Artificial intelligence for diabetes care: current and future prospects.

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study.

AIMS: To develop and externally test deep learning (DL) models for assessing the image quality of three-dimensional (3D) macular scans from Cirrus and Spectralis optical coherence tomography devices. METHODS: We retrospectively collected two data sets including 2277 Cirrus 3D scans and 1557 Spectralis 3D scans, respectively, for training (70%), fine-tuning (10%) and internal validation (20%) from electronic medical and research records at The Chinese University of Hong Kong Eye Centre and the Hong Kong Eye Hospital. Scans with various eye diseases (eg, diabetic macular oedema, age-related macular degeneration, polypoidal choroidal vasculopathy and pathological myopia), and scans of normal eyes from adults and children were included. Two graders labelled each 3D scan as gradable or ungradable, according to standardised criteria. We used a 3D version of the residual network (ResNet)-18 for Cirrus 3D scans and a multiple-instance learning pipline with ResNet-18 for Spectralis 3D scans. Two deep learning (DL) models were further tested via three unseen Cirrus data sets from Singapore and five unseen Spectralis data sets from India, Australia and Hong Kong, respectively. RESULTS: In the internal validation, the models achieved the area under curves (AUCs) of 0.930 (0.885-0.976) and 0.906 (0.863-0.948) for assessing the Cirrus 3D scans and Spectralis 3D scans, respectively. In the external testing, the models showed robust performance with AUCs ranging from 0.832 (0.730-0.934) to 0.930 (0.906-0.953) and 0.891 (0.836-0.945) to 0.962 (0.918-1.000), respectively. CONCLUSIONS: Our models could be used for filtering out ungradable 3D scans and further incorporated with a disease-detection DL model, allowing a fully automated eye disease detection workflow.

Interpretable Machine Learning-Based Risk Score for Predicting Ten-Year Corneal Graft Survival After Penetrating Keratoplasty and Deep Anterior Lamellar Keratoplasty in Asian Eyes.

PURPOSE: To predict 10-year graft survival after deep anterior lamellar keratoplasty (DALK) and penetrating keratoplasty (PK) using a machine learning (ML)-based interpretable risk score. METHODS: Singapore Corneal Transplant Registry patients (n = 1687) who underwent DALK (n = 524) or PK (n = 1163) for optical indications (excluding endothelial diseases) were followed up for 10 years. Variable importance scores from random survival forests were used to identify variables associated with graft survival. Parsimonious analysis using nested Cox models selected the top factors. An ML-based clinical score generator (AutoScore) converted identified variables into an interpretable risk score. Predictive performance was evaluated using Kaplan-Meier (KM) curves and time-integrated AUC (iAUC) on an independent testing set. RESULTS: Mean recipient age was 51.8 years, 54.1% were male, and majority were Chinese (60.0%). Surgical indications included corneal scar (46.5%), keratoconus (18.3%), and regraft (16.2%). Five-year and ten-year KM survival was 93.4% and 92.3% for DALK, compared with 67.6% and 56.6% for PK (log-rank P < 0.001). Five factors were identified by ML algorithm as predictors of 10-year graft survival: recipient sex, preoperative visual acuity, choice of procedure, surgical indication, and active inflammation. AutoScore stratified participants into low-risk and high-risk groups-with KM survival of 73.6% and 39.0%, respectively (log-rank P < 0.001). ML analysis outperformed traditional Cox regression in predicting graft survival beyond 5 years (iAUC 0.75 vs. 0.69). CONCLUSIONS: A combination of ML and traditional techniques identified factors associated with graft failure to derive a clinically interpretable risk score to stratify PK and DALK patients-a technique that may be replicated in other corneal transplant programs.

Transition probabilities of diabetic retinopathy and death in an Asian population with diabetes.

PURPOSE: To evaluate the dynamic transitions in diabetic retinopathy (DR) severity over time and associated risk factors in an Asian population with diabetes. DESIGN: Longitudinal cohort study METHODS: We analyzed data from 9481 adults in the Singapore Integrated Diabetic Retinopathy Screening Program (2010-2015) with linkage to death registry. A multistate Markov model adjusted for age, sex, systolic blood pressure (SBP), diabetes duration, HbA1c, and body mass index (BMI) was applied to estimate annual transition probabilities between four DR states (no, mild, moderate, and severe/proliferative) and death, and the mean sojourn time in each state. RESULTS: The median assessment interval was 12 months, with most patients having 3 assessments. Annual probabilities for DR progression (no-to-mild, mild-to-moderate and moderate-to-severe/proliferative) were 6.1 %, 7.0 % and 19.3 %, respectively; and for regression (mild-to-no, moderate-to-mild and severe-to-moderate) were 55.4 %, 17.3 % and 4.4 %, respectively. Annual mortality rates from each DR state were 1.2 %, 2.0 %, 18.7 %, and 30.0 %. The sojourn time in each state were 8.2, 0.8, 0.8 and 2.2 years. Higher HbA1c and SBP levels were associated with progression of no-mild and mild-moderate DR, and diabetes duration with no-to-mild and moderate-to-severe/proliferative DR. Lower HbA1c levels were associated with regression from mild-to-no and moderate-to-mild, and higher BMI with mild-to-no DR. CONCLUSIONS: Our results suggest a prolonged duration (∼8 years) in developing mild DR, with faster transitions (within a year) from mild or moderate states. Moderate/above DR greatly increases the probability of progression and death as compared to mild DR/below. HbA1c was associated with both progression as well as regression.

Prevalence, associated risk factors; and patient and economic impact of multiple sensory impairment in a multi-ethnic elderly population in Singapore: the PIONEER study.

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 < 12 on the Sniffin' Sticks test) impairments across the spectrum of laterality (any, unilateral, combination [of unilateral and bilateral], and bilateral). RESULTS: Among 2,057 participants (mean ± SD 72.2 ± 0.2 years; 53.1% female), the national census-adjusted prevalence rates of any, unilateral, combination, and bilateral MSI were 20.6%, 1.2%, 12.2%, and 7.2%, respectively. Older age, male gender, low socioeconomic status (SES), and smoking (all p < 0.05) were independently associated with higher likelihood of any MSI. Compared to those with no sensory loss, those with MSI had significantly decreased mobility (range 5.4%-9.2%), had poor functioning (OR range 3.25-3.45) and increased healthcare costs (range 4-6 folds) across the laterality spectrum. Additionally, bilateral MSI had a significant decrease in HRQoL (5.5%, p = 0.012). CONCLUSIONS: MSI is a highly prevalent medical condition, with 1 in 5; and almost 1 in 10 community-dwelling older Asians having any and bilateral MSI, respectively, with a higher likelihood in men, smokers, and those with low SES. Critically, MSI has a substantial negative impact on patient health and economic outcomes across the laterality spectrum. Sensory testing is critical to detect and refer individuals with MSI for management to improve their functional independence and QoL.

Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study.

BACKGROUND: The Diabetic Retinopathy Extended Screening Study (DRESS) aims to develop and validate a new DR/diabetic macular edema (DME) risk stratification model in patients with Type 2 diabetes (DM) to identify low-risk groups who can be safely assigned to biennial or triennial screening intervals. We describe the study methodology, participants' baseline characteristics, and preliminary DR progression rates at the first annual follow-up. METHODS: DRESS is a 3-year ongoing longitudinal study of patients with T2DM and no or mild non-proliferative DR (NPDR, non-referable) who underwent teleophthalmic screening under the Singapore integrated Diabetic Retinopathy Programme (SiDRP) at four SingHealth Polyclinics. Patients with referable DR/DME (> mild NPDR) or ungradable fundus images were excluded. Sociodemographic, lifestyle, medical and clinical information was obtained from medical records and interviewer-administered questionnaires at baseline. These data are extracted from medical records at 12, 24 and 36 months post-enrollment. Baseline descriptive characteristics stratified by DR severity at baseline and rates of progression to referable DR at 12-month follow-up were calculated. RESULTS: Of 5,840 eligible patients, 78.3% (n = 4,570, median [interquartile range [IQR] age 61.0 [55-67] years; 54.7% male; 68.0% Chinese) completed the baseline assessment. At baseline, 97.4% and 2.6% had none and mild NPDR (worse eye), respectively. Most participants had hypertension (79.2%) and dyslipidemia (92.8%); and almost half were obese (43.4%, BMI ≥ 27.5 kg/m2). Participants without DR (vs mild DR) reported shorter DM duration, and had lower haemoglobin A1c, triglycerides and urine albumin/creatinine ratio (all p < 0.05). To date, we have extracted 41.8% (n = 1909) of the 12-month follow-up data. Of these, 99.7% (n = 1,904) did not progress to referable DR. Those who progressed to referable DR status (0.3%) had no DR at baseline. CONCLUSIONS: In our prospective study of patients with T2DM and non-referable DR attending polyclinics, we found extremely low annual DR progression rates. These preliminary results suggest that extending screening intervals beyond 12 months may be viable and safe for most participants, although our 3-year follow up data are needed to substantiate this claim and develop the risk stratification model to identify low-risk patients with T2DM who can be assigned biennial or triennial screening intervals.

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. METHODS: We 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 <60 mL/min/1.73 m2) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A1c, systolic blood pressure, and BMI. RESULTS: Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR). CONCLUSIONS: This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection 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. METHODS: Data from four cohorts were analyzed: Singapore Epidemiology of Eye Diseases (SEED) (n = 8802), Copenhagen Chronic Kidney Disease (CPH) (n = 916), Singapore Diabetic Nephropathy (n = 714), and UK Biobank (UKBB) (n = 103,051). Prevalent CKD (stages 3-5) was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2; incident severe CKD as CKD-related mortality or kidney failure occurring within 10 years. We used multivariable regressions to identify, among 146 blood metabolites, those associated with CKD, and quantify the corresponding increase in performance. RESULTS: Chronic kidney disease prevalence (stages 3-5) and severe incidence were 11.4% and 2.2% in SEED, and 2.3% and 0.2% in UKBB. Firstly, phenylalanine (Odds Ratio [OR] 1-SD increase = 1.83 [1.73, 1.93]), tyrosine (OR = 0.75 [0.71, 0.79]), docosahexaenoic acid (OR = 0.90 [0.85, 0.95]), citrate (OR = 1.41 [1.34, 1.47]) and triglycerides in medium high density lipoprotein (OR = 1.07 [1.02, 1.13]) were associated with prevalent stages 3-5 CKD. Mendelian randomization analyses suggested causal relationships. Adding these metabolites beyond traditional risk factors increased the area under the curve (AUC) by 3% and the sensitivity by 7%. Secondly, lactate (HR = 1.33 [1.08, 1.64]) and tyrosine (HR = 0.74 [0.58, 0.95]) were associated with incident severe CKD among individuals with eGFR < 90 mL/min/1.73 m2 at baseline. These metabolites increased the c-index by 2% and sensitivity by 5% when added to traditional risk factors. CONCLUSION: The performance improvements of CKD detection and prediction achieved by adding metabolites to traditional risk factors are modest and further research is necessary to fully understand the clinical implications of these findings.

Synergistic Impact of Visual and Cognitive Impairments on Health-Related Quality of Life in a Multiethnic Asian Population.

INTRODUCTION: The concomitant impact of visual impairment (VI) and cognitive impairment (CI) on health-related quality of life (HRQoL) in older adults is unclear. We aimed to determine the synergistic effect of baseline VI and CI on HRQoL decline at 6 years in multiethnic Asians. METHODS: We included Chinese, Malay, and Indian adults aged ≥60 years who participated in baseline (2004-2011) and 6-year (2011-2017) follow-up visits of the Singapore Epidemiology of Eye Diseases Study, a population-based cohort study in Singapore. Visual acuity (VA) was objectively measured at both visits, with VI defined as presenting VA >0.3 LogMAR in the better eye. CI was defined as Abbreviated Mental Test scores of ≤6 and ≤8 for individuals with ≤6 and >6 years of formal education, respectively. HRQoL was measured using the European Quality of Life-5 Dimensions (EQ-5D) questionnaire. HRQoL decline was defined as the difference in the composite EQ-5D scores at baseline and 6-year follow-up and deemed clinically meaningful if the reduction was equal to or larger than the minimal clinically important difference. Multivariable linear regression assessed the independent associations and synergism (ß interaction) between baseline VI and CI on EQ-5D decline. RESULTS: Of the 2,433 participants (mean [SD] age: 67.6 [5.5]) at baseline, 559, 120, and 151 had VI only, CI only, and both impairments, respectively. HRQoL decline in individuals with baseline comorbid VI-CI was clinically meaningful and was 2.0 times (ß = -0.044, 95% confidence interval: -0.077 to -0.010) and 3.7 times (ß = -0.065, 95% confidence interval: -0.11 to -0.022) larger than those with VI only and CI only, respectively. Importantly, there was a significant synergism (ß interaction = -0.048, 95% confidence interval: -0.095 to -0.001) between baseline VI and CI as predictors of HRQoL decline, suggesting that individuals having both conditions concurrently had a greater HRQoL reduction than the sum in those with VI alone and CI alone. The affected HRQoL domains included mobility and usual activities. CONCLUSIONS: Concomitant VI-CI potentiated HRQoL decline to a greater extent than the sum of individual contributions of VI and CI, suggesting synergism. Our results suggest that rehabilitative interventions such as the use of mobility aids and occupational therapy are needed to maintain HRQoL in older adults with concomitant VI-CI. Moreover, preventive interventions targeting at early detection and management of both VI and CI may also be beneficial.

A deep learning system for retinal vessel calibre improves cardiovascular risk prediction in Asians with chronic kidney disease.

Backgraund: Cardiovascular disease (CVD) and mortality is elevated in chronic kidney disease (CKD). Retinal vessel calibre in retinal photographs is associated with cardiovascular risk and automated measurements may aid CVD risk prediction. Methods: Retrospective cohort study of 860 Chinese, Malay and Indian participants aged 40-80 years with CKD [estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2] who attended the baseline visit (2004-2011) of the Singapore Epidemiology of Eye Diseases Study. Retinal vessel calibre measurements were obtained by a deep learning system (DLS). Incident CVD [non-fatal acute myocardial infarction (MI) and stroke, and death due to MI, stroke and other CVD] in those who were free of CVD at baseline was ascertained until 31 December 2019. Risk factors (established, kidney, and retinal features) were examined using Cox proportional hazards regression models. Model performance was assessed for discrimination, fit, and net reclassification improvement (NRI). Results: Incident CVD occurred in 289 (33.6%) over mean follow-up of 9.3 (4.3) years. After adjusting for established cardiovascular risk factors, eGFR [adjusted HR 0.98 (95% CI: 0.97-0.99)] and retinal arteriolar narrowing [adjusted HR 1.40 (95% CI: 1.17-1.68)], but not venular dilation, were independent predictors for CVD in CKD. The addition of eGFR and retinal features to established cardiovascular risk factors improved model discrimination with significantly better fit and better risk prediction according to the low (<15%), intermediate (15-29.9%), and high (30% or more) risk categories (NRI 5.8%), and with higher risk thresholds (NRI 12.7%). Conclusions: Retinal vessel calibre measurements by DLS were significantly associated with incident CVD independent of established CVD risk factors. Addition of kidney function and retinal vessel calibre parameters may improve CVD risk prediction among Asians with CKD.

Cardiovascular disease and thinning of retinal nerve fiber layer in a multi-ethnic Asian population: the Singapore epidemiology of eye diseases study.

Introduction: Our 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. Methods: 6,024 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. Results: We observed a significant association between CVD history and thinner average RNFL (ß = -1.63; 95% CI, -2.70 to -0.56; p = 0.003). This association was consistent for superior (ß = -1.79, 95% CI, -3.48 to -0.10; p = 0.038) and inferior RNFL quadrant (ß = -2.14, 95% CI, -3.96 to -0.32; p = 0.021). Of the CVD types, myocardial infarction particularly showed significant association with average (ß = -1.75, 95% CI, -3.08 to -0.42; p = 0.010), superior (ß = -2.22, 95% CI, -4.36 to -0.09; p = 0.041) and inferior (ß = -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 (ß = -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 (ß = -0.22, 95% CI, -1.15 to 0.71; p = 0.641). Discussion: In 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.

Six-Year Incidence of Visual Impairment in a Multiethnic Asian Population: The Singapore Epidemiology of Eye Diseases Study.

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 Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.

The Impact of Hearing Impairment on Health Indicators in a Multiethnic Population of Older Adults in Singapore.

Background and Objectives: To determine the impact of hearing impairment (HI) on health indicators in a multiethnic Singaporean population of older adults. Research Design and Methods: In this cross-sectional, population-based study, pure-tone averages of air-conduction thresholds at 500 Hz, 1,000 Hz, 2,000 Hz, and 4,000 Hz were calculated for each ear. Eight categories of HI were defined ranging from: 1: No HI to 8: Bilateral severe HI. Health indicators included hearing-related quality of life (H-QoL), depressive symptoms, frailty, gait speed, instrumental activities of daily living, sarcopenia, and cognitive impairment. Multivariable regression models determined the independent associations between HI and outcomes. Results: A total of 2,503 older adults (mean age ± SD 73.4 ± 8.4; 55.2% female participants) were enrolled. Of these, 289 (11.6%), 259 (10.4%), 798 (31.9%), 303 (12.1%), 515 (20.6%), 52 (2.1%), 155 (6.2%), and 115 (4.6%) had hearing levels in Cats 1 to 8, respectively; and 20 (0.8%) used a hearing aid. Compared to those with no HI, participants with unilateral mild HI (Cat 2) had a 107% reduction in H-QoL (ß: 0.63; CI: 0.18, 1.09, p = .006), increasing to a 2,816% reduction (ß: 16.78; CI: 13.25, 20.31, p < .001) in those with bilateral severe HI-Cat 8 (p-trend < .001). Those with Cat 8 also had lower gait speed and we observed a nonsignificant increase in odds of frailty as HI worsened. Discussion and Implications: H-QoL is affected across the spectrum of severity and laterality of HI. Interventions to alleviate the effects of HI and provision of QoL support are warranted. Other health indicators were only affected in late stages, suggesting that slowing disease progression is crucial in clinical management.

Circulating Metabolites and Cardiovascular Disease in Asians with Chronic Kidney Disease.

INTRODUCTION: Chronic kidney disease (CKD) is a growing public health problem, with significant burden of cardiovascular disease and mortality. The risk of cardiovascular disease in CKD is elevated beyond that predicted by traditional cardiovascular risk factors, suggesting that other factors may account for this increased risk. Through metabolic profiling, this study aimed to investigate the associations between serum metabolites and prevalent cardiovascular disease in Asian patients with CKD to provide insights into the complex interactions between metabolism, cardiovascular disease and CKD. METHODS: This was a single-center cross-sectional study of 1,122 individuals from three ethnic cohorts in the population-based Singapore Epidemiology of Eye Disease (SEED) study (153 Chinese, 262 Indians, and 707 Malays) aged 40-80 years with CKD (estimated glomerular filtration rate <60 mL/min/1.73 m2). Nuclear magnetic resonance spectroscopy was used to quantify 228 metabolites from the participants' serum or plasma. Prevalent cardiovascular disease was defined as self-reported myocardial infarction, angina, or stroke. Multivariate logistic regression identified metabolites independently associated with cardiovascular disease in each ethnic cohort. Metabolites with the same direction of association with cardiovascular disease in all three cohorts were selected and subjected to meta-analysis. RESULTS: Cardiovascular disease was present in 275 (24.5%). Participants with cardiovascular disease tend to be male; of older age; with hypertension, hyperlipidemia, and diabetes; with lower systolic and diastolic blood pressure (BP); lower high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol than those without cardiovascular disease. After adjusting for age, sex, systolic BP, diabetes, total cholesterol, and HDL cholesterol, 10 lipoprotein subclass ratios and 6 other metabolites were significantly associated with prevalent cardiovascular disease in at least one cohort. Meta-analysis with Bonferroni correction for multiple comparisons found that lower tyrosine, leucine, and valine concentrations and lower cholesteryl esters to total lipid ratio in intermediate-density lipoprotein (IDL) were associated with cardiovascular disease. CONCLUSION: In Chinese, Indian, and Malay participants with CKD, prevalent cardiovascular disease was associated with tyrosine, leucine, valine, and cholesteryl esters to total lipid ratios in IDL. Increased cardiovascular risk in CKD patients may be contributed by altered amino acid and lipoprotein metabolism. The presence of CKD and ethnic differences may affect interactions between metabolites in health and disease, hence greater understanding will allow us to better risk stratify patients, and also individualize care with consideration of ethnic disparities.

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). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic 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). Results: ML model Elastic Net (EN) had the best AUC (95% CI) 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. Conclusions: Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites. Funding: This 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.

Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes.

OBJECTIVE: To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations. MATERIALS AND METHODS: We trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional). RESULTS: In SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid. DISCUSSION AND CONCLUSION: There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD.

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 8.3 years. The simplified risk models were validated in two independent multiethnic Singapore cohorts (N = 12,720). RESULTS: The established risk models had moderate-to-good discrimination (area under the receiver operating characteristic curves, AUCs 0.762 - 0.828) but a lack of fit (P-values < 0.05). Simplified risk models that included fewer predictors (age, BMI, systolic blood pressure, triglycerides, and HbA1c or FPG) showed good discrimination in all cohorts (AUCs ≥ 0.810), and sufficiently captured differences between the ethnic groups. While recalibration improved fit the simplified models in validation cohorts, there remained evidence of miscalibration in Chinese (p ≤ 0.012). CONCLUSIONS: Simplified risk models including HbA1c or FPG had good discrimination in predicting incidence of T2D in three major Asian ethnic groups. Risk functions with HbA1c performed as well as those with FPG.

Factors influencing myopia stabilisation in young myopic adult Singaporeans.

PURPOSE: To evaluate factors influencing stabilisation of myopia in the Singapore Cohort of Risk factors for Myopia. METHODS: We evaluated the longitudinal natural history of 424 myopic participants from 1999 to 2022. The outcome was the change in myopia from the adolescence follow-up visit (aged 12-19 years) to the adulthood follow-up visit (aged 26-33 years). Association of predictive factors, including baseline spherical error, gender, ethnicity, parental myopia, time outdoor, near work and age at adolescence, was examined with the dichotomous outcome of adult myopia progression (≤ -1.00 dioptres (D) over 10 years) using multiple logistic regression and progression in linear regression models. RESULTS: For the primary outcome, the mean rate of progression of the outcome was found to be -0.04±0.09 D per year from the adolescent to the adulthood follow-up visits. 82.3% (95% CI 78.3% to 85.8%) had myopia stabilisation, with progression of less than 1.00 D over 10 years while 61.3% (95% CI 56.5% to 66.0%) of the subjects had progression of less than 0.50 D. In logistic regression models, both male gender (p=0.035) and non-Chinese ethnicity (p=0.032) were more likely to achieve myopia stabilisation while in linear multivariate regression models, males had a significantly slower degree of myopia progression (p=0.021). CONCLUSION: 5 in 6 Singaporean young adults had myopia stabilisation. Male gender is 2 times and non-Chinese ethnicities are 2.5 times more likely to achieve myopia stabilisation. However, a proportion of myopes continue to exhibit a clinically significant degree of progression in adulthood.

Eyes as the windows into cardiovascular disease in the era of big data.

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.

Association of blood pressure with incident diabetic microvascular complications among diabetic patients: Longitudinal findings from the UK Biobank.

Background: Evidence suggests a correlation of blood pressure (BP) level with presence of diabetic microvascular complications (DMCs), but the effect of BP on DMCs incidence is not well-established. We aimed to explore the associations between BP and DMCs (diabetic retinopathy, diabetic kidney disease, and diabetic neuropathy) risk in participants with diabetes. Methods: This study included 23 030 participants, free of any DMCs at baseline, from the UK Biobank. We applied multivariable-adjusted Cox regression models to estimate BP-DMCs association and constructed BP genetic risk scores (GRSs) to test their association with DMCs phenotypes. Differences in incidences of DMCs were also compared between the 2017 ACC/AHA and JNC 7 guidelines (traditional criteria) of hypertension. Results: Compared to systolic blood pressure (SBP)<120 mm Hg, participants with SBP≥160 mm Hg had a hazard ratio (HR) of 1.50 (95% confidence interval (CI) = 1.09, 2.06) for DMCs. Similarly, DMCs risk increased by 9% for every 10 mm Hg of higher SBP at baseline (95% CI = 1.04, 1.13). The highest tercile SBP GRS was associated with 32% higher DMCs risk (95% CI = 1.11, 1.56) compared to the lowest tercile. We found no significant differences in DMCs incidence between JNC 7 and 2017 ACC/AHA guidelines. Conclusions: Genetic and epidemiological evidence suggests participants with higher SBP had an increased risk of DMCs, but hypertension defined by 2017 ACC/AHA guidelines may not impact DMCs incidence compared with JNC 7 criteria, contributing to the care and prevention of DMCs.