Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients' meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.

翻译：通过连续血糖监测（CGM）收集的餐后血糖反应信息，对于评估个体代谢状态和支持个性化饮食处方具有重要意义。然而，CGM监测仪产生的数据复杂性对现有分析方法提出了挑战。CGM数据通常表现出显著的个体内变异性，并具有天然的多层次结构。本研究受AEGIS研究中非糖尿病人群CGM数据分析的启发。该数据集包含个体在不同日期内详细的进餐时间与营养信息。本研究主要关注患者餐后CGM血糖反应，并探讨其与饮食特征及患者特征的时变关联。针对此问题，我们提出了一种基于多层次功能模型的新分析框架，其中包括一种新的功能混合R平方系数。这些模型的应用阐明了三个关键点：（i）在进行饮食推荐时，分析整个功能域内血糖反应的重要性；（ii）血糖正常者与糖尿病前期患者代谢反应的差异，尤其在脂质摄入方面；（iii）在对此科学问题建模时纳入随机性个体效应的重要性。