Digital Diabetology: Using a Digital Twin to Optimize Diabetes Management

• Applicant: Professorin Dr. Claudia Eberle
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics ; Biomedical Systems Technology
• Term: from 2021 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 497483233

According to the International Diabetes Federation (IDF), more than 460 million people worldwide are affected by diabetes mellitus, corresponding to a prevalence of almost 10% of the population. Digital diabetes management could make an important contribution to improve the therapy and quality of life of these patients. Although technologies such as continuous glucose monitoring (CGM), smartphones or insulin pumps are available today, new concepts must be developed for individual digital therapy support.To this end, the project is focusing on the development of a diabetological diabetic twin. Its core is formed by in silico models of metabolic dynamics that also include CGM sensor dynamics and the patient's food intake and movement. These models have already been established and used by the applicant in a number of studies to analyze observations from in vivo animal models. Skills will be progressively added to the digital twin: these include real-time estimation of current metabolic state and simulation and prediction of glucose and insulin trajectories. Based on retrospective measurements, it will also be possible to adapt the digital twin to the individual characteristics of the patient. This will improve the quality of the digital twin, which will then also allow diagnostic conclusions. The research goal of the project is to investigate if the concept of a digital twin can be transferred to diabetology and used there in a beneficial way for optimized diabetes management. For this purpose, a prototype will first be put into operation and assessed in the context of the current literature. In the context of a retrospective analysis of animal models, the concept can be validated on the basis of controlled metabolic tests such as the Oral Glucose Tolerance Test (OGTT). This is followed by the clinical extension of the digital twin using the example of various forms of diabetes mellitus. Therefore, the twin is extended by clinically relevant parameters, e.g. fasting glucose, HOMA, HbA1c, body weight, body mass index (BMI), total cholesterol, low density lipoprotein (LDL), etc., and its predictive quality is analyzed. Finally, an overall concept for the design of a digital therapy support based on a digital twin is proposed. The project is conducted by Prof. Dr. med. Claudia Eberle at Fulda University of Applied Sciences.

• DFG Programme: Research Grants