Imaging, Genetic, and Demographic Factors Associated With Conversion to Neovascular Age-Related Macular Degeneration: Secondary Analysis of a Randomized Clinical Trial

Morph-SSL: Self-Supervision With Longitudinal Morphing for Forecasting AMD Progression From OCT Volumes

The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL. To address this issue, we propose Morph-SSL, a novel Self-supervised Learning (SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans from different visits and involves morphing the scan from the previous visit to the next. The Decoder predicts the transformation for morphing and ensures a smooth feature manifold that can generate intermediate scans between visits through linear interpolation. Next, the Morph-SSL trained features are input to a Classifier which is trained in a supervised manner to model the cumulative probability distribution of the time to conversion with a sigmoidal function. Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The Classifier was evaluated with a five-fold cross-validation on 2418 scans from 343 eyes with clinical labels of the conversion date. The Morph-SSL features achieved an AUC of 0.779 in predicting the conversion to nAMD within the next 6 months, outperforming the same network when trained end-to-end from scratch or pre-trained with popular SSL methods. Automated prediction of the future risk of nAMD onset can enable timely treatment and individualized AMD management.

3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression From Longitudinal OCTs

Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six-month interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies, artificial intelligence (AI) has emerged as an essential instrument for intelligent analysis across a range of fields. AI has been proven to be highly effective in ophthalmology, where it is frequently used for identifying, diagnosing, and typing retinal diseases. An increasing number of researchers have begun to comprehensively map patients' retinal diseases using AI, which has made individualized clinical prediction and treatment possible. These include prognostic improvement, risk prediction, progression assessment, and interventional therapies for retinal diseases. Researchers have used a range of input data methods to increase the accuracy and dependability of the results, including the use of tabular, textual, or image-based input data. They also combined the analyses of multiple types of input data. To give ophthalmologists access to precise, individualized, and high-quality treatment strategies that will further optimize treatment outcomes, this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Ocular Neovascular Conversion and Systemic Bleeding Complications in Patients with Age-Related Macular Degeneration on Anticoagulants

PURPOSE

Conversion to neovascular disease in patients with non-neovascular age-related macular degeneration (AMD) initiated on direct oral anticoagulants (DOACs) compared with matched patients treated with warfarin.

DESIGN

Retrospective cohort study.

PARTICIPANTS

The study included 20 300 patients and 13 387 patients with non-neovascular AMD initiated on DOACs or warfarin, respectively, before propensity score matching (PSM).

METHODS

TriNetX was used to identify patients diagnosed with non-neovascular AMD stratified by treatment with DOACs or warfarin with at least 6 months of follow-up. Propensity score matching was performed to control for baseline demographics and medical comorbidities.

MAIN OUTCOME MEASURES

Relative risk (RR) of developing neovascular AMD, macular hemorrhage (MH), vitreous hemorrhage (VH), and requiring an ocular intervention (intravitreal anti-VEGF therapy or pars plana vitrectomy [PPV]) within 6 months and 1 year. Patients with chronic atrial fibrillation (AF) on anticoagulation were separately evaluated for the same measures within 5 years after initiating therapy.

RESULTS

Treatment with warfarin was associated with a higher risk of developing neovascular AMD at 6 months (RR, 1.24, 95% confidence interval [CI], 1.12-1.39; P < 0.001) and 1 year (RR, 1.26, 95% CI, 1.14-1.40; P < 0.001) when compared with matched patients treated with DOACs. There was an increased risk of requiring intravitreal anti-VEGF therapy (6 months: RR, 1.30; 95% CI, 1.13-1.49; P < 0.001; 1 year: RR, 1.31, 95% CI, 0.72-2.05; P < 0.001) and PPV (6 months: RR, 2.13; 95% CI, 1.16-3.94; P = 0.01; 1 year: RR, 2.29, 95% CI, 1.30-4.05; P = 0.003). Among patients with AMD and AF treated with warfarin, there was an increased risk of ocular complications (neovascular AMD: RR, 1.25; 95% CI, 1.14-1.38; P < 0.001; MH: RR, 1.86; 95% CI, 1.47-2.35; P < 0.001; VH: RR, 2.22; 95% CI, 1.51-3.26; P < 0.001) and need for intravitreal anti-VEGF therapy (RR, 1.34; 95% CI, 1.18-1.52; P < 0.001) over an extended 5-year period. There was no significant difference in the development of major systemic hemorrhagic events between the 2 cohorts over 5 years.

CONCLUSIONS

Patients with non-neovascular AMD treated with warfarin were more likely to develop neovascular disease and require ocular intervention for hemorrhagic complications when compared with matched patients initiated on DOACs.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

OCT Prognostic Biomarkers for Progression to Late Age-related Macular Degeneration: A Systematic Review and Meta-analysis

TOPIC

To evaluate which OCT prognostic biomarkers best predict the risk of progression from early/intermediate to late age-related macular degeneration (AMD).

CLINICAL RELEVANCE

Among > 100 OCT prognostic biomarkers for AMD, it is unclear which are the most relevant for clinicians and researchers to focus on. This review evaluated which OCT biomarkers confer the greatest magnitude of prediction for progression to late AMD.

METHODS

Study protocol was registered on PROSPERO (CRD42023400166). PubMed and Embase were searched from inception to March 2, 2023, and eligible studies assessed following the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. The primary outcome was any quantified risk of progression from treatment-naive early/intermediate AMD to late AMD, including hazard ratios (HRs), odds ratios (ORs), and standardized mean differences (at baseline, between eyes with versus without progression), subgrouped by each OCT biomarker. Further meta-analyses were subgrouped by progression to geographic atrophy or neovascularization.

RESULTS

A total of 114 quantified OCT prognostic biomarkers were identified. With high GRADE certainty of evidence, the greatest magnitudes of prediction to late AMD belonged to: external limiting membrane abnormality (OR, 15.42 [7.63, 31.17]), ellipsoid zone abnormality (OR, 10.8 [4.58, 25.46]), interdigitation zone abnormality (OR, 7.68 [2.57, 23]), concurrent large drusen and reticular pseudodrusen (HR, 6.73 [1.35, 33.65], hyporeflective drusen cores (HR, 2.48 [1.8, 3.4]; OR 1.85 [1.29, 2.66]), intraretinal hyperreflective foci (IHRF; HR, 2.16 [0.92, 5.07]; OR 5.08 [3.26, 7.92]), and large drusen (HR, 2.01 [1.35, 2.99]); OR, 1.98 [1.27, 3.08]). There was greater risk of geographic atrophy for IHRF and hyporeflective drusen cores (P < 0.05), and neovascularization for ellipsoid zone abnormality (P < 0.05). Other OCT biomarkers such as drusenoid pigment epithelium detachment, shallow irregular retinal pigment epithelium elevations, and nascent geographic atrophy exhibited large magnitudes of risk but required further studies for validation.

CONCLUSION

This review synthesizes the 6 most relevant OCT prognostic biomarkers for AMD with greater predictive ability than large drusen alone, for clinicians and researchers to focus on. Further study is required to validate other biomarkers with less than high certainty of evidence, and assess how the copresence of biomarkers may affect risks.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Spatial Analysis Reveals Vascular Changes in Retinal and Choroidal Vessel Perfusion in Intermediate AMD With Reticular Pseudodrusen

Purpose

To examine the effect of reticular pseudodrusen (RPD) on retinal and choroidal vessel perfusion (VP) topography in intermediate age-related macular degeneration (iAMD) using refined spatial analyses.

Methods

This was a retrospective cross-sectional study of 120 individuals with 30 iAMDRPD, 60 iAMDno_RPD, and 30 normal eyes, propensity-score matched by age, sex, and presence of cardiovascular-related disease. VP of the superficial and deep retinal and choriocapillaris vascular slabs was assessed from 6 × 6-mm optical coherence tomography angiography (OCTA) scans divided into 126 × 126 grids, with adjustment for various person- and eye-level factors. Grid-wise VP differences (%) among the groups were spatially assessed according to analyses based on the Early Treatment for Diabetic Retinopathy Study (ETDRS), eccentricity (µm), and degree (°).

Results

VP was significantly decreased between iAMDRPD and iAMDno_RPD, across all vascular slabs in various ETDRS sectors (up to -2.16%; 95% confidence interval, -2.99 to -1.34; P < 0.05). Eccentricity analyses revealed more complex patterns: a bisegmented relationship where VP in iAMDRPD eyes decreased linearly toward 1000 µm then returned toward similar values as iAMDno_RPD, plateauing around 2000 µm in the superficial and 3000 µm in the deep retina (R2 = 0.57-0.9; P < 0.001). Degree-based analysis further showed that the greatest VP differences in iAMDRPD eyes were commonly located superiorly and nasally across all vascular slabs (P < 0.05).

Conclusions

RPD appears to compound the vascular impact of iAMD, displaying complex spatial patterns beyond the ETDRS sectors. This highlights the importance of considering spatial delineations for future work regarding the role of RPD and vascular dysfunction.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

Study protocol on prevalence of non-exudative macular neovascularisation and its contribution to prediction of exudation in fellow eyes with unilateral exudative AMD (EYE-NEON)

PURPOSE

Fellow eyes of patients with unilateral neovascular age-related macular degeneration (nAMD) are at risk of developing macular neovascularisation (MNV). These eyes may first develop subclinical non-exudative MNV (neMNV) before they leak to form exudative MNV (eMNV). The EYE NEON study is a 2-year study aimed at estimating the prevalence and incidence of neMNV and evaluating its role as a predictor for conversion to neovascular AMD.

METHODS

EYE NEON is a multicentre study that will run in retinal clinics across 25 National Health Service with the aim to recruit 800 patients with new onset nAMD in the first eye. The fellow-eye with no evidence of nAMD at baseline will be the study eye. All study eyes will have OCT and OCTA done at first and second year following first anti-VEGF treatment to the first eye (non-study eye), with new onset nAMD. We will estimate the prevalence and incidence of neMNV over 2 years, rate of conversion from neMNV to eMNV and numbers initiated on treatment for neovascular AMD in the study eye will be reported. Predictive models of conversion including neMNV with other demographic and imaging parameters will be developed.

CONCLUSION

The study design with proposed target sample size is sufficient to evaluate the retinal imaging characteristics of the study eyes with and without neMNV and develop predictive models to inform risk of conversion to nAMD.

Spatial Cluster Patterns of Retinal Sensitivity Loss in Intermediate Age-Related Macular Degeneration Features

Purpose

To examine spatial patterns of retinal sensitivity loss in the three key features of intermediate age-related macular degeneration (iAMD).

Methods

One-hundred individuals (53 iAMD, 47 normal) underwent 10-2 mesopic microperimetry testing in one eye. Pointwise sensitivities (dB) were corrected for age, sex, iAMD status, and co-presence of co-localized key iAMD features: drusen load, pigmentary abnormalities, and reticular pseudodrusen (RPD). Clusters (labeled by ranks of magnitude C-2, C-1, C0) were derived from pointwise sensitivities and then assessed by quadrants and eccentricity/rings.

Results

Two clusters of decreased sensitivities were evident in iAMD versus normal: C-2, -1.67 dB (95% CI (confidence intervals), -2.36 to -0.98; P < 0.0001); C-1, -0.93 dB (95% CI, -1.5 to -0.36; P < 0.01). One cluster of decreased sensitivity was independently associated each with increased drusen load (13.57 µm increase per -1 dB; P < 0.0001), pigmentary abnormalities (C-1: -2.23 dB; 95% CI, -3.36 to -1.1; P < 0.01), and RPD (C-1: -1.07 dB; 95% CI, -2 to -0.14; P < 0.01). Sensitivity loss in iAMD was biased toward the superior and central macula (P = 0.16 to <0.0001), aligning with structural distributions of features. However, sensitivity loss associated with drusen load also extended to the peripheral macula (P < 0.0001) with paracentral sparing, which was discordant with the central distribution of drusen.

Conclusions

Drusen load, pigmentary abnormalities, and RPD are associated with patterns of retinal sensitivity loss commonly demonstrating superior and central bias. Results highlighted that a clinical focus on these three key iAMD features using structural measures alone does not capture the complex, spatial extent of vision-related functional impairment in iAMD.

Translational Relevance

Defining the spatial patterns of retinal sensitivity loss in iAMD can facilitate a targeted visual field protocol for iAMD assessment.

Levodopa Is Associated with Reduced Development of Neovascular Age-Related Macular Degeneration

OBJECTIVE

To determine whether levodopa (L-DOPA) is associated with a reduced likelihood of developing neovascular age-related macular degeneration (AMD).

DESIGN

Three studies were performed: retrospective analyses in the Vestrum Health Retina Database (#1-2) and case-control analysis in the Merative MarketScan Research Databases (#3).

PARTICIPANTS

Eyes with neovascular AMD and 2 years of follow-up (#1). Eyes with non-neovascular AMD and 1 to 5 years of follow-up (#2). Patients aged ≥ 55 years with newly diagnosed neovascular AMD matched to controls without neovascular AMD (#3).

METHODS

Eyes were divided into 2 groups (#1-2): exposed to L-DOPA before or on the date of neovascular (#1) or nonneovascular (#2) AMD diagnosis, and eyes not exposed to L-DOPA. We extracted AMD risk factors, number of intravitreal injections (#1), and conversion rate to neovascular AMD (#2). We calculated the percentage of newly diagnosed neovascular AMD cases and matched controls exposed to any L-DOPA and determined the cumulative 2-year dose in grams by tertiles (< 100 mg, approximately 100-300 mg, and approximately > 300 mg per day, #3).

MAIN OUTCOME MEASURES

Number of intravitreal injections (#1) and detection of new-onset neovascular AMD (#2-3) after adjusting for AMD risk factors.

RESULTS

In the Vestrum database, eyes with neovascular AMD that were exposed to L-DOPA underwent 1 fewer intravitreal injection over 2 years (N = 84 088 control vs. 530 L-DOPA eyes, P = 0.006). In eyes with nonneovascular AMD (N = 42 081-203 155 control vs. 314-1525 L-DOPA eyes), L-DOPA exposure was associated with a reduced risk of conversion to neovascular AMD by 21% at year 2 (P = 0.029), 35% at years 3 to 4 (P < 0.001), and 28% at year 5 (P = 0.024). In the MarketScan databases (N = 86 900 per group), cumulative 2-year doses of L-DOPA between approximately 100 to 300 mg per day and approximately > 300 mg were associated with decreased odds of developing neovascular AMD by 15% (odds ratio [OR], 0.85; 95% confidence interval [CI], 0.75-0.97) and 23% (OR, 0.77; 95% CI, 0.67-0.87), respectively.

CONCLUSIONS

Levodopa use was associated with reduced detection of new-onset neovascular AMD. A prospective, randomized clinical trial should be considered to investigate whether low-dose L-DOPA reduces neovascular AMD conversion.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

LncRNA NORAD defects deteriorate the formation of age-related macular degeneration

Long noncoding RNAs (lncRNAs) play important roles in the development of age-related macular degeneration (AMD). However, the effect of long non-coding RNA activated by DNA damage (NORAD) on AMD remains unknown. This study aimed to investigate the effect of NORAD on RPE cell senescence and degeneration. Irradiated adult retinal pigment epithelial cell line-19 (ARPE-19) and sodium iodate-treated mice were used as in vitro and in vivo AMD models. Results showed that irradiation-induced AMD characteristics of ARPE-19 and NORAD-knockdown aggravated cell cycle arrest in the G2/M phase, cell apoptosis and cell senescence along with the increased expression of phosphorylated P53 (p-P53) and P21. AMD factors C3, ICAM-1, APP, APOE, and VEGF-A were also increased by NORAD-knockdown. Moreover, NORAD-knockdown increased irradiation-induced reduction of mitochondrial homeostasis factors, (i.e., TFAM and POLG) and mitochondrial respiratory chain complex genes (i.e., ND1 and ND5) along with mitochondrial reactive oxygen species (ROS). We also identified a strong interaction of NORAD and PGC-1α and sirtuin 1 (SIRT1) in ARPE-19; that is, NORAD knockdown increases the acetylation of PGC-1α. In NORAD knockout mice, NORAD-knockout accelerated the sodium iodate-reduced retinal thickness reduction, function impairment and loss of retinal pigment in the fundus. Therefore, NORAD-knockdown accelerates retinal cell senescence, apoptosis, and AMD markers via PGC-1α acetylation, mitochondrial ROS, and the p-P53-P21signaling pathway, in which NORAD-mediated effect on PGC-1α acetylation might occur through the direct interaction with PGC-1α and SIRT1.

An Analysis of the Usage of Retinal Imaging Technology in the Detection of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a disease that worsens the central vision of numerous individuals across the globe. Ensuring that patients are diagnosed accurately and that their symptoms are carefully monitored is essential to ensure that adequate care is delivered. To accomplish this objective, retinal imaging technology is necessary to assess the pathophysiology that is required to give an accurate diagnosis of AMD. The purpose of this review is to assess the ability of various retinal imaging technologies such as optical coherence tomography (OCT), color fundus retinal photography, fluorescein angiography, and fundus photography. The statistical methods that were conducted yielded results that suggested that using OCT in conjunction with other imaging technologies results in a higher detection of symptoms among patients that have AMD. Further investigation should be conducted to ascertain the validity of the conclusions that were stated within the review.

Trends and disparities in disease burden of age-related macular degeneration from 1990 to 2019: Results from the global burden of disease study 2019

Objectives

This study aims to estimate the trends and disparities in the worldwide burden for health of AMD, overall and by age, sex, socio-demographic index (SDI), region, and nation using prevalence and years lived with disability (YLDs) from Global Burden of Disease (GBD) study 2019.

Methods

This retrospective study presents the prevalent AMD cases and YLDs from 1990-2019, as well as the age-standardized prevalence rate (ASPR) and age-standardized YLD rate (ASYR) of AMD. To measure changes over time, estimated annual percentage changes (EAPCs) of the age-standardized rates (ASRs) were analyzed globally, then studied further by sex, SDI, region, and nation. We included data from the 2019 Global Burden of Disease (GBD) database to examine AMD prevalence and YLDs from 1990-2019 in 204 countries and territories, as well as demographic information such as age, sex, SDI, region, and nation.

Results

Globally, the number of prevalent AMD cases increased from 3,581,329.17 (95% uncertainty interval [UI], 3,025,619.4-4,188,835.7) in 1990 to 7,792,530 (95% UI, 6,526,081.5-9,159,394.9) in 2019, and the number of YLDs increased from 296,771.93 (95% uncertainty interval [UI], 205,462.8-418,699.82) in 1990 to 564,055.1 (95% UI, 392,930.7-789,194.64) in 2019. The ASPR of AMD had a decreased trend with an EAPC of -0.15 (95% confidence interval [CI], -0.2 to -0.11) from 1990 to 2019, and the ASYR of AMD showed a decreased trend with an EAPC of -0.71 (95% confidence interval [CI], -0.78 to -0.65) during this period. The prevalence and YLDs of AMD in adults over 50 years of age showed a significant increase. The prevalence and YLDs of AMD were significantly higher in females than males, overall. The ASPRs and ASYRs in low SDI regions was greater than in high SDI regions from 1990 to 2019. In addition, increases in prevalence and YLDs differed by regions and nations, as well as level of socio-economic development.

Conclusion

The number of prevalent cases and YLDs due to AMD increased over 30 years and were directly linked to age, sex, socio-economic status, and geographic location. These findings can not only guide public health work but also provide an epidemiological basis for global strategy formulation regarding this global health challenge.

The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review

In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.

Melanin-like Nanoparticles as an Alternative to Natural Melanin in Retinal Pigment Epithelium Cells and Their Therapeutic Effects against Age-Related Macular Degeneration

Melanin is a natural pigment that is widely distributed in many parts of the human body, such as the skin and retinal pigment epithelium (RPE) in eyes. In contrast to skin melanin, which is being constantly synthesized by the epidermal melanocytes, melanin in the RPE does not regenerate. Melanin is known to function as a potential radical scavenger and photoprotective agent. However, the protective effects of melanin against oxidative stress decline with increasing age. This phenomenon has been correlated with the pathogenesis of age-related macular degeneration (AMD). To increase the potential antioxidant and photoprotective characteristics of melanin, we designed a therapeutic strategy for replenishment of melanin using PEGylated synthetic melanin-like nanoparticles (MNPs) in the RPE for the treatment of AMD. We performed experiments using AMD-like cellular and mouse models and demonstrated that MNPs are biocompatible and selectively target reactive oxygen species (ROS) with powerful antioxidant properties. MNPs can traffic and accumulate in the RPE and are exclusively located in cytosol, but not the nucleus and mitochondria of the cells, for at least 3 months after a single-dose intravitreal injection. Our findings demonstrate that MNPs are able to substitute for natural melanin in the RPE and suggest the potential efficacy of MNPs as a natural radical scavenger against oxidative stress in ROS-related diseases, such as AMD.

Reticular Pseudodrusen Are Associated With More Advanced Para-Central Photoreceptor Degeneration in Intermediate Age-Related Macular Degeneration

Purpose

The purpose of this study was to examine retinal topographical differences between intermediate age-related macular degeneration (iAMD) with reticular pseudodrusen (RPD) versus iAMD without RPD, using high-density optical coherence tomography (OCT) cluster analysis.

Methods

Single eyes from 153 individuals (51 with iAMD+RPD, 51 with iAMD, and 51 healthy) were propensity-score matched by age, sex, and refraction. High-density OCT grid-wise (60 × 60 grids, each approximately 0.01 mm2 area) thicknesses were custom-extracted from macular cube scans, then compared between iAMD+RPD and iAMD eyes with correction for confounding factors. These "differences (µm)" were clustered and results de-convoluted to reveal mean difference (95% confidence interval [CI]) and topography of the inner retina (retinal nerve fiber, ganglion cell, inner plexiform, and inner nuclear layers) and outer retina (outer plexiform/Henle's fiber/outer nuclear layers, inner and outer segments, and retinal pigment epithelium-to-Bruch's membrane [RPE-BM]). Differences were also converted to Z-scores using normal data.

Results

In iAMD+RPD compared to iAMD eyes, the inner retina was thicker (up to +5.89 [95% CI = +2.44 to +9.35] µm, P < 0.0001 to 0.05), the outer para-central retina was thinner (up to -3.21 [95% CI = -5.39 to -1.03] µm, P < 0.01 to 0.001), and the RPE-BM was thicker (+3.38 [95% CI = +1.05 to +5.71] µm, P < 0.05). The majority of effect sizes (Z-scores) were large (-3.13 to +1.91).

Conclusions

OCT retinal topography differed across all retinal layers between iAMD eyes with versus without RPD. Greater para-central photoreceptor thinning in RPD eyes was suggestive of more advanced degeneration, whereas the significance of inner retinal thickening was unclear. In the future, quantitative evaluation of photoreceptor thicknesses may help clinicians monitor the potential deleterious effects of RPD on retinal integrity.

High-Density Optical Coherence Tomography Analysis Provides Insights Into Early/Intermediate Age-Related Macular Degeneration Retinal Layer Changes

Purpose

To topographically map all of the thickness differences in individual retinal layers between early/intermediate age-related macular degeneration (AMDearly/AMDint) and normal eyes and to determine interlayer relationships.

Methods

Ninety-six AMDtotal (48 AMDearly and 48 AMDint) and 96 normal eyes from 192 participants were propensity-score matched by age, sex, and refraction. Retrospective optical coherence tomography (OCT) macular cube scans were acquired, and high-density (60 × 60 0.01-mm2) grid thicknesses were custom extracted for comparison between AMDtotal and normal eyes corrected for confounding. Resultant "normal differences" underwent cluster, interlayer correlation, and dose-response analyses for the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer + Henle's fiber layer (ONL+HFL), inner and outer segment (IS/OS) thickness, and retinal pigment epithelium (RPE) to Bruch's membrane (BM) thickness.

Results

AMDtotal inner retinal clusters demonstrated extensively thinned RNFL, GCL, IPL, and paracentral INL and thickened INL elsewhere, with normal difference means ranging from -8.13 µm (95% confidence interval [CI], -11.12 to -5.13) to 1.58 µm (95% CI, 1.07-2.09) (P < 0.0001 to P < 0.05). Outer retinal clusters displayed thinned paracentral OPL/ONL+HFL, central IS/OS, and peripheral RPE-BM and thickened central RPE-BM, with means ranging from -1.31 µm (95% CI, -2.06 to -0.55) to 2.99 µm (95% CI, 0.97-5.01] (P < 0.0001 to P <0.05). Effect sizes (-2.56 to 9.93 SD), cluster sizes, and eccentricity effects varied. All interlayer correlations were negligible to moderate regardless of AMD severity. Only the RPE-BM was partly thicker with greater AMD severity (up to 5.44 µm; 95% CI, 4.88-6.00; P < 0.01).

Conclusions

From the early stage, AMD eyes demonstrate thickness differences compared to normal with unique topographies across all retinal layers. Poor interlayer correlations highlight that the outer retina inadequately reflects complete retinal health. The clinical importance of OCT assessment across all individual retinal layers in early/intermediate AMD requires further investigation.

SD-OCT Biomarkers and the Current Status of Artificial Intelligence in Predicting Progression from Intermediate to Advanced AMD

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the Western World. Optical coherence tomography (OCT) has revolutionized the diagnosis and follow-up of AMD patients. This review focuses on SD-OCT imaging biomarkers which were identified as predictors for progression in intermediate AMD to late AMD, either geographic atrophy (GA) or choroidal neovascularization (CNV). Structural OCT remains the most compelling modality to study AMD features related to the progression such as drusen characteristics, hyperreflective foci (HRF), reticular pseudo-drusen (RPD), sub-RPE hyper-reflective columns and their impact on retinal layers. Further on, we reviewed articles that attempted to integrate biomarkers that have already proven their involvement in intermediate AMD progression, in their models of artificial intelligence (AI). By combining structural biomarkers with genetic risk and lifestyle the predictive ability becomes more accurate.

Non-invasive testing for early detection of neovascular macular degeneration in unaffected second eyes of older adults: EDNA diagnostic accuracy study

BACKGROUND

Neovascular age-related macular degeneration is a leading cause of sight loss, and early detection and treatment is important. For patients with neovascular age-related macular degeneration in one eye, it is usual practice to monitor the unaffected eye. The test used to diagnose neovascular age-related macular degeneration, fundus fluorescein angiography, is an invasive test. Non-invasive tests are available, but their diagnostic accuracy is unclear.

OBJECTIVES

The primary objective was to determine the diagnostic monitoring performance of tests for neovascular age-related macular degeneration in the second eye of patients with unilateral neovascular age-related macular degeneration. The secondary objectives were the cost-effectiveness of tests and to identify predictive factors of developing neovascular age-related macular degeneration.

DESIGN

This was a multicentre, prospective, cohort, comparative diagnostic accuracy study in a monitoring setting for up to 3 years. A Cox regression risk prediction model and a Markov microsimulation model comparing cost-effectiveness of the index tests over 25 years were used.

SETTING

This took place in hospital eye services.

PARTICIPANTS

Participants were adults (aged 50-95 years) with newly diagnosed (within the previous 6 weeks) neovascular age-related macular degeneration in one eye and an unaffected second (study) eye who were attending for treatment injections in the first eye and who had a study eye baseline visual acuity of ≥ 68 Early Treatment Diabetic Retinopathy Study letters.

INTERVENTIONS

The index tests were Amsler chart (completed by participants), fundus clinical examination, optical coherence tomography, self-reported vision assessment (completed by participants) and visual acuity. The reference standard was fundus fluorescein angiography.

MAIN OUTCOME MEASURES

The main outcome measures were sensitivity and specificity; the performance of the risk predictor model; and costs and quality-adjusted life-years.

RESULTS

In total, 552 out of 578 patients who consented from 24 NHS hospitals (n = 16 ineligible; n = 10 withdrew consent) took part. The mean age of the patients was 77.4 years (standard deviation 7.7 years) and 57.2% were female. For the primary analysis, 464 patients underwent follow-up fundus fluorescein angiography and 120 developed neovascular age-related macular degeneration on fundus fluorescein angiography. The diagnostic accuracy [sensitivity (%) (95% confidence interval); specificity (%) (95% confidence interval)] was as follows: optical coherence tomography 91.7 (85.2 to 95.6); 87.8 (83.8 to 90.9)], fundus clinical examination [53.8 (44.8 to 62.5); 97.6 (95.3 to 98.9)], Amsler [33.7 (25.1 to 43.5); 81.4 (76.4 to 85.5)], visual acuity [30.0 (22.5 to 38.7); 66.3 (61.0 to 71.1)] and self-reported vision [4.2 (1.6 to 9.8); 97.0 (94.6 to 98.5)]. Optical coherence tomography had the highest sensitivity across all secondary analyses. The final prediction model for neovascular age-related macular degeneration in the non-affected eye included smoking status, family history of neovascular age-related macular degeneration, the presence of nodular drusen with or without reticular pseudodrusen, and the presence of pigmentary abnormalities [c-statistic 0.66 (95% confidence interval 0.62 to 0.71)]. Optical coherence tomography monitoring generated the greatest quality-adjusted life-years gained per patient (optical coherence tomography, 5.830; fundus clinical examination, 5.787; Amsler chart, 5.736, self-reported vision, 5.630; and visual acuity, 5.600) for the lowest health-care and social care costs (optical coherence tomography, £19,406; fundus clinical examination, £19,649; Amsler chart, £19,751; self-reported vision, £20,198; and visual acuity, £20,444) over the lifetime of the simulated cohort. Optical coherence tomography dominated the other tests or had an incremental cost-effectiveness ratio below the accepted cost-effectiveness thresholds (£20,000) across the scenarios explored.

LIMITATIONS

The diagnostic performance may be different in an unselected population without any history of neovascular age-related macular degeneration; the prediction model did not include genetic profile data, which might have improved the discriminatory performance.

CONCLUSIONS

Optical coherence tomography was the most accurate in diagnosing conversion to neovascular age-related macular degeneration in the fellow eye of patients with unilateral neovascular age-related macular degeneration. Economic modelling suggests that optical coherence tomography monitoring is cost-effective and leads to earlier diagnosis of and treatment for neovascular age-related macular degeneration in the second eye of patients being treated for neovascular age-related macular degeneration in their first eye.

FUTURE WORK

Future works should investigate the role of home monitoring, improved risk prediction models and impact on long-term visual outcomes.

STUDY REGISTRATION

This study was registered as ISRCTN48855678.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 8. See the NIHR Journals Library website for further project information.

Should clinical automated perimetry be considered for routine functional assessment of early/intermediate age-related macular degeneration (AMD)? A systematic review of current literature

Purpose

There is growing interest in functional testing for early/intermediate age‐related macular degeneration (iAMD). However, systematic evaluation of existing clinical functional tests is lacking. This systematic review examines evidence for using clinical automated perimetry in routine assessment of early/iAMD.

Recent findings

PubMed, Web of Science Core Collection, and Embase were searched from inception to October 2020 to answer, is there evidence of visual field defects in early/iAMD, and if so, are early/iAMD visual field defects linked to real‐world patient outcomes? Articles using clinical automated perimetry (commercially accessible and non‐modified devices/protocols) were included. Microperimetry was excluded as this has yet to be incorporated into clinical guidelines. The primary outcome was global visual field indices including mean deviation (MD), pattern standard deviation (PSD), mean sensitivity (MS) and frequency of defects. The secondary outcome was any real‐world patient outcome including quality of life and/or activities of daily living indices. Twenty‐six studies were eligible for inclusion and all studies were observational. There was consistent evidence of worsened MD, PSD, MS and frequency of defects for early/iAMD compared to normal eyes under photopic, low‐photopic and scotopic conditions. Meta‐analysis of studies using standard automated perimetry (SAP) under photopic conditions revealed worsened MD (−1.52dB [−2.27, −0.78 dB]) and MS (−1.47dB [−2, −0.94 dB]) in early/iAMD compared to normal eyes, representing large statistical effect sizes but non‐clinically meaningful reductions. There was insufficient data for meta‐analyses regarding other clinical automated perimetry protocols. Only one study assessed a real‐world patient outcome (on‐road driving performance), with no significant link to visual field outcomes in early/iAMD.

Summary

Significant reduction of global visual field indices is present in early/iAMD, but not clinically meaningful using SAP under photopic conditions. Translational relevance of visual field outcomes to patient outcomes in early/iAMD remains unclear. Thus, SAP under photopic conditions is unlikely to be useful for routine assessment of early/iAMD.

Location-Specific Thickness Patterns in Intermediate Age-Related Macular Degeneration Reveals Anatomical Differences in Multiple Retinal Layers

Purpose

To examine individual retinal layers’ location-specific patterns of thicknesses in intermediate age-related macular degeneration (iAMD) using optical coherence tomography (OCT).

Methods

OCT macular cube scans were retrospectively acquired from 84 iAMD eyes of 84 participants and 84 normal eyes of 84 participants propensity-score matched on age, sex, and spherical equivalent refraction. Thicknesses of the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer + Henle's fiber layer (ONL_+HFL), inner- and outer-segment layers (IS/OS), and retinal pigment epithelium to Bruch's membrane (RPE-BM) were calculated across an 8 × 8 grid (total 24° × 24° area). Location-specific analysis was performed using cluster_(normal) and grid_(iAMD)-to-cluster_(normal) comparisons.

Results

In iAMD versus normal eyes, the central RPE-BM was thickened (mean difference ± SEM up to 27.45% ± 7.48%, P < 0.001; up to 7.6 SD-from-normal), whereas there was thinned outer (OPL, ONL_+HFL, and non-central RPE-BM, up to −6.76% ± 2.47%, P < 0.001; up to −1.6 SD-from-normal) and inner retina (GCL and IPL, up to −4.83% ± 1.56%, P < 0.01; up to −1.7 SD-from-normal) with eccentricity-based effects. Interlayer correlations were greater against the ONL_+HFL (mean |r| ± SEM 0.19 ± 0.03, P = 0.14 to < 0.0001) than the RPE-BM (0.09 ± 0, P = 0.72 to < 0.0001).

Conclusions

Location-specific analysis suggests altered retinal anatomy between iAMD and normal eyes. These data could direct clinical diagnosis and monitoring of AMD toward targeted locations.

Imaging and artificial intelligence for progression of age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.

The Role of Oxidative Stress and the Importance of miRNAs as Potential Biomarkers in the Development of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is the primary cause of blindness in developed countries. With the progressive aging of the population, AMD is a significant ophthalmological problem in the population over 50 years of age. The etiology of AMD is known to be based on various biochemical, immunological and molecular pathways and to be influenced by a range of genetic and environmental elements. This review provides an overview of the pathophysiological role of oxidative stress and free radicals in the retina with a special focus on the DNA repair efficiency and enzymatic antioxidant defense. It also presents a correlation between miRNA profile and AMD, and indicates their involvement in inflammation, angiogenesis, increased oxidation of cellular components, enzymatic antioxidant capacity and DNA repair efficiency, which play particularly important roles in AMD pathogenesis. Gene silencing by miRNAs can induce changes in antioxidant enzymes, leading to a complex interplay between redox imbalance by free radicals and miRNAs in modulating cellular redox homeostasis.

Probabilistic Forecasting of Anti-VEGF Treatment Frequency in Neovascular Age-Related Macular Degeneration

Purpose

To probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain-optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings.

Methods

SD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure.

Results

In a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25-2.96] (R2 = 0.390) using random forest regression and 2.66 injections/year [2.31-3.01] (R2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models.

Conclusions

The proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings.

Translational Relevance

Prediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Radial Peripapillary Capillary Plexus Sparing and Underlying Retinal Vascular Impairment in Intermediate Age-Related Macular Degeneration

Purpose

To examine location-specific retinal vascular changes in intermediate age-related macular degeneration (iAMD) using age-matched, high-density en face optical coherence tomography angiography (OCTA) cluster analysis.

Methods

En face OCTA images of the 6 × 6 mm macular area were retrospectively acquired from 60 iAMD eyes and 60 age-matched normal eyes and then subdivided into 126 × 126 (47.62 × 47.62 µm) grids within the superficial and deep vascular complex. Grid-wise vessel perfusion (VP) were compared between iAMD and normal eyes from the corresponding 10-yearly age cohort, forming difference plots. Difference plots were further separated by normative topographical map spatial clusters (C_1-6), derived from normal_database eyes (n = 236, 20–81 years old).

Results

Overall difference plots showed decreased VP in the superficial (−12.19%) and deep vascular complex (−6.44%) of iAMD compared to normal eyes (P < 0.0001 both comparisons). Cluster-based difference plots highlighted nonuniform changes in the superficial vascular complex, with sparing of VP at the nasal macula (corresponding to the radial peripapillary capillary plexus) versus decreased VP toward the temporal macula and foveal avascular zone (FAZ) (C_1–6 all comparisons P < 0.0001, except C₁ vs. C₂P > 0.99 and C₄ vs. C₅P = 0.11). The deep vascular complex displayed diffusely decreased VP, greater at the FAZ (P < 0.0001).

Conclusions

High-density en face OCTA cluster analysis suggests relative sparing of the radial peripapillary capillary plexus and impairment of underlying retinal vasculature, supporting potential anterograde transsynaptic degeneration in iAMD. These location-specific data may better guide future diagnostic and management protocol of iAMD.

ACAD10 protein expression and Neurobehavioral assessment of Acad10-deficient mice

Acyl-CoA dehydrogenase 10 (Acad10)-deficient mice develop impaired glucose tolerance, peripheral insulin resistance, and abnormal weight gain. In addition, they exhibit biochemical features of deficiencies of fatty acid oxidation, such as accumulation of metabolites consistent with abnormal mitochondrial energy metabolism and fasting induced rhabdomyolysis. ACAD10 has significant expression in mouse brain, unlike other acyl-CoA dehydrogenases (ACADs) involved in fatty acid oxidation. The presence of ACAD10 in human tissues was determined using immunohistochemical staining. To characterize the effect of ACAD10 deficiency on the brain, micro-MRI and neurobehavioral evaluations were performed. Acad10-deficient mouse behavior was examined using open field testing and DigiGait analysis for changes in general activity as well as indices of gait, respectively. ACAD10 protein was shown to colocalize to mitochondria and peroxisomes in lung, muscle, kidney, and pancreas human tissue. Acad10-deficient mice demonstrated subtle behavioral abnormalities, which included reduced activity and increased time in the arena perimeter in the open field test. Mutant animals exhibited brake and propulsion metrics similar to those of control animals, which indicates normal balance, stability of gait, and the absence of significant motor impairment. The lack of evidence for motor impairment combined with avoidance of the center of an open field arena and reduced vertical and horizontal exploration are consistent with a phenotype characterized by elevated anxiety. These results implicate ACAD10 function in normal mouse behavior, which suggests a novel role for ACAD10 in brain metabolism.

Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers

We propose a hybrid sequential prediction model called “Deep Sequence”, integrating radiomics-engineered imaging features, demographic, and visual factors, with a recursive neural network (RNN) model in the same platform to predict the risk of exudation within a future time-frame in non-exudative AMD eyes. The proposed model provides scores associated with risk of exudation in the short term (within 3 months) and long term (within 21 months), handling challenges related to variability of OCT scan characteristics and the size of the training cohort. We used a retrospective clinical trial dataset that includes 671 AMD fellow eyes with 13,954 observations before any signs of exudation for training and validation in a tenfold cross validation setting. Deep Sequence achieved high performance for the prediction of exudation within 3 months (0.96 ± 0.02 AUCROC) and within 21 months (0.97 ± 0.02 AUCROC) on cross-validation. Training the proposed model on this clinical trial dataset and testing it on an external real-world clinical dataset showed high performance for the prediction within 3-months (0.82 AUCROC) but a clear decrease in performance for the prediction within 21-months (0.68 AUCROC). While performance differences at longer time intervals may be derived from dataset differences, we believe that the high performance and generalizability achieved in short-term predictions may have a high clinical impact allowing for optimal patient follow-up, adding the possibility of more frequent, detailed screening and tailored treatments for those patients with imminent risk of exudation.

Predicting Progression to Advanced Age-Related Macular Degeneration from Clinical, Genetic, and Lifestyle Factors Using Machine Learning

PURPOSE

Current prediction models for advanced age-related macular degeneration (AMD) are based on a restrictive set of risk factors. The objective of this study was to develop a comprehensive prediction model applying a machine learning algorithm allowing selection of the most predictive risk factors automatically.

DESIGN

Two population-based cohort studies.

PARTICIPANTS

The Rotterdam Study I (RS-I; training set) included 3838 participants 55 years of age or older, with a median follow-up period of 10.8 years, and 108 incident cases of advanced AMD. The Antioxydants, Lipids Essentiels, Nutrition et Maladies Oculaires (ALIENOR) study (test set) included 362 participants 73 years of age or older, with a median follow-up period of 6.5 years, and 33 incident cases of advanced AMD.

METHODS

The prediction model used the bootstrap least absolute shrinkage and selection operator (LASSO) method for survival analysis to select the best predictors of incident advanced AMD in the training set. Predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC).

MAIN OUTCOME MEASURES

Incident advanced AMD (atrophic, neovascular, or both), based on standardized interpretation of retinal photographs.

RESULTS

The prediction model retained (1) age, (2) a combination of phenotypic predictors (based on the presence of intermediate drusen, hyperpigmentation in one or both eyes, and Age-Related Eye Disease Study simplified score), (3) a summary genetic risk score based on 49 single nucleotide polymorphisms, (4) smoking, (5) diet quality, (6) education, and (7) pulse pressure. The cross-validated AUC estimation in RS-I was 0.92 (95% confidence interval [CI], 0.88-0.97) at 5 years, 0.92 (95% CI, 0.90-0.95) at 10 years, and 0.91 (95% CI, 0.88-0.94) at 15 years. In ALIENOR, the AUC reached 0.92 at 5 years (95% CI, 0.87-0.98). In terms of calibration, the model tended to underestimate the cumulative incidence of advanced AMD for the high-risk groups, especially in ALIENOR.

CONCLUSIONS

This prediction model reached high discrimination abilities, paving the way toward making precision medicine for AMD patients a reality in the near future.

Risk factors for progression of age-related macular degeneration

Purpose

Age‐related macular degeneration (AMD) is a degenerative disease of the macula, often leading to progressive vision loss. The rate of disease progression can vary among individuals and has been associated with multiple risk factors. In this review, we provide an overview of the current literature investigating phenotypic, demographic, environmental, genetic, and molecular risk factors, and propose the most consistently identified risk factors for disease progression in AMD based on these studies. Finally, we describe the potential use of these risk factors for personalised healthcare.

Recent findings

While phenotypic risk factors such as drusen and pigment abnormalities become more important to predict disease progression during the course of the disease, demographic, environmental, genetic and molecular risk factors are more valuable at earlier disease stages. Demographic and environmental risk factors such as age and smoking are consistently reported to be related to disease progression, while other factors such as sex, body mass index (BMI) and education are less often associated. Of all known AMD variants, variants that are most consistently reported with disease progression are rs10922109 and rs570618 in CFH, rs116503776 in C2/CFB/SKIV2L, rs3750846 in ARMS2/HTRA1 and rs2230199 in C3. However, it seems likely that other AMD variants also contribute to disease progression but to a lesser extent. Rare variants have probably a large effect on disease progression in highly affected families. Furthermore, current prediction models do not include molecular risk factors, while these factors can be measured accurately in the blood. Possible promising molecular risk factors are High‐Density Lipoprotein Cholesterol (HDL‐C), Docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), zeaxanthin and lutein.

Summary

Phenotypic, demographic, environmental, genetic and molecular risk factors can be combined in prediction models to predict disease progression, but the selection of the proper risk factors for personalised risk prediction will differ among individuals and is dependent on their current disease stage. Future prediction models should include a wider set of genetic variants to determine the genetic risk more accurately, and rare variants should be taken into account in highly affected families. In addition, adding molecular factors in prediction models may lead to preventive strategies and personalised advice.

Why Miss the Chance? Incidental Findings while Telescreening for Diabetic Retinopathy

PURPOSE

To report on incidental pathological findings met while screening for Diabetic Retinopathy (DR) in Diabetes Clinics (DC) by ophthalmologist-graded digital fundus imaging.

METHODS

At the DC of Pescara (central Italy), for 3,859 eyes of 1,930 consecutive patients having not undergone fundus examination in the last year, two mydriatic fundus digital images, taken with a CenterVue DRS Digital Retinal Camera, were sent along with Best Corrected Visual Acuity, on a "store-and-forward" basis, to an ophthalmologist trained in DR screening, and graded according to the UK Diabetic Eye Screening Programme. Incidental fundus abnormalities other than DR were reported.

RESULTS

No adverse event to mydriasis was reported. One hundred and eighty eyes (4.66%) were ungradable. Among the 3,679 gradable ones, 1,105 (30.04%) showed different degrees of DR (R1 to R3), and 126 (3.42%) maculopathy (M1). Any Age-Related Macular Degeneration was present in 387 eyes (10.52%), any optic disc and parapapillary area features suspect for glaucoma in 562 eyes (15.27%), any hypertensive retinopathy in 1,263 eyes (34.33%), vitreoretinal interface disease in 252 eyes (6.84%), myopic choroidopathy in 92 eyes (2.50%), disc pallor in 31 eyes (0.84%). Mean time was 5 min for screening, 2 min for grading.

CONCLUSION

Teleretinography is a well-established, cost-effective procedure in DR screening. Along with increased attendance, locating a digital camera in a DC with a retina-specialist grader results in finding fundus pathologies also beyond DR, very similarly to fundus examination in an outpatient ophthalmic setting.

Involvement of microRNA-34a in Age-Related Susceptibility to Oxidative Stress in ARPE-19 Cells by Targeting the Silent Mating Type Information Regulation 2 Homolog 1/p66shc Pathway: Implications for Age-Related Macular Degeneration

The aging retinal pigment epithelium and oxidative stress, mediated by reactive oxygen species (ROS) accumulation, have been implicated in the mechanisms of age-related macular degeneration (AMD). The expression level of the adapter protein p66shc, a key protein that regulates cellular oxidative stress, is relatively low under normal conditions because of the effects of silent mating type information regulation 2 homolog 1 (SIRT1) on the binding of fully deacetylated histone H3' to the p66shc promoter region, thus inhibiting p66shc transcription and expression. The equilibrium between SIRT1 and p66shc is disrupted in the presence of various stresses, including AMD. As a major target gene, SIRT1 is regulated by microRNA-34a (miR-34a), and overexpression of miR-34a results in significant inhibition of post-transcriptional expression of SIRT1. Furthermore, our recent studies demonstrated that miR-34a is significantly upregulated, accompanied by reduced tolerance to oxidative stress in hydrogen peroxide-induced prematurely senescent ARPE-19 cells. Moreover, the expression of SIRT1 is decreased, whereas that of p66shc is increased in these cells. Accordingly, miR-34a may play a key role in age-related susceptibility to oxidative stress in ARPE-19 cells by targeting the SIRT1/p66shc pathway, leading to AMD. In this review article, we discuss the functions of miR-34a in modulating the SIRT1/p66shc pathway in age-related conditions, including AMD.