Type 2 Diabetes (T2D) poses an increasing global health threat, demanding effective glycemic assessment to support personalized and improved diabetes care. Wearable sensors such as continuous glucose monitors (CGM) and fitness trackers offer many valuable insights for glycemic assessment. However, effectively analyzing these data requires integration with essential individual-level context. Existing methods are often based on traditional machine learning (ML) and rely primarily on historical blood glucose measurements and overlook personalized information, which limits their performance across diverse diabetes populations. Recent advances in large language models (LLMs) have demonstrated their ability to integrate diverse data modalities while modeling sequential dependencies, motivating the exploration of their potential for personalized glycemic assessment. In this paper, we propose GlyLLM, an LLM-powered framework for modeling CGM-based glycemic dynamics through the integration of wearable sensor data and structured metadata. GlyLLM can leverage the extensive prior knowledge of pre-trained LLMs and achieve sensor-text semantic abstraction at decision time. Experiments on two related tasks on the AI-READI dataset demonstrate that our model outperforms traditional ML methods by an average of 13.66\% in Root Mean Squared Error (RMSE) for glucose forecasting and 13.08\% in Area Under the Receiver Operating Characteristic (AUROC) for diabetes categorization. Additionally, our ablation study shows that diabetes surveys and biometric tests are more critical than other health information for glycemic assessment. Our work presents a promising step toward harnessing the power of LLMs to advance personalized glycemic assessment in T2D care.

翻译：二型糖尿病（T2D）日益成为全球性健康威胁，亟需有效的血糖评估手段以支持个性化糖尿病管理的优化。连续血糖监测仪（CGM）和健身追踪器等可穿戴传感器为血糖评估提供了诸多宝贵见解，然而，有效分析这些数据需要整合关键的患者个体化背景信息。现有方法多基于传统机器学习（ML），主要依赖历史血糖检测值，忽视了个体差异信息，这限制了其在多样化糖尿病人群中的性能。大语言模型（LLM）的最新进展已展现出其整合多模态数据并建模时序依赖关系的能力，这激发了探索其在个性化血糖评估中潜力的研究。本文提出GlyLLM框架，该框架基于大语言模型，通过整合可穿戴传感器数据和结构化元数据，对CGM血糖动态进行建模。GlyLLM能利用预训练大语言模型的广泛先验知识，在决策时刻实现传感器数据与文本语义的抽象融合。在AI-READI数据集上的两项相关任务实验中，我们的模型在血糖预测的均方根误差（RMSE）上平均优于传统机器学习方法13.66%，在糖尿病分类的受试者工作特征曲线下面积（AUROC）上平均提升13.08%。此外，消融研究表明，糖尿病调查问卷和生物特征检测结果对血糖评估的关键性高于其他健康信息。本研究为利用大语言模型推进二型糖尿病个性化血糖评估迈出了前瞻性一步。