Tear Proteomic Signatures Reveal And Predict Keratoconus Progression In A Case-Control Cohort

The primary aim of this study was to comprehensively characterize the tear-fluid proteomic landscape and to delineate specific biomarkers capable of distinguishing between keratoconus (KC) risk profile for progression, stable KC, and healthy corneal tissue (CT), thereby establishing molecular indicators associated with the risk and dynamics of disease progression and to integrate molecular findings with corneal tomographic metrics in order to identify biological patterns associated with structural instability.

Currently, serial corneal topography serves as the primary method for determination of progression, requiring detailed trend analysis of the collected morphometric data. In this context, the identification of tear fluid biomarker capable of distinguishing between stable and progressive keratoconus, when compared with normal corneas, may enable earlier diagnosis, guide therapeutic decision-making, and represent potential new avenues for future treatment strategies.

A cross-sectional case–control pilot study was conducted including 58 participants. Subjects were classified into three groups: KC with tomographic risk profile for progression (n19), clinically stable KC (n19), and healthy controls (n20). Risk stratification was based on predefined Scheimpflug tomography (Pentacam®) Belin/Ambrósio deviation (BAD-D) increase ≥ 1.5 across the three most recent examinations. Tear samples were collected (Schirmer strips) and analyzed by micro–liquid chromatography coupled with tandem mass spectrometry. Differential protein abundance was assessed with moderated t tests controlled by a false discovery rate <0.05; fold-change thresholds of 0.5 and 1.5 denoted relevant down- or up-regulation.

ARTmax was the main tomographic parameter significantly different between risk-profile and stable KC after correction. Proteomic analysis identified 2 490 tear proteins, of which 32 were differentially expressed between groups. Compared with stable KC, the risk profile group demonstrated coordinated downregulation of epithelial structural keratins. Stable KC demonstrated increased abundance of structural keratins. Both KC groups showed up regulation of oxidative stress–related enzymes, including GSTT1 and ADH1C relative to controls.  GSTT was expressed at approximately twice the level in stable patients compared with patients at risk of progression. GSTT1 expression correlated negatively with ARTmax.

Keratoconus with tomographic risk profile is characterized by loss of relational structural integrity, captured by ARTmax, and by distinct tear proteomic patterns reflecting oxidative stress activation and epithelial structural remodeling. Enhanced GSTT1 expression in stable KC may reflect a comparatively stronger redox-buffering mechanism, potentially contributing to preservation of epithelial structural organization despite underlying ectatic architecture. Integration of tomographic indices with tear proteomics may improve early risk stratification and inform personalized management strategies in keratoconus.