SES health gradients in late life: testing models of gene-environment interplay in an international twin consortium

晚年的 SES 健康梯度：在国际双胞胎联盟中测试基因与环境相互作用的模型

• 批准号: 10190761
• 负责人: Brian K. Finch
• 金额: $ 55.61万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10190761
• 关键词: Accounting; Activities of Daily Living; Address; Adult; Affect; Age; Aging; Area; Australia; Blood Pressure; Body mass index; Capital; Characteristics; Chronic Disease; Classification; Cognitive; Cohort Studies; Collaborations; Coronary Arteriosclerosis; Country; Data; Data Collection; Data Set; Denmark; Disease; Disease susceptibility; Dizygotic Twins; Economic Conditions; Education; Elderly; Emotional; Environment; Environmental Risk Factor; Exposure to; Financial Hardship; Finland; Genes; Genetic; Genetic Predisposition to Disease; Genotype; Goals; Hand Strength; Health; Health Policy; Health Promotion; Health Status; Healthy People 2020; Human; Income; Individual; Inherited; Intake; International; Life Cycle Stages; Life Style; Loneliness; Measures; Mediating; Mental Depression; Methods; Modeling; Monozygotic twins; Nature; Occupational; Occupations; Outcome; Participant; Phenotype; Physical Exercise; Poverty; Prevention strategy; Quantitative Genetics; Research; Research Personnel; Resources; Risk; Role; Sampling; Sex Differences; Social Class; Social Policies; Social status; Social stratification; Societies; Socioeconomic Status; Stress; Structure; Sweden; Testing; Time; Toxic effect; Translating; Twin Multiple Birth; Twin Studies; Vision; Woman; Women' s Health; Work; cohort; cost effective; design; disorder risk; environmental enrichment for laboratory animals; genome-wide; health economics; health inequalities; indexing; innovation; low socioeconomic status; memory process; men; physical conditioning; polygenic risk score; population health; processing speed; public health priorities; pulmonary function; safety net; sex; social; social health determinants; social inequality; socioeconomics; tool

Inequalities in health outcomes are targeted by Healthy People 2020 as a public health priority. The social stratification of health is well documented, pervasive, and of growing concern because it appears to be increasing over time. Reducing these social class disparities will require greater understanding of how social class impacts health than we currently have. While most research focuses on individual-level socioeconomic status—defined as social status that accrues to occupational classification, education, and income—new research has begun to focus on the macro-economic environment. Further, although both genetic (G) and environmental (E) factors are known to contribute to the SES-health gradient, the mechanisms by which the two sets of factors combine to influence health outcomes (i.e., GE interplay) are poorly understood. Models of GE interplay differ in their environmental focus (disease-triggering effects of toxic environments vs. health- promoting benefits of favorable environments) and the expected genetic contribution to disease (maximized in adverse environments, in favorable environments, or at both extremes) Understanding whether high-SES preferentially promotes good health among a genetically selected subset of individuals (i.e., social enhancement), whether low-SES triggers poor health among a genetically vulnerable subset of individuals (i.e., diathesis-stress), or both, is essential for translating research in this area into effective prevention strategies. The consortium on Interplay of Genes and Environments across Multiple Studies (IGEMS) with 16 existing longitudinal twin studies in the U.S., Sweden, Denmark, Finland, and Australia is poised to evaluate these models. IGEMS includes more than 52,000 individual twins, with over 7000 identical twin pairs for within-pair difference models, and over 12,600 dizygotic twin pairs with nearly 4000 opposite sex pairs for sex-difference models. The sample spans a wide age range (15 to 103 years at intake) and includes a set of well-characterized longitudinal phenotypes, including harmonized measures of physical health (e.g., subjective health, chronic disease indicators, body mass index, lung function, blood pressure, activities of daily living, and grip strength), cognitive health (verbal ability, spatial ability, memory, and processing speed), and emotional health (depression, loneliness) as well as measures of multiple facets of SES (e.g., occupation, education, financial strain). In addition, a large subset of IGEMS participants has genome wide genotyping from which we have computed polygenic risk scores (PRS). We will use co-twin control/within pair models, quantitative genetic moderation, sex-limitation models, and PRS analyses to investigate specific mechanisms of the SES-health gradient at the individual level as well as the country and historical cohort level (e.g., country-level indices of social inequality). The proposed study reflects an innovative vision by the investigators who—rather than addressing the research questions through new data collection—successfully created a collaboration among 16 large international studies with relevant phenotypes, making this study highly cost-effective and efficient.

《2020 年健康人民》将健康结果的不平等作为公共卫生优先事项。社会 健康分层有据可查、普遍存在，并且日益受到关注，因为它似乎 随着时间的推移而增加。减少这些社会阶层差异需要更深入地了解社会如何 阶级对健康的影响比我们现在的要大。虽然大多数研究都集中在个人层面的社会经济 地位——定义为根据职业分类、教育和收入而产生的社会地位——新 研究开始集中于宏观经济环境。此外，虽然遗传（G）和 众所周知，环境（E）因素会导致社会经济地位-健康梯度，即社会经济地位与健康梯度的机制。 人们对组合影响健康结果的两组因素（即 GE 相互作用）知之甚少。型号 GE 的相互作用在环境焦点上有所不同（有毒环境引发疾病的影响与健康环境的影响） 促进有利环境的益处）和预期的遗传对疾病的贡献（最大化 不利环境、有利环境或两个极端）了解高SES是否 优先促进基因选择的个体子集（即社会 增强），低SES是否会引发遗传易受影响的个体子集（即， 素质-压力）或两者兼而有之，对于将该领域的研究转化为有效的预防策略至关重要。 基因与环境相互作用跨多项研究联盟 (IGEMS) 拥有 16 个现有项目 美国、瑞典、丹麦、芬兰和澳大利亚的纵向双胞胎研究准备评估这些 模型。 IGEMS 包括超过 52,000 个双胞胎，其中有超过 7000 对同卵双胞胎 差异模型，超过12,600对异卵双胞胎和近4000对异性对的性别差异 模型。该样本涵盖了广泛的年龄范围（入学时 15 岁至 103 岁），并包括一组特征明确的 纵向表型，包括身体健康的统一衡量标准（例如主观健康、慢性 疾病指标、体重指数、肺功能、血压、日常生活活动能力和握力）， 认知健康（语言能力、空间能力、记忆力和处理速度）和情绪健康 （抑郁、孤独）以及社会经济地位多个方面的衡量标准（例如职业、教育、经济状况） 拉紧）。此外，IGEMS 参与者的很大一部分进行了全基因组基因分型，从中我们可以得到 计算多基因风险评分（PRS）。我们将使用双胞胎控制/内对模型，定量遗传 适度、性别限制模型和 PRS 分析来研究 SES 健康的具体机制 个人层面以及国家和历史队列层面的梯度（例如，国家层面的指数 社会不平等）。拟议的研究反映了研究人员的创新愿景，而不是 通过新的数据收集解决研究问题——成功地建立了合作 16项相关表型的大型国际研究，使得本研究极具成本效益和效率。