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Original Article
Clinical Study C-Peptide-Based Index Is More Related to Incident Type 2 Diabetes in Non-Diabetic Subjects than Insulin-Based Index
Jong-Dai Kim1orcid, Sung Ju Kang1, Min Kyung Lee2, Se Eun Park3, Eun Jung Rhee3, Cheol-Young Park3, Ki-Won Oh3, Sung-Woo Park3, Won-Young Lee3orcid
Endocrinology and Metabolism 2016;31(2):320-327.
DOI: https://doi.org/10.3803/EnM.2016.31.2.320
Published online: June 21, 2016

1Division of Endocrinology, Department of Internal Medicine, Konyang University Hospital, Konyang University College of Medicine, Daejeon, Korea.

2Division of Endocrinology, Department of Internal Medicine, Myongji Hospital, Seonam University College of Medicine, Goyang, Korea.

3Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.

Corresponding author: Won-Young Lee. Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul 03181, Korea. Tel: +82-2-2001-2440, Fax: +82-2-2001-1578, wonyoung2.lee@samsung.com
• Received: May 14, 2016   • Revised: May 7, 2016   • Accepted: May 16, 2016

Copyright © 2016 Korean Endocrine Society

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Background
    Diabetes can be efficiently prevented by life style modification and medical therapy. So, identification for high risk subjects for incident type 2 diabetes is important. The aim of this study is to identify the best β-cell function index to identify high risk subjects in non-diabetic Koreans.
  • Methods
    This is a retrospective longitudinal study. Total 140 non-diabetic subjects who underwent standard 2-hour 75 g oral glucose tolerance test from January 2007 to February 2007 at Kangbuk Samsung Hospital and followed up for more than 1 year were analyzed (mean follow-up, 54.9±16.4 months). The subjects were consist of subjects with normal glucose tolerance (n=44) and subjects with prediabetes (n=97) who were 20 years of age or older. Samples for insulin and C-peptide levels were obtained at 0 and 30 minutes at baseline.
  • Results
    Thirty subjects out of 140 subjects (21.4%) developed type 2 diabetes. When insulin-based index and C-peptide-based index are compared between progressor and non-progressor to diabetes, all C-peptide-based indices were statistically different between two groups, but only insulinogenic index and disposition index among insulin-based index were statistically different. C-peptide-based index had higher value of area under receiver operating characteristic curve (AROC) value than that of insulin-based index. "C-peptidogenic" index had highest AROC value among indices (AROC, 0.850; 95% confidence interval, 0.761 to 0.915). C-peptidogenic index had significantly higher AROC than insulinogenic index (0.850 vs. 0.731 respectively; P=0.014).
  • Conclusion
    C-peptide-based index was more closely related to incident type 2 diabetes in non-diabetic subjects than insulin-based index.
The number of subjects with type 2 diabetes and prediabetes is increasing [1]. Current prevalence of prediabetes in Koreans aged over 30 years or older is 19.3% [2]. Up to 50% of prediabetes can progress into diabetes within 10 years [3].
Patients with type 2 diabetes have higher mortality and morbidity compared to those without diabetes [4]. Once diabetes is diagnosed, β-cell function is usually declined [5]. So preventing diabetes is important. Even if it failed to prevent diabetes, delaying the onset of diabetes is also of value. As the development of microvascular complication is also dependent on duration of diabetes [6], delaying the onset of diabetes as late as possible is important to prevent microvascular complications.
Accurate identification of high risk subjects is important, since diabetes can be prevented through life style intervention or medication [7]. Abdul-Ghani et al. [8] reported that oral disposition index calculated by using insulin during oral glucose tolerance test (OGTT) is the best predictor for future development of diabetes in non-diabetic subjects.
Whereas considerable portion of insulin that is released into portal vein from pancreatic islets is cleared by liver at first pass transit, C-peptide that almost always is co-secreted with insulin at same molecules, is not cleared by liver [9]. So, we hypothesized that C-peptide-based index would be more accurately reflects β-cell function. The aim of this study is to compare insulin based index with C-peptide-based index for prediction of incident type 2 diabetes in Korean non-diabetic subjects.
Subjects
The study population consisted of subjects who had undergone comprehensive health examinations and subsequently undergone OGTT at the Kangbuk Samsung Hospital. All subjects were 20 years of age or older. After health examination, subjects who had fasting glucose equal to or higher than 100 mg/dL were referred to outpatient clinic at Department of Endocrinology of Kangbuk Samsung Hospital. They have undergone standard 2-hour (2-h) 75 g OGTT from January 2007 to February 2007. Among them, total 140 subjects that were followed up for more than 1 year through outpatient clinics or annual health check-up at Kangbuk Samsung Hospital, so that we can identify progression to diabetes or not, were eligible for our analysis.
At baselines, subjects in whom OGTT results were satisfied to diabetes criteria and subjects who had a history of diabetes or currently using insulin or oral anti-diabetic drugs based on self-report questionnaire, were excluded. Subjects who had malignancies, anemia, pregnancy, chronic kidney disease, or liver cirrhosis were also excluded. One hundred forty subjects included in this analysis were without diabetes at baseline (subjects with impaired fasting glucose, subjects with impaired glucose tolerance, and subjects with normal glucose tolerance). The study was approved by the Institutional Review Board at Kangbuk Samsung Hospital. Informed consent requirement was waived because personal identifying information was not accessed.
Oral glucose tolerance test
Participants underwent a 2-h 75 g OGTT following a 10-hour overnight fast. Subjects ingested a solution containing 75 g of dextrose, and venous blood samples were obtained at 0, 30, and 120 minutes for the determination of plasma glucose level. Samples for insulin and C-peptide were obtained at 0 and 30 minutes.
Anthropometric and laboratory measurements
Height, weight, and waist circumferences were measured. Body mass index (kg/m2) was calculated as body weight in kilograms divided by height in meters squared. Blood pressures were measured using a sphygmomanometer (Vital Signs Monitor 300 series, Welch Allyn Inc., Skaneateles Falls, NY, USA) after at least 5 minutes of rest. Blood samples were collected following an overnight fast. Plasma glucose concentrations were determined using a Beckman glucose analyzer II (Beckman Instruments, Fullerton, CA, USA). The assay coefficient of variation (CV) for glucose was <1.5%. Serum insulin levels were measured using an immunoradiometric assay (DIA-source, Nivells, Belgium) following the manufacturer's recommendations. The intra-assay CV was 1.8% and the interassay CV was 6.3%. C-peptide was measured using a radioimmunoassay method with a commercial kit (DIAsource). The intra-assay CV was 6.5% and the interassay CV was 9.4%. Glycated hemoglobin (HbA1c) was measured using an immunoturbidimetric assay with a Cobra Integra 800 automatic analyzer (Roche Diagnostics, Basel, Switzerland) with a reference value range of 4.4% to 6.4%. HbA1c measurements were standardized to the reference method aligned with the Diabetes Control and Complications Trial and the National Glycohemoglobin Standardization Program (NGSP) standards. The intra-assay CV was 2.3% and the interassay CV was 2.4%, both of which are within the NGSP acceptable range. Plasma lipids, including total cholesterol, triglyceride, high density lipoprotein cholesterol (HDL-C), and low density lipoprotein cholesterol, were measured by enzymatic colorimetric assay (Siemens, Tarrytown, NY, USA). Serum high-sensitivity C-reactive protein (hs-CRP) levels were measured by nephelometric assay using a BNII nephelometer (Dade Behring, Deerfield, IL, USA). Apolipoprotein B (apoB) and apoA-I levels were measured with the nephelometric method using a BNII system (Dade Behring Co., Marburg, Germany). Creatinine, alanine aminotransferase, γ-glutamyl transpeptidase, uric acid were measured by standard laboratory methods. All of the laboratory tests were undertaken at the same laboratory.
Assessment for the development of diabetes
The development of diabetes was assessed from the annual records of all participants or in each outpatient clinic visits. The development of diabetes was defined as fasting plasma glucose ≥126 mg/dL or HbA1c ≥6.5% or reporting to have developed diabetes in self-reported questionnaire at each visit. Subjects who had a history of diabetes or currently using insulin or oral anti-diabetic drugs based on annual check-up self-report questionnaire at each visit were considered to have diabetes. Progressor was defined as who developed incident type 2 diabetes.
Variables and calculation
(1) The homeostatic model assessment of insulin resistance (HOMA-IR) was calculated using the following formula [10]: HOMA-IR=(fasting insulin [µIU/mL]×fasting glucose [mmol/L])/22.5)
(2) HOMA-IR by C-peptide was calculated using replacing insulin with C-peptide in HOMA-IR formula.
(3) Insulinogenic index was calculated using the following formula [11]: insulinogenic index=(insulin30min-insulin0min)/(glucose30min-glucose0min).
(4) C-peptidogenic index was calculated replacing insulin with C-peptide in insulinogenic index formula (we propose this term for formula that replace insulin with C-peptide in insulinogenic index formula).
(5) Oral disposition index (by insulin) was calculated as ratio of insulinogenic index to HOMA-IR.
(6) Oral disposition index based on C-peptide was calculated as ratio of C-peptidogenic index to HOMA-IR by C-peptide (we propose this term formula that replace insulin to C-peptide in oral disposition index formula).
Statistical analysis
Continuous variables are reported as mean±SD or median (95% confidence interval) if not normally distributed. Comparisons of continuous variables between groups were performed using unpaired t test or Mann-Whitney U test. Categorical data are expressed as number (percentages) and were compared using the chi-square test. Backward stepwise multivariate binary logistic regression analyses were performed in order to find the risk factors for future development of type 2 diabetes. To compare the ability for predicting incident type 2 diabetes, we used area under the receiver operating characteristic curve (ROC), and then used the DeLong algorithm for determination of statistical significance [12]. P<0.05 was considered statistically significant. All statistical analyses were performed using IBM PASW version 18.0 (IBM, Armonk, NY, USA) except for ROC curve analysis, which was performed using MedCalc for Windows version 12.5 (MedCalc Software, Ostend, Belgium).
Total 140 subjects were followed-up for 54.9±16.4 months. During this period, 30 subjects (21.4%) developed type 2 diabetes. Subjects who progressed to type 2 diabetes were older, showed higher triglyceride, lower HDL-C, and higher hs-CRP (Table 1). In multivariate regression analysis, age, glucose, insulin, and C-peptide-based parameters are independent predictor of incident type 2 diabetes (data were not shown).
When insulin-based index and C-peptide-based index were compared between progressor and non-progressor, while all C-peptide-based index were statistically different between two groups, only insulinogenic index and disposition index assessed by insulin were statistically different among insulin-based indexes (Table 2). In multivariate binary logistic regression analysis, C-peptidogenic index was independent predictor for incident type 2 diabetes even after adjusted for glucose parameters (HbA1c, OGTT 120 minutes glucose, and insulinogenic index) (Table 3). In ROC curve analysis, C-peptide-based index had higher value of area under ROC curve (AROC) than insulin based index. C-peptidogenic index had highest AROC value among parameters. C-peptidogenic index had significantly higher AROC than insulinogenic index (Table 4).
While insulin resistance indices based on insulin had no significant difference between two groups, all indices assessing insulin secretion (both insulin and C-peptide-based indices) had significant differences between the two groups (Table 2). Also in ROC curve analysis, insulin resistance indices had lower AROC value than insulin secretion indices (Table 4).
In this retrospective longitudinal study, insulin secretion defect was the major determinant for the progression to diabetes from prediabetes. C-peptidogenic index was more closely related to the progression to diabetes than insulinogenic index and other glucose parameters.
There were some reports that C-peptide-based indices were better indices than insulin based indices for the evaluation of pathophysiology of diabetes. Fasting C-peptide multiplied by fasting glucose was better associated with insulin resistance measured as hyperinsulinemic-euglycemic clamps than HOMA-IR [13]. Meier et al. [14] reported that C-peptide-based index was more closely correlated than insulin-based index with β-cell mass. Loopstra-Masters et al. [15] report that proinsulinto-C-peptide ratio were stronger predictor of diabetes in comparison with proinsulin-to-insulin ratios.
There are some hypothesis why C-peptide-based index is more closely related to diabetes progression in prediabetes. First, C-peptide is may be better index because C-peptide doesn't undergo hepatic extraction, so C-peptide may more accurately reflect pre-hepatic β-cell secretion. Pre-hepatic β-cell insulin secretion can be estimated by plasma C-peptide level [1617]. Second, C-peptides have more steady clearance than insulin [16]. Insulin clearance is influenced by various factors. In insulin resistant state, activity of insulin degrading enzyme is increased. Hepatic extraction of insulin is also increased in insulin resistance state [18]. Third, C-peptide has lower within-subject and between-subject variation than insulin, so C-peptides were more reproducible for the determination of β-cell function [1920]. Fourth, recent research has revealed various action of C-peptide as a bioactive peptide. C-peptide can inhibit nuclear factor κB, reduce reactive oxygen species, and activate AMP-activated protein kinase [21222324]. C-peptide has the insulinomimetic effect and may also interact synergistically with insulin by disaggregating hexameric insulin into active monomeric form [925].
There are some differences and common points of our study with other studies. In Insulin Resistance Atherosclerosis Study prospective cohort study for non-diabetic subjects, C-peptide-to-proinsulin ratio was more stronger predictor for diabetes progression than insulin-to-proinsulin ratio [15]. Our study showed different results to that study in that we used stimulated C-peptide rather than basal C-peptide. So we could evaluated β-cell secretion function more accurately. Utzschneider et al. [26] reported that oral disposition index based on insulin predicts the development of future diabetes above and beyond fasting and 2-h glucose levels in prospective cohort study for non-diabetic Japanese Americans, but this study did not measure C-peptide. In our study, C-peptidogenic index had more strong to predict future development of diabetes than insulinogenic index. Also in our study, C-peptidogenic index was more closely related to future development of diabetes than fasting and 2-h glucose. In Japanese non-diabetic cohort study [27], similarly in our study, insulin secretion had a greater impact on the incidence of type 2 diabetes than insulin resistance. But that study did not compare insulin secretion index to glucose parameter. We compared C-peptidogenic index to glucose parameters.
This is first report that C-peptide-based insulin secretion index is better than insulin based insulin secretion index for predicting diabetes progression in prediabetes. C-peptidogenic index also had better value to traditional criteria for the diagnosis of prediabetes (0 minute glucose, 120 minutes glucose, and HbA1c) for the prediction of diabetes progression.
Our study has several limitations. Our study is not prospective study and has relatively small sample size. Because we didn't measure 60 minutes glucose in OGTT, we could not compare C-peptidogenic index to 60 minutes glucose that previously reported as the best index for predicting diabetes. We did not have data for the family history of diabetes. So we could not adjust the data for it. In determining the progression to diabetes, we did not check post prandial 2-h glucose or OGTT, we were only dependent on fasting plasma glucose, HbA1c and the questionnaire; thus, we have limitation in that we did not clearly diagnose new diabetes case during follow-up.
From this study, we could suggest that in Korean non-diabetic subjects, C-peptidogenic index was most strong predictor for future diabetes development. Validation of this finding is warranted in more large scale study, in other ethnic groups, and in prospective studies.

CONFLICTS OF INTEREST: No potential conflict of interest relevant to this article was reported.

  • 1. International Diabetes Federation. IDF diabetes atlas; 6th ed. Brussels: International Diabetes Federation; 2013.
  • 2. Jeon JY, Ko SH, Kwon HS, Kim NH, Kim JH, Kim CS, et al. Prevalence of diabetes and prediabetes according to fasting plasma glucose and HbA1c. Diabetes Metab J 2013;37:349–357. ArticlePubMedPMC
  • 3. Gerstein HC, Santaguida P, Raina P, Morrison KM, Balion C, Hunt D, et al. Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies. Diabetes Res Clin Pract 2007;78:305–312. ArticlePubMed
  • 4. Laakso M. Epidemiology of diabetic dyslipidemia. Diabetes Rev 1995;3:408–422.
  • 5. Bagust A, Beale S. Deteriorating beta-cell function in type 2 diabetes: a long-term model. QJM 2003;96:281–288. ArticlePubMedPDF
  • 6. Writing Team for the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group. Effect of intensive therapy on the microvascular complications of type 1 diabetes mellitus. JAMA 2002;287:2563–2569. ArticlePubMedPMC
  • 7. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346:393–403. ArticlePubMedPMC
  • 8. Abdul-Ghani MA, Williams K, DeFronzo RA, Stern M. What is the best predictor of future type 2 diabetes? Diabetes Care 2007;30:1544–1548. ArticlePubMed
  • 9. Grunberger G, Qiang X, Li Z, Mathews ST, Sbrissa D, Shisheva A, et al. Molecular basis for the insulinomimetic effects of C-peptide. Diabetologia 2001;44:1247–1257. ArticlePubMedPDF
  • 10. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412–419. ArticlePubMedPDF
  • 11. Seltzer HS, Allen EW, Herron AL Jr, Brennan MT. Insulin secretion in response to glycemic stimulus: relation of delayed initial release to carbohydrate intolerance in mild diabetes mellitus. J Clin Invest 1967;46:323–335. ArticlePubMedPMC
  • 12. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–845. ArticlePubMed
  • 13. Ohkura T, Shiochi H, Fujioka Y, Sumi K, Yamamoto N, Matsuzawa K, et al. 20/(fasting C-peptide × fasting plasma glucose) is a simple and effective index of insulin resistance in patients with type 2 diabetes mellitus: a preliminary report. Cardiovasc Diabetol 2013;12:21ArticlePubMedPMC
  • 14. Meier JJ, Menge BA, Breuer TG, Muller CA, Tannapfel A, Uhl W, et al. Functional assessment of pancreatic beta-cell area in humans. Diabetes 2009;58:1595–1603. ArticlePubMedPMC
  • 15. Loopstra-Masters RC, Haffner SM, Lorenzo C, Wagenknecht LE, Hanley AJ. Proinsulin-to-C-peptide ratio versus proinsulin-to-insulin ratio in the prediction of incident diabetes: the Insulin Resistance Atherosclerosis Study (IRAS). Diabetologia 2011;54:3047–3054. ArticlePubMedPDF
  • 16. Van Cauter E, Mestrez F, Sturis J, Polonsky KS. Estimation of insulin secretion rates from C-peptide levels. Comparison of individual and standard kinetic parameters for C-peptide clearance. Diabetes 1992;41:368–377. ArticlePubMed
  • 17. Breda E, Cobelli C. Insulin secretion rate during glucose stimuli: alternative analyses of C-peptide data. Ann Biomed Eng 2001;29:692–700. ArticlePubMed
  • 18. Hsieh SD, Kanazawa Y, Akanuma Y. Serum free C-peptide response to oral glucose loading as a parameter for the monitoring of pancreatic B-cell function in diabetic patients. Diabetes Res Clin Pract 1985;1:109–114. ArticlePubMed
  • 19. Gottsater A, Landin-Olsson M, Fernlund P, Gullberg B, Lernmark A, Sundkvist G. Pancreatic beta-cell function evaluated by intravenous glucose and glucagon stimulation. A comparison between insulin and C-peptide to measure insulin secretion. Scand J Clin Lab Invest 1992;52:631–639. ArticlePubMed
  • 20. Utzschneider KM, Prigeon RL, Tong J, Gerchman F, Carr DB, Zraika S, et al. Within-subject variability of measures of beta cell function derived from a 2 h OGTT: implications for research studies. Diabetologia 2007;50:2516–2525. ArticlePubMedPDF
  • 21. Luppi P, Cifarelli V, Tse H, Piganelli J, Trucco M. Human C-peptide antagonises high glucose-induced endothelial dysfunction through the nuclear factor-kappaB pathway. Diabetologia 2008;51:1534–1543. ArticlePubMedPDF
  • 22. Cifarelli V, Geng X, Styche A, Lakomy R, Trucco M, Luppi P. C-peptide reduces high-glucose-induced apoptosis of endothelial cells and decreases NAD(P)H-oxidase reactive oxygen species generation in human aortic endothelial cells. Diabetologia 2011;54:2702–2712. ArticlePubMedPDF
  • 23. Bhatt MP, Lim YC, Hwang J, Na S, Kim YM, Ha KS. C-peptide prevents hyperglycemia-induced endothelial apoptosis through inhibition of reactive oxygen species-mediated transglutaminase 2 activation. Diabetes 2013;62:243–253. ArticlePubMed
  • 24. Bhatt MP, Lim YC, Kim YM, Ha KS. C-peptide activates AMPKα and prevents ROS-mediated mitochondrial fission and endothelial apoptosis in diabetes. Diabetes 2013;62:3851–3862. ArticlePubMedPMC
  • 25. Kubota M, Sato Y, Khookhor O, Ekberg K, Chibalin AV, Wahren J. Enhanced insulin action following subcutaneous co-administration of insulin and C-peptide in rats. Diabetes Metab Res Rev 2014;30:124–131. ArticlePubMed
  • 26. Utzschneider KM, Prigeon RL, Faulenbach MV, Tong J, Carr DB, Boyko EJ, et al. Oral disposition index predicts the development of future diabetes above and beyond fasting and 2-h glucose levels. Diabetes Care 2009;32:335–341. ArticlePubMedPMC
  • 27. Morimoto A, Tatsumi Y, Deura K, Mizuno S, Ohno Y, Miyamatsu N, et al. Impact of impaired insulin secretion and insulin resistance on the incidence of type 2 diabetes mellitus in a Japanese population: the Saku study. Diabetologia 2013;56:1671–1679. ArticlePubMedPDF
Table 1

Baseline Characteristics between Progressors and Non-Progressors to Diabetes

enm-31-320-i001.jpg
Characteristic Total (n=140) Non-progressor (n=110) Progressor (n=30) P value
Age, yr 48.8±10.3 47.8±9.8 52.2±11.0 0.009
Sex, female/male 25.7/74.3 29.0/71.0 25.5/74.5 0.689
Follow-up interval, mo 54.9±16.4 53.7±16.8 58.9±14.8 0.099
Body mass index, kg/m2 24.7±2.6 24.5±2.6 25.6±2.3 0.063
SBP, mm Hg 122.2±14.0 121.3±13.6 126.1±15.2 0.122
DBP, mm Hg 77.2±8.8 77.1±8.5 78.1±10.1 0.596
ALT, U/L 33.9±17.0 32.8±16.4 37.5±18.8 0.156
GGT, U/L 43.8±41.3 40.5±40.6 55.8±42.2 0.102
Total cholesterol, mmol/L 5.16±0.93 5.15±0.95 5.22±0.86 0.733
HDL-C, mmol/L 1.33±0.25 1.35±0.24 1.24±0.27 0.028
LDL-C, mmol/L 3.00±0.71 3.00±0.68 3.03±0.81 0.840
Triglycerides, mmol/L 1.75±0.92 1.68±0.91 2.01±0.94 0.015
Uric acid, mmol/L 0.339±0.086 0.335±0.088 0.344±0.081 0.638
hs-CRP, mg/L 1.23±1.87 1.18±0.200 1.42±1.30 0.010
ApoA1, g/L 1.558±0.256 1.558±0.208 1.553±0.383 0.140
ApoB, g/L 1.052±0.218 1.042±0.212 110.1±24.4 0.231
Lp(a), mg/L 203±144 214±156 161±214 0.697
HbA1c, % (mmol/mol) 5.70±0.36
(38.8±3.9)
5.63±0.31
(38.0±3.3)
6.00±0.39
(42.0±4.2)
<0.001
Glucose, mmol/L
 0 Minute 5.68±0.57 5.60±0.55 6.03±0.56 <0.001
 30 Minutes 9.25±1.75 8.97±1.65 10.21±1.75 <0.001
 120 Minutes 7.46±1.75 7.17±1.65 8.61±1.72 <0.001
NGT 44 (31.4) 42 (38.2) 2 (6.7) <0.001

Values are expressed as mean±SD or number (%).

SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine aminotransferase; GGT, γ-glutamyl transpeptidase; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; ApoA1, apolipoprotein A1; ApoB, apolipoprotein B; Lp(a), lipoprotein a; HbA1c, glycated hemoglobin; NGT, normal glucose tolerance.

Table 2

Comparison between Insulin Based Index and C-Peptide-Based Index for Diabetes Progression

enm-31-320-i002.jpg
Variable Non-progressor Progressor P value
Insulin based index
 Fasting insulin, pmol/L 71.5±22.9 75.0±23.6 0.211
 HOMA-IR 2.35±1.00 2.56±0.94 0.141
 30 Minutes insulin, pmol/L 341.7±211.8 274.3±140.3 0.067
 Δ Insulin, pmol/L 275.0±29.8 182.0±133.3 0.063
 Insulinogenic index, pmol/mmol 103.2±91.2 36.6±28.1 0.003
 Oral disposition index by insulin 49.1±50.6 15.3±11.7 0.005
C-peptide-based index
 Fasting C-peptide, nmol/L 1.06±0.65 1.10±0.32 0.038
 HOMA-IR by C-peptidea 0.79±0.48 0.89±0.29 0.014
 30 Minutes C-peptide, nmol/L 2.79±1.79 2.09±0.68 0.018
 Δ C-peptide, nmol/L 1.81±1.60 1.02±0.64 0.001
 C-peptidogenic indexb, nmol/mmol 0.639±0.468 0.233±0.117
 Oral disposition index by C-peptidec 0.954±0.725 0.324±0.257 <0.001

Values are expressed as mean±SD.

HOMA-IR, homeostatic model assessment of insulin resistance.

aHOMA-IR by C-peptide=fasting glucose×fasting C-peptide/22.5; bC-peptidogenic index=(C-peptide30min-C-peptide0min)/(glucose30min-glucose0min); cOral disposition index by C-peptide=C-peptidogenic index/HOMA-IR by C-peptide.

Table 3

Multivariate Regression Analysis Results for Predicting Incident Diabetes in Nondiabetic Subjects

enm-31-320-i003.jpg
Variable Model 1 Model 2 Model 3 Model 4
OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value
C-peptidogenic index 0.001
(0.000-0.051)
0.001a 0.001
(0.000-0.063)
0.002a 0.001
(0.000-0.258)
0.014a 0.001
(0.000-0.258)
0.014a
Insulinogenic index 0.989 0.531
Age 1.011 0.731 0.992 0.921 0.990 0.882
Sex 0.719 0.658 0.259 0.166 0.259 0.166
BMI 1.024 0.856 0.927 0.747 0.984 0.956
ALT 1.017 0.349 1.017 0.573
eGFR 0.968 0.252 0.968 0.252
hs-CRP 104.1 0.328 104.1 0.328
TC 1.009 0.366 1.009 0.366
HDL-C 0.917 0.151 0.917 0.151
HbA1c 7.096 0.261 7.096 0.261
OGTT 120 minutes glucose 1.030
(1.004-1.057)
0.026a 1.030
(1.004-1.057)
0.026a

OR, odds ratio; CI, confidence interval; BMI, body mass index; ALT, alanine transferase; eGFR, estimated glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein; TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; HbA1C, glycated hemoglobin; OGTT, oral glucose tolerance test.

aFor P<0.005.

Table 4

Comparison between Glycemic and β-Cell Function Markers Future Development of Type 2 Diabetes

enm-31-320-i004.jpg
Variable AROC 95% CI Cutoff Sensitivity, % Specificity, %
Fasting plasma glucose, mmol/L 0.756 0.673-0.828 5.66 60.9 76.7
30 Minutes glucose, mmol/L 0.734 0.639-0.815 9.27 64.4 75.0
120 Minutes glucose, mmol/L 0.782 0.691-0.856 7.77 66.4 70.0
HbA1c, % (mmol/mol) 0.789 0.699-0.862 5.9 (41.0) 86.4 56.7
HOMA-IR 0.641 0.542-0.732 2.818 79.5 47.6
HOMA-IR by C-peptide 0.705 0.608-0.789 0.997 85.4 52.4
Insulinogenic index, pmol/mol 0.731 0.629-0.817 30.4 68.8 61.1
C-peptidogenic index, nmol/mola 0.850 0.761-0.915 0.245 86.8 64.7

AROC, area under receiver operating characteristic curve; CI, confidence interval; HbA1c, glycated hemoglobin; HOMA-IR, homeostatic model assessment of insulin resistance.

aAROC comparison between insulinogenic index vs. C-peptidogenic index, P=0.014.

Figure & Data

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    Citations to this article as recorded by  
    • Insulinogenic index and early phase insulin secretion predict increased risk of worsening glucose tolerance and of cystic fibrosis-related diabetes
      Kathryn J. Potter, Valérie Boudreau, Anne Bonhoure, François Tremblay, Annick Lavoie, Maité Carricart, Peter A. Senior, Rémi Rabasa-Lhoret
      Journal of Cystic Fibrosis.2023; 22(1): 50.     CrossRef
    • First-phase insulin secretion: can its evaluation direct therapeutic approaches?
      Gianfranco Di Giuseppe, Gea Ciccarelli, Laura Soldovieri, Umberto Capece, Chiara M.A. Cefalo, Simona Moffa, Enrico C. Nista, Michela Brunetti, Francesca Cinti, Antonio Gasbarrini, Alfredo Pontecorvi, Andrea Giaccari, Teresa Mezza
      Trends in Endocrinology & Metabolism.2023; 34(4): 216.     CrossRef
    • Insights on C-peptide in diabetes
      Anuj Maheshwari
      IP Journal of Nutrition, Metabolism and Health Science.2023; 6(2): 63.     CrossRef
    • Human Milk Oligosaccharides in Maternal Serum Respond to Oral Glucose Load and Are Associated with Insulin Sensitivity
      Marie-Therese Weiser-Fuchs, Elena Maggauer, Mireille N. M. van Poppel, Bence Csapo, Gernot Desoye, Harald C. Köfeler, Andrea Groselj-Strele, Slave Trajanoski, Herbert Fluhr, Barbara Obermayer-Pietsch, Evelyn Jantscher-Krenn
      Nutrients.2023; 15(18): 4042.     CrossRef
    • Elevated Peripheral Brain-Derived Neurotrophic Factor Level Associated With Decreasing Insulin Secretion May Forecast Memory Dysfunction in Patients With Long-Term Type 2 Diabetes
      Xi Huang, Zuolin Xie, Chenchen Wang, Shaohua Wang
      Frontiers in Physiology.2022;[Epub]     CrossRef
    • Predictors of Glycemic Outcomes at 1 Year Following Pediatric Total Pancreatectomy With Islet Autotransplantation
      Sarah E. Swauger, Lindsey N. Hornung, Deborah A. Elder, Appakalai N. Balamurugan, David S. Vitale, Tom K. Lin, Jaimie D. Nathan, Maisam Abu-El-Haija
      Diabetes Care.2022; 45(2): 295.     CrossRef
    • Data-driven subgroups of type 2 diabetes, metabolic response, and renal risk profile after bariatric surgery: a retrospective cohort study
      Violeta Raverdy, Ricardo V Cohen, Robert Caiazzo, Helene Verkindt, Tarissa Beatrice Zanata Petry, Camille Marciniak, Benjamin Legendre, Pierre Bauvin, Estelle Chatelain, Alain Duhamel, Elodie Drumez, Naima Oukhouya-Daoud, Mikael Chetboun, Gregory Baud, Em
      The Lancet Diabetes & Endocrinology.2022; 10(3): 167.     CrossRef
    • The Relationship between the Lipid Accumulation Product and Beta-cell Function in Korean Adults with or without Type 2 Diabetes Mellitus: The 2015 Korea National Health and Nutrition Examination Survey
      Hye Eun Cho, Seung Bum Yang, Mi Young Gi, Ju Ae Cha, so Young Park, Hyun Yoon
      Endocrine Research.2022; 47(2): 80.     CrossRef
    • Measures of Maternal Metabolic Health as Predictors of Severely Low Milk Production
      Laurie A. Nommsen-Rivers, Erin A. Wagner, Dayna M. Roznowski, Sarah W. Riddle, Laura P. Ward, Amy Thompson
      Breastfeeding Medicine.2022; 17(7): 566.     CrossRef
    • Prediabetes: From diagnosis to prognosis
      Teodora Beljić-Živković
      Galenika Medical Journal.2022; 1(1): 57.     CrossRef
    • C‐peptide determination in the diagnosis of type of diabetes and its management: A clinical perspective
      Ernesto Maddaloni, Geremia B. Bolli, Brian M. Frier, Randie R. Little, Richard D. Leslie, Paolo Pozzilli, Raffaela Buzzetti
      Diabetes, Obesity and Metabolism.2022; 24(10): 1912.     CrossRef
    • Fasting Proinsulin Independently Predicts Incident Type 2 Diabetes in the General Population
      Sara Sokooti, Wendy A. Dam, Tamas Szili-Torok, Jolein Gloerich, Alain J. van Gool, Adrian Post, Martin H. de Borst, Ron T. Gansevoort, Hiddo J. L. Heerspink, Robin P. F. Dullaart, Stephan J. L. Bakker
      Journal of Personalized Medicine.2022; 12(7): 1131.     CrossRef
    • C-peptide is a predictor of telomere shortening: A five-year longitudinal study
      Racha Ghoussaini, Hani Tamim, Martine Elbejjani, Maha Makki, Lara Nasreddine, Hussain Ismaeel, Mona P. Nasrallah, Nathalie K. Zgheib
      Frontiers in Endocrinology.2022;[Epub]     CrossRef
    • Association between early-pregnancy serum C-peptide and risk of gestational diabetes mellitus: a nested case–control study among Chinese women
      Xue Yang, Yi Ye, Yi Wang, Ping Wu, Qi Lu, Yan Liu, Jiaying Yuan, Xingyue Song, Shijiao Yan, Xiaorong Qi, Yi-Xin Wang, Ying Wen, Gang Liu, Chuanzhu Lv, Chun-Xia Yang, An Pan, Jianli Zhang, Xiong-Fei Pan
      Nutrition & Metabolism.2022;[Epub]     CrossRef
    • Human C-peptide is a ligand of the elastin-receptor-complex and therewith central to human vascular remodelling and disease in metabolic syndrome
      Gert Wensvoort
      Medical Hypotheses.2022; 168: 110964.     CrossRef
    • Lactiplantibacillus plantarum DSM20174 Attenuates the Progression of Non-Alcoholic Fatty Liver Disease by Modulating Gut Microbiota, Improving Metabolic Risk Factors, and Attenuating Adipose Inflammation
      José I. Riezu-Boj, Miguel Barajas, Tania Pérez-Sánchez, María J. Pajares, Miriam Araña, Fermín I. Milagro, Raquel Urtasun
      Nutrients.2022; 14(24): 5212.     CrossRef
    • Depression Augments Plasma APOA4 without Changes of Plasma Lipids and Glucose in Female Adolescents Carrying G Allele of APOA4 rs5104
      Qi Wei Guo, Yan Jun Si, Yi Lin Shen, Xu Chen, Mei Yang, Ding Zhi Fang, Jia Lin
      Journal of Molecular Neuroscience.2021; 71(10): 2060.     CrossRef
    • Insulinemic and Inflammatory Dietary Patterns Show Enhanced Predictive Potential for Type 2 Diabetes Risk in Postmenopausal Women
      Qi Jin, Ni Shi, Desmond Aroke, Dong Hoon Lee, Joshua J. Joseph, Macarius Donneyong, Darwin L. Conwell, Phil A. Hart, Xuehong Zhang, Steven K. Clinton, Zobeida Cruz-Monserrate, Theodore M. Brasky, Rebecca Jackson, Lesley F. Tinker, Simin Liu, Lawrence S. P
      Diabetes Care.2021; 44(3): 707.     CrossRef
    • Analytical and clinical comparison between two different chemiluminescent enzyme immunoassays for the measurement of C-peptide in serum
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      Minerva Biotechnology and Biomolecular Research.2021;[Epub]     CrossRef
    • Pregnancy Serum DLK1 Concentrations Are Associated With Indices of Insulin Resistance and Secretion
      Clive J Petry, Keith A Burling, Peter Barker, Ieuan A Hughes, Ken K Ong, David B Dunger
      The Journal of Clinical Endocrinology & Metabolism.2021; 106(6): e2413.     CrossRef
    • May C-peptide index be a new marker to predict proteinuria in anemic patients with type 2 diabetes mellitus?
      Bilal Katipoglu, Mustafa Comoglu, Ihsan Ates, Nisbet Yilmaz, Dilek Berker
      Endocrine Regulations.2020; 54(1): 1.     CrossRef
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      European Journal of Preventive Cardiology.2020; 27(18): 1934.     CrossRef
    • Circulating levels of selected adipokines in women with gestational diabetes and type 2 diabetes
      David Karasek, Ondrej Krystynik, Dominika Goldmannova, Lubica Cibickova, Jan Schovanek
      Journal of Applied Biomedicine.2020; 18(2-3): 54.     CrossRef
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      Journal of Diabetes and its Complications.2020; 34(2): 107464.     CrossRef
    • High Plasma Resistin Levels Portend the Insulin Resistance-Associated Susceptibility to Early Cognitive Decline in Patients with Type 2 Diabetes Mellitus
      Chenchen Wang, Xi Huang, Sai Tian, Rong Huang, Dan Guo, Hongyan Lin, Jiaqi Wang, Shaohua Wang
      Journal of Alzheimer's Disease.2020; 75(3): 807.     CrossRef
    • Linearization of the Disposition Index equation allows evaluation of secretion-sensitivity coupling slopes
      Kieren J. Mather, Melinda Chen, Tamara S. Hannon
      Journal of Diabetes and its Complications.2020; 34(7): 107589.     CrossRef
    • Impact of interval walking training managed through smart mobile devices on albuminuria and leptin/adiponectin ratio in patients with type 2 diabetes
      Jelizaveta Sokolovska, Karina Ostrovska, Leonora Pahirko, Gunita Varblane, Ksenija Krilatiha, Austris Cirulnieks, Inese Folkmane, Valdis Pirags, Janis Valeinis, Aija Klavina, Leo Selavo
      Physiological Reports.2020;[Epub]     CrossRef
    • Gender diversity of insulin sensitivity markers among patients of type 2 diabetes mellitus in northern India: A cross-sectional analytical study
      Ravi Kant, Poonam Yadav, Surekha Kishore
      Journal of Family Medicine and Primary Care.2020; 9(7): 3315.     CrossRef
    • Associations of Plasma BACE1 Level and BACE1 C786G Gene Polymorphism with Cognitive Functions in Patients with Type 2 Diabetes: A Cross- Sectional Study
      Sai Tian, Rong Huang, Dan Guo, Hongyan Lin, Jiaqi Wang, Ke An, Shaohua Wang
      Current Alzheimer Research.2020; 17(4): 355.     CrossRef
    • PPARGC1A Gene Promoter Methylation as a Biomarker of Insulin Secretion and Sensitivity in Response to Glucose Challenges
      José L. Santos, Bernardo J. Krause, Luis Rodrigo Cataldo, Javier Vega, Francisca Salas-Pérez, Paula Mennickent, Raúl Gallegos, Fermín I. Milagro, Pedro Prieto-Hontoria, J. Ignacio Riezu-Boj, Carolina Bravo, Albert Salas-Huetos, Ana Arpón, José E. Galgani,
      Nutrients.2020; 12(9): 2790.     CrossRef
    • Plasma C-Peptide and Risk of Developing Type 2 Diabetes in the General Population
      Sara Sokooti, Lyanne M. Kieneker, Martin H. de Borst, Anneke Muller Kobold, Jenny E. Kootstra-Ros, Jolein Gloerich, Alain J. van Gool, Hiddo J. Lambers Heerspink, Ron T Gansevoort, Robin P.F. Dullaart, Stephan J. L. Bakker
      Journal of Clinical Medicine.2020; 9(9): 3001.     CrossRef
    • Effect of Anthocyanins Supplementation on Serum IGFBP-4 Fragments and Glycemic Control in Patients with Fasting Hyperglycemia: A Randomized Controlled Trial


      Liping Yang, Zhaomin Liu, Wenhua Ling, Li Wang, Changyi Wang, Jianping Ma, Xiaolin Peng, Jianying Chen
      Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2020; Volume 13: 3395.     CrossRef
    • Adipose Mitochondrial Respiratory Capacity in Obesity is Impaired Independently of Glycemic Status of Tissue Donors
      Britta Wessels, Julius Honecker, Theresa Schöttl, Lynne Stecher, Martin Klingenspor, Hans Hauner, Thomas Skurk
      Obesity.2019; 27(5): 756.     CrossRef
    • Higher Plasma Level of Nampt Presaging Memory Dysfunction in Chinese Type 2 Diabetes Patients with Mild Cognitive Impairment
      Xi Huang, Chenchen Wang, Sai Tian, Rong Huang, Dan Guo, Haoqiang Zhang, Jijing Shi, Shaohua Wang
      Journal of Alzheimer's Disease.2019; 70(1): 303.     CrossRef
    • Increased Ratio of Global O-GlcNAcylation to Tau Phosphorylation at Thr212 Site Is Associated With Better Memory Function in Patients With Type 2 Diabetes
      Rong Huang, Sai Tian, Jing Han, Rongrong Cai, Hongyan Lin, Dan Guo, Jiaqi Wang, Shaohua Wang
      Frontiers in Physiology.2019;[Epub]     CrossRef
    • Pancreatic fat content is associated with β-cell function and insulin resistance in Chinese type 2 diabetes subjects
      Ting Lu, Yao Wang, Ting Dou, Bizhen Xue, Yuanyuan Tan, Jiao Yang
      Endocrine Journal.2019; 66(3): 265.     CrossRef
    • Effectiveness of Eriomin® in managing hyperglycemia and reversal of prediabetes condition: A double‐blind, randomized, controlled study
      Carolina B. Ribeiro, Fernanda M. Ramos, John A. Manthey, Thais B. Cesar
      Phytotherapy Research.2019; 33(7): 1921.     CrossRef
    • Association between plasma adipsin level and mild cognitive impairment in Chinese patients with type 2 diabetes: a cross-sectional study
      Dan Guo, Yang Yuan, Rong Huang, Sai Tian, Jiaqi Wang, Hongyan Lin, Ke An, Jin Han, Shaohua Wang
      BMC Endocrine Disorders.2019;[Epub]     CrossRef
    • Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia
      Bernd Richter, Bianca Hemmingsen, Maria-Inti Metzendorf, Yemisi Takwoingi
      Cochrane Database of Systematic Reviews.2018;[Epub]     CrossRef
    • OGTT 1h serum C-peptide to plasma glucose concentration ratio is more related to beta cell function and diabetes mellitus
      Hongmei Zhang, Bingxian Bian, Fan Hu, Qing Su
      Oncotarget.2017; 8(31): 51786.     CrossRef
    • Articles inEndocrinology and Metabolismin 2016
      Won-Young Lee
      Endocrinology and Metabolism.2017; 32(1): 62.     CrossRef
    • Obesity Influence on Insulin Activity and Resting Metabolic Rate in Type 2 Diabetes
      Rodica Doros, Daniela Lixandru, Laura Petcu, Ariana Picu, Manuela Mitu, Janeta Tudosoiu, Constantin Ionescu-Tîrgoviste
      Romanian Journal of Diabetes Nutrition and Metabolic Diseases.2016; 23(4): 377.     CrossRef
    • Insulin Secretory Capacity and Insulin Resistance in Korean Type 2 Diabetes Mellitus Patients
      Jong-Dai Kim, Won-Young Lee
      Endocrinology and Metabolism.2016; 31(3): 354.     CrossRef
    • Prediction of Diabetes Using Serum C-Peptide
      Hye Seung Jung
      Endocrinology and Metabolism.2016; 31(2): 275.     CrossRef

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