Search

Abstract Primary open-angle glaucoma typically presents as two subtypes. This study aimed to elucidate the shared and distinct genetic architectures of normal-tension (NTG) and high-tension glaucoma (HTG), motivated by the need to develop intraocular pressure (IOP)-independent drug targets for the disease. We conducted a comprehensive multi-ethnic meta-analysis, prioritized variants based on functional annotation, and explored drug-gene interactions. We further assessed the genetic overlap between NTG and HTG using pairwise GWAS analysis. We identified 22 risk loci associated with NTG, 17 of which have not previously been reported for NTG. Two loci, BMP4 and TBKBP1, have not previously been associated with glaucoma at the genome-wide significance level. Our results indicate that while there is a significant overlap in risk loci between tension subtypes, the magnitude of the effect tends to be lower in NTG compared to HTG, particularly for IOP-related loci. Additionally, we identified a potential role for biologic immunomodulatory treatments as neuroprotective agents.

Abstract Structural variants (SVs) are significant contributors to inter-individual genetic variation associated with traits and diseases. Current SV studies using whole-genome sequencing (WGS) have a largely Eurocentric composition, with little known about SV diversity in other ancestries, particularly from Asia. Here, we present a WGS catalogue of 73,035 SVs from 8392 Singaporeans of East Asian, Southeast Asian and South Asian ancestries, of which ~65% (47,770 SVs) are novel. We show that Asian populations can be stratified by their global SV patterns and identified 42,239 novel SVs that are specific to Asian populations. 52% of these novel SVs are restricted to one of the three major ancestry groups studied (Indian, Chinese or Malay). We uncovered SVs affecting major clinically actionable loci. Lastly, by identifying SVs in linkage disequilibrium with single-nucleotide variants, we demonstrate the utility of our SV catalogue in the fine-mapping of Asian GWAS variants and identification of potential causative variants. These results augment our knowledge of structural variation across human populations, thereby reducing current ancestry biases in global references of genetic variation afflicting equity, diversity and inclusion in genetic research.

Background: Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors. Main text: We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n ​= ​5), OCT-based (n ​= ​1), and external ocular photo-based (n ​= ​3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79–0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%–68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%–84.00% and specificity of 74.00%–86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models. Conclusions: The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.

Abstract Background Housing has been associated with dementia risk and disability, but associations of housing with differential patterns of neuropsychiatric symptoms (NPS) among dementia-free older adults remain to be explored. The present study sought to explore the contribution of housing status on NPS and subsyndromes associated with cognitive dysfunction in community-dwelling dementia-free elderly in Singapore. Methods A total of 839 dementia-free elderly from the Epidemiology of Dementia in Singapore (EDIS) study aged ≥ 60 were enrolled in the current study. All participants underwent clinical, cognitive, and neuropsychiatric inventory (NPI) assessments. The housing status was divided into three categories according to housing type. Cognitive function was measured by a comprehensive neuropsychological battery. The NPS were assessed using 12-term NPI and were grouped into four clinical subsyndromes: psychosis, hyperactivity, affective, and apathy. Associations of housing with composite and domain-specific Z-scores, as well as NPI scores, were assessed using generalized linear models (GLM). Binary logistic regression models analysed the association of housing with the presence of NPS and significant NPS (NPI total scores ≥ 4). Results Better housing status (5-room executive apartments, condominium, or private housing) was associated with better NPS (OR = 0.49, 95%CI = 0.24 to 0.98, P

The Meishan-Chiayi area of western Taiwan has a large probability of producing a major earthquake in the near future. Historically, one of the largest and most damaging of Taiwan’s earthquakes occurred there. It is, therefore, important to have a well-constrained upper crustal 3-D shear-wave velocity model that can be used to accurately determine ground motion predictions and fault geometry models used in seismic hazard and risk modelling. In this study, we carried out an ambient noise tomography experiment using 100 seismometers deployed with a ∼2 km spacing on a 20 by 20 km grid. The reliable periods of phase velocity from Rayleigh waves are 0.6 to 6.8 s, providing a well-resolved Vs structure from the surface to a depth of around 4 km. The velocity model displays a prominent, roughly northeast-striking change in Vs that follows the projected surface trace of the blind Chiayi thrust. The uplift of relatively higher Vs rocks in its hanging wall, together with a negative to positive change in dVs suggests that it dips gently eastward across the study area. A northward thickening of the lower Vs crust, together with a high negative dVs in the north of the study area is related to an increased thickness of foreland basin rocks across the Meishan fault. The Vs and dVs models provide reasonable evidence that the Meishan fault can be traced at a high angle from its surface rupture to the base of the model at 4 km depth. It cuts the Chiayi thrust.

Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR. Design: Retrospective study. Participants: A total of 48 269 retinal images from the open-source Kaggle DR detection dataset, the Messidor-1 dataset and the Singapore Epidemiology of Eye Diseases (SEED) study were included. Methods: Using 41 614 retinal photographs from the Kaggle dataset, we developed 5 CNN (Visual Geometry Group 19, ResNet50, InceptionV3, DenseNet201, and EfficientNetV2S) and 4 ViTs models (VAN_small, CrossViT_small, ViT_small, and Hierarchical Vision transformer using Shifted Windows [SWIN]_tiny) for the detection of referable DR. We defined the presence of referable DR as eyes with moderate or worse DR. The comparative performance of all 9 models was evaluated in the Kaggle internal test dataset (with 1045 study eyes), and in 2 external test sets, the SEED study (5455 study eyes) and the Messidor-1 (1200 study eyes). Main Outcome Measures: Area under operating characteristics curve (AUC), specificity, and sensitivity. Results: Among all models, the SWIN transformer displayed the highest AUC of 95.7% on the internal test set, significantly outperforming the CNN models (all P

Abstract Background Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina’s unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. Main text This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson’s disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. Conclusion AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina’s unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Abstract Background To determine the prevalence, risk factors; and impact on patient health and economic outcomes across the laterality spectrum of multiple sensory impairment (MSI) in a multi-ethnic older Asian population. Methods In this population-based study of Singaporeans aged ≥ 60 years, MSI was defined as concomitant vision (visual acuity > 0.3 logMAR), hearing (pure-tone air conduction average > 25 dB), and olfactory (score

Cardiovascular disease (CVD) is the leading cause of death in Asians. We aimed to examine the validity and reliability of self-reported (SR) CVD in 6762 Chinese, Malay, and Indian adults aged 40–80 years who attended the baseline (November 2004) and 6-year follow-up visit (2011–2017) of a population-based cohort study in Singapore. CVD was defined based on the presence of existing (prevalent) or new onset (incident) cases of acute myocardial infarction (AMI) or stroke. The validity of SR-CVD was assessed by comparing it against diagnosed CVD using sensitivity and specificity. The reliability of SR-CVD was evaluated by calculating the percentage of positive agreement between baseline and follow-up visits. The sensitivity and specificity of SR-CVD were 62.7% and 93.8% for prevalent SR-CVD and 50.9% and 98.5% for incident SR-CVD. The negative predictive value (NPV) was 98.1% for both prevalent and incident SR-CVD. The reliability of positive self-reports between the baseline and follow-up was substantial, at 85%. The excellent specificity and NPV of SR-CVD suggest that it could serve as a valuable tool for excluding AMI and stroke. However, its moderate sensitivity suggests that positive SR-CVD reports should prompt further clinical evaluation to prevent potential false positives.

ObjectiveThe aim of this study was to evaluate the accuracy, comprehensiveness, and safety of a publicly available large language model (LLM)—ChatGPT in the sub-domain of glaucoma.DesignEvaluation of diagnostic test or technology.Subjects, participants, and/or controlsWe seek to evaluate the responses of an artificial intelligence chatbot ChatGPT (version GPT-3.5, OpenAI).Methods, intervention, or testingWe curated 24 clinically relevant questions in the domain of glaucoma. The questions spanned four categories: pertaining to diagnosis, treatment, surgeries, and ocular emergencies. Each question was posed to the LLM and the responses obtained were graded by an expert grader panel of three glaucoma specialists with combined experience of more than 30 years in the field. For responses which performed poorly, the LLM was further prompted to self-correct. The subsequent responses were then re-evaluated by the expert panel.Main outcome measuresAccuracy, comprehensiveness, and safety of the responses of a public domain LLM.ResultsThere were a total of 24 questions and three expert graders with a total number of responses of n = 72. The scores were ranked from 1 to 4, where 4 represents the best score with a complete and accurate response. The mean score of the expert panel was 3.29 with a standard deviation of 0.484. Out of the 24 question-response pairs, seven (29.2%) of them had a mean inter-grader score of 3 or less. The mean score of the original seven question-response pairs was 2.96 which rose to 3.58 after an opportunity to self-correct (z-score − 3.27, p = 0.001, Mann–Whitney U). The seven out of 24 question-response pairs which performed poorly were given a chance to self-correct. After self-correction, the proportion of responses obtaining a full score increased from 22/72 (30.6%) to 12/21 (57.1%), (p = 0.026, χ2 test).ConclusionLLMs show great promise in the realm of glaucoma with additional capabilities of self-correction. The application of LLMs in glaucoma is still in its infancy, and still requires further research and validation.

Background In adult patients with high myopia (HM), progressive axial elongation poses a significant risk for the development of subsequent ocular complications that may lead to visual impairment. Effective strategies to reduce or prevent further axial elongation in highly myopic adult patients have not been available so far. Recent studies suggested that medically lowering intraocular pressure (IOP) may reduce axial elongation.Objective This clinical randomised controlled trial (RCT) aims to evaluate the efficacy of medical IOP reduction in adult patients with progressive HM (PHM).Trial design Single-centre, open-label, prospective RCT.Methods This RCT will recruit 152 participants with PHM at the Zhongshan Ophthalmic Center (ZOC). Randomised in a ratio of 1:1, participants will receive IOP-lowering eyedrops (intervention group) or will be followed without treatment (control group) for 12 months. Follow-up visits will be conducted at 1, 6 and 12 months after baseline. Only one eye per eligible participant will be included for analysis. The primary outcome is the change in axial length (AL) within the study period of 12 months. Secondary outcomes include the incidence and progression of visual field (VF) defects, changes in optic disc morphology and incidence and progression of myopic maculopathy. Difference in AL changes between the two groups will be analysed using linear regression analysis. For the secondary outcomes, a multifactor Poisson regression within a generalised linear model will be used to estimate the relative risk of progression in VF defects and myopic maculopathy, and the rate of thinning in retinal nerve fibre layer and ganglion cell-inner plexiform will be assessed through Kaplan-Meier curves and log-rank tests.Ethics and dissemination Full ethics approval for this trial has been obtained from the Ethics Committee of ZOC, Sun Yat-sen University, China (ID: 2023KYPJ110). Results of this trial will be disseminated through peer-reviewed journals and conference presentations.Trial registration number NCT05850936.

Abstract X-chromosomal genetic variants are understudied but can yield valuable insights into sexually dimorphic human traits and diseases. We performed a sex-stratified cross-ancestry X-chromosome-wide association meta-analysis of seven kidney-related traits (n = 908,697), identifying 23 loci genome-wide significantly associated with two of the traits: 7 for uric acid and 16 for estimated glomerular filtration rate (eGFR), including four novel eGFR loci containing the functionally plausible prioritized genes ACSL4, CLDN2, TSPAN6 and the female-specific DRP2. Further, we identified five novel sex-interactions, comprising male-specific effects at FAM9B and AR/EDA2R, and three sex-differential findings with larger genetic effect sizes in males at DCAF12L1 and MST4 and larger effect sizes in females at HPRT1. All prioritized genes in loci showing significant sex-interactions were located next to androgen response elements (ARE). Five ARE genes showed sex-differential expressions. This study contributes new insights into sex-dimorphisms of kidney traits along with new prioritized gene targets for further molecular research.

Abstract Background Cataract diagnosis typically requires in-person evaluation by an ophthalmologist. However, color fundus photography (CFP) is widely performed outside ophthalmology clinics, which could be exploited to increase the accessibility of cataract screening by automated detection. Methods DeepOpacityNet was developed to detect cataracts from CFP and highlight the most relevant CFP features associated with cataracts. We used 17,514 CFPs from 2573 AREDS2 participants curated from the Age-Related Eye Diseases Study 2 (AREDS2) dataset, of which 8681 CFPs were labeled with cataracts. The ground truth labels were transferred from slit-lamp examination of nuclear cataracts and reading center grading of anterior segment photographs for cortical and posterior subcapsular cataracts. DeepOpacityNet was internally validated on an independent test set (20%), compared to three ophthalmologists on a subset of the test set (100 CFPs), externally validated on three datasets obtained from the Singapore Epidemiology of Eye Diseases study (SEED), and visualized to highlight important features. Results Internally, DeepOpacityNet achieved a superior accuracy of 0.66 (95% confidence interval (CI): 0.64–0.68) and an area under the curve (AUC) of 0.72 (95% CI: 0.70–0.74), compared to that of other state-of-the-art methods. DeepOpacityNet achieved an accuracy of 0.75, compared to an accuracy of 0.67 for the ophthalmologist with the highest performance. Externally, DeepOpacityNet achieved AUC scores of 0.86, 0.88, and 0.89 on SEED datasets, demonstrating the generalizability of our proposed method. Visualizations show that the visibility of blood vessels could be characteristic of cataract absence while blurred regions could be characteristic of cataract presence. Conclusions DeepOpacityNet could detect cataracts from CFPs in AREDS2 with performance superior to that of ophthalmologists and generate interpretable results. The code and models are available at https://github.com/ncbi/DeepOpacityNet ( https://doi.org/10.5281/zenodo.10127002 ).

Abstract Aim Diversified students in higher education and the complexity and difficulty of the evidence‐based nursing course perceived by students challenge nursing educators. Differentiated instruction can provide students with various opportunities to learn and meet the learning needs of students with different academic abilities and strengths, which may be a solution. This study aimed to apply differentiated instruction to design the undergraduate evidence‐based nursing course and evaluate the effects of differentiated instruction on students' learning outcomes and learning satisfaction. Design One‐group pretest–posttest pre‐experimental design was applied. Methods Ninety‐eight undergraduate nursing students enrolled in the evidence‐based nursing course 2020 participated in this study. Students' learning outcomes including preferred learning styles, classroom engagement, collaborative learning, attitudes towards evidence‐based nursing, learning satisfaction and evidence‐based nursing knowledge were measured using validated questionnaires. Results The differentiated instruction increased students' learning interests, promoted focused and independent thinking, and enhanced academic achievement. Students' classroom engagement, attitudes towards evidence‐based nursing, evidence‐based nursing knowledge and learning satisfaction were improved after the course. The course designed with differentiated instruction provided a supportive learning environment and furnished a vivid pedagogical way for the unique nursing profession. Patient or Public Contribution Positive results of the study support the application of differentiated instruction in the evidence‐based nursing course. The study indicates that the application of differentiated instruction in mixed‐ability classrooms in the evidence‐based nursing course improved students' learning outcomes, attitudes towards evidence‐based nursing, evidence‐based nursing knowledge and learning satisfaction. In clinical settings where nurses are even more diverse in academic education, clinical experiences and learning preferences, differentiated instruction can be a suitable application for in‐service training and education to promote nurses' enthusiasm for professional learning.

Abstract Neovascular age-related macular degeneration (nAMD), along with its clinical subtype known as polypoidal choroidal vasculopathy (PCV), are among the leading causes of vision loss in elderly Asians. In a genome-wide association study (GWAS) comprising 3,128 nAMD (1,555 PCV and 1,573 typical nAMD), and 5,493 controls of East Asian ancestry, we identify twelve loci, of which four are novel ( $$P \, < \, 1.19\times {10}^{-8}$$ P < 1.19 × 10 − 8 ). Substantial genetic sharing between PCV and typical nAMD is noted (r g = 0.666), whereas collagen extracellular matrix and fibrosis-related pathways are more pronounced for PCV. Whole-exome sequencing in 259 PCV patients revealed functional rare variants burden in collagen type I alpha 1 chain gene (COL1A1; $$P=1.05\times {10}^{-6}$$ P = 1.05 × 10 − 6 ) and potential enrichment of functional rare mutations at AMD-associated loci. At the GATA binding protein 5 (GATA5) locus, the most significant GWAS novel loci, the expressions of genes including laminin subunit alpha 5 (Lama5), mitochondrial ribosome associated GTPase 2 (Mtg2), and collagen type IX alpha 3 chain (Col9A3), are significantly induced during retinal angiogenesis and subretinal fibrosis in murine models. Furthermore, retinoic acid increased the expression of LAMA5 and MTG2 in vitro. Taken together, our data provide insights into the genetic basis of AMD pathogenesis in the Asian population.

Abstract Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

BackgroundDiabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes. ObjectiveThis study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors. MethodsWe used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate

Purpose: To evaluate the relationships between chronic kidney disease (CKD) with retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) thickness profiles of eyes in Asian and White populations. Design: Cross-sectional analysis. Participants: A total of 5066 Asian participants (1367 Malays, 1772 Indians, and 1927 Chinese) from the Singapore Epidemiology of Eye Diseases Study (SEED) were included, consisting of 9594 eyes for peripapillary RNFL analysis and 8661 eyes for GCIPL analysis. Additionally, 45 064 White participants (87 649 eyes) from the United Kingdom Biobank (UKBB) were included for both macular RNFL analysis and GCIPL analysis. Methods: Nonglaucoma participants aged ≥ 40 years with complete data for estimated glomerular filtration rate (eGFR) were included from both SEED and UKBB. In SEED, peripapillary RNFL and GCIPL thickness were measured by Cirrus HD-OCT 4000. In UKBB, macular RNFL and GCIPL were measured by Topcon 3D-OCT 1000 Mark II. Chronic kidney disease was defined as eGFR < 60 ml/min/1.73 m2 in both data sets. To evaluate the associations between kidney function status with RNFL and GCIPL thickness profiles, multivariable linear regression with generalized estimating equation models were performed in SEED and UKBB data sets separately. Main Outcome Measures: Average peripapillary and macular RNFL thickness and macular GCIPL thickness. Results: In SEED, after adjusting for age, gender, ethnicity, systolic blood pressure, antihypertensive medication, diabetes, hyperlipidemia, body mass index, smoking status, and intraocular pressure, presence of CKD (β = −1.31; 95% confidence interval [CI], −2.37 to −0.26; P = 0.015) and reduced eGFR (per 10 ml/min/1.73 m2; β = −0.32; 95% CI, −0.50 to −0.13; P = 0.001) were associated with thinner average peripapillary RNFL. Presence of CKD (β = −1.63; 95% CI, −2.42 to −0.84) and reduced eGFR (per 10 ml/min/1.73 m2; β = −0.30; 95% CI, −0.44 to −0.16) were consistently associated with thinner GCIPL in SEED (all P < 0.001). In UKBB, after adjusting for the above-mentioned covariates (except ethnicity), reduced eGFR (per 10 ml/min/1.73 m2; β = −0.06; 95% CI, −0.10 to −0.01; P = 0.008) was associated with thinner macular RNFL and CKD (β = −0.62; 95% CI, −1.16 to −0.08; P = 0.024) was associated with thinner average GCIPL. Conclusion: We consistently observed associations between CKD and thinning of RNFL and GCIPL across Asian and White populations' eyes. These findings further suggest that compromised kidney function is associated with RNFL and GCIPL thinning. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Objective: To develop and validate a deep learning algorithm that could automate the annotation of scleral spur (SS) and segmentation of anterior chamber (AC) structures for measurements of AC, iris, and angle width parameters in anterior segment OCT (ASOCT) scans. Design: Cross-sectional study. Subjects: Data from 2 population-based studies (i.e., the Singapore Chinese Eye Study and Singapore Malay Eye Study) and 1 clinical study on angle-closure disease were included in algorithm development. A separate clinical study on angle-closure disease was used for external validation. Method: Image contrast of ASOCT scans were first enhanced with CycleGAN. We utilized a heat map regression approach with coarse-to-fine framework for SS annotation. Then, an ensemble network of U-Net, full resolution residual network, and full resolution U-Net was used for structure segmentation. Measurements obtained from predicted SSs and structure segmentation were measured and compared with measurements obtained from manual SS annotation and structure segmentation (i.e., ground truth). Main Outcome Measures: We measured Euclidean distance and intraclass correlation coefficients (ICC) to evaluate SS annotation and Dice similarity coefficient for structure segmentation. The ICC, Bland–Altman plot, and repeatability coefficient were used to evaluate agreement and precision of measurements. Results: For SS annotation, our algorithm achieved a Euclidean distance of 124.7 μm, ICC ≥ 0.95, and a 3.3% error rate. For structure segmentation, we obtained Dice similarity coefficient ≥ 0.91 for cornea, iris, and AC segmentation. For angle width measurements, ≥ 95% of data points were within the 95% limits-of-agreement in Bland–Altman plot with insignificant systematic bias (all P > 0.12). The ICC ranged from 0.71–0.87 for angle width measurements, 0.54 for IT750, 0.83–0.85 for other iris measurements, and 0.89–0.99 for AC measurements. Using the same SS coordinates from a human expert, measurements obtained from our algorithm were generally less variable than measurements obtained from a semiautomated angle assessment program. Conclusion: We developed a deep learning algorithm that could automate SS annotation and structure segmentation in ASOCT scans like human experts, in both open-angle and angle-closure eyes. This algorithm reduces the time needed and subjectivity in obtaining ASOCT measurements. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Purpose: To examine the 6-year incidence of visual impairment (VI) and identify risk factors associated with VI in a multiethnic Asian population. Design: Prospective, population-based, cohort study. Participants: Adults aged ≥ 40 years were recruited from the Singapore Epidemiology of Eye Diseases cohort study at baseline. Eligible subjects were re-examined after 6 years. Subjects included in the final analysis had a mean age of 56.1 ± 8.9 years, and 2801 (50.5%) were female. Methods: All participants underwent standardized examination and interviewer-administered questionnaire at baseline. Incidences were standardized to the Singapore Population Census 2010. A Poisson binomial regression model was used to evaluate the associations between baseline factors and incident presenting VI. Main Outcome Measures: Incident presenting VI was assessed at the 6-year follow-up visit. Visual impairment (presenting visual acuity < 20/40), low vision (presenting visual acuity < 20/40 but ≥ 20/200), and blindness (presenting visual acuity < 20/200) were defined based on United States definition. Results: A total of 5551 subjects (2188 Chinese, 1837 Indians, and 1526 Malays) were evaluated, of whom 514 developed incident presenting VI over 6 years. Malays had a higher incidence of low vision and blindness (13.0%; 0.6%) than Indians (7.0%; 0.1%) and Chinese (7.7%; 0.2%). Among Malay individuals with VI at baseline, 52.8% remained visually impaired after 6 years, which was considerably higher than Chinese (32.4%) and Indians (37.2%). Older age (per decade; relative risk [RR] = 1.59), a history of cardiovascular disease (RR = 1.38), current smoking (RR = 1.31), smaller housing type (1- to 2-room public flat; RR = 2.01), and no formal education (RR = 1.63) at baseline were associated with a higher risk of incident VI (all P ≤ 0.027). Older age (> 60 years) contributed the highest population attributable risk to incident VI (27.1%), followed by lower monthly income (Singapore dollar < $2000; 26.4%) and smaller housing type (24.7%). Overall, undercorrected refractive error (49.1%) and cataract (82.6%) were leading causes for low vision and blindness, respectively. This was consistently observed across the 3 ethnicities. Conclusions: In this multiethnic Asian population, Malays had a higher VI incidence compared to Indians and Chinese. Leading causes of VI are mostly treatable, suggesting that more efforts are needed to further mitigate preventable visual loss. Financial Disclosure(s): The authors have no proprietary or commercial interest in any materials discussed in this article.

Purpose: For OCT retinal thickness measurements to be used as a prodromal age-related macular degeneration (AMD) risk marker, the 3-dimensional (3D) topographic variation of the relationship between genetic susceptibility to AMD and retinal thickness needs to be assessed. We aimed to evaluate individual retinal layer thickness changes and topography at the macula that are associated with AMD genetic susceptibility. Design: Genetic association study. Participants: A total of 1579 healthy participants (782 Chinese, 353 Malays, and 444 Indians) from the multiethnic Singapore Epidemiology of Eye Diseases study were included. Methods: Spectral-domain OCT and automatic segmentation of individual retinal layers were performed to produce 10 retinal layer thickness measurements at each ETDRS subfield, producing 3D topographic information. Age-related macular degeneration genetic susceptibility was represented via single nucleotide polymorphisms (SNPs) and aggregated via whole genome (overall) and pathway-specific age-related macular degeneration polygenic risk score (PRSAMD). Main Outcome Measures: Associations of individual SNPs, overall PRSAMD, and pathway-specific PRSAMD with retinal thickness were analyzed by individual retinal layer and ETDRS subfield. Results: CFH rs10922109, ARMS2-HTRA1 rs3750846, and LIPC rs2043085 were the top AMD susceptibility SNPs associated with retinal thickness of individual layers (P

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes.Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: “Some College” (yes/no, for any education beyond high school) and “Graduated College” (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10−8) and suggestive (p < 1 × 10−6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals).Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue.Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

Abstract Background We investigated risk factors influencing the outcome of unexpected ovarian carcinomas. Methods We reviewed the ovarian carcinoma patients treated at atertiary medical institution between 2000 and 2017 and analyze the clinico‐pathological characteristics, treatment strategies, recurrence status, and outcome. Results A total of 112 women (65 primary laparoscopic surgery [LSC] and 47 laparotomic surgery [LAPA]) were included in the analysis. The LSC group had smaller ovarian tumors (10.5 ± 7.3 cm vs. 16.6 ± 8.7 cm, p = 0.031) and higher incidence of subsequent staging surgery (56.9% vs. 25.5%, p = 0.0001) compared to the LAPA group. There were 98/112 (86.6%) of early stages (I/II) diseases. The difference between the recurrent rate (27.7% vs. 31.9%), disease‐free survival (DFS), and overall survival (OS) were not significant among surgical groups. In the multivariate analysis, FIGO stage (stage II hazard ratio [HR] 6.61, p = 0.007; stage III HR 8.40, p = 0.002) was the only prognostic factor for DFS. FIGO stage (stage II HR 20.78, p = 0.0001; stage III HR 7.99, p = 0.017), histological type (mucinous HR 12.49, p = 0.036), and tumor grade (grade 3 HR 35.01, p = 0.003) were independent prognostic factors for OS, while women with latency >28 days from primary to staging surgery had significantly poorer OS (p = 0.008). Women with latency >28 days between primary surgery and adjuvant chemotherapy had similar DFS (p = 0.31) and a trend of poorer OS (p = 0.064). Conclusions The prognosis of unexpected ovarian cancer is independent from the primary surgical procedure and comprehensive staging surgery should be performed at close proximity after the diagnosis of unexpected ovarian malignancy.

A multi-ethnic meta-analysis of exome array data from over 27,000 participants identifies several rare variants that could contribute to risk of ocular refractive error.

Abstract Studies using machine learning (ML) approaches have reported high diagnostic accuracies for glaucoma detection. However, none assessed model performance across ethnicities. The aim of the study is to externally validate ML models for glaucoma detection from optical coherence tomography (OCT) data. We performed a prospective, cross-sectional study, where 514 Asians (257 glaucoma/257 controls) were enrolled to construct ML models for glaucoma detection, which was then tested on 356 Asians (183 glaucoma/173 controls) and 138 Caucasians (57 glaucoma/81 controls). We used the retinal nerve fibre layer (RNFL) thickness values produced by the compensation model, which is a multiple regression model fitted on healthy subjects that corrects the RNFL profile for anatomical factors and the original OCT data (measured) to build two classifiers, respectively. Both the ML models (area under the receiver operating [AUC] = 0.96 and accuracy = 92%) outperformed the measured data (AUC = 0.93; P

Abstract Background Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5–10% (termed as borderline-QRISK3 group) using the UK Biobank. Methods Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups. Results Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30–1.52) with a 13.1% (95% CI, 11.7–14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010–0.017) in non-statin cohort, 0.013 (0.007–0.019) in stage 1 hypertension cohort, and 0.023 (0.018–0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3. Conclusions Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD.

Cardiovascular disease (CVD) is a major cause of mortality and morbidity worldwide and imposes significant socioeconomic burdens, especially with late diagnoses. There is growing evidence of strong correlations between ocular images, which are information-dense, and CVD progression. The accelerating development of deep learning algorithms (DLAs) is a promising avenue for research into CVD biomarker discovery, early CVD diagnosis, and CVD prognostication. We review a selection of 17 recent DLAs on the less-explored realm of DL as applied to ocular images to produce CVD outcomes, potential challenges in their clinical deployment, and the path forward. The evidence for CVD manifestations in ocular images is well documented. Most of the reviewed DLAs analyze retinal fundus photographs to predict CV risk factors, in particular hypertension. DLAs can predict age, sex, smoking status, alcohol status, body mass index, mortality, myocardial infarction, stroke, chronic kidney disease, and hematological disease with significant accuracy. While the cardio-oculomics intersection is now burgeoning, very much remain to be explored. The increasing availability of big data, computational power, technological literacy, and acceptance all prime this subfield for rapid growth. We pinpoint the specific areas of improvement toward ubiquitous clinical deployment: increased generalizability, external validation, and universal benchmarking. DLAs capable of predicting CVD outcomes from ocular inputs are of great interest and promise to individualized precision medicine and efficiency in the provision of health care with yet undetermined real-world efficacy with impactful initial results.

Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.

The advents of information technologies have led to the creation of ever-larger datasets. Also known as big data, these large datasets are characterized by its volume, variety, velocity, veracity, and value. More importantly, big data has the potential to expand traditional research capabilities, inform clinical practice based on real-world data, and improve the health system and service delivery. This review first identified the different sources of big data in ophthalmology, including electronic medical records, data registries, research consortia, administrative databases, and biobanks. Then, we provided an in-depth look at how big data analytics have been applied in ophthalmology for disease surveillance, and evaluation on disease associations, detection, management, and prognostication. Finally, we discussed the challenges involved in big data analytics, such as data suitability and quality, data security, and analytical methodologies.

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Clinically significant genetic variation in Asian populations is under-characterized. Here, the authors show the diversity in prevalence and spectrum of human disease and pharmacogenetic variants in a multi-ethnic Asian population.

IntroductionOur study aimed to examine the relationship between cardiovascular diseases (CVD) with peripapillary retinal fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thickness profiles in a large multi-ethnic Asian population study.Methods6,024 Asian subjects were analyzed in this study. All participants underwent standardized examinations, including spectral domain OCT imaging (Cirrus HD-OCT; Carl Zeiss Meditec). In total, 9,188 eyes were included for peripapillary RNFL analysis (2,417 Malays; 3,240 Indians; 3,531 Chinese), and 9,270 eyes (2,449 Malays, 3,271 Indians, 3,550 Chinese) for GCIPL analysis. History of CVD was defined as a self-reported clinical history of stroke, myocardial infarction, or angina. Multivariable linear regression models with generalized estimating equations were performed, adjusting for age, gender, ethnicity, diabetes, hypertension, hyperlipidaemia, chronic kidney disease, body mass index, current smoking status, and intraocular pressure.ResultsWe observed a significant association between CVD history and thinner average RNFL (β = −1.63; 95% CI, −2.70 to −0.56; p = 0.003). This association was consistent for superior (β = −1.79, 95% CI, −3.48 to −0.10; p = 0.038) and inferior RNFL quadrant (β = −2.14, 95% CI, −3.96 to −0.32; p = 0.021). Of the CVD types, myocardial infarction particularly showed significant association with average (β = −1.75, 95% CI, −3.08 to −0.42; p = 0.010), superior (β = −2.22, 95% CI, −4.36 to −0.09; p = 0.041) and inferior (β = −2.42, 95% CI, −4.64 to −0.20; p = 0.033) RNFL thinning. Among ethnic groups, the association between CVD and average RNFL was particularly prominent in Indian eyes (β = −1.92, 95% CI, −3.52 to −0.33; p = 0.018). CVD was not significantly associated with average GCIPL thickness, albeit a consistent negative direction of association was observed (β = −0.22, 95% CI, −1.15 to 0.71; p = 0.641).DiscussionIn this large multi-ethnic Asian population study, we observed significant association between CVD history and RNFL thinning. This finding further validates the impact of impaired systemic circulation on RNFL thickness.

Background Case-finding is a recommended approach for dementia early detection in the community.Aims To investigate the discriminant validity and cost-effectiveness of a stepwise dementia case-finding approach in a Singaporean older adult community.Methods The two-phase study was conducted in the community from 2009 to 2015 in Singapore. A total of 3780 participants (age ≥60 years) completed phase I (a brief cognitive screening); 918 completed phase II and were included in the final analysis. In phase I, all participants were administered the Abbreviated Mental Test (AMT) and the Progressive Forgetfulness Question (PFQ). Those who screened positive on either test were invited to phase II, whereby the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and a formal neuropsychological battery were administered, followed by the research diagnosis of no cognitive impairment, cognitive impairment no dementia (CIND)-Mild (≤2 impaired cognitive domains), CIND-Moderate (>2 impaired domains) or dementia. Receiver operating characteristic curve analyses were conducted for the different cognitive instruments. All discriminant indices were calculated, including sensitivity, specificity, positive and negative predictive values (NPV) and accuracy. Cost-effectiveness analysis was conducted by estimating the amount of screening time needed and the number of older adults requiring re-evaluation in two case-finding scenarios, ie, with or without preselection by the PFQ.Results The stepwise case-finding approach (preselection by the PFQ, then MMSE or MoCA or AMT) showed an excellent NPV (>99%) and accuracy (>86%) for excluding dementia-free cases. Without preselection by the PFQ, screening time for the three cognitive tools were 317.5, 317.5 and 254 hours, with 159, 302 and 175 screen-positive older adults involved in further evaluation. By adopting the stepwise case-finding approach, total screening time were 156.5, 156.5 and 126.2 hours, which decreased by 50.7%, 50.7% and 50.3% as compared with those without preselection. Furthermore, after preselection, only 98, 167 and 145 screen-positive older adults required further evaluation, corresponding to a reduction of 38.4%, 44.7% and 17.1% in the numbers compared with those without preselection.Conclusions A stepwise approach for dementia case-finding should be implemented in the community to minimise the time and resources needed for large-scale early detection of dementia.

Background Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways. Methods To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses. Results Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology. Conclusions Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.

Summary: Background: Large language models (LLMs) are garnering wide interest due to their human-like and contextually relevant responses. However, LLMs’ accuracy across specific medical domains has yet been thoroughly evaluated. Myopia is a frequent topic which patients and parents commonly seek information online. Our study evaluated the performance of three LLMs namely ChatGPT-3.5, ChatGPT-4.0, and Google Bard, in delivering accurate responses to common myopia-related queries. Methods: We curated thirty-one commonly asked myopia care-related questions, which were categorised into six domains—pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis. Each question was posed to the LLMs, and their responses were independently graded by three consultant-level paediatric ophthalmologists on a three-point accuracy scale (poor, borderline, good). A majority consensus approach was used to determine the final rating for each response. ‘Good’ rated responses were further evaluated for comprehensiveness on a five-point scale. Conversely, ‘poor’ rated responses were further prompted for self-correction and then re-evaluated for accuracy. Findings: ChatGPT-4.0 demonstrated superior accuracy, with 80.6% of responses rated as ‘good’, compared to 61.3% in ChatGPT-3.5 and 54.8% in Google Bard (Pearson's chi-squared test, all p ≤ 0.009). All three LLM-Chatbots showed high mean comprehensiveness scores (Google Bard: 4.35; ChatGPT-4.0: 4.23; ChatGPT-3.5: 4.11, out of a maximum score of 5). All LLM-Chatbots also demonstrated substantial self-correction capabilities: 66.7% (2 in 3) of ChatGPT-4.0's, 40% (2 in 5) of ChatGPT-3.5's, and 60% (3 in 5) of Google Bard's responses improved after self-correction. The LLM-Chatbots performed consistently across domains, except for ‘treatment and prevention’. However, ChatGPT-4.0 still performed superiorly in this domain, receiving 70% ‘good’ ratings, compared to 40% in ChatGPT-3.5 and 45% in Google Bard (Pearson's chi-squared test, all p ≤ 0.001). Interpretation: Our findings underscore the potential of LLMs, particularly ChatGPT-4.0, for delivering accurate and comprehensive responses to myopia-related queries. Continuous strategies and evaluations to improve LLMs’ accuracy remain crucial. Funding: Dr Yih-Chung Tham was supported by the National Medical Research Council of Singapore (NMRC/MOH/HCSAINV21nov-0001).

[This corrects the article DOI: 10.1371/journal.pdig.0000193.].

Scaffoldings play a critical role as temporary structures in supporting construction processes. Accidents at construction sites frequently stem from issues related to scaffoldings, including insufficient support caused by deviations from the construction design, insecure rod connections, or absence of cross-bracing, which result in uneven loading and potential collapse, leading to casualties. This research introduces a novel approach employing deep learning (i.e., YOLO v5) and augmented reality (AR), termed the scaffolding assembly deficiency detection system (SADDS), designed to aid field inspectors in discerning deficiencies within scaffolding assemblies. Inspectors have the flexibility to utilize SADDS through various devices, such as video cameras, mobile phones, or AR goggles, for the automated identification of deficiencies in scaffolding assemblies. The conducted test yielded satisfactory results, with a mean average precision of 0.89 and individual precision values of 0.96, 0.82, 0.90, and 0.89 for qualified frames and frames with the missing cross-tie rod, missing lower-tie rod, and missing footboard deficiencies, respectively. Subsequent field tests conducted at two construction sites demonstrated improved system performance compared to the training test. Furthermore, the advantages and disadvantages of employing mobile phones and AR goggles were discussed, elucidating certain limitations of the SADDS system, such as self-occlusion and efficiency issues.

Introduction: Long axial length (AL) is a risk factor for myopia. Although family studies indicate that AL has an important genetic component with heritability estimates up to 0.94, there have been few reports of AL-associated loci.Methods: Here, we conducted a multiethnic genome-wide association study (GWAS) of AL in 19,420 adults of European, Latino, Asian, and African ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, with replication in a subset of the Consortium for Refractive Error and Myopia (CREAM) cohorts of European or Asian ancestry. We further examined the effect of the identified loci on the mean spherical equivalent (MSE) within the GERA cohort. We also performed genome-wide genetic correlation analyses to quantify the genetic overlap between AL and MSE or myopia risk in the GERA European ancestry sample.Results: Our multiethnic GWA analysis of AL identified a total of 16 genomic loci, of which 5 are novel. We found that all AL-associated loci were significantly associated with MSE after Bonferroni correction. We also found that AL was genetically correlated with MSE (rg = −0.83; SE, 0.04; p = 1.95 × 10−89) and myopia (rg = 0.80; SE, 0.05; p = 2.84 × 10−55). Finally, we estimated the array heritability for AL in the GERA European ancestry sample using LD score regression, and found an overall heritability estimate of 0.37 (s.e. = 0.04).Discussion: In this large and multiethnic study, we identified novel loci, associated with AL at a genome-wide significance level, increasing substantially our understanding of the etiology of AL variation. Our results also demonstrate an association between AL-associated loci and MSE and a shared genetic basis between AL and myopia risk.

IntroductionAge-related macular degeneration (AMD) is one of the leading causes of vision impairment globally and early detection is crucial to prevent vision loss. However, the screening of AMD is resource dependent and demands experienced healthcare providers. Recently, deep learning (DL) systems have shown the potential for effective detection of various eye diseases from retinal fundus images, but the development of such robust systems requires a large amount of datasets, which could be limited by prevalence of the disease and privacy of patient. As in the case of AMD, the advanced phenotype is often scarce for conducting DL analysis, which may be tackled via generating synthetic images using Generative Adversarial Networks (GANs). This study aims to develop GAN-synthesized fundus photos with AMD lesions, and to assess the realness of these images with an objective scale.MethodsTo build our GAN models, a total of 125,012 fundus photos were used from a real-world non-AMD phenotypical dataset. StyleGAN2 and human-in-the-loop (HITL) method were then applied to synthesize fundus images with AMD features. To objectively assess the quality of the synthesized images, we proposed a novel realness scale based on the frequency of the broken vessels observed in the fundus photos. Four residents conducted two rounds of gradings on 300 images to distinguish real from synthetic images, based on their subjective impression and the objective scale respectively.Results and discussionThe introduction of HITL training increased the percentage of synthetic images with AMD lesions, despite the limited number of AMD images in the initial training dataset. Qualitatively, the synthesized images have been proven to be robust in that our residents had limited ability to distinguish real from synthetic ones, as evidenced by an overall accuracy of 0.66 (95% CI: 0.61–0.66) and Cohen’s kappa of 0.320. For the non-referable AMD classes (no or early AMD), the accuracy was only 0.51. With the objective scale, the overall accuracy improved to 0.72. In conclusion, GAN models built with HITL training are capable of producing realistic-looking fundus images that could fool human experts, while our objective realness scale based on broken vessels can help identifying the synthetic fundus photos.

Abstract Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of “biological age” has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into antiageing interventions. Numerous potential biomarkers of ageing have been proposed, spanning from molecular changes and imaging characteristics to clinical phenotypes. In this review, we will start off with a discussion of the development of ageing biomarkers, then we will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective. Additionally, we will discuss the challenges, potential applications, and future opportunities in this field. While research on ageing biomarkers has led to significant progress and applications, further investigations are still necessary. We anticipate that future breakthroughs in this field will involve exploring potential mechanisms, developing biomarkers by combining various data sources or employing new technologies, and validating the clinical value of existing and emerging biomarkers through comprehensive collaboration and longitudinal studies.

A large-scale GWAS provides insight on diabetes-dependent genetic effects on the glomerular filtration rate, a common metric to monitor kidney health in disease.

Abstract To determine the differential impact of the irreversible eye diseases on vision-related quality of life (VRQoL) in a multi-ethnic Asian population. 2652 participants from the Singapore Epidemiology of Eye Disease Study, with any of the following early and late-stage eye conditions including age-related macular degeneration (AMD, n = 158), diabetic retinopathy (DR, n = 105; non vision threatening [non-VTDR]; VTDR), glaucoma (n = 57) and myopic macular degeneration (MMD, n = 106), or none of the above (controls, 2226 [83.9%]) were included. Rasch-scaled scores of the Emotional well-being Mobility and Reading subscales of the Impact of Vision Impairment (IVI) questionnaire, collectively referred to as “VRQoL” were assessed. Multivariable linear regression analyses and pairwise comparisons adjusting for age, gender, ethnicity, socio-economic status, BMI, smoking, alcohol use, presence of systemic diseases and presenting VI were performed to assess and compare the impact of the presence and severity of each eye condition on the three IVI domains. Multivariable adjusted pairwise comparisons of VRQoL between early stages of the four eye diseases showed no significant differences (all P > 0.05). For late stage diseases, individuals with VTDR had significantly larger decrements in Emotional well-being compared to glaucoma (β − 0.81; 95% CI − 1.47 to − 0.16) and MMD (β − 1.17; 95% CI − 2.16 to − 0.18); and Reading decrements compared to glaucoma (β − 0.66; 95% CI − 1.22 to − 0.11). When compared to late glaucoma, individuals with late AMD (β − 0.76; 95% CI − 1.50 to − 0.01) had significantly larger IVI Mobility subscale decrements. VTDR and late AMD, appear to have the greatest impact on VRQoL, compared to late glaucoma and MMD, suggesting a differential impact of late-stage eye disease categorization on VRQoL.

Marie Loh, Weihua Zhang et al. use a genome-wide association study meta-analysis to examine variants associated with Type 2 diabetes (T2D) in South Asian and European ancestry cohorts. Their results provide further insights into the genetic mechanisms underlying T2D across ancestral populations.

Abstract This study aimed to describe the topographic variation of the macula’s choroidal angioarchitecture using three-dimensional (3D) choroidal vascularity index (CVI) of healthy eyes from an Asian population and to investigate the associations of CVI. 50 participants were recruited via stratified randomisation based on subfoveal choroidal thickness from the Singapore Epidemiology of Eye Diseases Study. Macular volume scans were acquired using spectral-domain optical coherence tomography with enhanced depth imaging. CVI was assessed based on B-scan binarisation and choroid segmentation. The 3D CVI of the whole, superior, central, and inferior macula were 62.92 ± 1.57%, 62.75 ± 1.93%, 63.35 ± 1.72%, and 62.66 ± 1.70%, respectively, pairwise comparisons P all > 0.05). 3D CVI (Whole Macula) and 2D CVI (Subfoveal) were associated only with each other and not with other ocular and systemic factors. 2D CVI (Subfoveal) had a moderate agreement with 3D CVI (Central Macula) [intraclass corelation coefficient (ICC) = 0.719], and had poorer agreement with 3D CVI of the whole macula, superior, and inferior macula (ICC = 0.591, 0.483, and 0.394, respectively). Scanning volume did not influence 3D CVI measurements. In conclusion, 3D CVI demonstrated no significant topographic variation. CVI was not correlated with demographic or ocular structural features. 2D CVI of the fovea is partially representative of 3D CVI of the macula.

Summary: Genomic researchers increasingly utilize commercial cloud service providers (CSPs) to manage data and analytics needs. CSPs allow researchers to grow Information Technology (IT) infrastructure on demand to overcome bottlenecks when combining large datasets. However, without adequate security controls, the risk of unauthorized access may be higher for data stored on the cloud. Additionally, regulators are mandating data access patterns and specific security protocols for the storage and use of genomic data. While CSP provides tools for security and regulatory compliance, building the necessary controls required for cloud solutions is not trivial. Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud-native genomics data repository and analytics platform that implements a “five-safes” framework to provide security and governance controls to data contributors and users, leveraging CSP for sharing and analysis of genomic datasets without the risk of security breaches or running afoul of regulations.

Anterior chamber depth (ACD) is a major risk factor of angle closure disease, and has been used in angle closure screening in various populations. However, ACD is measured from ocular biometer or anterior segment optical coherence tomography (AS-OCT), which are costly and may not be readily available in primary care and community settings. Thus, this proof-of-concept study aims to predict ACD from low-cost anterior segment photographs (ASPs) using deep-learning (DL). We included 2,311 pairs of ASPs and ACD measurements for algorithm development and validation, and 380 pairs for algorithm testing. We captured ASPs with a digital camera mounted on a slit-lamp biomicroscope. Anterior chamber depth was measured with ocular biometer (IOLMaster700 or Lenstar LS9000) in data used for algorithm development and validation, and with AS-OCT (Visante) in data used for testing. The DL algorithm was modified from the ResNet-50 architecture, and assessed using mean absolute error (MAE), coefficient-of-determination (R2), Bland-Altman plot and intraclass correlation coefficients (ICC). In validation, our algorithm predicted ACD with a MAE (standard deviation) of 0.18 (0.14) mm; R2 = 0.63. The MAE of predicted ACD was 0.18 (0.14) mm in eyes with open angles and 0.19 (0.14) mm in eyes with angle closure. The ICC between actual and predicted ACD measurements was 0.81 (95% CI 0.77, 0.84). In testing, our algorithm predicted ACD with a MAE of 0.23 (0.18) mm; R2 = 0.37. Saliency maps highlighted the pupil and its margin as the main structures used in ACD prediction. This study demonstrates the possibility of predicting ACD from ASPs via DL. This algorithm mimics an ocular biometer in making its prediction, and provides a foundation to predict other quantitative measurements that are relevant to angle closure screening. Author summary This proof-of-concept study aimed to predict anterior chamber depth (ACD) quantitatively from anterior segment photographs (ASPs) using deep-learning. Anterior chamber depth is a major and consistent risk factor of primary angle closure disease (PACD), which is a major cause of glaucoma-induced blindness at the later stages. Our study is motivated by the lack of an appropriate screening tool for PACD, where clinical tests lack repeatability and imaging devices are too costly to adopt in primary care and community settings for screening. In this study, we included 2,311 pairs of ASPs and ACD measurements in algorithm development, and 380 pairs for algorithm testing. We modified the ResNet-50 convolutional neural network (CNN) in developing our algorithm. Our algorithm was able to predict ACD with a mean absolute error of 0.18mm in algorithm validation, and 0.23mm in algorithm testing. We obtained an intraclass correlation coefficient (ICC) of 0.81 in validation, indicating good agreement between actual and predicted ACD measurements. Importantly, our algorithm highlighted the pupil and its margins in predicting ACD, which is similar to how actual measurements were obtained from imaging devices. Our study shows that depth (i.e., ACD) may be predicted from 2-dimension photographs using deep-learning. This algorithm provides a foundation for predicting other relevant parameters of PACD, which may be further combined into a screening algorithm for the disease at a later stage.

Objective: Lipid dysregulation and complement system (CS) activation are 2 important pathophysiology pathways for age-related macular degeneration (AMD). We hypothesized that the relationship between lipids and AMD may also differ according to CS genotype profile. Thus, the objective was to investigate the relationships between lipid-related metabolites and AMD according to CS genotypes. Design: Population-based cross-sectional study. Participants: A total of 6947 participants from Singapore Epidemiology of Eye Diseases study with complete relevant data were included. Methods: We investigated a total of 32 blood lipid–related metabolites from nuclear magnetic resonance metabolomics data including lipoproteins and their subclasses, cholesterols, glycerides, and phospholipids, as well as 4 CS single nucleotide polymorphisms (SNPs): rs10922109 (complement factor H), rs10033900 (complement factor I), rs116503776 (C2-CFB-SKIV2L), and rs2230199 (C3). We first investigated the associations between AMD and the 32 lipid-related metabolites using multivariable logistic regression models. Then, to investigate whether the effect of lipid-related metabolites on AMD differ according to the CS SNPs, we tested the possible interactions between the CS SNPs and the lipid-related metabolites. Main Outcome Measures: Age-related macular degeneration was defined using the Wisconsin grading system. Results: Among the 6947 participants, the prevalence of AMD was 6.1%, and the mean age was 58.3 years. First, higher levels of cholesterol in high-density lipoprotein (HDL) and medium and large HDL particles were associated with an increased risk of AMD, and higher levels of serum total triglycerides (TG) and several very-low–density lipoprotein subclass particles were associated with a decreased risk of AMD. Second, these lipids had significant interaction effects on AMD with 2 CS SNPs: rs2230199 and rs116503776 (after correction for multiple testing). For rs2230199, in individuals without risk allele, higher total cholesterol in HDL2 was associated with an increased AMD risk (odds ratio [OR] per standard deviation increase, 1.20; 95% confidence interval (CI), 1.06–1.37; P = 0.005), whereas, in individuals with at least 1 risk allele, higher levels of these particles were associated with a decreased AMD risk (OR, 0.69; 95% CI, 0.45–1.05; P = 0.079). Conversely, for rs116503776, in individuals without risk allele, higher serum total TG were associated with a decreased AMD risk (OR, 0.84; 95% CI, 0.74–0.95; P = 0.005), whereas, in individuals with 2 risk alleles, higher levels of these particles were associated with an increased risk of AMD (OR, 2.3, 95% CI, 0.99–5.39, P = 0.054). Conclusions: Lipid-related metabolites exhibit opposite directions of effects on AMD according to CS genotypes. This indicates that lipid metabolism and CS may have synergistic interplay in the AMD pathogenesis.

BACKGROUND Cytochrome P450 family 8 subfamily B member 1 (CYP8B1) generates 12α-hydroxylated bile acids (BAs) that are associated with insulin resistance in humans.METHODS To determine whether reduced CYP8B1 activity improves insulin sensitivity, we sequenced CYP8B1 in individuals without diabetes and identified carriers of complete loss-of-function (CLOF) mutations utilizing functional assays.RESULTS Mutation carriers had lower plasma 12α-hydroxylated/non–12α-hydroxylated BA and cholic acid (CA)/chenodeoxycholic acid (CDCA) ratios compared with age-, sex-, and BMI-matched controls. During insulin clamps, hepatic glucose production was suppressed to a similar magnitude by insulin, but glucose infusion rates to maintain euglycemia were higher in mutation carriers, indicating increased peripheral insulin sensitivity. Consistently, a polymorphic CLOF CYP8B1 mutation associated with lower fasting insulin in the AMP-T2D-GENES study. Exposure of primary human muscle cells to mutation-carrier CA/CDCA ratios demonstrated increased FOXO1 activity, and upregulation of both insulin signaling and glucose uptake, which were mediated by increased CDCA. Inhibition of FOXO1 attenuated the CDCA-mediated increase in muscle insulin signaling and glucose uptake. We found that reduced CYP8B1 activity associates with increased insulin sensitivity in humans.CONCLUSION Our findings suggest that increased circulatory CDCA due to reduced CYP8B1 activity increases skeletal muscle insulin sensitivity, contributing to increased whole-body insulin sensitization.FUNDING Biomedical Research Council/National Medical Research Council of Singapore.

Abstract Background Hypertriglyceridemia has emerged as a critical coronary artery disease (CAD) risk factor. Rare loss-of-function (LoF) variants in apolipoprotein C-III have been reported to reduce triglycerides (TG) and are cardioprotective in American Indians and Europeans. However, there is a lack of data in other Europeans and non-Europeans. Also, whether genetically increased plasma TG due to ApoC-III is causally associated with increased CAD risk is still unclear and inconsistent. The objectives of this study were to verify the cardioprotective role of earlier reported six LoF variants of APOC3 in South Asians and other multi-ethnic cohorts and to evaluate the causal association of TG raising common variants for increasing CAD risk. Methods We performed gene-centric and Mendelian randomization analyses and evaluated the role of genetic variation encompassing APOC3 for affecting circulating TG and the risk for developing CAD. Results One rare LoF variant (rs138326449) with a 37% reduction in TG was associated with lowered risk for CAD in Europeans (p = 0.007), but we could not confirm this association in Asian Indians (p = 0.641). Our data could not validate the cardioprotective role of other five LoF variants analysed. A common variant rs5128 in the APOC3 was strongly associated with elevated TG levels showing a p-value 2.8 × 10− 424. Measures of plasma ApoC-III in a small subset of Sikhs revealed a 37% increase in ApoC-III concentrations among homozygous mutant carriers than the wild-type carriers of rs5128. A genetically instrumented per 1SD increment of plasma TG level of 15 mg/dL would cause a mild increase (3%) in the risk for CAD (p = 0.042). Conclusions Our results highlight the challenges of inclusion of rare variant information in clinical risk assessment and the generalizability of implementation of ApoC-III inhibition for treating atherosclerotic disease. More studies would be needed to confirm whether genetically raised TG and ApoC-III concentrations would increase CAD risk.