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

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dc.authorid0000-0003-3590-2656
dc.authorid0000-0002-0933-6198
dc.authorid0000-0003-0022-5704
dc.contributor.authorSaygili, Emre Sedar
dc.contributor.authorBatman, Adnan
dc.contributor.authorKarakilic, Ersen
dc.date.accessioned2026-02-03T12:02:46Z
dc.date.available2026-02-03T12:02:46Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractAims: 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.
dc.identifier.doi10.1016/j.diabres.2025.112453
dc.identifier.issn0168-8227
dc.identifier.issn1872-8227
dc.identifier.pmid40914229
dc.identifier.scopus2-s2.0-105016007170
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.diabres.2025.112453
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34863
dc.identifier.volume229
dc.identifier.wosWOS:001574030100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Ireland Ltd
dc.relation.ispartofDiabetes Research and Clinical Practice
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260130
dc.subjectBeta-cell function
dc.subjectC -peptide
dc.subjectClinical decision support systems
dc.subjectMachine learning
dc.subjectMixed-meal tolerance test
dc.subjectType 1 diabetes mellitus
dc.titlePredicting stimulated C-peptide in type 1 diabetes using machine learning: a web-based tool from the T1D exchange registry
dc.typeArticle

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