Translational vision science & technology · 2026
Personalized Dosing System Guided by Cross-Scale Metabolic Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.
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Li Xuejing, Wang Caixia, Duan Jialiang, Shang Qingli
Аннотация
PURPOSE: Long-term anti-vascular endothelial growth factor (VEGF) response in neovascular age-related macular degeneration (nvAMD) varies widely, with no validated predictive biomarkers. We aimed to establish a metabolic imaging biomarker-based framework for personalized efficacy prediction and dosing.
METHODS: In this single-center retrospective study, patients with clinically confirmed nvAMD and age-matched controls (cataract surgery with normal fundus or non-neovascular AMD) were enrolled. Aqueous humor was analyzed by ultra-high-performance liquid chromatography tandem mass spectrometry. Patients with nvAMD received anti-VEGF therapy (three doses then as needed, followed for ≥12 months) and were classified by structural/functional outcomes as responders or nonresponder. Comparative metabolomics identified phenotype-associated metabolites. An integrated prediction model was built using orthogonal partial least squares discriminant analysis, receiver operating characteristic analysis, and multivariate logistic regression.
RESULTS: The aqueous humor of patients with nvAMD showed lipid remodeling and suppressed purine metabolism. Eight key metabolites were identified as phenotype-associated biomarkers (indole-3-acetic acid, N-acetyl-L-alanine, xanthine, arachidonoyl lysophosphatidic acid, taurine, indole-3-lactic acid, 4-acetoxyphenol, and Lys-Ile). Among them, indole-3-acetic acid and N-acetyl-L-alanine showed the highest predictive performance for long-term treatment response (area under the curve = 0.802 each). Integrating metabolic markers with baseline imaging and clinical parameters improved predictive accuracy (area under the curve = 0.86). Longitudinal analysis showed partial normalization of metabolite levels in responders after anti-VEGF therapy.
CONCLUSIONS: nvAMD involves responder-specific metabolic reprogramming. Integrating aqueous metabolites with imaging biomarkers enables accurate pretreatment prediction of anti-VEGF efficacy, supporting personalized therapy.
TRANSLATIONAL RELEVANCE: This work links aqueous metabolic profiling to clinical decision-making by identifying biomarkers predictive of long-term anti-vascular endothelial growth factor response. Their integration with routine imaging yields a pretreatment stratification model (area under the curve = 0.86), which may inform individualized monitoring and earlier regimen adjustment in predicted nonresponders.
METHODS: In this single-center retrospective study, patients with clinically confirmed nvAMD and age-matched controls (cataract surgery with normal fundus or non-neovascular AMD) were enrolled. Aqueous humor was analyzed by ultra-high-performance liquid chromatography tandem mass spectrometry. Patients with nvAMD received anti-VEGF therapy (three doses then as needed, followed for ≥12 months) and were classified by structural/functional outcomes as responders or nonresponder. Comparative metabolomics identified phenotype-associated metabolites. An integrated prediction model was built using orthogonal partial least squares discriminant analysis, receiver operating characteristic analysis, and multivariate logistic regression.
RESULTS: The aqueous humor of patients with nvAMD showed lipid remodeling and suppressed purine metabolism. Eight key metabolites were identified as phenotype-associated biomarkers (indole-3-acetic acid, N-acetyl-L-alanine, xanthine, arachidonoyl lysophosphatidic acid, taurine, indole-3-lactic acid, 4-acetoxyphenol, and Lys-Ile). Among them, indole-3-acetic acid and N-acetyl-L-alanine showed the highest predictive performance for long-term treatment response (area under the curve = 0.802 each). Integrating metabolic markers with baseline imaging and clinical parameters improved predictive accuracy (area under the curve = 0.86). Longitudinal analysis showed partial normalization of metabolite levels in responders after anti-VEGF therapy.
CONCLUSIONS: nvAMD involves responder-specific metabolic reprogramming. Integrating aqueous metabolites with imaging biomarkers enables accurate pretreatment prediction of anti-VEGF efficacy, supporting personalized therapy.
TRANSLATIONAL RELEVANCE: This work links aqueous metabolic profiling to clinical decision-making by identifying biomarkers predictive of long-term anti-vascular endothelial growth factor response. Their integration with routine imaging yields a pretreatment stratification model (area under the curve = 0.86), which may inform individualized monitoring and earlier regimen adjustment in predicted nonresponders.