Sex Distributions in Non-ABCA4 Autosomal Macular Dystrophies

Purpose

We sought to explore whether sex imbalances are discernible in several autosomally inherited macular dystrophies.

Methods

We searched the electronic patient records of our large inherited retinal disease cohort, quantifying numbers of males and females with the more common (non-ABCA4) inherited macular dystrophies (associated with BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3). BEST1 cases were subdivided into typical autosomal dominant and recessive disease. For PRPH2, only patients with variants at codons 172 or 142 were included. Recessive PROM1 and recessive RP1L1 cases were excluded because these variants give a more widespread or peripheral degeneration. The proportion of females was calculated for each condition; two-tailed binomial testing was performed. Where a significant imbalance was found, previously published cohorts were also explored.

Results

Of 325 patients included, numbers for BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3 were 152, 35, 30, 50, 14, and 44, respectively. For autosomal dominant Best disease (n = 115), there were fewer females (38%; 95% confidence interval [CI], 29-48%; P = 0.015). For EFEMP1-associated disease (n = 35), there were significantly more females (77%; 95% CI, 60%-90%; P = 0.0019). No significant imbalances were seen for the other genes. When pooling our cohort with previous large dominant Best disease cohorts, the proportion of females was 37% (95% CI, 31%-43%; P = 1.2 × 10-5). Pooling previously published EFEMP1-cases with ours yielded an overall female proportion of 62% (95% CI, 54%-69%; P = 0.0023).

Conclusions

This exploratory study found significant sex imbalances in two autosomal macular dystrophies, suggesting that sex could be a modifier. Our findings invite replication in further cohorts and the investigation of potential mechanisms.

Spectrum of Genetic Variants in the Most Common Genes Causing Inherited Retinal Disease in a Large Molecularly Characterized United Kingdom Cohort

PURPOSE

Inherited retinal disease (IRD) is a leading cause of blindness. Recent advances in gene-directed therapies highlight the importance of understanding the genetic basis of these disorders. This study details the molecular spectrum in a large United Kingdom (UK) IRD patient cohort.

DESIGN

Retrospective study of electronic patient records.

PARTICIPANTS

Patients with IRD who attended the Genetics Service at Moorfields Eye Hospital between 2003 and July 2020, in whom a molecular diagnosis was identified.

METHODS

Genetic testing was undertaken via a combination of single-gene testing, gene panel testing, whole exome sequencing, and more recently, whole genome sequencing. Likely disease-causing variants were identified from entries within the genetics module of the hospital electronic patient record (OpenEyes Electronic Medical Record). Analysis was restricted to only genes listed in the Genomics England PanelApp R32 Retinal Disorders panel (version 3.24), which includes 412 genes associated with IRD. Manual curation ensured consistent variant annotation and included only plausible disease-associated variants.

MAIN OUTCOME MEASURES

Detailed analysis was performed for variants in the 5 most frequent genes (ABCA4, USH2A, RPGR, PRPH2, and BEST1), as well as for the most common variants encountered in the IRD study cohort.

RESULTS

We identified 4415 individuals from 3953 families with molecularly diagnosed IRD (variants in 166 genes). Of the families, 42.7% had variants in 1 of the 5 most common IRD genes. Complex disease alleles contributed to disease in 16.9% of affected families with ABCA4-associated retinopathy. USH2A exon 13 variants were identified in 43% of affected individuals with USH2A-associated IRD. Of the RPGR variants, 71% were clustered in the ORF15 region. PRPH2 and BEST1 variants were associated with a range of dominant and recessive IRD phenotypes. Of the 20 most prevalent variants identified, 5 were not in the most common genes; these included founder variants in CNGB3, BBS1, TIMP3, EFEMP1, and RP1.

CONCLUSIONS

We describe the most common pathogenic IRD alleles in a large single-center multiethnic UK cohort and the burden of disease, in terms of families affected, attributable to these variants. Our findings will inform IRD diagnoses in future patients and help delineate the cohort of patients eligible for gene-directed therapies under development.

FINANCIAL DISCLOSURE(S)

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

Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography

Purpose

Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype-phenotype correlations.

Methods

International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function.

Results

Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants.

Conclusions

This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease.

Translational Relevance

Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies.

Description of a patient cohort with Hereditary Sensory Neuropathy Type 1 without retinal disease Macular Telangiectasia type 2 - implications for retinal screening in HSN1

BACKGROUND AND AIMS

Pathogenic variants in the genes encoding serine palmitoyl transferase (SPTLC1 or SPTLC2) are the most common causes of the rare peripheral nerve disorder Hereditary Sensory Neuropathy Type 1 (HSN1). Macular telangiectasia type 2 (MacTel), a retinal disorder associated with disordered serine-glycine metabolism and has been described in some patients with HSN1. This study aims to further investigate this association in a cohort of people with HSN1.

METHODS

Fourteen patients with a clinically and genetically confirmed diagnosis of HSN1 from the National Hospital for Neurology and Neurosurgery (NHNN, University College London Hospitals NHS Foundation Trust, London, United Kingdom) were recruited to the MacTel Registry, between July 2018 and April 2019. Two additional patients were identified from the dataset of the international clinical registry study (www.lmri.net). Ocular examination included fundus autofluorescence, blue light and infrared reflectance, macular pigment optical density mapping, and optical coherence tomography.

RESULTS

Twelve patients had a pathogenic variant in the SPTLC1 gene, with p.Cys133Trp in eleven cases (92%) and p.Cys133Tyr in one case (8%). Four patients had a variant in the SPTLC2 gene. None of the patients showed clinical evidence of MacTel.

INTERPRETATION

The link between HSN1 and MacTel seems more complex than can solely be explained by the genetic variants. An extension of the spectrum of SPTLC1/2-related disease with phenotypic pleiotropy is proposed. HSN1 patients should be screened for visual symptoms and referred for specialist retinal screening, but the association of the two diseases is likely to be variable and remains unexplained. This article is protected by copyright. All rights reserved.

Treatment of chronic diabetic macular oedema with intravitreal fluocinolone acetonide implant; real-life analysis of outcomes during overall treatment period

PURPOSE

To assess the clinical efficacy of the fluocinolone acetonide (FA) intravitreal implant (Iluvien, Alimera Sciences) over a 12-month period in a population resistant to treatment with first-line anti-VEGF agents.

METHODS

This study is a retrospective cohort study assessing functional and anatomical outcomes in 13 eyes of 12 patients treated for diabetic macular oedema (DMO) with a single fluocinolone implant (FA) (Iluvien) under real-world conditions. The follow-up period includes the time of first intravitreal treatment (incl anti-VEGF or short-lasting steroids) given until 12 months post FA implant insertion. Primary outcomes were best corrected visual acuity (BCVA), measured using the modified Early Treatment Diabetic Retinopathy Study (ETDRS) grading scale, and central foveal thickness (CFT), measured using Topcon 3DOCT-2000 (Topcon Inc) SD-OCT imaging. Mean BCVA and CFT were measured before anti-VEGF treatment, after anti-VEGF treatment, at the time of Iluvien implant insertion, and 6 and 12 months after Iluvien implant insertion. The t-paired sample test was used to ascertain statistical significance of changes in comparison of two samples while the ANOVA analysis was used in comparison of three or more samples.

RESULTS

The baseline BCVA (SD) of the cohort prior to initiation of anti-VEGF treatment was 47.45 (12.27) ETDRS letters whilst the mean CFT (SD) was 579 (203) microns. Following completion of anti-VEGF therapy, the mean improvement in vision was 8.9 ETDRS letters (p = 0.1) whilst the mean reduction in CFT was 197 microns (p = 0.028). Mean BCVA (SD) at the time of insertion of the FA implant was 55.15 (11.16) ETDRS letters and mean (SD) CFT at time of insertion of the FA was 454.62 μm (109.51). Following the 12-month treatment period with the FA implant, BCVA (SD) was 62.15 (10.25) ETDRS letters (p = 0.0331) and the mean (SD) CFT was 404.36 μm (142.92), a change of -50.26 μm from baseline (p = 0.0369).

CONCLUSIONS

This study has shown that statistically significant improvements in BCVA and CFT can be achieved over a 12-month period with the Iluvien implant. The implant has been shown to be a safe option in the treatment of DMO and may have a role to play in achieving good functional and anatomical outcomes in DMO while also reducing the frequency of follow-up appointments required to maintain stable vision in the working-age population.

Acute posterior multifocal placoid pigment epitheliopathy (APMPPE)

Background

Acute posterior multifocal placoid pigment epitheliopathy (APMPPE) is a rare inflammatory eye disease, affecting the inner choroid and the outer retina. Recent advances in multimodal imaging have been important in the understanding of the pathophysiology of the disease, allowing a better characterization of the morphology of this condition.

Methods

Narrative review.

Results

In this review, a comprehensive overview of clinical features, imaging findings, treatment management, and long-term outcomes of patients with APMPPE will be provided.

Conclusions

Although APMPPE was originally believed to be a self-limited condition with a good prognosis, the disease can be recurrent and result in significant loss of vision function. Fundus imaging plays an important role in the diagnosis and management of the disease, allowing to evaluate response to treatment and onset of complications.

Choroidal involvement in non-infectious posterior scleritis

Purpose

To provide a comprehensive overview of choroidal involvement in non-infectious posterior scleritis; including different imaging modalities and their clinical usefulness.

Methods

Narrative review.

Results

Posterior scleritis is an uncommon yet potentially sight-threatening inflammation of the sclera. During the disease process, inflammation can spread to the adjacent choroid, causing different manifestations of choroidal involvement: (1) increased choroidal thickness, (2) choroidal vasculitis, (3) presentation as a choroidal or subretinal mass in nodular posterior scleritis, and (4) choroidal folds, choroidal effusion and exudative retinal detachment.

Conclusions

Clinical characteristics and multimodal imaging can aid in diagnosing and monitoring disease progression and response to treatment in non-infectious posterior scleritis with choroidal involvement.

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

Question

Is deep learning–based segmentation of macular disease in optical coherence tomography (OCT) suitable for clinical use?

Findings

In this diagnostic study of OCT data from 173 patients with age-related macular degeneration or diabetic macular edema, model segmentations qualitatively ranked better or comparable for clinical applicability to 1 or more expert grader segmentations in 127 scans (73%) by a panel of 3 retinal specialists. Scans with high quantitative accuracy scores were not reliably associated with higher rankings.

Meaning

These findings suggest that qualitative evaluation adds to quantitative approaches when assessing clinical applicability of segmentation tools and clinician satisfaction in practice.

Importance

Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning systems for automatic OCT segmentation have recently been developed. However, the potential clinical applicability of these systems is largely unknown.

Objective

To evaluate a deep learning model for whole-volume segmentation of 4 clinically important pathological features and assess clinical applicability.

Design, Setting, Participants

This diagnostic study used OCT data from 173 patients with a total of 15 558 B-scans, treated at Moorfields Eye Hospital. The data set included 2 common OCT devices and 2 macular conditions: wet age-related macular degeneration (107 scans) and diabetic macular edema (66 scans), covering the full range of severity, and from 3 points during treatment. Two expert graders performed pixel-level segmentations of intraretinal fluid, subretinal fluid, subretinal hyperreflective material, and pigment epithelial detachment, including all B-scans in each OCT volume, taking as long as 50 hours per scan. Quantitative evaluation of whole-volume model segmentations was performed. Qualitative evaluation of clinical applicability by 3 retinal experts was also conducted. Data were collected from June 1, 2012, to January 31, 2017, for set 1 and from January 1 to December 31, 2017, for set 2; graded between November 2018 and January 2020; and analyzed from February 2020 to November 2020.

Main Outcomes and Measures

Rating and stack ranking for clinical applicability by retinal specialists, model-grader agreement for voxelwise segmentations, and total volume evaluated using Dice similarity coefficients, Bland-Altman plots, and intraclass correlation coefficients.

Results

Among the 173 patients included in the analysis (92 [53%] women), qualitative assessment found that automated whole-volume segmentation ranked better than or comparable to at least 1 expert grader in 127 scans (73%; 95% CI, 66%-79%). A neutral or positive rating was given to 135 model segmentations (78%; 95% CI, 71%-84%) and 309 expert gradings (2 per scan) (89%; 95% CI, 86%-92%). The model was rated neutrally or positively in 86% to 92% of diabetic macular edema scans and 53% to 87% of age-related macular degeneration scans. Intraclass correlations ranged from 0.33 (95% CI, 0.08-0.96) to 0.96 (95% CI, 0.90-0.99). Dice similarity coefficients ranged from 0.43 (95% CI, 0.29-0.66) to 0.78 (95% CI, 0.57-0.85).

Conclusions and Relevance

This deep learning–based segmentation tool provided clinically useful measures of retinal disease that would otherwise be infeasible to obtain. Qualitative evaluation was additionally important to reveal clinical applicability for both care management and research.