Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data
RESEARCH DESIGN AND METHODS We recruited individuals with T1D using continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) 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 1,057 healthy individuals to 47,863 meals were also integrated into the model. The performance of the models was evaluated using 10-fold cross validation.
RESULTS 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 emulating standard of care (correlation of R=0.59 compared to 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 minutes prior to meal, meal carbohydrate content and meal’s carbohydrate/fat ratio were the most influential features to the model.
CONCLUSIONS 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 based on meals with expected low glycemic response.