USE OF MACHINE LEARNING TO PREDICT INDIVIDUAL POSTPRANDIAL GLYCEMIC RESPONSES TO FOOD AMONG INDIVIDUALS WITH TYPE 2 DIABETES IN INDIA

Abstract

ABSTRACTBackgroundType 2 diabetes (DM2) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the world’s most populous country, India. Nutrition is a critical and evidence-based component of effective blood sugar control and most dietary advice emphasizes carbohydrate and calorie reduction. Emerging global evidence demonstrates marked inter-individual differences in post-prandial glucose response (PPGR) although no such data exists in India and prior studies have primarily evaluated PPGR variation in individuals without diabetes.MethodsThis prospective cohort study seeks to characterize the PPGR variability in Indians with diabetes and to identify factors associated with these differences. Adults with type 2 diabetes and a hemoglobin A1c ≥7 are being enrolled from 14 sites around India. Subjects wear a continuous glucose monitor, eat a series of standardized meals, and record all free-leaving foods, activities, and medication use for a 14-day period. The study’s primary outcome is PPGR, calculated as the incremental area under the curve 2 hours after each logged meal.DiscussionThis study will provide the first large scale examination variability in blood sugar responses to food in India and will be among the first to estimate PPGR variability for individuals with DM2 in any jurisdiction. Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with DM2.