This study provides population-based data on the prevalence of and risk factors for age-related eye diseases in people of Malay ethnicity in Singapore. Data from this study allow further understanding of the etiology and impact of eye diseases in this ethnic group.

The proteins found in tears have an important role in the maintenance of the ocular surface and changes in the quality and quantity of tear components reflect changes in the health of the ocular surface. In this study, we have used quantitative proteomics, iTRAQ technology coupled with 2D-nanoLC-nano-ESI-MS/MS and with a statistical model to uncover proteins that are significantly and reliably changed in the tears of dry eye patients in an effort to reveal potential biomarker candidates. Fifty-six patients with dry eye and 40 healthy subjects were recruited for this study. In total, 93 tear proteins were identified with a ProtScore >or=2 (>or=99% confidence). Associated with dry eye were 6 up-regulated proteins, alpha-enolase, alpha-1-acid glycoprotein 1, S100 A8 (calgranulin A), S100 A9 (calgranulin B), S100 A4 and S100 A11 (calgizzarin) and 4 down-regulated proteins, prolactin-inducible protein (PIP), lipocalin-1, lactoferrin and lysozyme. Receiver operating curves (ROC) were evaluated for individual biomarker candidates and a biomarker panel. With the use of a 4-protein biomarker panel, the diagnostic accuracy for dry eye was 96% (sensitivity, 91.0%; specificity, 90.0%). Two biomarker candidates (alpha-enolase and S100 A4) generated from iTRAQ experiments were successfully verified using an ELISA assay. The levels of these 10 tear proteins reflect aqueous secretion deficiency by lacrimal gland, inflammatory status of the ocular surface. The clinical classification of the severity of the dry eye condition was successfully correlated to the proteomics by using three proteins that are associated with inflammation, alpha1-acid glycoprotein 1, S100 A8 and S100 A9. The nine tear protein biomarker candidates (except alpha1-acid glycoprotein 1) were also verified using an independent age-matched patient sample set. This study demonstrated that iTRAQ technology combined with 2D-nanoLC-nanoESI-MS/MS quantitative proteomics is a powerful tool for biomarker discovery.