ArticleInvestigative ophthalmology & visual science2024
Distinct Clinical Effects of Two RP1L1 Hotspots in East Asian Patients With Occult Macular Dystrophy (Miyake Disease): EAOMD Report 4.
Article in Investigative ophthalmology & visual science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Review of Four Refined Clinical Entities in Hereditary Retinal Disorders from Japan.International journal of molecular sciences · 2025Review
- Phenotypic and Genotypic Characterization of RP1L1-Associated Retinopathy.Investigative ophthalmology & visual science · 2025Article
- Sex Distributions in Non-ABCA4 Autosomal Macular Dystrophies.Investigative ophthalmology & visual science · 2024Article
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Authors and funding
21 authors at 10 institutions in 4 countries.
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Abstract
Purpose: To characterize the clinical effects of two RP1L1 hotspots in patients with East Asian occult macular dystrophy (OMD). Methods: Fifty-one patients diagnosed with OMD harboring monoallelic pathogenic RP1L1 variants (Miyake disease) from Japan, South Korea, and China were enrolled. Patients were classified into two genotype groups: group A, p.R45W, and group B, missense variants located between amino acids (aa) 1196 and 1201. The clinical parameters of the two genotypes were compared, and deep learning based on spectral-domain optical coherence tomographic (SD-OCT) images was used to distinguish the morphologic differences. Results: Groups A and B included 29 and 22 patients, respectively. The median age of onset in groups A and B was 14.0 and 40.0 years, respectively. The median logMAR visual acuity of groups A and B was 0.70 and 0.51, respectively, and the survival curve analysis revealed a 15-year difference in vision loss (logMAR 0.22). A statistically significant difference was observed in the visual field classification, but no significant difference was found in the multifocal electroretinographic classification. High accuracy (75.4%) was achieved in classifying genotype groups based on SD-OCT images using machine learning. Conclusions: Distinct clinical severities and morphologic phenotypes supported by artificial intelligence-based classification were derived from the two investigated RP1L1 hotspots: a more severe phenotype (p.R45W) and a milder phenotype (1196-1201 aa). This newly identified genotype-phenotype association will be valuable for medical care and the design of therapeutic trials.
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