Evaluating the Predictive Performance of Metabolic Biomarkers for 20- Year Incidence Diabetes in the Yazd Healthy Heart Cohort: A Machine Learning Analysis

Authors

  • Akram Ghadiri-Anari Diabetes Research Center, Shahid Sadoughi University of Medical Science, Yazd, Iran
  • Seyedeh Mahdieh Namayandeh Clinical Research Development Center, Afshar Hospital, Shahid Sadoughi University of Medical Sciences, Yazd, Iran2. Clinical Research Development Center, Afshar Hospital, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
  • Mohammadreza Ahi Clinical Research Development Center, Afshar Hospital, Shahid Sadoughi University of Medical Sciences, Yazd, Iran2. Clinical Research Development Center, Afshar Hospital, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

DOI:

https://doi.org/10.18502/ijdl.v26i3.22507

Keywords:

Type 2 diabetes, Machine learning, Metabolic biomarkers, Cohort, Long-term prediction

Abstract

Background: Type 2 diabetes is a major global health challenge, necessitating the development of accurate prediction models for early intervention. This study aimed to assess the performance of machine learning models in predicting the long-term incidence of diabetes and to identify predictive metabolic biomarkers based on data from the Yazd Healthy Heart Cohort.

Methods: This retrospective study was conducted on 906 non-diabetic individuals from the Yazd Healthy Heart Cohort with a 20-year follow-up. Five machine learning models (Logistic Regression, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest) were implemented based on 17 demographic, clinical, and biochemical variables. The data were randomly split into 70% for training and 30% for testing. Model performance was evaluated using 10-fold cross-validation and metrics including accuracy, sensitivity, specificity, F1-Score, and Area Under the Curve- Receiver Operator Characteristic (AUC-ROC).

Results: During the study, 340 participants (37.5%) developed diabetes. None of the models achieved satisfactory performance (AUC> 0.8). The best performance was observed for the LDA model in the 10-year prediction, with an accuracy of 73% and an AUC of 0.70, although its sensitivity was low (47%). The composite indices triglyceride-glucose (TyG) and atherogenic plasma index (AIP) were identified as the strongest predictors across most models.

Conclusion: Although composite biomarkers such as TyG show significant predictive potential, conventional machine learning models using baseline data are insufficient for predicting diabetes incidence over long-term horizons. The development of dynamic models that incorporate temporal changes in variables and complex pathophysiological interactions is essential to achieve acceptable accuracy in long-term prediction.

Published

2026-09-04

Issue

Volume (26) issue (3)

Section

Articles