PrediabetesDM-PIONEERNCT07854002
Multimodal Risk Factor Graph Mapping, Metabolic Subtyping, and Digital Intelligent Chronic Disease Management for Prediabetes: An Integrated Cohort and Case-Control Clinical Study
An observational study of Prediabetes and Prediabetes (Insulin Resistance, Impaired Glucose Tolerance), run by Nanjing Medical University.
Trial summary
This observational study aims to understand the metabolic characteristics and long-term changes in adults with prediabetes and to identify risk and protective factors associated with progression to diabetes or return to normal glucose regulation. The study will include adults aged 20 to 69 years with prediabetes. Read more about this study
- Who is it for?
- Ages 20 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- Prediabetes, Prediabetes (Insulin Resistance, Impaired Glucose Tolerance), Impaired Fasting Glucose
- How many people
- 30,000 participants
- Where
- No sites listed yet
- Study dates
- 8 Oct 2026to 31 Aug 2029
- 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 20 to 70 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Adults aged 20 to 69 years will be recruited from participating hospitals, previous cohort populations, and health examination programs. The study population will include individuals with prediabetes and individuals with normal glucose metabolism who serve as a comparison group. Participants with prediabetes will meet at least one criterion for impaired fasting glucose, impaired glucose tolerance, or elevated HbA1c without meeting diagnostic criteria for diabetes. Participants in the normal glucose metabolism group will have normal fasting glucose, 2-hour glucose during a 75-g oral glucose tolerance test, and HbA1c, with no previous diagnosis of diabetes or prediabetes.
Who may be able to join
- Participants must meet all of the following common criteria (Items 1-3) and meet either the population definition in Item 4 or Item 5:
- Aged ≥20 years and <70 years on the date of signing the informed consent form. No restriction on sex. The overall multicenter recruitment plan aims to maintain an approximately 1:1 male-to-female ratio.
- Able to use smartphone-based applications, including a WeChat mini-program or mobile application, and willing to participate in up to 3 years of follow-up and digital health management. Written informed consent must be provided by the participant or his/her legal guardian.
- Prediabetes group: Participants must not meet diagnostic criteria for diabetes and must meet at least one of the following criteria for prediabetes:
- Impaired fasting glucose (IFG): 6.1 mmol/L ≤ fasting plasma glucose (FPG) <7.0 mmol/L; Impaired glucose tolerance (IGT): 7.8 mmol/L ≤ 2-hour plasma glucose (2h-PG) <11.1 mmol/L during a 75-g oral glucose tolerance test (OGTT); Hemoglobin A1c (HbA1c): 5.7% ≤ HbA1c <6.5%. Normal glucose metabolism group: FPG <6.1 mmol/L, 2-hour plasma glucose <7.8 mmol/L during a 75-g OGTT, and HbA1c <5.7%, with no previous diagnosis of diabetes or prediabetes.
Who cannot join
- Participants meeting any of the following criteria will be excluded:
- Previous diagnosis of type 2 diabetes, type 1 diabetes, or other specific types of diabetes, including gestational diabetes mellitus or pancreatogenic diabetes.
- Clinically significant severe cardiac, hepatic, or renal dysfunction, or an advanced major medical condition, including:
- estimated glomerular filtration rate (eGFR) <45 mL/min/1.73 m²; acute myocardial infarction within 6 months before baseline; unstable angina; congestive heart failure, New York Heart Association (NYHA) class III-IV; severe arrhythmia; stroke, including cerebral infarction or intracerebral hemorrhage, within 6 months before baseline.
- Active malignancy currently being treated with chemotherapy, radiotherapy, or immunotherapy; acute or chronic infection or autoimmune disease that may compromise the safety of follow-up; psychiatric disorders, including severe depression, schizophrenia, or bipolar disorder; or severe cognitive impairment or impaired consciousness that may affect adherence to study questionnaires or the reliability of follow-up data.
- Pregnant or breastfeeding women, or women planning pregnancy within the next 24 months.
- Current participation in another interventional clinical trial, or participation in another drug or medical device clinical trial within 3 months before baseline; or participants considered by the investigator to be at high risk of withdrawal or loss to follow-up for non-medical reasons, such as frequent changes in permanent residence.
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
This observational study aims to understand the metabolic characteristics and long-term changes in adults with prediabetes and to identify risk and protective factors associated with progression to diabetes or return to normal glucose regulation. The study will include adults aged 20 to 69 years with prediabetes.
The main questions this study aims to answer are:
Read more
Which clinical characteristics, lifestyle factors, social and environmental factors, and genetic factors are associated with progression or improvement of prediabetes? How do blood glucose levels, pancreatic islet function, and other metabolic indicators change over time in people with prediabetes?
Participants will complete questionnaires about their health status and lifestyle at baseline and during follow-up. According to the study schedule, they will also undergo blood glucose and other metabolic assessments, including an oral glucose tolerance test (OGTT). Some participants will undergo additional assessments, such as body composition analysis, liver elasticity measurement, and arterial stiffness testing. Blood, urine, and other biological samples may also be collected for further research.
Participants will be followed over time to assess changes in glucose metabolism and lifestyle. Researchers will evaluate factors associated with return to normal glucose regulation, persistent prediabetes, or progression to diabetes. The findings may help improve early identification and personalized management of people at high risk of developing diabetes.
Read the detailed description
This is a multicenter observational study integrating prospective and retrospective cohort data with a case-control component to investigate the development and progression of prediabetes in Chinese adults. The study is designed to characterize the heterogeneity of prediabetes by combining clinical, metabolic, lifestyle, environmental, genetic, and longitudinal health data. No pharmacological treatment is assigned by the study, and participation does not replace or modify routine clinical care.
The study has three major components. First, data from existing cohorts, including the previous cohort, participating hospitals, and health examination programs will be harmonized to identify clinical, behavioral, environmental, and genetic factors associated with prediabetes and its long-term progression. Multivariable statistical methods and machine-learning approaches will be used to identify important risk factors and explore interactions among genetic background, environmental exposure, lifestyle, and metabolic characteristics.
Second, a prospective observational cohort will undergo standardized baseline and follow-up assessments. Data collection may include questionnaires on health status and lifestyle, anthropometric measurements, oral glucose tolerance testing, biochemical measurements, body composition assessment, liver transient elastography, pulse wave velocity, urine testing, and continuous glucose monitoring. Blood and other biological samples will be collected for selected research analyses, including genetic testing and metabolomic profiling. Participants will be followed longitudinally to characterize changes in glucose metabolism and related metabolic phenotypes.
Third, a case-control component will compare participants with prediabetes with individuals who have normal glucose regulation but other metabolic risk factors. Genetic, metabolic, behavioral, and environmental characteristics will be compared between groups to identify factors associated with abnormal glucose regulation. Genetic information from genome-wide genotyping will also be used to construct polygenic risk scores, which will be integrated with clinical and metabolic data.
Longitudinal data will be used to identify metabolic subtypes of prediabetes and to develop prediction models for future changes in glucose regulation. Unsupervised clustering and other machine-learning methods will be used to characterize distinct metabolic phenotypes. Time-series data will also be used to develop and validate prediction models, including Transformer-based models, for estimating the risk of future progression of glucose abnormalities.
The planned observational cohort will include at least 30,000 participants. This sample size was selected to provide an adequate number of longitudinal outcome events for multivariable risk modeling and high-dimensional machine-learning analyses. A separate case-control component will include approximately 3,000 participants, with equal numbers of prediabetes cases and metabolically at-risk controls.
Standardized operating procedures will be used across participating centers for participant recruitment, informed consent, data collection, laboratory testing, sample handling, follow-up, data management, and quality control. Study personnel and clinical research coordinators will receive standardized training before participating in study procedures.
Each participant will be assigned a unique study identifier. Study identifiers, barcodes, scheduled assessment times, sample information, and completion status will be used to maintain traceability between participants, clinical assessments, biological samples, and study data.
Data will be obtained from multiple sources, including hospital information systems, structured questionnaires, case report forms, laboratory systems, study examinations, wearable devices, and continuous glucose monitoring systems. Where applicable, registry data will be compared with source medical records, laboratory records, case report forms, and device-generated data to evaluate data accuracy and completeness.
Automated and manual data checks will be used to identify values outside predefined ranges, inconsistent information across variables, duplicate records, missing data, and other potential data-quality problems. Suspected errors will be reviewed against available source records before correction. Key study procedures and protocol deviations will be documented and remain traceable.
A standardized data dictionary and coding framework will be used across participating centers. The data dictionary will define study variables, data sources, formats, coding rules, allowable values, and relevant units. Data will be stored in a centralized, de-identified research database with access controls and data security procedures.
Quality monitoring will include routine centralized review and planned site-level monitoring. Monitoring will focus on informed consent, participant identification, completeness of key study assessments, timing of oral glucose tolerance testing, sample tracking, device data, protocol deviations, and consistency between source information and the research database.
Missing or incomplete data will be characterized before analysis. Depending on the extent and pattern of missingness, statistical approaches such as multiple imputation may be used. Sensitivity analyses will be performed to evaluate the robustness of major findings to missing data and different analytical assumptions.
Statistical analyses will include descriptive analyses, multivariable regression, longitudinal risk modeling, subgroup and interaction analyses, and machine-learning approaches. Logistic regression and time-to-event models will be used where appropriate to evaluate associations between baseline or longitudinal factors and changes in glucose regulation. Machine-learning and clustering methods will be used to identify metabolic subtypes and develop prediction models. Model performance will be evaluated using internal and, where available, external validation datasets.
- Conditions
- Prediabetes, Prediabetes (Insulin Resistance, Impaired Glucose Tolerance), Impaired Fasting Glucose, Impaired Glucose Tolerance (Prediabetes)
- Keywords
- Prediabetes, metabolic disease, Glucose Metabolism, Insulin Resistance, Beta-cell Function, Diabetes
- Medical subject headings
- Prediabetic State, Insulin Resistance, Glucose Intolerance, Metabolic Diseases, Diabetes Mellitus
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 cohort | Adults with prediabetes, defined as impaired fasting glucose, impaired glucose tolerance, and/or HbA1c 5.7-6.4%, without a diagnosis of type 2 diabetes. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Glycemic status transition
Participants will be classified according to their glycemic status during follow-up as: (1) progression to type 2 diabetes, defined as fasting plasma glucose (FPG) ≥7.0 mmol/L, 2-hour plasma glucose (2h-PG) ≥11.1 mmol/L, or HbA1c ≥6.5%; (2) persistent prediabetes; or (3) reversion to normal glucose regulation, defined as FPG <6.1 mmol/L and HbA1c <5.7%. The proportion of participants in each glycemic outcome category will be assessed.
Measured: From baseline through 3 years of follow-up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Oct 2026
- Study start8 Oct 2026 (estimated)
- Primary completion28 Feb 2029 (estimated)
- Study completion31 Aug 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for PrediabetesStudies of the same condition, open and under way.
- 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
- Other
- Time perspective
- Other
- Enrolment
- 30,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Nanjing Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- The Affiliated Hospital Of Guizhou Medical University, The First Affiliated Hospital of Zhengzhou University, Suqian First Hospital, Jiangsu Taizhou People's Hospital, The First People's Hospital of Lianyungang, The Second People's Hospital of Huai'an, Wuxi People's Hospital, Nantong First People's Hospital, Yancheng First People's Hospital, The First Hospital of Jilin University, Fujian Medical University Union Hospital, Shandong Provincial Hospital, The First Affiliated Hospital of Nanchang University, People's Hospital of Xinjiang Uygur Autonomous Region, Xijing Hospital, Dalian Central Hospital, Xinqiao Hospital of Chongqing, Suzhou Municipal Hospital, Wuhu City Second People's Hospital, Shangrao People's Hospital, The Fourth Affiliated Hospital of Nanjing Medical University, Changzhou Second People's Hospital affiliated with Nanjing Medical University
- Responsible party
- Principal investigator, Yang Tao, Nanjing Medical University
- Investigators
- Tao Yang, Principal investigator, The First Affiliated Hospital with Nanjing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07854002
- Sponsor's protocol ID
- 2026-SR-0067
- Other IDs
- 2025ZD0550700
- Acronym
- DM-PIONEER
- Status last verified
- Sep 2026
- First submitted
- 27 Sep 2026
- Last updated
- 2 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be routinely shared with external researchers because the study includes sensitive clinical, genetic, and other multimodal data that are subject to participant privacy protection, institutional data governance, and applicable regulations on human genetic resources and data security. De-identified data may be considered for controlled scientific use only after appropriate institutional, ethical, and regulatory review and approval. Aggregate study results will be disseminated through scientific publications and other approved channels.
Publications
Fu Q, Sun M, Tang W, Wang Z, Cao M, Zhu Z, Lu L, Bi Y, Ning G, Yang T. A Chinese risk score model for identifying postprandial hyperglycemia without oral glucose tolerance test. Diabetes Metab Res Rev. 2014 May;30(4):284-90. doi: 10.1002/dmrr.2490.
PubMed 24154991Fu Q, Dai H, Shen S, He Y, Zheng S, Jiang H, Gu P, Sun M, Zhu X, Xu K, Yang T. Interactions of genes with alcohol consumption affect insulin sensitivity and beta cell function. Diabetologia. 2025 Jan;68(1):116-127. doi: 10.1007/s00125-024-06291-5. Epub 2024 Oct 19.
PubMed 39425782Li G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, Li H, Li H, Jiang Y, An Y, Shuai Y, Zhang B, Zhang J, Thompson TJ, Gerzoff RB, Roglic G, Hu Y, Bennett PH. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008 May 24;371(9626):1783-9. doi: 10.1016/S0140-6736(08)60766-7.
PubMed 18502303Gong Q, Zhang P, Wang J, Ma J, An Y, Chen Y, Zhang B, Feng X, Li H, Chen X, Cheng YJ, Gregg EW, Hu Y, Bennett PH, Li G; Da Qing Diabetes Prevention Study Group. Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. Lancet Diabetes Endocrinol. 2019 Jun;7(6):452-461. doi: 10.1016/S2213-8587(19)30093-2. Epub 2019 Apr 26.
PubMed 31036503Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, Keinanen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M; Finnish Diabetes Prevention Study Group. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001 May 3;344(18):1343-50. doi: 10.1056/NEJM200105033441801.
PubMed 11333990Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002 Feb 7;346(6):393-403. doi: 10.1056/NEJMoa012512.
PubMed 11832527Lu J, He J, Li M, Tang X, Hu R, Shi L, Su Q, Peng K, Xu M, Xu Y, Chen Y, Yu X, Yan L, Wang T, Zhao Z, Qin G, Wan Q, Chen G, Dai M, Zhang D, Gao Z, Wang G, Shen F, Luo Z, Qin Y, Chen L, Huo Y, Li Q, Ye Z, Zhang Y, Du R, Cheng D, Liu C, Wang Y, Wu S, Yang T, Deng H, Li D, Lai S, Bloomgarden ZT, Chen L, Zhao J, Mu Y, Ning G, Wang W, Bi Y; 4C Study Group. Predictive Value of Fasting Glucose, Postload Glucose, and Hemoglobin A1c on Risk of Diabetes and Complications in Chinese Adults. Diabetes Care. 2019 Aug;42(8):1539-1548. doi: 10.2337/dc18-1390. Epub 2019 May 31.
PubMed 31152120Wu Y, Fan X, Shi Y, Yuan Z, Zhang Y, Han J, Yuan Z, Li M, Cheng Y, Feng X, Wang Z, Xuan R, Dong Y, Tian Y, Dong H, Guo Q, Song Y, Zhao J. Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK Biobank. BMJ Public Health. 2025 Feb 12;3(1):e001539. doi: 10.1136/bmjph-2024-001539. eCollection 2025.
PubMed 40017947Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Melloni GEM, Kanoni S, Rayner NW, Bocher O, Arruda AL, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Thangam M, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, Gonzalez-Villalpando C, Gonzalez-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Hakaste L, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jorgensen T, Kamanu FK, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Lall K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Melander O, Metspalu A, Mo H, Morris AD, Moura FA, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schonherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stancakova A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W; VA Million Veteran Program; Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Pare G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Kottgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Ahlqvist E, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zollner S, Magi R, Marston NA, Ruff CT, van Heel DA, Finer S, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, Zeggini E. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature. 2024 Mar;627(8003):347-357. doi: 10.1038/s41586-024-07019-6. Epub 2024 Feb 19.
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PubMed 32345662
Study at a glance
From the registry record, last updated by the sponsor on 2 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Risk Factor Graph Mapping, Metabolic Subtyping, and Digital Intelligent Chronic Disease Management for Prediabetes: An Integrated Cohort and Case-Control Clinical Study
- ClinicalTrials.gov ID
- NCT07854002
- Acronym / sponsor ID
- DM-PIONEER / 2026-SR-0067
- Conditions
- Prediabetes, Prediabetes (Insulin Resistance, Impaired Glucose Tolerance), Impaired Fasting Glucose, Impaired Glucose Tolerance (Prediabetes)
- Keywords
- Prediabetes, metabolic disease, Glucose Metabolism, Insulin Resistance, Beta-cell Function, Diabetes
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 30,000 participants (estimated)
- Lead sponsor
- Nanjing Medical UniversityUniversity, hospital or other
- Collaborators
- The Affiliated Hospital Of Guizhou Medical University, The First Affiliated Hospital of Zhengzhou University, Suqian First Hospital, Jiangsu Taizhou People's Hospital, The First People's Hospital of Lianyungang, The Second People's Hospital of Huai'an, Wuxi People's Hospital, Nantong First People's Hospital, Yancheng First People's Hospital, The First Hospital of Jilin University, Fujian Medical University Union Hospital, Shandong Provincial Hospital, The First Affiliated Hospital of Nanchang University, People's Hospital of Xinjiang Uygur Autonomous Region, Xijing Hospital, Dalian Central Hospital, Xinqiao Hospital of Chongqing, Suzhou Municipal Hospital, Wuhu City Second People's Hospital, Shangrao People's Hospital, The Fourth Affiliated Hospital of Nanjing Medical University, Changzhou Second People's Hospital affiliated with Nanjing Medical University
- Study start
- 8 Oct 2026 (estimated)
- Primary completion
- 28 Feb 2029 (estimated)
- Study completion
- 31 Aug 2029 (estimated)
- First posted
- 2 Oct 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.
This observational study aims to understand the metabolic characteristics and long-term changes in adults with prediabetes and to identify risk and protective factors associated with progression to diabetes or return to normal glucose regulation. The study will include adults aged 20 to 69 years with prediabetes.
The main questions this study aims to answer are:
Which clinical characteristics, lifestyle factors, social and environmental factors, and genetic factors are associated with progression or improvement of prediabetes? How do blood glucose levels, pancreatic islet function, and other metabolic indicators change over time in people with prediabetes?
Participants will complete questionnaires about their health status and lifestyle at baseline and during follow-up. According to the study schedule, they will also undergo blood glucose and other metabolic assessments, including an oral glucose tolerance test (OGTT). Some participants will undergo additional assessments, such as body composition analysis, liver elasticity measurement, and arterial stiffness testing. Blood, urine, and other biological samples may also be collected for further research.
Participants will be followed over time to assess changes in glucose metabolism and lifestyle. Researchers will evaluate factors associated with return to normal glucose regulation, persistent prediabetes, or progression to diabetes. The findings may help improve early identification and personalized management of people at high risk of developing diabetes.
Detailed description
This is a multicenter observational study integrating prospective and retrospective cohort data with a case-control component to investigate the development and progression of prediabetes in Chinese adults. The study is designed to characterize the heterogeneity of prediabetes by combining clinical, metabolic, lifestyle, environmental, genetic, and longitudinal health data. No pharmacological treatment is assigned by the study, and participation does not replace or modify routine clinical care.
The study has three major components. First, data from existing cohorts, including the previous cohort, participating hospitals, and health examination programs will be harmonized to identify clinical, behavioral, environmental, and genetic factors associated with prediabetes and its long-term progression. Multivariable statistical methods and machine-learning approaches will be used to identify important risk factors and explore interactions among genetic background, environmental exposure, lifestyle, and metabolic characteristics.
Second, a prospective observational cohort will undergo standardized baseline and follow-up assessments. Data collection may include questionnaires on health status and lifestyle, anthropometric measurements, oral glucose tolerance testing, biochemical measurements, body composition assessment, liver transient elastography, pulse wave velocity, urine testing, and continuous glucose monitoring. Blood and other biological samples will be collected for selected research analyses, including genetic testing and metabolomic profiling. Participants will be followed longitudinally to characterize changes in glucose metabolism and related metabolic phenotypes.
Third, a case-control component will compare participants with prediabetes with individuals who have normal glucose regulation but other metabolic risk factors. Genetic, metabolic, behavioral, and environmental characteristics will be compared between groups to identify factors associated with abnormal glucose regulation. Genetic information from genome-wide genotyping will also be used to construct polygenic risk scores, which will be integrated with clinical and metabolic data.
Longitudinal data will be used to identify metabolic subtypes of prediabetes and to develop prediction models for future changes in glucose regulation. Unsupervised clustering and other machine-learning methods will be used to characterize distinct metabolic phenotypes. Time-series data will also be used to develop and validate prediction models, including Transformer-based models, for estimating the risk of future progression of glucose abnormalities.
The planned observational cohort will include at least 30,000 participants. This sample size was selected to provide an adequate number of longitudinal outcome events for multivariable risk modeling and high-dimensional machine-learning analyses. A separate case-control component will include approximately 3,000 participants, with equal numbers of prediabetes cases and metabolically at-risk controls.
Standardized operating procedures will be used across participating centers for participant recruitment, informed consent, data collection, laboratory testing, sample handling, follow-up, data management, and quality control. Study personnel and clinical research coordinators will receive standardized training before participating in study procedures.
Each participant will be assigned a unique study identifier. Study identifiers, barcodes, scheduled assessment times, sample information, and completion status will be used to maintain traceability between participants, clinical assessments, biological samples, and study data.
Data will be obtained from multiple sources, including hospital information systems, structured questionnaires, case report forms, laboratory systems, study examinations, wearable devices, and continuous glucose monitoring systems. Where applicable, registry data will be compared with source medical records, laboratory records, case report forms, and device-generated data to evaluate data accuracy and completeness.
Automated and manual data checks will be used to identify values outside predefined ranges, inconsistent information across variables, duplicate records, missing data, and other potential data-quality problems. Suspected errors will be reviewed against available source records before correction. Key study procedures and protocol deviations will be documented and remain traceable.
A standardized data dictionary and coding framework will be used across participating centers. The data dictionary will define study variables, data sources, formats, coding rules, allowable values, and relevant units. Data will be stored in a centralized, de-identified research database with access controls and data security procedures.
Quality monitoring will include routine centralized review and planned site-level monitoring. Monitoring will focus on informed consent, participant identification, completeness of key study assessments, timing of oral glucose tolerance testing, sample tracking, device data, protocol deviations, and consistency between source information and the research database.
Missing or incomplete data will be characterized before analysis. Depending on the extent and pattern of missingness, statistical approaches such as multiple imputation may be used. Sensitivity analyses will be performed to evaluate the robustness of major findings to missing data and different analytical assumptions.
Statistical analyses will include descriptive analyses, multivariable regression, longitudinal risk modeling, subgroup and interaction analyses, and machine-learning approaches. Logistic regression and time-to-event models will be used where appropriate to evaluate associations between baseline or longitudinal factors and changes in glucose regulation. Machine-learning and clustering methods will be used to identify metabolic subtypes and develop prediction models. Model performance will be evaluated using internal and, where available, external validation datasets.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prediabetes cohort | – | –Adults with prediabetes, defined as impaired fasting glucose, impaired glucose tolerance, and/or HbA1c 5.7-6.4%, without a diagnosis of type 2 diabetes. |
Who may qualify
- Ages
- 20 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Adults aged 20 to 69 years will be recruited from participating hospitals, previous cohort populations, and health examination programs. The study population will include individuals with prediabetes and individuals with normal glucose metabolism who serve as a comparison group. Participants with prediabetes will meet at least one criterion for impaired fasting glucose, impaired glucose tolerance, or elevated HbA1c without meeting diagnostic criteria for diabetes. Participants in the normal glucose metabolism group will have normal fasting glucose, 2-hour glucose during a 75-g oral glucose tolerance test, and HbA1c, with no previous diagnosis of diabetes or prediabetes.
Key inclusion criteria (5 listed)
- Participants must meet all of the following common criteria (Items 1-3) and meet either the population definition in Item 4 or Item 5:
- Aged ≥20 years and <70 years on the date of signing the informed consent form. No restriction on sex. The overall multicenter recruitment plan aims to maintain an approximately 1:1 male-to-female ratio.
- Able to use smartphone-based applications, including a WeChat mini-program or mobile application, and willing to participate in up to 3 years of follow-up and digital health management. Written informed consent must be provided by the participant or his/her legal guardian.
- Prediabetes group: Participants must not meet diagnostic criteria for diabetes and must meet at least one of the following criteria for prediabetes:
- Impaired fasting glucose (IFG): 6.1 mmol/L ≤ fasting plasma glucose (FPG) <7.0 mmol/L; Impaired glucose tolerance (IGT): 7.8 mmol/L ≤ 2-hour plasma glucose (2h-PG) <11.1 mmol/L during a 75-g oral glucose tolerance test (OGTT); Hemoglobin A1c (HbA1c): 5.7% ≤ HbA1c <6.5%. Normal glucose metabolism group: FPG <6.1 mmol/L, 2-hour plasma glucose <7.8 mmol/L during a 75-g OGTT, and HbA1c <5.7%, with no previous diagnosis of diabetes or prediabetes.
Key exclusion criteria (7 listed)
- Previous diagnosis of type 2 diabetes, type 1 diabetes, or other specific types of diabetes, including gestational diabetes mellitus or pancreatogenic diabetes.
- Clinically significant severe cardiac, hepatic, or renal dysfunction, or an advanced major medical condition, including:
- estimated glomerular filtration rate (eGFR) <45 mL/min/1.73 m²; acute myocardial infarction within 6 months before baseline; unstable angina; congestive heart failure, New York Heart Association (NYHA) class III-IV; severe arrhythmia; stroke, including cerebral infarction or intracerebral hemorrhage, within 6 months before baseline.
- Active malignancy currently being treated with chemotherapy, radiotherapy, or immunotherapy; acute or chronic infection or autoimmune disease that may compromise the safety of follow-up; psychiatric disorders, including severe depression, schizophrenia, or bipolar disorder; or severe cognitive impairment or impaired consciousness that may affect adherence to study questionnaires or the reliability of follow-up data.
- Pregnant or breastfeeding women, or women planning pregnancy within the next 24 months.
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
Glycemic status transition
Time frame: From baseline through 3 years of follow-up
Participants will be classified according to their glycemic status during follow-up as: (1) progression to type 2 diabetes, defined as fasting plasma glucose (FPG) ≥7.0 mmol/L, 2-hour plasma glucose (2h-PG) ≥11.1 mmol/L, or HbA1c ≥6.5%; (2) persistent prediabetes; or (3) reversion to normal glucose regulation, defined as FPG <6.1 mmol/L and HbA1c <5.7%. The proportion of participants in each glycemic outcome category will be assessed.
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 2 Oct 2026.
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- Individual participant dataSharing planned: No. Individual participant data will not be routinely shared with external researchers because the study includes sensitive clinical, genetic, and other multimodal data that are subject to participant privacy protection, institutional data governance, and applicable regulations on human genetic resources and data security. De-identified data may be considered for controlled scientific use only after appropriate institutional, ethical, and regulatory review and approval. Aggregate study results will be disseminated through scientific publications and other approved channels.
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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.
Tao Yang principal investigator
The First Affiliated Hospital with Nanjing Medical University
Questions about this trial
What is this clinical trial studying?
An observational study of Prediabetes and Prediabetes (Insulin Resistance, Impaired Glucose Tolerance), run by Nanjing Medical University.
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 5 inclusion and 7 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 2 Oct 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 Tao Yang (0086-13851498409, yangt@njmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Glycemic status transition (time frame: From baseline through 3 years of follow-up). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07854002, which also lists 12 linked publications.
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 2 Oct 2026.