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This is a randomized study comparing outcomes of patients diagnosed with Type 2 Diabetes (T2D) who are enrolled into the Twin Health Precision Treatment (TPT) system versus usual care. The study will last for a year with a 1 year optional extension for the TPT arm patients to continue for another year, and for the usual care (UC) patients to cross over to the TPT treatment for a year.
150 patients will be enrolled with 100 being randomized to the TPT arm and 50 being enrolled to the UC arm
Full description
This is a randomized study comparing outcomes of usual care patients diagnosed with Type 2 Diabetes (T2D) with patients diagnosed with T2D enrolled onto Twin Health's TPT system. Twin Precision Treatment (TPT) system utilizes live, exercise and nutrition coaching based on computer algorithm learning to attempt to improve patients overall health and reverse type 2 diabetes (T2D). The TPT system does so by measuring and addressing glucose aberrations associated with meals. Using Whole Body Digital Twin (WBDT) platform powered by Artificial Intelligence (AI) and the Internet of Things (IoT) technologies, the Whole Body Digital Twin platform captures data on up to 174 health markers, up to 3000 daily data points to provide precision nutrition guidance to the patient that precisely balances 87 essential nutrient factors. The platform captures daily weight using Bluetooth scales and captures daily blood pressure, particularly in patients with underlying HTN using Bluetooth blood pressure measurement. Additionally, the Whole Body Digital Twin platform captures daily physical activity and sleep data, and provides precision guidance on activity and sleep for the patient to follow.
The machine learning algorithm is devised to integrate these multi-dimensional data and accurately predict personalized glucose responses. Dietary intake is a central determinant of blood glucose levels, and thus, to achieve optimal glucose levels, it is imperative to make food choices that induce normal postprandial glycemic responses. Thus, the platform will suggest the right food to the right participant at the right time.
Depending on the likes and dislikes of the patient, the Whole Body Digital Twin platform will recommend a meal plan that is balanced across macro, micro and biota nutrients to reduce glucotoxicity and lipotoxicity, which helps in ameliorating inflammation, fatty liver and insulin resistance. This precise management of nutrition, activity and sleep ensures that the average blood glucose of the day will be consistently maintained within the optimal range. The intervention will continuously offer precision nutrition, precision sleep and precision activity recommendations. Nutritional, activity, and sleep counseling will be provided by trained health coaches through the app and via telephone.
In the usual care of type 2 diabetes, glucose-lowering medications are added progressively with lifestyle modification to improve glycemia and optimize glycated hemoglobin values (HbA1c) so as to reduce the risk of developing long-term complications. Standard of care is to take such a glucose-centric approach to T2D management rather than focusing treatment on the underlying root causes of the disease. It is rare to attempt to target remission of the diabetes process since remission occurs in usual care regimens in only a very small percentage of patients with type 2 diabetes (21). Studies that target other metabolic disease states, such as obesity, have been proven to also impact T2D progression and achieve remission states, but these types of approaches are rare in the usual care setting (22). Only three therapeutic approaches have been associated with remission of diabetes: 1. Bariatric surgery, 2. Very Low-Calorie diet, and 3. Ketogenic diet with near elimination of carbohydrate (23). Our study attempts to determine if precision nutritional, activity and sleep guidance associated with the Whole Body Digital Twin (WBDT) platform and Twin Precision Treatment (TPT) can lead to diabetes remission in a population of patients with T2D.
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150 participants in 2 patient groups
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Shannon Downie; Ari Smith-Coleman
Data sourced from clinicaltrials.gov
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