Endocrine journal · 2025
Utility of the random C-peptide and random C-peptide index at diagnosis as a predictor of disease type and long-term insulin secretory capacity in children with diabetes mellitus.
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Numasawa Keita, Shiga Kentaro, Kitao Makiko, Konno Hiroaki, Nishiyama Kuniyuki, Ohsugi Koji, Ebina Kanako, Kikuchi Nobuyuki, Ito Shuichi
Аннотация
The random C-peptide and random C-peptide index (CPI) have been shown to be useful in assessing endogenous insulin secretory capacity in adults with type 2 diabetes. This study aimed to clarify the utility of C-peptide and the CPI in early classification of long-term insulin-dependent status in pediatric diabetes patients. A total of 204 patients aged ≤15 years who received an initial diagnosis of acute-onset type 1 diabetes mellitus (T1DM), slowly progressive insulin-dependent diabetes mellitus (SPIDDM), or type 2 diabetes mellitus (T2DM) at Yokohama City University Medical Center between April 1, 2003 and March 31, 2018 were included. The acute-onset T1DM, SPIDDM, and T2DM groups included 140, 8, and 56 patients, respectively. The median random C-peptide values were 0.67, 3.18, and 4.16 ng/mL and median random CPI values were 0.19, 2.02, and 2.53 for acute-onset T1DM, SPIDDM, and T2DM cases, respectively (acute-onset T1DM vs. T2DM, p < 0.001 (C-peptide), p < 0.001 (CPI), acute-onset T1DM vs. SPIDDM, p < 0.001 (C-peptide), p < 0.001 (CPI), SPIDDM vs. T2DM, p = 0.04 (C-peptide), p = 0.19 (CPI)). Receiver operating characteristic analysis cutoff values of C-peptide levels in differentiating acute-onset T1DM from SPIDDM and acute-onset T1DM from T2DM were 1.60 ng/mL (sensitivity 87.5%, specificity 90.6%) and 1.81 ng/mL (sensitivity 91.1%, specificity 93.5%), while the respective CPI values were 0.46 (100% sensitivity, 77% specificity) and 1.05 (92.1% sensitivity, 87.5% specificity). This study indicates that the random C-peptide and random CPI at diagnosis are helpful in the early classification of childhood diabetes and determining an appropriate time to introduce insulin and predicting the subsequent clinical course.