New Polygenic Risk Score to Predict High Myopia in Singapore Chinese Children

IMI-Management and Investigation of High Myopia in Infants and Young Children

Purpose

The purpose of this study was to evaluate the epidemiology, etiology, clinical assessment, investigation, management, and visual consequences of high myopia (≤-6 diopters [D]) in infants and young children.

Findings

High myopia is rare in pre-school children with a prevalence less than 1%. The etiology of myopia in such children is different than in older children, with a high rate of secondary myopia associated with prematurity or genetic causes. The priority following the diagnosis of high myopia in childhood is to determine whether there is an associated medical diagnosis that may be of greater overall importance to the health of the child through a clinical evaluation that targets the commonest features associated with syndromic forms of myopia. Biometric evaluation (including axial length and corneal curvature) is important to distinguishing axial myopia from refractive myopia associated with abnormal development of the anterior segment. Additional investigation includes ocular imaging, electrophysiological tests, genetic testing, and involvement of pediatricians and clinical geneticists is often warranted. Following investigation, optical correction is essential, but this may be more challenging and complex than in older children. Application of myopia control interventions in this group of children requires a case-by-case approach due to the lack of evidence of efficacy and clinical heterogeneity of high myopia in young children.

Conclusions

High myopia in infants and young children is a rare condition with a different pattern of etiology to that seen in older children. The clinical management of such children, in terms of investigation, optical correction, and use of myopia control treatments, is a complex and often multidisciplinary process.

A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

BACKGROUND

High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ -6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER.

METHODS

The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression.

FINDINGS

In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17-21%), 2% (1-3%), 8% (7-10%) and 6% (3-9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75-0.81), 0.58 (0.53-0.64), 0.71 (0.69-0.74) and 0.67 (0.62-0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92-1.24).

INTERPRETATION

Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted for.

FUNDING

Supported by the Welsh Government and Fight for Sight (24WG201).

Artificial intelligence and digital solutions for myopia

Myopia as an uncorrected visual impairment is recognized as a global public health issue with an increasing burden on health-care systems. Moreover, high myopia increases one's risk of developing pathologic myopia, which can lead to irreversible visual impairment. Thus, increased resources are needed for the early identification of complications, timely intervention to prevent myopia progression, and treatment of complications. Emerging artificial intelligence (AI) and digital technologies may have the potential to tackle these unmet needs through automated detection for screening and risk stratification, individualized prediction, and prognostication of myopia progression. AI applications in myopia for children and adults have been developed for the detection, diagnosis, and prediction of progression. Novel AI technologies, including multimodal AI, explainable AI, federated learning, automated machine learning, and blockchain, may further improve prediction performance, safety, accessibility, and also circumvent concerns of explainability. Digital technology advancements include digital therapeutics, self-monitoring devices, virtual reality or augmented reality technology, and wearable devices - which provide possible avenues for monitoring myopia progression and control. However, there are challenges in the implementation of these technologies, which include requirements for specific infrastructure and resources, demonstrating clinically acceptable performance and safety of data management. Nonetheless, this remains an evolving field with the potential to address the growing global burden of myopia.

Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children

Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify "at-risk" children for early intervention.

Analysis and modeling of myopia-related factors based on questionnaire survey

With the rapid development of science and technology, the trend of low age myopia is becoming increasingly significant. The latest national survey done by the Chinese government found that more than 80% of Chinese teenagers suffer from myopia. Adolescent myopia is closely related to living environment, heredity, and living habits. Quantifying the relationship between myopia and living environment, heredity, and living habits is conductive to the prevention and intervention of adolescent myopia. In this study, we investigated the relationships between four main factors (environment, habits, parental vision, and demographic) and myopia status by analyzing the questionnaire data. Data were collected from Chengdu, China in 2021, including 2808 myopia samples and 5693 non-myopia samples, with a total of 22 features. Then, these 22 features were inputted into three machine learning algorithms to discriminate the two classes of samples. Results show that the computational model could produce an AUC of 0.768. To pick out the most important features which play important roles in classification, we used incremental feature selection strategy to screen the 22 features. As a result, we found that the 4 most influential features with XGBoost could achieve a competitive AUC of 0.764. To further investigate the risk and protective factors affecting adolescent myopia, we used OR values derived from MLE-LR to analyze the relationship between 22 features and adolescent myopia. Results showed that the age variable was the most significant risk factor for myopia, followed by the myopia status of parents. The most protective factor for eyesight is the measure taken by the children, followed by the distance between books and eyes when reading. These discoveries can guide the prevention and control of myopia in children and adolescents.

Myopia Genetics and Heredity

Myopia is the most common eye condition leading to visual impairment and is greatly influenced by genetics. Over the last two decades, more than 400 associated gene loci have been mapped for myopia and refractive errors via family linkage analyses, candidate gene studies, genome-wide association studies (GWAS), and next-generation sequencing (NGS). Lifestyle factors, such as excessive near work and short outdoor time, are the primary external factors affecting myopia onset and progression. Notably, besides becoming a global health issue, myopia is more prevalent and severe among East Asians than among Caucasians, especially individuals of Chinese, Japanese, and Korean ancestry. Myopia, especially high myopia, can be serious in consequences. The etiology of high myopia is complex. Prediction for progression of myopia to high myopia can help with prevention and early interventions. Prediction models are thus warranted for risk stratification. There have been vigorous investigations on molecular genetics and lifestyle factors to establish polygenic risk estimations for myopia. However, genes causing myopia have to be identified in order to shed light on pathogenesis and pathway mechanisms. This report aims to examine current evidence regarding (1) the genetic architecture of myopia; (2) currently associated myopia loci identified from the OMIM database, genetic association studies, and NGS studies; (3) gene-environment interactions; and (4) the prediction of myopia via polygenic risk scores (PRSs). The report also discusses various perspectives on myopia genetics and heredity.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

A multicenter Spanish study of atropine 0.01% in childhood myopia progression

To evaluate the efficacy and safety of atropine 0.01% eye drops for myopia control in a multicentric pediatric Spanish cohort. An interventional, prospective, multicenter study was designed. Children aged between 6 and 14 years, with myopia between - 2.00 D to - 6.00 D, astigmatism < 1.50 D and documented previous annual progression greater than - 0.5 D (cycloplegic spherical equivalent, SE) were included. Once nightly atropine 0.01% eye drops in each eye were prescribed to all participants for 12 months. Age, gender, ethnicity and iris color were registered. All patients underwent the same follow-up protocol in every center: baseline visit, telephone consultation 2 weeks later and office controls at 4, 8 and 12 months. At each visit, best-corrected visual acuity, and cycloplegic autorefraction were assessed. Axial length (AL), anterior chamber depth and pupil diameter were measured on an IOL Master (Carl Zeiss Meditec, Inc, Dublin, CA). Adverse effects were registered in a specific questionnaire. Mean changes in cycloplegic SE and AL in the 12 months follow-up were analyzed. SE progression during treatment was compared with the SE progression in the year before enrollment for each patient. Correlation between SE and AL, and annual progression distribution were evaluated. Progression risk factors were analyzed by multivariate logistic regression analyses. Of the 105 recruited children, 92 completed the treatment. Mean SE and AL changes were - 0.44 ± 0.41 D and 0.27 ± 0.20 mm respectively. Mean SE progression was lower than the year before treatment (- 0.44 ± 0.41 D versus - 1.01 ± 0.38 D; p < 0.0001). An inverse correlation between SE progression and AL progression (r: - 0.42; p < 0.0001) was found. Fifty-seven patients (62%) had a SE progression less than - 0.50 D. No risk factors associated with progression could be identified in multivariate analyses. Mean pupil diameter increment at 12-months visit was 0.74 ± 1.76 mm. The adverse effects were mild and infrequent, and decreased over the time. Atropine 0.01% is effective and safe for myopia progression control in a multicentric Spanish children cohort. We believe this efficacy might be extensible to the myopic pediatric population from Western countries with similar social and demographic features. More studies about myopia progression risk factors among atropine treated patients are needed.