Photoreceptor degeneration in ABCA4-associated retinopathy and its genetic correlates

Pfau, Maximilian; Cukras, Catherine A; Huryn, Laryssa A.; Zein, Wadih M.; Ullah, Ehsan; Boyle, Marisa P.; Turriff, Amy; Chen, Michelle A.; Hinduja, Aarti; Siebel, Hermann E.A.; Hufnagel, Robert B.; Jeffrey, Brett G.; Brooks, Brian P.

doi:10.1172/jci.insight.155373

Cited by 19 publications

(13 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies have used different approaches to quantify variant severity and derived metrics relating to delay in disease initiation (based on perimetric or Optical Coherence Tomography (OCT) ellipsoid zone data). 25 , 26 We found significant positive correlation between our ERG-based scoring of variant severity and these metrics ( r = 0.639 and r = 0.668 for correlation between our scores and the metrics of Pfau et al. 25 and Cideciyan et al, 26 respectively).…”

Section: Discussionsupporting

confidence: 54%

See 1 more Smart Citation

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

Glinton

Calcagni

Lilaonitkul

et al. 2022

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 54%

“… 25 , 26 We found significant positive correlation between our ERG-based scoring of variant severity and these metrics ( r = 0.639 and r = 0.668 for correlation between our scores and the metrics of Pfau et al. 25 and Cideciyan et al, 26 respectively). These correlations are depicted in Supplementary Figure S4 .…”

Section: Discussionsupporting

confidence: 54%

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

Glinton

Calcagni

Lilaonitkul

et al. 2022

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

show abstract

“…Future versions of our model could refine the areas beyond just the center subfield and outer ring. For example, instead of analyzing changes within ETDRS regions, which can be subject to floor effects 25 , our model could be extended to estimate expected rod and cone survival for all A-scans.…”

Section: Discussionmentioning

confidence: 99%

Modeling rod and cone photoreceptor cell survivalin vivousing optical coherence tomography

Whitmore

DeLuca

Andorf

et al. 2022

Preprint

View full text Add to dashboard Cite

Many retinal diseases involve the loss of light-sensing photoreceptor cells (rods and cones) over time. The severity and distribution of photoreceptor loss varies widely across diseases and affected individuals, so characterizing the degree and pattern of photoreceptor loss can clarify pathophysiology and prognosis. Currently, in vivo visualization of individual photoreceptors requires technology such as adaptive optics, which has numerous limitations and is not widely used. By contrast, optical coherence tomography (OCT) is nearly ubiquitous in daily clinical practice given its ease of image acquisition and detailed visualization of retinal structure. However, OCT cannot resolve individual photoreceptors, and no OCT-based method exists to distinguish between the loss of rods versus cones. Here, we present a computational model that quantitatively estimates rod versus cone photoreceptor loss from OCT. Using histologic data of human photoreceptor topography, we constructed an OCT-based reference model to simulate outer nuclear layer thinning caused by differential loss of rods and cones. The model was able to estimate rod and cone loss using in vivo OCT data from patients with Stargardt disease and healthy controls. Our model provides a powerful new tool to quantify photoreceptor loss using OCT data alone, with potentially broad applications for research and clinical care.

show abstract

“…Segmentation of the outer nuclear layer (outer plexiform layer/outer nuclear layer boundary to external limiting membrane), the photoreceptor inner segments (external limiting membrane to ellipsoid zone), the outer segments (ellipsoid zone to the RPE–drusen complex [RPEDC]), the RPEDC (RPE to BrM), and the choroid was performed applying a fully automated deep-learning–based segmentation algorithm to the OCT B-scans. 23 – 26 The algorithm was previously shown in eyes with atrophic macular degeneration to segment with an accuracy within the limits of human-expert inter-reader-variability. 23 Based on these segmentations of all 121 B-scans, en face thickness maps were created for each of the segmented layers.…”

Section: Methodsmentioning

confidence: 99%

Choriocapillaris Flow Signal Impairment in Patients With Pseudoxanthoma Elasticum

Loewinger

Pfau

Herrmann

et al. 2023

Invest. Ophthalmol. Vis. Sci.

Self Cite

View full text Add to dashboard Cite

Purpose To quantify choriocapillaris flow alterations in patients with pseudoxanthoma elasticum (PXE) in pre-atrophic stages and its association with structural changes of the choroid and outer retina. Methods Thirty-two eyes of 21 patients with PXE and 35 healthy eyes of 35 controls were included. The density of choriocapillaris flow signal deficits (FDs) was quantified on 6 × 6-mm optical coherence tomography angiography (OCTA) images. Spectral-domain optical coherence tomography (SD-OCT) images were analyzed for thicknesses of the choroid and outer retinal microstructure and correlated with choriocapillaris FDs in the respective Early Treatment Diabetic Retinopathy Study subfield. Results The multivariable mixed model analysis for choriocapillaris FDs revealed significantly higher FDs associated with the group (PXE patients vs. controls +13.6; 95% confidence interval [CI] 9.87–17.3; P < 0.001), with increasing age (+0.22% per year; 95% CI 0.12–0.33; P < 0.001), and with retinal location (significantly higher FDs in nasal compared to temporal subfields). Choroidal thickness (CT) did not differ significantly between both groups ( P = 0.078). The CT and choriocapillaris FDs were inversely correlated (−1.92 µm per %FDs; interquartile range −2.81 to −1.03; P < 0.001). Larger values of the choriocapillaris FDs were associated with significant thinning of the overlying photoreceptor layers (outer segments: −0.21 µm per %FDs, P < 0.001; inner segments: −0.12 µm per %FDs, P = 0.001; outer nuclear layer: −0.72 µm per %FDs; P < 0.001). Conclusions Patients with PXE display significant alterations of the choriocapillaris on OCTA even in pre-atrophic stages and in the absence of significant choroidal thinning. The analysis favors choriocapillaris FDs over choroidal thickness as a potential early outcome measure for future interventional trials in PXE. Further, increased FDs in nasal compared to temporal locations mirror the centrifugal spread of Bruch's membrane calcification in PXE.

show abstract

Photoreceptor degeneration in ABCA4-associated retinopathy and its genetic correlates

Cited by 19 publications

References 45 publications

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

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

Modeling rod and cone photoreceptor cell survivalin vivousing optical coherence tomography

Choriocapillaris Flow Signal Impairment in Patients With Pseudoxanthoma Elasticum

Contact Info

Product

Resources

About