Identification of hydroxyapatite spherules provides new insight into subretinal pigment epithelial deposit formation in the aging eye.

Accumulation of protein- and lipid-containing deposits external to the retinal pigment epithelium (RPE) is common in the aging eye, and has long been viewed as the hallmark of age-related macular degeneration (AMD). The cause for the accumulation and retention of molecules in the sub-RPE space, however, remains an enigma. Here, we present fluorescence microscopy and X-ray diffraction evidence for the formation of small (0.5-20 μm in diameter), hollow, hydroxyapatite (HAP) spherules in Bruch's membrane in human eyes. These spherules are distinct in form, placement, and staining from the well-known calcification of the elastin layer of the aging Bruch's membrane. Secondary ion mass spectrometry (SIMS) imaging confirmed the presence of calcium phosphate in the spherules and identified cholesterol enrichment in their core. Using HAP-selective fluorescent dyes, we show that all types of sub-RPE deposits in the macula, as well as in the periphery, contain numerous HAP spherules. Immunohistochemical labeling for proteins characteristic of sub-RPE deposits, such as complement factor H, vitronectin, and amyloid beta, revealed that HAP spherules were coated with these proteins. HAP spherules were also found outside the sub-RPE deposits, ready to bind proteins at the RPE/choroid interface. Based on these results, we propose a novel mechanism for the growth, and possibly even the formation, of sub-RPE deposits, namely, that the deposit growth and formation begin with the deposition of insoluble HAP shells around naturally occurring, cholesterol-containing extracellular lipid droplets at the RPE/choroid interface; proteins and lipids then attach to these shells, initiating or supporting the growth of sub-RPE deposits

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Development and validation of novel clinical endpoints in intermediate age-related macular degeneration in MACUSTAR

Background Currently, no validated clinical endpoints for treatment studies exist for intermediate age-related macular degeneration (iAMD). Objective The European MACUSTAR study aims to develop and clinically validate adequate clinical endpoints for future treatment studies in iAMD and to identify early determinants of disease progression to late stage AMD. Material and methods The MACUSTAR study protocol was developed by an international consortium of researchers from academia, the pharmaceutical industry and medical device companies. The MACUSTAR project is funded by the Innovative Medicines Initiative 2 (IMI2) of the European Union. Results The MACUSTAR study consists of a cross-sectional and a longitudinal investigation. A total of 750 subjects with early, intermediate and late AMD as well as control subjects with no signs of AMD will be included with a follow-up period of 3 years. Overall, 20 European study centers are involved. Conclusion The MACUSTAR project will generate large high-quality datasets, which will allow clinical validation of novel endpoints for future interventional trials in iAMD. The aim is that these endpoints will be accepted as suitable for medication approval studies by the regulatory authorities and that understanding of the disease process will be improved

Relationship between nerve fiber layer hemorrhages and outcomes in central retinal vein occlusion

PURPOSE. To evaluate the depth and pattern of retinal hemorrhage in acute central retinal vein occlusion (CRVO) and to correlate these with visual and anatomic outcomes. METHODS. Retinal hemorrhages were evaluated with color fundus photography and fluorescein angiography at baseline and follow-up. Snellen visual acuity (VA), central foveal thickness (CFT), extent of retinal ischemia, and development of neovascularization were analyzed. RESULTS. 108 eyes from 108 patients were evaluated. Mean age was 63.6 ± 16.1 years with a predilection for the right eye (73.1). Average follow-up was 17.2 ± 19.2 months. Mean VA at baseline was 20/126 and 20/80 at final follow-up. Baseline (P = 0.005) and final VA (P = 0.02) in eyes with perivascular nerve fiber layer (NFL) hemorrhages were significantly worse than in eyes with deep hemorrhages alone. Baseline CFT was greater in the group with perivascular hemorrhages (826 ± 394 μm) compared to the group with deep hemorrhages alone (455 ± 273 μm, P < 0.001). The 10 disc areas of retinal ischemia was more common in patients with perivascular (80.0) and peripapillary (31.3) versus deep hemorrhages alone (16.1, P < 0.001). Neovascularization of the iris was more common, although this differrence was not significant, in the groups with peripapillary (14.3) and perivascular (2.0) NFL versus deep hemorrhages alone (0.0). CONCLUSIONS. NFL retinal hemorrhages at baseline correlate with more severe forms of CRVO, with greater macular edema, poorer visual outcomes, and greater risk of ischemia and neovascularization. This may be related to the organization of the retinal capillary plexus. The depth and pattern of distribution of retinal hemorrhages in CRVO may provide an easily identifiable early biomarker of CRVO prognosis. Copyright 2020 The Author

Ophthalmology

OBJECTIVE: In the current study we aimed to identify metabolites associated with age-related macular degeneration (AMD) by performing the largest metabolome association analysis in AMD to date. In addition, we aimed to determine the effect of AMD-associated genetic variants on metabolite levels, and aimed to investigate associations between the identified metabolites and activity of the complement system, one of the main AMD-associated disease pathways. DESIGN: Case-control assocation analysis of metabolomics data. SUBJECTS: 2,267 AMD cases and 4,266 controls from five European cohorts. METHODS: Metabolomics was performed using a high-throughput H-NMR metabolomics platform, which allows the quantification of 146 metabolite measurements and 79 derivative values. Metabolome-AMD associations were studied using univariate logistic regression analyses. The effect of 52 AMD-associated genetic variants on the identified metabolites was investigated using linear regression. In addition, associations between the identified metabolites and activity of the complement pathway (defined by the C3d/C3 ratio) were investigated using linear regression. MAIN OUTCOME MEASURES: Metabolites associated with AMD RESULTS: We identified 60 metabolites that were significantly associated with AMD, including increased levels of large and extra-large HDL subclasses and decreased levels of VLDL, amino acids and citrate. Out of 52 AMD-associated genetic variants, seven variants were significantly associated with 34 of the identified metabolites. The strongest associations were identified for genetic variants located in or near genes involved in lipid metabolism (ABCA1, CETP, APOE, LIPC) with metabolites belonging to the large and extra-large HDL subclasses. In addition, 57 out of 60 metabolites were significantly associated with complement activation levels, and these associations were independent of AMD status. Increased large and extra-large HDL levels and decreased VLDL and amino acid levels were associated with increased complement activation. CONCLUSIONS: Lipoprotein levels were associated with AMD-associated genetic variants, while decreased essential amino acids may point to nutritional deficiencies in AMD. We observed strong associations between the vast majority of the AMD-associated metabolites and systemic complement activation levels, independent of AMD status. This may indicate biological interactions between the main AMD disease pathways, and suggests that multiple pathways may need to be targeted simultaneously for successful treatment of AMD