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

项目总结 糖尿病治疗的关键目标是将糖尿病并发症降至最低，并提高 糖尿病患者。来自大规模随机试验和流行病学研究的深入分析 已经表明，糖化血红蛋白(HbA1c)，评估血糖控制的主要指标，单独 可能不能充分预测糖尿病并发症的风险。接近正常的HbA1c水平并不一致 改善糖尿病并发症。糖化血红蛋白水平相近的人可能有明显不同的血糖谱 有不同的频率和幅度的低血糖和高血糖。血糖变异性(GV)是指 血糖水平的全天波动(日内GV)或日间GV(日间GV)是一种 糖尿病并发症的新预测指标。通过连续血糖可以实时测量GV 监控(CGM)。在CGM中进行了大量研究以了解GV，导致CGM在 1型糖尿病患者的临床实践。然而，很少有研究解决影响全球价值链的因素 2型糖尿病(T2D)，在如何缓解GV和改善糖尿病方面留下了巨大的知识空白 以T2D表示的结果。个人生活方式行为、情绪幸福感和社会营造环境的作用 在血糖控制方面已经得到了很好的证明。此外，生活方式的实时数据，如日常睡眠， 正在产生体力活动和饮食。然而，这些因素是如何与GV联系在一起的还没有 接受检查。这项研究将是第一个前瞻性地研究 睡眠、体力活动、饮食、情绪健康和GV，结合人与人之间的联系和 具有T2D的不同种族/民族的成年人的人内变异性。我们将使用实时数据捕获 战略，生态瞬时评估，以衡量生活方式因素和情绪幸福感。三个24小时 将进行饮食召回，以收集营养水平数据。我们还将使用动作记录仪作为客观 睡眠和体力活动的测量与CGM配合使用，这是一种实时、不引人注目的血糖测量方法，将 在为期14天的研究期间，参与者处于失明状态。研究的具体目的是：(1)识别风险 通过检查人与人之间的关系，研究不同类型成人T2D中高GV的相关因素 在人口统计学、临床因素、生活方式(睡眠、体力活动、饮食)因素中，每个因素的变异性 生活方式因素、情绪幸福感、社会构建的环境因素和日间GV，(2)识别- GV的个人预测者通过检查以下内容来告知个性化糖尿病干预的发展- 睡眠、体力活动、饮食、情绪健康和一天内GV之间的关系，以及(3) 考察哪些生活方式因素在情绪幸福感和心理健康之间起中介或调节作用 不同的T2D成人中的GV。这项拟议研究的结果将指导临床决策 为医疗保健提供者提供使用CGM的有效治疗并告知个性化的发展 减少GV和改善糖尿病结局的干预措施。

PROJECT SUMMARY The pivotal goals of diabetes treatment are to minimize diabetes complications and improve quality of life for individuals with diabetes. In-depth analyses from large scale, randomized trials and epidemiological studies have indicated that glycated hemoglobin (HbA1c), the primary metric for assessing glycemic control, alone may not sufficiently predict the risk of diabetes complications. Near-normal HbA1c level did not consistently improve diabetes complications. People with similar HbA1c values can have markedly different glucose profiles with varying frequencies and amplitudes of hypo- and hyper-glycemia. Glucose variability (GV) refers to fluctuations in blood glucose levels throughout the day (intra-day GV) or over days (inter-day GV) and is an emerging predictor of diabetes complications. GV can be measured in real-time through continuous glucose monitoring (CGM). Considerable research in CGM to understand GV has led to the growing use of CGM in clinical practice among individuals with type 1 diabetes. However, few studies address factors that affect GV in type 2 diabetes (T2D), leaving large gaps in knowledge about how to mitigate GV and improve diabetes outcomes in T2D. Roles of individuals’ lifestyle behaviors, emotional well-being and social-built environments in glucose control have been well documented. Also, real-time data on lifestyle such as day-to-day sleep, physical activity and diet are being generated. However, how these factors are associated with GV has yet to be examined. This study will be one of the first to prospectively examine the temporal relationships among sleep, physical activity, diet, emotional well-being, and GV, incorporating between-person associations and within-person variability among adults of diverse race/ethnicity with T2D. We will use a real-time, data capture strategy, ecological momentary assessment to measure lifestyle factors and emotional well-being. Three 24-hr dietary recalls will be conducted to collect nutrient-level data. We will also use actigraphy as objective measures of sleep and physical activity paired with CGM, a real-time, unobtrusive glucose measure that will be blinded from participants during a study period of 14 days. The specific aims of the study are to: (1) identify risk factors associated with high GV among diverse adults with T2D by examining between-person associations among demographics, clinical factors, lifestyle (sleep, physical activity, diet) factors, the variability of each lifestyle factor, emotional well-being, social-built environmental factors and inter-day GV, (2) identify ‘within- person predictors’ of GV to inform the development of personalized diabetes interventions by examining within- person associations among sleep, physical activity, diet, emotional well-being and intra-day GV, and (3) examine which lifestyle factors either mediate or moderate the relationship between emotional well-being and GV among diverse adults with T2D. The findings from the proposed study will guide clinical decision-making for healthcare providers to facilitate effective treatment using CGM and inform the development of personalized interventions to mitigate GV and improve diabetes outcomes.