Personalized Therapy Algorithm to Predict Hypoglycemic Drug Response in Type 2 Diabetes Trial

Effective management of type 2 diabetes mellitus (T2DM) is hampered by a lack of personalized approaches, often leading to trial-and-error drug selection and suboptimal glycemic control. This increases the risk of severe cardiovascular complications. Current standard-of-care lacks robust, personified predictors to guide initial drug choice among diverse classes like SGLT-2 inhibitors, DPP-4 inhibitors, GLP-1 receptor agonists, and sulfonylureas. This gap necessitates predictive biomarkers and tailored therapeutic strategies to improve patient outcomes and prevent adverse events.

This interventional, randomized controlled, open-label trial is designed to determine highly specific personified predictors of therapeutic response across different hypoglycemic drug groups. The study will compare SGLT-2 inhibitors, DPP-4 inhibitors, GLP-1 receptor agonists, and sulfonylureas in patients with type 2 diabetes mellitus. The primary objective is to develop an algorithm for personalized therapy selection. Secondary objectives include designing an organizational and methodological model for preventing cardiovascular complications and creating an automated decision-making system for therapy selection, aiming to reduce cardiovascular events compared to a traditional approach.