Predictive Utility of Genetic Risk for Myopic Maculopathy Presence and Progression in a Chinese High Myopia Cohort

遗传风险对中国高度近视人群近视性黄斑病变发生和进展的预测价值

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Abstract

PURPOSE: To evaluate the predictive utility of genetic risk for predicting the presence and longitudinal progression of myopic maculopathy among Chinese pediatric and adult high myopia cohorts. METHODS: Highly myopic participants (n = 623, spherical equivalence [SE] <-6.00 diopters [D]) were recruited from 2011 to 2012 and followed up with 2-year intervals. Multivariate logistic regression models and time-to-event analyses using Cox proportional hazards models were adopted to estimate the presence and progression of myopic maculopathy in cross-sectional analyses and follow-up visits. Conventional factors included age, gender, education level, SE, or axial length. Genetic risk factors included polygenic risk scores (PRSs) derived from genome-wide significant single-nucleotide polymorphisms associated with myopia. RESULTS: The participants had a mean age of 21.02 ± 11.85 years and an average SE of -9.76 ± 3.25 D. For the prediction of the presence of myopia maculopathy, the PRS had an odds ratio of 2.62 (95% confidence interval [CI], 1.43-4.85; P = 0.002) and resulted in an area under the receiver operating characteristic curve (AUC) of 0.589. However, adding PRS to the conventional models did not significantly improve prediction (P = 0.155). Likewise, over a follow-up of 4.94 ± 2.75 years, participants in the uppermost PRS quartile demonstrated a 2.33-fold (95% CI, 1.17-4.84; P = 0.018) elevated risk of myopic maculopathy progression compared to the lowest-risk group. The PRS yielded an AUC of 0.578 for forecasting the progression of myopic maculopathy. Integrating the PRS with conventional models did not enhance the prediction accuracy significantly (P = 0.575). CONCLUSIONS: The PRS alone fails to predict myopic maculopathy among Chinese highly myopic pediatric and adult populations, and the enhancement in prediction performance is quite limited when it is added to conventional predictors.

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