Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions.

PURPOSE OF REVIEW: Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting. RECENT FINDINGS: Artificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice. SUMMARY: Artificial intelligence based retinal microvasculature analysis may supplement existing CVD risk stratification approach. Although technical and socioeconomic challenges remain, we envision artificial intelligence based microvasculature analysis to have major clinical and research impacts in the future, through screening for high-risk individuals especially in less-developed areas and identifying new retinal biomarkers for CVD research.

A MULTITASK DEEP-LEARNING SYSTEM FOR ASSESSMENT OF DIABETIC MACULAR ISCHEMIA ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGES.

PURPOSE: We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical coherence tomography angiography (OCTA) images. METHODS: This study included 7,194 OCTA images with diabetes mellitus for training and primary validation and 960 images from three independent data sets for external testing. A trinary classification for image quality assessment and the presence or absence of DMI for DMI assessment were labeled on all OCTA images. Two DenseNet-161 models were built for both tasks for OCTA images of superficial and deep capillary plexuses, respectively. External testing was performed on three unseen data sets in which one data set using the same model of OCTA device as of the primary data set and two data sets using another brand of OCTA device. We assessed the performance by using the area under the receiver operating characteristic curves with sensitivities, specificities, and accuracies and the area under the precision-recall curves with precision. RESULTS: For the image quality assessment, analyses for gradability and measurability assessment were performed. Our deep-learning system achieved the area under the receiver operating characteristic curves >0.948 and area under the precision-recall curves >0.866 for the gradability assessment, area under the receiver operating characteristic curves >0.960 and area under the precision-recall curves >0.822 for the measurability assessment, and area under the receiver operating characteristic curves >0.939 and area under the precision-recall curves >0.899 for the DMI assessment across three external validation data sets. Grad-CAM demonstrated the capability of our deep-learning system paying attention to regions related to DMI identification. CONCLUSION: Our proposed multitask deep-learning system might facilitate the development of a simplified assessment of DMI on OCTA images among individuals with diabetes mellitus at high risk for visual loss.

Association between blood pressure and retinal arteriolar and venular diameters in Chinese early adolescent children, and whether the association has gender difference: a cross-sectional study.

BACKGROUND: To establish the independent association between blood pressure (BP) and retinal vascular caliber, especially the retinal venular caliber, in a population of 12-year-old Chinese children. METHODS: We have examined 1501 students in the 7th grade with mean age of 12.7 years. A non-mydriatic fundus camera (Canon CR-2, Tokyo, Japan) was used to capture 450 fundus images of the right eyes. Retinal vascular caliber was measured using a computer-based program (IVAN). BP was measured using an automated sphygmomanometer (HEM-907, Omron, Kyoto, Japan). RESULTS: The mean retinal arteriolar caliber was 145.3 µm (95% confidence interval [CI], 110.6-189.6 µm) and the mean venular caliber was 212.7 µm (95% CI, 170.6-271.3 µm). After controlling for age, sex, axial length, BMI, waist, spherical equivalent, birth weight, gestational age and fellow retinal vessel caliber, children in the highest quartile of BP had significantly narrower retinal arteriolar caliber than those with lower quartiles (P for trend< 0.05). Each 10-mmHg increase in BP was associated with narrowing of the retinal arterioles by 3.00 µm (multivariable-adjusted P < 0.001), and the results were consist in three BP measurements. The association between BP measures and retinal venular caliber did not persist after adjusting for fellow arteriolar caliber. And there was no significant interaction between BP and sex, age, BMI, and birth status. CONCLUSIONS: In a large population of adolescent Chinese children, higher BP was found to be associated with narrower retinal arterioles, but not with retinal venules. Sex and other confounding factors had no effect on the relationship of BP and retinal vessel diameter.

Peripapillary retinal nerve fibre layer thickness and its association with refractive error in Chinese children: the Anyang Childhood Eye Study.

BACKGROUND: To report the thickness of the peripapillary retinal nerve fibre layer (pRNFL) in Chinese children and examine its association with refractive error, axial length (AL) and optic disc parameters. DESIGN: Population-based cross-sectional study. PARTICIPANTS: A total of 2893 seven-year-old children from 11 randomly selected primary schools in Anyang, central China. METHODS: Participants underwent ophthalmic examinations including optical biometry, cycloplegic autorefraction and spectral-domain ocular coherence tomography. MAIN OUTCOME MEASURES: Retinal nerve fibre layer thickness in 16 radial sections, cycloplegic spherical equivalent, AL. RESULTS: The mean (SD) average RNFL thickness was 102.01(8.02) µm. The average RNFL thickness decreased with smaller disc area (r = 0.18, R2 = 0.03, P < 0.0001), bigger cup area (r = -0.11, R2 = 0.01, P < 0.0001), smaller rim area (r = 0.28, R2 = 0.08, P < 0.0001), smaller nerve head volume (r = 0.27, R2 = 0.07, P < 0.0001), longer AL (r = -0.15, R2 = 0.02, P < 0.0001) and a negative spherical equivalent (r = 0.11, R2 = 0.01, P < 0.0001). Hyperopic children had a thicker RNFL than emmetropic children [102.45(8.13) µm vs. 100.81 (7.18) µm, P < 0.001]. Myopic children had thinner RNFL than emmetropic children [99.17 (7.69) µm vs. 100.81 (7.18) µm, P < 0.05]. CONCLUSION: Retinal nerve fibre layer thickness decreased with increasing AL, higher myopia, bigger cup area, smaller disc and rim area, and a smaller nerve head volume, but the coefficient of determination for all these associations was small. The RNFL in myopes was significantly thinner than emmetropes or hyperopes, but with small absolute differences. The study provides RNFL values for healthy 7-year-old Chinese children. Follow up of this cohort to observe the change of RNFL thickness with myopia and possible change in detected associations with age is planned.

[Research advances of retinal nerve fiber layer thickness and its association with myopia].

Recently, the distribution characteristics of retinal nerve fiber layer thickness in myopic population have raised scholars' attention. The retinal nerve fiber layer thickness is varied with different refractive statuses, and is correlated to many factors like age, eye elongation, and fundus changes. Further exploration of the relationship between myopia and retinal structure and function will promote our understanding and knowledge of the pathogenesis of myopia. The article reviews the structure characteristics of the retinal nerve fiber layer, its associations with demographic characteristics, its characteristics in myopia, and the structural-functional relationship.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Assessment of Parafoveal Diabetic Macular Ischemia on Optical Coherence Tomography Angiography Images to Predict Diabetic Retinal Disease Progression and Visual Acuity Deterioration.

Importance: The presence of diabetic macular ischemia (DMI) on optical coherence tomography angiography (OCTA) images predicts diabetic retinal disease progression and visual acuity (VA) deterioration, suggesting an OCTA-based DMI evaluation can further enhance diabetic retinopathy (DR) management. Objective: To investigate whether an automated binary DMI algorithm using OCTA images provides prognostic value on DR progression, diabetic macular edema (DME) development, and VA deterioration in a cohort of patients with diabetes. Design, Setting, and Participants: In this cohort study, DMI assessment of superficial capillary plexus and deep capillary plexus OCTA images was performed by a previously developed deep learning algorithm. The presence of DMI was defined as images exhibiting disruption of fovea avascular zone with or without additional areas of capillary loss, while absence of DMI was defined as images presented with intact fovea avascular zone outline and normal distribution of vasculature. Patients with diabetes were recruited starting in July 2015 and were followed up for at least 4 years. Cox proportional hazards models were used to evaluate the association of the presence of DMI with DR progression, DME development, and VA deterioration. Analysis took place between June and December 2022. Main Outcomes and Measures: DR progression, DME development, and VA deterioration. Results: A total of 321 eyes from 178 patients were included for analysis (85 [47.75%] female; mean [SD] age, 63.39 [11.04] years). Over a median (IQR) follow-up of 50.41 (48.16-56.48) months, 105 eyes (32.71%) had DR progression, 33 eyes (10.28%) developed DME, and 68 eyes (21.18%) had VA deterioration. Presence of superficial capillary plexus-DMI (hazard ratio [HR], 2.69; 95% CI, 1.64-4.43; P < .001) and deep capillary plexus-DMI (HR, 3.21; 95% CI, 1.94-5.30; P < .001) at baseline were significantly associated with DR progression, whereas presence of deep capillary plexus-DMI was also associated with DME development (HR, 4.60; 95% CI, 1.15-8.20; P = .003) and VA deterioration (HR, 2.12; 95% CI, 1.01-5.22; P = .04) after adjusting for age, duration of diabetes, fasting glucose, glycated hemoglobin, mean arterial blood pressure, DR severity, ganglion cell-inner plexiform layer thickness, axial length, and smoking at baseline. Conclusions and Relevance: In this study, the presence of DMI on OCTA images demonstrates prognostic value for DR progression, DME development, and VA deterioration.

Effect of Text Messaging Parents of School-Aged Children on Outdoor Time to Control Myopia: A Randomized Clinical Trial.

Importance: Myopia in school-aged children is a public health issue worldwide; consequently, effective interventions to prevent onset and progression are required. Objective: To investigate whether SMS text messages to parents increase light exposure and time outdoors in school-aged children and provide effective myopia control. Design, Setting, and Participants: This randomized clinical trial was conducted in China from May 2017 to May 2018, with participants observed for 3 years. Of 528 965 primary school-aged children from Anyang, 3113 were randomly selected. Of these, 268 grade 2 schoolchildren were selected and randomly assigned to SMS and control groups. Data were analyzed from June to December 2021. Interventions: Parents of children in the SMS group were sent text messages twice daily for 1 year to take their children outdoors. All children wore portable light meters to record light exposure on 3 randomly selected days (2 weekdays and 1 weekend day) before and after the intervention. Main Outcomes and Measures: The co-primary outcomes were change in axial length (axial elongation) and change in spherical equivalent refraction (myopic shift) from baseline as measured at the end of the intervention and 3 years later. A secondary outcome was myopia prevalence. Results: Of 268 grade 2 schoolchildren, 121 (45.1%) were girls, and the mean (SD) age was 8.4 (0.3) years. Compared with the control group, the SMS intervention group demonstrated greater light exposure and higher time outdoors during weekends, and the intervention had significant effect on axial elongation (coefficient, 0.09; 95% CI, 0.02-0.17; P = .01). Axial elongation was lower in the SMS group than in the control group during the intervention (0.27 mm [95% CI, 0.24-0.30] vs 0.31 mm [95% CI, 0.29-0.34]; P = .03) and at year 2 (0.39 mm [95% CI, 0.35-0.42] vs 0.46 mm [95% CI, 0.42-0.50]; P = .009) and year 3 (0.30 mm [95% CI, 0.27-0.33] vs 0.35 mm [95% CI, 0.33-0.37]; P = .005) after the intervention. Myopic shift was lower in the SMS group than in the control group at year 2 (-0.69 diopters [D] [95% CI, -0.78 to -0.60] vs -0.82 D [95% CI, -0.91 to -0.73]; P = .04) and year 3 (-0.47 D [95% CI, -0.54 to -0.39] vs -0.60 D [95% CI, -0.67 to -0.53]; P = .01) after the intervention, as was myopia prevalence (year 2: 38.3% [51 of 133] vs 51.1% [68 of 133]; year 3: 46.6% [62 of 133] vs 65.4% [87 of 133]). Conclusions and Relevance: In this randomized clinical trial, SMS text messages to parents resulted in lower axial elongation and myopia progression in schoolchildren over 3 years, possibly through increased outdoor time and light exposure, showing promise for reducing myopia prevalence. Trial Registration: Chinese Clinical Trial Registry Identifier: ChiCTR-IOC-17010525.

Deep Learning-Based Optical Coherence Tomography and Optical Coherence Tomography Angiography Image Analysis: An Updated Summary.

ABSTRACT: Deep learning (DL) is a subset of artificial intelligence based on deep neural networks. It has made remarkable breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, there are rising interests in applying DL methods to analyze optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images. Studies showed that OCT and OCTA image evaluation by DL algorithms achieved good performance for disease detection, prognosis prediction, and image quality control, suggesting that the incorporation of DL technology could potentially enhance the accuracy of disease evaluation and the efficiency of clinical workflow. However, substantial issues, such as small training sample size, data preprocessing standardization, model robustness, results explanation, and performance cross-validation, are yet to be tackled before deploying these DL models in real-time clinics. This review summarized recent studies on DL-based image analysis models for OCT and OCTA images and discussed the potential challenges of clinical deployment and future research directions.

Brain Activation Induced by Myopic and Hyperopic Defocus From Spectacles.

Purpose: To assess neural changes in perceptual effects induced by myopic defocus and hyperopic defocus stimuli in ametropic and emmetropic subjects using functional magnetic resonance imaging (fMRI). Methods: This study included 41 subjects with a mean age of 26.0 ± 2.9 years. The mean spherical equivalence refraction was -0.54 ± 0.51D in the emmetropic group and -3.57 ± 2.27D in the ametropic group. The subjects were instructed to view through full refractive correction, with values of +2.00D to induce myopic defocus state and -2.00D to induce hyperopic defocus state. This was carried over in three random sessions. Arterial spin labeling (ASL) perfusion was measured using fMRI to obtain quantified regional cerebral blood flow (rCBF). Behavioral tests including distant visual acuity (VA) and contrast sensitivity (CS), were measured every 5 min for 30 min. Results: Myopic defocus induced significantly greater rCBF increase in four cerebral regions compared with full correction: right precentral gyrus, right superior temporal gyrus, left inferior parietal lobule, and left middle temporal gyrus (P < 0.001). The differences were less significant in low myopes than emmetropes. In the hyperopic defocus session, the increased responses of rCBF were only observed in the right and left precentral gyrus. Myopic defocused VA and CS improved significantly within 5 min and reached a plateau shortly after. Conclusion: This study revealed that myopic defocus stimuli can significantly increase blood perfusion in visual attention-related cerebral regions, which suggests a potential direction for future investigation on the relationship between retinal defocus and its neural consequences.

Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.

Purpose: Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left). Methods: A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2). Results: For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively. Conclusions: Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities. Translational Relevance: The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.

A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis.

OBJECTIVE: Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. RESEARCH DESIGN AND METHODS: We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent data sets from Singapore, Hong Kong, the U.S., China, and Australia. RESULTS: In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920-0.954), 0.958 (0.930-0.977), and 0.965 (0.948-0.977) for the primary data set obtained from CIRRUS, SPECTRALIS, and Triton OCTs, respectively, in addition to AUROCs >0.906 for the external data sets. For further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940-0.995), 0.951 (0.898-0.982), and 0.975 (0.947-0.991) for the primary data set and >0.894 for the external data sets. CONCLUSIONS: We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.

UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification.

Deep learning has achieved remarkable success in the optical coherence tomography (OCT) image classification task with substantial labelled B-scan images available. However, obtaining such fine-grained expert annotations is usually quite difficult and expensive. How to leverage the volume-level labels to develop a robust classifier is very appealing. In this paper, we propose a weakly supervised deep learning framework with uncertainty estimation to address the macula-related disease classification problem from OCT images with the only volume-level label being available. First, a convolutional neural network (CNN) based instance-level classifier is iteratively refined by using the proposed uncertainty-driven deep multiple instance learning scheme. To our best knowledge, we are the first to incorporate the uncertainty evaluation mechanism into multiple instance learning (MIL) for training a robust instance classifier. The classifier is able to detect suspicious abnormal instances and abstract the corresponding deep embedding with high representation capability simultaneously. Second, a recurrent neural network (RNN) takes instance features from the same bag as input and generates the final bag-level prediction by considering the individually local instance information and globally aggregated bag-level representation. For more comprehensive validation, we built two large diabetic macular edema (DME) OCT datasets from different devices and imaging protocols to evaluate the efficacy of our method, which are composed of 30,151 B-scans in 1,396 volumes from 274 patients (Heidelberg-DME dataset) and 38,976 B-scans in 3,248 volumes from 490 patients (Triton-DME dataset), respectively. We compare the proposed method with the state-of-the-art approaches, and experimentally demonstrate that our method is superior to alternative methods, achieving volume-level accuracy, F1-score and area under the receiver operating characteristic curve (AUC) of 95.1%, 0.939 and 0.990 on Heidelberg-DME and those of 95.1%, 0.935 and 0.986 on Triton-DME, respectively. Furthermore, the proposed method also yields competitive results on another public age-related macular degeneration OCT dataset, indicating the high potential as an effective screening tool in the clinical practice.

Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning.

Glaucoma is the leading cause of irreversible blindness in the world. Structure and function assessments play an important role in diagnosing glaucoma. Nowadays, Optical Coherence Tomography (OCT) imaging gains increasing popularity in measuring the structural change of eyes. However, few automated methods have been developed based on OCT images to screen glaucoma. In this paper, we are the first to unify the structure analysis and function regression to distinguish glaucoma patients from normal controls effectively. Specifically, our method works in two steps: a semi-supervised learning strategy with smoothness assumption is first applied for the surrogate assignment of missing function regression labels. Subsequently, the proposed multi-task learning network is capable of exploring the structure and function relationship between the OCT image and visual field measurement simultaneously, which contributes to classification performance improvement. It is also worth noting that the proposed method is assessed by two large-scale multi-center datasets. In other words, we first build the largest glaucoma OCT image dataset (i.e., HK dataset) involving 975,400 B-scans from 4,877 volumes to develop and evaluate the proposed method, then the model without further fine-tuning is directly applied on another independent dataset (i.e., Stanford dataset) containing 246,200 B-scans from 1,231 volumes. Extensive experiments are conducted to assess the contribution of each component within our framework. The proposed method outperforms the baseline methods and two glaucoma experts by a large margin, achieving volume-level Area Under ROC Curve (AUC) of 0.977 on HK dataset and 0.933 on Stanford dataset, respectively. The experimental results indicate the great potential of the proposed approach for the automated diagnosis system.

Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy.

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2-4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.

Distribution and associations of intraocular pressure in 7- and 12-year-old Chinese children: The Anyang Childhood Eye Study.

PURPOSE: To report the intraocular pressure (IOP) and its association with myopia and other factors in 7 and 12-year-old Chinese children. METHODS: All children participating in the Anyang Childhood Eye Study underwent non-contact tonometry as well as measurement of central corneal thickness (CCT), axial length, cycloplegic auto-refraction, blood pressure, height and weight. A questionnaire was used to collect other relevant information. Univariable and multivariable analysis were performed to determine the associations of IOP. RESULTS: A total of 2760 7-year-old children (95.4%) and 2198 12-year-old children (97.0%) were included. The mean IOP was 13.5±3.1 mmHg in the younger cohort and 15.8±3.5 mmHg in older children (P<0.0001). On multivariable analysis, higher IOP in the younger cohort was associated with female gender (standardized regression coefficient [SRC], 0.11, P<0.0001), increasing central corneal thickness (SRC, 0.39, P<0.0001), myopia (SRC, 0.05, P = 0.03), deep anterior chamber (SRC, 0.07, P<0.01), smaller waist (SRC, 0.07, P<0.01) and increasing mean arterial pressure (SRC, 0.13, P<0.0001). In the older cohort, higher IOP was again associated with female gender (SRC, 0.16, P<0.0001), increasing central corneal thickness (SRC, 0.43, P<0.0001), deep anterior chamber (SRC, 0.09, P<0.01), higher body mass index (SRC, 0.07, P = 0.04) and with increasing mean arterial pressure (SRC, 0.09, P = 0.01), age at which reading commenced (SRC, 0.10, P<0.01) and birth method (SRC, 0.09, P = 0.01), but not with myopia (SRC, 0.09, P = 0.20). CONCLUSION: In Chinese children, higher IOP was associated with female gender, older age, thicker central cornea, deeper anterior chamber and higher mean arterial pressure. Higher body mass index, younger age at commencement of reading and being born of a caesarean section was also associated with higher IOP in adolescence.

Chinese Eye Exercises and Myopia Development in School Age Children: A Nested Case-control Study.

Chinese eye exercises have been implemented in China as an intervention for controlling children's myopia for over 50 years. This nested case-control study investigated Chinese eye exercises and their association with myopia development in junior middle school children. Outcome measures were the onset and progression of myopia over a two-year period. Cases were defined as 1. Myopia onset (cycloplegic spherical equivalent ≤ -0.5 diopter in non-myopic children). 2. Myopia progression (myopia shift of ≥1.0 diopter in those who were myopic at baseline). Two independent investigators assessed the quality of Chinese eye exercises performance at the end of the follow-up period. Of 260 children at baseline (mean age was 12.7 ± 0.5 years), 201 were eligible for this study. There was no association between eye exercises and the risk of myopia-onset (OR = 0.73, 95%CI: 0.24-2.21), nor myopia progression (OR = 0.79, 95%CI: 0.41-1.53). The group who performed high quality exercises had a slightly lower myopia progression of 0.15 D than the children who did not perform the exercise over a period of 2 years. However, the limited sample size, low dosage and performance quality of Chinese eye exercises in children did not result in statistical significance and require further studies.

The association of TGFB1 genetic polymorphisms with high myopia: a systematic review and meta-analysis.

OBJECTIVE: The TGFB1 gene is among the most studied genes in high myopia due to its role in scleral remodeling. But reported findings of association on TGFB1 and high myopia are inconsistent. This present study is to evaluate the association of TGFB1 polymorphisms and high myopia. METHODS: A comprehensive literature search was conducted on studies published up to April 5, 2015. Summary odds ratios (ORs) and 95% confidence intervals were analyzed. Heterogeneity across studies was evaluated by Cochran Q statistic test and the I(2) index. Sensitivity analyses were conducted by the approach of one-study remove to assess the influence of single study on the combined effect. RESULTS: Eight studies were included in this study for meta-analysis. Rs1982073 was associated with high myopia in dominant model (OR=1.64; 95% CI=1.04~2.58; P<0.05), heterozygous model (OR=1.54; 95% CI=1.02~2.33; P<0.05), homozygous model (OR=1.90; 95% CI=1.01~3.55; P=0.05) and allelic model (OR=1.36; 95% CI=1.01~1.84; P=0.05). However, there was no statistical significance when Bonferroni correction was considered. Rs4803455 was associated with high myopia in recessive model (OR=0.40; 95% CI=0.25~0.64; P<0.01) and homozygous model (OR=0.42; 95% CI=0.26~0.68; P<0.01). Rs1800469 was associated with high myopia in allelic model (OR=0.78; 95% CI=0.64~0.96; P<0.05). And the associations can withstand Bonferroni correction in models mentioned above when referring to rs4803455 (P<0.01) and rs1800469 (P<0.05). CONCLUSIONS: Meta-analysis of existing data revealed a suggestive association of TGFB1 rs1982073 and rs4803455 with high myopia.