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

NCT07386756 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2026-04-15

No results posted yet for this study

Summary

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.

Conditions

Sponsors & Collaborators

  • Peking Union Medical College Hospital

    lead OTHER

Principal Investigators

  • Xinhua Xiao · Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China

Eligibility

Min Age
35 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-04-30
Primary Completion
2027-12-31
Completion
2027-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07386756 on ClinicalTrials.gov