Decoding the genetic comorbidity network of Alzheimer's disease.

Alzheimer's disease (AD) has emerged as the most prevalent and complex neurodegenerative disorder among the elderly population. However, the genetic comorbidity etiology for AD remains poorly understood. In this study, we conducted pleiotropic analysis for 41 AD phenotypic comorbidities, identifying ten genetic comorbidities with 16 pleiotropy genes associated with AD. Through biological functional and network analysis, we elucidated the molecular and functional landscape of AD genetic comorbidities. Furthermore, leveraging the pleiotropic genes and reported biomarkers for AD genetic comorbidities, we identified 50 potential biomarkers for AD diagnosis. Our findings deepen the understanding of the occurrence of AD genetic comorbidities and provide new insights for the search for AD diagnostic markers.

GWAS-by-subtraction reveals an IOP-independent component of primary open angle glaucoma.

The etiology of primary open angle glaucoma is constituted by both intraocular pressure-dependent and intraocular pressure-independent mechanisms. However, GWASs of traits affecting primary open angle glaucoma through mechanisms independent of intraocular pressure remains limited. Here, we address this gap by subtracting the genetic effects of a GWAS for intraocular pressure from a GWAS for primary open angle glaucoma to reveal the genetic contribution to primary open angle glaucoma via intraocular pressure-independent mechanisms. Seventeen independent genome-wide significant SNPs were associated with the intraocular pressure-independent component of primary open angle glaucoma. Of these, 7 are located outside known normal tension glaucoma loci, 11 are located outside known intraocular pressure loci, and 2 are novel primary open angle glaucoma loci. The intraocular pressure-independent genetic component of primary open angle glaucoma is associated with glaucoma endophenotypes, while the intraocular pressure-dependent component is associated with blood pressure and vascular permeability. A genetic risk score for the intraocular pressure-independent component of primary open angle glaucoma is associated with 26 different retinal micro-vascular features, which contrasts with the genetic risk score for the intraocular pressure-dependent component. Increased understanding of these intraocular pressure-dependent and intraocular pressure-independent components provides insights into the pathogenesis of glaucoma.

Associations of static and dynamic iris parameters in healthy Chinese individuals: the Handan Eye Study.

PURPOSE: To determine the associations between the demographic factors (age and sex) and physiological dynamic iris changes and explore the associated factors for iris cross-sectional area (IA) change in healthy Chinese individuals. METHODS: This cross-sectional study included individuals aged ≥40 years with an open angle and underwent anterior segment optical coherence tomography under light and dark conditions from the follow-up cohort of the Handan Eye Study. Ocular data from the right eye were analyzed. The Pearson correlation coefficient was used to assess the relationship between age and iris parameters, including iris thickness (IT), IA, and iris curvature (IC), as well as the pupil diameter (PD) in the dark, and their changes from light to dark conditions. Linear regression analysis was performed to identify the potential factors associated with IA change. RESULTS: The final analysis included 465 healthy individuals. PD in dark, IA change and PD change decreased with age (P < 0.001), whereas IC increased with age (P < 0.001). IT and IT change were smaller, and IC was larger in women than that in men (P = 0.021, 0.007, and 0.010, respectively). Older age (P = 0.041), larger lens thickness (P = 0.013), larger IC change (P < 0.001), and smaller PD change (P < 0.001) were significantly associated with a smaller IA change. CONCLUSIONS: Our study demonstrated the associations of static and dynamic iris parameters in healthy Chinese individuals. The findings provided a possible explanation for the higher prevalence of primary angle closure disease in elderly and female populations.

The Onset and Progression of Myopia Slows in Chinese 15-Year-Old Adolescents Following Vocational Rather Than Academic School Pathways.

Purpose: The purpose of this study was to investigate the changes in spherical equivalent (SE) and axial length (AL) and cumulative incidence of myopia and high myopia in Chinese 15-year-old adolescents entering a non-academic stream of senior high school education. Methods: A total of 880 first-born twins with a baseline age range of 7 to 15 years were enrolled and followed annually until 18 years of age. Cycloplegic refractions and AL were examined. Educational exposure was divided into academic high school (AHS) and vocational high school (VHS) streams. A piecewise linear mixed-effects model was used to estimate the effect of education exposures on SE development, the slope before the age of 15 years (ß2), and the slope change at the age of 15 years (ß3) was compared between the 2 groups. Results: The curves of refractive development in a myopic direction changed in parallel in the AHS and VHS group before 15 years. For nonmyopic children, ß2 was -0.19 and -0.20 diopters (D)/year (P = 0.270), and ß3 was 0.16 and 0.14 D/year (P = 0.270), in the AHS and VHS groups, respectively. Among patients with myopia, ß2 was -0.52 and -0.54 D/year (P = 0.500), and ß3 was 0.37 and 0.32 D/year (P = 0.004), in the AHS and VHS groups, respectively. The trends in AL were similar. The 3-year cumulative incidence of myopia was 35.3% (AHS) versus 14.7% (VHS; P < 0.001), and that of high myopia was 5.7% and 3.3% (P = 0.129). Conclusions: Students undertaking a VHS rather than an AHS education have slower myopic shifts in refraction and less incident myopia after the age of 15 years.

Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study.

BACKGROUND: For medical artificial intelligence (AI) training and validation, human expert labels are considered the gold standard that represents the correct answers or desired outputs for a given data set. These labels serve as a reference or benchmark against which the model's predictions are compared. OBJECTIVE: This study aimed to assess the accuracy of a custom deep learning (DL) algorithm on classifying diabetic retinopathy (DR) and further demonstrate how label errors may contribute to this assessment in a nationwide DR-screening program. METHODS: Fundus photographs from the Lifeline Express, a nationwide DR-screening program, were analyzed to identify the presence of referable DR using both (1) manual grading by National Health Service England-certificated graders and (2) a DL-based DR-screening algorithm with validated good lab performance. To assess the accuracy of labels, a random sample of images with disagreement between the DL algorithm and the labels was adjudicated by ophthalmologists who were masked to the previous grading results. The error rates of labels in this sample were then used to correct the number of negative and positive cases in the entire data set, serving as postcorrection labels. The DL algorithm's performance was evaluated against both pre- and postcorrection labels. RESULTS: The analysis included 736,083 images from 237,824 participants. The DL algorithm exhibited a gap between the real-world performance and the lab-reported performance in this nationwide data set, with a sensitivity increase of 12.5% (from 79.6% to 92.5%, P<.001) and a specificity increase of 6.9% (from 91.6% to 98.5%, P<.001). In the random sample, 63.6% (560/880) of negative images and 5.2% (140/2710) of positive images were misclassified in the precorrection human labels. High myopia was the primary reason for misclassifying non-DR images as referable DR images, while laser spots were predominantly responsible for misclassified referable cases. The estimated label error rate for the entire data set was 1.2%. The label correction was estimated to bring about a 12.5% enhancement in the estimated sensitivity of the DL algorithm (P<.001). CONCLUSIONS: Label errors based on human image grading, although in a small percentage, can significantly affect the performance evaluation of DL algorithms in real-world DR screening.

Long-Term Prediction and Risk Factors for Incident Visual Field Defect in Nonpathologic High Myopia.

Purpose: To investigate the long-term patterns and risk factors of visual field defect (VFD) development in nonpathologic high myopia (HM) over an 8-year follow-up. Methods: This was an observational cohort study. The VFD classification adhered to the Glaucoma Suspects with High Myopia Study Group. Logistic regression models with generalized estimating equations were used to identify risk factors for VFD development. Results: A total of 330 eyes from 194 patients were included. Among them, 49.4% of eyes developed VFD, with enlarged blind spot and nonspecific defect ranked as the most common VFDs, followed by partial arcuate defect, vertical step, nasal step, paracentral defect, and combined defects. Longer axial length (odds ratio [OR] = 1.43 per 1-mm increase; 95% CI, 1.04-1.95; P = 0.026), thinner central corneal thickness (OR = 1.01 per 1-µm decrease; 95% CI, 1.003-1.02; P = 0.013), worse mean deviation of visual field (OR = 1.51 per 1-dB decrease; 95% CI, 1.14-2.00; P = 0.004), and the presence of peripapillary γ-zone (OR = 5.57; 95% CI, 3.06-10.15; P < 0.001) at baseline correlated with the development of any VFD. By incorporating these factors, the prediction models achieved area under the curves of 0.789 (95% CI, 0.726-0.853) and 0.828 (95% CI, 0.714-0.943) for discriminating the development of any VFD and moderate/severe VFD, respectively, with good calibration power. Conclusions: The development of VFD occurred frequently in individuals with nonpathologic HM and can be effectively predicted using relevant metrics. The findings will aid in expanding our knowledge of optic neuropathy in HM.

Estimating biological age from retinal imaging: a scoping review.

BACKGROUND/AIMS: The emerging concept of retinal age, a biomarker derived from retinal images, holds promise in estimating biological age. The retinal age gap (RAG) represents the difference between retinal age and chronological age, which serves as an indicator of deviations from normal ageing. This scoping review aims to collate studies on retinal age to determine its potential clinical utility and to identify knowledge gaps for future research. METHODS: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist, eligible non-review, human studies were identified, selected and appraised. PubMed, Scopus, SciELO, PsycINFO, Google Scholar, Cochrane, CINAHL, Africa Wide EBSCO, MedRxiv and BioRxiv databases were searched to identify literature pertaining to retinal age, the RAG and their associations. No restrictions were imposed on publication date. RESULTS: Thirteen articles published between 2022 and 2023 were analysed, revealing four models capable of determining biological age from retinal images. Three models, 'Retinal Age', 'EyeAge' and a 'convolutional network-based model', achieved comparable mean absolute errors: 3.55, 3.30 and 3.97, respectively. A fourth model, 'RetiAGE', predicting the probability of being older than 65 years, also demonstrated strong predictive ability with respect to clinical outcomes. In the models identified, a higher predicted RAG demonstrated an association with negative occurrences, notably mortality and cardiovascular health outcomes. CONCLUSION: This review highlights the potential clinical application of retinal age and RAG, emphasising the need for further research to establish their generalisability for clinical use, particularly in neuropsychiatry. The identified models showcase promising accuracy in estimating biological age, suggesting its viability for evaluating health status.

Efficacy and safety of penetrating canaloplasty versus ab externo canaloplasty for primary open-angle glaucoma: A randomized controlled trial.

PURPOSE: To report the 2-year efficacy and safety of penetrating canaloplasty versus ab externo canaloplasty for the treatment of primary open-angle glaucoma (POAG). SETTING: A single surgical site in China. DESIGN: This was a prospective, randomized controlled trial. POAG patients were randomly assigned to the penetrating canaloplasty or ab externo canaloplasty group. METHODS: This study enrolled POAG patients who underwent penetrating canaloplasty or ab externo canaloplasty randomly. Surgical success, intraocular pressure (IOP), number of glaucoma medications, and surgical complications were evaluated until 24 months post-operatively. Surgical success was defined as 6 mmHg ≤ IOP ≤21 mmHg with an IOP reduction ≥20%, which included qualified success (with or without medications) and complete success (without medications). RESULTS: A total of 52 eyes (45 patients) were randomly assigned to one of two groups: the penetrating canaloplasty group (PCP, n = 26) or the ab externo canaloplasty group (CP, n = 26). The probabilities of qualified success and complete success were 92.3% and 76.9%, respectively, in the PCP group and 64.1% and 52.1%, respectively, in the CP group at 24 months (p = 0.013, p = 0.042, log-rank test). The mean IOP decreased from 30.8 ± 10.7 and 28.6 ± 11.8 mmHg to 14.1 ± 3.3 mmHg in the PCP group and 22.1 ± 13.6 mmHg in the CP group at year two (p = 0.007). The PCP group also received fewer medications (0.2 ± 0.5) than did the CP group (0.7 ± 1.2) at year two (p = 0.038). Post-operative complications were similar, and the most common complications were transient IOP elevation and hyphema in the PCP group (42.3%, 46.2%) and the CP group (38.5%, 23.1%) (p > 0.05). CONCLUSIONS: Compared to ab externo canaloplasty, penetrating canaloplasty had a greater surgical success rate and better IOP reduction with a comparable rate of complications.

Choroidal Vascularity and Axial Length Elongation in Highly Myopic Children: A 2-Year Longitudinal Investigation.

Purpose: To examine the influence of subfoveal choroidal thickness (SFCT) and choroidal vascularity index (CVI) on axial length (AL) elongation over a 2-year period in highly myopic children. Methods: In this is prospective, longitudinal, observational study, 163 participants (74%), who were 8 to 18 years of age with bilateral high myopia (sphere ≤ -6.0 D) and without pathologic myopia, completed follow-up visits over 2 years. All participants underwent baseline and follow-up ocular examinations, including swept-source optical coherence tomography (SS-OCT) and AL measurements. SFCT and CVI were derived from SS-OCT scans using a deep-learning-based program for choroidal structure assessment. Results: The mean age of the participants at baseline was 15.0 years (±2.3), with males constituting 47% of the cohort. An inverse relationship was observed between AL elongation and increases in baseline age, baseline SFCT, and CVI, as well as a decrease in baseline AL. Adjusting for other factors, every 10-µm increase in SFCT and each 1% increase in CVI were associated with decreases in AL elongation of 0.007 mm (95% confidence interval [CI], -0.013 to -0.002; P = 0.011) and 0.010 mm (95% CI, -0.019 to 0.000; P = 0.050), respectively. The incorporation of SFCT or CVI into predictive models improved discrimination over models using only age, gender, and baseline AL (both P < 0.05, likelihood ratio test). Conclusions: Our findings suggest a possible association between a thinner choroid and increased AL elongation over 2 years in children with high myopia, after adjusting for potential baseline risk factors such as age, gender, and initial AL.

A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children.

The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images. Based on deep learning architecture, DeepMyopia had been trained and internally validated on a large cohort of retinal fundus images (n = 1,638,315) and then externally tested on datasets from seven sites in China (n = 22,060). Our results demonstrated robustness of DeepMyopia, with AUCs of 0.908, 0.813, and 0.810 for 1-, 2-, and 3-year myopia onset prediction with the internal test set, and AUCs of 0.796, 0.808, and 0.767 with the external test set. DeepMyopia also effectively stratified children into low- and high-risk groups (p < 0.001) in both test sets. In an emulated randomized controlled trial (eRCT) on the Shanghai outdoor cohort (n = 3303) where DeepMyopia showed effectiveness in myopia prevention compared to NonCyc-based model, with an adjusted relative reduction (ARR) of -17.8%, 95% CI: -29.4%, -6.4%. DeepMyopia-assisted interventions attained quality-adjusted life years (QALYs) of 0.75 (95% CI: 0.53, 1.04) per person and avoided blindness years of 13.54 (95% CI: 9.57, 18.83) per 1 million persons compared to natural lifestyle with no active intervention. Our findings demonstrated DeepMyopia as a reliable and efficient AI-based decision support system for intervention guidance for children.

Shared whole environmental etiology between Alzheimer's disease and age-related macular degeneration.

The comorbidity of Alzheimer's disease (AD) and age-related macular degeneration (AMD) has been established in clinical and genetic studies. There is growing interest in determining the shared environmental factors associated with both conditions. Recent advancements in record linkage techniques enable us to identify the contributing factors to AD and AMD from a wide range of variables. As such, we first constructed a knowledge graph based on the literature, which included all statistically significant risk factors for AD and AMD. An environment-wide association study (EWAS) was conducted to assess the contribution of various environmental factors to the comorbidity of AD and AMD based on the UK biobank. Based on the conditional Q-Q plots and Bayesian algorithm, several shared environmental factors were identified, which could be categorized into the domains of health condition, biological sample parameters, body index, and attendance availability. Finally, we generated a shared etiology landscape for AD and AMD by combining existing knowledge with our novel findings.

Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review.

Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Conventional manual evaluation techniques for MGD face limitations characterized by inefficiencies, high subjectivity, limited big data processing capabilities, and a dearth of quantitative analytical tools. With rapidly advancing artificial intelligence (AI) techniques revolutionizing ophthalmology, studies are now leveraging sophisticated AI methodologies--including computer vision, unsupervised learning, and supervised learning--to facilitate comprehensive analyses of meibomian gland (MG) evaluations. These evaluations employ various techniques, including slit lamp examination, infrared imaging, confocal microscopy, and optical coherence tomography. This paradigm shift promises enhanced accuracy and consistency in disease evaluation and severity classification. While AI has achieved preliminary strides in meibomian gland evaluation, ongoing advancements in system development and clinical validation are imperative. We review the evolution of MG evaluation, juxtapose AI-driven methods with traditional approaches, elucidate the specific roles of diverse AI technologies, and explore their practical applications using various evaluation techniques. Moreover, we delve into critical considerations for the clinical deployment of AI technologies and envisages future prospects, providing novel insights into MG evaluation and fostering technological and clinical progress in this arena.

Malignant glaucoma treated by low-dose laser cycloplasty: a 1-year multicenter prospective non-comparative study.

AIM: To report a one-year clinical outcomes of low-dose laser cycloplasty (LCP) among malignant glaucoma patients. METHODS: In this prospective, multicenter, non-comparative clinical study, participants with malignant glaucoma were recruited and underwent LCP at eight ophthalmic centers in China. Patients were followed up at 1wk, 1, 3, 6, and 12mo. Intraocular pressure (IOP), number of glaucoma medications, anterior chamber depth (ACD), and complications were recorded. Anatomical success was defined as the reformation of the anterior chamber based on slit-lamp biomicroscopy. Recurrence was defined by the presence of a shallow or flat anterior chamber after initial recovery from treatment. RESULTS: A total of 34 eyes received LCP. Mean IOP and medications decreased from 36.1±11.5 mm Hg with 3.3±1.5 glaucoma medications pre-treatment to 20.9±9.8 mm Hg (P<0.001) with 2.9±1.6 medications (P=0.046) at 1d, and 17.4±6.7 mm Hg (P<0.001) with 1.3±1.7 medications (P<0.001) at 12mo. The ACD increased from 1.1±0.8 mm at baseline to 1.7±1.0 mm and to 2.0±0.5 mm at 1d and 12mo, respectively. A total of 32 (94.1%) eyes achieved initial anatomical success. During follow-up, 2 (5.9%) eyes failed and 8 (23.5%) eyes relapsed, yielding a 12-month anatomical success rate of 64.3%. Complications including anterior synechia (8.82%), choroidal/ciliary detachment (5.88%) and hypopyon (2.94%) were observed within 1wk. CONCLUSION: LCP is simple, safe, and effective in reforming the anterior chamber in malignant glaucoma.

ChatFFA: An ophthalmic chat system for unified vision-language understanding and question answering for fundus fluorescein angiography.

Existing automatic analysis of fundus fluorescein angiography (FFA) images faces limitations, including a predetermined set of possible image classifications and being confined to text-based question-answering (QA) approaches. This study aims to address these limitations by developing an end-to-end unified model that utilizes synthetic data to train a visual question-answering model for FFA images. To achieve this, we employed ChatGPT to generate 4,110,581 QA pairs for a large FFA dataset, which encompassed a total of 654,343 FFA images from 9,392 participants. We then fine-tuned the Bootstrapping Language-Image Pre-training (BLIP) framework to enable simultaneous handling of vision and language. The performance of the fine-tuned model (ChatFFA) was thoroughly evaluated through automated and manual assessments, as well as case studies based on an external validation set, demonstrating satisfactory results. In conclusion, our ChatFFA system paves the way for improved efficiency and feasibility in medical imaging analysis by leveraging generative large language models.

Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts.

PURPOSE: To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and to evaluate their usefulness in predicting DR development and progression. DESIGN: Multicenter, multiethnic cohort study. PARTICIPANTS: This study included 17 675 participants from the UK Biobank (UKB) who had baseline prediabetes or diabetes, identified in accordance with the 2021 American Diabetes Association guidelines, and were free of baseline DR and an additional 638 participants with type 2 diabetes mellitus from the Guangzhou Diabetic Eye Study (GDES) for external validation. Diabetic retinopathy was determined by ICD-10 codes in the UKB cohort and revised ETDRS grading criteria in the GDES cohort. METHODS: Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance (NMR) assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort using NMR technology. Model assessments included the concordance (C) statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical usefulness in both cohorts. MAIN OUTCOME MEASURES: DR development and progression and retinal microvascular damage. RESULTS: Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C statistic, 0.802 [95% confidence interval (CI), 0.760-0.843] vs. 0.751 [95% CI, 0.706-0.796]; P = 5.56 × 10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C statistic, 0.807 [95% CI, 0.711-0.903] vs. 0.617 [95% CI, 0.494-0.740]; P = 1.68 × 10-4) and progression (C statistic, 0.797 [95% CI, 0.712-0.882] vs. 0.665 [95% CI, 0.545-0.784]; P = 0.003) in the external GDES cohort. Improvements in NRIs, IDIs, and clinical usefulness also were evident in both cohorts (all P < 0.05). In addition, lactate and citrate were associated with microvascular damage across macular and optic nerve head regions among Chinese GDES (all P < 0.05). CONCLUSIONS: Metabolomic profiling may be effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Metabolic profiling reveals circulating biomarkers associated with incident and prevalent Parkinson's disease.

The metabolic profile predating the onset of Parkinson's disease (PD) remains unclear. We aim to investigate the metabolites associated with incident and prevalent PD and their predictive values in the UK Biobank participants with metabolomics and genetic data at the baseline. A panel of 249 metabolites was quantified using a nuclear magnetic resonance analytical platform. PD was ascertained by self-reported history, hospital admission records and death registers. Cox proportional hazard models and logistic regression models were used to investigate the associations between metabolites and incident and prevalent PD, respectively. Area under receiver operating characteristics curves (AUC) were used to estimate the predictive values of models for future PD. Among 109,790 participants without PD at the baseline, 639 (0.58%) individuals developed PD after one year from the baseline during a median follow-up period of 12.2 years. Sixty-eight metabolites were associated with incident PD at nominal significance (P < 0.05), spanning lipids, lipid constituent of lipoprotein subclasses and ratios of lipid constituents. After multiple testing corrections (P < 9 × 10-4), polyunsaturated fatty acids (PUFA) and omega-6 fatty acids remained significantly associated with incident PD, and PUFA was shared by incident and prevalent PD. Additionally, 14 metabolites were exclusively associated with prevalent PD, including amino acids, fatty acids, several lipoprotein subclasses and ratios of lipids. Adding these metabolites to the conventional risk factors yielded a comparable predictive performance to the risk-factor-based model (AUC = 0.766 vs AUC = 0.768, P = 0.145). Our findings suggested metabolic profiles provided additional knowledge to understand different pathways related to PD before and after its onset.