gms | German Medical Science

Although an elevated intraocular pressure represents the main risk factor, it cannot explain the glaucoma disease in all patients. Previous studies could provide hints for an involvement of autoantibodies in the pathogenesis of the disease. The aim of this study was to analyze the use of autoantibody repertoires for the diagnosis of glaucoma. Furthermore, we attempted to test the glaucoma-specificity of these antibodies comparing them to antibody repertoires found in retinal diseases and to confirm some of these reactivities by Proteinchip analyses.

420 patients were divided into four groups: healthy volunteers without any ocular disorders (n=150), patients with primary open angle glaucoma (POAG, n=96), normal tension glaucoma (NTG, n=74). To test the robustness of the glaucoma detection, in an additional procedure 100 patients with other ocular disorders (e.g. retinal diseases) were included in the non-glaucoma control group (CTRL2). All groups were matched for age and gender. The sera of patients were tested against Western blots of retinal and optic nerve antigens. The autoantibody patterns were digitized and subsequently analyzed by multivariate statistical techniques and artificial neural networks. Some of the antibody reactivities were confirmed using Seldi (surface enhanced laser desorption and ionization) mass spectrometry. Therefore, bioactivated chip surfaces (PS10, Ciphergen, Fremont, USA) and Protein-A beads were used to capture the antibodies against retinal and optic nerve antigens.

All groups revealed complex autoantibody patterns against ocular antigens. After randomly dividing patients into test and training sets, the analysis by means of artificial neural networks was performed. The diagnostic power of this antibody approach for the diagnosis of glaucoma could be assessed by calculating receiver operating (ROC) curves. Including both healthy subjects and other retinal diseases as controls, the artificial neural network could reach a sensitivity of 83.5%, a specificity of 85.2%, and an area under curve (r-value, ROC-curve) of 0.85. The Seldi analysis could demonstrate significant differences (P<0.05) in the antibody reactivities between all groups according to the Western blot results.

In this study, we could demonstrate that pattern matching algorithms such as artificial neural networks could be used to detect glaucoma based on autoantibody patterns specific for this disease. Furthermore, the glaucoma specificity of these antibody profiles could be proved by comparison to antibody profiles in patients suffering from retinal diseases. The use of other technologies such as Seldi-TOF for the antibody detection could facilitate the use of the analysis of antibody profiles for mass screening of patients.