Saygili, Emre SedarBatman, AdnanKarakilic, Ersen2026-02-032026-02-0320250168-82271872-8227https://doi.org/10.1016/j.diabres.2025.112453https://hdl.handle.net/20.500.12428/34863Aims: 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.eninfo:eu-repo/semantics/closedAccessBeta-cell functionC -peptideClinical decision support systemsMachine learningMixed-meal tolerance testType 1 diabetes mellitusArticle22910.1016/j.diabres.2025.112453Q1WOS:0015740301000012-s2.0-10501600717040914229Q1