Latent Growth Curve Paths to Longevity: The Terman Study

描述（由申请人提供）：从幼儿期开始的发育生物心理社会轨迹的差异可能会将那些特别健康的老年人与那些过早死亡或患有慢性疾病的人区分开来。纵向轨迹的变化将使用潜在的增长曲线的方法对1,528个人从特曼生命周期研究，一个全面的8十年档案。虽然研究中的个体是根据儿童时期的智力和前途来选择的，但在后来的成功、长寿和疾病（包括痴呆症）方面存在很大差异。80年来，在丰富的社会心理、健康和死亡率信息中，还有待考虑生命历程轨迹。该项目将使用动态建模方法来研究个体特征的稳定性和变化模式，包括身体活动水平，社会支持和个性，以及作为原因特异性死亡率函数的生长曲线变化（疾病学家编码）。儿童期数据将用作生长轨迹变化的预测因子，例如出生体重、护理、儿童期疾病、发育里程碑、性别、社会经济地位和参与者出生时的父母年龄。成年期发生的生活事件也将被视为健康和心理社会轨迹转折点的潜在来源，例如离婚或配偶死亡。分析中将统计考虑样本和家庭因素，例如智力和父母寿命。与请求（RFA）一致，目的是使用新的建模技术来扩大现有研究的使用和合作，以评估健康和心理轨迹的模式以及它们随时间的动态相互关系，以便区分那些达到老年的人以及他们是否达到健康状态。

DESCRIPTION (provided by applicant): Disparities in developmental biopsychosocial trajectories beginning in early childhood may distinguish those who reach advanced age exceptionally healthy from those who suffer an early death or chronic disease. Variations in longitudinal trajectories will be examined using a latent growth curve approach on 1,528 individuals from the Terman Life Cycle Study, a comprehensive 8-decade archive. Whilst individuals in the study were selected for childhood intelligence and promise, there was substantial variation in later success, longevity, and disease, including dementing illnesses. Life course trajectories have yet to be considered across the 8-decades of rich psychosocial, health and mortality information available. This project will use dynamical modeling approaches to examine patterns of stability and change for individual characteristics, including physical activity level, social support, and personality, and variations in growth curves as a function of cause-specific mortality (nosologist coded). Childhood data will be used as predictors of variations in growth trajectories, for example, birth weight, nursing, childhood illnesses, developmental milestones, gender, socioeconomic status, and parental age at participant's birth. Life events occurring in adulthood will also be examined as potential sources of turning points in health and psychosocial trajectories, for example, divorce or death of spouse. Sample and familial factors such as intelligence and parental longevity will be statistically accounted for in analyses. Consistent with the Request (RFA), the aim is to use new modeling techniques to expand use of and collaboration on an existing study to assess patterns of health and psychosocial trajectories as well as their dynamic interrelations across time in order to differentiate among those who reach old age and whether they reach it in good health.