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

NCT07386756 | Not Yet Recruiting

• 1 other identifier: 2026-1-4011
• Study Type: observational
• Target: 1,000
• Locations: 0 countries
• Sites: N/A

Brief 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

Prediabetes

Keywords

prediabetes; continuous glucose monitoring (CGM); artificial intelligence; early diagnosis; personal intervention

Outcome Measures

Primary Outcomes (1)

• oral glucose tolerance test

  Baseline period and one year after enrollment

Secondary Outcomes (1)

• glycosylated hemoglobin

  Baseline period and one year after enrollment

Study Arms (2)

prediabetes group

No interventions

healthy control group

no interventions

Eligibility Criteria

• Age: 35 Years - 75 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

People with high risk of diabetes

You may qualify if:

• Voluntarily participate in this study, sign a written informed consent form, and be able to adhere to the study protocol for regular follow-up visits;
• Age≥35 years and Chinese Diabetes Risk Score≥25 points (i.e., individuals at high risk for diabetes based on traditional factors); ③Normal blood glucose levels at baseline, as determined by fasting blood glucose, HbA1c, or OGTT testing (i.e., fasting blood glucose \< 6.1 mmol/L AND HbA1c \< 5.7% AND 2-hour OGTT glucose \< 7.8 mmol/L).

You may not qualify if:

• Diagnosed with diabetes or prediabetes: History of diabetes or meeting diagnostic criteria for diabetes at baseline screening (fasting blood glucose ≥7.0 mmol/L or HbA1c ≥6.5%) or prediabetes criteria (i.e., impaired fasting glucose: 6.1-6.9 mmol/L; and/or impaired glucose tolerance: 7.8-11.0 mmol/L; and/or HbA1c 5.7%-6.5%);
• Conditions severely affecting blood glucose control: severe cardiac, hepatic, or renal insufficiency (e.g., NYHA Class III-IV heart failure, cirrhosis, renal failure with eGFR \<30 mL/min/1.73 m²);
• Severe complications or comorbidities: recent (within 6 months) macrovascular events (e.g., myocardial infarction, stroke);
• Malignancy currently active or undergoing treatment;
• Severe psychiatric or cognitive impairment preventing study compliance;
• Pregnant women, lactating women, or women planning pregnancy within the next year; ⑦ Severe allergy or intolerance to the CGM sensor patch; ⑧ Plans to relocate outside the study center's coverage area within the next year, preventing completion of follow-up; ⑨ Inability or unwillingness to use a smartphone or smart device, which would impair data collection.

Sponsors & Collaborators

• Peking Union Medical College Hospital: lead

MeSH Terms

Conditions

Prediabetic State; Disease

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• 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

  STUDY DIRECTOR

Central Study Contacts

Xinhua Xiao

CONTACT

86-10-139 1183; xiaoxh2014@vip.163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 27, 2026
• First Posted: February 4, 2026
• Study Start: April 30, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: April 15, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share