Glucose tolerance and insulin resistance/sensitivity associate with retinal layer characteristics: the LIFE-Adult-Study.

AIMS/HYPOTHESIS: As the prevalence of insulin resistance and glucose intolerance is increasing throughout the world, diabetes-induced eye diseases are a global health burden. We aim to identify distinct optical bands which are closely related to insulin and glucose metabolism, using non-invasive, high-resolution spectral domain optical coherence tomography (SD-OCT) in a large, population-based dataset. METHODS: The LIFE-Adult-Study randomly selected 10,000 participants from the population registry of Leipzig, Germany. Cross-sectional, standardised phenotyping included the assessment of various metabolic risk markers and ocular imaging, such as SD-OCT-derived thicknesses of ten optical bands of the retina. Global and Early Treatment Diabetic Retinopathy Study (ETDRS) subfield-specific optical retinal layer thicknesses were investigated in 7384 healthy eyes of 7384 participants from the LIFE-Adult-Study stratified by normal glucose tolerance, prediabetes (impaired fasting glucose and/or impaired glucose tolerance and/or HbA1c 5.7-6.4% [39-47 mmol/mol]) and diabetes. The association of optical retinal band characteristics with different indices of glucose tolerance (e.g. fasting glucose, area under the glucose curve), insulin resistance (e.g. HOMA2-IR, triglyceride glucose index), or insulin sensitivity (e.g. estimated glucose disposal rate [eGDR], Stumvoll metabolic clearance rate) was determined using multivariable linear regression analyses for the individual markers adjusted for age, sex and refraction. Various sensitivity analyses were performed to validate the observed findings. RESULTS: In the study cohort, nine out of ten optical bands of the retina showed significant sex- and glucose tolerance-dependent differences in band thicknesses. Multivariable linear regression analyses revealed a significant, independent, and inverse association between markers of glucose intolerance and insulin resistance (e.g. HOMA2-IR) with the thickness of the optical bands representing the anatomical retinal outer nuclear layer (ONL, standardised ß=-0.096; p<0.001 for HOMA2-IR) and myoid zone (MZ; ß=-0.096; p<0.001 for HOMA2-IR) of the photoreceptors. Conversely, markers of insulin sensitivity (e.g. eGDR) positively and independently associated with ONL (ß=0.090; p<0.001 for eGDR) and MZ (ß=0.133; p<0.001 for eGDR) band thicknesses. These global associations were confirmed in ETDRS subfield-specific analyses. Sensitivity analyses further validated our findings when physical activity, neuroanatomical cell/tissue types and ETDRS subfield categories were investigated after stratifying the cohort by glucose homeostasis. CONCLUSIONS/INTERPRETATION: An impaired glucose homeostasis associates with a thinning of the optical bands of retinal ONL and photoreceptor MZ. Changes in ONL and MZ thicknesses might predict early metabolic retinal alterations in diabetes.

Normative Percentiles of Retinal Nerve Fiber Layer Thickness and Glaucomatous Visual Field Loss.

Purpose: Circumpapillary retinal nerve fiber layer thickness (RNFLT) measurement aids in the clinical diagnosis of glaucoma. Spectral domain optical coherence tomography (SD-OCT) machines measure RNFLT and provide normative color-coded plots. In this retrospective study, we investigate whether normative percentiles of RNFLT (pRNFLT) from Spectralis SD-OCT improve prediction of glaucomatous visual field loss over raw RNFLT. Methods: A longitudinal database containing OCT scans and visual fields from Massachusetts Eye & Ear glaucoma clinic patients was generated. Reliable OCT-visual field pairs were selected. Spectralis OCT normative distributions were extracted from machine printouts. Supervised machine learning models compared predictive performance between pRNFLT and raw RNFLT inputs. Regional structure-function associations were assessed with univariate regression to predict mean deviation (MD). Multivariable classification predicted MD, pattern standard deviation, MD change per year, and glaucoma hemifield test. Results: There were 3016 OCT-visual field pairs that met the reliability criteria. Spectralis norms were found to be independent of age, sex, and ocular magnification. Regional analysis showed significant decrease in R2 from pRNFLT models compared to raw RNFLT models in inferotemporal sectors, across multiple regressors. In multivariable classification, there were no significant improvements in area under the curve of receiver operating characteristic curve (ROC-AUC) score with pRNFLT models compared to raw RNFLT models. Conclusions: Our results challenge the assumption that normative percentiles from OCT machines improve prediction of glaucomatous visual field loss. Raw RNFLT alone shows strong prediction, with no models presenting improvement by the manufacturer norms. This may result from insufficient patient stratification in tested norms. Translational Relevance: Understanding correlation of normative databases to visual function may improve clinical interpretation of OCT data.

PyVisualFields: A Python Package for Visual Field Analysis.

Purpose: Artificial intelligence (AI) methods are changing all areas of research and have a variety of capabilities of analysis in ophthalmology, specifically in visual fields (VFs) to detect or predict vision loss progression. Whereas most of the AI algorithms are implemented in Python language, which offers numerous open-source functions and algorithms, the majority of algorithms in VF analysis are offered in the R language. This paper introduces PyVisualFields, a developed package to address this gap and make available VF analysis in the Python language. Methods: For the first version, the R libraries for VF analysis provided by vfprogression and visualFields packages are analyzed to define the overlaps and distinct functions. Then, we defined and translated this functionality into Python with the help of the wrapper library rpy2. Besides maintaining, the subsequent versions' milestones are established, and the third version will be R-independent. Results: The developed Python package is available as open-source software via the GitHub repository and is ready to be installed from PyPI. Several Jupyter notebooks are prepared to demonstrate and describe the capabilities of the PyVisualFields package in the categories of data presentation, normalization and deviation analysis, plotting, scoring, and progression analysis. Conclusions: We developed a Python package and demonstrated its functionality for VF analysis and facilitating ophthalmic research in VF statistical analysis, illustration, and progression prediction. Translational Relevance: Using this software package, researchers working on VF analysis can more quickly create algorithms for clinical applications using cutting-edge AI techniques.

Assessing Surface Shapes of the Optic Nerve Head and Peripapillary Retinal Nerve Fiber Layer in Glaucoma with Artificial Intelligence.

Purpose: To assess 3-dimensional surface shape patterns of the optic nerve head (ONH) and peripapillary retinal nerve fiber layer (RNFL) in glaucoma with unsupervised artificial intelligence (AI). Design: Retrospective study. Participants: Patients with OCT scans obtained between 2016 and 2020 from Massachusetts Eye and Ear. Methods: The first reliable Cirrus (Carl Zeiss Meditec, Inc) ONH OCT scans from each eye were selected. The ONH and RNFL surface shape was represented by the vertical positions of the inner limiting membrane (ILM) relative to the lowest ILM vertical position in each eye. Nonnegative matrix factorization was applied to determine the ONH and RNFL surface shape patterns, which then were correlated with OCT and visual field (VF) loss parameters and subsequent VF loss rate. We tested whether using ONH and RNFL surface shape patterns improved the prediction accuracy for associated VF loss and subsequent VF loss rates measured by adjusted r 2 and Bayesian information criterion (BIC) difference compared with using established OCT parameters alone. Main Outcome Measures: Optic nerve head and RNFL surface shape patterns and prediction of the associated VF loss and subsequent VF loss rates. Results: We determined 14 ONH and RNFL surface shape patterns using 9854 OCT scans from 5912 participants. Worse mean deviation (MD) was most correlated (r = 0.29 and r = 0.24, Pearson correlation; each P < 0.001) with lower coefficients of patterns 10 and 12 representing inferior and superior para-ONH nerve thinning, respectively. Worse MD was associated most with higher coefficients of patterns 5, 4, and 9 (r = -0.16, r = -0.13, and r = -0.13, respectively), representing higher peripheral ONH and RNFL surfaces. In addition to established ONH summary parameters and 12-clock-hour RNFL thickness, using ONH and RNFL surface patterns improved (BIC decrease: 182, 144, and 101, respectively; BIC decrease ≥ 6; strong model improvement) the prediction of accompanied MD (r 2 from 0.32 to 0.37), superior (r 2 from 0.27 to 0.31), and inferior (r 2 from 0.17 to 0.21) paracentral loss and improved (BIC decrease: 8 and 8, respectively) the prediction of subsequent VF MD loss rates (r 2 from 0 to 0.13) and inferior paracentral loss rates (r 2 from 0 to 0.16). Conclusions: The ONH and RNFL surface shape patterns quantified by unsupervised AI techniques improved the structure-function relationship and subsequent VF loss rate prediction.

Race and Ethnicity Differences in Disease Severity and Visual Field Progression Among Glaucoma Patients.

PURPOSE: Investigate associations of race/ethnicity and preferred language with baseline glaucoma severity, VF test frequency and disease progression. DESIGN: Retrospective cohort study. METHODS: Patients receiving VF testing at a tertiary eyecare center between 1998 and 2020 with self-identified race, ethnicity and preferred language were included. Outcome measures were VF MD and age at first visit, VF test frequency, VF MD progression. RESULTS: Among 29,891 patients with VF measurements between 1998 and 2020, 55.1% were female, 71.0% self-identified as White/Caucasian, 14.0% as Black/African American, 7.4% as Asian and 6.4% as Hispanic, and 11.2% preferred a language other than English. Mean VF MD at presentation was worse among Black (-9.3±9.7 dB), Asian (-6.2±7.6 dB) and Hispanic (-8.3±9.3 dB) patients (vs. Whites [-5.5±7.3 dB, p<0.001] or non-Hispanics [-6.2±7.8 dB, p<0.001]). After controlling for age, gender and English proficiency, disparities in glaucoma severity at presentation were reduced, especially among Asian and Hispanic patients. Despite greater severity at presentation, Black patients had lower VF test frequency/person-years (1.07±0.53) compared to Whites (1.12±0.52, p=0.006) and worse VF MD progression (-0.43 dB/year, 95% CI -0.67 to -0.28, p<0.001). In contrast, Hispanics had a higher VF frequency vs. non-Hispanics (1.18±0.64 vs. 1.11±0.52, p<0.001), and no difference in VF progression (p=0.77). CONCLUSIONS: Black, Asian and Hispanic patients had greater baseline severity vs. Whites. Unlike other groups, Black patients had a lower VF frequency vs. Whites and greater VF progression. Disparities in baseline severity were partially explained by English proficiency, especially for Asian and Hispanic patients.

Age, Gender, and Laterality of Retinal Vascular Occlusion: A Retrospective Study from the IRIS® Registry.

PURPOSE: Retinal vascular occlusion is a leading cause of profound irreversible visual loss, but the understanding of the disease is insufficient. We systematically investigated the age, gender, and laterality at the onset of retinal artery occlusion (RAO) and retinal vein occlusion (RVO) in the Intelligent Research in Sight (IRIS®) Registry. DESIGN: Retrospective registry cohort. PARTICIPANTS: Patients with retinal vascular occlusion participating in the IRIS® Registry. METHODS: Patients who received a diagnosis of retinal vascular occlusion between 2013 and 2017 were included. Those with unspecified gender or laterality were excluded when conducting the relevant analyses. Patients were categorized into RAO, with subtypes transient retinal artery occlusion (TRAO), partial retinal artery occlusion (PRAO), branch retinal artery occlusion (BRAO), and central retinal artery occlusion (CRAO), and into RVO, with subtypes venous engorgement (VE), branch retinal vein occlusion (BRVO), and central retinal vein occlusion (CRVO). Age was evaluated as a categorical variable (5-year increments). We investigated the association of age, gender, and laterality with the onset frequency of retinal vascular occlusion subtypes. MAIN OUTCOME MEASURES: The frequency of onset of RAO and RVO subtypes by age, gender and laterality. RESULTS: A total of 1 251 476 patients with retinal vascular occlusion were included, 23.8% of whom had RAO, whereas 76.2% had RVO. Of these, 1 248 656 and 798 089 patients were selected for analyses relevant to gender and laterality, respectively. The onset frequency of all subtypes increased with age. PRAO, BRAO, CRAO, and CRVO presented more frequently in men (53.5%, 51.3%, 52.6%, and 50.4%, respectively), whereas TRAO, VE, and BRVO presented more frequently in women (54.9%, 56.0%, and 54.5% respectively). All RAO subtypes and BRVO showed a right-eye onset preference (TRAO, 51.7%; PRAO, 54.4%; BRAO, 53.5%; CRAO, 53.4%; and BRVO, 51.0%), whereas VE and CRVO exhibited a left-eye onset preference (53.3% and 50.9%, respectively). CONCLUSIONS: Although retinal vascular occlusion incidence increases with age regardless of subtypes, we found various subtype-specific disease-onset differences related to gender and, in particular, ocular laterality. These findings may improve understanding of the specific cause of retinal vascular occlusions of different subtypes and their relationships with structural and anatomic asymmetries of the vascular system.

Demyelination of the Optic Nerve: An Underlying Factor in Glaucoma?

Neurodegenerative disorders are characterized by typical neuronal degeneration and axonal loss in the central nervous system (CNS). Demyelination occurs when myelin or oligodendrocytes experience damage. Pathological changes in demyelination contribute to neurodegenerative diseases and worsen clinical symptoms during disease progression. Glaucoma is a neurodegenerative disease characterized by progressive degeneration of retinal ganglion cells (RGCs) and the optic nerve. Since it is not yet well understood, we hypothesized that demyelination could play a significant role in glaucoma. Therefore, this study started with the morphological and functional manifestations of demyelination in the CNS. Then, we discussed the main mechanisms of demyelination in terms of oxidative stress, mitochondrial damage, and immuno-inflammatory responses. Finally, we summarized the existing research on the relationship between optic nerve demyelination and glaucoma, aiming to inspire effective treatment plans for glaucoma in the future.

Association Between Diabetes, Diabetic Retinopathy, and Glaucoma.

PURPOSE OF REVIEW: The strength of the relationship between diabetes, diabetic retinopathy (DR), and glaucoma remains controversial. We review evidence supporting and refuting this association and explore mechanistic pathological and treatment relationships linking these diseases. RECENT FINDINGS: While studies have shown diabetes/DR may increase the risk for glaucoma, this remains inconsistently demonstrated. Diabetes/DR may contribute toward glaucomatous optic neuropathy indirectly (either by increasing intraocular pressure or vasculopathy) or through direct damage to the optic nerve. However, certain elements of diabetes may slow glaucoma progression, and diabetic treatment may concurrently be beneficial in glaucoma management. Diabetes plays a significant role in poor outcomes after glaucoma surgery. While the relationship between diabetes/DR and glaucoma remains controversial, multiple mechanistic links connecting pathophysiology and management of diabetes, DR, and glaucoma have been made. However, a deeper understanding of the causes of disease association is needed.

Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression.

Purpose: To develop and test machine learning classifiers (MLCs) for determining visual field progression. Methods: In total, 90,713 visual fields from 13,156 eyes were included. Six different progression algorithms (linear regression of mean deviation, linear regression of the visual field index, Advanced Glaucoma Intervention Study algorithm, Collaborative Initial Glaucoma Treatment Study algorithm, pointwise linear regression [PLR], and permutation of PLR) were applied to classify each eye as progressing or stable. Six MLCs were applied (logistic regression, random forest, extreme gradient boosting, support vector classifier, convolutional neural network, fully connected neural network) using a training and testing set. For MLC input, visual fields for a given eye were divided into the first and second half and each location averaged over time within each half. Each algorithm was tested for accuracy, sensitivity, positive predictive value, and class bias with a subset of visual fields labeled by a panel of three experts from 161 eyes. Results: MLCs had similar performance metrics as some of the conventional algorithms and ranged from 87% to 91% accurate with sensitivity ranging from 0.83 to 0.88 and specificity from 0.92 to 0.96. All conventional algorithms showed significant class bias, meaning each individual algorithm was more likely to grade uncertain cases as either progressing or stable (P ≤ 0.01). Conversely, all MLCs were balanced, meaning they were equally likely to grade uncertain cases as either progressing or stable (P ≥ 0.08). Conclusions: MLCs showed a moderate to high level of accuracy, sensitivity, and specificity and were more balanced than conventional algorithms. Translational Relevance: MLCs may help to determine visual field progression.