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

点击翻译按钮获取中文摘要

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.