We aimed to identify and compare microRNAs (miRNAs) from individual aqueous humor samples between normal-tension glaucoma (NTG) patients and normal controls. Aqueous humor (80 to 120 µl) was collected before cataract surgery. Six stable NTG patients and seven age-matched controls were included in the final analysis. RNA sequencing was conducted for RNA samples extracted from the 13 aqueous humor samples, and bioinformatics analysis was employed for the miRNA targets and related pathways. Two hundred and twenty-eight discrete miRNAs were detected in the aqueous humor and consistently expressed in all samples. Eight significantly upregulated miRNAs were found in the NTG patients compared to the controls (fold-change > 2, p < 0.05). They were hsa-let-7a-5p, hsa-let-7c-5p, hsa-let-7f-5p, hsa-miR-192-5p, hsa-miR-10a-5p, hsa-miR-10b-5p, hsa-miR-375, and hsa-miR-143-3p. These miRNAs were predicted to be associated with the biological processes of apoptosis, autophagy, neurogenesis, and aging in the gene ontology categories. The related Kyoto encyclopedia of genes and genomes pathways were extracellular matrix-receptor interaction, mucin-type O-glycan biosynthesis, biotin metabolism, and signaling pathways regulating the pluripotency of stem cells. The differentially expressed miRNA in the NTG samples compared to the controls suggest the possible roles of miRNA in the pathogenesis of NTG. The underlying miRNA-associated pathways further imply novel targets for the pathogenesis of NTG.

Purpose: The purpose of this study was to present the results of our investigation into the risk of glaucoma development in patients with chronic renal disease (CRD). Methods: The present retrospective cohort study used the Korean National Health Insurance Service data, which consisted of 1,025,340 random subjects who were tracked from 2002 to 2013. Newly diagnosed glaucoma and CRD were included on the basis of the Korean Classification of Disease codes. The CRD group consisted of patients who received an initial CRD diagnosis between January 2003 and December 2007 as an index period (n = 3640). The control group (n = 17,971) was selected using 1:5 propensity-score matching using social and demographic factors, along with the year of enrollment. Each group subject was followed until 2013. We used multivariate Cox proportional hazard regression analysis to compare the risk of glaucoma development between the two groups. Results: Glaucoma consecutively developed in 4.3% in the CRD group and 2.8% in the control group (P < 0.0001). CRD increased the risk of glaucoma development (hazard ratio [HR] = 1.63, 95% confidence interval [CI] = 1.34–1.98] after adjusting for age, sex, comorbidities, residence, household income, and the year of enrollment. In multivariate Cox regression analysis, patients with comorbidity of hypertension, diabetes mellitus, or aged ≥ 50 years showed a significantly higher risk of glaucoma development (all P < 0.008). Conclusions: A significant association between CRD and following development of glaucoma was revealed after adjusting the potential confounding factors.

We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch’s membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learning model. Discriminating early-stage glaucoma and GS is challenging and a deep-learning model may be helpful to clinicians. NTG accounts for an average 77% of open-angle glaucoma in Asians. BMO-MRW is a new structural parameter that has advantages in assessing neuroretinal rim tissue more accurately than conventional parameters. A dataset consisted of 229 eyes out of 277 GS and 168 eyes of 285 patients with early NTG. A deep-learning algorithm was developed to discriminate between GS and early NTG using a training set, and its accuracy was validated in the testing dataset using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). The deep neural network model (DNN) achieved highest diagnostic performance, with an AUC of 0.966 (95%confidence interval 0.929–1.000) in classifying either GS or early NTG, while AUCs of 0.927–0.947 were obtained by other machine-learning models. The performance of the DNN model considering all three OCT-based parameters was the highest (AUC 0.966) compared to the combinations of just two parameters. As a single parameter, BMO-MRW (0.959) performed better than RNFL alone (0.914).

We aimed to obtain microRNA (miRNA) profiles of patients with pseudoexfoliation (PEX) glaucoma or normal-tension glaucoma (NTG) compared to normal controls using individual aqueous humor (AH) samples and investigate the role of miRNAs in the pathogenesis of PEX glaucoma compared to NTG in Korean. AH (80-120 µl) was collected before cataract surgery or trabeculectomy from 26 Korean subjects (eleven with PEX glaucoma, age-matched eight NTG, and seven controls). RNA sequencing was conducted for RNA samples extracted from 26 AH samples. Bioinformatics analysis was performed for targets and related pathways. A total of 334 and 291 discrete miRNAs were detected in AH samples of PEX glaucoma and NTG patients, respectively. Two significantly upregulated miRNAs (hsa-miR-30d-5p and hsa-miR-320a) and ten significantly downregulated miRNAs (hsa-miR-3156-5p, hsa-miR-4458, hsa-miR-6717-5p, hsa-miR-6728-5p, hsa-miR-6834-5p, hsa-miR-6864-5p, hsa-miR-6879-5p, hsa-miR-877-3p, hsa-miR-548e-3p, and hsa-miR-6777-5p) in PEX glaucoma patients compared to control (fold-change > 2, p < 0.05) were found. In NTG patients, ten significantly upregulated and two downregulated miRNAs compared to control were found. Only hsa-miR-6777-5p was commonly downregulated in both PEX glaucoma and NTG patients. Related pathways were proteoglycans in cancer, glioma, and TGF-beta signaling pathway in PEX glaucoma. These differentially expressed miRNAs between PEX glaucoma and NTG samples suggest the possible role of miRNA in the pathogenesis of glaucoma, further implying that pathogenic mechanisms may differ between different types of glaucoma.

We aimed to quantitatively analyze the exosome and its cargo in individual aqueous humor (AH) samples from pseudoexfoliation (PEX) glaucoma patients compared to controls using a novel detection platform. We investigated the size distribution and measured the quantitative exosome particle counts in each AH sample. AH (80–120 µL) was obtained during cataract surgery or glaucoma filtering surgery from 12 Korean subjects (six with PEX glaucoma and six age-matched controls). The mean size of the exosomes was 58.9 ± 18.5 nm measured by a tangential flow filtration system using single-particle interferometric reflectance imaging sensor. Exosome particle count in each CD 63, CD 81, and CD9 spot was significantly greater in PEX glaucoma than in controls in total, CD 63, CD9, syntenin, and scattering(all p < 0.003). The CD63 spot showed a particle count of 8319.1 ± 797.7 in PEX glaucoma patients and 4786.8 ± 1302.1 in controls (p = 1.88E−11). Individual fluorescent capture spot images also revealed denser exosome particles in PEX patients than in controls. Syntenin, indicating exosomal origin, was detected in all AH samples. Exosomes differentially detected in AH suggest the possible role of exosomes in the pathogenesis of PEX glaucoma.

The aim of this study was to predict three visual filed (VF) global indexes, mean deviation (MD), pattern standard deviation (PSD), and visual field index (VFI), from optical coherence tomography (OCT) parameters including Bruch's Membrane Opening-Minimum Rim Width (BMO-MRW) and retinal nerve fiber layer (RNFL) based on a deep-learning model. Subjects consisted of 224 eyes with Glaucoma suspects (GS), 245 eyes with early NTG, 58 eyes with moderate stage of NTG, 36 eyes with PACG, 57 eyes with PEXG, and 99 eyes with POAG. A deep neural network (DNN) algorithm was developed to predict values of VF global indexes such as MD, VFI, and PSD. To evaluate performance of the model, mean absolute error (MAE) was determined. The MAE range of the DNN model on cross validation was 1.9–2.9 (dB) for MD, 1.6–2.0 (dB) for PSD, and 5.0 to 7.0 (%) for VFI. Ranges of Pearson’s correlation coefficients were 0.76–0.85, 0.74–0.82, and 0.70–0.81 for MD, PSD, and VFI, respectively. Our deep-learning model might be useful in the management of glaucoma for diagnosis and follow-up, especially in situations when immediate VF results are not available because VF test requires time and space with a subjective nature.