNA甲基化（Illumina EPIC芯片）和基因表达（RNA Seq）。目标2将研究 衰老的生物标志物分布在生命过程中，并在几个关键领域相互关联。 人口统计学此外，使用已经产生的免疫细胞甲基化（FF在9岁和15岁）和RNA（AH 年龄24-32），我们将预测随后的衰老的成人生物标志物。利用统一的调查数据，目标3将 研究衰老的生物标志物与一系列早期生活和成年健康的背景测量之间的联系 表型结果将提供深入了解哪些措施，从血液化学，甲基化，和RNA可以 在可观察到的症状出现之前，可以用来检查环境对衰老的影响。

As the US rapidly transitions into an aging society, aging-associated diseases are increasing in both prevalence and cost. Compounding this concern is evidence that cohorts entering adulthood and midlife today are less healthy than preceding generations were at those ages. The faster rate of aging among recent cohorts is cause for concern, and necessitates earlier detection of aging-associated diseases, often well before midlife. Determining the individual physiological changes linked to aging and health outcomes provides important information about influences on the aging process, as well as identifying potential areas for intervention to increase healthy lifespan and longevity. A variety of approaches to measuring physiological aging using sets of biomarkers have been suggested in recent years—but currently little is known about how they correlate with each other, how those relationships change over the life course, and to what degree the biomarkers of aging are generalizable to population subgroups (by sex, race/ethnicity, and socioeconomic status (SES)). Early life social adversities show strong and lasting associations with aging and aging-related diseases. However, a key unanswered question is the degree to which biomarkers of aging across the life course link early life context to later life health. To understand how biomarkers of aging correlate across the life course and link to SES and social context we draw upon survey and biorepository data and samples from three large nationally representative panel studies: the Health and Retirement Study (HRS; representative of US population over age 50; biomarker data for ages 51-110), the National Longitudinal Study of Adolescent to Adult Health (Add Health; representative of adolescents in grades 7-12 in 1994; biomarker data ages 24-42) and the Fragile Families and Child Wellbeing Study (FFCW; representative of birth in large US Cities 1998-2000; biomarker data ages 9-24). The harmonization of biomarkers and survey data across these three panel studies provides an unprecedented opportunity to discover how biomarkers of aging correlate over the life course and how they correlate with SES and other social contextual factors associated with aging. Aim 1 will produce harmonized data and measures for the research community from three national panel studies with special focus on biomarkers: aging blood-based biomarkers (IL-6, TNFa, CRP, GDF15, IGF-1, Cystatin C, NT-proBNP, and hbA1c), DNA methylation (Illumina EPIC chip), and gene expression (RNA Seq). Aim 2 will examine how the biomarkers of aging are distributed and correlate with each other over the life course and across several key demographics. Also, using already generated immune cell methylation (FF at ages 9 and 15) and RNA (AH age 24-32) we will predict subsequent adult biomarkers of aging. Using the harmonized survey data, Aim 3 will examine the link between biomarkers of aging and a set of contextual measures of early life and adult health phenotypes. Results will provide insight into which measures from blood chemistry, methylation, and RNA can be used to examine contextual influences on aging long before observable symptoms arise.