Work

Instrumenting Carotid Sonography Biomarkers and Polygenic Risk Score As a Novel Screening Approach for Retinal Detachment

Year: 2025

Type: article

Abstract: Purpose: Retinal detachment (RD) is a vision-threatening condition that manifests silently before abrupt disease onset; thus, most of the RD at-risk individuals are left unchecked until the first RD attack. Methods: To establish an RD risk-informing system for a broader population, we utilized carotid ultrasonography (CUS) biometrics, RD polygenic risk score (PRSRD), and clinical covariates (COVs) to assess RD risk predisposition factors. First, a backpropagation logistic regression model identified RD-associated CUS biomarkers and further incorporated them as a multivariable RD-risk nomogram. Next, a PRSRD model was established with the selected single-nucleotide polymorphisms (SNPs) curated as high functional expression candidates in the retina single-cell RNA datasets. Finally, a three-component RD prediction model (CUS, PRSRD, and COVs) was assembled by logistic cumulative analysis. Results: Demographic analysis reported hypertension (HTN) status was associated with RD (odds ratio [OR] = 1.601). The CUS regression model revealed that the minimum flow of the right internal carotid artery (ICA-Qmin; OR = 1.04) and the time-averaged maximum velocity of the right common carotid artery (CCA-TAMAX; OR = 1.03) were associated with increased RD risk. Notably, genome-wide association studies (GWAS) identified three significant SNPs (IGFBPL1 rs117248428, OR = 1.63; CELF2 rs56168975, OR = 1.72; and PAX6 rs11825821, OR = 1.61; P < 5.00 × 10-6) that are highly expressed at the RD border of the retinal pigment epithelium and choroid. Finally, the three-component model demonstrated state-of-the-art RD prediction (AUCHTN+ = 0.95, AUCHTN- = 0.93). Conclusions: Based on instrumenting CUS images and genetic PRSRD, we are proposing a screening method for RD at-risk patients. Translational Relevance: Results from this study demonstrated the combination of CUS and GWAS as a cost-effective, population-wide screening framework for identifying RD at-risk individuals.

Source: Translational Vision Science & Technology

DOI: 10.1167/tvst.14.4.16