Personal and social-built environmental factors of glucose variability among multi ethnic groups of adults with type 2 diabetes

项目摘要 由于气候变化，极端环境温度（Ta）变得越来越普遍， 与心脏代谢疾病的发病率和死亡率增加有关。高血压引起的脱水 Ta刺激肝脏中的胰岛素生成并促进胰岛素抵抗。增加血液流向皮肤 在热浪期间增加心脏需求，导致个体心脏缺血、梗塞和中风 心血管疾病（CVD）。因此，糖尿病患者特别容易受到 由于对热和CVD合并症的生理反应受损而引起的热浪。葡萄糖变异性（GV）， 通过连续葡萄糖监测（CGM）测量的血糖水平波动是在给定的时间间隔内血糖水平的波动 是动态血糖控制的重要新兴参数。糖化 血红蛋白（HbA 1c）是过去2至3个月内平均血糖水平的标志物， 用于预测糖尿病并发症的主要指标。然而，对大规模、随机、 临床试验和流行病学研究表明，单独HbA 1c可能不足以预测 糖尿病并发症到目前为止，气候变化和健康（CCH）研究已经在人口- 汇总数据和HbA 1c水平。尽管之前的研究提供了对气候的宝贵见解， 变化和糖尿病护理，不同气候下人群水平的平均HbA 1c水平和汇总数据 区域使得难以理解Ta对个体患者的真实的影响。迫切需要 解决CCH研究中的差距，研究气候变化下的生活方式行为对Ta的反应，社会建设 环境，和糖尿病的结果与高时空粒度。我们将收集有时间戳的生活方式 使用生态瞬时评估（EMA）和活动记录仪收集数据，并进行24小时饮食回忆。CGM， 在14天的观察期内，将对参与者进行实时、不显眼的葡萄糖测量。 我们将从气象站收集每小时和每日Ta，从每日地面天气数据中估计每日Ta 在北美的1 km网格上，开发高分辨率（1 km 2）时空分辨的每小时Ta 在康涅狄格州使用卫星数据和机器学习方法的模型。还将要求与会者 佩戴个人Ta传感器。利用GV和多水平个人（人口统计学、合并症）数据， 生活方式，以及我们的父母研究提供的社会环境因素，并将它们与Ta数据联系起来，我们 提出以下具体目标：（1）通过检查，识别易受高或低Ta影响的风险群体 人口统计学、合并症、生活方式（体力活动、睡眠、饮食行为/饮食）、社会构建环境 和GV在2个季节的多种族T2 D成人中各14天，（2）检查Ta对 随时间变化的生活方式因素和GV，在14天内使用1 km 2时的每小时Ta估计值， 决议，以告知未来的个性化行为干预，和（3）评估的可行性和效用， 可穿戴Ta传感器，并将数据与模型开发的每小时Ta和观察到的每小时Ta数据进行比较。

Project Summary Extreme ambient temperature (Ta), which is becoming more common due to climate change, has been associated with the increased incidence of cardiometabolic disease and mortality. Dehydration induced by high Ta stimulates gluconeogenesis in the liver and promotes insulin resistance. Increased blood flow to the skin during heatwaves increases cardiac demand, resulting in cardiac ischemia, infarction, and stroke in individuals with pre-existing cardiovascular disease (CVD). Thus, individuals with diabetes are particularly vulnerable to heat waves due to impaired physiological responses to heat and CVD comorbidities. Glucose variability (GV), measured by continuous glucose monitoring (CGM), is fluctuations in blood glucose levels over a given interval of time (intra-day or inter-day) and is an important emerging parameter of dynamic glycemic control. Glycated hemoglobin (HbA1c), a marker of the average blood glucose levels over the previous 2 to 3 months, is the primary metric used to predict diabetic complications. However, in-depth analyses from large scale, randomized trials, and epidemiological studies have indicated that HbA1c alone may not sufficiently predict the risk of diabetes complications. To date, climate change and health (CCH) research has been done at the population- level with aggregated data and HbA1c levels. Although the previous studies provided valuable insight into climate change and diabetes care, the mean HbA1c level at the population level with aggregated data in different climate zones makes it difficult to understand the real impact of Ta on individual patients. There is an urgent need to address gaps in CCH research studying lifestyle behavioral responses to Ta under climate change, social-built environment, and diabetes outcomes with high spatiotemporal granularity. We will collect time-stamped lifestyle data using ecological momentary assessment (EMA) and actigraphy and perform 24-hour dietary recalls. CGM, a real-time, unobtrusive glucose measure will be blinded from participants during the 14-day observation period. We will collect hourly and daily Ta from weather stations, daily Ta estimates from the Daily Surface Weather Data on a 1-km Grid for North America, and develop a high resolution (1 km2) spatiotemporally resolved hourly Ta model in Connecticut using satellite data with a machine learning approach. Participants will also be asked to wear a personal Ta sensor. Leveraging data in GV and multi-level personal (demographic, comorbidities), lifestyle, and social-built environmental factors that our parent study provides, and linking them to Ta data, we propose the following specific aims: (1) identify the risk groups who are vulnerable to high or low Ta by examining demographics, comorbidities, lifestyle (physical activity, sleep, eating behaviors/diet), social-built environment and GV among multi-ethnic adults with T2D for 14 days each in 2 seasons, (2) examine the influence of Ta on time-varying lifestyle factors and GV, within-person across 14 days using the hourly Ta estimates at 1 km2 special resolution to inform future personalized behavioral interventions, and (3) evaluate the feasibility and utility of the wearable Ta sensor and compare the data with the model developed hourly Ta and observed hourly Ta data.