From real world data to real world evidence to improve outcomes in neuro-ophthalmology.

Real-world data (RWD) can be defined as all data generated during routine clinical care. This includes electronic health records, disease-specific registries, imaging databanks, and data linkage to administrative databases. In the field of neuro-ophthalmology, the intersection of RWD and clinical practice offers unprecedented opportunities to understand and treat rare diseases. However, translating RWD into real-world evidence (RWE) poses several challenges, including data quality, legal and ethical considerations, and sustainability of data sources. This review explores existing RWD sources in neuro-ophthalmology, such as patient registries and electronic health records, and discusses the challenges of data collection and standardisation. We focus on research questions that need to be answered in neuro-ophthalmology and provide an update on RWE generated from various RWD sources. We review and propose solutions to some of the key barriers that can limit translation of a collection of data into impactful clinical evidence. Careful data selection, management, analysis, and interpretation are critical to generate meaningful conclusions.

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(Ortho-k) among the children who, despite undergoing Ortho-k treatment, exhibited an axial elongation of at least 0.50mm over 1 year. DESIGN: Multicenter, randomized, parallel-group, single-blind clinical trial (ClinicaTrials.gov,NCT04722874). PARTICIPANTS: Eligible children were aged 8-13 years with a cycloplegic spherical equivalent refraction of -1.00 to -5.00 diopters in the initial Ortho-k fitting examination and had annual axial length (AL) elongation ≥ 0.50 mm despite undergoing Ortho-k for 1 year. A total of 48 children were enrolled from March 2021 to January 2022, and the final follow-up was completed in March 2023. METHODS: Children were randomly assigned to the RLRL combined with Ortho-k(RCO) or the Ortho-k group in a 2:1 ratio. The Ortho-k group wore Ortho-k at least 8 hours per night, while the RCO group received daily RLRL therapy twice a day for 3 minutes in addition to Ortho-k wearing. 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 post-randomization follow-up using the modified intention-to-treat principle. RESULTS: A total of 47(97.9%) children were included in the analysis (30 in the RCO group and 17 in the Ortho-k group). The mean axial elongation rate before the trial was 0.60mm/year in the RCO group and 0.61mm/year in the Ortho-k group. After 12 months following the intended intervention, the adjusted mean AL changes were -0.02mm(95% CI, -0.08 to +0.03 mm) in the RCO group and 0.27mm(0.19-0.34 mm) in the Ortho-k group. The adjusted mean difference in AL change was -0.29mm(-0.44 to -0.14mm) between the RCO and Ortho-k groups. The percentage of children achieving an uncorrected visual acuity greater than 20/25 was similar in the RCO (64.3%) and Ortho-k (65.5%) groups (Chi2 test, P=0.937). CONCLUSIONS: Combining RLRL therapy with Ortho-k may offer a promising approach to optimize axial elongation control among myopic children. This approach also potentially allows children to achieve satisfactory visual acuity, reducing the daytime dependence on corrective eyewear.

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.

Epidemiologic association and shared genetic architecture between cataract and hearing difficulties among middle-aged and older adults.

Age-related cataract and hearing difficulties are major sensory disorders that often co-exist in the global-wide elderly and have a tangible influence on the quality of life. However, the epidemiologic association between cataract and hearing difficulties remains unexplored, while little is known about whether the two share their genetic etiology. We first investigated the clinical association between cataract and hearing difficulties using the UK Biobank covering 502,543 individuals. Both unmatched analysis (adjusted for confounders) and a matched analysis (one control matched for each patient with cataract according to confounding factors) were undertaken and confirmed that cataract was associated with hearing difficulties (OR, 2.12; 95% CI, 1.98-2.27; OR, 2.03; 95% CI, 1.86-2.23, respectively). Furthermore, we explored and quantified the shared genetic architecture of these two complex sensory disorders at the common variant level using the bivariate causal mixture model (MiXeR) and conditional/conjunctional false discovery rate method based on the largest available genome-wide association studies of cataract (N = 585,243) and hearing difficulties (N = 323,978). Despite detecting only a negligible genetic correlation, we observe polygenic overlap between cataract and hearing difficulties and identify 6 shared loci with mixed directions of effects. Follow-up analysis of the shared loci implicates candidate genes QKI, STK17A, TYR, NSF, and TCF4 likely contribute to the pathophysiology of cataracts and hearing difficulties. In conclusion, this study demonstrates the presence of epidemiologic association between cataract and hearing difficulties and provides new insights into the shared genetic architecture of these two disorders at the common variant level.

Development and validation of a smartwatch algorithm for differentiating physical activity intensity in health monitoring.

To develop and validate a machine learning based algorithm to estimate physical activity (PA) intensity using the smartwatch with the capacity to record PA and determine outdoor state. Two groups of participants, including 24 adults (13 males) and 18 children (9 boys), completed a sequential activity trial. During each trial, participants wore a smartwatch, and energy expenditure was measured using indirect calorimetry as gold standard. The support vector machine algorithm and the least squares regression model were applied for the metabolic equivalent (MET) estimation using raw data derived from the smartwatch. Exercise intensity was categorized based on MET values into sedentary activity (SED), light activity (LPA), moderate activity (MPA), and vigorous activity (VPA). The classification accuracy was evaluated using area under the ROC curve (AUC). The METs estimation accuracy were assessed via the mean absolute error (MAE), the correlation coefficient, Bland-Altman plots, and intraclass correlation (ICC). A total of 24 adults aged 21-34 years and 18 children aged 9-13 years participated in the study, yielding 1790 and 1246 data points for adults and children respectively for model building and validation. For adults, the AUC for classifying SED, MVPA, and VPA were 0.96, 0.88, and 0.86, respectively. The MAE between true METs and estimated METs was 0.75 METs. The correlation coefficient and ICC were 0.87 (p < 0.001) and 0.89, respectively. For children, comparable levels of accuracy were demonstrated, with the AUC for SED, MVPA, and VPA being 0.98, 0.89, and 0.85, respectively. The MAE between true METs and estimated METs was 0.80 METs. The correlation coefficient and ICC were 0.79 (p < 0.001) and 0.84, respectively. The developed model successfully estimated PA intensity with high accuracy in both adults and children. The application of this model enables independent investigation of PA intensity, facilitating research in health monitoring and potentially in areas such as myopia prevention and control.

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.

Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia.

Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucial for halting progressive visual impairment in glaucoma patients, as there is no cure available. This narrative review aims to: identify reasons for the significant under-diagnosis of glaucoma globally, particularly in Australia, elucidate the role of primary healthcare in glaucoma diagnosis using Australian healthcare as an example, and discuss how recent advances in artificial intelligence (AI) can be implemented to improve diagnostic outcomes. Glaucoma is a prevalent disease in ageing populations and can have improved visual outcomes through appropriate treatment, making it essential for general medical practice. In countries such as Australia, New Zealand, Canada, USA, and the UK, optometrists serve as the gatekeepers for primary eye care, and glaucoma detection often falls on their shoulders. However, there is significant variation in the capacity for glaucoma diagnosis among eye professionals. Automation with Artificial Intelligence (AI) analysis of optic nerve photos can help optometrists identify high-risk changes and mitigate the challenges of image interpretation rapidly and consistently. Despite its potential, there are significant barriers and challenges to address before AI can be deployed in primary healthcare settings, including external validation, high quality real-world implementation, protection of privacy and cybersecurity, and medico-legal implications. Overall, the incorporation of AI technology in primary healthcare has the potential to reduce the global prevalence of undiagnosed glaucoma cases by improving diagnostic accuracy and efficiency.

Evaluation of the Observational Associations and Shared Genetics Between Glaucoma With Depression and Anxiety.

Purpose: Glaucoma, a leading cause of blindness worldwide, is suspected to exhibit a notable association with psychological disturbances. This study aimed to investigate epidemiological associations and explore shared genetic architecture between glaucoma and mental traits, including depression and anxiety. Methods: Multivariable logistic regression and Cox proportional hazards regression models were employed to investigate longitudinal associations based on UK Biobank. A stepwise approach was used to explore the shared genetic architecture. First, linkage disequilibrium score regression inferred global genetic correlations. Second, MiXeR analysis quantified the number of shared causal variants. Third, specific shared loci were detected through conditional/conjunctional false discovery rate (condFDR/conjFDR) analysis and characterized for biological insights. Finally, two-sample Mendelian randomization (MR) was conducted to investigate bidirectional causal associations. Results: Glaucoma was significantly associated with elevated risks of hospitalized depression (hazard ratio [HR] = 1.54; 95% confidence interval [CI], 1.01-2.34) and anxiety (HR = 2.61; 95% CI, 1.70-4.01) compared to healthy controls. Despite the absence of global genetic correlations, MiXeR analysis revealed 300 variants shared between glaucoma and depression, and 500 variants shared between glaucoma and anxiety. Subsequent condFDR/conjFDR analysis discovered 906 single-nucleotide polymorphisms (SNPs) jointly associated with glaucoma and depression and two associated with glaucoma and anxiety. The MR analysis did not support robust causal associations but indicated the existence of pleiotropic genetic variants influencing both glaucoma and depression. Conclusions: Our study enhances the existing epidemiological evidence and underscores the polygenic overlap between glaucoma and mental traits. This observation suggests a correlation shaped by pleiotropic genetic variants rather than being indicative of direct causal relationships.

Seasonal Variations in Vitamin D Levels and the Incident Dementia Among Older Adults Aged ≥60 Years in the UK Biobank.

Background: Limited knowledge exists regarding the association between dementia incidence and vitamin D insufficiency/deficiency across seasons. Objective: This study aimed to evaluate the impact of seasonal serum vitamin D (25(OH)D) levels on dementia and its subtypes, considering potential modifiers. Methods: We analyzed 193,003 individuals aged 60-73 at baseline (2006-2010) from the UK Biobank cohort, with follow-up until 2018. 25(OH)D were measured at baseline, and incident dementia cases were identified through hospital records, death certificates, and self-reports. Results: Out of 1,874 documented all-cause dementia cases, the median follow-up duration was 8.9 years. Linear and nonlinear associations between 25(OH)D and dementia incidence across seasons were observed. In multivariable-adjusted analysis, 25(OH)D deficiency was associated with a 1.5-fold (95% CIs: 1.2-2.0), 2.2-fold (1.5-3.0), 2.0-fold (1.5-2.7), and 1.7-fold (1.3-2.3) increased incidence of all-cause dementia in spring, summer, autumn, and winter, respectively. Adjusting for seasonal variations, 25(OH)D insufficiency and deficiency were associated with a 1.3-fold (1.1-1.4) and 1.8-fold (1.6-2.2) increased dementia incidence, respectively. This association remained significant across subgroups, including baseline age, gender, and education levels. Furthermore, 25(OH)D deficiency was associated with a 1.4-fold (1.1-1.8) and 1.5-fold (1.1-2.0) higher incidence of Alzheimer's disease and vascular dementia, respectively. These associations remained significant across all subgroups. Conclusions: 25(OH)D deficiency is associated with an increased incidence of dementia and its subtypes throughout the year.

Leading determinants of incident dementia among individuals with and without the apolipoprotein E ε4 genotype: a retrospective cohort study.

BACKGROUND: Little is known regarding the leading risk factors for dementia/Alzheimer's disease (AD) in individuals with and without APOE4. The identification of key risk factors for dementia/Alzheimer's disease (AD) in individuals with and without the APOE4 gene is of significant importance in global health. METHODS: Our analysis included 110,354 APOE4 carriers and 220,708 age- and sex-matched controls aged 40-73 years at baseline (between 2006-2010) from UK Biobank. Incident dementia was ascertained using hospital inpatient, or death records until January 2021. Individuals of non-European ancestry were excluded. Furthermore, individuals without medical record linkage were excluded from the analysis. Moderation analysis was tested for 134 individual factors. RESULTS: During a median follow-up of 11.9 years, 4,764 cases of incident all-cause dementia and 2065 incident AD cases were documented. Hazard ratios (95% CIs) for all-cause dementia and AD associated with APOE4 were 2.70(2.55-2.85) and 3.72(3.40-4.07), respectively. In APOE4 carriers, the leading risk factors for all-cause dementia included low self-rated overall health, low household income, high multimorbidity risk score, long-term illness, high neutrophil percentage, and high nitrogen dioxide air pollution. In non-APOE4 carriers, the leading risk factors included high multimorbidity risk score, low overall self-rated health, low household income, long-term illness, high microalbumin in urine, high neutrophil count, and low greenspace percentage. Population attributable risk for these individual risk factors combined was 65.1%, and 85.8% in APOE4 and non-APOE4 carriers, respectively. For 20 risk factors including multimorbidity risk score, unhealthy lifestyle habits, and particulate matter air pollutants, their associations with incident dementia were stronger in non-APOE4 carriers. For only 2 risk factors (mother's history of dementia, low C-reactive protein), their associations with incident all-cause dementia were stronger in APOE4 carriers. CONCLUSIONS: Our findings provide evidence for personalized preventative approaches to dementia/AD in APOE4 and non-APOE4 carriers. A mother's history of dementia and low levels of C-reactive protein were more important risk factors of dementia in APOE4 carriers whereas leading risk factors including unhealthy lifestyle habits, multimorbidity risk score, inflammation and immune-related markers were more predictive of dementia in non-APOE4 carriers.

Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis.

Background: We aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia. Methods: We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study. Findings: With the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m. Interpretation: Our findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations. Funding: This project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Metabolomic age and risk of 50 chronic diseases in community-dwelling adults: A prospective cohort study.

It is unclear how metabolomic age is associated with the risk of a wide range of chronic diseases. Our analysis included 110,692 participants (training: n = 27,673; testing: n = 27,673; validating: n = 55,346) aged 39-71 years at baseline (2006-2010) from the UK Biobank. Incident chronic diseases were identified using inpatient records, or death registers until January 2021. Predicted metabolomic age was trained and tested based on 168 metabolomics. Metabolomic age was linked to the risk of 50 diseases in the validation dataset. The median follow-up duration for individual diseases ranged from 11.2 years to 11.9 years. After controlling for false discovery rate, chronological age-adjusted age gap (CAAG) was significantly associated with the incidence of 25 out of 50 chronic diseases. After adjustment for full covariates, associations with 15 chronic diseases remained significant. Greater CAAG was associated with increased risk of eight cardiometabolic disorders (including cardiovascular diseases and diabetes), some cancers, alcohol use disorder, chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease and age-related macular degeneration. The association between CAAG and risk of peripheral vascular disease, other cardiac diseases, fracture, cataract and thyroid disorder was stronger among individuals with unhealthy diet than in those with healthy diet. The association between CAAG and risk of some conditions was stronger in younger individuals, those with metabolic disorders or low education. Metabolomic age plays an important role in the development of multiple chronic diseases. Healthy diet and high education may mitigate the risk for some chronic diseases due to metabolomic age acceleration.

Frailty and risk of microvascular disease in adults with prediabetes.

BACKGROUND AND AIMS: To assess the relationship between frailty phenotypes and the risk of MVD among prediabetics in two prospective cohorts. METHODS: The study included 66,068 and 226 participants with prediabetes from the UK Biobank (UKB) and Chinese Ocular Imaging Project (COIP) in Guangzhou, China, respectively. Frailty was evaluated using the Fried phenotype, which includes weight loss, fatigue, low grip strength, low physical activity, and slow walking pace. The outcome was incident microvascular diseases, including diabetic retinopathy, nephropathy, and neuropathy in UKB, and decline rate of retinal capillary density in COIP. Cox models were used to calculate hazard ratios (HRs) and 95 % confidential intervals (CIs), and mixed linear model was used to determine the ß and 95 % CIs. RESULTS: At baseline, 27,491 (41.6 %) and 3332 (5.0 %) prediabetics were classified as pre-frail and frail, respectively in UKB. During a median follow-up of 8.9 years, 3784 cases of incident microvascular diseases were identified. Pre-frailty and frailty were significantly associated with a higher risk of microvascular diseases (HR 1.21 [1.12, 1.30] for pre-frailty; HR 1.60 [1.42, 1.81] for frailty). Compared to no frailty, the adjusted HRs for frailty were 1.42 (0.73, 2.76) for retinopathy, 1.49 (1.31, 1.70) for nephropathy, and 2.37 (1.69, 3.33) for neuropathy. Fatigue and walking pace were the strongest mediators of frailty and microvascular diseases. In the COIP, the lowest handgrip strength group exhibited 62%-63 % faster annually decline in retinal capillary density compared with the highest group (all P<0.05). CONCLUSIONS: Each frailty point is important for prediabetics because both pre-frailty and frailty phenotypes are strongly associated with an increased risk of microvascular diseases and its subtypes. Lower handgrip strength presents with faster decline in retinal capillary density.

Ocular manifestations of obstructive sleep apnea: a systematic review and meta-analysis.

BACKGROUND: The association of obstructive sleep apnea (OSA) with development of eye diseases is unclear. This current systematic review and meta-analysis attempts to summarize and analyze associations between OSA and ocular disorders in the literature. METHODS: PubMed, EMBASE, Google Scholar, Web Of Science, and Scopus databases were searched from 1901 to July 2022 in accordance with the Preferred Reporting in Systematic Review & Meta-Analysis (PRISMA). Our primary outcome assessed the association between OSA and the odds of developing floppy eyelid syndrome (FES), glaucoma, non-arteritic anterior ischemic optic neuropathy (NAION), retinal vein occlusion (RVO), keratoconus (KC), idiopathic intracranial hypertension (IIH), age-related macular degeneration (AMD), and central serous chorioretinopathy (CSR) through odds ratio calculated at the 95% confidence interval. RESULTS: Forty-nine studies were included for systematic review and meta-analysis. The pooled OR estimate was highest for NAION [3.98 (95% CI 2.38, 6.66)], followed by FES [3.68 (95% CI 2.18, 6.20)], RVO [2.71(95% CI 1.83, 4.00)], CSR [2.28 (95% CI 0.65, 7.97)], KC [1.87 (95% CI 1.16, 2.99)], glaucoma [1.49 (95% CI 1.16, 1.91)], IIH [1.29 (95% CI 0.33, 5.01)], and AMD [0.92 [95% CI 0.24, 3.58] All observed associations were significant (p < 0.001) aside from IIH and AMD. CONCLUSION: OSA is significantly associated with NAION, FES, RVO, CSR, KC, and glaucoma. Clinicians should be informed of these associations so early recognition, diagnosis, and treatment of eye disorders can be addressed in at-risk groups, and early referral to ophthalmic services is made to prevent vision disturbances. Similarly, ophthalmologists seeing patients with any of these conditions should consider screening and referring patients for assessment of possible OSA.

Anisometropia and its association with refraction development in highly myopic children.

CLINICAL RELEVANCE: Anisometropia can affect visual development in children. Investigations of anisometropia in high myopes would explore potential causes related to anisometropia, highlighting the management of anisometropia in high myopia. BACKGROUND: The prevalence of anisometropia ranged from 0.6% to 4.3% in general paediatric population and from 7% to 14% in myopes. Anisometropia is regarded as an associated factor for myopia development, while myopia progression is a stimulus driving anisometropic development. The purpose of this study was to investigate the prevalence of anisometropia and its association with refraction development in Chinese children with high myopia. METHODS: In the cohort study, a total of 1,577 highly myopic (spherical equivalent ≤-5.0D) children aged 4-18 years were included. Refractive parameters (dioptre of sphere, dioptre of cylinder, corneal curvature radius, and axial length) of both eyes were measured after cycloplegia. The prevalence and degree of anisometropia were compared among refractive groups (non-parametric tests or chi-square tests), and regression analyses were used to determine associated factors of anisometropia. The statistical significance was set to P < 0.05 (two-tailed). RESULTS: In highly myopic children with a mean (standard deviation) age of 13.06 (2.80) years, the proportions of spherical equivalent anisometropia, cylindrical anisometropia and spherical anisometropia ≥1.00 D were 34.5%, 21.9% and 39.9%, respectively. There was more spherical equivalent anisometropia associated with more severe astigmatism (P for trend <0.001). In the multivariate regression analysis, more spherical equivalent anisometropia, cylindrical anisometropia and spherical anisometropia were associated with higher degrees of astigmatism (standard beta = -0.175, -0.148 and -0.191, respectively). More spherical anisometropia was associated with better spherical power (standard beta = 0.116). CONCLUSION: The proportion of anisometropia in highly myopic children was high, compared with previously reported general population, and more severe anisometropia was associated with higher degree of cylindrical power, but not spherical power.

Deep neural network-estimated age using optical coherence tomography predicts mortality.

The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT). A total of 84,753 high-quality OCT images from 53,159 individuals in the UK Biobank were included, among which 12,631 3D-OCT images from 8,541 participants without any reported medical conditions at baseline were used to develop an age prediction model. For the remaining 44,618 participants, OCT age gap, the difference between the OCT-predicted age and chronological age, was calculated for each participant. Cox regression models assessed the association between OCT age gap and mortality. The DNN model predicted age with a mean absolute error of 3.27 years and showed a strong correlation of 0.85 with chronological age. After a median follow-up of 11.0 years (IQR 10.9-11.1 years), 2,429 deaths (5.44%) were recorded. For each 5-year increase in OCT age gap, there was an 8% increased mortality risk (hazard ratio [HR] = 1.08, CI:1.02-1.13, P = 0.004). Compared with an OCT age gap within ± 4 years, OCT age gap less than minus 4 years was associated with a 16% decreased mortality risk (HR = 0.84, CI: 0.75-0.94, P = 0.002) and OCT age gap more than 4 years showed an 18% increased risk of death incidence (HR = 1.18, CI: 1.02-1.37, P = 0.026). OCT imaging could serve as an ageing biomarker to predict biological age with high accuracy and the OCT age gap, defined as the difference between the OCT-predicted age and chronological age, can be used as a marker of the risk of mortality.

Impacts of Chronic Kidney Disease on Retinal Neurodegeneration: A Cross-Cohort Analysis.

PURPOSE: To assess the cross-sectional and longitudinal associations between chronic kidney disease (CKD) and ganglion cell-inner plexiform layer (GCIPL) thickness in a UK Biobank population and a Chinese cohort. DESIGN: Prospective observational cohort study and cross-sectional study. METHOD: This study included 23,014 individuals without neurodegenerative diseases from the UK Biobank, and 3 years of annual follow-up data of 2197 individuals from a Chinese cohort. Three groups were defined by estimated glomerular filtration rate (eGFR) based on serum creatinine classifying CKD severity as no CKD, mild CKD, and moderate to severe CKD (MS-CKD). GCIPL thickness, measured using optical coherence tomography, was analyzed through linear regression over time to determine its decline rate in micrometers per year. Linear regression models were used to assess the correlation between renal function and both the baseline GCIPL thickness and the GCIPL decline rate. RESULTS: The cross-sectional analysis in a largely white population showed that poorer renal function negatively correlated with GCIPL thickness with a mean of 0.15 µm thinner (95% confidence interval [CI] -0.30 to -0.01; P = .042) in mild CKD and 0.83 µm thinner (95% CI -1.34 to -0.32; P = .001) in MS-CKD compared with that of control subjects without CKD. Longitudinal analysis in the Chinese cohort showed that the GCIPL decreased more rapidly in persons with poorer renal function. After correcting for all confounding factors, the rate of GCIPL thinning was 0.30 µm/year (95% CI -0.41 to -0.19; P < .001) more in the mild CKD group and 0.52 µm/year (95% CI -0.79 to -0.26; P < .001) more in the MS-CKD group compared with control subjects without CKD. This relationship also occurred in individuals with diabetes or hypertension. CONCLUSIONS: Poor renal function was associated with a lower baseline GCIPL thickness in the UK population and a faster decline rate in Chinese participants. However, the detailed underlying mechanisms still need further exploration.

Cutoff values of axial length/corneal radius ratio for determining myopia vary with age among 3-18 years old children and adolescents.

PURPOSE: To investigate the effectiveness and cutoffs of axial length/corneal radius (AL/CR) ratio for myopia detection in children by age. METHODS: Totally, 21 kindergartens and schools were enrolled. Non-cycloplegic autorefraction (NCAR), axial length (AL), horizontal and vertical meridian of corneal radius (CR1, CR2), and cycloplegic autorefraction were measured. Receiver operating characteristic (ROC) curve was used to obtain the effectiveness and cutoff for myopia detection. RESULTS: Finally, 7803 participants aged 3-18 years with mean AL/CR ratio of 2.99 ± 0.16 were included. Area under the ROC curve (AUC) of AL/CR ratio for myopia detection (0.958 for AL/CR1, 0.956 for AL/CR2, 0.961 for AL/CR) was significantly larger than that of AL (0.919, all P < 0.001), while AUCs of the three were similar with different cutoffs (> 2.98, > 3.05, and > 3.02). When divided by age, the ROC curves of AL/CR ratio in 3- to 5-year-olds showed no significance or low accuracy (AUCs ≤ 0.823) in both genders. In ≥ 6-year-olds, the accuracies were promising (AUCs ≥ 0.883, all P < 0.001), the cutoffs basically increased with age (from > 2.93 in 6-year-olds to > 3.07 in 18-year-olds among girls, and from > 2.96 in 6-year-olds to > 3.07 in 18-year-olds among boys). In addition, boys presented slightly larger cutoffs than girls in all ages except for 16 and 18 years old. For children aged 3-5 years, AL/CR ratio or AL combined with NCAR increased AUC to > 0.900. CONCLUSION: AL/CR ratio provided the best prediction of myopia with age-dependent cutoff values for all but preschool children, and the cutoffs of boys were slightly larger than those of girls. For preschool children, AL/CR ratio or AL combined with NCAR is recommended to achieve satisfactory accuracy. AL/CR ratio calculated by two meridians showed similar predictive power but with different cutoffs.

Retinal age gap as a predictive biomarker for future risk of clinically significant diabetic retinopathy.

AIMS: Retinal age derived from fundus images has been verified as a novel ageing biomarker. We aim to explore the association between retinal age gap (retinal age minus chronological age) and incident diabetic retinopathy (DR). METHODS: Retinal age prediction was performed by a deep learning model, trained and validated based on 19,200 fundus images of 11,052 disease-free participants. Retinal age gaps were determined for 2311 patients with diabetes who had no history of diabetic retinopathy at baseline. DR events were ascertained by data linkage to hospital admissions. Cox proportional hazards regression models were performed to evaluate the association between retinal age gaps and incident DR. RESULTS: During the median follow-up period of 11.0 (interquartile range: 10.8-11.1) years, 183 of 2311 participants with diabetes developed incident DR. Each additional year of the retinal age gap was associated with a 7% increase in the risk of incident DR (hazard ratio [HR] = 1.07, 95% confidence interval [CI] 1.02-1.12, P = 0.004), after adjusting for confounding factors. Participants with retinal age gaps in the fourth quartile had a significantly higher DR risk compared to participants with retinal age gaps in the lowest quartile (HR = 2.88, 95% CI 1.61-5.15, P < 0.001). CONCLUSIONS: We found that higher retinal age gap was associated with an increased risk of incident DR. As an easy and non-invasive biomarker, the retinal age gap may serve as an informative tool to facilitate the individualized risk assessment and personalized screening protocol for DR.

Macular and peripapillary Choroidal Vascularity Index in children with different refractive status.

OBJECTIVES: To characterize choroidal vascular changes in children with different refractive status. METHODS: A study including 5864 children aged 6-9 years was performed to investigate the choroidal vascular index (CVI) in myopic, emmetropic and hyperopic eyes. Each participant had a comprehensive ocular examination with cycloplegic autorefraction performed, axial length (AL) measured and Swept Source-Optical Coherence Tomography (SS-OCT) scans acquired. Choroidal thickness (ChT) was measured by built-in software, and CVI was calculated using a previously validated self-developed algorithm. RESULTS: The mean ChT and CVI were 275.88 ± 53.34 µm and 34.91 ± 3.83 in the macula region, and 191.96 ± 46.28 µm and 32.35 ± 4.21 in the peripapillary region. CVI was significantly lowest for myopes, followed by emmetropes and hyperopes (P < 0.001). CVI varied between different sectors separated by the Early Treatment of Diabetic Retinopathy Study (ETDRS) grid (P < 0.001). Macular CVI decreased horizontally from nasal to temporal quadrant with lowest in center fovea, and vertically from superior to inferior quadrants. Peripapillary CVI was highest in the nasal and lowest in the inferior sector. Multiple regression showed that spherical equivalent (SE), AL, intraocular pressure (IOP), ChT, age, and gender were significantly related to CVI (P < 0.05). CONCLUSIONS: In children, the distribution of CVI in the posterior pole is not uniform. A decreased CVI was observed from hyperopia to myopia and was associated with decreased SE, elongated AL, and choroidal thinning. Further study of changes in CVI during myopia onset and progression is required to better understand the role of the choroidal vasculature in myopia development.