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Updated: Aug 7, 2025
Inducement and Evaluation of a Murine Model of Experimental Myopia
Published on: January 22, 2019
Baseline metrics that may predict future myopia in young children
Fuensanta A Vera-Diaz1, Ashutosh Jnawali1, Athanasios Panorgias1
1New England College of Optometry, Boston, Massachusetts, USA.
Insights
This study identified key metrics to predict myopia in children. The axial length/corneal radius ratio (AXL/CR) emerged as the most effective predictor for identifying high-risk children.
Area of Science:
- Ophthalmology and Vision Science
- Pediatric Eye Health
- Biometry and Refractive Error
Background:
- Myopia, or nearsightedness, is a growing global health concern, particularly in children.
- Early identification of children at high risk for myopia is crucial for timely intervention.
- Understanding the structural and heritable factors influencing myopia development is essential.
Purpose of the Study:
- To investigate structural, functional, and heritable metrics for predicting future myopia in young children.
- To compare the predictive power of various ocular parameters in classifying children at high risk (HR) versus low risk (LR) for myopia.
Main Methods:
- Utilized baseline data from the PICNIC longitudinal study on 97 young children with functional emmetropia.
- Measured cycloplegic refractive error (M) and optical biometry, including axial length (AXL) and corneal radius (CR).
- Classified children as HR or LR for myopia based on parental history and refractive error, and analyzed metrics like AXL/CR and anterior chamber depth (ACD).
Main Results:
- Children classified as HR exhibited significantly longer axial length (AXL) and deeper anterior chamber depth (ACD) compared to LR children.
- Linear regression models indicated that central corneal thickness (CCT), ACD, posterior vitreous depth (PVD), corneal radius (CR), and age significantly predicted refractive error (M).
- The axial length/corneal radius ratio (AXL/CR) was found to be the most predictive metric for classifying children's myopia risk.
Conclusions:
- While refractive error (M) and axial length (AXL) are correlated, the AXL/CR ratio provides a more distinct classification of myopia risk in pre-myopic children.
- Further longitudinal analysis will confirm the predictability of each assessed metric for myopia progression.
Purpose:
We used baseline data from the PICNIC longitudinal study to investigate structural, functional, behavioural and heritable metrics that may predict future myopia in young children.
Methods:
Cycloplegic refractive error (M) and optical biometry were obtained in 97 young children with functional emmetropia. Children were classified as high risk (HR) or low risk (LR) for myopia based on parental myopia and M. Other metrics included axial length (AXL), axial length/corneal radius (AXL/CR) and refractive centile curves.
Results:
Based on the PICNIC criteria, 46 children (26 female) were classified as HR (M = +0.62 ± 0.44 D, AXL = 22.80 ± 0.64 mm) and 51 (27 female) as LR (M = +1.26 ± 0.44 D, AXL = 22.77 ± 0.77 mm). Based on centiles, 49 children were HR, with moderate agreement compared with the PICNIC classification (k = 0.65, p < 0.01). ANCOVA with age as a covariate showed a significant effect for AXL (p < 0.01), with longer AXL and deeper anterior chamber depth (ACD) (p = 0.01) in those at HR (differences AXL = 0.16 mm, ACD = 0.13 mm). Linear regression models showed that central corneal thickness (CCT), ACD, posterior vitreous depth (PVD) (=AXL - CCT - ACD-lens thickness (LT)), corneal radius (CR) and age significantly predicted M (R = 0.64, p < 0.01). Each 1.00 D decrease in hyperopia was associated with a 0.97 mm elongation in PVD and 0.43 mm increase in CR. The ratio AXL/CR significantly predicted M (R = -0.45, p < 0.01), as did AXL (R = -0.25, p = 0.01), although to a lesser extent.
Conclusions:
Although M and AXL were highly correlated, the classification of pre-myopic children into HR or LR was significantly different when using each parameter, with AXL/CR being the most predictive metric. At the end of the longitudinal study, we will be able to assess the predictability of each metric.
