Lipid-lowering drug and complement factor H genotyping-personalized treatment strategy for age-related macular degeneration.

We investigated whether the effect of lipid-lowering drugs (LLDs) on age-related macular degeneration (AMD) differs according to the main complement genetic variants in Singapore Epidemiology of Eye Diseases (SEED) ( = 5,579) and UK Biobank studies ( = 445,727). The effect of LLD was determined for each stratum of 20 complement genetic variants. In SEED, 484 individuals developed AMD and 216 showed progression over 6 years.

Contributions of Lipid-Related Metabolites and Complement Proteins to Early and Intermediate Age-Related Macular Degeneration.

Objective: Our objective was to determine the effects of lipids and complement proteins on early and intermediate age-related macular degeneration (AMD) stages using machine learning models by integrating metabolomics and proteomic data.

Design: Nested case-control study.

Subjects And Controls: The analyses were performed in a subset of the Singapore Indian Chinese Cohort (SICC) Eye Study.

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.

Background: Diabetic 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.

Objective: This 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.

Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease.

Background: The prevalence of chronic kidney disease (CKD) is high. Identification of cases with CKD or at high risk of developing it is important to tailor early interventions. The objective of this study was to identify blood metabolites associated with prevalent and incident severe CKD, and to quantify the corresponding improvement in CKD detection and prediction.

Ocular microvascular complications in diabetic retinopathy: insights from machine learning.

Introduction: Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, primarily driven by ocular microvascular complications from chronic hyperglycemia. Comprehending the complex relationship between microvascular changes in the eye and disease progression poses challenges, traditional methods assuming linear or logistical relationships may not adequately capture the intricate interactions between these changes and disease advances. Hence, the aim of this study was to evaluate the microvascular involvement of diabetes mellitus (DM) and non-proliferative DR with the implementation of non-parametric machine learning methods.

Associations between Chronic Kidney Disease and Thinning of Neuroretinal Layers in Multiethnic Asian and White Populations.

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.

Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults.

Background: Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).

Methods: We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y 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).

Risk prediction models for type 2 diabetes using either fasting plasma glucose or HbA1c in Chinese, Malay, and Indians: Results from three multi-ethnic Singapore cohorts.

Aims: To assess three well-established type 2 diabetes (T2D) risk prediction models based on fasting plasma glucose (FPG) in Chinese, Malays, and Indians, and to develop simplified risk models based on either FPG or HbA1c.

Methods: We used a prospective multiethnic Singapore cohort to evaluate the established models and develop simplified models. 6,217 participants without T2D at baseline were included, with an average follow-up duration of 8.

Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

Background: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown.

Methods: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists.

Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.

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.

Relationships between Lipid-Related Metabolites and Age-Related Macular Degeneration Vary with Complement Genotype.

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.

Relationship between dry eye symptoms and quality of life: associations and mediation analysis.

Purpose: (1) To determine the independent association of dry eye symptoms with health-related quality of life (HRQoL) in the Singapore population and (2) to further investigate which factors mediate this association.

Methods: In this cross-sectional study, 7707 participants were included. The presence of dry eye symptoms was defined as experiencing at least one out of the six symptoms either 'often' or 'all the time'.

Retinal vascular profile in predicting incident cardiometabolic diseases among individuals with diabetes.

Objective: 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.

Methods: 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).

Machine learning identifying peripheral circulating metabolites associated with intraocular pressure alterations.

Aims: To identify blood metabolite markers associated with intraocular pressure (IOP) in a population-based cross-sectional study.

Methods: This study was conducted in a multiethnic Asian population (Chinese, n=2805; Indians, n=3045; Malays, n=3041 aged 40-80 years) in Singapore. All subjects underwent standardised systemic and ocular examinations, and biosamples were collected.

Circulating Metabolic Biomarkers Are Consistently Associated With Type 2 Diabetes Risk in Asian and European Populations.

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).

Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk.

Background: ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA).

Objective: we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations.

Combining retinal and choroidal microvascular metrics improves discriminative power for diabetic retinopathy.

Purpose: To use optical coherence tomography angiography (OCTA) parameters from both the retinal and choroidal microvasculature to detect the presence and severity of diabetic retinopathy (DR).

Method: This is a cross-sectional case-control study. OCTA parameters from retinal vasculature, fovea avascular zone (FAZ) and choriocapillaris were evaluated from 3×3 mm fovea-centred scans.

Handheld chromatic pupillometry can accurately and rapidly reveal functional loss in glaucoma.

Background/aims: Early detection and treatment of glaucoma can delay vision loss. In this study, we evaluate the performance of handheld chromatic pupillometry (HCP) for the objective and rapid detection of functional loss in glaucoma.

Methods: In this clinic-based, prospective study, we enrolled 149 patients (median (IQR) years: 68.

High-Density Lipoprotein 3 Cholesterol and Primary Open-Angle Glaucoma: Metabolomics and Mendelian Randomization Analyses.

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.

Novel Serum and Urinary Metabolites Associated with Diabetic Retinopathy in Three Asian Cohorts.

Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus, a metabolic disorder, but understanding of its pathophysiology remains incomplete. Meta-analysis of three population-based cross-sectional studies (2004-11) representing three major Asian ethnic groups (aged 40-80 years: Chinese, 592; Malays, 1052; Indians, 1320) was performed. A panel of 228 serum/plasma metabolites and 54 urinary metabolites were quantified using nuclear magnetic resonance (NMR) spectroscopy.

COVID-19 awareness, knowledge and perception towards digital health in an urban multi-ethnic Asian population.

This study aimed to determine COVID-19-related awareness, knowledge, impact and preparedness among elderly Asians; and to evaluate their acceptance towards digital health services amidst the pandemic. 523 participants (177 Malays, 171 Indians, 175 Chinese) were recruited and underwent standardised phone interview during Singapore's lockdown period (07 April till 01 June 2020). Multivariable logistic regression models were performed to evaluate the associations between demographic, socio-economic, lifestyle, and systemic factors, with COVID-19 awareness, knowledge, preparedness, well-being and digital health service acceptance.