The role of the retinal vasculature in age-related macular degeneration: a spotlight on OCTA.

Age-related macular degeneration (AMD) remains a disease with high morbidity and an incompletely understood pathophysiological mechanism. The ocular blood supply has been implicated in the development of the disease process, of which most research has focused on the role of the choroid and choriocapillaris. Recently, interest has developed into the role of the retinal vasculature in AMD, particularly with the advent of optical coherence tomography angiography (OCTA), which enables non-invasive imaging of the eye's blood vessels. This review summarises the up-to-date body of work in this field including the proposed links between observed changes in the retinal vessels and the development of AMD and potential future directions for research in this area. The review highlights that the strongest evidence supports the observation that patients with early to intermediate AMD have reduced vessel density in the superficial vascular complex of the retina, but also emphasises the need for caution when interpreting such studies due to their variable methodologies and nomenclature.

Deep Learning-Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4).

Purpose: We introduce a deep learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). Design: Retrospective analysis of a large data set of retinal OCT images. Participants: A total of 3456 adults aged between 51 and 102 years whose OCT images were collected under the PINNACLE project. Methods: Our system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46 496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct 2 parallel 1.5-hour semistructured interviews with 2 independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome Measures: We checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD, and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late-stage wet and dry AMD against an established clinical grading system and a demographic baseline model. Results: Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognized as known biomarkers used in established grading systems, and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid, and thick from thin choroids, and, in simulation, outperformed clinically used grading systems in prognostic value. Conclusions: Using self-supervised deep learning, we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep learning tools can accelerate the discovery of novel prognostic biomarkers. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease.

The human immune system displays substantial variation between individuals, leading to differences in susceptibility to autoimmune disease. We present single-cell RNA sequencing (scRNA-seq) data from 1,267,758 peripheral blood mononuclear cells from 982 healthy human subjects. For 14 cell types, we identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTLs, with most showing cell type-specific effects on gene expression. We subsequently show how eQTLs have dynamic allelic effects in B cells that are transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to interindividual variation in immune function. Finally, using a Mendelian randomization approach, we identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. This work brings together genetic epidemiology with scRNA-seq to uncover drivers of interindividual variation in the immune system.

Demyelinating Events Following Initiation of Anti-TNFα Therapy in the British Society for Rheumatology Biologics Registry in Rheumatoid Arthritis.

OBJECTIVE: To establish the incidence of demyelination in patients who have received anti-tumor necrosis factor alpha (anti-TNFα) therapy, through analysis of adverse events reported in a prospective cohort of patients receiving biological therapies. METHODS: A cohort study was performed on prospectively acquired data via the British Society for Rheumatology Biologics Register in Rheumatoid Arthritis. All potential demyelinating events during follow-up were extracted and classified as definite, probable, or possible blinded to treatment data. The point of starting an anti-TNF therapy in individuals with no prior reported demyelination was the time of exposure. Crude rates of demyelination and standardized incident rates (SIRs) compared with the general UK population were calculated. RESULTS: Thirty-five individuals with demyelinating events were identified from a total pool of 13,489. The median age at study entry was 44 years, and the median disease duration was 8 years; 71% were female. Events occurred a median of 3 (interquartile range 1-5) years from the start of the first anti-TNF therapy. Twenty-six events occurred in individuals still taking anti-TNFα therapy; of the other 9, 6 were within 90 days of drug withdrawal. The raw incidence of demyelination was 19.7/100,000 patient-years (95% CI 13.7-27.3). The SIR in the whole population was 1.38 (95% CI 0.96-1.92) and 0.83 (0.51-1.26) limited to definite/probable cases. CONCLUSIONS: Demyelination following anti-TNF therapy is uncommon. Patients receiving anti-TNFα therapy show a marginally increased SIR; this is lost in sensitivity analyses. Patients concerned about anti-TNFα-associated demyelination can be relatively reassured by these data.