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

Smadar ShiloAnastasia GodnevaMarianna RachmielTal KoremDmitry KolobkovTal KaradyNoam BarBat Chen WolfYitav Glantz-GashaiMichal CohenNehama Zuckerman LevinNaim ShehadehNoah GruberNeriya LevranShlomit KorenAdina WeinbergerOrit Pinhas-HamielEran Segal
Published in: Diabetes care (2021)
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.
Keyphrases
  • type diabetes
  • glycemic control
  • microbial community
  • physical activity
  • electronic health record
  • big data
  • smoking cessation
  • machine learning
  • weight loss
  • deep learning