Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study
BackgroundType 2 diabetes (T2D) 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 glucose control and m...
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JMIR Publications
2025-01-01
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| author | Niteesh K Choudhry Shweta Priyadarshini Jaganath Swamy Mridul Mehta |
| author_facet | Niteesh K Choudhry Shweta Priyadarshini Jaganath Swamy Mridul Mehta |
| author_sort | Niteesh K Choudhry |
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BackgroundType 2 diabetes (T2D) 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 glucose control and most dietary advice emphasizes carbohydrate and calorie reduction. Emerging global evidence demonstrates marked interindividual differences in postprandial glucose response (PPGR) although no such data exists in India and previous studies have primarily evaluated PPGR variation in individuals without diabetes.
ObjectiveThis prospective cohort study seeks to characterize the PPGR variability among individuals with diabetes living in India and to identify factors associated with these differences.
MethodsAdults with T2D and a hemoglobin A1c of ≥7 are being enrolled from 14 sites around India. Participants wear a continuous glucose monitor, eat a series of standardized meals, and record all free-living 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.
ResultsData collection commenced in May 2022, and the results will be ready for publication by September 2025. 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 T2D.
ConclusionsThis study will provide the first large scale examination variability in blood glucose responses to food in India and will be among the first to estimate PPGR variability for individuals with T2D in any jurisdiction.
Trial RegistrationClinical Trials Registry-India CTRI/2022/02/040619; https://tinyurl.com/mrywf6bf
International Registered Report Identifier (IRRID)DERR1-10.2196/59308 |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
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| series | JMIR Research Protocols |
| spelling | doaj-art-cbeb0cf538b54400bc9646224bc57bb32025-01-23T14:46:10ZengJMIR PublicationsJMIR Research Protocols1929-07482025-01-0114e5930810.2196/59308Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort StudyNiteesh K Choudhryhttps://orcid.org/0000-0001-7719-2248Shweta Priyadarshinihttps://orcid.org/0000-0002-4034-6634Jaganath Swamyhttps://orcid.org/0009-0004-5477-6274Mridul Mehtahttps://orcid.org/0009-0009-4663-5486 BackgroundType 2 diabetes (T2D) 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 glucose control and most dietary advice emphasizes carbohydrate and calorie reduction. Emerging global evidence demonstrates marked interindividual differences in postprandial glucose response (PPGR) although no such data exists in India and previous studies have primarily evaluated PPGR variation in individuals without diabetes. ObjectiveThis prospective cohort study seeks to characterize the PPGR variability among individuals with diabetes living in India and to identify factors associated with these differences. MethodsAdults with T2D and a hemoglobin A1c of ≥7 are being enrolled from 14 sites around India. Participants wear a continuous glucose monitor, eat a series of standardized meals, and record all free-living 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. ResultsData collection commenced in May 2022, and the results will be ready for publication by September 2025. 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 T2D. ConclusionsThis study will provide the first large scale examination variability in blood glucose responses to food in India and will be among the first to estimate PPGR variability for individuals with T2D in any jurisdiction. Trial RegistrationClinical Trials Registry-India CTRI/2022/02/040619; https://tinyurl.com/mrywf6bf International Registered Report Identifier (IRRID)DERR1-10.2196/59308https://www.researchprotocols.org/2025/1/e59308 |
| spellingShingle | Niteesh K Choudhry Shweta Priyadarshini Jaganath Swamy Mridul Mehta Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study JMIR Research Protocols |
| title | Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study |
| title_full | Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study |
| title_fullStr | Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study |
| title_full_unstemmed | Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study |
| title_short | Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study |
| title_sort | use of machine learning to predict individual postprandial glycemic responses to food among individuals with type 2 diabetes in india protocol for a prospective cohort study |
| url | https://www.researchprotocols.org/2025/1/e59308 |
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