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NCT07386756

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

Not yet recruiting Last updated 15 April 2026
What this trial tests

trial in Prediabetes in 1,000 participants. Not yet recruiting.

Timeline
30 April 2026
Primary endpoint
31 December 2027
31 December 2027

Quick facts

Lead sponsorPeking Union Medical College Hospital
StatusNot yet recruiting
Study typeOBSERVATIONAL
Enrollment1,000
Start date30 April 2026
Primary completion31 December 2027
Estimated completion31 December 2027

Conditions studied

Sponsor

Peking Union Medical College Hospital

Who can join

Adults 35 to 75, any sex, with Prediabetes. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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Other recruiting trials for Prediabetes

Currently open trials in the same condition.

Other Peking Union Medical College Hospital trials

Trials by the same sponsor.

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Data sources for this page

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