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的健康预测质量。这一目标将通过构建一组具有不同复杂程度的新的健康预测模型来实现，评估其预测的质量，并使用经过验证的模型来预测不同情景下未来癌症、冠心病(CHD)、中风、糖尿病和阿尔茨海默病(AD)的患病率。这些模型将使用五个数据集中有关影响健康和生存的因素的信息，包括Framingham心脏研究(FHS)、与联邦医疗保险文件合并的健康和退休研究(HRS-M)、与联邦医疗保险记录相关联的国家长期护理调查(NLTCS-M)、监测、流行病学和最终结果数据与联邦医疗保险记录合并(SEER-M)以及5%的联邦医疗保险(5%-M)文件。最复杂的模型将使用关于遗传和非遗传因素的信息，并考虑基因对健康和生存的多效性、多基因和特定年龄的影响，以及与衰老相关的变化的动态机制。将解决以下具体目标：1.使用具有不同复杂程度的模型预测2020年、2025年、2030年和2035年癌症、冠心病、中风、糖尿病和阿尔茨海默病的流行年龄模式，该模型使用SEER-M和5%-M文件、NLTCS-M和HRS-M(没有遗传数据)在不同情况下为男性和女性构建。在FHS和HRS-M数据中，使用为健康性状的多效性遗传效应而丰富的候选基因组区域，识别显示个体和多效性与健康和生存特征相关的遗传变异组。识别与选定的遗传变异相关的基因，并评估它们在代谢、信号通路和疾病网络中的作用。构建多基因评分指数并评估其对健康和生存特征的影响。3.预测与目标1相同的疾病患病率的年龄模式和时间范围，但采用先进的建模方法，结合遗传变异对健康和生存的多效性、多基因和特定年龄的影响的遗传信息，并使用不同的假设。使用可用数据的子集测试健康预测的质量。在上面指定的时间范围的健康预测中使用经过验证的模型。4.使用扩展的多状态健康和死亡率模型，通过将健康转变风险作为遗传因素以及观察到的协变量和生理变量的函数来预测上述疾病流行的年龄模式。为此，使用FHS和HRS-M数据评估后续出生队列的过渡风险及其时间趋势。使用可用数据的子集测试健康预测的质量。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提供的模型进行健康预测的结果。就在上述规定的时间范围内在健康预测中正确使用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.