PrediabetesNCT07386756
Early Precise Identification and Intervention Strategies for Individuals at High Risk of Prediabetes
An observational study of Prediabetes, run by Peking Union Medical College Hospital.
Trial 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. Read more about this study
- Who is it for?
- Ages 35 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Prediabetes
- How many people
- 1,000 participants
- Where
- No sites listed yet
- Study dates
- 30 Apr 2026to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 35 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: People with high risk of diabetes
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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.
Read the detailed description
In recent years, the global prevalence of prediabetes has surged rapidly, with approximately 9% of adults exhibiting impaired glucose tolerance and 6% showing impaired fasting glucose. More concerning is the rising prevalence of prediabetes in China. Cross-sectional studies indicate that the prevalence of prediabetes in China increased from 35.7% to 38.1% between 2013 and 2018. Pre-diabetes not only significantly elevates diabetes risk (with annual conversion rates as high as 5-10%) but is also closely associated with multiple adverse clinical outcomes including all-cause mortality, cardiovascular and cerebrovascular diseases, microvascular complications, retinopathy, tumors, and dementia. Therefore, timely identification of pre-diabetic individuals and effective early intervention are critical measures for curbing the diabetes epidemic and mitigating related complications.
Existing screening methods for prediabetes exhibit notable limitations. Current domestic and international guidelines primarily recommend fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and glycated hemoglobin (HbA1c) as diagnostic criteria for prediabetes. However, these methods suffer from cumbersome procedures, delayed diagnosis, and inadequate detection of occult hyperglycemia, making them ill-suited for large-scale screening and precise diagnosis of prediabetes. Therefore, developing a more efficient, precise, and convenient early identification system for prediabetes is urgently needed.
Among novel glucose monitoring technologies, CGM demonstrates significant advantages. CGM provides continuous, comprehensive, and reliable 24-hour glucose information, detects occult hyperglycemia and hypoglycemia, and reveals trends and characteristics of glucose fluctuations. This facilitates a shift from HbA1c-based glucose management to a comprehensive glucose management model, while being less susceptible to interference from factors such as anemia and chronic kidney disease. Our research group previously applied CGM technology to type 2 diabetes patients, securing the Chinese invention patent "Method for Optimizing Blood Glucose Management and Monitoring in Type 2 Diabetes Using Continuous Glucose Monitoring Technology (CN 109637677A)". However, research on applying CGM to prediabetes screening is still in its infancy. Currently, there is a lack of early identification and stratified intervention systems for high-risk prediabetes populations, as well as scalable personalized intervention strategies.
Prediabetes is associated with visceral fat accumulation. Body composition analysis offers a non-invasive and convenient method for detecting body fat distribution, muscle mass percentage, and other parameters, demonstrating significant potential in prediabetes risk assessment. However, the independent predictive value of each relevant parameter and the optimal cutoff points require further investigation.
Furthermore, metabolomics and proteomics research has identified multiple potential biomarkers for prediabetes. However, most biomarker detection relies on high-end platforms like mass spectrometry or nuclear magnetic resonance, facing challenges such as high testing costs, insufficient standardization, lack of validation in large prospective cohorts, and undefined diagnostic cut-off points, which limit their clinical translation.
This project will employ novel intelligent monitoring technologies such as CGM and body composition analysis. By integrating multidimensional data including clinical information, laboratory indicators, and biomarkers, and leveraging artificial intelligence and big data technologies, it aims to establish a comprehensive "screen-assess-prevent" system for high-risk populations. This system will cover early diagnosis, precise classification, and personalized intervention, thereby advancing diabetes prevention efforts, improving early detection rates of prediabetes, effectively prevent diabetes and related complications, and provide scientific evidence and practical pathways for proactive chronic disease health management.
- Conditions
- Prediabetes
- Keywords
- prediabetes, continuous glucose monitoring (CGM), artificial intelligence, early diagnosis, personal intervention
- Medical subject headings
- Prediabetic State, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| prediabetes group | No interventions | |
| healthy control group | no interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
oral glucose tolerance test
Measured: Baseline period and one year after enrollment
Secondary outcomes (1)
glycosylated hemoglobin
Measured: Baseline period and one year after enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Feb 2026
- Study start30 Apr 2026 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PrediabetesStudies of the same condition, open and under way.
- More trials run by Peking Union Medical College HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Xinhua Xiao, Study director, 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
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07386756
- Sponsor's protocol ID
- 2026-1-4011
- Status last verified
- Jan 2026
- First submitted
- 27 Jan 2026
- Last updated
- 15 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Precise Identification and Intervention Strategies for Individuals at High Risk of Prediabetes
- ClinicalTrials.gov ID
- NCT07386756
- Acronym / sponsor ID
- 2026-1-4011
- Conditions
- Prediabetes
- Keywords
- prediabetes, continuous glucose monitoring (CGM), artificial intelligence, early diagnosis, personal intervention
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Study start
- 30 Apr 2026 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 4 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
In recent years, the global prevalence of prediabetes has surged rapidly, with approximately 9% of adults exhibiting impaired glucose tolerance and 6% showing impaired fasting glucose. More concerning is the rising prevalence of prediabetes in China. Cross-sectional studies indicate that the prevalence of prediabetes in China increased from 35.7% to 38.1% between 2013 and 2018. Pre-diabetes not only significantly elevates diabetes risk (with annual conversion rates as high as 5-10%) but is also closely associated with multiple adverse clinical outcomes including all-cause mortality, cardiovascular and cerebrovascular diseases, microvascular complications, retinopathy, tumors, and dementia. Therefore, timely identification of pre-diabetic individuals and effective early intervention are critical measures for curbing the diabetes epidemic and mitigating related complications.
Existing screening methods for prediabetes exhibit notable limitations. Current domestic and international guidelines primarily recommend fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), and glycated hemoglobin (HbA1c) as diagnostic criteria for prediabetes. However, these methods suffer from cumbersome procedures, delayed diagnosis, and inadequate detection of occult hyperglycemia, making them ill-suited for large-scale screening and precise diagnosis of prediabetes. Therefore, developing a more efficient, precise, and convenient early identification system for prediabetes is urgently needed.
Among novel glucose monitoring technologies, CGM demonstrates significant advantages. CGM provides continuous, comprehensive, and reliable 24-hour glucose information, detects occult hyperglycemia and hypoglycemia, and reveals trends and characteristics of glucose fluctuations. This facilitates a shift from HbA1c-based glucose management to a comprehensive glucose management model, while being less susceptible to interference from factors such as anemia and chronic kidney disease. Our research group previously applied CGM technology to type 2 diabetes patients, securing the Chinese invention patent "Method for Optimizing Blood Glucose Management and Monitoring in Type 2 Diabetes Using Continuous Glucose Monitoring Technology (CN 109637677A)". However, research on applying CGM to prediabetes screening is still in its infancy. Currently, there is a lack of early identification and stratified intervention systems for high-risk prediabetes populations, as well as scalable personalized intervention strategies.
Prediabetes is associated with visceral fat accumulation. Body composition analysis offers a non-invasive and convenient method for detecting body fat distribution, muscle mass percentage, and other parameters, demonstrating significant potential in prediabetes risk assessment. However, the independent predictive value of each relevant parameter and the optimal cutoff points require further investigation.
Furthermore, metabolomics and proteomics research has identified multiple potential biomarkers for prediabetes. However, most biomarker detection relies on high-end platforms like mass spectrometry or nuclear magnetic resonance, facing challenges such as high testing costs, insufficient standardization, lack of validation in large prospective cohorts, and undefined diagnostic cut-off points, which limit their clinical translation.
This project will employ novel intelligent monitoring technologies such as CGM and body composition analysis. By integrating multidimensional data including clinical information, laboratory indicators, and biomarkers, and leveraging artificial intelligence and big data technologies, it aims to establish a comprehensive "screen-assess-prevent" system for high-risk populations. This system will cover early diagnosis, precise classification, and personalized intervention, thereby advancing diabetes prevention efforts, improving early detection rates of prediabetes, effectively prevent diabetes and related complications, and provide scientific evidence and practical pathways for proactive chronic disease health management.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| prediabetes group | – | –No interventions |
| healthy control group | – | –no interventions |
Who may qualify
- Ages
- 35 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- People with high risk of diabetes
Key inclusion criteria (2 listed)
- 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;
Key exclusion criteria (6 listed)
- 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%);
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
oral glucose tolerance test
Time frame: Baseline period and one year after enrollment
1 secondary outcome
glycosylated hemoglobin
Time frame: Baseline period and one year after enrollment
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Apr 2026.
- Individual participant dataSharing planned: No
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Prediabetes
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Xinhua Xiao study director
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
Questions about this trial
What is this clinical trial studying?
An observational study of Prediabetes, run by Peking Union Medical College Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 15 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xinhua Xiao (86-10-139 1183, xiaoxh2014@vip.163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
oral glucose tolerance test (time frame: Baseline period and one year after enrollment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Apr 2026; AzTrials last refreshed its copy on 26 Oct 2026.