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Higher adiposity predicts greater intra-individual inconsistencies in postprandial glycemic measurements—an analysis of three randomized controlled trials in Asian populations



Acute glycemic responses offer important insights into glucose homeostasis although the repeatability of these measurements particularly in Asian populations remains unclear. This research aimed to critically investigate the inconsistencies of the postprandial glycemic profile within individuals, and identify potential variables predicting greater inconsistencies.


This was a secondary analysis of three randomized controlled trials which fed subjects with glucose (and other carbohydrate-rich foods), and measured postprandial blood glucose at regular intervals. Intra-individual rank-order consistency in the glycemic profile between acute glucose treatments was evaluated and compared against demographic, anthropometric and cardio-metabolic health related indicators to delineate potential confounding variables. Correlations between the incremental area under curve at 120 min (iAUC120 min) for glucose and the carbohydrate-rich foods were further explored.


Rank-order consistency was identified to be moderate, with intra-individual inconsistencies marginally lower than inter-individual inconsistencies. Notably, greater inconsistencies within individuals were directly correlated with BMI and fat-mass index (P < 0.01) albeit non-significant for age, ethnicity, and other cardio-metabolic health-related risk indicators. Across the trials, there were positive monotonic correlations between the iAUC120 min for glucose and simple sugars (sucrose, isomaltulose), as well as different varieties of rice (jasmine white, Bapatla brown, Bapatla white; p < 0.05). However, there were a lack of associations between iAUC120 min for glucose with pastas (semolina and wholegrain penne, spaghetti) and mee pok noodles.


There are inherent inconsistencies in postprandial glycemic measurements within individuals, particularly among those with higher adiposity. These confounders need to be kept in mind for appropriate and meaningful interpretations of glycemia.

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Fig. 1: Flow diagram of population selection process for secondary analysis of acute feeding trials.
Fig. 2: Rank-order associations between iAUC120 min of glucose and intervention foods in NCT04653207.
Fig. 3: Rank-order associations between iAUC120 min of glucose and intervention foods in NCT03646812.
Fig. 4: Rank-order associations between iAUC120 min of glucose and intervention foods in NCT04228341.

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Data availability

Data described in the manuscript, code book, and analytic code will be made available upon request pending application and approval.


  1. Bogdanet D, O’Shea P, Lyons C, Shafat A, Dunne F. The oral glucose tolerance test — is it time for a change? — A literature review with an emphasis on pregnancy. J Clin Med. 2020;9:3451.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Karakaya J, Aksoy DY, Harmanci A, Karaagaoglu E, Gurlek A. Predictive ability of fasting plasma glucose for a diabetic 2-h postload glucose value in oral glucose tolerance test: spectrum effect. J Diabetes Complicat. 2007;21:300–5.

    Article  Google Scholar 

  3. Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM, et al. Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr. 1981;34:362–6.

    Article  CAS  PubMed  Google Scholar 

  4. Gordon BA, Fraser SF, Bird SR, Benson AC. Reproducibility of multiple repeated oral glucose tolerance tests. Diabetes Res Clin Pract. 2011;94:e78–82.

    Article  CAS  PubMed  Google Scholar 

  5. Van de Velde FP, Dierickx A, Depypere H, Delanghe JR, Kaufman JM, Lapauw B. Reproducibility and least significant differences of oral glucose tolerance test-derived parameters in a postmenopausal population without diabetes. Diabetes Metab. 2017;43:484–7.

    Article  PubMed  Google Scholar 

  6. Kostopoulou E, Partsalaki I, Spiliotis BE, Skiadopoulos S, Gil APR. Repetitiveness of the oral glucose tolerance test in children and adolescents. World J Clin Pediatr. 2021;10:29–39.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Jagannathan R, DuBose CW, Mabundo LS, Chung ST, Ha J, Sherman A, et al. The OGTT is highly reproducible in Africans for the diagnosis of diabetes: implications for treatment and protocol design. Diabetes Res Clin Pract. 2020;170:108523.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ko GTC, Chan JCN, Woo J, Lau E, Yeung VTF, Chow CC, et al. The reproducibility and usefulness of the oral glucose tolerance test in screening for diabetes and other cardiovascular risk factors. Ann Clin Biochem. 1998;35:62–7.

    Article  PubMed  Google Scholar 

  9. Matthan NR, Ausman LM, Meng H, Tighiouart H, Lichtenstein AH. Estimating the reliability of glycemic index values and potential sources of methodological and biological variability. Am J Clin Nutr. 2016;104:1004–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Yabe D, Seino Y, Fukushima M, Seino S. β cell dysfunction versus insulin resistance in the pathogenesis of type 2 diabetes in east Asians. Curr Diab Rep. 2015;15:602.

    Article  PubMed  Google Scholar 

  11. Ke C, Narayan KMV, Chan JCN, Jha P, Shah BR. Pathophysiology, phenotypes and management of type 2 diabetes mellitus in Indian and Chinese populations. Nat Rev Endocrinol. 2022;18:413–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Leow MKS. Characterization of the Asian phenotype - An emerging paradigm with clinicopathological and human research implications. Int J Med Sci. 2017;14:639–47.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lim U, Ernst T, Buchthal SD, Latch M, Albright CL, Wilkens LR, et al. Asian women have greater abdominal and visceral adiposity than Caucasian women with similar body mass index. Nutr Diabetes. 2011;1:e6–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Camps SG, Lim J, Koh MXN, Henry CJ. The glycaemic and insulinaemic response of pasta in Chinese and indians compared to Asian carbohydrate staples: taking spaghetti back to Asia. Nutrients. 2021;13:451.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Goh SY, Ang SBin, Bee YM, Chen RYT, Gardner D, Ho E, et al. Ministry of health clinical practice guidelines: diabetes mellitus. Singap Med J. 2014;55:334–47.

    Article  CAS  Google Scholar 

  16. Allison DB, Paultre F, Maggio C, Mezzitis N, Pi-Sunyer FX. The use of areas under curves in diabetes research. Diabetes Care 1995;18:245–50.

    Article  CAS  PubMed  Google Scholar 

  17. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155–63.

    Article  PubMed  PubMed Central  Google Scholar 

  18. World Health Organization. Waist circumference and waist-hip ratio: Report of a WHO expert consultation. World Health Organization. Geneva; 2008.

  19. World Health Organization. Classification of diabetes mellitus 2019. World Health Organization. 2019.

  20. Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 International Society of Hypertension global hypertension practice guidelines. Hypertension. 2020;75:1334–57.

    Article  CAS  PubMed  Google Scholar 

  21. Lee YS, Biddle S, Chan MF, Cheng A, Cheong M, Chong YS, et al. Health promotion board–ministry of health clinical practice guidelines: Obesity. Singap Med J. 2016;57:292–300.

    Article  Google Scholar 

  22. Williams SM, Venn BJ, Perry T, Brown R, Wallace A, Mann JI, et al. Another approach to estimating the reliability of glycaemic index. Br J Nutr. 2008;100:364–72.

    Article  CAS  PubMed  Google Scholar 

  23. Wolever TMS. Effect of blood sampling schedule and method of calculating the area under the curve on validity and precision of glycaemic index values. Br J Nutr. 2004;91:295–300.

    Article  CAS  PubMed  Google Scholar 

  24. Wolever TMS, Brand-Miller JC, Abernethy J, Astrup A, Atkinson F, Axelsen M, et al. Measuring the glycemic index of foods: Interlaboratory study. Am J Clin Nutr. 2008;87:247S–57S.

    Article  CAS  PubMed  Google Scholar 

  25. Venn BJ, Williams SM, Mann JI. Comparison of postprandial glycaemia in Asians and Caucasians. Diabet Med. 2010;27:1205–8.

    Article  CAS  PubMed  Google Scholar 

  26. Selvin E, Crainiceanu CM, Brancati FL, Coresh J. Short-term variability in measures of glycemia and implications for the classification of diabetes. Arch Intern Med. 2007;167:1545–51.

    Article  CAS  PubMed  Google Scholar 

  27. Babbar R, Heni M, Peter A, de Angelis MH, Häring HU, Fritsche A, et al. Prediction of glucose tolerance without an oral glucose tolerance test. Front Endocrinol. 2018;9:82.

    Article  Google Scholar 

  28. Mooy JM, Grootenhuis PA, De Vries H, Kostense PJ, Popp-Snijders C, Bouter LM, et al. Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the hoorn study. Diabetologia. 1996;39:298–305.

    Article  CAS  PubMed  Google Scholar 

  29. Gromova LV, Fetissov SO, Gruzdkov AA. Mechanisms of glucose absorption in the small intestine in health and metabolic diseases and their role in appetite regulation. Nutrients. 2021;13:2474.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fournel A, Marlin A, Abot A, Pasquio C, Cirillo C, Cani PD, et al. Glucosensing in the gastrointestinal tract: Impact on glucose metabolism. Am J Physiol. 2016;310:G645–58.

    Google Scholar 

  31. American Diabetes Association. Postprandial blood glucose. Diabetes Care. 2001;24:775–8.

    Article  Google Scholar 

  32. Abdul-Ghani MA, Stern MP, Lyssenko V, Tuomi T, Groop L, DeFronzo RA. Minimal contribution of fasting hyperglycemia to the incidence of type 2 diabetes in subjects with normal 2-h plasma glucose. Diabetes Care. 2010;33:557–61.

    Article  CAS  PubMed  Google Scholar 

  33. Bergman M, Manco M, Satman I, Chan J, Inês Schmidt M, Sesti G, et al. International Diabetes Federation Position Statement on the 1-hour post-load plasma glucose for the diagnosis of intermediate hyperglycaemia and type 2 diabetes. Diabetes Res Clin Pract. 2024;209:111589.

    Article  CAS  PubMed  Google Scholar 

  34. Temelkova-Kurktschiev TS, Koehler C, Henkel E, Leonhardt W, Fuecker K, Hanefeld M. Postchallenge plasma glucose and glycemic spikes are more strongly associated with atherosclerosis than fasting glucose or HbA1c level. Diabetes Care. 2000;23:1830–4.

    Article  CAS  PubMed  Google Scholar 

  35. Qatanani M, Lazar MA. Mechanisms of obesity-associated insulin resistance: Many choices on the menu. Genes Dev. 2007;21:1443–55.

    Article  CAS  PubMed  Google Scholar 

  36. Buscemi S, Verga S, Cottone S, Azzolina V, Buscemi B, Gioia D, et al. Glycaemic variability and inflammation in subjects with metabolic syndrome. Acta Diabetol. 2009;46:55–61.

    Article  CAS  PubMed  Google Scholar 

  37. Klimontov VV, Semenova JF. Glucose variability in subjects with normal glucose tolerance: relations with body composition, insulin secretion and sensitivity. Diabetes Metab Syndr Clin Res Rev. 2022;16:102387.

    Article  CAS  Google Scholar 

  38. Vega-López S, Ausman LM, Griffith JL, Lichtenstein AH. Interindividual variability and intra-individual reproducibility of glycemic index values for commercial white bread. Diabetes Care. 2007;30:1412–7.

    Article  PubMed  Google Scholar 

  39. Zhou Z, Sun B, Huang S, Zhu C, Bian M. Glycemic variability: adverse clinical outcomes and how to improve it? Cardiovasc Diabetol. 2020;19:102.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Bouchi R, Babazono T, Mugishima M, Yoshida N, Nyumura I, Toya K, et al. Fluctuations in HbA1c are associated with a higher incidence of cardiovascular disease in Japanese patients with type 2 diabetes. J Diabetes Investig. 2012;3:148–55.

    Article  CAS  PubMed  Google Scholar 

  41. Li L, Zou X, Huang Q, Han X, Zhou X, Ji L. Do east Asians with normal glucose tolerance have worse β-cell function? A meta-analysis of epidemiological studies. Front Endocrinol. 2021;12:780557.

    Article  Google Scholar 

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This research was funded by the A*STAR Industry Alignment Fund – Pre-Positioning Programme (IAF-PP) – Food Structure Engineering for Nutrition and Health Programme (Grant ID no: H17/01/a0/A11 & H18/01/a0/B11).

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Authors and Affiliations



The authors’ responsibilities were as follows – DWKT, SP, and CJH conceptualized and designed the research; SGC, JL, MXNK, and CJH conducted the original clinical trials and provided essential data for the secondary analysis; DWKT, SP, and CJH analyzed the data; DWKT, SP and JL wrote the original manuscript under the supervision of CJH; DWKT and CJH have primary responsibility for final content; all authors read, reviewed and approved of the final manuscript.

Corresponding authors

Correspondence to Darel Wee Kiat Toh or Christiani Jeyakumar Henry.

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The authors declare no competing interests.

Ethical approval

This secondary analysis was performed in accordance with the Declaration of Helsinki, and was reviewed by the Agency for Science, Technology and Research Institutional Review Board (A*STAR IRB; reference number: 2022-067). The 3 acute trials selected were approved by the National Healthcare Group Domain Specific Review Board (NHG DSRB; reference numbers: 2018/00025, 2018/00622 and 2018/01079 respectively). Written informed consent was obtained from all participants.

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Toh, D.W.K., Ponnalagu, S., Camps, S.G. et al. Higher adiposity predicts greater intra-individual inconsistencies in postprandial glycemic measurements—an analysis of three randomized controlled trials in Asian populations. Eur J Clin Nutr (2024).

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