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

综述：尽管医疗保健支出大幅增加，预期寿命也有所增加 老年人中，超过三分之二的人患有多种与年龄相关的慢性病。慢性病占了 75%的医疗成本和资源利用率。估计显示，老年人(年龄)的数量 到2060年，美国的≥)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 老龄化和将老龄化与年龄相关的疾病联系起来的机制，以及它们在性别上的差异， 时间顺序年龄(CA)(青年-成年人、中年人、青年-老年人、老年人和最年长的老年人)和种族- 种族特征仍然不完整。提高对老龄化和健康以及人口亚群差异的洞察力 因此是必不可少的。我们建议的工作的长期目标是改善老年人的生活质量 减少与年龄有关的残疾和发病率。而CA是许多慢性疾病的主要危险因素 疾病方面，有明显的证据表明衰老的速度，表现为生理机能的衰退 系统，被称为生物年龄(BA)，在同一CA的个体之间fi明显不同。因此， 研究BA是至关重要的。BA是不可直接测量的，但可以从潜在的衰老生物标志物中推断出来。 尽管有各种方法来量化BA，但准确的估计仍然是一个挑战。确实有 方法上的差距和明显的改进机会。例如，克勒梅拉-杜巴尔的模型， 最广泛使用的工具，假设生物标记物和年龄之间的线性关系及其在 纵向数据和不同的人口亚群是未被探索的。这项提议的目标 研究是开发一种改进的BA估计方法，并检查老化轨迹和 它的决定因素和结果按性别、年龄和种族-民族划分。我们的中心假设是我们会 通过使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，对一大类线性和非线性关系进行建模，以及 适应人口亚群的差异。这反过来将允许更有效地调查 老龄化及其风险因素和后果。我们将使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 根据关键的人口统计数据评估加速老龄化的潜在决定因素。估计的能力 BA的准确可以产生深远的影响，包括在衰老研究中的直接应用 年龄相关疾病的发病机制和未来发病预测。我们在不同人群中的发现 分组可以扩大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.