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

SAYGILI E. S., Batman A., KARAKILIÇ E.

Diabetes Research and Clinical Practice, cilt.229, 2025 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 229
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.diabres.2025.112453
  • Dergi Adı: Diabetes Research and Clinical Practice
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, CINAHL, EMBASE, Index Islamicus, MEDLINE, Veterinary Science Database
  • Anahtar Kelimeler: Beta-cell function, C-peptide, Clinical decision support systems, Machine learning, Mixed-meal tolerance test, Type 1 diabetes mellitus
  • Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet

Özet

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. The cohort was randomly split into training (70%) and test (30%) sets. Five clinical variables—age at diagnosis, diabetes duration, HbA1c, non-fasting glucose, and non-fasting C-peptide—were selected via recursive feature elimination. Four ML algorithms (random forest [RF], XGBoost, LightGBM, and ordinal logistic regression) were trained with 10-fold cross-validation. Results: The RF model showed the highest performance: AUC 0.94 (95% CI: 0.92–0.96), sensitivity 0.84 (95% CI: 0.80–0.89), and specificity 0.92 (95% CI: 0.90–0.94) in cross-validation. In the test set, AUC was 0.97, sensitivity 88%, and specificity 94%. Notably, 17.7% of individuals with undetectable non-fasting C-peptide had measurable levels after MMTT. Conclusions: This ML model provides a practical, non-invasive tool for estimating beta-cell function in T1D and is available online at https://cpeptide.streamlit.app.