Alterations in Tear Proteomes of Adults with Pre-Diabetes and Type 2 Diabetes Mellitus but Without Diabetic Retinopathy.

Background: Type 2 diabetes mellitus (T2DM) is an epidemic chronic disease that affects millions of people worldwide. This study aims to explore the impact of T2DM on the tear proteome, specifically investigating whether alterations occur before the development of diabetic retinopathy.

Methods: Flush tear samples were collected from healthy subjects and subjects with preDM and T2DM. Tear proteins were processed and analyzed by mass spectrometry-based shotgun proteomics using a data-independent acquisition parallel acquisition serial fragmentation (diaPASEF) approach. Machine learning algorithms, including random forest, lasso regression, and support vector machine, and statistical tools were used to identify potential biomarkers.

Results: Machine learning models identified 17 proteins with high importance in classification. Among these, five proteins (cystatin-S, S100-A11, submaxillary gland androgen-regulated protein 3B, immunoglobulin lambda variable 3-25, and lambda constant 3) exhibited differential abundance across these three groups. No correlations were identified between proteins and clinical assessments of the ocular surface. Notably, the 17 important proteins showed superior prediction accuracy in distinguishing all three groups (healthy, preDM, and T2DM) compared to the five proteins that were statistically significant.

Conclusions: Alterations in the tear proteome profile were observed in adults with preDM and T2DM before the clinical diagnosis of ocular abnormality, including retinopathy.