[Point-of-care biomarkers of ocular surface disease: Current approaches and future perspectives]

Dry eye disease is a common multifactorial condition that significantly impairs patients' quality of life. In clinical practice, conventional diagnostic tools such as the Schirmer test, tear break-up time (TBUT), and ocular surface staining show poor correlation between clinical signs and patient-reported symptoms. In this context, point-of-care (POC) biomarkers represent a major advance by enabling objective, reproducible, rapid assessment of ocular surface abnormalities during routine visits. Among these biomarkers, lactoferrin reflects aqueous tear deficiency and can be measured in the clinic using the TearScan Lactoferrin Test® (Advanced Tear Diagnostics [ATD], Birmingham, Alabama, USA) or the Lactoplate® test (Department of Ophthalmology, University of Nijmegen, Nijmegen, The Netherlands). Matrix metalloproteinase-9 (MMP-9) is a pro-inflammatory enzyme overexpressed in moderate to severe dry eye disease and can be detected using the InflammaDry® POC test. Another clinically available test is tear osmolarity measurement, which reflects tear film homeostasis; assessment using the TearLab® system (TearLab Corporation, San Diego, California, USA) or the ScoutPro® Osmolarity System (Trukera Medical, Inc., Southlake, Texas, USA) is valuable for both diagnosis and therapeutic monitoring. Additional biomarkers currently being investigated in research settings include Human Leukocyte Antigen - DR isotype (HLA-DR), Intercellular Adhesion Molecule 1 (ICAM-1), inflammatory cytokines, lipocalin-1, and soluble mucin encoded by the Mucin 5 subtype AC (MUC5AC) gene. Although these markers provide important diagnostic and prognostic insights, their measurement relies on complex laboratory techniques, limiting routine clinical use. Ongoing technological innovations aim to miniaturize diagnostic devices, develop multiplex assays capable of simultaneously analyzing multiple biomarkers, and integrate artificial intelligence to enhance data interpretation. Collectively, these advances contribute to improved phenotyping of dry eye disease and support more personalized management strategies in clinical practice.