Genetics of Changes in Population Pyramids: Implications for Health Forecasting

描述（由申请人提供）：拟议研究的总体目标是显著提高美国老年人健康预测的质量。通过构建一套新的不同复杂程度的健康预测模型，评估其预测质量，并使用经过验证的模型预测不同情景下癌症、冠心病（CHD）、中风、糖尿病和阿尔茨海默病（AD）的未来患病率，从而实现这一目标。这些模型将使用五个数据集中有关影响健康和生存因素的信息，包括弗雷明汉心脏研究（FHS）、健康与退休研究与医疗保险档案（HRS-M）、与医疗保险记录相关的国家长期护理调查（NLTCS-M）、与医疗保险记录合并的监测、流行病学和最终结果数据（SEER-M）和5%医疗保险（5%- m）文件。最复杂的模型将使用有关遗传和非遗传因素的信息，并考虑到基因对健康和生存的多效性、多基因性和年龄特异性影响，以及与衰老有关的变化的动态机制。将讨论以下具体目标：利用SEER-M、5%-M文件、NLTCS-M和HRS-M（不含遗传数据）数据构建不同复杂程度的模型，预测2020年、2025年、2030年和2035年男性和女性癌症、冠心病、中风、糖尿病和AD患病率的年龄模式。利用对健康性状具有多效性遗传效应的候选基因组区域，在FHS和HRS-M数据中识别出与健康和生存性状存在个体和多效性关联的遗传变异集。确定与选定的遗传变异相关的基因，并评估其在代谢和信号通路以及疾病网络中的作用。构建多基因评分指标，评价其对健康和生存性状的影响。3. 预测目标1中相同疾病和时间范围的流行年龄模式，但采用先进的建模方法，纳入有关遗传变异对健康和生存的多效性、多基因和年龄特异性影响的遗传信息，并使用不同的情景。使用可用数据子集测试运行状况预测的质量。在上述指定的时间范围内使用经过验证的健康预测模型。4. 使用扩展的多状态健康和死亡率模型，通过考虑作为遗传因素功能的健康转变风险，以及观察到的协变量和生理变量，预测上述疾病流行的年龄模式。出于这些目的，使用FHS和HRS-M数据评估随后出生队列的转移风险及其时间趋势。使用可用数据子集测试运行状况预测的质量。在不同情景下使用经过验证的模型进行健康预测。比较使用本项目中构建的不同模型以及文献中可用模型的健康预测结果。就正确使用上述时间范围内的健康预测数据和模型提出建议。

DESCRIPTION (provided by applicant): The overall objective of the proposed research is to significantly improve quality of health forecasting for the US elderly. This objective will be reached by constructing a set of new health predicting models having different levels of complexity, evaluating quality of their predictions, and using verified models to predict future prevalence of cancer, coronary heart disease (CHD), stroke, diabetes, and Alzheimer's disease (AD) under different scenarios. The models will use information about factors affecting health and survival available in five datasets including the Framingham Heart Study (FHS), Health and Retirement study merged with Medicare files (HRS-M), National Long Term Care Survey linked to Medicare records (NLTCS-M), the Surveillance, the Epidemiology and End Results data merged with Medicare records (SEER-M), and the 5% Medicare (5%-M) file. The most sophisticated models will use information about genetic and non-genetic factors, and take pleiotropic, polygenic, and age-specific effects of genes on health and survival, as well as dynamic mechanisms of aging related changes, into account. The following specific aims will be addressed: 1. Predict age patterns of prevalence for cancer, CHD, stroke, diabetes, and AD for years 2020, 2025, 2030, and 2035 using models having different levels of complexity constructed using data from SEER-M, and 5%-M files, NLTCS-M and HRS-M (without genetic data) for males and females under different scenarios.2. Identify sets of genetic variants showing individual and pleiotropic associations with health and survival traits in the FHS and HRS-M data using candidate genomic regions enriched for pleiotropic genetic effects on health traits. Identify genes related to selected genetic variants and evaluate their roles in metabolic and signaling pathways and disease networks. Construct polygenic score indices and evaluate their influence on health and survival traits. 3. Predict age patterns of prevalence for the same diseases and time horizons as in Aim 1, however applying advanced modeling approaches incorporating the genetic information about pleiotropic, polygenic and age-specific effects of genetic variants on health and survival and using different scenarios. Test the quality of health predictions using subsets of available data. Use verified models in health forecasting for time horizons specified above. 4. Predict age patterns of prevalence of diseases listed above using extended multistate health and mortality models by considering risks of health transitions as functions of genetic factors, as well as observed covariates and physiological variables. For these purposes, evaluate risks of transitions and their time trends for subsequent birth cohorts using FHS and HRS-M data. Test quality of health predictions using subsets of available data. Use verified models in health forecasting under different scenarios. Compare results of health predictions using different models constructed in this project, as well as models available in the literature. Make recommendations concerning the proper use of data and models in health forecasting for time horizons specified above.