Social Modulation of Transitions in Diabetes: Synthesizing Data Sets and Biomeasure Assays

项目摘要 糖尿病和肥胖症患病率的上升是一种公共卫生危机。解决这个问题 流行病需要了解人口内部的差异，即人们如何在非 糖尿病、糖尿病前期和糖尿病状态，以及它们的发病率和死亡率。对所有人的全面研究 在具有全国代表性的美国老年人样本中，转变和结果对于准确地 预测未来的流行率和卫生保健需求。同样重要的是确定新的方法来 降低糖尿病风险、并发症和死亡率。虽然行为干预是有益的，但我们有 早期证据表明，人们的社交活动也可能独立地降低糖尿病风险和进展。 我们的中心假设是糖尿病轨迹中的很大一部分个体差异是 与不同的社会环境相关联。我们将通过(1)组合两个高- 高质量的国家数据集，每个数据集都有独特的优势，创建一个更多的综合数据集 比单独和(2)分析广泛的生物测量(包括A1C)更全面。这个 国家社会生活、健康和老龄化项目(NSHAP)收集了三波(2005， 2010和2015年(2015年生物测量将在此处进行检测)、健康数据、用药情况以及 对任何全国性老年人调查的社会关系和亲密伴侣的全面评估。 健康和退休研究(HRS)还收集了来自一家更大的 自2006年以来，每两年进行一次老年人样本调查。建议的分析组合了这些数据集，以增加 我们估计的精确度，同时利用NSHAP独特的社会和生物数据。我们开发的工具 为了组合和分析这些数据集，这些数据集也适用于其他慢性病和衰老状况 并将免费提供给研究界，以及完整的生物测量数据，提供 这是研究各种健康问题的宝贵公共资源。对于目标1，我们将估计 美国老年人口中非糖尿病、糖尿病前期和糖尿病状态的转换率 肥胖、健康状况和人口子组。对于目标2，我们将检验以下假设：社会因素 如大型社交网络、积极社交和亲密关系、社会支持、社会参与 社会压力缓冲、低水平的孤立和孤独与较低的 糖尿病的进展，甚至逆转。对于目标3，我们将测试糖尿病发病率的差异 过渡是通过特定的生理(炎症、应激生物学、性激素)和健康来调节的 行为机制(活动和睡眠)。了解社会因素影响的机制 糖尿病风险可能有助于1)更好地针对个人的社会背景进行特定类型的社会干预， 2)展示社会干预措施需要如何因糖尿病转型而变化，以及3)确定 作为社会干预的一部分，最有效地成为目标的社交网络。

Project Summary The epidemic increase in the prevalence of diabetes and obesity is a public health crisis. Addressing this epidemic requires understanding the variation within the population in how people transition among non- diabetic, pre-diabetic, and diabetic states, as well as their morbidity and mortality. A comprehensive study of all transitions and outcomes in a nationally-representative sample of US older adults is critical to accurately forecast future prevalence and demand for health care. Equally important is identifying novel approaches to reduce diabetes risk, its complications and mortality. While behavioral interventions are beneficial, we have early evidence that people’s social interactions may also independently reduce diabetes risk and progression. Our central hypothesis is that a significant portion of individual variation in diabetic trajectories is associated with different social environments. We will test our hypotheses by (1) combining two high- quality, national datasets, each with unique strengths, creating a synthesized data set more comprehensive than either alone and (2) assaying a broad panel of biomeasures (including A1C). The National Social Life, Health, and Aging Project (NSHAP) has collected longitudinal data at three waves (2005, 2010 and 2015 (2015 biomeasure to be assayed here), health data, medication usage, as well as the most comprehensive assessment of social relationships and intimate partners of any national survey of older adults. The Health and Retirement Study (HRS) has also collected longitudinal A1C and social data from a larger sample of older adults bi-yearly since 2006. The proposed analyses combine these datasets to increase the precision of our estimates, while leveraging NSHAP’s unique social and biological data. The tools we develop for combining and analyzing these datasets are applicable to other chronic diseases and conditions of aging and will be made freely available to the research community, along with complete biomeasure data, providing an invaluable public resource for studying a wide range of health issues. For Aim 1, we will estimate the transition rates among non-diabetic, pre-diabetic, and diabetic states in the U.S. population of older adults by obesity, health status, and demographic subgroups. For Aim 2, we will test the hypothesis that social factors such as large social networks, positive social and intimate relationships, social support, social participation, social stress-buffering and low levels of isolation and loneliness are associated with a lower likelihood of diabetes progression and even reversals. For Aim 3, we will test whether differences in the rate of diabetic transitions are mediated through specific physiological (inflammation, stress biology, sex steroids) and health behavior mechanisms (activity and sleep). Understanding the mechanisms by which social factors affect diabetes risk may help 1) better target specific types of social interventions to the social context of individuals, 2) demonstrate how social interventions need to vary by diabetic transition, and 3) identify key members of a social network that would be most effectively targeted as part of a social intervention.