What question did this study set out to answer?

To develop an interpretable machine learning model for classifying prediabetes using inflammatory biomarkers instead of traditional glycemic measures.

March 18, 2026Open Access

An inflammatory biomarker panel for prediabetes classification using interpretable machine learning

Key Points

To develop an interpretable machine learning model for classifying prediabetes using inflammatory biomarkers instead of traditional glycemic measures.
Developed machine learning models using data from 545 participants, including 140 with prediabetes.
Employed nested cross-validation for robust validation.
Managed feature collinearity using variance inflation factor (VIF).
Interpreted the final model using Shapley Additive Explanations (SHAP).
Identified a panel of inflammatory biomarkers (IL-10, IGF-1, CRP) for prediabetes classification.
Achieved an Area Under the Curve (AUC) of 0.711 on validation tests, demonstrating effective classification.
Established inflammation as a measurable indicator of early metabolic dysfunction.

Abstract

Objective Prediabetes is a silent condition that often goes undetected. However, timely interventions could prevent its progression to type 2 diabetes. Traditional glycemic markers, such as hemoglobin A1c (HbA1c), have limitations, creating a need for new diagnostic biomarkers. In this study, our objective was to develop an interpretable machine learning model using biomarkers related to oxidative stress, inflammation, and lipid metabolism to classify prediabetes independently of traditional glycemic markers, such as HbA1c. We also compared multiple biomarker panels to determine which biomarkers offer the highest predictive accuracy. Methods We developed and validated interpretable machine learning models using clinical and biomarker data from 545 participants (405 healthy controls and 140 with prediabetes). To ensure robust and generalizable findings, we employed a nested cross-validation technique, managed feature collinearity using the variance inflation factor (VIF), and interpreted the final model with Shapley Additive exPlanations (SHAP) Kapoor S, Narayanan A. Patterns. 4(9):100804 (2023); Vabalas A, et al. PLoS One. 14(11):e0224365 (2019); Lundberg SM, Lee SI. Adv Neural Inf Process Syst. 30:4768–77 (2017). Results Our approach identified a distinct panel of inflammatory biomarkers (IL-10, IGF-1, and CRP) capable of classifying prediabetes independently of traditional glycemic markers. This non-glycemic model achieved a promising Area Under the Curve (AUC) of 0.711 on holdout validation, establishing inflammation as a key and measurable indicator of early metabolic dysfunction. Conclusion Our findings introduce a novel panel of inflammatory biomarkers that show promise in the identification of prediabetes independently of traditional glucose-based measures. By highlighting inflammation as an early indicator of metabolic dysfunction, this approach may enhance precision in the detection of prediabetes. Longitudinal studies with larger and more diverse populations are essential to clinically validate these biomarkers and confirm their value in improving the early diagnosis and management of metabolic health.

An inflammatory biomarker panel for prediabetes classification using interpretable machine learning

Key Points

Abstract

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