LONGITUDINAL MODELS OF CORRELATES OF AGING AND LONGEVITY

衰老与寿命相关性的纵向模型

• 批准号: 2048745
• 负责人: MAX A WOODBURY
• 金额: $ 21.29万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1982
• 资助国家: 美国
• 起止时间: 1982-06-01 至 1998-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2048745
• 关键词: epidemiology; gender difference; human mortality; human population dynamics; human subject; longevity; longitudinal human study; patient /disease registry; racial /ethnic difference; vital statistics

The age-related physiological and behavioral changes underlying the association of aging and mortality with sex and race will be assessed in five data sets (Framingham; Kaunas; Duke Longitudinal I; Duke Longitudinal II; Evans County). These data sets represent a wide range of population characteristics needed to determine whether the associations noted in one data set can be replicated in a similarly constituted analysis in the other data sets. In these analyses, we will apply methodologies appropriate to the estimation of the parameters of aging processes. Two such methodologies, which have been extensively applied to the study of such processes, will be used. The first, Grade of Membership (GOM) analysis, is applicable when there are many discrete variables used to describe biomedical and behavioral states and it is desired to determine if there exist subgroups with different age trajectories within the study population. The second, a stochastic process model of human aging and mortality, describes changes in a longitudinally followed population as a function of two inter-related processes: a) a continuous state-continuous time auto-regressive model of change on appropriate measurements, and b) a discrete state process, conditional upon the "current" risk factor values, which models systematic loss from the study population. This model is well suited to describe physiological aging changes on a moderate number of well identified risk factors. In these investigations we will accomplish certain specific and substantive tasks. First, there will be continued development of the two analytic procedures. For example, the algorithm of the stochastic process model will be modified to allow much higher transition rates (e.g., 30 percent or more) than those typically found in morbidity and mortality processes. This will facilitate the analysis of certain social transitions (e.g., retirement) which occur at a high rate over a relatively short period. Second, using the GOM model we will identify subgroups with very different aging trajectories and survival chances, both within individual populations and within multiple, differently constituted populations. Third, using both methods, we will assess sex differences in the rates of change of certain physiological and cognitive parameters, the age-related correlation of physiological, cognitive and behavioral changes, and the relation of such processes to the risk of death. Of interest, is a study of the temporal lag in CVD morbidity and mortality between the sexes.

与年龄相关的生理和行为变化的基础 衰老与死亡率与性别和种族的关联将在 五个数据集（Framingham; Kaunas; Duke Longudinal I; Duke纵向 ii;埃文斯县）。 这些数据集代表了广泛的人口 确定关联是否在一个中指出的特征 数据集可以在另一个类似的分析中复制 数据集。 在这些分析中，我们将应用适当的方法 老化过程参数的估计。 两个这样的 方法论已广泛应用于此类的研究 过程将被使用。 第一个，成员资格（GOM）分析是 当有许多离散变量用于描述时适用 生物医学和行为状态，希望确定是否在那里 研究中存在具有不同年龄轨迹的亚组 人口。 第二个是人类衰老的随机过程模型 死亡率，描述了纵向跟踪人口的变化 两个相互关联过程的功能：a）连续状态连续 适当测量的时间自动回归模型，b）a 离散的状态过程，以“当前”风险因素值为条件， 哪些模型从研究人群中进行系统的损失。 这个模型很好 适合描述中等数量的生理衰老变化 确定的风险因素。 在这些调查中，我们将完成 某些特定的实质性任务。 首先，将继续 开发两个分析程序。 例如， 随机过程模型将被修改以允许更高 过渡率（例如30％或以上），比通常发现的过渡率 发病率和死亡过程。 这将有助于分析 某些社会过渡（例如，退休）以较高的速度发生 在相对较短的时间内。 第二，使用GOM模型，我们将 识别具有非常不同衰老轨迹和生存的亚组 在单个人口内和多个人群中的机会， 构成人群的不同。 第三，使用这两种方法，我们将 评估某些生理学变化率的性别差异和 认知参数，与年龄相关的生理相关性， 认知和行为变化，以及此类过程与 死亡风险。 感兴趣的是对CVD的时间滞后的研究 性别之间的发病率和死亡率。