Time-to-Peak C-Peptide and Peak Glucose during an Oral Glucose Tolerance Test (OGTT) Are Independent Risk Factors for Type 1 Diabetes (T1D) in Antibody-Positive (Ab+) Individuals

Prolonged fasting has shown beneficial effects in healthy individuals and in people with chronic diseases. In type 1 diabetes, the effect or even the feasibility of fasting is unclear. We aimed to assess the impact and safety of prolonged fasting in adults with type 1 diabetes. Glycemia was assessed during overnight fasting (12 hours) vs. prolonged fasting (36 hours) via an intermittently-scanned continuous glucose monitoring system. Anthropometric data, metabolic and hormonal markers were compared between both trial arms. After each fasting period, a 75 g oral glucose tolerance test was performed and plasma glucose levels and hormones were assessed. Data were compared via paired t-tests and mixed-model regressions (p ≤ 0.05). Twenty individuals with type 1 diabetes (7 females) with a mean ± SD age of 35 ± 11 years, body mass index (BMI) 24.8 ± 2.8 kg/m2 and HbA1c 54 ± 7 mmol/mol were included. Hypoglycemia/hour (70 mg/dL; <3.9 mmol/L) was similar in both trial arms (12 hrs: 0.07 ± 0.06 vs. 36 hrs: 0.05 ± 0.03, p=0.21). Glycemic excursions during the oral glucose tolerance test were not different after the two fasting periods. Beta-hydroxybutyrate levels were higher after prolonged fasting (p=0.0006). Our study showed that people with type 1 diabetes can safely perform a 36 hours fasting period with a low risk of hypoglycemia and ketoacidosis.Clinical Trial RegistrationDRKS.de, identifier DRKS00016148.

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Time to Peak Glucose and Peak C-Peptide During the Progression to Type 1 Diabetes in the Diabetes Prevention Trial and TrialNet Cohorts

10.2337/figshare.14974563.v2 ◽

2021 ◽

Author(s):

Michael G Voss ◽

David D Cuthbertson ◽

Mario M Cleves ◽

Ping Xu ◽

Carmella Evans-Molina ◽

...

Keyword(s):

Type 1 Diabetes ◽

Diabetes Prevention ◽

Prevention Trial ◽

Oral Glucose ◽

Z Score ◽

C Peptide ◽

Time To Peak ◽

Diabetes Progression ◽

Diabetes Development

Objective: To assess the progression of type 1 diabetes using time to peak glucose or C-peptide during oral glucose tolerance tests (OGTTs) in autoantibody positive (Ab+) relatives of people with type 1 diabetes. Methods: We examined 2-hour OGTTs of participants in the Diabetes Prevention Trial Type 1 (DPT-1) and TrialNet Pathway to Prevention (PTP) studies. We included 706 DPT-1 participants (Mean±SD age: 13.84±9.53 years; BMI-Z-Score: 0.33±1.07; 56.1% male) and 3,720 PTP participants (age: 16.01±12.33 Years, BMI-Z-Score 0.66±1.3; 49.7% male). Log-rank testing and Cox regression analyses with adjustments (age, sex, race, BMI Z-Score, HOMA-IR and peak Glucose/C-peptide levels, respectively) were performed. Results: In each of DPT-1 and PTP, higher 5-year diabetes progression risk was seen in those with time to peak glucose >30 min and time to peak C-peptide >60 min (p<0.001 for all groups), before and after adjustments. In models examining strength of association with diabetes development, associations were greater for time to peak C-peptide versus peak C-peptide value (DPT-1: X2 = 25.76 vs. X2 = 8.62 and PTP: X2 = 149.19 vs. X2 = 79.98; all p<0.001). Changes in the percentage of individuals with delayed glucose and/or C-peptide peaks were noted over time. Conclusions: In two independent at risk populations, we show that those with delayed OGTT peak times for glucose or C-peptide are at higher risk of diabetes development within 5 years, independent of peak levels. Moreover, time to peak C-peptide appears more predictive than the peak level, suggesting its potential use as a specific biomarker for diabetes progression.

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Different responses of insulin, C-peptide, and testosterone to an oral glucose tolerance test in two groups of women with polycystic ovarian syndrome

Acta Obstetricia Et Gynecologica Scandinavica ◽

10.3109/00016349609033311 ◽

1996 ◽

Vol 75 (2) ◽

pp. 166-169 ◽

Cited By ~ 8

Author(s):

Wu Xiao Ke ◽

Gu Qiu Shan ◽

Su Yan Hua

Keyword(s):

Glucose Tolerance ◽

Oral Glucose Tolerance Test ◽

Polycystic Ovarian Syndrome ◽

Glucose Tolerance Test ◽

Tolerance Test ◽

Oral Glucose ◽

Oral Glucose Tolerance ◽

C Peptide ◽

Ovarian Syndrome

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Postprandial serum C-peptide to plasma glucose concentration ratio correlates with oral glucose tolerance test- and glucose clamp-based disposition indexes

Metabolism ◽

10.1016/j.metabol.2013.05.022 ◽

2013 ◽

Vol 62 (10) ◽

pp. 1470-1476 ◽

Cited By ~ 25

Author(s):

Yoko Okuno ◽

Hisako Komada ◽

Kazuhiko Sakaguchi ◽

Tomoaki Nakamura ◽

Naoko Hashimoto ◽

...

Keyword(s):

Glucose Tolerance ◽

Oral Glucose Tolerance Test ◽

Plasma Glucose ◽

Glucose Concentration ◽

Concentration Ratio ◽

Glucose Tolerance Test ◽

Tolerance Test ◽

Oral Glucose ◽

Oral Glucose Tolerance ◽

C Peptide

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Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test

Frontiers in Endocrinology ◽

10.3389/fendo.2021.611147 ◽

2021 ◽

Vol 12 ◽

Author(s):

Micaela Morettini ◽

Laura Burattini ◽

Christian Göbl ◽

Giovanni Pacini ◽

Bo Ahrén ◽

...

Keyword(s):

Mathematical Model ◽

Insulin Secretion ◽

Clinical Practice ◽

Glucose Tolerance Test ◽

Glucagon Secretion ◽

Tolerance Test ◽

Oral Glucose ◽

Oral Glucose Tolerance ◽

C Peptide ◽

Proposed Model

Glucagon is secreted from the pancreatic alpha cells and plays an important role in the maintenance of glucose homeostasis, by interacting with insulin. The plasma glucose levels determine whether glucagon secretion or insulin secretion is activated or inhibited. Despite its relevance, some aspects of glucagon secretion and kinetics remain unclear. To gain insight into this, we aimed to develop a mathematical model of the glucagon kinetics during an oral glucose tolerance test, which is sufficiently simple to be used in the clinical practice. The proposed model included two first-order differential equations -one describing glucagon and the other describing C-peptide in a compartment remote from plasma - and yielded a parameter of possible clinical relevance (i.e., SGLUCA(t), glucagon-inhibition sensitivity to glucose-induced insulin secretion). Model was validated on mean glucagon data derived from the scientific literature, yielding values for SGLUCA(t) ranging from -15.03 to 2.75 (ng of glucagon·nmol of C-peptide-1). A further validation on a total of 100 virtual subjects provided reliable results (mean residuals between -1.5 and 1.5 ng·L-1) and a negative significant linear correlation (r = -0.74, p < 0.0001, 95% CI: -0.82 – -0.64) between SGLUCA(t) and the ratio between the areas under the curve of suprabasal remote C-peptide and glucagon. Model reliability was also proven by the ability to capture different patterns in glucagon kinetics. In conclusion, the proposed model reliably reproduces glucagon kinetics and is characterized by sufficient simplicity to be possibly used in the clinical practice, for the estimation in the single individual of some glucagon-related parameters.

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