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

老龄化轨迹和健康状况的人口亚组差异：方法与应用

• 批准号: 10259741
• 负责人: Serkalem Demissie
• 金额: $ 19.24万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-10 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10259741
• 关键词: Accounting; Address; African American; Age; Age of Onset; Aging; Alaska Native; Algorithms; American Indians; Area; Asians; Biological; Biological Markers; Biology of Aging; Blood Pressure; Characteristics; Chronic Disease; Chronology; Creatinine; Data; Development; Disease; Disease Progression; Elderly; Electronic Health Record; Ethnic Origin; Future; Gender; Genomics; Goals; Health; Health Care Costs; Health behavior; Health trends; Healthcare; Healthcare Systems; Heterogeneity; Hispanics; Individual; Intervention; Investigation; Knowledge; Life Expectancy; Link; Longitudinal Studies; Measures; Medical; Methodology; Methods; Modeling; Morbidity - disease rate; NIH Program Announcements; Native Hawaiian; Not Hispanic or Latino; Onset of illness; Outcome; Pacific Island Americans; Phenotype; Physiological; Population; Population Group; Population Heterogeneity; Public Health; Quality of life; Race; Research; Research Design; Resources; Risk Factors; Serum; Societies; Socioeconomic Factors; Statistical Methods; Stress; Subgroup; System; Testing; United States Department of Veterans Affairs; Veterans; Woman; Work; age related; base; biological research; cohort; demographics; design; direct application; disability; epidemiology study; ethnic difference; follow-up; functional decline; health care needs assessment; health difference; human old age (65+); implementation strategy; improved; insight; men; middle age; novel strategies; potential biomarker; prevent; public health relevance; risk prediction model; study population; tool; young adult

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.

摘要：尽管医疗保健支出大幅增加和预期寿命增加 老年人，超过三分之二的人患有多种与年龄有关的慢性疾病。慢性病帐户 75％的医疗保健成本和资源利用。估计表明老年人的数量（年龄 ≥65）在美国，到2060年将几乎翻一番，并变得更加多样化。 人口变化以及对公共卫生和医疗保健系统的影响，我们对 衰老和将老化与年龄相关条件联系起来的机制，以及它们如何因性别而差异， 年代年龄（CA）（在年轻人，中年，年轻，老年和最古老的年龄）和种族 - 种族仍然不完整。改善了对衰老和健康的见解，人口亚组的差异 因此至关重要。我们拟议的工作的长期目标是改善老年人的生活质量 成人通过减少与年龄相关的残疾和发病率。而CA是许多慢性的主要危险因素 疾病，有明确的证据表明，衰老率表现为生理功能下降 系统并被称为生物年龄（BA），在同一个人的个体之间显着不同。那， 研究BA至关重要。 BA不是直接测量的，而是根据衰老的潜在生物标志物推断出来的。 尽管存在各种量化BA的方法，但准确的估计仍然是一个挑战。有 方法论上的差距和明确的改进机会。例如，克莱梅拉（Klemera – Uboubal）的模型， 使用最广泛的工具，假设生物标志物与年龄之间的线性关系及其应用 纵向数据和异质种群亚组是出乎意料的。提出的目标 研究是为了开发一种改进的BA估计方法，并检查衰老轨迹和 其决定者和性别，年龄和种族种族的结果。我们的中心假设是我们将 通过使用通用的加性模型来实现BA估计的显着改善 使用纵向研究设计对广泛的线性和非线性关系进行建模，并 占据人口亚组差异。反过来，这将允许对 衰老及其风险因素和结果。我们将使用两个特定目标检验中心假设。目标1： 开发一种新的和改进的算法，以更准确地估计BA；目标2：量化衰老和 根据关键人口统计学评估加速衰老的潜在决定。估计能力 BA准确地可以具有深远的影响，包括在衰老调查中的直接应用 机制和对年龄相关条件的未来发作的预测。我们在潜水员人口中的发现 亚组可以提高我们对老年人健康趋势和差异的理解范围， 并为特定人群的医疗保健需求评估和实施策略提供信息。