Continuous glucose monitoring: determinants and prediction of diabetes mellitus development in the Framingham Heart Study

项目摘要 几乎一半的成年人患有糖尿病（DM）或糖尿病前期（preDM），但其中许多人 未确诊的疾病目前的糖尿病诊断和风险预测是基于单一的“快照” 测量，包括空腹血糖、餐后血糖和血红蛋白（Hb）A1 c。然而，在这方面， 一些不属于传统DM生物标志物的高风险类别的个体偶尔会出现 血糖波动与糖尿病前期甚至糖尿病患者相似。了解这些因素的决定因素 血糖模式以及它们是否会导致发生DM的风险将阐明其病理生理学 负责向DM的进展，并可改善DM风险预测。我们将使用连续 葡萄糖监测（CGM），以测量2700名成人（平均年龄58岁）的血糖模式， 基于社区的心脏研究第三代队列和Omni 2，一个多种族队列。我们 旨在描述健康个体的大样本中的标准血糖模式（其中大多数人不 糖尿病患者），探讨标准临床指标（体重指数，空腹血糖，HbA 1c），血液 代谢物、肠道微生物组、饮食模式、体力活动、DM家族史和多基因风险评分 与CGM衍生的血糖变量相关。我们将探讨的CGM衍生变量包括时间 血糖范围（例如70-140 mg/dL）、高/低血糖发作、平均血糖和血糖 可变性此外，我们将研究是否CGM衍生的血糖变量预测糖尿病的发展 超过2-3年的随访（通过每年自我报告的健康史），并与 心血管疾病（横断面）。我们的研究将提供信息，可以改善预测 糖尿病和糖尿病并发症。我们的发现也可能导致新的发现， 糖尿病有针对性的预防。

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.