Multimodal Risk Factor Graph Mapping, Metabolic Subtyping, and Digital Intelligent Chronic Disease Management for Prediabetes: An Integrated Cohort and Case-Control Clinical Study

NCT07854002 | Not Yet Recruiting DMC

• 2 other identifiers: 2026-SR-00672025ZD0550700
• Study Type: observational
• Target: 30,000
• Locations: 0 countries
• Sites: N/A

Brief 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. 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.

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

Outcome Measures

Primary Outcomes (1)

• 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.

  From baseline through 3 years of follow-up

Study Arms (1)

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.

Eligibility Criteria

• Age: 20 Years - 70 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Changzhou Second People's Hospital affiliated with Nanjing Medical University: collaborator
• Nanjing Medical University: lead
• The Affiliated Hospital Of Guizhou Medical University: collaborator
• The First Affiliated Hospital of Zhengzhou University: collaborator
• Suqian First Hospital: collaborator
• Jiangsu Taizhou People's Hospital: collaborator
• The First People's Hospital of Lianyungang: collaborator
• The Second People's Hospital of Huai'an: collaborator
• Wuxi People's Hospital: collaborator
• Nantong First People's Hospital: collaborator
• Yancheng First People's Hospital: collaborator
• The First Hospital of Jilin University: collaborator
• Fujian Medical University Union Hospital: collaborator
• Shandong Provincial Hospital: collaborator
• The First Affiliated Hospital of Nanchang University: collaborator
• People's Hospital of Xinjiang Uygur Autonomous Region: collaborator
• Xijing Hospital: collaborator
• Dalian Central Hospital: collaborator
• Xinqiao Hospital of Chongqing: collaborator
• Suzhou Municipal Hospital: collaborator
• Wuhu City Second People's Hospital: collaborator
• Shangrao People's Hospital: collaborator
• The Fourth Affiliated Hospital of Nanjing Medical University: collaborator

Related Publications (12)

• 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.

  PMID: 24154991 BACKGROUND

• Fu 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.

  PMID: 39425782 BACKGROUND

• Li 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.

  PMID: 18502303 BACKGROUND

• Gong 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.

  PMID: 31036503 BACKGROUND

• Tuomilehto 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.

  PMID: 11333990 BACKGROUND

• Knowler 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.

  PMID: 11832527 BACKGROUND

• Lu 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.

  PMID: 31152120 BACKGROUND

• Wu 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.

  PMID: 40017947 BACKGROUND

• Suzuki 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.

  PMID: 38374256 BACKGROUND

• Zhang L, Zhang Y, Shen S, Wang X, Dong L, Li Q, Ren W, Li Y, Bai J, Gong Q, Kuang H, Qi L, Lu Q, Cheng W, Liu Y, Yan S, Wu D, Fang H, Hou F, Wang Y, Yang Z, Lian X, Du J, Sun N, Ji L, Li G; China Diabetes Prevention Program Study Group. Safety and effectiveness of metformin plus lifestyle intervention compared with lifestyle intervention alone in preventing progression to diabetes in a Chinese population with impaired glucose regulation: a multicentre, open-label, randomised controlled trial. Lancet Diabetes Endocrinol. 2023 Aug;11(8):567-577. doi: 10.1016/S2213-8587(23)00132-8. Epub 2023 Jul 3.

  PMID: 37414069 BACKGROUND

• Echouffo-Tcheugui JB, Perreault L, Ji L, Dagogo-Jack S. Diagnosis and Management of Prediabetes: A Review. JAMA. 2023 Apr 11;329(14):1206-1216. doi: 10.1001/jama.2023.4063.

  PMID: 37039787 BACKGROUND

• Li Y, Teng D, Shi X, Qin G, Qin Y, Quan H, Shi B, Sun H, Ba J, Chen B, Du J, He L, Lai X, Li Y, Chi H, Liao E, Liu C, Liu L, Tang X, Tong N, Wang G, Zhang JA, Wang Y, Xue Y, Yan L, Yang J, Yang L, Yao Y, Ye Z, Zhang Q, Zhang L, Zhu J, Zhu M, Ning G, Mu Y, Zhao J, Teng W, Shan Z. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. BMJ. 2020 Apr 28;369:m997. doi: 10.1136/bmj.m997.

  PMID: 32345662 BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

Stored biospecimens will include plasma, whole blood, extracted genomic DNA, urine samples collected before and after the OGTT, and optional stool samples for genetic, metabolic, and other protocol-specified research analyses.

MeSH Terms

Conditions

Prediabetic State; Insulin Resistance; Glucose Intolerance; Metabolic Diseases; Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Nutritional and Metabolic Diseases; Endocrine System Diseases; Hyperinsulinism; Hyperglycemia

Study Officials

• Tao Yang

  The First Affiliated Hospital with Nanjing Medical University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Tao Yang

CONTACT

0086-13851498409; yangt@njmu.edu.cn

Jingyang Gao

CONTACT

0086-13127526220; gaojingyang2011@163.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor and Director, Department of Endocrinology

Study Record Dates

• First Submitted: September 27, 2026
• First Posted: October 2, 2026
• Study Start (Estimated): October 8, 2026
• Primary Completion (Estimated): February 28, 2029
• Study Completion (Estimated): August 31, 2029
• Last Updated: October 2, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share