Peripheral blood mononuclear cell respiratory function is associated with progressive glaucomatous vision loss.

Intraocular pressure (IOP) is currently the only modifiable risk factor for glaucoma and all licensed treatments lower IOP. However, many patients continue to lose vision despite IOP-lowering treatment. Identifying biomarkers for progressive vision loss would have considerable clinical utility. We demonstrate that lower peripheral blood mononuclear cell (PBMC) oxygen consumption rate (OCR) is strongly associated with faster visual field (VF) progression in patients treated by lowering IOP (P < 0.001, 229 eyes of 139 participants), explaining 13% of variance in the rate of progression. In a separate reference cohort of untreated patients with glaucoma (213 eyes of 213 participants), IOP explained 16% of VF progression variance. OCR is lower in patients with glaucoma (n = 168) than in controls (n = 50; P < 0.001) and is lower in patients with low baseline IOP (n = 99) than those with high baseline IOP (n = 69; P < 0.01). PBMC nicotinamide adenine dinucleotide (NAD) levels are lower in patients with glaucoma (n = 29) compared to controls (n = 25; P < 0.001) and strongly associated with OCR (P < 0.001). Our results support PBMC OCR and NAD levels as new biomarkers for progressive glaucoma.

Estimating the Distribution of True Rates of Visual Field Progression in Glaucoma.

Purpose: The purpose of this study was to estimate the distribution of the true rates of progression (RoP) of visual field (VF) loss. Methods: We analyzed the progression of mean deviation over time in series of ≥ 10 tests from 3352 eyes (one per patient) from 5 glaucoma clinics, using a novel Bayesian hierarchical Linear Mixed Model (LMM); this modeled the random-effect distribution of RoPs as the sum of 2 independent processes following, respectively, a negative exponential distribution (the "true" distribution of RoPs) and a Gaussian distribution (the "noise"), resulting in a skewed exGaussian distribution. The exGaussian-LMM was compared to a standard Gaussian-LMM using the Watanabe-Akaike Information Criterion (WAIC). The random-effect distributions were compared to the empirical cumulative distribution function (eCDF) of linear regression RoPs using a Kolmogorov-Smirnov test. Results: The WAIC indicated a better fit with the exGaussian-LMM (estimate [standard error]: 192174.4 [721.2]) than with the Gaussian-LMM (192595 [697.4], with a difference of 157.2 [22.6]). There was a significant difference between the eCDF and the Gaussian-LMM distribution (P < 0.0001), but not with the exGaussian-LMM distribution (P = 0.108). The estimated mean (95% credible intervals, CIs) "true" RoP (-0.377, 95% CI = -0.396 to -0.359 dB/year) was more negative than the observed mean RoP (-0.283, 95% CI = -0.299 to -0.268 dB/year), indicating a bias likely due to learning in standard LMMs. Conclusions: The distribution of "true" RoPs can be estimated with an exGaussian-LMM, improving model accuracy. Translational Relevance: We used these results to develop a fast and accurate analytical approximation for sample-size calculations in clinical trials using standard LMMs, which was integrated in a freely available web application.

Relationship between Intraocular Pressure Fluctuation and Visual Field Progression Rates in the United Kingdom Glaucoma Treatment Study.

PURPOSE: To investigate whether intraocular pressure (IOP) fluctuation is associated independently with the rate of visual field (VF) progression in the United Kingdom Glaucoma Treatment Study. DESIGN: Randomized, double-masked, placebo-controlled multicenter trial. PARTICIPANTS: Participants with ≥5 VFs (213 placebo, 217 treatment). METHODS: Associations between IOP metrics and VF progression rates (mean deviation [MD] and five fastest locations) were assessed with linear mixed models. Fluctuation variables were mean Pascal ocular pulse amplitude (OPA), standard deviation (SD) of diurnal Goldmann IOP (diurnal fluctuation), and SD of Goldmann IOP at all visits (long-term fluctuation). Fluctuation values were normalized for mean IOP to make them independent from the mean IOP. Correlated nonfluctuation IOP metrics (baseline, peak, mean, supine, and peak phasing IOP) were combined with principal component analysis, and principal component 1 (PC1) was included as a covariate. Interactions between covariates and time from baseline modeled the effect of the variables on VF rates. Analyses were conducted separately in the two treatment arms. MAIN OUTCOME MEASURES: Associations between IOP fluctuation metrics and rates of MD and the five fastest test locations. RESULTS: In the placebo arm, only PC1 was associated significantly with the MD rate (estimate, -0.19 dB/year [standard error (SE), 0.04 dB/year]; P < 0.001), whereas normalized IOP fluctuation metrics were not. No variable was associated significantly with MD rates in the treatment arm. For the fastest five locations in the placebo group, PC1 (estimate, -0.58 dB/year [SE, 0.16 dB/year]; P < 0.001), central corneal thickness (estimate, 0.26 dB/year [SE, 0.10 dB/year] for 10 µm thicker; P = 0.01) and normalized OPA (estimate, -3.50 dB/year [SE, 1.04 dB/year]; P = 0.001) were associated with rates of progression; normalized diurnal and long-term IOP fluctuations were not. In the treatment group, only PC1 (estimate, -0.27 dB/year [SE, 0.12 dB/year]; P = 0.028) was associated with the rates of progression. CONCLUSIONS: No evidence supports that either diurnal or long-term IOP fluctuation, as measured in clinical practice, are independent factors for glaucoma progression; other aspects of IOP, including mean IOP and peak IOP, may be more informative. Ocular pulse amplitude may be an independent factor for faster glaucoma progression. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Spatial Summation in the Glaucomatous Macula: A Link With Retinal Ganglion Cell Damage.

Purpose: The purpose of this study was to test whether functional loss in the glaucomatous macula is characterized by an enlargement of Ricco's area (RA) through the application of a computational model linking retinal ganglion cell (RGC) damage to perimetric sensitivity. Methods: One eye from each of 29 visually healthy subjects <40 years old, 30 patients with glaucoma, and 20 age-similar controls was tested with a 10-2 grid with stimuli of 5 different area sizes. Structural estimates of point-wise RGC density were obtained from optical coherence tomography (OCT) scans. Structural and functional data from the young healthy cohort were used to estimate the parameters of a computational spatial summation model to generate a template. The template was fitted with a Bayesian hierarchical model to estimate the latent RGC density in patients with glaucoma and age-matched controls. We tested two alternative hypotheses: fitting the data by translating the template horizontally (H1: change in RA) or vertically (H2: loss of sensitivity without a change in RA). Root mean squared error (RMSE) of the model fits to perimetric sensitivity were compared. Ninety-five percent confidence intervals were bootstrapped. The dynamic range of the functional and structural RGC density estimates was denoted by their 1st and 99th percentiles. Results: The RMSE was 2.09 (95% CI = 1.92-2.26) under H1 and 2.49 (95% CI = 2.24-2.72) under H2 (P < 0.001). The average dynamic range for the structural RGC density estimates was only 11% that of the functional estimates. Conclusions: Macular sensitivity loss in glaucoma is better described by a model in which RA changes with RGC loss. Structural measurements have limited dynamic range.

Investigating the Spatiotemporal Summation of Perimetric Stimuli in Dry Age-Related Macular Degeneration.

Purpose: To measure achromatic spatial, temporal, and spatiotemporal summation in dry age-related macular degeneration (AMD) compared to healthy controls under conditions of photopic gaze-contingent perimetry. Methods: Twenty participants with dry AMD (mean age, 74.6 years) and 20 healthy controls (mean age, 67.8 years) performed custom, gaze-contingent perimetry tests. An area-modulation test generated localized estimates of Ricco's area (RA) at 2.5° and 5° eccentricities along the 0°, 90°, 180°, and 270° meridians. Contrast thresholds were measured at the same test locations for stimuli of six durations (3.7-190.4 ms) with a Goldmann III stimulus (GIII, 0.43°) and RA-scaled stimuli. The upper limit (critical duration) of complete temporal summation (using the GIII stimulus) and spatiotemporal summation (using the RA stimuli) was estimated using iterative two-phase regression analysis. Results: Median (interquartile range [IQR]) RA estimates were significantly larger in AMD participants (2.5°: 0.21 [0.09-0.41] deg2; 5°: 0.32 [0.15-0.65 deg2]) compared to healthy controls (2.5°: 0.08 [0.05-0.13] deg2; 5°: 0.15 [0.08-0.22] deg2) at all test locations (all P < 0.05). No significant difference in median critical duration was found in AMD participants with the GIII stimulus (19.6 [9.9-30.4] ms) and RA-scaled stimuli (22.9 [13.9-40.3] ms) compared to healthy controls (GIII: 17.0 [11.3-24.0] ms; RA-scaled: 22.4 [14.3-33.1] ms) at all test locations (all P > 0.05). Conclusions: Spatial summation is altered in dry AMD, without commensurate changes in temporal summation. Translational Relevance: The sensitivity of perimetry to AMD may be improved by utilizing stimuli that probe alterations in spatial summation in the disease.

Validating Trend-Based End Points for Neuroprotection Trials in Glaucoma.

Purpose: The purpose of this study was to evaluate the power of trend-based visual field (VF) progression end points against long-term development of event-based end points accepted by the US Food and Drug Administration (FDA). Methods: One eye from 3352 patients with ≥10 24-2 VFs (median = 11 years) follow-up were analyzed. Two FDA-compatible criteria were applied to these series to label "true-progressed" eyes: ≥5 locations changing from baseline by more than 7 dB (FDA-7) or by more than the expected test-retest variability (GPA-like) in 2 consecutive tests. Observed rates of progression (RoP) were used to simulate trial-like series (2 years) randomly assigned (1000 times) to a "placebo" or a "treatment" arm. We simulated neuroprotective "treatment" effects by changing the proportion of "true progressed" eyes in the two arms. Two trend-based methods for mean deviation (MD) were assessed: (1) linear mixed model (LMM), testing average difference in RoP between the two arms, and (2) time-to-progression (TTP), calculated by linear regression as time needed for MD to decline by predefined cutoffs from baseline. Power curves with 95% confidence intervals were calculated for trend and event-based methods on the simulated series. Results: The FDA-7 and GPA-like progression was achieved by 45% and 55% of the eyes in the clinical database. LMM and TTP had similar power, significantly superior to the event-based methods, none of which reached 80% power. All methods had a 5% false-positive rate. Conclusions: The trend-based methods can efficiently detect treatment effects defined by long-term FDA-compatible progression. Translational Relevance: The assessment of the power of trend-based methods to detect clinically relevant progression end points.

Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning.

Purpose: To assess the performance of a perimetric strategy using structure-function predictions from a deep learning (DL) model. Methods: Visual field test-retest data from 146 eyes (75 patients) with glaucoma with (median [5th-95th percentile]) 10 [7, 10] tests per eye were used. Structure-function predictions were generated with a previously described DL model using cicumpapillary optical coherence tomography (OCT) scans. Structurally informed prior distributions were built grouping the observed measured sensitivities for each predicted value and recalculated for each subject with a leave-one-out approach. A zippy estimation by sequential testing (ZEST) strategy was used for the simulations (1000 per eye). Ground-truth sensitivities for each eye were the medians of the test-retest values. Two variations of ZEST were compared in terms of speed (average total number of presentations [NP] per eye) and accuracy (average mean absolute error [MAE] per eye), using either a combination of normal and abnormal thresholds (ZEST) or the calculated structural distributions (S-ZEST) as prior information. Two additional versions of these strategies employing spatial correlations were tested. Results: S-ZEST was significantly faster, with a mean average NP of 213.87 (SD = 28.18), than ZEST, with a mean average NP of 255.65 (SD = 50.27) (P < 0.001). The average MAE was smaller for S-ZEST (1.98; SD = 2.37) than ZEST (2.43; SD = 2.69) (P < 0.001). Spatial correlations further improved both strategies (P < 0.001), but the differences between ZEST and S-ZEST remained significant (P < 0.001). Conclusions: DL structure-function predictions can significantly improve perimetric tests. Translational Relevance: DL structure-function predictions from clinically available OCT scans can improve perimetry in glaucoma patients.

The Role of Mitophagy in Glaucomatous Neurodegeneration.

This review aims to provide a better understanding of the emerging role of mitophagy in glaucomatous neurodegeneration, which is the primary cause of irreversible blindness worldwide. Increasing evidence from genetic and other experimental studies suggests that mitophagy-related genes are implicated in the pathogenesis of glaucoma in various populations. The association between polymorphisms in these genes and increased risk of glaucoma is presented. Reduction in intraocular pressure (IOP) is currently the only modifiable risk factor for glaucoma, while clinical trials highlight the inadequacy of IOP-lowering therapeutic approaches to prevent sight loss in many glaucoma patients. Mitochondrial dysfunction is thought to increase the susceptibility of retinal ganglion cells (RGCs) to other risk factors and is implicated in glaucomatous degeneration. Mitophagy holds a vital role in mitochondrial quality control processes, and the current review explores the mitophagy-related pathways which may be linked to glaucoma and their therapeutic potential.

Spatiotemporal summation of perimetric stimuli in healthy observers.

Spatial summation of perimetric stimuli has been used to derive conclusions about the spatial extent of retinal-cortical convergence, mostly from the size of the critical area of summation (Ricco's area, RA) and critical number of retinal ganglion cells (RGCs). However, spatial summation is known to change dynamically with stimulus duration. Conversely, temporal summation and critical duration also vary with stimulus size. Such an important and often neglected spatiotemporal interaction has important implications for modeling perimetric sensitivity in healthy observers and for formulating hypotheses for changes measured in disease. In this work, we performed experiments on visually heathy observers confirming the interaction of stimulus size and duration in determining summation responses in photopic conditions. We then propose a simplified computational model that captures these aspects of perimetric sensitivity by modelling the total retinal input, the combined effect of stimulus size, duration, and retinal cones-to-RGC ratio. We additionally show that, in the macula, the enlargement of RA with eccentricity might not correspond to a constant critical number of RGCs, as often reported, but to a constant critical total retinal input. We finally compare our results with previous literature and show possible implications for modeling disease, especially glaucoma.

Investigating the linkage between mesopic spatial summation and variations in retinal ganglion cell density across the central visual field.

PURPOSE: The relationship between perimetric stimulus area and Ricco's area (RA) determines measured thresholds and the sensitivity of perimetry to retinal disease. The nature of this relationship, in addition to effect of retinal ganglion cell (RGC) number on this, is currently unknown for the adaptation conditions of mesopic microperimetry. In this study, achromatic mesopic spatial summation was measured across the central visual field to estimate RA with the number of RGCs underlying RA also being established. METHODS: Achromatic luminance thresholds were measured for six incremental spot stimuli (0.009-2.07 deg2 ) and 190.4 ms duration, at four locations, each at 2.5°, 5° and 10° eccentricity in five healthy observers (mean age 61.4 years) under mesopic conditions (background 1.58 cd/m2 ). RA was estimated using two-phase regression analysis with the number of RGCs underlying RA being calculated using normative histological RGC counts. RESULTS: Ricco's area exhibited a small but statistically insignificant increase between 2.5° and 10° eccentricity. Compared with photopic conditions, RA was larger, with the difference between RA and the Goldmann III stimulus (0.43°) being minimised. RGC number underlying RA was also higher than reported for photopic conditions (median 70 cells, IQR 36-93), with no significant difference being observed across test locations. CONCLUSIONS: Ricco's area and the number of RGCs underlying RA do not vary significantly across the central visual field in mesopic conditions. However, RA is larger and more similar to the standard perimetric Goldmann III stimulus under mesopic compared with photopic adaptation conditions. Further work is required to determine if compensatory enlargements in RA occur in age-related macular degeneration, to establish the optimal stimulus parameters for AMD-specific microperimetry.

Ageing changes in retinal outer nuclear layer thickness and cone photoreceptor density using adaptive optics-free imaging.

PURPOSE: To investigate age-related changes of the outer nuclear layer (ONL) thickness and cone density, and their associations in healthy participants using a modified, narrow scan-angle Heidelberg Retina Angiograph (HRA2). METHODS: Retinal cones were imaged outside the fovea at 8.8° eccentricity and cone density was compared to ONL thickness measurements obtained by Spectral-Domain Optical Coherence Tomography (SD-OCT) at the same locations. Fifty-six eyes of 56 healthy participants with a median age (interquartile range, IQR) of 37 years (29-55) were included. RESULTS: Median (IQR) cone count was 7,472 (7,188, 7,746) cones/mm2 and median (IQR) ONL thickness was 56 (52, 60) µm for healthy participants. Both cone density and ONL thickness were negatively associated with age: cone density, R2 = 0.16 (F(1,54) = 10.41, P = 0.002); ONL thickness, R2 = 0.12 (F(1,54) = 7.41, P = 0.009). No significant association was seen between cone density and ONL thickness (R2 = 0.03; F(1,54) = 1.66, P = 0.20). CONCLUSION: Cone density was lower, and ONL thinner, in older compared to younger participants, therefore, image-based structural measures should be compared to age-related data. However, cone density and ONL thickness were not strongly associated, indicating that determinants of ONL thickness measurements other than cone density measurements, and including measurement error, have a major influence.

Two-Year Visual Field Outcomes of the Treatment of Advanced Glaucoma Study (TAGS).

PURPOSE: to compare visual field (VF) progression between the 2 arms of the Treatment of Advanced Glaucoma Study (TAGS). DESIGN: Post hoc analysis of VF data from a 2-arm, multicenter, randomized controlled clinical trial. METHODS: A total of 453 patients with newly diagnosed advanced open-angle glaucoma in at least 1 eye from 27 centers in the United Kingdom were randomized to either trabeculectomy (n = 227) or medication in their index eye (n = 226) and followed-up for 2 years with 2 24-2 VF tests at baseline, 4, 12, and 24 months. Data were analyzed for participants with a reliable VF (false positive rate < 15%) at baseline and at least 2 other time points. Average difference in rate of progression (RoP) was analyzed using a hierarchical Bayesian model. Time for each eye to progress from baseline beyond specific cut-offs (0.5, 1, 1.5, and 2 dB) was compared using survival analysis. RESULTS: This study analyzed 211 eyes in the trabeculectomy first arm and 203 eyes in the medication first arm. The average RoP (estimate [95% credible intervals]) was -0.59 [-0.88, -0.31] dB/year in the medication first arm and -0.40 [-0.67, -0.13] dB/year in the trabeculectomy first arm. The difference was not significant (Bayesian P-value = .353). More eyes progressed in the medication first arm, but this difference was not significant. CONCLUSIONS: There was no significant difference in the average RoP at 2 years.

Detecting glaucoma from multi-modal data using probabilistic deep learning.

Objective: To assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields. Design: Algorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study. Subjects and participants: Fundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models. Main outcome measures: Accuracy and area under the receiver-operating characteristic curve (AUC). Methods: Fundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model. Results: The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89-0.92), 0.89 (0.88-0.91), and 0.94 (0.92-0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92-0.95), 0.98 (0.98-0.99), and 0.98 (0.98-0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively. Conclusion and relevance: Probabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making.

Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer.

The clinical diagnostic evaluation of optic neuropathies relies on the analysis of the thickness of the retinal nerve fibre layer (RNFL) by optical coherence tomography (OCT). However, false positives and false negatives in the detection of RNFL abnormalities are common. Here we show that an algorithm integrating measurements of RNFL thickness and reflectance from standard wide-field OCT scans can be used to uncover the trajectories and optical texture of individual axonal fibre bundles in the retina and to discern distinctive patterns of loss of axonal fibre bundles in glaucoma, compressive optic neuropathy, optic neuritis and non-arteritic anterior ischaemic optic neuropathy. Such optical texture analysis can detect focal RNFL defects in early optic neuropathy, as well as residual axonal fibre bundles in end-stage optic neuropathy that were indiscernible by conventional OCT analysis and by red-free RNFL photography. In a diagnostic-performance study, optical texture analysis of the RNFL outperformed conventional OCT in the detection of glaucoma, as defined by visual-field testing or red-free photography. Our findings show that optical texture analysis of the RNFL for the detection of optic neuropathies is highly sensitive and specific.

Predicting Visual Fields From Optical Coherence Tomography via an Ensemble of Deep Representation Learners.

PURPOSE: To develop and validate a deep learning method of predicting visual function from spectral domain optical coherence tomography (SD-OCT)-derived retinal nerve fiber layer thickness (RNFLT) measurements and corresponding SD-OCT images. DESIGN: Development and evaluation of diagnostic technology. METHODS: Two deep learning ensemble models to predict pointwise VF sensitivity from SD-OCT images (model 1: RNFLT profile only; model 2: RNFLT profile plus SD-OCT image) and 2 reference models were developed. All models were tested in an independent test-retest data set comprising 2181 SD-OCT/VF pairs; the median of ∼10 VFs per eye was taken as the best available estimate (BAE) of the true VF. The performance of single VFs predicting the BAE VF was also evaluated. The training data set comprised 954 eyes of 220 healthy and 332 glaucomatous participants, and the test data set, 144 eyes of 72 glaucomatous participants. The main outcome measures included the pointwise prediction mean error (ME), mean absolute error (MAE), and correlation of predictions with the BAE VF sensitivity. RESULTS: The median mean deviation was -4.17 dB (-14.22 to 0.88). Model 2 had excellent accuracy (ME 0.5 dB, SD 0.8) and overall performance (MAE 2.3 dB, SD 3.1), and significantly (paired t test) outperformed the other methods. For single VFs predicting the BAE VF, the pointwise MAE was 1.5 dB (SD 0.7). The association between SD-OCT and single VF predictions of the BAE pointwise VF sensitivities was R2 = 0.78 and R2 = 0.88, respectively. CONCLUSIONS: Our method outperformed standard statistical and deep learning approaches. Predictions of BAEs from OCT images approached the accuracy of single real VF estimates of the BAE.

Genome-Wide Association Study Identifies Two Common Loci Associated with Pigment Dispersion Syndrome/Pigmentary Glaucoma and Implicates Myopia in its Development.

PURPOSE: To identify genetic variants associated with pigment dispersion syndrome (PDS) and pigmentary glaucoma (PG) in unrelated patients and to further understand the genetic and potentially causal relationships between PDS and associated risk factors. DESIGN: A 2-stage genome-wide association meta-analysis with replication and subsequent in silico analyses including Mendelian randomization. PARTICIPANTS: A total of 574 cases with PG or PDS and 52 627 controls of European descent. METHODS: Genome-wide association analyses were performed in 4 cohorts and meta-analyzed in 3 stages: (1) a discovery meta-analysis was performed in 3 cohorts, (2) replication was performed in the fourth cohort, and (3) all 4 cohorts were meta-analyzed to increase statistical power. Two-sample Mendelian randomization was used to determine whether refractive error and intraocular pressure exert causal effects over PDS. MAIN OUTCOME MEASURES: The association of genetic variants with PDS and whether myopia exerts causal effects over PDS. RESULTS: Significant association was present at 2 novel loci for PDS/PG. These loci and follow-up analyses implicate the genes gamma secretase activator protein (GSAP) (lead single nucleotide polymorphism [SNP]: rs9641220, P = 6.0×10-10) and glutamate metabotropic receptor 5 (GRM5)/TYR (lead SNP: rs661177, P = 3.9×10-9) as important factors in disease risk. Mendelian randomization showed significant evidence that negative refractive error (myopia) exerts a direct causal effect over PDS (P = 8.86×10-7). CONCLUSIONS: Common SNPs relating to the GSAP and GRM5/TYR genes are associated risk factors for the development of PDS and PG. Although myopia is a known risk factor, this study uses genetic data to demonstrate that myopia is, in part, a cause of PDS and PG.

Hierarchical Censored Bayesian Analysis of Visual Field Progression.

Purpose: To develop a Bayesian model (BM) for visual field (VF) progression accounting for the hierarchical, censored and heteroskedastic nature of the data. Methods: Three versions of a hierarchical BM were developed: a simple linear (Hi-linear); censored at 0 dB (Hi-censored); heteroskedastic censored (Hi-HSK). For the latter, we modeled the test variability according to VF sensitivity using a large test-retest cohort (1396 VFs, 146 eyes with glaucoma). We analyzed a large cohort of 44,371 VF tests from 3352 eyes from five glaucoma clinics. We quantified the bias in the estimated rate-of-progression, the detection of progression (Hit-rate [HR]), the median time-to-progression and the prediction error of future observations (mean absolute error [MAE]). HR and time-to-progression were compared at matched false-positive-rate (FPR), quantified using permutations of a separate test-retest cohort (360 tests, 30 eyes with glaucoma). BMs were compared to simple linear regression and Permutation-Analyses-of Pointwise-Linear-Regression. Differences in time-to-progression were tested using survival analysis. Results: Censored models showed the smallest bias in the rate-of-progression. The three BMs performed very similarly in terms of HR and time-to-progression and always better than the other methods. The average reduction in time-to-progression was 37% with the BMs (P < 0.001) at 5% FPR. MAE for prediction was very similar among methods. Conclusions: Bayesian hierarchical models improved the detection of VF progression. Accounting for censoring improves the precision of the estimates, but minimal effect is provided by accounting for heteroskedasticity. Translational Relevance: These results are relevant for quantification of VF progression in practice and for clinical trials.

Neuroprotection in Glaucoma: NAD+/NADH Redox State as a Potential Biomarker and Therapeutic Target.

Glaucoma is the leading cause of irreversible blindness worldwide. Its prevalence and incidence increase exponentially with age and the level of intraocular pressure (IOP). IOP reduction is currently the only therapeutic modality shown to slow glaucoma progression. However, patients still lose vision despite best treatment, suggesting that other factors confer susceptibility. Several studies indicate that mitochondrial function may underlie both susceptibility and resistance to developing glaucoma. Mitochondria meet high energy demand, in the form of ATP, that is required for the maintenance of optimum retinal ganglion cell (RGC) function. Reduced nicotinamide adenine dinucleotide (NAD+) levels have been closely correlated to mitochondrial dysfunction and have been implicated in several neurodegenerative diseases including glaucoma. NAD+ is at the centre of various metabolic reactions culminating in ATP production-essential for RGC function. In this review we present various pathways that influence the NAD+(H) redox state, affecting mitochondrial function and making RGCs susceptible to degeneration. Such disruptions of the NAD+(H) redox state are generalised and not solely induced in RGCs because of high IOP. This places the NAD+(H) redox state as a potential systemic biomarker for glaucoma susceptibility and progression; a hypothesis which may be tested in clinical trials and then translated to clinical practice.

Do Additional Testing Locations Improve the Detection of Macular Perimetric Defects in Glaucoma?

PURPOSE: To evaluate the ability of additional central testing locations to improve detection of macular visual field (VF) defects in glaucoma. DESIGN: Prospective cross-sectional study. PARTICIPANTS: Four hundred forty healthy people and 499 patients with glaucomatous optic neuropathy (GON) were tested with a fundus tracked perimeter (CMP; CenterVue) using a 24-2 grid with 12 additional macular locations (24-2+). METHODS: Glaucomatous optic neuropathy was identified based on expert evaluation of optic nerve head photographs and OCT scans, independently of the VF. We defined macular defects as locations with measurements outside the 5% and 2% normative limits on total deviation (TD) and pattern deviation (PD) maps within the VF central 10°. Classification was based on the total number of affected macular locations (overall detection) or the largest number of affected macular locations connected in a contiguous cluster (cluster detection). Criteria based on the number of locations and cluster size were used to obtain equivalent specificity between the 24-2 grid and the 24-2+ grids, calculated using false detections in the healthy cohort. Partial areas under the receiver operating characteristic curve (pAUCs) were also compared at specificities of 95% or more. MAIN OUTCOME MEASURES: Matched specificity comparison of the ability to detect glaucomatous macular defects between the 24-2 and 24-2+ grids. RESULTS: At matched specificity, cluster detection identified more macular defects with the 24-2+ grid compared with the 24-2 grid. For example, the mean increase in percentage of detection was 8% (95% confidence interval [CI], 5%-11%) and 10% (95% CI, 7%-13%) for 5% TD and PD maps, respectively, and 5% (95% CI, 2%-7%) and 6% (95% CI, 4%-8%) for the 2% TD and PD maps, respectively. Good agreement was found between the 2 grids. The improvement measured by pAUCs was also significant but generally small. The percentage of eyes with macular defects ranged from about 30% to 50%. Test time for the 24-2+ grid was longer (21% increase) for both cohorts. Between 74% and 98% of defects missed by the 24-2 grid had at least 1 location with sensitivity of < 20 dB. CONCLUSIONS: Visual field examinations with additional macular locations can improve the detection of macular defects in GON modestly without loss of specificity when appropriate criteria are selected.