Objectives: The objective of this study was to identify novel autoantibodies specific for open-angle glaucoma (OAG), including normal-tension glaucoma (NTG) and primary open-angle glaucoma (POAG), using proteome-wide autoantibody screening and to determine their utility for diagnosis. Methods: We conducted proteome-wide autoantibody screening by wet protein arrays. Autoantibody reactivity in the plasma of OAG patients (50 NTG and 69 POAG patients) was quantitatively analyzed and compared to that of controls (35 cataract patients). The area under the curve (AUC) of the receiver operating characteristic (ROC) and multivariate analyses were used to determine diagnostic potential in patients with OAG. Results: Based on differences in autoantibody titers and positivity rates, four autoantibodies against ETNK1, VMAC, NEXN, and SUN1 were selected as potential biomarkers to discriminate OAG and cataract. In discrimination between POAG and cataract, the AUCs of ETNK1 and VMAC were calculated to be 0.820 (95%CI: 0.733–0.907) and 0.889 (95%CI: 0.818–0.959), respectively. Furthermore, the combination of these four antibodies demonstrated diagnostic potential for OAG with an AUC of 0.828 (95%CI: 0.757–0.898) by multivariate analysis. Conclusions: Four new glaucoma-associated autoantibodies were identified in this study. The differences in autoantibody patterns in the plasma between glaucoma and cataract patients support their potential utility as biomarkers for glaucoma screening.

Objectives: Previously, we identified four open-angle glaucoma (OAG)-associated autoantibodies (anti-ETNK1, anti-VMAC, anti-NEXN, and anti-SUN1) using proteome-wide autoantibody screening by wet protein arrays. The objective of this exploratory study was to evaluate the diagnostic performance of these four glaucoma-associated autoantibodies using automated machine learning. Methods: Plasma samples from 119 patients with OAG and 35 patients with cataracts as controls were enrolled for the study. All machine-learning analyses were performed in Python 3.9.16 (GCC 11.2.0) using scikit-learn 1.2.2 and PyCaret 3.0.1. Variables included plasma levels of the autoantibodies, age, sex, and intra-ocular pressure (IOP). Probability calibration (Platt/sigmoid and isotonic) was assessed with reliability curves and Brier scores. Model explainability was examined with permutation importance, SHAP values, and an ablation analysis removing one autoantibody at a time. Results: The tuned random forest achieved an out-of-fold (OOF) area under the receiver-operating characteristic curve (ROC–AUC) of 0.852 (±0.040), an average precision (AP) of 0.950, and an F1 score of 0.865. Isotonic mapping improved agreement between predicted and empirical probabilities. Among these four autoantibodies, VMAC was the most important factor for the model’s prediction. Conclusions: A machine learning model using four autoantibodies from blood samples showed potential for diagnosing OAG.