Genetics of Changes in Population Pyramids: Implications for Health Forecasting

人口金字塔变化的遗传学：对健康预测的影响

• 批准号: 8629370
• 负责人: ANATOLIY I YASHIN
• 金额: $ 59.5万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8629370
• 关键词: Accounting; Address; Affect; Age; Alzheimer' s Disease; Birth; Comorbidity; Coronary heart disease; Data; Data Set; Diabetes Mellitus; Disease; Education; Elderly; Female; Framingham Heart Study; Future; Gender; Genes; Genetic; Genomics; Habits; Health; Health Transition; Healthcare Systems; Human Genome; Individual; Internet; Link; Literature; Long-Term Care; Longevity; Malignant Neoplasms; Marital Status; Medical Surveillance; Medicare; Metabolic Pathway; Modeling; Mutation; Pattern; Physiological; Population; Prevalence; Race; Recommendation; Records; Research; Resources; Retirement; Risk; Risk Estimate; Role; Signal Pathway; Smoking; Specific qualifier value; Stroke; Surveys; Testing; Time; Uncertainty; age related; cohort; data modeling; drinking; functional improvement; genetic association; genetic variant; improved; indexing; male; mortality; non-genetic; population health; public health relevance; trait; trend

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.

描述（由申请人提供）：拟议研究的总体目标是显着提高美国老年人的健康预测质量。将通过构建具有不同水平复杂性，评估预测质量的一组新的健康预测模型，并使用经过验证的模型来预测癌症，冠心病（CHD），中风，糖尿病和阿尔茨海默氏病（AD）在不同风景下的未来患病率。这些模型将使用有关影响健康和生存因素的信息，包括五个数据集可用的因素，包括弗雷明汉心脏研究（FHS），健康和退休研究，与医疗保险文件（HRS-M）合并，与Medicare Records（NLTCS-M）相关的国家长期护理调查（NLTCS-M）链接，并与Medicare Records（Medicare Medicare（Medicare Records）和5％的Medicare Records（5％）和5％的Medicare Records（以及5％）以及5％的Medicare Records（以及5％）和5％。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基因，多基因和年龄特异性影响，以及相关变化的动态机制。将解决以下具体目的：1。预测2020、2025、2025、2030和2035年的癌症，冠心病，中风，糖尿病和广告的年龄模式，使用具有不同级别的复杂性的模型，使用SEER-M和5％-M文件，NLTCS-M和HRS-M（无遗传数据）的模型构建了不同级别的复杂性。确定一组遗传变异，显示了FHS和HRS-M数据中与健康和HRS-M数据中健康和生存特征的个人和多效性相关性，该遗传变异使用富含对健康特征的多效性遗传影响的候选基因组区域。识别与选定的遗传变异有关的基因，并评估其在代谢和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AIM 1相同疾病和时间范围的患病率的年龄模式，但是采用了遗传变异对健康和生存以及使用不同情况的遗传学，多基因和年龄特异性的遗传信息的先进建模方法。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内使用经过验证的模型。 4.使用扩展的多态健康和死亡率模型来预测上述疾病的患病率模式，通过将健康转变的风险视为遗传因素的功能，以及观察到的协变量和生理变量。为了这些目的，使用FHS和HRS-M数据评估过渡风险及其时间趋势。使用可用数据子集测试健康预测质量。在不同情况下使用经过验证的模型进行健康预测。使用本项目中构建的不同模型以及文献中可用的模型比较健康预测的结果。提出有关在上述时间范围内正确使用数据和模型在健康预测中的建议。