Abstract / Summary
Abstract Background Oral fluids may provide an alternative to traditional invasive sampling methods for screening and monitoring chronic conditions, such as Type 2 diabetes mellitus (T2DM), which is commonly assessed using blood HbA1c levels. We aimed to identify putative proteomic signatures in oral rinse samples to differentiate individuals with low versus high HbA1c levels using a machine learning (ML) approach. Methods Participants were selected from an existing population cohort (The Maastricht Study), and oral rinse samples were derived from the associated Biobank. A two-threshold ML approach used the population median threshold (37 mmol/mol), followed by the clinical cutoff for elevated HbA1c (48 mmol/mol), the diagnostic threshold for diabetes, to stratify participants into two HbA1c-based groups. Proteomic analysis of the oral rinse samples was performed using the Proximity Extension Assay (Olink ® ) targeting 92 cardiometabolic proteins. The ML approach employed repeated nested cross-validation with Boruta feature selection and evaluated Logistic Regression, Random Forest, and XGBoost. SHapley Additive exPlanations (SHAP) were used to quantify feature importance and interpret the contribution and directionality of individual proteins in the model predictions. Post hoc analyses evaluated the levels of identified proteins across HbA1c and T2DM categories and assessed potential confounding factors, including periodontitis, an oral inflammatory disease. Results Among the 176 participants, no discriminatory proteins were identified at the population median threshold. Using the clinical cutoff, Logistic Regression achieved the highest performance (ROC AUC: 0.78 ± 0.09; 95% CI: [0.74, 0.82]). REG1A (Regenerating Islet-Derived Protein 1 Alpha), also known as Pancreatic Stone Protein, emerged as the sole oral biomarker for elevated blood HbA1c. In the post hoc multivariable analysis, after adjusting for potential confounders, REG1A remained significantly associated with elevated HbA1c, and REG1A levels showed significant positive trends across the clinically relevant HbA1c and T2DM categories (normal, prediabetes, and diabetes). Conclusions REG1A, a protein linked to pancreatic β-cell regeneration, emerged as an oral biomarker for elevated HbA1c levels. This finding suggests that oral rinse proteomics may offer a simple, non-invasive tool for screening and monitoring elevated HbA1c levels, with potential applicability to point-of-care testing and large epidemiological studies. However, validation in larger cohorts is warranted.