Purpose: To analyze and compare the high performance liquid chromatography (HPLC) runs of tear proteins from diabetic (DIDRY) and non-diabetic (DRY) dry-eye patients, and healthy subjects (CTRL). The patterns were analyzed using multivariate statistical methods.
Methods: Tears (total 56 eyes: CTRL: n=16, DIDRY: n=21, and DRY: n=19) were analyzed by HPLC, using a size-exclusion column with an eluent of 0.5 M sodium phosphate buffer. The patients were primarily grouped according to the results of the basic secretory test (BST) in combination with subjective symptoms such as burning, foreign body sensations, tearing, and "dryness" of the eyes. Patients with BST values < or = 10 mm/5 min plus two subjective symptoms were grouped as dry-eye patients. Before statistical analysis, each HPLC run was quantitatively analyzed using ScanPacK software (ScanPacK, Göttingen, Germany), and a data set was created from each HPLC run. The data were then analyzed by multivariate analysis of discriminance.
Results: The HPLC patterns of CTRL, DIDRY and DRY were significantly different (Wilks' lambda: 0.0209; p<0.01). The area of the sigA peak was significantly smaller (p<0.05) in dry-eye tears than controls. There was a good correlation between the extent of separation in the multivariate analysis and the BST value (r = -0.71). Classification of all samples resulted in 98% correct assignments.
Conclusions: The analysis of HPLC patterns and subsequent statistical evaluation are useful for the detection of dry eyes. The HPLC method and the statistical routines described allow a shorter analysis time than electrophoresis. HPLC analysis in combination with statistical analysis can be used as a diagnostic tool for the detection of dry eyes, and also improves the quality of analysis of disease-associated tear proteins in clinical research.