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METHODS article
Front. Endocrinol.
Sec. Clinical Diabetes
Volume 15 - 2024 |
doi: 10.3389/fendo.2024.1485464
This article is part of the Research Topic Digital Technology in the Management and Prevention of Diabetes: Volume II View all articles
Personalized Nutrition in Type 2 Diabetes Remission: Application of Digital Twin Technology for Predictive Glycemic Control
Provisionally accepted- 1 Bangalore Diabetes Center, Bangalore, India
- 2 Lilavati Hospital and Research Centre, Mumbai, Maharashtra, India
- 3 Twin Health, California, United States
Background: Type 2 Diabetes (T2D) is a complex condition marked by insulin resistance and beta-cell dysfunction. Traditional dietary interventions, such as low-calorie or low-carbohydrate diets, typically overlook individual variability in postprandial glycemic responses (PPGRs), which can lead to suboptimal management of the disease. Recent advancements suggest that personalized nutrition, tailored to individual metabolic profiles, may enhance the effectiveness of T2D management. Objective: This study aims to present the development and application of a Digital Twin (DT) technology-a machine learning (ML)-powered platform designed to predict and modulate PPGRs in T2D patients. By integrating continuous glucose monitoring (CGM), dietary data, and other physiological inputs, the DT provides individualized dietary recommendations to improve insulin sensitivity, reduce hyperinsulinemia, and support the remission of T2D.We developed a sophisticated DT platform that synthesizes real-time data from CGM, dietary logs, and other biometric inputs to create personalized metabolic models for T2D patients. The intervention is delivered via a mobile application, which dynamically adjusts dietary recommendations based on predicted PPGRs. This methodology is validated through a randomized controlled trial (RCT) assessing its impact on various metabolic markers, including HbA1c, metabolic-associated fatty liver disease (MAFLD), blood pressure, body weight, ASCVD risk, albuminuria, and diabetic retinopathy.Results: Preliminary data from the ongoing RCT and real-world study demonstrate the DT's capacity to generate significant improvements in glycemic control and metabolic health. The DTdriven personalized nutrition plan has been associated with reductions in HbA1c, enhanced beta-cell function, and normalization of hyperinsulinemia, supporting sustained T2D remission.Additionally, the DT's predictions have contributed to improvements in MAFLD markers, blood pressure, and cardiovascular risk factors, highlighting its potential as a comprehensive management tool.The DT technology represents a novel and scalable approach to personalized nutrition in T2D management. By addressing individual variability in PPGRs, this method offers a promising alternative to conventional dietary interventions, with the potential to improve longterm outcomes and reduce the global burden of T2D.
Keywords: Digital twin technology, personalized nutrition, Type 2 diabetes remission, Predictive Glycemic Control, machine learning
Received: 23 Aug 2024; Accepted: 28 Oct 2024.
Copyright: © 2024 Shamanna, Joshi, Thajudeen, Shah, Poon, Mohamed and Mohammed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Paramesh Shamanna, Bangalore Diabetes Center, Bangalore, India
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