Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data

Smadar Shilo, Anastasia Godneva, Marianna Rachmiel, Tal Korem, Dmitry Kolobkov, Tal Karady, Noam Bar, Bat Chen Wolf, Yitav Glantz-Gashai, Michal Cohen, Nehama Zuckerman Levin, Naim Shehadeh, Noah Gruber, Neriya Levran, Shlomit Koren, Adina Weinberger, Orit Pinhas-Hamiel, Eran Segal

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)502-511
Number of pages10
JournalDiabetes Care
Volume45
Issue number3
DOIs
StatePublished - 1 Mar 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 by the American Diabetes Association.

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