Population subgroup difference in aging trajectory and health: Methods and application

摘要：尽管医疗保健支出大幅增加， 在老年人中，超过三分之二的人患有多种与年龄有关的慢性疾病。慢性病占 75%的医疗费用和资源利用率。估计数显示，老年人（年龄 到2060年，美国65岁以上的人口将几乎翻一番，并变得更加多样化。尽管预计 人口变化，以及对公共卫生和医疗保健系统的影响，我们对 衰老以及将衰老与年龄相关疾病联系起来的机制，以及它们如何因性别而异， 年龄（CA）（在成年人，中年人，中年人，老年人和老年人），和种族- 种族划分仍不完整。更深入地了解老龄化和健康，以及人口亚组的差异 因此至关重要。我们建议的工作的长期目标是提高老年人的生活质量， 减少与年龄有关的残疾和发病率。虽然CA是许多慢性疾病的主要危险因素， 疾病，有明确的证据表明，衰老的速度，表现在生理功能的下降， 生物年龄（BA），在同一CA的个体之间存在显著差异。因此，在本发明中， 研究BA是必要的。BA不能直接测量，但可以从衰老的潜在生物标志物中推断出来。 虽然存在各种方法来量化BA，但准确估计仍然是一个挑战。有 方法上的差距和明显的改进机会。例如，Klemera-Doubal的模型， 最广泛使用的工具，假设生物标志物和年龄之间的线性关系， 纵向数据和异质性人口亚组尚未探索。提出的目标 研究是开发一种改进的BA估计方法，并检查老化轨迹和 其决定因素和结果的性别，年龄和种族，民族。我们的核心假设是， 通过采用广义加性模型实现BA估计的显著改进， 使用纵向研究设计，对广泛的线性和非线性关系进行建模，以及 适应人口亚组差异。这反过来又将使人们能够更有效地调查 老龄化及其风险因素和结果。我们将用两个具体目标来检验中心假设。目标1： 开发一种新的改进算法，以更准确地估计BA;目标2：量化老化和 根据关键人口统计数据评估加速老化的潜在决定因素。估计能力 BA准确地可以有深刻的含义，包括在衰老研究中的直接应用 机制和预测未来发生的年龄相关的条件。我们在不同人群中的发现 分组可以提高我们对老年人健康趋势和差异的理解范围， 并为特定人群的卫生保健需求评估和实施战略提供信息。

SUMMARY: Despite the substantial increase in healthcare spending and increase in life expectancy among older adults, more than two-thirds live with multiple age-related chronic diseases. Chronic diseases account for 75% of the health care costs and resource utilization. Estimates show that the number of older adults (age ≥65) in the U.S. will almost double and become considerably more diverse by 2060. Despite the projected demographic changes, and the implications for public health and the health care system, our understanding of aging and the mechanisms that link aging to age-related conditions, and how they differ by gender, chronological age (CA) (in the young-adult, middle-age, young-old, the old, and the oldest-old), and race- ethnicity remains incomplete. Improved insights into aging and health, and differences in population subgroups are therefore essential. The long-term goal of our proposed work is to improve the quality of life among older adults by reducing age-related disability and morbidity. While CA is the main risk factor for many chronic illnesses, there is clear evidence that the rate of aging, manifested in decline of function in physiological systems and referred to as biological age (BA), differs significantly between individuals of the same CA. Thus, researching BA is essential. BA is not directly measureable, but inferred from potential biomarkers of aging. Although various approaches exist to quantify BA, accurate estimation remains a challenge. There are methodological gaps and clear opportunities for improvement. For example, Klemera–Doubal's model, the most widely used tool, assumes linear relationships between biomarkers and age and its application for longitudinal data and heterogeneous population subgroups is unexplored. The objectives of this proposed study are to develop an improved method for BA estimation, and examine differences in aging trajectory and its determinants and outcomes by gender, age, and race-ethnicity. Our central hypothesis is that we will achieve significant improvement in BA estimation by employing generalized additive models that allow modeling a broad class of linear and nonlinear relationships, using a longitudinal study design, and accommodating population subgroup differences. This, in turn, will permit a more effective investigation of aging and its risk factors and outcomes. We will test the central hypothesis using two specific aims. Aim 1: Develop a new and improved algorithm for more accurate estimation of BA; Aim 2: Quantify aging and evaluate potential determinants of accelerated aging according to key demographics. The ability to estimate BA accurately can have profound implications, including a direct application in the investigation of aging mechanisms and prediction of future onset of age-related conditions. Our findings in diverse population subgroups can improve the scope of our understanding of health trends and differences among older adults, and inform population-specific health care needs assessment and implementation strategies.