Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Backgruound: The Korean Endocrine Hormone Reference Standard Data Center (KEHRS DC) has created reference standards (RSs) for endocrine hormones since 2020. This study is the first of its kind, wherein the KEHRS DC established RSs for serum Cpeptide levels in a healthy Korean population.

Methods: Healthy Korean adults were recruited from May 2021 to September 2023. After excluding participants according to our criteria, serum samples were collected; each participant could then choose between fasting glucose only or fasting glucose plus an oral glucose tolerance test (OGTT). If their sample showed high glucose (≥100 mg/dL) or hemoglobin A1c (HbA1c) (≥5.70%), their C-peptide levels were excluded from analyzing the RSs.

Results: A total of 1,532 participants were recruited; however, only the data of 1,050 participants were analyzed after excluding those whose samples showed hyperglycemia or high HbA1c. Post-30-minute OGTT data from 342 subjects and post-120-minute OGTT data from 351 subjects were used. The means±2 standard deviations and expanded uncertainties of fasting, post-30-minute and 120-minute OGTT C-peptide levels were 1.26±0.82 and 0.34-3.18, 4.74±3.57 and 1.14-8.33, and 4.85±3.58 and 1.25-8.34 ng/mL, respectively. Serum C-peptide levels correlated with obesity, serum glucose levels, and HbA1c levels.

Conclusion: The RSs for serum C-peptide levels established in this study are expected to be useful in both clinical and related fields.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220213PMC
http://dx.doi.org/10.3803/EnM.2023.1888DOI Listing

Publication Analysis

Top Keywords

c-peptide levels
16
reference standards
8
korean endocrine
8
endocrine hormone
8
hormone reference
8
reference standard
8
standard data
8
data center
8
rss serum
8
healthy korean
8

Similar Publications

Objectives: To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepatic echotexture analysis.

Methods: This diagnostic accuracy study analyzed ultrasound images of fetal livers from pregnancies between 37 and 42 weeks, including cases with and without gestational diabetes mellitus (GDM). Images were stored in Digital Imaging and Communications in Medicine (DICOM) format, annotated by experts, and converted to segmented masks after quality checks.

View Article and Find Full Text PDF

Purpose: Previous studies have shown that serum uric acid (UA) levels are significantly higher in patients with bipolar disorder (BD) than in patients with depressive disorder (DD), schizophrenia, and healthy controls. Currently, studies generally report that there is a complex bidirectional interaction between mood disorders (MD) and hyperuricemia (HUA). We investigated the prevalence and related factors of hyperuricemia in Chinese patients with mood disorders to find out potential mechanisms and build a predictive model.

View Article and Find Full Text PDF

Non-islet cell tumor hypoglycemia (NICTH) is a rare paraneoplastic syndrome resulting from excessive secretion of pro-insulin-like growth factor 2 (proIGF-2). This leads to hypoinsulinemic hypoglycemia and, in some cases, acromegaly. We report the case of a 52-year-old woman with NICTH syndrome who had decreased levels of insulin-like growth factor 1 (IGF1), insulin, C-peptide, and growth hormone (GH).

View Article and Find Full Text PDF

Background: The long-term clinical efficacy of intraportal islet transplantation is hampered by islet loss due to inflammation, oxidative stress, and insufficient vascularization. This study explores the venous sac as an alternative implantation site for islet transplantation in large animal models.

Methods: An immunosuppressed, diabetic cynomolgus monkey received allogeneic islet implants in its mesenteric venous sac, with metabolic assessments over 112 days.

View Article and Find Full Text PDF

Predicting stimulated C-peptide in type 1 diabetes using machine learning: a web-based tool from the T1D exchange registry.

Diabetes Res Clin Pract

September 2025

Division of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine, Canakkale Onsekiz Mart University, Canakkale, Turkey.

Aims: The mixed-meal tolerance test (MMTT), though considered the gold standard for evaluating residual beta-cell function in type 1 diabetes mellitus (T1D), is impractical for routine use. We aimed to develop and validate a machine learning (ML) model to predict MMTT-stimulated C-peptide categories using routine clinical data.

Methods: Data from 319 individuals in the T1D Exchange Registry with complete MMTT and clinical information were analyzed.

View Article and Find Full Text PDF