Clinical Research Directory

Development of PI-ML Algorithm for Prediction of the Real-time Risk for Developing Pre-diabetes

Sponsor: Jelizaveta Sokolovska

Summary

In this prospective, non-randomized, monocentric study, data will be collected from otherwise healthy individuals with overweight/obese grade I to increase data availability in the pre-diabetes field (impaired glucose intolerance), and to validate the outputs of an algorithm for the "physics-informed machine learning (PIML)" designed to estimate the real-time risk of prediabetes. Each participant will take part in the study for 4 months, including 3 onsite visits. During the screening visit, participants' eligibility will be determined by checking the inclusion and exclusion criteria after detailed information and obtaining informed consent by the investigator. Blood will be withdrawn for exclusion of existing prediabetes/diabetes at the fasted state. For women in reproductive age, a urinary pregnancy test will be performed. After getting the results of blood tests (glucose and HbA1c), participants will be asked to participate in study. On the visit 1, eligible participants will arrive at the study centre in a fasting state. Blood samples will be collected and participants will get vials and instructions for collection of stool and urine samples. Anthropometric data, lifestyle habit (cigarette, alcohol consumption) and family history will be collected. A 6-minute walking test to determine VO2 max will then be performed. Lap counts and time will be manually recorded using a sports watch. The Polar H10 heart rate monitor chest strap will be used to record heart rate (HR) throughout the test. To measure resting HR and heart rate recovery (HRR), participants will be asked to sit still for 5 minutes before the walking test and for 2 minutes after the test. Participants will receive a blinded Abbott Libre Pro glucose sensor, which they will wear for the next 14-days. Further, participants will be provided with a Fitbit Charge 5 health and fitness wristband. For validation purposes some part of study participants will be kindly asked to test newly develop wrist-worn device (EDIBit). With the help of 24-hour food recall, study subjects will be trained by medical staff on how to correctly enter their food intake in the Study app for completion of digital 3-day food diaries. They will be asked to fill in the diaries for 3 days after study visit1 and 3 days before study visit2. They will also receive a food frequency questionnaire during visit1. The second study visit will run nearly identical to study visit1 (except for food frequency questionnaire which will be omitted). During this visit, participants will receive information sheets on physical activity and dietary recommendations. The third and last visit will run nearly identically to the study visit2, except that no new glucose sensor will be inserted and also stool samples will not be collected.

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

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