diabetes and metabolism 


Cardiometabolic health (blood pressure, heart rate, glucose and insulin levels) can be monitored in real-time with the help of wearables. Wearables as blood glucose sensors and smart watches are efficient in monitoring bio-signals in real time. However, what to do with a wealth of data? How to use it in order to create a personalized metabolic digital twin? A metabolic digital twin could keep track of our daily calory intake and feed diet information into a metabolic model that could predict our body’s response to certain foods. The personalized response can forecast benefits or disadvantages in a patient with health conditions such as obesity, hyperlipidemias, and type II diabetes. Artificial Intelligence (AI) and Machine Learning (ML) could be used to retrofit these forecasts and predictions back into the metabolic model to strengthen it. A digital twin of the patient’s metabolic profile has the potential to circumvent these problems.

Besides model prediction and treatment optimization, digital twins can increase disease awareness and patient education via serous gaming. A game like SugarVita  can be combined with wearables and the digital twin model to improve patient health in two ways: i) by helping the patient to age better, lower its body weight, and decrease the need of bariatric surgery, and ii) by educating patients in choosing the right foods to prevent blood glucose spikes after a meal.             

Problem definition

Can you create a digital twin of the patient’s metabolic profile where different foods can represent appropriate spikes in blood sugar, while integrating wearables bio-signals to personalize the treatment of diseases like obesity, hyperlipidemias or type II diabetes?. You can also create a digital twin of your metabolic profile in order to know which are your preferred superfoods to maintain health, physical activity and mental focus, and prevent aggressive glucose spikes?

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