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Aims This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD. Methods and results We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD’s prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively. Conclusion The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.

Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications.The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets.Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index.We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting.The online version contains supplementary material available at 10.1007/s13167-022-00301-5.

To evaluate the transancestry portability of current myopia polygenic risk scores (PRSs) to predict high myopia (HM) and myopic macular degeneration (MMD) in an Asian population.Population-based study.A total of 5894 adults (2141 Chinese, 1913 Indian, and 1840 Malay) from the Singapore Epidemiology of Eye Diseases study were included in the analysis. The mean ± standard deviation age was 57.05 ± 9.31 years. A total of 361 adults had a diagnosis of HM (spherical equivalent [SE]-5.00 diopters [D]) from refraction measurements, 240 individuals had a diagnosis of MMD graded by the International Photographic Classification and Grading System for Myopic Maculopathy criteria from fundus photographs, and 3774 individuals were control participants without myopia (SE-0.5 D).The PRS, derived from 687 289 HapMap3 single nucleotide polymorphisms (SNPs) from the largest genome-wide association study of myopia in Europeans to date (n = 260 974), was assessed on its ability to predict patients with HM and MMD versus control participants.The primary outcomes were the area under the receiver operating characteristic curve (AUC) to predict HM and MMD.The PRS had an AUC of 0.73 (95% confidence interval [CI], 0.70-0.75) for HM and 0.66 (95% CI, 0.63-0.70) for MMD versus no myopia. The inclusion of the PRS with other predictors (age, sex, educational attainment [EA], and ancestry; age-by-ancestry, sex-by-ancestry, and EA-by-ancestry interactions; and 20 genotypic principal components) increased the AUC to 0.84 (95% CI, 0.82-0.86) for HM and 0.79 (95% CI, 0.76-0.82) for MMD. Individuals with a PRS in the top 5% showed up to a 4.66 (95% CI, 3.34-6.42) times higher risk of HM developing and up to a 3.43 (95% CI, 2.27-5.05) times higher risk of MMD developing compared with the remaining 95% of individuals.The PRS is a good predictor for HM and facilitates the identification of high-risk children to prevent myopia progression to HM. In addition, the PRS also predicts MMD and helps to identify high-risk adults with myopia who require closer monitoring for myopia-related complications.

Mw 6.3 and 6.0 earthquakes occurred on January 27 and 29 in 2020 in the southeastern Solomon Islands which is one of the most seismically active areas in the southern Pacific. To investigate the seismogenic mechanism and structure of the southeastern Solomon Islands, we locate the foreshocks-main-shock-aftershocks sequence of two moderate earthquakes recorded by the regional seismic network. In this study, we establish the new database and locate earthquakes for analyzing the seismogenic structures and deriving the new regional 1D velocity model. Based on the special distribution of the foreshock-aftershock sequence, the interaction of subduction and transform zones between the Pacific and the Australia Plates leads to the near-vertical dip-slip tear structure in the southeastern Solomon Islands. Confirmed with PREM and the new 1D velocity model for testing the robustness of the earthquake locations, the seismic gap at depths from 25 to 35 km is observed as the “jelly sandwich” rheology. In addition, seismic events with large-amplitude, high-frequency signals could be observed in the fore-arc area is due to waves that guided by the subducted Australia plate. In order to improve the capability of earthquake detection, we generate templates from foreshocks-aftershocks sequence to detect repeating or near-repeating seismicity. Our study provides unprecedented seismic data and velocity model for the study area that could benefit understanding detailed structure beneath the region and promoting the initial reference model for locating earthquakes and seismic tomography.

To develop a classification system of visual field (VF) abnormalities in highly myopic eyes with and without glaucoma.Secondary analysis of VF data from a longitudinal cohort study.One thousand eight hundred ninety-three VF tests from 1302 eyes (825 individuals).All participants underwent VF testing (Humphrey 24-2 Swedish interactive threshold algorithm standard program; Carl Zeiss Meditec) and detailed ophthalmic examination. A comprehensive set of VF defect patterns was defined via observation of the 1893 VF reports, literature review, and consensus meetings. The classification system comprised 4 major types of VF patterns, including normal type, glaucoma-like defects (paracentral defect, nasal step, partial arcuate defect, arcuate defect), high myopia-related defects (enlarged blind spot, vertical step, partial peripheral rim, nonspecific defect), and combined defects (nasal step with enlarged blind spot). A subset (n = 1000) of the VFs was used to evaluate the interobserver and intraobserver agreement and weighted κ values of the classification system by 2 trained readers. The prevalence of various VF patterns and their associated factors were determined.The classification of VF in highly myopic eyes and its associated risk factors.We found that normal type, glaucoma-like defects, high myopia-related defects, and combined defects accounted for 74.1%, 10.8%, 15.0%, and 0.1% of all unique VF tests, respectively. The interobserver and intraobserver agreements were89%, and the corresponding κ values were 0.86 or more between readers. Both glaucoma-like and high myopia-related VF defects were associated with older age (odds ratios [ORs], 1.07 [95% confidence interval (CI), 1.04-1.10; P0.001] and 1.06 [95% CI, 1.04-1.10; P0.001]) and longer axial length (ORs, 1.65 [95% CI, 1.32-2.07; P0.001] and 1.37 [95% CI, 1.11-1.68; P = 0.003]). Longer axial length showed a stronger effect on the prevalence of glaucoma-like VF defects than on the prevalence of high myopia-related VF defects (P = 0.036).We propose a new and reproducible classification system of VF abnormalities for nonpathologic high myopia. Applying a comprehensive classification system will facilitate communication and comparison of findings among studies.

PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. DESIGN: DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. PARTICIPANTS: A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). METHODS: Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9–7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%–100%) and posterior subcapsular cataract (PSC; scale 0%–100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. MAIN OUTCOME MEASURES: Mean squared error (MSE). RESULTS: On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. CONCLUSIONS: DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.

Context While Asians have a higher risk of type 2 diabetes (T2D) than Europeans for a given body mass index (BMI), it remains unclear whether the same markers of metabolic pathways are associated with diabetes. Objective We evaluated associations between metabolic biomarkers and incidence of T2D in 3 major Asian ethnic groups (Chinese, Malay, and Indian) and a European population. Methods We analyzed data from adult males and females of 2 cohorts from Singapore (n = 6393) consisting of Chinese, Malays, and Indians and 3 cohorts of European-origin participants from Finland (n = 14 558). We used nuclear magnetic resonance to quantify 154 circulating metabolic biomarkers at baseline and performed logistic regression to assess associations with T2D risk adjusted for age, sex, BMI and glycemic markers. Results Of the 154 metabolic biomarkers, 59 were associated with higher risk of T2D in both Asians and Europeans (P Conclusion Metabolic biomarkers across several biological pathways were consistently associated with T2D risk in Asians and Europeans.

Purpose To develop a novel deep-learning approach that can describe the structural phenotype of the glaucomatous ONH and can be used as a robust glaucoma diagnosis tool. Design Retrospective, deep-learning approach diagnosis study. Method We trained a deep learning network to segment three neural-tissue and four connective-tissue layers of the ONH. The segmented OCT images were then processed by a customized autoencoder network with an additional parallel branch for binary classification. The encoder part of the autoencoder reduced the segmented OCT images into a low-dimensional latent space (LS); whereas the decoder and the classification branches reconstructed the images and classified them as glaucoma or non-glaucoma, respectively. We performed principal component analysis on the latent parameters and identified the principal components (PCs). Subsequently, the magnitude of each PC was altered in steps and reported how it impacted the morphology of the ONH. Results The image reconstruction quality and diagnostic accuracy increased with the size of the LS. With 54 parameters in the LS, the diagnostic accuracy was 92.0±2.3% with a sensitivity of 90.0±2.4% (at 95% specificity), and the corresponding Dice coefficient for the reconstructed images was 0.86±0.04. By changing the magnitudes of PC in steps, we were able to reveal how the morphology of the ONH changes as one transitions from a ‘non-glaucoma’ to a ‘glaucoma’ condition. Conclusions Our network was able to identify novel biomarkers of the ONH for glaucoma diagnosis. Specifically, the structural features identified by our algorithm were found to be related to clinical observations of glaucoma.

Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven associations to many systemic diseases, the eye could potentially provide a credible perspective as a novel screening tool. This systematic review aims to summarize the current applications of ocular image-based artificial intelligence on the detection of systemic diseases and suggest future trends for systemic disease screening.A systematic search was conducted on September 1, 2021, using 3 databases-PubMed, Google Scholar, and Web of Science library. Date restrictions were not imposed and search terms covering ocular images, systemic diseases, and artificial intelligence aspects were used.Thirty-three papers were included in this systematic review. A spectrum of target diseases was observed, and this included but was not limited to cardio-cerebrovascular diseases, central nervous system diseases, renal dysfunctions, and hepatological diseases. Additionally, one- third of the papers included risk factor predictions for the respective systemic diseases.Ocular image - based artificial intelligence possesses potential diagnostic power to screen various systemic diseases and has also demonstrated the ability to detect Alzheimer and chronic kidney diseases at early stages. Further research is needed to validate these models for real-world implementation.

Purpose We hypothesized that the effect of blood lipid–related metabolites on primary open-angle glaucoma (POAG) would differ according to specific lipoprotein particles and lipid sub-fractions. We investigated the associations of blood levels of lipoprotein particles and lipid sub-fractions with POAG. Design Cross-sectional study. Participants Individuals recruited for the baseline visit of the population-based Singapore Epidemiology of Eye Disease study (n = 8503). Methods All participants underwent detailed standardized ocular and systemic examinations. A total of 130 blood lipid–related metabolites were quantified using a nuclear magnetic resonance metabolomics platform. The analyses were conducted in 2 stages. First, we investigated whether and which lipid-related metabolites were directly associated with POAG using regression analyses followed by Bayesian network modeling. Second, we investigated if any causal relationship exists between the identified lipid-related metabolites, if any, and POAG using 2-sample Mendelian randomization (MR) analysis. We performed genome-wide association studies (GWAS) on high-density lipoprotein (HDL) 3 cholesterol (after inverse normal transformation) and used the top variants associated with HLD3 cholesterol as instrumental variables (IVs) in the MR analysis. Main Outcome Measure Primary open-angle glaucoma. Results Of the participants, 175 (2.1%) had POAG. First, a logistic regression model showed that total HDL3 cholesterol (negatively) and phospholipids in very large HDL (positively) were associated with POAG. Further analyses using a Bayesian network analysis showed that only total HDL3 cholesterol was directly associated with POAG (odds ratio [OR], 0.72 per 1 standard deviation increase in HDL3 cholesterol; 95% confidence interval [CI], 0.61–0.84), independently of age, gender, intraocular pressure (IOP), body mass index (BMI), education level, systolic blood pressure, axial length, and statin medication. Using 5 IVs identified from the GWAS and with the inverse variance weighted MR method, we found that higher levels of HDL3 cholesterol were associated with a decreased odds of POAG (OR, 0.91; 95% CI, 0.84–0.99, P = 0.021). Other MR methods, including weighted median, mode-based estimator, and contamination mixture methods, derived consistent OR estimates. None of the routine lipids (blood total, HDL, or low-density lipoprotein [LDL] cholesterol) were associated with POAG. Conclusions Overall, these results suggest that the relationship between HDL3 cholesterol and POAG might be causal and specific, and that dysregulation of cholesterol transport may play a role in the pathogenesis of POAG.

Background and ObjectivesAge-related cognitive impairment is driven by the complex interplay of neurovascular and neurodegenerative disease. There is a strong relationship between cerebral microbleeds (CMBs), cerebral amyloid angiopathy (CAA), and the cognitive decline observed in conditions such as Alzheimer disease. However, in the early, preclinical phase of cognitive impairment, the extent to which CMBs and underlying CAA affect volumetric changes in the brain related to neurodegenerative disease remains unclear.MethodsWe performed cross-sectional analyses from 3 large cohorts: The Northern Manhattan Study (NOMAS), Alzheimer's Disease Neuroimaging Initiative (ADNI), and the Epidemiology of Dementia in Singapore study (EDIS). We conducted a confirmatory analysis of 82 autopsied cases from the Brain Arterial Remodeling Study (BARS). We implemented multivariate regression analyses to study the association between 2 related markers of cerebrovascular disease—MRI-based CMBs and autopsy-based CAA—as independent variables and volumetric markers of neurodegeneration as dependent variables. NOMAS included mostly dementia-free participants age 55 years or older from northern Manhattan. ADNI included participants living in the United States age 55–90 years with a range of cognitive status. EDIS included community-based participants living in Singapore age 60 years and older with a range of cognitive status. BARS included postmortem pathologic samples.ResultsWe included 2,657 participants with available MRI data and 82 autopsy cases from BARS. In a meta-analysis of NOMAS, ADNI, and EDIS, superficial CMBs were associated with larger gray matter (β = 4.49 ± 1.13,p= 0.04) and white matter (β = 4.72 ± 2.1,p= 0.03) volumes. The association between superficial CMBs and larger white matter volume was more evident in participants with 1 CMB (β = 5.17 ± 2.47,p= 0.04) than in those with ≥2 CMBs (β = 1.97 ± 3.41,p= 0.56). In BARS, CAA was associated with increased cortical thickness (β = 6.5 ± 2.3,p= 0.016) but not with increased brain weight (β = 1.54 ± 1.29,p= 0.26).DiscussionSuperficial CMBs are associated with larger morphometric brain measures, specifically white matter volume. This association is strongest in brains with fewer CMBs, suggesting that the CMB/CAA contribution to neurodegeneration may not relate to tissue loss, at least in early stages of disease.

Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we report the development and validation of a retinal photograph-based, deep-learning algorithm for automated detection of visually significant cataracts, using more than 25,000 images from population-based studies. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 96.6%. External testing performed across three studies showed AUROCs of 91.6–96.5%. In a separate test set of 186 eyes, we further compared the algorithm’s performance with 4 ophthalmologists’ evaluations. The algorithm performed comparably, if not being slightly more superior (sensitivity of 93.3% versus 51.7–96.6% by ophthalmologists and specificity of 99.0% versus 90.7–97.9% by ophthalmologists). Our findings show the potential of a retinal photograph-based screening tool for visually significant cataracts among older adults, providing more appropriate referrals to tertiary eye centers.

Table S1: Description of the BCAC studies contributing to COGS. Table S2: Predicted effects of BRCA1 and BRCA2 variants included in the iCOGS array on protein function. Table S3: Frequency of BRCA1 and BRCA2 variants from iCOGS in breast cancer cases and controls. Table S4: Family studies of Y3035S showing scores for each family by constant relative risk and 75% penetrance. Supplementary References.

Breast cancer risks conferred by many germline missense variants in the BRCA1 and BRCA2 genes, often referred to as variants of uncertain significance (VUS), have not been established. In this study, associations between 19 BRCA1 and 33 BRCA2 missense substitution variants and breast cancer risk were investigated through a breast cancer case–control study using genotyping data from 38 studies of predominantly European ancestry (41,890 cases and 41,607 controls) and nine studies of Asian ancestry (6,269 cases and 6,624 controls). The BRCA2 c.9104A>C, p.Tyr3035Ser (OR = 2.52; P = 0.04), and BRCA1 c.5096G>A, p.Arg1699Gln (OR = 4.29; P = 0.009) variant were associated with moderately increased risks of breast cancer among Europeans, whereas BRCA2 c.7522G>A, p.Gly2508Ser (OR = 2.68; P = 0.004), and c.8187G>T, p.Lys2729Asn (OR = 1.4; P = 0.004) were associated with moderate and low risks of breast cancer among Asians. Functional characterization of the BRCA2 variants using four quantitative assays showed reduced BRCA2 activity for p.Tyr3035Ser compared with wild-type. Overall, our results show how BRCA2 missense variants that influence protein function can confer clinically relevant, moderately increased risks of breast cancer, with potential implications for risk management guidelines in women with these specific variants. Cancer Res; 77(11); 2789–99. ©2017 AACR.

Figure S1: BCRA2 p.Y3035S Pedigrees. Figure S2: Western blot of ectopically expressed full-length wildtype and mutant hBRCA2 protein in VC8 DR-GFP cells subjected to HDR assay. Figure S3: Purification of wildtype and mutant BRCA2 from human cells. Figure S4: Position of BRCA2 DNA binding domain (DBD) variants on a ribbon diagram of the murine DBD crystal structure.

The global "myopia boom" has raised significant international concerns. Despite a higher myopia prevalence in Asia, previous large-scale genome-wide association studies (GWASs) were mostly based on European descendants. Here, we report a GWAS of spherical equivalent (SE) in 1852 Chinese Han individuals with extreme SE from Guangzhou (631 -6.00D and 574 0.00D) and Wenzhou (593 -6.00D and 54 -1.75D), followed by a replication study in two independent cohorts with totaling 3538 East Asian individuals. The discovery GWAS and meta-analysis identify three novel loci, which show genome-wide significant associations with SE, including 1q25.2 FAM163A, 10p11.22 NRP1/PRAD3, and 10p11.21 ANKRD30A/MTRNR2L7, together explaining 3.34% of SE variance. 10p11.21 is successfully replicated. The allele frequencies of all three loci show significant differences between major continental groups (P 0.001). The SE reducing (more myopic) allele of rs10913877 (1q25.2 FAM163A) demonstrates the highest frequency in East Asians and much lower frequencies in Europeans and Africans (EAS = 0.60, EUR = 0.20, and AFR = 0.18). The gene-based analysis additionally identifies three novel genes associated with SE, including EI24, LHX5, and ARPP19. These results provide new insights into myopia pathogenesis and indicate the role of genetic heterogeneity in myopia epidemiology among different ethnicities.

Purpose To determine whether eyes with pathologic myopia can be identified and whether each type of myopic maculopathy lesion on fundus photographs can be diagnosed by deep learning (DL) algorithms. Design A DL algorithm was developed to recognize myopic maculopathy features and to categorize the myopic maculopathy automatically. Participants We examined 7020 fundus images from 4432 highly myopic eyes obtained from the Advanced Clinical Center for Myopia. Methods Deep learning (DL) algorithms were developed to recognize the key features of myopic maculopathy with 5176 fundus images. These algorithms were also used to develop a Meta-analysis for Pathologic Myopia (META-PM) study categorizing system (CS) by adding a specific processing layer. Models and the system were evaluated by 1844 fundus image. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to determine the performance of each DL algorithm. The rate of correct predictions was used to determine the performance of the META-PM study CS. Main Outcome Measures Four trained DL models were able to recognize the lesions of myopic maculopathy accurately with high sensitivity and specificity. The META-PM study CS also showed a high accuracy and was qualified to be used in a semiautomated way during screening for myopic maculopathy in highly myopic eyes. Results The sensitivity of the DL models was 84.44% for diffuse atrophy, 87.22% for patchy atrophy, 85.10% for macular atrophy, and 37.07% for choroidal neovascularization, and the AUC values were 0.970, 0.978, 0.982, and 0.881, respectively. The rate of total correct predictions from the META-PM study CS was 87.53%, with rates of 90.18%, 95.28%, 97.50%, and 91.14%, respectively, for each type of lesion. The META-PM study CS showed an overall rate of 92.08% in detecting pathologic myopia correctly, which was defined as having myopic maculopathy equal to or more serious than diffuse atrophy. Conclusions The novel DL models and system can achieve high sensitivity and specificity in identifying the different types of lesions of myopic maculopathy. These results will assist in the screening for pathologic myopia and subsequent protection of patients against low vision and blindness caused by myopic maculopathy.

Vision impairment (VI) is associated with poor quality of life and increased risk of falls. Few prospective data are available on Asians. This study investigates the longitudinal impact of VI and the major eye diseases on mortality risk in Asians.Prospective cohort study with meta-analysis.We conducted a multi-ethnic prospective study of adults (40-80 years old) in the Singapore Epidemiology of Eye Diseases Study (baseline: 2004-2011). All-cause mortality was obtained from the National Death Registry until May 2017. VI was defined by best-corrected visual acuity (BCVA)20/40 in the better eye. Major eye diseases were assessed using standard protocols. We examined associations using multivariate-adjusted Cox proportional hazards regression models. Finally, we conducted a meta-analysis of the associations between VI and mortality.Of 9,986 participants, 1,210 deaths occurred (12.1%) over a median follow-up of 8.8 years. Compared to participants with normal vision, persons with VI had increased risk of mortality (hazards ratio [HR]: 1.53; 95% confidence interval [CI:] 1.30-1.81) in multivariate models. In ethnicity-specific analyses, this association was significant across Chinese (HR: 1.63; 95% CI: 1.08-2.48); Malays (HR: 1.31; 95% CI: 1.06-1.62); and Indians (HR: 2.25; 95% CI: 1.61-3.15). Cataract, under-corrected refractive errors (URE), and diabetic retinopathy (DR) were significantly associated with mortality (HRs: 1.30, 1.22, and 1.54, respectively). In a meta-analysis of 12 studies including 58,034 persons, VI was associated with 30% increased risk of mortality (HR: 1.3; 95% CI: 1.2-1.5).In this multi-ethnic Asian population, VI and preventable eye conditions (cataract, URE, and DR) were associated with mortality, emphasizing the need for early detection and intervention to prevent and treat VI and major eye diseases.

Topic To provide updated estimates on the global prevalence and number of people with diabetic retinopathy (DR) through 2045. Clinical Relevance The International Diabetes Federation (IDF) estimated the global population with diabetes mellitus (DM) to be 463 million in 2019 and 700 million in 2045. Diabetic retinopathy remains a common complication of DM and a leading cause of preventable blindness in the adult working population. Methods We conducted a systematic review using PubMed, Medline, Web of Science, and Scopus for population-based studies published up to March 2020. Random effect meta-analysis with logit transformation was performed to estimate global and regional prevalence of DR, vision-threatening DR (VTDR), and clinically significant macular edema (CSME). Projections of DR, VTDR, and CSME burden were based on population data from the IDF Atlas 2019. Results We included 59 population-based studies. Among individuals with diabetes, global prevalence was 22.27% (95% confidence interval [CI], 19.73%–25.03%) for DR, 6.17% (95% CI, 5.43%–6.98%) for VTDR, and 4.07% (95% CI, 3.42%–4.82%) for CSME. In 2020, the number of adults worldwide with DR, VTDR, and CSME was estimated to be 103.12 million, 28.54 million, and 18.83 million, respectively; by 2045, the numbers are projected to increase to 160.50 million, 44.82 million, and 28.61 million, respectively. Diabetic retinopathy prevalence was highest in Africa (35.90%) and North American and the Caribbean (33.30%) and was lowest in South and Central America (13.37%). In meta-regression models adjusting for habitation type, response rate, study year, and DR diagnostic method, Hispanics (odds ratio [OR], 2.92; 95% CI, 1.22–6.98) and Middle Easterners (OR, 2.44; 95% CI, 1.51–3.94) with diabetes were more likely to have DR compared with Asians. Discussion The global DR burden is expected to remain high through 2045, disproportionately affecting countries in the Middle East and North Africa and the Western Pacific. These updated estimates may guide DR screening, treatment, and public health care strategies.

One of the promises of precision medicine is to understand and act on inter-individual genetic differences in drug responses. SNPdrug3D contains the complete genomic landscape of missense single nucleotide variants (SNV) across the human proteome and at population-wide level that could affect drug binding. In the Singapore SG10K Health and global gnomAD cohorts, comprising variations in over 80,000 individuals, we identified ~ 1.17 million variants mapped to residues near bound drug molecules in protein-drug complexes relative to ~ 6000 drug molecules and experimentally verified effects of selected SNVs, including previously uncharacterized variants, on drug binding in relevant proteins ranging from kinases to cytochrome P450s (CYPs). The latter led to a specific predictor for interpreting variants in the CYP family that vastly outperforms existing tools in the prediction of pharmacogenetic effects. By placing variants and drugs in structural context, SNPdrug3D aids drug development by early flagging of resistance potential through population-specific variability.

AimsTo report the 6-year incidence of optical coherence tomography (OCT)-derived age-related changes in drusen volume and related systemic and ocular associations.MethodsChinese adults aged 40 years and older were assessed at baseline and 6 years with colour fundus photography (CFP) and spectral domain (SD) OCT. CFPs were graded for age-related macular degeneration (AMD) features and drusen volume was generated using commercially available automated software.ResultsA total of 4172 eyes of 2580 participants (mean age 58.12±9.03 years; 51.12% women) had baseline and 6-year follow-up CFP for grading, of these, 2130 eyes of 1305 participants had gradable SD-OCT images, available for analysis. Based on CFP grading, 136 (3.39%) participants developed incident early AMD and 10 (0.25%) late AMD. Concurrently, retinal pigment epithelial-Bruch’s membrane (RPE-BrC) volumes decreased, remained stable and increased in 6.8%, 78.5% and 14.7%, respectively, over 6 years. In eyes where RPE-BrC volumes were >0 mm3at baseline, this was associated with two-fold higher prevalence rate of any AMD at baseline (pConclusionAMD incidence detected at 6 years on CFP and correlated OCT-derived drusen volume measurement change is low. Older age and some systemic risk factors are associated with drusen volume change, and our data provide new insights into relationship between systemic risk factors and outer retinal morphology in Asian eyes.

Topic Glaucoma is the leading cause of irreversible blindness, despite having good prognosis with early treatment. We evaluated the global extent of undetected glaucoma and the factors associated with it in this systematic review and meta-analysis. Clinical Relevance Undetected glaucoma increases the risk of vision impairment, which leads to detrimental effects on the quality-of-life and socioeconomic well-being of those affected. Detailed information on the extent and factors associated with undetected glaucoma aid in the development of public health interventions. Methods We conducted a systematic review and meta-analysis of population-based studies published between January 1, 1990, and June 1, 2020. Article search was conducted in online databases (PubMED, Web-of-Science), grey literatures (OpenGrey), and nongovernment organization reports. Our outcome measure was the proportion of glaucoma cases that were undetected previously. Manifest glaucoma included any form of glaucoma reported in the original studies and may include primary open-angle glaucoma (POAG), primary angle-closure-glaucoma, secondary glaucoma, or a combination thereof. Undetected glaucoma was defined as glaucoma cases that were undetected prior to diagnosis in the respective study. Random-effect meta-analysis was used to estimate the pooled proportion of undetected glaucoma. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Meta-analysis of Observational Studies in Epidemiology guidelines in our study. Results We identified 61 articles from 55 population-based studies (n = 189 359 participants; n = 6949 manifest glaucoma). Globally, more than half of all glaucoma cases were undetected previously on average in each geographical region. Africa (odds ratio [OR], 12.70; 95% confidence interval [CI], 4.91–32.86) and Asia (OR, 3.41; 95% CI, 1.63–7.16) showed higher odds of undetected glaucoma as compared with Europe. Countries with low Human Development Index (HDI; Discussion Undetected glaucoma is highly prevalent across diverse communities worldwide and more common in Africa and Asia. Strategies to improve detection are needed to prevent excess visual disability and blindness resulting from glaucoma.

Objective: Age-related macular degeneration(AMD) has become a major cause of visual impairment worldwide, especially in the elderly. Estimates of incidence, progression rates, and risk factors of AMD vary among studies, complicating the understanding of its epidemiology. Methods: For this systematic review and meta-analysis, literature published up to March 1, 2021, was searched in both English and Chinese databases. Hierarchical Bayesian approaches were used to estimate pooled incidence, progression, and 95% credible intervals (CrIs). Results: Thirty studies were included. The pooled annual early and late AMD incidence rates were 1.59 (95% CrI: 1.18–2.11) and 0.23 (95% CrI: 0.14–0.34) per 100 person-years, respectively. The annual progression rate of AMD was 5.5 (95% CrI: 2.3–8.8) per 100 person-years. Smoking was an independent risk factor for both early and late AMD, whereas age, high-density lipoprotein cholesterol, and alcohol consumption were risk factors for early AMD incidence only. The projected number of new cases of early and late AMD in 2050 would be 39.05 million (95% CrI: 23.12–63.57) and 6.41 million (95% CrI: 3.37–13.22), respectively. Conclusion: The prediction the number of new cases of AMD is not equal across the globe. Our findings indicate the need for more rigorous control and prevention measures in AMD focus on its risk factors for early intervention. The epidemiological estimates reported in this study could inform to identify effective strategies for preventing AMD worldwide.

Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital expenditures while paying only for IT capacity consumed by their project. Cloud computing also allows researchers to overcome common network and storage bottlenecks encountered when combining or re-analysing large datasets. However, cloud computing presents a new set of challenges. Without adequate security controls, the risk of unauthorised access may be higher for data stored on the cloud. In addition, regulators are increasingly mandating data access patterns and specific security protocols on the storage and use of genomic data to safeguard rights of the study participants. While CCPs provide tools for security and regulatory compliance, utilising these tools to build the necessary controls required for cloud solutions is not trivial as such skill sets are not commonly found in a genomics lab. The Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud native genomics data repository and analytics platform focusing on security and regulatory compliance. Using a “five-safes” framework (Safe Purpose, Safe People, Safe Settings, Safe Data and Safe Output), RAPTOR provides security and governance controls to data contributors and users leveraging cloud computing for sharing and analysis of large genomic datasets without the risk of security breaches or running afoul of regulations. RAPTOR can also enable data federation with other genomic data repositories using GA4GH community-defined standards, allowing researchers to boost the statistical power of their work and overcome geographic and ancestry limitations of data sets

We investigated risk factors influencing the outcome of unexpected ovarian carcinomas.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.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 latency28 days from primary to staging surgery had significantly poorer OS (p = 0.008). Women with latency28 days between primary surgery and adjuvant chemotherapy had similar DFS (p = 0.31) and a trend of poorer OS (p = 0.064).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.

BackgroundMachine learning (ML) techniques improve disease prediction by identifying the most relevant features in multi-dimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).MethodsWe utilized longitudinal data from 1365 Chinese, Malay and Indian participants aged 40-80 years with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) 2 with at least 25% decrease in eGFR at follow-up from baseline. 339 features including participant characteristics, retinal imaging, genetic and blood metabolites were used as predictors. Performances of several ML models were compared to each other and to logic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC).ResultsML model, Elastic Net (EN) had the best AUC (95% confidence interval) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2% and 65.9% vs. 73.0% and 72.8% by LR. The top-15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR and metabolites related to lipids, lipoproteins, fatty acids and ketone bodies.ConclusionsOur results showed ML together with feature selection improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors including metabolites.Funding SupportThis study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Conflicts of interestNone declared.

AIMSUsing machine learning integrated with clinical and metabolomic data to identify biomarkers associated with diabetic kidney disease (DKD) and diabetic retinopathy (DR), and to improve the performance of DKD/DR detection models beyond traditional risk factors.METHODSWe examined a population-based cross-sectional sample of 2,772 adults with type 1 or type 2 diabetes from Singapore Epidemiology of Eye Diseases study (SEED, 2004-2011). LASSO logistic regression (LASSO) and gradient boosting decision tree (GBDT) were used to select markers of prevalent DKD (defined as an eGFR < 60ml/min/1.73m2) and prevalent DR (defined as an ETDRS severity level ≥ 20) from an expanded set of 19 established risk factors and 220 NMR-quantified circulating metabolites. Risk assessment models were developed based on the variable selection results and externally validated in UK Biobank (n=5,843, 2007-2010). Model performance (AUC with 95% CI, sensitivity, and specificity) of machine learning was compared to that of traditional logistic regression adjusted for age, gender, diabetes duration, HbA1c%, systolic BP, and BMI.RESULTSSEED participants had a median age of 61.7 years, with 49.1% female, 20.2% having DKD, and 25.4% having DR. UK Biobank participants had a median age of 61.0 years, with 39.2% female, 6.4% having DKD, and 5.7% having DR. Both algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with CVD, hypertension medication, and three metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density-lipoprotein); While DR was additionally associated with HbA1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional logistic regression in both internal (AUC: 0.832-0.838 vs. 0.743 for DKD, and 0.779-0.790 vs. 0.764 for DR) and external validation (AUC: 0.737-0.790 vs. 0.692 for DKD, and 0.778 vs. 0.760 for DR).CONCLUSIONSMachine-learned biomarkers suggested insulin resistance to be a primary factor associated with diabetic microvascular complications. Integrating machine learning with biomedical big data enabled biomarker discovery from a wide range of correlated variables, which may facilitate our understanding of the disease mechanisms and improve disease screening.

The prominent rise of digital health in ophthalmology is evident in the current age of Industry 4.0. Despite the many facets of digital health, there has been a greater slant in interest and focus on artificial intelligence recently. Other major elements of digital health like wearables could also substantially impact patient-focused outcomes but have been relatively less explored and discussed. In this review, we comprehensively evaluate the use of non-artificial intelligence digital health tools in ophthalmology. 53 papers were included in this systematic review - 25 papers discuss virtual or augmented reality, 14 discuss mobile applications and 14 discuss wearables. Most papers focused on the use of technologies to detect or rehabilitate visual impairment, glaucoma and age-related macular degeneration. Overall, the findings on patient-focused outcomes with the adoption of these technologies are encouraging. Further validation, large-scale studies and earlier consideration of real-world barriers are warranted to enable better real-world implementation.

Topic Visual impairment (VI) and cognitive impairment (CIM) are prevalent age-related conditions that impose substantial burden on the society. Findings on the hypothesized bidirectional association of VI and CIM remains equivocal. Hence, we conducted a systematic review and meta-analysis to examine this bidirectional relationship. Clinical Relevance Sixty percent risk of CIM has not been well elucidated in the literature. A bidirectional relationship between VI and CIM may support the development of strategies for early detection and management of risk factors for both conditions in older people. Methods PubMed, Embase, and Cochrane Central registers were searched systematically for observational studies, published from inception until April 6, 2020, in adults 40 years of age or older reporting objectively measured VI and CIM assessment using clinically validated cognitive screening tests or diagnostic evaluation. Meta-analyses on cross-sectional and longitudinal associations between VI and CIM outcomes (any CIM assessed using screening tests and clinically diagnosed dementia) were examined. Random effect models were used to generate pooled odds ratios (ORs) and 95% confidence intervals (CIs). We also examined study quality, publication bias, and heterogeneity. Results Forty studies were included (n = 47 913 570). Meta-analyses confirmed that persons with VI were more likely to have CIM, with significantly higher odds of: (1) any CIM (cross-sectional: OR, 2.38 [95% CI, 1.84–3.07]; longitudinal: OR, 1.66 [95% CI, 1.46–1.89]) and (2) clinically diagnosed dementia (cross-sectional: OR, 2.43 [95% CI, 1.48–4.01]; longitudinal: OR, 2.09 [95% CI, 1.37–3.21]) compared with persons without VI. Significant heterogeneity was explained partially by differences in age, sex, and follow-up duration. Also, some evidence suggested that individuals with CIM, relative to cognitively intact persons, were more likely to have VI, with most articles (8/9 [89%]) reporting significantly positive associations; however, meta-analyses on this association could not be conducted because of insufficient data. Discussion Overall, our work suggests that VI is a risk factor of CIM, although further work is needed to confirm the association of CIM as a risk factor for VI. Strategies for early detection and management of both conditions in older people may minimize individual clinical and public health consequences.

PurposeTo evaluate the performance of swept source optical coherence tomography (SS-OCT) to detect gonioscopic angle closure using different classification algorithms.MethodsThis was a cross-sectional study of 2028 subjects without ophthalmic symptoms recruited from a community-based clinic. All subjects underwent gonioscopy and SS-OCT (Casia, Tomey Corporation, Nagoya, Japan) under dark room conditions. For each eye, 8 out of 128 frames (22.5° interval) were selected to measure anterior chamber parameters namely anterior chamber width, depth, area and volume (ACW, ACD, ACA, and ACV), lens vault (LV), iris curvature (IC), iris thickness (IT) from 750 µm and 2000 µm from the scleral spur, iris area and iris volume. Five diagnostic algorithms—stepwise logistic regression, random forest, multivariate adaptive regression splines, recursive partitioning and Naïve Bayes were evaluated for detection of gonioscopic angle closure (defined as ≥2 closed quadrants). The performance of the horizontal frame was compared with that of other meridians.ResultsData from 1988 subjects, including 143 (7.2%) with gonioscopic angle closure, were available for analysis. They were divided into two groups: training (1391, 70%) and validation (597, 30%). The best algorithm for detecting gonioscopic angle closure was stepwise logistic regression with an area under the curve of 0.91 (95% CI 0.88 to 0.93) using all parameters, and 0.88 (95% CI 0.82 to 0.93) using only ACA, LV and IC of the horizontal meridian scan.ConclusionsA stepwise logistic regression model incorporating SS-OCT measurements has a high diagnostic ability to detect gonioscopic angle closure.

PURPOSE To examine the association between near work, screen time including TV and outdoor time with myopia in children from the Sunflower Myopia Asian Eye Epidemiology Consortium (AEEC). METHODS We analysed AEEC cross-sectional data (12 241 children) on risk factors (near work, screen time including TV and outdoor time) and myopia of six population-based studies (China, Hong Kong and Singapore). Cycloplegic refraction and axial length (AL) measurements were included. Risk factors were determined using questionnaires. Data were pooled from each study, and multivariable regression analysis was performed to evaluate the associations between risks factors and myopia, spherical equivalent (SE) and AL. RESULTS Among the included children, 52.1% were boys, 98.1% were Chinese and 69.7% lived in urban areas. Mean±standard deviation (SD) for age was 8.8 ± 2.9 years, for SE was -0.14 ± 1.8 D and for AL was 23.3 ± 1.1 mm. Myopia prevalence was 30.6%. In multivariate analysis, more reading and writing (OR = 1.17; 95% CI, 1.11-1.24), more total near work (OR = 1.05; 95% CI, 1.02-1.09) and less outdoor time (OR = 0.82, 95% CI, 0.75-0.88) were associated with myopia (p's

Purpose To describe the distribution and determinants of choroidal thickness (CT) in participants in a population study based on spectral-domain (SD)-OCT measurements. Design Population-based, cross-sectional study. Participants Ethnic Chinese, Indian, and Malay adults aged more than 50 years without any retinal diseases (e.g., diabetic retinopathy, macular edema, age-related macular degeneration, central serous chorioretinopathy) that might affect the CT were recruited from the Singapore Epidemiology of Eye Diseases Study. Methods Choroidal imaging was performed by SD-OCT (Spectralis, Heidelberg Engineering, Heidelberg, Germany) in enhanced depth imaging (EDI) mode. Subfoveal choroidal thickness (SFCT) was measured on the foveal line scan by 2 retinal experts independently (YS and KT), and the average was used in the analyses. In Chinese and Indian cohorts in whom macular raster scans were captured, the manufacturer-supplied research software (Heyex SP-X version 6.4.8.116; Heidelberg Engineering) was used to obtain automated segmentation yielding mean choroidal thickness in each of the 9 ETDRS grid sectors. Main Outcome Measures Subfoveal choroidal thickness and regional CT in the 9 ETDRS grid sectors. Results For the SFCT analysis, 2794 eyes of 1619 participants (Chinese, Indian, and Malay ) were included. The mean age was 60.9 years (standard deviation, 7.7), and 797 (49.2%) were male. Mean SFCT was 255.2 μm (standard deviation, 102.6). The normal range of SFCT was 106 to 447 μm (corresponding to 5th and 95th percentile limits of SFCT, respectively). In multivariable models, thinner SFCT was associated with older age, female gender, longer axial length, and Malay (vs. Chinese) ethnicity. In the subset of Chinese and Indian eyes (n = 1842) in whom regional variation was evaluated, the choroid was thickest at the superior and temporal sectors and thinner at the inferior and nasal sectors. Conclusions Subfoveal choroidal thickness is influenced by age, gender, and ethnicity along with regional differences even within individual eyes. Subfoveal choroidal thickness also shows a wide range in physiologic limits. These data may be used as a reference in future studies.

AimsTo characterise the association between visual field (VF) defects and myopic macular degeneration (MMD) in highly myopic adults without glaucoma.MethodsParticipants (n=106; 181 eyes) with high myopia (HM; spherical equivalent ≤−5.0 D or axial length (AL) ≥26 mm), after excluding glaucoma and glaucoma suspects, from the Singapore Epidemiology of Eye Diseases-HM study were included in this cross-sectional study. Humphrey VF (central 24–2 threshold), cup-disc ratio (CDR) and intraocular pressure (IOP) measurements were performed. Mean deviation (MD) and pattern SD (PSD), VF defects (normal or abnormal; pResultsMean age was 55.4±9.9 years and 51.9% were women (AL=26.7±1.1 mm). MMD eyes had lower MD (−3.8±2.9 dB vs −1.1±1.4 dB) and higher PSD (2.8±1.7 dB vs 1.7±0.6 dB). A higher percentage of MMD eyes (n=48) had abnormal VF (62.5% vs 28.6%; pConclusionHighly myopic adults with MMD may have VF loss when compared with highly myopic patients without MMD even in adults without glaucoma.

When fire disasters are imminent, disaster prevention measures and relief plans are critical. However, “fire education” to equip civilians with the know-how to cope with various emergencies and protect themselves is perhaps more important. To provide learning materials which are interesting, appealing, flexible, safe, and efficient for fire education, this study adopts an augmented reality (AR) technique to create an interactive fire education learning environment. This study constructs an interactive learning environment for fire education and examines the learning effectiveness between an AR card and tangible-user-interface-based (TUI) AR in motivation, fire prevention attitude, and cognitive learning outcome. In the proposed AR interactive learning environment, Kolb’s experiential learning cycle is used to guide students’ learning process. There were 170 experimental participants in this study; 86 were assigned to the experimental group using the TUI, and 84 to the control group using AR cards. The AR system integrated with experiential learning could improve students’ fire prevention attitudes and learning outcomes in both groups. Moreover, the experimental group performed better in the cognitive learning outcome and had lower mental loads than the control group. However, no difference in the fire prevention attitude between the two groups was found. In other words, the “object-linked” characteristics provided by TUI-AR benefits cognitive performance rather than attitude. To conclude, this research presents an important perspective on the introduction of technology into fire education that contributes to the broader body of teaching and learning on school fire education.

To determine the 6-year incidence of primary glaucoma and its risk factors in an Indian population in Singapore.Cohort population-based study.Indian adults between 40 and 80 years of age living in Singapore. A total of 3400 participants underwent the baseline examination between 2007 and 2009. Of them, 2200 (response rate, 75.5% of those eligible) participated in the 6-year follow-up visit between 2013 and 2015.Standardized examination and investigations were performed, including indentation gonioscopy, intraocular pressure (IOP) measurement, optic disc examination, and static automated perimetry. Glaucoma was defined according to the International Society Geographical and Epidemiological Ophthalmology criteria.Incidences of primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG).The age-standardized incidences of primary glaucoma, POAG, and PACG were 1.68% (95% confidence interval [CI], 1.21%-2.32%), 1.37% (95% CI, 0.94%-1.96%), and 0.32% (95% CI, 0.15%-0.66%), respectively. For eyes with ocular hypertension (n = 45) at baseline, the incidence of POAG was 4.83% (95% CI, 1.24%-17.21%) and for eyes with primary angle closure (n = 14) or suspected primary angle closure (n = 101), the incidence of PACG developing was 1.82% (95% CI, 0.37%-9.68%). Baseline factors associated significantly with risk of primary glaucoma developing included older age (per decade; odds ratio [OR], 1.84; 95% CI, 1.44-2.36; P0.001), larger vertical cup-to-disc ratio (per 0.1 unit; OR, 2.78; 95% CI, 2.12-3.64; P0.001), and higher IOP (per millimeter of mercury; OR, 1.25; 95% CI, 1.13-1.38; P0.001). However, 71 of 85 eyes (84.5%) diagnosed with incident glaucoma had IOP of less than 21 mmHg at the time of examination.Our study showed that the overall age-standardized incidence of primary glaucoma was 1.68% in an Indian population in Singapore. In our population, the incidence of POAG was only half and the incidence of PACG was similar to the incidence reported by the Chennai Eye Disease Study.

Purpose Visual impairment (VI) can have a detrimental impact on vision-related quality of life (VRQoL), but it is still unclear how this relationship varies with age across the VI spectrum. We determined the age-stratified, cross-sectional, and longitudinal associations between VI severity and VRQoL. Design The baseline and follow-up Singapore Chinese Eye Studies (SCES-1/-2; 2009–2011 and 2015–2017). Participants A total of 3068 SCES-1 participants (mean age [standard deviation {SD}]: 59.5 [9.8] years; 50.2% female) and 1919 SCES-2 participants (mean age [SD]: 56.8 [8.3] years; 49.9% female). Methods Visual impairment was defined as visual acuity (VA) of > 0.3 logarithm of the minimum angle of resolution (logMAR) units; VI severity as mild-moderate (logMAR scores less than the median of all individuals with VI) and severe (logMAR scores median or greater); and VI incidence as VI absence at baseline, but evident at follow-up. Age was stratified into 40 to 49 years, 50 to 64 years, and ≥65 years. Main Outcome Measures Rasch-transformed scores from the 32-item Impact of Visual Impairment (IVI) questionnaire were used to measure the “Reading,” “Mobility,” and “Emotional” domains of VRQoL. Multiple linear regression models determined the age-stratified associations of prevalent and incident VI with all 3 VRQoL outcomes, adjusted for potential confounders. Results Of the 807 persons with prevalent VI, 55.9% had mild-moderate and 44.1% had severe VI. Compared with no VI, age-stratified analyses showed that VRQoL decrements were significant only in the older age groups (mild-moderate VI: 6.2% and 8.1% reduction in Mobility and Reading scores in those aged ≥ 65 years; severe VI: 8.5% to 13.4% reductions in the 3 VRQoL scores in those aged ≥ 50 years). This interaction with older age became more pronounced with incident VI (N = 168), where decrements in all 3 VRQoL domains were evident only in those aged ≥65 years compared with persons without incident VI. Conclusions Our results suggest that the VI-VRQoL associations are driven mainly by older individuals aged ≥65 years, highlighting the need for effective regular screening and early intervention modalities to prevent the presence and onset of VI, and subsequent VRQoL declines, in these individuals.

To investigate whether recently identified genetic loci for primary angle-closure glaucoma (PACG) are associated with disease severity.Case-control study.Eight hundred four PACG patients and 943 control participants of Chinese ethnicity from Singapore.The 8 PACG-associated single nucleotide polymorphisms (SNPs; rs11024102 at PLEKHA7, rs3753841 at COL11A1, rs1015213 located between PCMTD1 and ST18 on chromosome 8q, rs3816415 at EPDR1, rs1258267 at CHAT, rs736893 at GLIS3, rs7494379 at FERMT2, and rs3739821 mapping in between DPM2 and FAM102A) identified from genome-wide association studies were tested for association with disease severity using logistic regression adjusted for age and gender. A P value of 0.006 was set as significant after Bonferroni correction for testing of 8 loci. We also calculated the weighted genetic risk score (GRS) weighted by the estimated individual SNP effect size on PACG calculated as logarithm of the odds ratio (OR). Disease severity was based on the visual field mean deviation (MD) and classified as early to moderate (MD,-12 dB) and severe (MD,-20 dB).Association of PACG loci with severe disease.Of the 804 PACG patients, genotyping data were available for 768 individuals and included 436 with mild-to-moderate PACG and 206 with severe PACG. The PACG patients were significantly older (mean age, 64.3 ± 9.1 years vs. 56.4 ± 8.9 years; P0.001) and there were proportionately more women compared with control participants (58.4% vs. 49.0%; P0.001). Of the 8 loci investigated, we observed significant evidence of association with severe PACG at 1 SNP, namely rs3816415 in EPDR1 (OR, 2.03; 95% confidence interval [CI], 1.49-2.78; P = 1 × 10Our results show that EPDR1 is associated significantly with severe PACG, suggesting that it may predispose patients to more aggressive disease development. Individuals with PACG with a higher GRS were associated with a higher risk of severe PACG.

Objective To report two cases of for primary peritoneal serous carcinoma (PPSC) to present with gastrointestinal manifestations that mimic colorectal cancer. Case report There were two patients with initial presentations of fatigue with iron deficiency anemia, and tenesmus with bloody stool. Tumors of the ascending colon and rectum were detected by colonofiberoscope, and pathologic reports of tumor biopsies revealed adenocarcinoma of suspected gynecologic origin. Both patients underwent optimal debulking surgery without macroscopic residual tumor, and then received adjuvant carboplatin and paclitaxel chemotherapy with bevacizumab. Conclusions PPSC can clinically present like primary colorectal carcinoma. The differential diagnosis requires special staining of several markers for tumor tissues.

Background/aimsObesity is a well-known risk factor for diabetes, but its association with diabetic retinopathy (DR) is inconclusive, in particular in Asians. We aimed to assess whether body mass index (BMI) is associated with the presence and severity of DR in Asian populations with diabetes.MethodsPooled analysis of individual-level cross-sectional data from 10 010 adults with diabetes who participated in 12 population-based studies conducted in China, India, Japan, Russia (Asian), Singapore and South Korea that were part of the Asian Eye Epidemiology Consortium (AEEC). BMI was calculated as weight in kilograms divided by height in square metres and categorised into normal (2, reference), overweight (25–29.9 kg/m2) and obese (≥30 kg/m2). Any-DR (n=1669) and vision-threatening DR (VTDR, n=489) were assessed from digital retinal photographs and graded according to standard protocols. Each study was analysed separately using multivariable logistic regression models adjusted for age, sex, haemoglobin A1c%, systolic blood pressure and diabetes duration, and the estimated odds ratios (ORs) and 95% confidence interval (CIs) from all studies were then combined using random-effects models.ResultsIn multivariable models, obesity showed a significant inverse association with any-DR (pooled OR (95% CI) =0.74 (0.59 to 0.91)) and VTDR (0.75 (0.60 to 0.93)). Similarly, in continuous analysis, BMI showed a significant inverse association with both any-DR (0.93 (0.87 to 0.99)) and VTDR (0.79 (0.68 to 0.92) per SD increase). Overweight did not show a significant association with any-DR.ConclusionsAmong Asian adults with diabetes, both BMI and obesity showed an inverse association with DR. These findings warrant confirmation in further longitudinal studies.

PurposeTo evaluate the effect of signal strength (SS) on optical coherence tomography (OCT) parameters, and devise an algorithm to adjust the effect, when acceptable SS cannot be obtained.Methods5085 individuals (9582 eyes), aged ≥40 years from the Singapore Epidemiology of Eye Diseases population-based study were included. Everyone underwent a standardised ocular examination and imaging with Cirrus HD-OCT. Effect of SS was evaluated using multiple structural breaks linear mixed-effect models. Expected change for increment in SS between 4 and 10 for individual parameter was calculated. Subsequently we devised and evaluated an algorithm to adjust OCT parameters to higher SS.ResultsAverage retinal nerve fibre layer (RNFL) thickness showed shift of 4.11 µm from SS of 5 to 6. Above 6, it increased by 1.72 and 3.35 µm to 7 and 8; and by 1.09 µm (per unit increase) above 8 SS. Average ganglion cell-inner plexiform layer (GCIPL) thickness shifted 5.15 µm from SS of 5 to 6. Above 6, increased by 0.94 µm from 7 to 8; and by 0.16 µm (per unit increase) above 8 SS. When compared with reference in an independent test set, the algorithm produced less systemic bias. Algorithm-adjusted average RNFL was 0.549 µm thinner than the reference, while the unadjusted one was 2.841 µm thinner (pConclusionsOCT parameters can be adjusted for poor SS using an algorithm. This can potentially assist in diagnosis and monitoring of glaucoma when scans with acceptable SS cannot be acquired from patients in clinics.

BackgroundIn 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR). However, this new equation is not ethnic-specific, and prevalence of CKD in Asians is known to differ from other ethnicities. This study evaluates the impact of the 2009 and 2021 creatinine-based eGFR equations on the prevalence of CKD in multiple Asian cohorts.MethodsEight population-based studies from China, India, Russia (Asian), Singapore and South Korea provided individual-level data (n = 67,233). GFR was estimated using both the 2009 CKD-EPI equation developed using creatinine, age, sex, and race (eGFRcr [2009, ASR]) and the 2021 CKD-EPI equation developed without race (eGFRcr [2021, AS]). CKD was defined as an estimated glomerular filtration rate (eGFR) FindingsOf 67,233 adults, CKD prevalence was 8.6% (n = 5800/67,233) using eGFRcr (2009, ASR) and 6.4% (n = 4307/67,233) using eGFRcr (2021, AS). With the latter, CKD prevalence was reduced across all eight studies, ranging from −7.0% (95% CI −8.5% to −5.4%) to −0.4% (−1.3% to 0.5%), and across all subgroups except those in the BMI < 18.5% subgroup. Net reclassification index (NRI) was significant at −2.33% (p < 0.001). No individuals were reclassified as a higher (more severe) eGFR category, while 1.7%−4.2% of individuals with CKD were reclassified as one eGFR category lower when eGFRcr (2021, AS) rather than eGFRcr (2009, ASR) was used.InterpretationeGFRcr (2021, AS) consistently provided reduced CKD prevalence and higher estimation of GFR among Asian cohorts than eGFRcr (2009, ASR). Based on current risk-stratified approaches to CKD management, more patients reclassified to lower-risk GFR categories could help reduce inappropriate care and its associated adverse effects among Asian renal patients. Comparison of both equations to predict progression to renal failure or adverse outcomes using prospective studies are warranted.FundingNational Medical Research Council, Singapore.

To determine the longitudinal associations between retinal vascular profile (RVP) and four major cardiometabolic diseases; and to quantify the predictive improvements when adding RVP beyond traditional risk factors in individuals with diabetes.Subjects were enrolled from the Singapore Epidemiology of Eye Disease (SEED) study, a multi-ethnic population-based cohort. Four incident cardiometabolic diseases, calculated over a ~ 6-year period, were considered: cardiovascular disease (CVD), hypertension (HTN), diabetic kidney disease (DKD), and hyperlipidemia (HLD). The RVP-vessel tortuosity, branching angle, branching coefficient, fractal dimension, vessel caliber, and DR status-was characterized at baseline using a computer-assisted program. Traditional risk factors at baseline included age, gender, ethnicity, smoking, blood pressure (BP), HbA1c, estimated glomerular filtration rate (eGFR), or cholesterol. The improvements in predictive performance when adding RVP (compared with only traditional risk factors) was calculated using several metrics including area under the receiver operating characteristics curve (AUC) and net reclassification improvement (NRI).Among 1770 individuals with diabetes, incidences were 6.3% (n = 79/1259) for CVD, 48.7% (n = 166/341) for HTN, 14.6% (n = 175/1199) for DKD, and 59.4% (n = 336/566) for HLD. DR preceded the onset of CVD (RR 1.85[1.14;3.00]) and DKD (1.44 [1.06;1.96]). Narrower arteriolar caliber preceding the onset of HTN (0.84 [0.72;0.99]), and changes in arteriolar branching angle preceded the onset of CVD (0.78 [0.62;0.98]) and HTN (1.15 [1.03;1.29]). The largest predictive improvement was found for HTN with AUC increment of 3.4% (p = .027) and better reclassification of 11.4% of the cases and 4.6% of the controls (p = .008).We found that RVPs improved the prediction of HTN in individuals with diabetes, but add limited information for CVD, DKD, and HLD predictions.