Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder characterized by insulin resistance and β-cell dysfunction, resulting in impaired insulin secretion and glucose tolerance. The 2-hour oral glucose tolerance test (OGTT) is commonly used for diagnosing T2DM and classifying glucose tolerance status as normal, impaired, or diabetic. Emerging evidence suggests that the morphology of the OGTT glucose curve (particularly monophasic versus biphasic patterns) may provide valuable insights into underlying variations in insulin sensitivity and β-cell function [1-3]. The shape of the glucose response curve during OGTT could serve as a novel and intuitive biomarker for early identification of metabolic risk.

We conducted a cross-sectional study examining glucose curve morphology during a 2-hour OGTT and its association with physiological, metabolic, and behavioral parameters in healthy Singaporean adults. Our analysis suggests that curve morphology assessment provides additional insights into metabolic health, potentially enhancing early risk stratification. The study comprised 157 non-diabetic, non-obese, multi-ethnic Singaporean adults (mean age 46.4 years), stratified by age, gender, and habitual physical activity. Using a novel curve classification method based on cubic smoothing splines to identify stationary points, we classified glucose curves as monophasic, biphasic, triphasic, or unclassified. We found that 76.4% of participants exhibited monophasic curves, 21.7% showed biphasic responses, and only two participants (1.3%) demonstrated triphasic responses. This distribution is consistent with the predominance of monophasic responses observed in previous studies [1,4].

Multiple surrogate measures, including homeostasis model assessment for insulin resistance (HOMA-IR), Matsuda index, triglyceride (TG)/high-density lipoprotein (HDL) ratio, and homeostasis model assessment of β-cell function (HOMA-β), were used to assess insulin resistance and β-cell function. Biphasic responders demonstrated lower insulin resistance, enhanced β-cell function, and significant differences in lipid ratios (TC/HDL, low-density lipoprotein/HDL) and systolic blood pressure, compared with monophasic responders. Physically active individuals were more likely to exhibit a biphasic curve compared to sedentary counterparts (24.4% vs. 18.3%).

Fig. 1 depicts the mean and confidence intervals of HOMA-IR, Matsuda index, TG/HDL ratio, and HOMA-β by exercise profile and age group. Among participants with biphasic response curves, the differences in insulin resistance and β-cell function did not reach statistical significance when parsed by exercise profile or age. However, notable disparities were identified for younger individuals with monophasic curves, highlighting the importance of physical activity in mediating metabolic health.

Among adults younger than 50 years, biphasic patterns were observed in 31.7% of active individuals versus 12.8% of sedentary individuals. Within the monophasic subgroup, active individuals showed significantly lower HOMA-IR, higher Matsuda index, and improved HOMA-β scores. These findings suggest that habitual exercise not only improves insulin sensitivity but may also shift the glycemic response pattern towards a more favorable biphasic shape. These observations align with previous evidence linking biphasic glucose curves to a reduced risk of developing T2DM [2,3]. Fundamentally, insulin secretion by pancreatic β-cells exhibits a biphasic profile, with an acute insulin response (AIR) characterized by a short-lived spike peaking at 2 to 4 minutes before returning to baseline at 10-15 minutes correlating to the first-phase insulin release (FPIR) in an intravenous glucose tolerance test, also known as glucose-stimulated insulin secretion, followed by a second-phase gradual and more sustained insulin response for 2 to 3 hours [5]. The plasma glucose response during an OGTT conceivably mirrors this coordinated β-cell insulin secretion profile, which expectedly is biphasic in the healthy state [6]. The biphasic glucose response may thus reflect more effective early-phase insulin secretion and improved peripheral glucose uptake, both characteristic features of enhanced insulin sensitivity [1,7]. The earliest detectable β-cell defect among those developing insulin resistance and prediabetes is a blunting or loss of the AIR or FPIR, resulting in a monophasic insulin profile, which accounts for a monophasic glucose response on OGTT [8]. While previous studies have primarily focused on individuals with diabetes or those at elevated risk, our findings extend this insight to a healthy, normoglycaemic population—demonstrating that glucose curve morphology can differentiate underlying metabolic phenotypes even in the absence of overt disease.

We observed gender-specific differences in glucose curve morphology and associated metabolic parameters. Female participants showed lower fasting glucose, lower TG/HDL ratios, and higher HDL levels. These findings align with previous research suggesting that sex hormones and differences in adipose tissue distribution influence glucose–insulin regulation and lipid metabolism [9]. These observations warrant further investigation to understand sex-specific metabolic phenotypes and their implications for disease risk.

Our study cohort was unique as none of the individuals were obese, and all were free from known metabolic, cardiovascular, or endocrine disorders. Therefore, the observed differences cannot be attributed to overt disease. Our findings underscore that clinically relevant metabolic heterogeneity exists even in apparently healthy populations, and curve morphology may help identify individuals at the earliest stages of metabolic dysfunction. From a public health perspective, this has important implications for diabetes prevention. As OGTTs are already performed in clinical and research settings, incorporating curve shape analysis requires no additional testing burden. Asian populations, including Singaporeans, tend to develop T2DM at younger ages and lower body mass index levels compared with Western populations. Leveraging routine OGTT data to detect early insulin resistance or subtle impairments in β-cell function through curve shape analysis offers a scalable, cost-effective approach to risk stratification. This strategy could enhance the precision of national screening programmes and support personalized lifestyle intervention platforms.

We recommend incorporating OGTT curve morphology into future cohort studies and intervention trials targeting diet, physical activity, and pharmacological strategies in individuals at risk of diabetes. Providing real-time feedback on glucose curve patterns could enhance patient engagement and motivation during risk assessment. To avoid multiple venous blood sampling timepoints to generate accurate OGTT curves, it is possible that well calibrated wearable glucose sensing devices such as continuous glucose monitoring systems may be used in future during OGTT that can facilitate the analysis of OGTT curve morphology [10]. Our findings demonstrate that even in healthy, normoglycaemic individuals, curve morphology reflects meaningful metabolic differences. The biphasic pattern—more prevalent among younger, physically active adults—is associated with lower insulin resistance, more favorable lipid profiles, and enhanced β-cell function, supporting its potential use for earlier risk identification and the development of personalized preventive strategies.

Institutional Review Board (IRB) approval was granted on July 9, 2015 by the IRB of Nanyang Technological University, Singapore (IRB number: IRB-2015-05-029). We confirm that all methods used in this study were performed in accordance with the relevant guidelines and regulations. Informed consent was not obtained from patients as this was a retrospective study using de-identified data.

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

Fig. 1.

Mean and confidence interval of (A) homeostasis model assessment for insulin resistance (HOMA-IR), (B) Matsuda index, (C) triglyceride (TG)/high-density lipoprotein (HDL) ratio, and (D) homeostasis model assessment of β-cell function (HOMA-β) by exercise profile, the shape of glucose curve, and age group. The number in each bar denotes the number of subjects in the corresponding group. ^aP<0.05.

REFERENCES

• 1. Kim JY, Michaliszyn SF, Nasr A, Lee S, Tfayli H, Hannon T, et al. The shape of the glucose response curve during an oral glucose tolerance test heralds biomarkers of type 2 diabetes risk in obese youth. Diabetes Care 2016;39:1431-9.ArticlePubMedPMCPDF
• 2. Manco M, Nolfe G, Pataky Z, Monti L, Porcellati F, Gabriel R, et al. Shape of the OGTT glucose curve and risk of impaired glucose metabolism in the EGIR-RISC cohort. Metabolism 2017;70:42-50.ArticlePubMed
• 3. Abdul-Ghani MA, Lyssenko V, Tuomi T, Defronzo RA, Groop L. The shape of plasma glucose concentration curve during OGTT predicts future risk of type 2 diabetes. Diabetes Metab Res Rev 2010;26:280-6.ArticlePubMed
• 4. Tura A, Morbiducci U, Sbrignadello S, Winhofer Y, Pacini G, Kautzky-Willer A, et al. Shape of glucose, insulin, C-peptide curves during a 3-h oral glucose tolerance test: any relationship with the degree of glucose tolerance? Am J Physiol Regul Integr Comp Physiol 2011;300:R941-8.ArticlePubMed
• 5. Curry DL, Bennett LL, Grodsky GM. Dynamics of insulin secretion by the perfused rat pancreas. Endocrinology 1968;83:572-84.ArticlePubMed
• 6. Mitrakou A, Kelley D, Mokan M, Veneman T, Pangburn T, Reilly J, et al. Role of reduced suppression of glucose production and diminished early insulin release in impaired glucose tolerance. N Engl J Med 1992;326:22-9.ArticlePubMed
• 7. Tschritter O, Fritsche A, Shirkavand F, Machicao F, Haring H, Stumvoll M, et al. Assessing the shape of the glucose curve during an oral glucose tolerance test. Diabetes Care 2003;26:1026-33.ArticlePubMedPDF
• 8. Meyer C, Pimenta W, Woerle HJ, Van Haeften T, Szoke E, Mitrakou A, et al. Different mechanisms for impaired fasting glucose and impaired postprandial glucose tolerance in humans. Diabetes Care 2006;29:1909-14.ArticlePubMedPDF
• 9. Tramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, et al. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia 2020;63:453-61.ArticlePubMedPDF
• 10. Leonard A, Judware IR, Vanscoy LL, Paranjape SM, Peeler D, Wu M, et al. Feasibility of using continuous glucose monitoring to detect glycemic abnormalities in children with cystic fibrosis. Horm Res Paediatr 2024 Nov 27 [Epub]. https://doi.org/10.1159/000542786.Article

Figure & Data

References

Citations

Citations to this article as recorded by  

About this article

Meng F, Abisheganaden JA, Ang GY, Kannapiran PSR, Yap MMC, Lee SY, Leow MKS, Lim CL. Oral Glucose Tolerance Test Glucose Curve Morphology and Metabolic Health in Healthy Adults: Insights from a Singaporean Cross-Sectional Study. Diabetes Metab J. 2025;49(5):1133-1136.

DOI: https://doi.org/10.4093/dmj.2025.0703.

Download citation ↓