Mathematical modeling of glucose dynamics during a meal in individuals with type 2 diabetes from continuous glucose monitoring data.

Diabetes mellitus is a chronic condition in which the body is unable to regulate blood glucose levels. Uncontrolled elevation of blood glucose is caused by the deficiency or ineffectiveness of the hormone insulin. This state of hyperglycemia can be detrimental to the body in the long term and, beyond a certain threshold, can lead to comatose states and death. The most common forms of diabetes are type 1 diabetes (T1DM), which has an autoimmune etiology and results in the pancreas’s inability to produce insulin, and type 2 diabetes (T2DM), characterized by reduced insulin effectiveness. There is no cure for this disease, which dimensions are becoming a global pandemic. Therefore, innovative interventions are needed for diabetes control and treatment. In cases of advanced T2DM and all patients with T1DM, treatment involves monitoring in- terstitial glucose levels and administering insulin as needed. This process can be carried out by measuring glucose only when necessary (Self-Monitoring Blood Glucose, SMBG) or by implanting a superficial sensor with a needle placed at the interstitial level, which measures glucose every few minutes (continuous glucose monitoring, CGM). CGM allows for very frequent and minimally invasive measurements, significantly impacting the patient’s quality of life less. The aim of this thesis is to create a model that simulates glucose metabolism and assesses the body’s tolerance based solely on CGM data in the case of type 2 diabetic subjects. To achieve this, a database of 100 virtual subjects monitored during a 75-gram glucose meal was used. The results are promising and provide an indication of disease progression with good estimation accuracy. This method could be very useful in the future for both diabetic and pre-diabetic patients to monitor the progression of this condition in a minimally invasive manner.