Personalized nutrition in type 2 diabetes remission: application of digital twin technology for predictive glycemic control

Abstract

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.

Methods: 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 DT-driven 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.

Conclusion: 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 long-term outcomes and reduce the global burden of T2D.

Keywords: digital twin technology; machine learning; personalized nutrition; predictive glycemic control; type 2 diabetes remission.

Figure 1

Digital Twin Platform: Member’s Whole Body Digital Twin for Chronic Disease Reversal. This figure illustrates the Digital Twin Platform’s use of real-time sensor data to create a Whole Body Digital Twin, enabling continuous, personalized health interventions across various domains for optimal well-being.

Figure 2

Whole Body Digital Twin^® (WBDT) mobile app User Interface. The WBDT Member App interface centers on the daily Action Score, which aggregates data from health modules like Sleep, Breathing, Activity, Nutrition, and Medicine. Users receive actionable steps, such as measuring weight, to stay aligned with their personalized health plan. The app promotes seamless interaction with health metrics, encouraging consistent engagement.

Figure 3

Flowchart illustrating the process for training a machine-learned model to output a representation of a patient’s metabolic health. This figure outlines the process used by the digital twin module to train a machine-learning model for predicting a patient’s metabolic state. The process begins with retrieving historical biological and patient data (710) to train a baseline model (720) reflecting population-level trends. Next, a patient-specific dataset (730) is generated, and a personalized model (740) is trained to predict individual metabolic responses. Finally, current biological and patient data are inputted into the trained model (750) to output real-time metabolic states. Data sources include lab tests, sensor data, and patient-recorded measurements, enabling precise metabolic monitoring and health predictions. This figure is taken from the patent US 2021/0196195 A1.

Figure 4

Process of Predicting and Verifying Patient-Specific Metabolic Responses Using a Machine-Learned Model. (A) This figure depicts the process of implementing a machine-learned model to predict a patient’s specific metabolic response. The model uses wearable sensor data (805), lab test data (810), and symptom data (815) to establish an initial metabolic state (825). Once sufficient training data for a patient exists, the digital twin module (450) can predict the patient’s metabolic response (850) based on input biosignals (830), such as nutrition (835), medication (840), and lifestyle data (845). The predicted metabolic response reflects changes in the patient’s health, corresponding to the input biosignals. (B) This figure illustrates the comparison process between a patient’s predicted metabolic state and true metabolic state. During a given time period, wearable sensor data (805), lab test data (810), and symptom data (815) are used to generate a patient’s true metabolic state (860) via the digital twin module (450). Simultaneously, input biosignals (830), such as nutrition, medication, and lifestyle data, are processed to predict the patient’s metabolic state (870). The Response Review Module (880) compares the true and predicted metabolic states to identify discrepancies, helping detect any errors in the biosignal inputs that might have contributed to the differences. This figure is taken from the patent US 2021/0196195 A1.

Figure 5

Predictor Features for Digital Twin Model Development. The predictor model includes demographic info (age, sex, BMI, gender), activity level (steps, active/sedentary minutes), sleep data, blood test results (HbA1c, HOMA), food nutrition data, recent glucose features, medications, and time of day.