Project Abstract Almost half of adults have either diabetes mellitus (DM) or prediabetes (preDM), but many of those have undiagnosed conditions. Current DM diagnosis and risk prediction are based on single “snapshot” measurements, including fasting blood glucose, postprandial glucose, and hemoglobin (Hb)A1c. However, some individuals that do not fall into high-risk categories by traditional DM biomarkers have occasional glycemic excursions similar to individuals with preDM or even DM. Understanding the determinants of these glycemic patterns and whether they confer risk in developing DM will elucidate the pathophysiology responsible for the progression toward DM and could improve DM risk prediction. We will use continuous glucose monitoring (CGM) to measure glycemic patterns in 2700 adults (mean age 58 years) from the community-based Framingham Heart Study Third Generation cohort and Omni 2, a multi-ethnic cohort. We aim to describe normative glycemic patterns in a large sample of healthy individuals (most of whom do not have DM), exploring how standard clinical measures (body mass index, fasting blood glucose, HbA1c), blood metabolites, gut microbiome, dietary patterns, physical activity, family history of DM, and polygenic risk score for DM relate to CGM-derived glycemic variables. The CGM-derived variables we will explore include time spent in glycemic ranges (e.g. 70-140 mg/dL), hyper/hypoglycemic episodes, mean glucose and glucose variability. Furthermore, we will examine whether CGM-derived glycemic variables predict development of DM over 2-3 years follow-up (through an annual self-reported health history) and relate to prevalence of cardiovascular disease (cross-sectionally). Our study will provide information that could improve the prediction of developing DM and DM complications. Our findings may also lead to new discoveries that will tailor and target prevention of DM.

Project narrative The goal of this project is to collect continuous glucose monitoring data in a healthy community-based sample to determine whether patterns identified in the rise and fall of glucose levels throughout the day can help predict risk of developing diabetes mellitus. Through linking patterns of dynamic glucose levels with diet and physical activity (among other factors like genetics, bacteria in the gut, and the prevalence of cardiovascular disease), this project may also lead to new discoveries that will tailor and target prevention of diabetes mellitus.