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Objectives: To determine the association between telomere length (TL) and age-related macular degeneration (AMD) and examine the potential variations with sex and ethnicity. Methods: Population-based, cross-sectional study. A total of 52,083 participants from the UK Biobank were included. Leucocyte TL, measured using quantitative polymerase chain reaction assay, was presented as the ratio of telomere repeat copy number relative to that of a single copy gene, and then log-transformed and Z-standardised. AMD cases were identified based on a combination of in-patient, self-reported and primary care data, and furtherly classified as early, intermediate and late AMD using the Beckmann classification system (based on more severe eye). Results: Among the 52,083 participants aged 60.2 ± 5.4 years, 725 were any-AMD cases. AMD patients had shorter TL than those without AMD (−0.22 ± 0.95 vs. −0.10 ± 0.99, P= 0.001). In multivariable model, shorter TL (per standard deviation) was significantly associated with higher odds of AMD in Whites (OR:1.09; 95% CI: 1.01–1.18; P= 0.036). When stratified by sex and ethnicity, this association was only significant in White women (OR:1.14; 95%CI: 1.02, 1.27; P= 0.018), but not in men and nonwhite populations (all P≥ 0.335). Among white women, the association was more pronounced (OR:1.47; 95%CI:1.23–1.77; P< 0.001) for intermediate/late AMD but not for early AMD (P= 0.789). Conclusions: Shorter TL was associated with any AMD in white women but not in men and other ethnicities. Our findings highlight the potential role of telomere length in the pathogenesis of AMD and the importance of considering sex and ethnicity variation in this research area.

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 m2at 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. Graphical Abstract:

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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.MethodsIn 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’. The EuroQoL-5 dimensions (EQ-5D) utility instrument (raw scores converted to UK time trade-off (TTO) values) was used to assess generic HRQoL and the overall score from the Visual Functioning Questionnaire for visual functioning. The association between dry eye symptoms and EQ-5D was investigated using multivariable linear regression, adjusting for demographic and socioeconomic information, comorbidities, systemic and ocular examinations results. Mediation analysis was used to determine whether certain factors mediated this association.ResultsAfter adjusting for relevant factors, those with dry eye symptoms had significantly lower HRQoL (difference in EQ-5D TTO: −0.062 (95% CI −0.073 to –0.050)), with the inability to open eyes affected the most (−0.101 (95% CI −0.161 to –0.042)), followed by a sandy sensation (−0.089 (95% CI −0.121 to –0.058)), a burning sensation (−0.070 (95% CI −0.105 to –0.036)), red eyes (−0.059 (95% CI −0.082 to –0.036)), a dry sensation (−0.058 (95% CI −0.072 to –0.044)) and crusting of eyelids (−0.040 (95% CI −0.071 to –0.008)). Visual functioning and the presence of recent falls accounted for 8.63% (4.98%–14.5%) and 2.93% (0.04%–5.68%) of the indirect relationship between dry eye and HRQoL, respectively.ConclusionDry eye symptoms were independently associated with poor HRQoL. Moreover, this was partly mediated by reduced visual functioning and experiencing recent falls. Our results suggest that efforts to reduce severity of dry eye symptoms are essential to optimise patients’ overall functioning and well-being.

PurposeTo use optical coherence tomography angiography (OCTA) parameters from both the retinal and choroidal microvasculature to detect the presence and severity of diabetic retinopathy (DR).MethodThis is a cross-sectional case–control study. OCTA parameters from retinal vasculature, fovea avascular zone (FAZ) and choriocapillaris were evaluated from 3×3 mm2fovea-centred scans. Areas under the receiver operating characteristic (ROC) curve were used to compare the discriminative power on the presence of diabetes mellitus (DM), the presence of DR and need for referral: group 1 (no DM vs DM no DR), group 2 (no DR vs any DR) and group 3 (non-proliferative DR (NPDR) vs proliferative DR (PDR)).Results35 eyes from 27 participants with no DM and 132 eyes from 75 with DM were included. DR severity was classified into three groups: no DR group (62 eyes), NPDR (51 eyes), PDR (19 eyes). All retinal vascular parameters, FAZ parameters and choriocapillaris parameters were strongly altered with DR stages (p<0.01), except for the deep plexus FAZ area (p=0.619). Choriocapillaris parameters allowed to better discriminate between no DM versus DM no DR group compared with retinal parameters (areas under the ROC curve=0.954 vs 0.821, p=0.006). A classification model including retinal and choroidal microvasculature significantly improved the discrimination between DR and no DR compared with each parameter separately (p=0.029).ConclusionsEvaluating OCTA parameters from both the retinal and choroidal microvasculature in 3×3 mm scans improves the discrimination of DM and early DR.

Background/aimsEarly 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.MethodsIn this clinic-based, prospective study, we enrolled 149 patients (median (IQR) years: 68.5 (13.6) years) with confirmed glaucoma and 173 healthy controls (55.2 (26.7) years). Changes in pupil size in response to 9 s of exponentially increasing blue (469 nm) and red (640 nm) light-stimuli were assessed monocularly using a custom-built handheld pupillometer. Pupillometric features were extracted from individual traces and compared between groups. Features with the highest classification potential, selected using a gradient boosting machine technique, were incorporated into a generalised linear model for glaucoma classification. Receiver operating characteristic curve analyses (ROC) were used to compare the performance of HCP, optical coherence tomography (OCT) and Humphrey Visual Field (HVF).ResultsPupillary light responses were altered in glaucoma compared with controls. For glaucoma classification, HCP yielded an area under the ROC curve (AUC) of 0.94 (95% CI 0.91 to 0.96), a sensitivity of 87.9% and specificity of 88.4%. The classification performance of HCP in early-moderate glaucoma (visual field mean deviation (VFMD) > -12 dB; AUC=0.91 (95% CI 0.87 to 0.95)) was similar to HVF (AUC=0.91) and reduced compared with OCT (AUC=0.97; p=0.01). For severe glaucoma (VFMD ≤ -12 dB), HCP had an excellent classification performance (AUC=0.98, 95% CI 0.97 to 1) that was similar to HVF and OCT.ConclusionHCP allows for an accurate, objective and rapid detection of functional loss in glaucomatous eyes of different severities.

AimsTo use machine learning (ML) to determine the relative contributions of modifiable and non-modifiable clinical, metabolic, genetic, lifestyle and socioeconomic factors on the risk of major eye diseases.MethodsWe conducted analyses in a cross-sectional multi-ethnic population-based study (n=10 033 participants) and determined a range of modifiable and non-modifiable risk factors of common eye diseases, including diabetic retinopathy (DR), non-diabetic-related retinopathy (NDR); early and late age-related macular degeneration (AMD); nuclear, cortical and posterior subcapsular (PSC) cataract; and primary open-angle (POAG) and primary angle-closure glaucoma (PACG). Risk factors included individual characteristics, metabolic profiles, genetic background, lifestyle patterns and socioeconomic status (n~100 risk factors). We used gradient boosting machine to estimate the relative influence (RI) of each risk factor.ResultsAmong the range of risk factors studied, the highest contributions were duration of diabetes for DR (RI=22.1%), and alcohol consumption for NDR (RI=6.4%). For early and late AMD, genetic background (RI~20%) and age (RI~15%) contributed the most. Axial length was the main risk factor of PSC (RI=30.8%). For PACG, socioeconomic factor (mainly educational level) had the highest influence (20%). POAG was the disease with the highest contribution of modifiable risk factors (cumulative RI~35%), followed by PACG (cumulative RI ~30%), retinopathy (cumulative RI between 20% and 30%) and late AMD (cumulative RI ~20%).ConclusionThis study illustrates the utility of ML in identifying factors with the highest contributions. Risk factors possibly amenable to interventions were intraocular pressure (IOP) and Body Mass Index (BMI) for glaucoma, alcohol consumption for NDR and levels of HbA1c for DR.

In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment.

Diabetic retinopathy (DR) research has had significant advancements over the past decades. We analysed the impact and characteristics of the top 100 (T100) most-cited articles in DR research. The Scopus database was searched for articles published from 1960 to June 2020 by two independent investigators. The T100 DR articles were published between 1961 and 2017 with median citations of 503 (range: 306–20 100); 84% were published after 1990. More than half (59%) were published in general medical/diabetes journals while 37% in ophthalmology journals. The top six journals contributed to 56% of the T100: Ophthalmology(n=13), Archives of Ophthalmology(n=12), Diabetes(n=9), New England Journal of Medicine(n=8), Journal of the American Medical Association(n=7) and The Lancet(n=7). Although observational studies were most popular (33%), randomised controlled trials (RCTs, 24%) published in journals with higher impact factor (IF) and citations (median IF and citations=7.113, 503 vs 21.437, 696.5, both p-value<0.05). 33 of the T100 were cited by several international DR clinical guidelines. The USA contributed to 63% of T100, but 18% of articles published after 2000 came from Asia. More than 80% of both first and last authors were men. Artificial intelligence (AI) to screen for DR ranked 14th and 99th despite recent publications in 2016 and 2017, respectively. To conclude, our T100 analysis showed that RCTs were most-cited and more articles were published in non-ophthalmology than ophthalmology journals. It highlights the impact the T100 DR has in shaping guidelines used to date in DR management, identifies AI for DR screening as an emerging area and shows a contemporary rise of Asian contribution in DR research.

Though the domain of big data and artificial intelligence in health care continues to evolve, there is a lack of systemic methods to improve data quality and streamline the preparation process. To address this, we aimed to develop an automated sorting system (RetiSort) that accurately labels the type and laterality of retinal photographs.

Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies.

Eyes and kidneys have numerous structural, developmental, physiologic, and pathogenic pathways in common, suggesting that many kidney and eye diseases may be interlinked. Studies suggest that the prevalence of eye diseases and vision impairment are higher among persons with end-stage kidney disease and earlier stages of chronic kidney disease (CKD) than in those without. Ocular morbidity in persons with CKD and end-stage kidney disease may be due to the following risk factors: (1) underlying conditions and risk factors for CKD such as diabetes or hypertension, (2) metabolic disorders associated with CKD, (3) uremia and anemia, and (4) CKD treatment. Among the chief eye diseases, diabetic retinopathy and age-related macular degeneration are most consistently associated with CKD. Further research for eye diseases such as glaucoma and cataract is needed to determine their relationships with CKD. Despite the high prevalence and burden of vision impairment among persons with CKD, eye screening in patients with CKD is not currently recommended as standard practice. This review suggests that patients with CKD should be encouraged to undergo a complete eye examination. Furthermore, physicians should be aware that patients undergoing dialysis may develop acute eye problems such as acute glaucoma, and appropriate referral to ophthalmologists should be considered in those with a history of glaucoma or recent ocular surgery. Interdisciplinary collaboration between nephrologists and ophthalmologists will ensure enhanced and appropriate management of patients with CKD.

Introduction:Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, with a benchmark of 10% for 10-year CVD risk determining clinical intervention. Yet, effects on clinical practice are limited and the barriers call for a simple, non-invasive risk stratification tool. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aimed to further validate our biomarker, Reti-CVD, 1)to detect risk group of ≥10% in 10-year CVD risk and 2)enhance risk assessment in individuals with QRISK3 of 7.5%-10% (termed as borderline-QRISK3 group) using the UK Biobank.Methods:RetiCVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of RetiCVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding RetiCVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups.Results:Among 45,233 participants with no history of CVD, 6.7% had CVD events during the 11-year follow-up. RetiCVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.34 (95% confidence interval [CI], 1.26-1.42) with a 14.7% (95% CI, 13.6-15.9%) 10-year CVD risk in RetiCVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in RetiCVD-high-risk group (12.8% in non-statin cohort [n=16240], 11.5% in stage 1 hypertension cohort [n=5102], and 14.9% in middle-aged cohort [n=11,474]). C statistics increased by 0.013 (0.010-0.017) in non-statin cohort, 0.017 (0.010-0.024) in stage 1 hypertension cohort, and 0.022 (0.017-0.027) in middle-aged cohort for CVD event prediction after adding RetiCVD to QRISK3.Conclusion:RetiCVD has the potential to identify individuals with ≥10% 10-year CVD risk who are likely to benefit from earlier, upstream preventative CVD interventions. For borderline-QRISK3 individuals, RetiCVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in early vulnerable adult groups.

Background:In our previous study, we developed a deep-learning-based novel cardiovascular disease (CVD) risk stratification system based on retinal photographs, Reti-CVD. This study aims to further validate Reti-CVD in the US population of the Age-Related Eye Disease Studies (AREDS).Methods:Our testing cohort comprises of AREDS participants who have undergone retinal photography. We evaluated the ability of Reti-CVD to predict atherosclerotic CVD (ASCVD) events (non-fatal and fatal events, and fatal events) using Cox proportional-hazards models. Reti-CVD scores were then calculated and stratified into three groups based on optimized cut-off values from AREDS. Upon further stratifying Reti-CVD scores, we evaluated whether adding Reti-CVD to traditional risk factors could improve risk prediction.Results:Among 3,555 participants, 282 (7.9%) had non-fatal and fatal ASCVD events, and 84 (2.4%) had fatal ASCVD during the 13-year follow-up. Reti-CVD was significantly associated with an increased risk of ASCVD, demonstrating an adjusted hazard ratio (HR) trend of 1.13 (95% CI, 1.03-1.24) for non-fatal and fatal ASCVD events, and an adjusted HR trend of 1.27 (1.07-1.52) for fatal ASCVD events. Reti-CVD significantly improved the overall predictive performance of traditional risk models with a continuous net reclassification index (NRI) of 0.247 (0.106-0.364) for non-fatal and fatal ASCVD events, and 0.232 (0.107-0.359) for fatal ASCVD events. In heatmaps, traditional risk features such as arteriovenous nicking and arteriolar narrowing were well-detected by the Reti-CVD algorithm.Conclusions:A deep-learning-based, retinal photograph-derived new CVD biomarker, Reti-CVD, can be utilized as a risk stratification tool in predicting ASCVD risk in the US population. With the improvements observed on the current risk stratification by traditional risk models, Reti-CVD has considerable potential as an enhanced CVD risk stratification tool applicable in the general US population.

Le fingolimod et le natalizumab partagent la même indication de traitement de seconde intention dans la SEP rémittente-récurrente (RR). Pourtant, l’efficacité de ces deux molécules est-elle comparable ?