UNLABELLED: The shape of the glycemic curve during the oral glucose tolerance test (OGTT), interpreted in the correct context, can predict impaired glucose homeostasis. Our aim was to reveal information inherent in the 3 h glycemic trajectory that is of physiological relevance concerning the disruption of glycoregulation and complications beyond, such as components of metabolic syndrome (MS). METHODS: In 1262 subjects (1035 women, 227 men) with a wide range of glucose tolerance, glycemic curves were categorized into four groups: monophasic, biphasic, triphasic, and multiphasic. The groups were then monitored in terms of anthropometry, biochemistry, and timing of the glycemic peak. RESULTS: Most curves were monophasic (50%), then triphasic (28%), biphasic (17.5%), and multiphasic (4.5%). Men had more biphasic curves than women (33 vs. 14%, respectively), while women had more triphasic curves than men (30 vs. 19%, respectively) (p < 0.01). Monophasic curves were more frequent in people with impaired glucose regulation and MS compared to bi-, tri-, and multiphasic ones. Peak delay was the most common in monophasic curves, in which it was also most strongly associated with the deterioration of glucose tolerance and other components of MS. CONCLUSION: The shape of the glycemic curve is gender dependent. A monophasic curve is associated with an unfavorable metabolic profile, especially when combined with a delayed peak.

Faculty of Science Charles University 128 00 Prague Czech Republic

Institute of Endocrinology 110 00 Prague Czech Republic

Institute of Neuroscience National Research Council 351 22 Padova Italy

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Hroboň P., Škodová M., Kučová P., Votápková J. Comparison of different approaches for estimation of prevalence of type 2 diabetes mellitus in the Czech Republic. Vnitr. Lek. 2020;66:e33–e37. doi: 10.36290/vnl.2020.095. PubMed DOI

International Association for the Study of Obesity Website. [(accessed on 1 January 2021)]. Available online: www.iaso.org.

Hameed I., Masoodi S.R., Mir S.A., Nabi M., Ghazanfar K., Ganai B.A. Type 2 diabetes mellitus: From a metabolic disorder to an inflammatory condition. World J. Diabetes. 2015;6:598–612. doi: 10.4239/wjd.v6.i4.598. PubMed DOI PMC

Hartstra A.V., Bouter K.E., Bäckhed F., Nieuwdorp M. Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care. 2015;38:159–165. doi: 10.2337/dc14-0769. PubMed DOI

Palatianou M.E., Simos Y.V., Andronikou S.K., Kiortsis D.N. Long-term metabolic effects of high birth weight: A critical review of the literature. Horm. Metab. Res. 2014;46:911–920. doi: 10.1055/s-0034-1395561. PubMed DOI

Kajantie E., Strang-Karlsson S., Hovi P., Wehkalampi K., Lahti J., Kaseva N., Järvenpää A.L., Räikkönen K., Eriksson J.G., Andersson S. Insulin sensitivity and secretory response in adults born preterm: The Helsinki Study of Very Low Birth Weight Adults. J. Clin. Endocrinol. Metab. 2015;100:244–250. doi: 10.1210/jc.2014-3184. PubMed DOI

Harder T., Rodekamp E., Schellong K., Dudenhausen J.W., Plagemann A. Birth weight and subsequent risk of type 2 diabetes: A meta-analysis. Am. J. Epidemiol. 2007;165:849–857. doi: 10.1093/aje/kwk071. PubMed DOI

Vaag A., Brøns C., Gillberg L., Hansen N.S., Hjort L., Arora G.P., Thomas N., Broholm C., Ribel-Madsen R., Grunnet L.G. Genetic, nongenetic and epigenetic risk determinants in developmental programming of type 2 diabetes. Acta Obstet. Gynecol. Scand. 2014;93:1099–1108. doi: 10.1111/aogs.12494. PubMed DOI

Vejrazkova D., Lukasova P., Vankova M., Bradnova O., Vacinova G., Vcelak J., Cirmanova V., Andelova K., Krejci H., Bendlova B. Gestational diabetes-metabolic risks of adult women with respect to birth weight. Physiol. Res. 2015;64:S135–S145. doi: 10.33549/physiolres.933089. PubMed DOI

Stein S.A., Maloney K.L., Pollin T.I. Genetic Counseling for Diabetes Mellitus. Curr. Genet. Med. Rep. 2014;2:56–67. doi: 10.1007/s40142-014-0039-5. PubMed DOI PMC

Brunetti A., Chiefari E., Foti D. Recent advances in the molecular genetics of type 2 diabetes mellitus. World J. Diabetes. 2014;5:128–140. doi: 10.4239/wjd.v5.i2.128. PubMed DOI PMC

Bouret S., Levin B.E., Ozanne S.E. Gene-environment interactions controlling energy and glucose homeostasis and the developmental origins of obesity. Physiol. Rev. 2015;95:47–82. doi: 10.1152/physrev.00007.2014. PubMed DOI PMC

Franks P.W., Mesa J.L., Harding A.H., Wareham N.J. Gene-lifestyle interaction on risk of type 2 diabetes. Nutr. Metab. Cardiovasc. Dis. 2007;17:104–124. doi: 10.1016/j.numecd.2006.04.001. PubMed DOI

O’Rahilly S. Human genetics illuminates the paths to metabolic disease. Nature. 2009;462:307–314. doi: 10.1038/nature08532. PubMed DOI

Morris A.P., Voight B.F., Teslovich T.M., Ferreira T., Segrè A.V., Steinthorsdottir V., Strawbridge R.J., Khan H., Grallert H., Mahajan A., et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 2012;44:981–990. doi: 10.1038/ng.2383. PubMed DOI PMC

Scott R.A., Lagou V., Welch R.P., Wheeler E., Montasser M.E., Luan J., Mägi R., Strawbridge R.J., Rehnberg E., Gustafsson S., et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 2012;44:991–1005. doi: 10.1038/ng.2385. PubMed DOI PMC

Vassy J.L., Meigs J.B. Is genetic testing useful to predict type 2 diabetes? Best Pract. Res. Clin. Endocrinol. Metab. 2012;26:189–201. doi: 10.1016/j.beem.2011.09.002. PubMed DOI PMC

Muniyappa R., Lee S., Chen H., Quon M.J. Current approaches for assessing insulin sensitivity and resistance in vivo: Advantages, limitations, and appropriate usage. Am. J. Physiol. Endocrinol. Metab. 2008;294:E15–E26. doi: 10.1152/ajpendo.00645.2007. PubMed DOI

Stumvoll M., Mitrakou A., Pimenta W., Jenssen T., Yki-Järvinen H., Van Haeften T., Renn W., Gerich J. Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity. Diabetes Care. 2000;23:295–301. doi: 10.2337/diacare.23.3.295. PubMed DOI

Kim J.Y., Michaliszyn S.F., Nasr A., Lee S., Tfayli H., Hannon T., Hughan K.S., Bacha F., Arslanian S. 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;39:1431–1439. doi: 10.2337/dc16-0352. PubMed DOI PMC

Bervoets L., Mewis A., Massa G. The shape of the plasma glucose curve during an oral glucose tolerance test as an indicator of Beta cell function and insulin sensitivity in end-pubertal obese girls. Horm. Metab. Res. 2015;47:445–451. doi: 10.1055/s-0034-1395551. PubMed DOI

Chung S.T., Ha J., Onuzuruike A.U., Kasturi K., Galvan-De La Cruz M., Bingham B.A., Baker R.L., Utumatwishima J.N., Mabundo L.S., Ricks M., et al. Time to glucose peak during an oral glucose tolerance test identifies prediabetes risk. Clin. Endocrinol. 2017;87:484–491. doi: 10.1111/cen.13416. PubMed DOI PMC

Tura A., Ludvik B., Nolan J.J., Pacini G., Thomaseth K. Insulin and C-peptide secretion and kinetics in humans: Direct and model-based measurements during OGTT. Am. J. Physiol. Endocrinol. Metab. 2001;281:E966–E974. doi: 10.1152/ajpendo.2001.281.5.E966. PubMed DOI

Pacini G., Mari A. Methods for clinical assessment of insulin sensitivity and beta-cell function. Best Pract. Res. Clin. Endocrinol. Metab. 2003;17:305–322. doi: 10.1016/S1521-690X(03)00042-3. PubMed DOI

Tura A., Chemello G., Szendroedi J., Göbl C., Færch K., Vrbíková J., Pacini G., Ferrannini E., Roden M. Prediction of clamp-derived insulin sensitivity from the oral glucose insulin sensitivity index. Diabetologia. 2018;61:1135–1141. doi: 10.1007/s00125-018-4568-4. PubMed DOI

Tura A., Morbiducci U., Sbrignadello S., Winhofer Y., Pacini G., Kautzky-Willer A. 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–R948. doi: 10.1152/ajpregu.00650.2010. PubMed DOI

Retnakaran R., Qi Y., Goran M.I., Hamilton J.K. Evaluation of proposed oral disposition index measures in relation to the actual disposition index. Diabet. Med. 2009;26:1198–1203. doi: 10.1111/j.1464-5491.2009.02841.x. PubMed DOI

Tura A., Kautzky-Willer A., Pacini G. Insulinogenic indices from insulin and C-peptide: Comparison of beta-cell function from OGTT and IVGTT. Diabetes Res. Clin. Pract. 2006;72:298–301. doi: 10.1016/j.diabres.2005.10.005. PubMed DOI

Bergman R.N., Stefanovski D., Buchanan T.A., Sumner A.E., Reynolds J.C., Sebring N.G., Xiang A.H., Watanabe R.M. A better index of body adiposity. Obesity. 2011;19:1083–1089. doi: 10.1038/oby.2011.38. PubMed DOI PMC

Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III) JAMA. 2001;285:2486–2497. doi: 10.1001/jama.285.19.2486. PubMed DOI

Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertil. Steril. 2004;81:19–25. doi: 10.1016/j.fertnstert.2003.10.004. PubMed DOI

Ahrén B., Pacini G. Islet adaptation to insulin resistance: Mechanisms and implications for intervention. Diabetes Obes. Metab. 2005;7:2–8. doi: 10.1111/j.1463-1326.2004.00361.x. PubMed DOI

Ahrén B., Pacini G. Impaired adaptation of first-phase insulin secretion in postmenopausal women with glucose intolerance. Am. J. Physiol. 1997;273:E701–E707. doi: 10.1152/ajpendo.1997.273.4.E701. PubMed DOI

Meloun M., Hill M., Militký J., Kupka K. Transformation in the PC-aided biochemical data analysis. Clin. Chem. Lab. Med. 2000;38:553–559. doi: 10.1515/CCLM.2000.081. PubMed DOI

Meloun M., Militký J., Hill M., Brereton R.G. Crucial problems in regression modelling and their solutions. Analyst. 2002;127:433–450. doi: 10.1039/b110779h. PubMed DOI

Abdul-Ghani M.A., Lyssenko V., Tuomi T., Defronzo R.A., Groop L. The shape of plasma glucose concentration curve during OGTT predicts future risk of type 2 diabetes. Diabetes Metab. Res. Rev. 2010;26:280–286. doi: 10.1002/dmrr.1084. PubMed DOI

Manco M., Nolfe G., Pataky Z., Monti L., Porcellati F., Gabriel R., Mitrakou A., Mingrone G. Shape of the OGTT glucose curve and risk of impaired glucose metabolism in the EGIR-RISC cohort. Metabolism. 2017;70:42–50. doi: 10.1016/j.metabol.2017.02.007. PubMed DOI

Utzschneider K.M., Younes N., Rasouli N., Barzilay J.I., Banerji M.A., Cohen R.M., Gonzalez E.V., Ismail-Beigi F., Mather K.J., Raskin P., et al. Shape of the OGTT glucose response curve: Relationship with β-cell function and differences by sex, race, and BMI in adults with early type 2 diabetes treated with metformin. BMJ Open Diabetes Res. Care. 2021;9:e002264. doi: 10.1136/bmjdrc-2021-002264. PubMed DOI PMC

Kanauchi M., Kimura K., Kanauchi K., Saito Y. Beta-cell function and insulin sensitivity contribute to the shape of plasma glucose curve during an oral glucose tolerance test in non-diabetic individuals. Int. J. Clin. Pract. 2005;59:427–432. doi: 10.1111/j.1368-5031.2005.00422.x. PubMed DOI

Vejrazkova D., Vankova M., Vcelak J., Krejci H., Anderlova K., Tura A., Pacini G., Sumova A., Sladek M., Bendlova B. The rs10830963 Polymorphism of the MTNR1B Gene: Association with Abnormal Glucose, Insulin and C-peptide Kinetics. Front. Endocrinol. 2022;13:868364. doi: 10.3389/fendo.2022.868364. PubMed DOI PMC

Kaga H., Tamura Y., Takeno K., Kakehi S., Someya Y., Funayama T., Furukawa Y., Suzuki R., Sugimoto D., Kadowaki S., et al. Shape of the glucose response curve during an oral glucose tolerance test is associated with insulin clearance and muscle insulin sensitivity in healthy non-obese men. J. Diabetes Investig. 2020;11:874–877. doi: 10.1111/jdi.13227. PubMed DOI PMC

Kramer C.K., Ye C., Hanley A.J., Connelly P.W., Sermer M., Zinman B., Retnakaran R. Delayed timing of post-challenge peak blood glucose predicts declining beta cell function and worsening glucose tolerance over time: Insight from the first year postpartum. Diabetologia. 2015;58:1354–1362. doi: 10.1007/s00125-015-3551-6. PubMed DOI

Wang X., Zhao X., Zhou R., Gu Y., Zhu X., Tang Z., Yuan X., Chen W., Zhang R., Qian C., et al. Delay in glucose peak time during the oral glucose tolerance test as an indicator of insulin resistance and insulin secretion in type 2 diabetes patients. J. Diabetes Investig. 2018;9:1288–1295. doi: 10.1111/jdi.12834. PubMed DOI PMC

Bonhoure A., Potter K.J., Colomba J., Boudreau V., Bergeron C., Desjardins K., Carricart M., Tremblay F., Lavoie A., Rabasa-Lhoret R. Peak glucose during an oral glucose tolerance test is associated with future diabetes risk in adults with cystic fibrosis. Diabetologia. 2021;64:1332–1341. doi: 10.1007/s00125-021-05423-5. PubMed DOI

La Grasta Sabolić L., Požgaj Šepec M., Cigrovski Berković M., Stipančić G. Time to the Peak, Shape of the Curve and Combination of These Glucose Response Characteristics During Oral Glucose Tolerance Test as Indicators of Early Beta-cell Dysfunction in Obese Adolescents. J. Clin. Res. Pediatr. Endocrinol. 2021;13:160–169. doi: 10.4274/jcrpe.galenos.2020.2020.0142. PubMed DOI PMC

Xie J.H., Liu Q., Yang Y., Liu Z.L., Hu S.H., Zhou X.R., Yuan G., Zhang M.X., Tao J., Yu X.F. Correlation of liver enzymes with diabetes and pre-diabetes in middle-aged rural population in China. J. Huazhong Univ. Sci. Technolog. Med. Sci. 2016;36:53–58. doi: 10.1007/s11596-016-1541-7. PubMed DOI

Vozarova B., Stefan N., Lindsay R.S., Saremi A., Pratley R.E., Bogardus C., Tataranni P.A. High alanine aminotransferase is associated with decreased hepatic insulin sensitivity and predicts the development of type 2 diabetes. Diabetes. 2002;51:1889–1895. doi: 10.2337/diabetes.51.6.1889. PubMed DOI

Kunutsor S.K., Apekey T.A., Walley J. Liver aminotransferases and risk of incident type 2 diabetes: A systematic review and meta-analysis. Am. J. Epidemiol. 2013;178:159–171. doi: 10.1093/aje/kws469. PubMed DOI