Identifying gene-by-environment interplay in health behavior

识别健康行为中基因与环境的相互作用

• 批准号: 10253129
• 负责人: Titus Johannes Galama
• 金额: $ 84.19万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10253129
• 关键词: Address; Adolescence; Adolescent; Adult; Affect; Age; Aging; Alcohol consumption; American; Caring; Cessation of life; Characteristics; Child; Childhood; Cigarette; Collection; Data; Data Set; Diet; Dimensions; Disability Insurance; Disease; Economics; Endowment; Environment; Environmental Exposure; Etiology; Evaluation; Evolution; Exhibits; Framingham Heart Study; Genes; Genetic; Genetic Predisposition to Disease; Genetic Risk; Genotype; Health; Health Professional; Health and Retirement Study; Health behavior; Health behavior outcomes; Individual; Intervention; Investigation; Knowledge; Labor Forces; Life; Life Cycle Stages; Long-Term Effects; Longevity; Longitudinal Studies; Measures; Medical; Methods; Modeling; Natural experiment; Nature; Obesity; Occupations; Parents; Pathway interactions; Pattern; Phase; Phenotype; Play; Policies; Population Group; Research; Research Personnel; Role; Sample Size; Schools; Shapes; Smoke; Smoking; Social Policies; Socioeconomic Status; Source; Structural Models; Structure; Taxation; Testing; Tobacco use; Variant; Withdrawal; base; biobank; experimental study; genetic analysis; genetic variant; genome wide association study; health disparity; high risk; improved; innovation; insight; intervention effect; low socioeconomic status; mortality; non-smoker; novel; physical inactivity; socioeconomics; theories; therapy design; unemployment insurance

Project summary / abstract Smoking, alcohol consumption, and obesity are the three leading causes of preventable disease and death in the U.S. Each year tobacco use alone kills nearly 440,000 Americans, who die up to 15 years earlier than nonsmokers. Risky health behaviors are more prevalent among low socioeconomic status (SES) groups, and significant sources of the substantial disparities in health between them. Such disparities are formed early in life and become more pronounced as individuals age. In this application we seek to test the hypothesis that protective socioeconomic and policy environments moderate the effects of high-risk genetic variants for smoking, alcohol consumption, and obesity; evaluate how such gene-by-environment (GxE) interplay evolves over the lifecycle and how it contributes to health disparities; generate information relevant to decision makers and health professionals; and when possible investigate the underlying mechanisms. Using results from genome-wide association studies (GWAS), we focus on specific genetic variants or aggregated genetic scores and specific dimensions of the socioeconomic and policy environment. The proposed research is a natural continuation of our project, “From understanding to reducing health disparities: a model-based evaluation” (R01 AG037398), in which we developed a theory of the formation and evolution of health disparities between SES groups, and informed by the theory, explored the extent to which disparities in health and longevity between SES groups are the result of differences in job-conditions, health behavior, medical care, and labor-force withdrawal. We also investigated the effects of possible policy interventions on health and health disparities. Two key findings of that research are that: (i) health behaviors play a very important role in health disparities between SES groups, and (ii) multiple factors (that have a genetic basis) influence both SES and health, and interact with SES in producing health. This suggests an important role for interactions between genes and the environment in shaping health behavior. With the very recent order of magnitude increases in the power of polygenic prediction, and the very recent genetic discovery results for several new smoking, alcohol consumption, and obesity phenotypes, it is now possible to incorporate into our analyses the effects of genetic predispositions, in interaction with the socioeconomic and policy environment, on these three risky health behaviors. The proposed project will integrate several complementary methods: descriptive analyses to explore associations for different levels of genetic risk and different measures of the social and policy environment at different stages of life; estimation of structural lifecycle models to better understand GxE interplay and make predictions; exploitation of natural experiments to address causality; and counterfactual analyses, using the structural models, to evaluate intervention alternatives.

项目摘要/摘要