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Reversing Metabolic Disease with Your (Digital) Twin


The Driver:

Twin Health recently raised $50 million in a series D financing to continue to expand its personalized Whole Body Digital Twin technology services, a dynamic model of an individual’s unique metabolism that is used for reversing and preventing chronic metabolic diseases like diabetes, for the company’s employer and health plan partners. Funding for this round was led by Temasek with support by ICONIQ Growth, Sofina, Peak XV, and Helena. According to Crunchbase, Twin Health has raised a total of $248.5 million in funding of four rounds since being founded in 2018.

Key Takeaways:

  • People diagnosed with diabetes account for one out of four health care dollars spent in the United States, with spending on insulin increasing from $8 billion in 2012 to over $22 billion in 2022 (American Diabetes Association) 

  • Diabetes was the 4th most frequent cause of inpatient hospital stays with a mean cost of approximately $12,000 and total inpatient costs of almost $8B (AHRQ)

  • Direct medical costs associated with diabetes care increased by 7% between 2017 and 2022, with Black Americans with diabetes paying the most in direct health expenditures (American Diabetes Association)

  • As of 2021, 38.4 million people in the United States are living with diabetes with 29.7M people having been diagnosed while an estimated 8.7 million or over 20% remain undiagnosed (American Diabetes Association)

The Story: 

Twin Health was founded in 2018 by CEO Jahangir Mohammed, an engineer and founder of Jaspar (a company that created a “switch on” for Internet of Things technology which enabled communication between a diverse set of objects, which resulted in the proliferation of twin technologies) and VP of Research Dr. Maluk Mohamed, and computer science engineer CTO Terrence Poon. Dr. Mohamed’s son had undergone a liver transplant and Dr. Mohamed conducted research on how sensors could be implanted in patients to facilitate improved post-transplant monitoring and thus reduce the amount of immunosuppressive drugs given to patients post-transplant. Separately, Jahangir Mohammed realized that about 40% of his family members were type 2 diabetic and wondered if twin technology (ex: a virtual copy of the human organs, tissues, cells or micro-environment that constantly adjusts to variations in the online data and can predict the future response of the human being or organ that it is meant to correspond to) could be applied to treat type 2 diabetes. Familial healthcare issues brought together the two founders to collaborate and create Whole Body Twin, which is a digital representation of one’s metabolism, which gives doctor’s access to thousands of data points through wearable sensors, clinical lab parameters, and self-reported preferences. 

The Differentiators:


According to the company, the company’s Whole Body Digital Twin™ is a digital representation of each person’s unique metabolism and delivers precise, personalized guidance about foods, sleep, activity, and breathing through the easy-to-use app. This is combined with a dedicated care team that monitors each patient's sensor data, offers personalized recommendations to improve responses to metabolic conditions like diabetes.

Unlike some competitors, Twin Health has been able to validate it’s results through randomized controlled trials. Twin Health completed the first randomized controlled trial for reversing chronic metabolic disease using Digital Twin health technology. Results showed that people diagnosed with type 2 diabetes with digital twin intervention had significantly better HbA1c levels than type 2 diabetics under standard care, with a 2.9% reduction from 9.0 to 6.1 within 6 months. After 1 year of digital Twin Health  intervention, there was a 72.7% remission rate in people with type 2 diabetes. After the first 6 months of intervention, patients had an average weight loss of 16.6 lbs., reducing BMI >30.

The Big Picture: 

As noted in “Digital twin in healthcare: Recent updates and challenges”, “the development of the technologies of big data, cloud computing, virtual reality, and the internet of things (IoT) has laid a technical foundation for the application of digital twin and thus provided clinicians and researchers with a more detailed dimension with which to study the occurrence and development of diseases and to conduct more precise diagnoses and treatments. A digital twin can simulate dosage effects or the device response before a specific treatment and thus indicate whether the medical device or treatment is appropriate for patients and improve the treatment of patients with different causes of disease” thereby helping reduce the cost of development while improving the effectiveness of treatments.

As noted above, this can be particularly relevant for diabetes where treatment costs can be staggering. For example, according to the American Diabetes Association’s Economic Report, the total annual costs for diabetes in 2022 were $412.9 billion, which includes $306.6 billion in direct medical costs and $106.3 billion in indirect costs. In addition, the report found that people diagnosed with diabetes account for one out of four health care dollars spent in the United States, with spending on insulin increasing from $8 billion in 2012 to over $22 billion in 2022. 

With treatments like Twin Health’s “Whole Body Twin” dramatic reductions in cost can be achieved. For example, according to the study conducted by the company, there was a 71% reduction in the use of high-cost medication with data from the study predicting a cumulative annual per patient savings of almost $8,000 due to the improvement of BMI, HbA1C, and blood pressure. In addition, treatments such as these can help patients reduce or eliminate the need for supplemental insulin entirely with Twin Health demonstrating that after a 90-day follow-up, patients discontinued the use of insulin, and about half stopped taking metformin. However, while digital twins and precision medicine hold great promise they must ensure they address the issues of data security, data privacy and data interoperability to achieve their full potential. 

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