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IIT Startup Develops Digital Twin for Health Monitoring, Demonstrates Effective Blood Sugar Control in its Study

Providing daily precision nutrition guidance based on food intake data can be advantageous for individuals managing metabolic disorders and type 2 diabetes.

Shivangi Rai
The digital twin, created using 174 health markers and over 3,000 data points collected every day from the individual using sensors. (Photo: Twitter/@iitm_respark)
The digital twin, created using 174 health markers and over 3,000 data points collected every day from the individual using sensors. (Photo: Twitter/@iitm_respark)

Twin Health, a startup based in IIT Madras’ research park, has developed an innovative approach to monitor an individual's health by creating a digital twin.

This digital twin is crafted using 174 health markers and collects over 3,000 data points daily through sensors, allowing individuals to keep track of their dietary choices and lifestyle. The creators claim that this approach can potentially reverse type 2 diabetes.

Recently, the evaluation report of the experiment was presented at the IITMRP. The study involved 206 prime patients and 82 individuals in a controlled group.

Twin Health leverages artificial intelligence (AI) to develop a digital twin, focusing on tracking metabolic health, including conditions such as diabetes, weight, blood pressure, cholesterol, insulin resistance, kidney issues, and diseases affecting the liver and pancreas. A coach assesses the data points, assisting individuals in losing weight, enhancing metabolism, and increasing physical activity. The evaluation report suggests a significant reduction in damage, reaching almost 58%.

The year-long treatment involves four phases, each lasting three months. During the initial three months, the individual's blood sugar is closely monitored and normalized. The subsequent three months focus on healing the underlying metabolism, leading to improved organ health by the end of six months. The sustained reversal of metabolic activities is observed by the 12th month.

The startup enrolled 206 individuals with type 2 diabetes in the random control group and 82 persons with T2D in the controlled group who did not follow the Twin Health process. The study revealed that over 90% of the precision treatment patients achieved normal blood sugar levels, ceased taking diabetes medication, and became diabetes-free.

In a year, 73% of the prime group achieved 'remission,' compared to none in the control group. Additionally, there was a remarkable 50% reduction in cardiovascular events in the precision treatment group.

The program utilizes a color-coded system categorizing foods as red, orange, and green. The study found that participants in the prime group shifted from consuming red and orange category foods to green, showcasing sustained engagement and adherence to the program.

The outcomes of the study suggest that daily precision nutrition guidance based on food intake data can be beneficial for individuals with metabolic disorders and type 2 diabetes. Ashok Jhunjhunwala, the president of IIT Madras Research Park, shared his experience as a borderline diabetic who participated in the experiment.

Despite challenges in adjusting his vegetarian diet, he reported shedding a few kilograms, experiencing increased energy levels, allowing him to extend his working hours from 12 to 15 hours a day.

Twin Health's method of creating a digital twin through AI and leveraging daily precision nutrition guidance has shown promising results in reversing type 2 diabetes and improving overall health. The study highlights the potential of personalized health monitoring and intervention strategies for individuals with metabolic disorders.

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