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.

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.

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.

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.

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.

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.

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.

Characteristics of myopic maculopathy in Chinese children and adolescents with high myopia.

AIMS: To investigate the characteristics of myopic maculopathy among highly myopic Chinese children and adolescents and explore its associated risk factors. METHODS: Children and adolescents aged 7-17 years with spherical equivalent (SE) ≤ -6.00 dioptres (D) were recruited. Myopic maculopathy was categorised based on the International Meta-Analysis of Pathological Myopia Classification. The extent of diffuse choroidal atrophy (DCA) was classified using Early Treatment Diabetic Retinopathy Study grid (ETDRS). The area of DCA was categorised into three classes relative to optic disk area (DA): A1 (≤1 DA), A2 (1 to ≤5 DA) and A3 (5 to ≤10 DA). Logistic regression was used to identify risk factors associated with myopic maculopathy. RESULTS: Of the 425 participants aged 13.66±2.67 years, the proportions of tessellated fundus and DCA were 11.76% and 12.24%, and no more severe fundus lesions or 'plus' lesions. The proportion of DCA was 27.03% in children under 11, significantly higher than the 9.12% observed in those aged 11 and older (p<0.001). The percentages of DCA involving the outer, middle and central circles of the ETDRS grid were 42.31%, 55.77% and 1.92%. Myopic maculopathy was significantly associated with younger age (p<0.001), longer axial length (AL; p<0.001) and larger ß-zone peripapillary atrophy (ß-PPA; p=0.012). CONCLUSION: In highly myopic children and adolescents, myopic maculopathy predominantly manifested as DCA (12.24%), with no cases of worse myopic maculopathy or 'plus' lesions. Younger age, longer AL and larger ß-PPA were risk factors for myopic maculopathy.

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.

Association of greenspace and natural environment with brain volumes mediated by lifestyle and biomarkers among urban residents.

OBJECTIVES: To examine the associaiton between environmental measures and brain volumes and its potential mediators. STUDY DESIGN: This was a prospective study. METHODS: Our analysis included 34,454 participants (53.4% females) aged 40-73 years at baseline (between 2006 and 2010) from the UK Biobank. Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. RESULTS: Greater proximity to greenspace buffered at 1000 m at baseline was associated with larger volumes of total brain measured 8.8 years after baseline assessment (standardized ß (95% CI) for each 10% increment in coverage: 0.013(0.005,0.020)), grey matter (0.013(0.006,0.020)), and white matter (0.011(0.004,0.017)) after adjustment for covariates and air pollution. The corresponding numbers for natural environment buffered at 1000 m were 0.010 (0.004,0.017), 0.009 (0.004,0.015), and 0.010 (0.004,0.016), respectively. Similar results were observed for greenspace and natural environment buffered at 300 m. The strongest mediator for the association between greenspace buffered at 1000 m and total brain volume was smoking (percentage (95% CI) of total variance explained: 7.9% (5.5-11.4%)) followed by mean sphered cell volume (3.3% (1.8-5.8%)), vitamin D (2.9% (1.6-5.1%)), and creatinine in blood (2.7% (1.6-4.7%)). Significant mediators combined explained 18.5% (13.2-25.3%) of the association with total brain volume and 32.9% (95% CI: 22.3-45.7%) of the association with grey matter volume. The percentage (95% CI) of the association between natural environment and total brain volume explained by significant mediators combined was 20.6% (14.7-28.1%)). CONCLUSIONS: Higher coverage percentage of greenspace and environment may benefit brain health by promoting healthy lifestyle and improving biomarkers including vitamin D and red blood cell indices.

Forecasting Myopic Maculopathy Risk Over a Decade: Development and Validation of an Interpretable Machine Learning Algorithm.

Purpose: The purpose of this study was to develop and validate prediction model for myopic macular degeneration (MMD) progression in patients with high myopia. Methods: The Zhongshan High Myopia Cohort for model development included 660 patients aged 7 to 70 years with a bilateral sphere of ≤-6.00 diopters (D). Two hundred twelve participants with an axial length (AL) ≥25.5 mm from the Chinese Ocular Imaging Project were used for external validation. Thirty-four clinical variables, including demographics, lifestyle, myopia history, and swept source optical coherence tomography data, were analyzed. Sequential forward selection was used for predictor selection, and binary classification models were created using five machine learning algorithms to forecast the risk of MMD progression over 10 years. Results: Over a median follow-up of 10.9 years, 133 patients (20.2%) showed MMD progression in the development cohort. Among them, 69 (51.9%) developed newly-onset MMD, 11 (8.3%) developed patchy atrophy from diffuse atrophy, 54 (40.6%) showed an enlargement of lesions, and 9 (6.8%) developed plus signs. Top six predictors for MMD progression included thinner subfoveal choroidal thickness, longer AL, worse best-corrected visual acuity, older age, female gender, and shallower anterior chamber depth. The eXtreme Gradient Boosting algorithm yielded the best discriminative performance (area under the receiver operating characteristic curve [AUROC] = 0.87 ± 0.02) with good calibration in the training cohort. In a less myopic external validation group (median -5.38 D), 48 patients (22.6%) developed MMD progression over 4 years, with the model's AUROC validated at 0.80 ± 0.008. Conclusions: Machine learning model effectively predicts MMD progression a decade ahead using clinical and imaging indicators. This tool shows promise for identifying "at-risk" high myopes for timely intervention and vision protection.

FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer.

Fundus fluorescein angiography (FFA) is a crucial diagnostic tool for chorioretinal diseases, but its interpretation requires significant expertise and time. Prior studies have used Artificial Intelligence (AI)-based systems to assist FFA interpretation, but these systems lack user interaction and comprehensive evaluation by ophthalmologists. Here, we used large language models (LLMs) to develop an automated interpretation pipeline for both report generation and medical question-answering (QA) for FFA images. The pipeline comprises two parts: an image-text alignment module (Bootstrapping Language-Image Pre-training) for report generation and an LLM (Llama 2) for interactive QA. The model was developed using 654,343 FFA images with 9392 reports. It was evaluated both automatically, using language-based and classification-based metrics, and manually by three experienced ophthalmologists. The automatic evaluation of the generated reports demonstrated that the system can generate coherent and comprehensible free-text reports, achieving a BERTScore of 0.70 and F1 scores ranging from 0.64 to 0.82 for detecting top-5 retinal conditions. The manual evaluation revealed acceptable accuracy (68.3%, Kappa 0.746) and completeness (62.3%, Kappa 0.739) of the generated reports. The generated free-form answers were evaluated manually, with the majority meeting the ophthalmologists' criteria (error-free: 70.7%, complete: 84.0%, harmless: 93.7%, satisfied: 65.3%, Kappa: 0.762-0.834). This study introduces an innovative framework that combines multi-modal transformers and LLMs, enhancing ophthalmic image interpretation, and facilitating interactive communications during medical consultation.

Myopia Control Effect of Repeated Low-Level Red-Light Therapy Combined with Orthokeratology: A Multicenter Randomized Controlled Trial.

PURPOSE: To investigate the efficacy and safety of repeated low-level red-light (RLRL) therapy combined with orthokeratology among children who, despite undergoing orthokeratology, exhibited an axial elongation of at least 0.50 mm over 1 year. DESIGN: Multicenter, randomized, parallel-group, single-blind clinical trial (ClinicaTrials.gov identifier, NCT04722874). PARTICIPANTS: Eligible children were 8-13 years of age with a cycloplegic spherical equivalent refraction of -1.00 to -5.00 diopters at the initial orthokeratology fitting examination and had annual axial length (AL) elongation of ≥0.50 mm despite undergoing orthokeratology. Forty-eight children were enrolled from March 2021 through January 2022, and the final follow-up was completed in March 2023. METHODS: Children were assigned randomly to the RLRL therapy combined with orthokeratology (RCO) group or to the orthokeratology group in a 2:1 ratio. The orthokeratology group wore orthokeratology lenses for at least 8 hours per night, whereas the RCO group received daily RLRL therapy twice daily for 3 minutes in addition to orthokeratology. MAIN OUTCOME MEASURES: The primary outcome was AL change measured at 12 months relative to baseline. The primary analysis was conducted in children who received the assigned intervention and completed at least 1 follow-up after randomization using the modified intention-to-treat principle. RESULTS: Forty-seven children (97.9%) were included in the analysis (30 in the RCO group and 17 in the orthokeratology group). The mean axial elongation rate before the trial was 0.60 mm/year and 0.61 mm/year in the RCO and orthokeratology groups, respectively. After 12 months, the adjusted mean AL changes were -0.02 mm (95% confidence interval [CI], -0.08 to +0.03 mm) in the RCO group and 0.27 mm (95% CI, 0.19-0.34 mm) in the orthokeratology group. The adjusted mean difference in AL change was -0.29 mm (95% CI, -0.44 to -0.14 mm) between the groups. The percentage of children achieving an uncorrected visual acuity of more than 20/25 was similar in the RCO (64.3%) and orthokeratology (65.5%) groups (P = 0.937). CONCLUSIONS: Combining RLRL therapy with orthokeratology may offer a promising approach to optimize axial elongation control among children with myopia. This approach also potentially allows children to achieve satisfactory visual acuity, reducing daytime dependence on corrective eyewear. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Shared genetic aetiology of Alzheimer's disease and age-related macular degeneration by APOC1 and APOE genes.

Background: Alzheimer's disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights into the underlying pathogenesis and inform integrated prevention and treatment for both diseases. Methods: A stratified quantile-quantile (QQ) plot was constructed to detect the pleiotropy among AD and AMD based on genome-wide association studies data from 17 008 patients with AD and 30 178 patients with AMD. A Bayesian conditional false discovery rate-based (cFDR) method was used to identify pleiotropic genes. UK Biobank was used to verify the pleiotropy analysis. Biological network and enrichment analysis were conducted to explain the biological reason for pleiotropy phenomena. A diagnostic test based on gene expression data was used to predict biomarkers for AD and AMD based on pleiotropic genes and their regulators. Results: Significant pleiotropy was found between AD and AMD (significant leftward shift on QQ plots). APOC1 and APOE were identified as pleiotropic genes for AD-AMD (cFDR <0.01). Network analysis revealed that APOC1 and APOE occupied borderline positions on the gene co-expression networks. Both APOC1 and APOE genes were enriched on the herpes simplex virus 1 infection pathway. Further, machine learning-based diagnostic tests identified that APOC1, APOE (areas under the curve (AUCs) >0.65) and their upstream regulators, especially ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and AMD (AUCs >0.8). Conclusion: In this study, we confirmed the genetic pleiotropy between AD and AMD and identified APOC1 and APOE as pleiotropic genes. Further, the integration of multiomics data identified ZNF131, ADNP2 and HINFP as novel diagnostic biomarkers for AD and AMD.

Association of age at diagnosis of diabetes with subsequent risk of age-related ocular diseases and vision acuity.

BACKGROUND: The importance of age on the development of ocular conditions has been reported by numerous studies. Diabetes may have different associations with different stages of ocular conditions, and the duration of diabetes may affect the development of diabetic eye disease. While there is a dose-response relationship between the age at diagnosis of diabetes and the risk of cardiovascular disease and mortality, whether the age at diagnosis of diabetes is associated with incident ocular conditions remains to be explored. It is unclear which types of diabetes are more predictive of ocular conditions. AIM: To examine associations between the age of diabetes diagnosis and the incidence of cataract, glaucoma, age-related macular degeneration (AMD), and vision acuity. METHODS: Our analysis was using the UK Biobank. The cohort included 8709 diabetic participants and 17418 controls for ocular condition analysis, and 6689 diabetic participants and 13378 controls for vision analysis. Ocular diseases were identified using inpatient records until January 2021. Vision acuity was assessed using a chart. RESULTS: During a median follow-up of 11.0 years, 3874, 665, and 616 new cases of cataract, glaucoma, and AMD, respectively, were identified. A stronger association between diabetes and incident ocular conditions was observed where diabetes was diagnosed at a younger age. Individuals with type 2 diabetes (T2D) diagnosed at < 45 years [HR (95%CI): 2.71 (1.49-4.93)], 45-49 years [2.57 (1.17-5.65)], 50-54 years [1.85 (1.13-3.04)], or 50-59 years of age [1.53 (1.00-2.34)] had a higher risk of AMD independent of glycated haemoglobin. T2D diagnosed < 45 years [HR (95%CI): 2.18 (1.71-2.79)], 45-49 years [1.54 (1.19-2.01)], 50-54 years [1.60 (1.31-1.96)], or 55-59 years of age [1.21 (1.02-1.43)] was associated with an increased cataract risk. T2D diagnosed < 45 years of age only was associated with an increased risk of glaucoma [HR (95%CI): 1.76 (1.00-3.12)]. HRs (95%CIs) for AMD, cataract, and glaucoma associated with type 1 diabetes (T1D) were 4.12 (1.99-8.53), 2.95 (2.17-4.02), and 2.40 (1.09-5.31), respectively. In multivariable-adjusted analysis, individuals with T2D diagnosed < 45 years of age [ß 95%CI: 0.025 (0.009,0.040)] had a larger increase in LogMAR. The ß (95%CI) for LogMAR associated with T1D was 0.044 (0.014, 0.073). CONCLUSION: The younger age at the diagnosis of diabetes is associated with a larger relative risk of incident ocular diseases and greater vision loss.

Myopia and Rate of Peripapillary Retinal Nerve Fiber Layer Thickness in Diabetic Patients Without Retinopathy: A 2-Year Longitudinal Study.

PURPOSE: The aim of this study was to investigate the association between myopia and longitudinal changes in peripapillary retinal nerve fiber layer (pRNFL) thickness in type 2 diabetic patients without diabetic retinopathy (DR). METHODS: A total of 1069 participants with a median follow-up time of 1.9 years were included in this study. The participants were categorized into four groups based on the presence of myopia (≤ -0.5 diopter [D]) and diabetes without DR, including a control group (n = 412), diabetes group (n = 416), myopia group (n = 115), and diabetes + myopia group (n = 126). Peripapillary average and sectoral RNFL measurements were obtained using 6 × 6 mm swept-source optical coherence tomography (SS-OCT) scans centered at the optic disc. The change rate of pRNFL, adjusted for age and sex, was calculated and compared among the four groups to investigate the impact of myopia and diabetes. RESULTS: The baseline estimated pRNFL thickness after adjustment for covariates was 113.7 µm, 116.2 µm, 108.0 µm, and 105.6 µm in the control, diabetes, myopia, and diabetes + myopia group, respectively (diabetes > control > myopia = diabetes + myopia, p < 0.001). The respective average pRNFL loss in the four groups was -0.48 µm/year, -1.11 µm/year, -1.23 µm/year, and -2.62 µm/year (all p < 0.01). The diabetes + myopia group exhibited a greater rate of average pRNFL reduction compared to the other groups (all p < 0.001). Multivariate analysis using a linear mixed-effects model showed that age, diabetes, axial length (AL), and baseline pRNFL thickness were significantly associated with the rate of average pRNFL reduction. CONCLUSIONS: The diabetes group showed a faster rate of average pRNFL thickness reduction compared to healthy controls, regardless of the presence of myopia. The average pRNFL thickness decreased more rapidly when diabetes and myopia were present simultaneously than in the individual diabetes or myopia group. Both diabetes and myopia were associated with accelerated pRNFL loss.

Role of lens in early refractive development: evidence from a large cohort of Chinese children.

AIMS: To document longitudinal changes in spherical equivalent refraction (SER) and related biometric factors during early refractive development. METHODS: This was a prospective cohort study of Chinese children, starting in 2018 with annual follow-ups. At each visit, children received cycloplegic autorefraction and ocular biometry measurements. Lens power (LP) was calculated using Bennett's formula. Children were divided into eight groups based on baseline age: the 3-year-old (n=426, 49.77% girls), 4-year-old (n=834, 47.36% girls), 6-year-old (n=292, 46.58% girls), 7-year-old (n=964, 43.46% girls), 9-year-old (n=981, 46.18% girls), 10-year-old (n=1181, 46.32% girls), 12-year-old (n=504, 49.01%) and 13-year-old (n=644, 42.70%) age groups. RESULTS: This study included right-eye data from 5826 children. The 3-year-old and 4-year-old age groups demonstrated an inflection point in longitudinal SER changes at a mild hyperopic baseline SER (+1 to +2 D), with children with more myopic SER showing hyperopic refractive shifts while those with more hyperopic SER showing myopic shifts. The hyperopic shift in SER was mainly attributed to rapid LP loss and was rarely seen in the older age groups. Axial elongation accelerated in the premyopia stage, accompanied by a partially counter-balancing acceleration of LP loss. For children aged 3-7 years, those with annual SER changes <0.25 D were all mildly hyperopic at baseline (mean: 1.23 D, 95% CI 1.20 to 1.27 D). CONCLUSION: Our findings suggest that during early refractive development, refractions cluster around or above +1.00 D. There is a pushback process in which increases in the rate of LP occur in parallel with increases in axial elongation.

Longitudinal Changes in Choroidal Thickness Varied With Refractive Progression in Myopic and Non-Myopic Children: A Two-Year Cohort Study.

Purpose: To evaluate the longitudinal changes in subfoveal choroidal thickness (SFCT) in children with different refractive status. Methods: A total of 2290 children 3 to 14 years old who attended the first year of kindergarten (G0), first year of primary school (G1), fourth year of primary school (G4), or first year of junior high school (G7) in Guangzhou, China, were recruited and followed up for 2 years. All participants received cycloplegic autorefraction, axial length measurement and SFCT measurement using a CIRRUS HD-OCT device. Children were divided into groups of persistent non-myopia (PNM), persistent myopia (PM), or newly developed myopia (NDM). Children in the PNM and PM groups were further divided into subgroups of stable refraction (absolute mean annual spherical equivalent refraction [SER] change < 0.5 D) and refractive progression (absolute mean annual SER change ≥ 0.5 D). Results: The mean ± SD ages for the G1 to G7 cohorts were 3.89 ± 0.30, 6.79 ± 0.47, 9.71 ± 0.34, and 12.54 ± 0.38, years, respectively. SFCT consistently decreased in the NDM group across the G1 to G7 cohorts (all P < 0.001) and exhibited variability across different age cohorts in the PNM and PM groups. Further subgroup analysis revealed significant thickening of SFCT in the PNM-stable group among the G0, G1, and G7 cohorts (all P < 0.05), whereas it remained stable among all cohorts in the PM-stable group (all P > 0.05). Conversely, SFCT exhibited thinning in the G4 and G7 cohorts in the PM-progressive group (both P < 0.01) and for the entire cohort of children in the PNM-progressive group (P = 0.012). Conclusions: SFCT increased in nonmyopic children with stable refraction, remained stable in myopic children maintained stable refraction, and decreased in those with refractive progression, whether they were myopic or not.