Despite technological advances, results from various clinical trials have repeatedly shown that many individuals with type 1 diabetes (T1D) do not achieve their glycemic goals. One of the major challenges in disease management is the administration of an accurate amount of insulin for each meal that will match the expected postprandial glycemic response (PPGR). The objective of this study was to develop a prediction model for PPGR in individuals with T1D.

We recruited individuals with T1D who were using continuous glucose monitoring and continuous subcutaneous insulin infusion devices simultaneously to a prospective cohort and profiled them for 2 weeks. Participants were asked to report real-time dietary intake using a designated mobile app. We measured their PPGRs and devised machine learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 900 healthy individuals to 41,371 meals were also integrated into the model. The performance of the models was evaluated with 10-fold cross validation.

A total of 121 individuals with T1D, 75 adults and 46 children, were included in the study. PPGR to 6,377 meals was measured. Our PPGR prediction model substantially outperforms a baseline model with emulation of standard of care (correlation of R = 0.59 compared with R = 0.40 for predicted and observed PPGR respectively; P < 10^–10). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 min prior to meal, meal carbohydrate content, and meal’s carbohydrate-to-fat ratio were the most influential features for the model.

Our model enables a more accurate prediction of PPGR and therefore may allow a better adjustment of the required insulin dosage for meals. It can be further implemented in closed loop systems and may lead to rationally designed nutritional interventions personally tailored for individuals with T1D on the basis of meals with expected low glycemic response.

尽管技术取得了进步，但各种临床试验的结果一再表明，许多 1 型糖尿病 (T1D) 患者并未达到其血糖目标。疾病管理的主要挑战之一是为每餐提供准确量的胰岛素，以匹配预期的餐后血糖反应 (PPGR)。本研究的目的是开发 T1D 个体 PPGR 的预测模型。

我们将同时使用连续血糖监测和连续皮下胰岛素输注装置的 T1D 患者招募到一个前瞻性队列中，并对他们进行为期 2 周的分析。参与者被要求使用指定的移动应用程序报告实时饮食摄入量。我们测量了他们的 PPGR，并设计了用于 PPGR 预测的机器学习算法，该算法集成了葡萄糖测量、胰岛素剂量、饮食习惯、血液参数、人体测量学、运动和肠道微生物群。该模型还整合了 900 名健康个体对 41,371 顿膳食的 PPGR 数据。模型的性能通过 10 倍交叉验证进行评估。

共有 121 名 T1D 患者、75 名成人和 46 名儿童被纳入研究。测量了 6,377 餐的 PPGR。我们的 PPGR 预测模型大大优于模拟护理标准的基线模型（R = 0.59 与 R = 0.40 分别预测和观察 PPGR 的相关性；^P < 10-10 ）。该模型在不同的亚群中是稳健的。特征归因分析显示，进餐开始时的血糖水平、进餐前 30 分钟的血糖趋势、进餐碳水化合物含量和进餐的碳水化合物与脂肪的比例是模型最有影响力的特征。

我们的模型能够更准确地预测 PPGR，因此可以更好地调整膳食所需的胰岛素剂量。它可以在闭环系统中进一步实施，并可能导致基于预期低血糖反应的膳食为 T1D 个体量身定制的合理设计的营养干预措施。