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CGM-Derived Glucose Phenotyping Reveals Hidden Prediabetes Risk and Reactive Hypoglycemia in Thai Adults with Central Obesity: A Machine Learning Approach

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Open Access
Article Type Research Article
Subject Medicine Group
OCLC JBRES Record
Nawinda Vanichakulthada*, Waiwut P2, Pitchaiprasert S, Jaroenying R, Hantragool S, Samhugkanee C, Jaruthamsophon C and Tarcome P
Issue: Volume7-Issue3
Pages: 1-11
Received: 2026-03-07
Accepted: 2026-03-13
Published: 2026-03-16

Abstract

Background: Abdominal obesity is a major driver of metabolic dysfunction, yet conventional screening methods fail to capture dynamic glycemic abnormalities. CGM-based phenotyping in non-diabetic, centrally obese Southeast Asian populations remains unexplored.
Objective: To identify glucose phenotypes (glucotypes) using machine learning analysis of Continuous Glucose Monitoring (CGM) data in Thai adults with central obesity and to examine cross-dataset reproducibility.
Methods: Two independent cohorts (discovery n = 104; validation n = 148) of participants enrolled in a lifestyle modification program underwent 14-day CGM monitoring. K-means clustering was performed on four standardized metrics: time above range (TAR%), time below range (TBR%), standard deviation of daily averages, and mean daily range. Prediabetes was defined according to ADA 2024 criteria (HbA1c 5.7 - 6.4% and/or fasting blood sugar 100 - 125 mg/dL). A sensitivity analysis using TAR% and TBR% only (n = 193) was conducted. Cluster stability was assessed via bootstrap resampling (1,000 iterations) and hierarchical clustering cross-validation.
Results: Three reproducible glucotypes were identified across both cohorts: (1) Postprandial Spikers (17 -26%), characterized by elevated TAR (12.7 - 14.2%), highest HbA1c (5.7 - 5.8%, p < 0.01), and prediabetes prevalence of 35 - 36% (p = 0.010); (2) Stable Glucose (62 - 76%), well-controlled across all metrics; and (3) Hypoglycemia-Prone (7 - 14%), with elevated TBR (9.1 - 13.2%), extreme variability, and zero prediabetes. The Hypoglycemia-Prone phenotype exhibited a glycemic pattern consistent with reactive hypoglycemia, although definitive mechanistic classification requires further investigation. Linear Discriminant Analysis (LDA) achieved 92.6% cross-validated accuracy. The relative risk of prediabetes in Spikers versus Stable was 2.52 (NNS = 4.6). Sensitivity analysis (n = 193, 2-feature) confirmed the identical glucotype structure (Silhouette = 0.544, ARI = 0.791).
Conclusion: Three clinically meaningful glucotypes were identified and replicated across independent cohorts and analytical approaches, confirming CGM-based phenotyping as a robust tool for precision metabolic screening in centrally obese populations. The consistent identification of hidden prediabetes risk and a phenotype suggestive of reactive hypoglycemia supports CGM deployment in diabetes prevention programs across Southeast Asia. Future studies incorporating insulin measurements and meal timing data are needed to confirm the mechanistic basis of the Hypoglycemia-Prone phenotype.

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© 2026 Vanichakulthada N, et al. Distributed under Creative Commons CC-BY 4.0 Creative CommonsAttribution

How to cite this article

Nawinda Vanichakulthada, Waiwut P, Pitchaiprasert S, Jaroenying R, Hantragool S, Samhugkanee C, Jaruthamsophon C, Tarcome P. CGM-Derived Glucose Phenotyping Reveals Hidden Prediabetes Risk and Reactive Hypoglycemia in Thai Adults with Central Obesity: A Machine Learning Approach. J Biomed Res Environ Sci. 2026 Mar 16; 7(3): 11. Doi: 10.37872/jbres2279

Subject area(s)

Metabolic SyndromesDiabetes

References

  1. Dianna J. Magliano, IDF Diabetes Atlas. 10th ed. International Diabetes Federation. Brussels: IDF; 2021.
  2. Aekplakorn W, Chariyalertsak S, Kessomboon P, Assanangkornchai S, Taneepanichskul S, Putwatana P. Prevalence of Diabetes and Relationship with Socioeconomic Status in the Thai Population: National Health Examination Survey, 2004-2014. J Diabetes Res. 2018 Mar 1;2018:1654530. doi: 10.1155/2018/1654530. PMID: 29687009; PMCID: PMC5852889.
  3. Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, Bosi E, Buckingham BA, Cefalu WT, Close KL, Cobelli C, Dassau E, DeVries JH, Donaghue KC, Dovc K, Doyle FJ 3rd, Garg S, Grunberger G, Heller S, Heinemann L, Hirsch IB, Hovorka R, Jia W, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Levine B, Mayorov A, Mathieu C, Murphy HR, Nimri R, Nørgaard K, Parkin CG, Renard E, Rodbard D, Saboo B, Schatz D, Stoner K, Urakami T, Weinzimer SA, Phillip M. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care. 2019 Aug;42(8):1593-1603. doi: 10.2337/dci19-0028. Epub 2019 Jun 8. PMID: 31177185; PMCID: PMC6973648.
  4. Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, Snyder M. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018 Jul 24;16(7):e2005143. doi: 10.1371/journal.pbio.2005143. PMID: 30040822; PMCID: PMC6057684.
  5. Chan JC, Malik V, Jia W, Kadowaki T, Yajnik CS, Yoon KH, Hu FB. Diabetes in Asia: epidemiology, risk factors, and pathophysiology. JAMA. 2009 May 27;301(20):2129-40. doi: 10.1001/jama.2009.726. PMID: 19470990.
  6. Unnikrishnan R, Anjana RM, Mohan V. Diabetes in South Asians: is the phenotype different? Diabetes. 2014 Jan;63(1):53-5. doi: 10.2337/db13-1592. PMID: 24357697.
  7. American Diabetes Association Professional Practice Committee. Standards of Care in Diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S1–S321.
  8. Dalmaijer ES, Nord CL, Astle DE. Statistical power for cluster analysis. BMC Bioinformatics. 2022 May 31;23(1):205. doi: 10.1186/s12859-022-04675-1. PMID: 35641905; PMCID: PMC9158113.
  9. Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53-65. doi: 10.1016/0377-0427(87)90125-7.
  10. Ruderman N, Chisholm D, Pi-Sunyer X, Schneider S. The metabolically obese, normal-weight individual revisited. Diabetes. 1998 May;47(5):699-713. doi: 10.2337/diabetes.47.5.699. PMID: 9588440.
  11. World Health Organization. Global action plan for the prevention and control of noncommunicable diseases 2013-2020. Geneva: WHO; 2013.
  12. Tao R, Yu X, Lu J, Shen Y, Lu W, Zhu W, Bao Y, Li H, Zhou J. Multilevel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes. BMJ Open Diabetes Res Care. 2021 Feb;9(1):e001869. doi: 10.1136/bmjdrc-2020-001869. PMID: 33627315; PMCID: PMC7908294.
  13. Mao Y, Tan KXQ, Seng A, Wong P, Toh SA, Cook AR. Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning. Health Data Sci. 2022 Apr 27;2022:9892340. doi: 10.34133/2022/9892340. PMID: 38487483; PMCID: PMC10880155.
  14. Chan NB, Li W, Aung T, Bazuaye E, Montero RM. Machine learning-based time in patterns for blood glucose fluctuation in the analysis of continuous glucose monitoring data. JMIR AI. 2024;3:e45450. doi: 10.2196/45450.
  15. Monnier L, Colette C, Owens DR. Glycemic variability: the third component of the dysglycemia in diabetes. Is it important? How to measure it? J Diabetes Sci Technol. 2008 Nov;2(6):1094-100. doi: 10.1177/193229680800200618. PMID: 19885298; PMCID: PMC2769808.
  16. Metwally AA. Utilizing continuous glucose monitoring and machine learning to identify metabolic subphenotypes. arXiv. 2024.
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