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

Abstract

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.

Keywords: Beta-cell function; C-peptide; Clinical decision support systems; Machine learning; Mixed-meal tolerance test; Type 1 diabetes mellitus.