Eyelid Disorders in Ophthalmology Practice: Results from a Large International Epidemiological Study in Eleven Countries

Tear and ocular surface disease biomarkers: A diagnostic and clinical perspective for ocular allergies and dry eye disease

Validated biomarkers to be used as biological tools for managing ocular surface diseases (OSDs) are still an unmet need in daily clinical practice. Many studies have contributed to the already extensive list of candidate biomarkers for these disorders. Dry eye (DE) and ocular allergy (OA) are complex and multifactorial diseases, often coexisting and with overlapping symptoms. The purpose of this review is to present a comprehensive updated revision of the most relevant biomarkers of DE and OA, with an emphasis on quantitative analyses and correlations with clinical parameter data. Analysis of biomarkers common for these pathologies has highlighted an important physiological process. Namely, the interleukin proteins (IL-1α, IL-1β and IL-17), tumour necrotic factor (TNFα) and interferon gamma (IFNγ; Th1-Th7 pathway) and IL-4, IL-5 and IL-13 (Th2 pathway) seem to represent similar inflammatory mechanisms. Moreover, changes in the levels of mucins (MUC1, MUC2, MUC4, MUC5 and MUC16) are common alterations in the tear film mucous layer. We also examine the current state of medical devices and the main limitations to their use in clinical practice. Translational research in biomarkers for clinical practice depends on a feasible transition from the laboratory to the point-of-care. This requires large-scale, coordinated clinical validation campaigns to select the biomarkers with the highest specificity and sensitivity and significant correlation with clinical parameters. Moreover, technical limitations of multiplexed quantitation systems must be overcome to detect and measure the levels of several biomarkers in very small samples. To ensure the future of biomarker research, significant progress is necessary in a number of fields. There is an urgent need for global unification of clinical classification and diagnostics criteria. Widespread integration of proteomic and transcriptomic data is paramount for performing meaningful analyses using appropriate bioinformatics tools and artificial intelligence systems.