Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
Diabetes Care. 2022 Mar 1;45(3):502-511. doi: 10.2337/dc21-1048.
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.
尽管技术不断进步，但各种临床试验的结果反复表明，许多 1 型糖尿病（T1D）患者无法达到血糖目标。疾病管理的主要挑战之一是为每顿饭准确地给予胰岛素，使其与预期的餐后血糖反应（PPGR）相匹配。本研究的目的是为 T1D 患者建立 PPGR 的预测模型。
我们招募了同时使用连续血糖监测和连续皮下胰岛素输注设备的 T1D 患者，进行前瞻性队列研究，并对他们进行了为期 2 周的分析。参与者被要求使用指定的移动应用程序实时报告饮食摄入情况。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，这些算法整合了血糖测量、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 名健康个体的 41371 餐 PPGR 数据。使用 10 倍交叉验证评估模型的性能。
共纳入 121 名 T1D 患者，其中 75 名成人和 46 名儿童。共测量了 6377 餐的 PPGR。我们的 PPGR 预测模型显著优于模仿标准护理的基线模型（预测和观察到的 PPGR 之间的相关性分别为 R = 0.59 和 R = 0.40；P < 10-10）。该模型在不同亚群中具有稳健性。特征归因分析表明，进餐时的血糖水平、进餐前 30 分钟的血糖趋势、进餐的碳水化合物含量和碳水化合物与脂肪的比例是该模型最具影响力的特征。
我们的模型能够更准确地预测 PPGR，从而可能更好地调整每餐所需的胰岛素剂量。它可以进一步应用于闭环系统，并可能导致根据预期低血糖反应的餐食，为 T1D 患者设计出合理的营养干预措施。
