TY - JOUR
T1 - Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data
AU - Shilo, Smadar
AU - Godneva, Anastasia
AU - Rachmiel, Marianna
AU - Korem, Tal
AU - Kolobkov, Dmitry
AU - Karady, Tal
AU - Bar, Noam
AU - Wolf, Bat Chen
AU - Glantz-Gashai, Yitav
AU - Cohen, Michal
AU - Levin, Nehama Zuckerman
AU - Shehadeh, Naim
AU - Gruber, Noah
AU - Levran, Neriya
AU - Koren, Shlomit
AU - Weinberger, Adina
AU - Pinhas-Hamiel, Orit
AU - Segal, Eran
N1 - Publisher Copyright:
© 2022 by the American Diabetes Association.
PY - 2022/3
Y1 - 2022/3
N2 - OBJECTIVE Despite technological advances, results from various clinical trials have repeat-edly 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. RESEARCH DESIGN AND METHODS 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 parame-ters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 900 healthy individuals to 41,371 meals were also integrated into the model. The per-formance of the models was evaluated with 10-fold cross validation. RESULTS 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 sub-stantially outperforms a baseline model with emulation of standard of care (cor-relation of R 5 0.59 compared with R 5 0.40 for predicted and observed PPGR respectively; P < 10210 ). 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 carbohy-drate-to-fat ratio were the most influential features for 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 nutri-tional interventions personally tailored for individuals with T1D on the basis of meals with expected low glycemic response.
AB - OBJECTIVE Despite technological advances, results from various clinical trials have repeat-edly 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. RESEARCH DESIGN AND METHODS 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 parame-ters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 900 healthy individuals to 41,371 meals were also integrated into the model. The per-formance of the models was evaluated with 10-fold cross validation. RESULTS 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 sub-stantially outperforms a baseline model with emulation of standard of care (cor-relation of R 5 0.59 compared with R 5 0.40 for predicted and observed PPGR respectively; P < 10210 ). 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 carbohy-drate-to-fat ratio were the most influential features for 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 nutri-tional interventions personally tailored for individuals with T1D on the basis of meals with expected low glycemic response.
UR - http://www.scopus.com/inward/record.url?scp=85125882146&partnerID=8YFLogxK
U2 - 10.2337/dc21-1048
DO - 10.2337/dc21-1048
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C2 - 34711639
AN - SCOPUS:85125882146
SN - 0149-5992
VL - 45
SP - 502
EP - 511
JO - Diabetes Care
JF - Diabetes Care
IS - 3
ER -
