Early Precise Identification and Intervention Strategies for Individuals at High Risk of Prediabetes

Prediabetes significantly increases the risk of developing diabetes, cardiovascular and cerebrovascular diseases, tumors, and dementia. Early identification and intervention have become a leading focus in current diabetes prevention and control research. Currently, prediabetes screening primarily relies on methods such as fasting blood glucose, oral glucose tolerance tests, and glycated hemoglobin. These approaches suffer from limitations including single-point assessment, static nature, cumbersome procedures, poor reproducibility, delayed diagnosis, and limited accuracy. Continuous glucose monitoring (CGM) technology offers advantages such as ease of use, dynamic continuous monitoring, and round-the-clock surveillance. It comprehensively captures glucose fluctuation patterns, enabling identification of occult hyperglycemia and glucose variability. Integrating artificial intelligence (AI) to perform deep analysis on CGM-generated big data holds promise for pioneering new pathways toward earlier and more precise identification of prediabetes. This project aims to establish a prospective prediabetes cohort integrating multidimensional data-including CGM parameters, body composition analysis, clinical indicators, and biomarkers-to develop novel diagnostic models for prediabetes. Building upon this foundation, we will construct an AI-driven prediabetes intervention management platform with intelligent decision support. This platform will generate personalized intervention strategies based on risk stratification, providing scientific evidence and practical support for advancing diabetes prevention and enabling precision management.

NCT Number	NCT07386756
Lead Sponsor	Peking Union Medical College Hospital
Conditions	Prediabetes
Enrollment	1,000 participants
Start Date	2026-04-30
Primary Completion	2027-12-31 (estimated)
Study Completion	2027-12-31 (estimated)
Updated on ClinicalTrials.gov	2026-04-15