Background: Several epidemiological studies have investigated the association between ambient air pollution and age-related macular degeneration (AMD). However, a consensus has not yet been reached. Our meta-analysis aimed to clarify this association. Methods: Databases, including PubMed, EMBASE, and Web of Science, were searched for relevant studies from 01 January 2000 to 30 January 2024. English-language, peer-reviewed studies using cross-sectional, prospective, or retrospective cohorts and case–control studies exploring this relationship were included. Two authors independently extracted data and assessed study quality. A random-effects model was used to calculate pooled covariate-adjusted odds ratios. Heterogeneity across studies was also tested. Results: We identified 358 relevant studies, of which eight were included in the meta-analysis. Four studies evaluated the association between particulate matter less than 2.5 μm in diameter (PM_2.5) and AMD, and three studies explored the relationship between nitrogen dioxide (NO₂) or ozone (O₃) and AMD. The pooled odds ratios were 1.16 (95% confidence interval [CI]: 1.11–1.21), 1.17 (95% CI: 1.09–1.25), and 1.06 (95% CI: 1.05–1.07), respectively. Conclusion: Current evidence suggests a concomitant positive but not causal relationship between PM_2.5, NO₂, or O₃ and AMD risk.

Background: The aim of this study was to explore the association between diabetic retinopathy (DR) and the variants of uncoupling proteins (UCPs) genes in a Chinese population of type 2 diabetes, in total and in patients of different glycemic status separately. Methods: This case-control study included a total of 3107 participants from two datasets, among which 662 were DR patients (21.31%). Eighteen tag single nucleotide polymorphisms (SNPs) of UCP1, UCP2, and UCP3 were selected as genetic markers. TaqMan probes, Sequenom MassARRAY MALDI-TOF mass spectrometry platform and Affymetrix Genome-Wide Human SNP Array were used for genotyping. Online SHEsis software was used for association analysis. Bonferroni correction was used for multiple comparisons correction. Results: Three SNPs of UCP1: rs7688743 (A allele, OR = 1.192, p = 0.013), rs3811787 (T allele, OR = 0.863, p = 0.023), and rs10011540 (G allele, OR = 1.368, p = 0.004) showed association with DR after the adjustment of glucose, but only rs10011540 was marginally significantly associated with DR when Bonferroni correction was strictly applied (p_adj = 0.048). In patients with uncontrolled glucose, rs7688743 (A allele, p = 0.012, OR = 1.309), rs10011540 (G allele, p = 0.033, OR = 1.432), and rs3811787 (T allele, p = 0.022, OR = 0.811) were associated with DR, while in participants with well controlled glucose, the rs2734827 of UCP3 was associated with DR (A allele, p = 0.017, OR = 0.532). Rs3811787 of UCP1 showed a protective effect to sight threatening DR (T allele, p = 0.007, OR = 0.490), and the association existed after the adjustment for environmental factors and the correction. In patients with uncontrolled glucose, the rs3811787 of UCP1 (T allele, p = 0.017, OR = 0.467) and the rs591758 of UCP3 (C allele, p = 0.026, OR = 0.103) were associated with STDR. While in those with well controlled glucose, only the rs7688743 of UCP1 showed a protective effect (A allele, p = 0.024, OR = 0.049). None of the associations remain significant when Bonferroni correction was strictly applied (all p < 0.05). Conclusions: The rs10011540 and rs3811787 of the UCP1 gene was marginally significantly associated with DR in Chinese type 2 diabetes patients. There might be different mechanisms of DR development in patients with different glycemic status.

Purpose: The purpose of this study was to evaluate the existence and regression of persistent Bergmeister’s papilla (PBP) in myopic eyes and determine its independent predictors. Methods: This cross-sectional population-based study included 472 eyes of 236 myopic children. PBPs were identified with swept-source optical coherence tomography (OCT) and were classified into three types (types I, II, and III) according to their morphologic features. Results: The mean patient age was 12.13 ± 2.60 years (range = 5–18 years), and 118 (50%) participants were boys. The prevalence of PBPs in our study was 67.8% (160/236). There were significant differences in height, spherical equivalent (SE), and axial length (AL) between the PBP and non-PBP groups (P < 0.05). Type I PBP was noted in 173 eyes (66.8%); type II PBP in 59 eyes (22.8%); and type III PBP in 27 eyes (10.4%). The three PBP types showed significant differences in height, AL, and SE (P < 0.001). Stepwise linear regression analysis indicated that the height (B = 4.497, P < 0.001), PBP existence or not (B = −1.434, P < 0.001), and the types of PBP (B = 0.566, P = 0.041) was an independent predictor for AL, respectively. PBP was detected more frequently in the nasal quadrant than in the inferior quadrant of the disc. Conclusions: PBP regression was closely related to the AL and could be used as a new biomarker to indicate the progression of myopia. Translational Relevance: Our analysis of the presence and morphology of PBP might enable clinicians to judge the progression of myopia.

Objectives: This study aims to detect characteristic fundus changes in pathological myopia using deep learning (DL)-based analysis of ultra-widefield (UWF) fundus imaging. Methods: Following the exclusion of low-quality images, this cross-sectional study used 1105 UWF images from 543 patients with high myopia to develop the model, along with 293 images from 150 patients with high myopia for external testing. All images were retrospectively collected from patients with high myopia at Shanghai General Hospital and Shanghai Eye Diseases Prevention and Treatment Center between 2018 and 2024. We trained a DL model based on an ophthalmology foundational model to detect myopic maculopathy (MM) and posterior staphyloma (PS). Results: The proposed RETFound-enhanced model demonstrated robust performance. For five-category classification of MM, it achieved 65.4% accuracy and an F1 score of 0.648, outperforming other methods. In three-category MM classification, it achieved 79.4% accuracy and an F1 score of 0.793. For PS detection, the model reached 84.1% accuracy, an F1 score of 0.814 and an area under the receiver operating characteristic curve (AUROC) of 0.886, highlighting its effectiveness as a screening tool. External validation showed consistent performance, with 64.4% accuracy for five-category MM classification, 79.8% accuracy for three-category classification and 81.2% accuracy for PS, confirming robustness across cohorts. Conclusions: This study presents an effective diagnostic model for pathological myopia using UWF fundus imaging and a foundation model. The integration of DL with non-mydriatic UWF fundus imaging demonstrates promising potential for applications in primary healthcare, particularly in underserved areas, enabling accessible screening for high myopia-related fundus changes.