Lower indoor spatial frequency increases the risk of myopia in children.

BACKGROUND/AIMS: Animal models have shown that the absence of high-frequency visual information can precipitate the onset of myopia, but this relationship remains unclear in humans. This study aims to explore the association between the spatial frequency content of the visual environment and myopia in children. METHODS: Images from the rooms of children and their frequently visited outdoor areas were taken by their parents and collected by the researcher through questionnaires. The spatial frequency was quantified using Matlab. Cycloplegic refraction was used to measure the spherical equivalent (SE), and IOL Master was used to measure axial length (AL) and corneal radius (CR). AL/CR ratio was calculated. RESULTS: The study included 566 children with an average age of (8.04±1.47) years, of which 270 were girls (47.7%), and the average SE was (0.70±1.21) D. Image analysis revealed that indoor spatial frequency slope was lower than that of the outdoor environment (-1.43±0.18 vs -1.11±0.23, p<0.001). There were 79 myopic individuals (14.0%). Images from indoor content of myopic children had a lower spatial frequency slope than non-myopic children (-1.47±0.16 vs 1.43±0.18, p=0.03) while there was no significant difference in outdoor spatial frequency slope. Regression analysis indicated that the indoor spatial frequency slope was positively associated with SE value (ß=0.60, p=0.02) and inversely related to myopia (OR=0.24, p<0.05). CONCLUSION: The spatial frequency of the outdoor environment is significantly higher than that of the indoor environment. Indoor spatial frequency is related to children's refractive status, with lower indoor spatial frequency being associated with a higher degree of myopia.

Effect modification of time spent outdoors on the association between early childhood overweight and myopia: a one-year follow-up study.

BACKGROUND: This study examined the moderating role of outdoor time on the relationship between overweight and myopia. METHODS: The data for this study was obtained from a prospective study in Shanghai, where non-myopic children wore wristwear and were followed up for 1 year. Eye examinations were performed at each visit. The modification effect was assessed on the additive scale using multivariable logistic regression, and relative excess risk due to interaction was used to calculate the modification effect. RESULTS: A total of 4683 non-myopic children were included with 32.20% being overweight at baseline. Following a 1-year period, 17.42% of children had myopia. When compared to those who spent <90 minutes outdoors, children who spent >120 had a relative risk of myopia onset that was reduced to 0.61. As time spent outdoors decreased, more risks of myopia onset were identified among overweight children than among normal children, the modification effect on the additive scale was -0.007, with ~70% of this effect attributed to the modifying influence of outdoor time. CONCLUSIONS: Increasing outdoor time can reduce myopia more among overweight children than normal. Future interventions should focus on outdoor activities among overweight children to reduce myopia risks.

Machine Learning-Based Integration of Metabolomics Characterisation Predicts Progression of Myopic Retinopathy in Children and Adolescents.

Myopic retinopathy is an important cause of irreversible vision loss and blindness. As metabolomics has recently been successfully applied in myopia research, this study sought to characterize the serum metabolic profile of myopic retinopathy in children and adolescents (4-18 years) and to develop a diagnostic model that combines clinical and metabolic features. We selected clinical and serum metabolic data from children and adolescents at different time points as the training set (n = 516) and the validation set (n = 60). All participants underwent an ophthalmologic examination. Untargeted metabolomics analysis of serum was performed. Three machine learning (ML) models were trained by combining metabolic features and conventional clinical factors that were screened for significance in discrimination. The better-performing model was validated in an independent point-in-time cohort and risk nomograms were developed. Retinopathy was present in 34.2% of participants (n = 185) in the training set, including 109 (28.61%) with mild to moderate myopia. A total of 27 metabolites showed significant variation between groups. After combining Lasso and random forest (RF), 12 modelled metabolites (mainly those involved in energy metabolism) were screened. Both the logistic regression and extreme Gradient Boosting (XGBoost) algorithms showed good discriminatory ability. In the time-validation cohort, logistic regression (AUC 0.842, 95% CI 0.724-0.96) and XGBoost (AUC 0.897, 95% CI 0.807-0.986) also showed good prediction accuracy and had well-fitted calibration curves. Three clinical characteristic coefficients remained significant in the multivariate joint model (p < 0.05), as did 8/12 metabolic characteristic coefficients. Myopic retinopathy may have abnormal energy metabolism. Machine learning models based on metabolic profiles and clinical data demonstrate good predictive performance and facilitate the development of individual interventions for myopia in children and adolescents.