Purpose: Cataract is the leading cause of blindness and cataract surgical rate (CSR) is used as a proxy indicator of access to cataract services in a country. The aim of this study was to explore the associations between the CSR and the economic development of countries in terms of gross domestic product per capital (GDP/P) and gross national income per capita (GNI/P). Methods: We systematically searched OVID (Medline and Embase), Pubmed, Embase.com, ISI Web of Science, and Cochrane Library databases, and retrieved additional data from unpublished reports. Cataract surgical rates and economic indicators (GDP/P, GNI/P) were collected for each country from 2005 to 2014. Complete data were used for the 50 largest countries according to World Health Organization (WHO) population estimates. Linear correlations between GDP/P and CSR were calculated. Cataract surgical rate data over two periods were used for analysis: 2005 to 2009 and 2010 to 2014 (CSR in 2009 or nearest year, CSR in 2014 or nearest year). Results: Over the study period, CSR data were available for 152 countries across both time periods. Most of the CSR data were obtained from nongovernment organization (NGO) reports, including WHO reports. A good linear correlation between CSR and GDP/P was found overall, nearest to 2009 (β = 0.162, Linear: y = 0.162x + 282.242; R² = 0.665, P < 0.001). Regression analysis of CSR nearest to 2014 produced similar findings, with significant correlations between CSR and GDP/P (Linear: y = 0.208x + 94.008; R² = 0.785, P < 0.001). When using GNI/P as an economic indicator, similarly excellent lines of fit were obtained. After adjusting for time and country, CSR was significantly associated with GDP/P (Coefficient = 0.147, R² = 0.759, P < 0.001), and GNI/P (Coefficient = 0.152, R² = 0.757, P < 0.001). Most countries had an increase in CSRs over time, with the greatest increases observed for Iran and Argentina. Conclusion: Cataract surgical rate and economic indicators are closely associated, indicating the strong influence of resource availability on healthcare delivery. Considering this relationship, it is important to be innovative in delivery of low-cost services and invest strategically in capacity development to meet cataract surgical need in low-resource settings.

Aim To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk. Methods A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality. Results The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality. Conclusions Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.

Background: To evaluate the long‐term efficacy and safety of continued repeated low‐level red‐light (RLRL) therapy on myopia control over 2 years, and the potential rebound effect after treatment cessation.Methods: The Chinese myopic children who originally completed the one‐year randomised controlled trial were enrolled. Children continued RLRL‐therapy were defined as RLRL‐RLRL group, while those who stopped and switched to single‐vision spectacle (SVS) in the second year were RLRL‐SVS group. Likewise, those who continued to merely wear SVS or received additional RLRL‐therapy were SVS‐SVS and SVS‐RLRL groups, respectively. RLRL‐therapy was provided by an at‐home desktop light device emitting red‐light of 650 nm and was administered for 3 min at a time, twice a day and 5 days per week. Changes in axial length (AL) and cycloplegic spherical equivalence refraction (SER) were measured.Results: Among the 199 children who were eligible, 138 (69.3%) children attended the examination and 114 (57.3%) were analysed (SVS‐SVS: n = 41; SVS‐RLRL: n = 10; RLRL‐SVS: n = 52; RLRL‐RLRL: n = 11). The baseline characteristics were balanced among four groups. In the second year, the mean changes in AL were 0.28 ± 0.14 mm, 0.05 ± 0.24 mm, 0.42 ± 0.20 mm and 0.12 ± 0.16 mm in SVS‐SVS, SVS‐RLRL, RLRL‐SVS and RLRL‐RLRL group, respectively (p < 0.001). The respective mean SER changes were −0.54 ± 0.39D, −0.09 ± 0.55D, −0.91 ± 0.48D, and −0.20 ± 0.56D (p < 0.001). Over the 2‐year period, axial elongation and SER progression were smallest in RLRL‐RLRL group (AL: 0.16 ± 0.37 mm; SER: −0.31 ± 0.79D), followed by SVS‐RLRL (AL: 0.44 ± 0.37 mm; SER: −0.96 ± 0.70D), RLRL‐SVS (AL: 0.50 ± 0.28 mm; SER: −1.07 ± 0.69D) and SVS‐SVS group (AL: 0.64 ± 0.29 mm; SER: −1.24 ± 0.63D). No self‐reported adverse events, functional or structural damages were noted.Conclusions: Continued RLRL therapy sustained promising efficacy and safety in slowing myopia progression over 2 years. A modest rebound effect was noted after treatment cessation.

It is unclear regarding associations of dietary patterns with a wide range of chronic diseases and which dietary score is more predictive of major chronic diseases. Using the UK Biobank, we examine associations of four individual healthy dietary scores with the risk of 48 individual chronic diseases. Higher Alternate Mediterranean Diet score is associated with a lower risk of 32 (all 8 cardiometabolic disorders, 3 out of 10 types of cancers, 7 out of 10 psychological/neurological disorders, 5 out of 6 digestive disorders, and 9 out of 14 other chronic diseases). Alternate Healthy Eating Index-2010 and Healthful Plant-based Diet Index are inversely associated with the risk of 29 and 23 individual chronic diseases, respectively. A higher Anti-Empirical Dietary Inflammatory Index is associated with a lower risk of 14 individual chronic diseases and a higher incidence of two diseases. Our findings support dietary guidelines for the prevention of most chronic diseases.

Retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age–chronological age) and incident Parkinson’s disease (PD). a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01–1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13–6.22, P = 0.024; HR = 4.86, 95% CI: 1.59–14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.