Physics Informed Machine Learning-based Prediction and Reversion of Impaired Fasting Glucose Management

Noncommunicable diseases (NCDs) such as cancer, cardiovascular diseases, and diabetes represent 74% of the disease burden globally and are the major causes of preventable premature deaths. (1,2).

Diabetes is a chronic NCD characterized by elevated glucose blood levels. In 2021, the prevalence of diabetes in Europe was 1 in 11 adults (61 million), a figure projected to rise to 69 million by 2045. (3) The global management and treatment of diabetes cost 988 billion dollars in 2021. Despite these expenditures, diabetes remains the third leading cause of death, accounting for 6.7 million deaths, with 1.1 millions of these in Europe alone. Among the different types, type-2 diabetes (T2D) comprises 90% of total diabetes cases, primarily presenting in adulthood. (3,4) Risk factors for T2D include genetic predisposition, family history, metabolic syndrome, obesity, physical inactivity, age, and ethnicity. There are 541 million adults worldwide with Impaired Glucose Tolerance (IGT), a significant risk factor for T2D. (5,6) IGT and Impaired Fasting Glucose (IFG) represent intermediate conditions within the "healthy-to-T2D transition" and are symptoms of prediabetes. (7,8) Notably, prediabetes represents an early-stage condition that can be reversed. Studies show that T2D progression can be reduced by approximately 58% within three years through lifestyle modifications. Physical activity of 30-54 minutes at least 2-5 days per week is recommended, as well as a healthy diet. (9) Efforts have been made to develop non-invasive diabetes risk prediction models based on clinically available parameters. (10) The onset of T2D involves complex, multiscale mechanisms starting from molecular, tissue, and organ levels, leading to dysfunction in physiological processes. Chronic inflammatory biomarkers play a significant role in T2D pathogenesis. Recognizing this multi-level approach is a step towards personalized disease diagnosis. However, there remain challenges related to modelling the "healthy-to-prediabetes transition" from both case study and methodological perspectives. (5,13,14) The objective of this project is the development of a prototype tool, aimed at real-time prediction of prediabetic risk. This tool will incorporate a series of patient-specific mathematical models simulating metabolism, pancreas hormone production, microbiome metabolites, inflammatory processes, and immune system response. These models were initially developed during the FP7 MISSION-T2D project and further developed into the implementation of an integrated, multilevel, and patient-specific model, incorporating genetic, metabolic, and nutritional data for the simulation and prediction of metabolic and inflammatory processes in the onset and progression of T2D. (14-18) The prediction algorithm will utilize a "physics-informed machine learning" (PIML) approach, combining a comprehensive dataset from both existing and new clinical trials, with continuous data input through wearable sensors. (19) The final algorithm will be hosted on a web-based platform where both medical professionals and patients can input data from multiple sources.

A dedicated prospective observational study described in this application will be conducted in Latvia recruiting adult participants with metabolic risk factors - overweight and obesity grade I, for data collection purposes to validate the developed machine learning PIML algorithm for pre-diabetes real-time risk prediction.

Data collections has three main purposes: I. Input Data for the in-silico MT2D model:

The input of the simulations includes the following discrete starting parameters: gender (M/F); weight; height; number of sessions of physical activity (0, 1, 2, 3); duration of the bout of physical activity (30, 60, 90 min); intensity in terms of % VO2max (40, 60); 3 meals per day; in each meal are specified the carbohydrates (low, medium, high), proteins (low, medium, high) and fats (low, medium, high).