A serum metabolomics study of patients with nAMD in response to anti-VEGF therapy

Abstract

intravitreal injection of anti-vascular endothelial growth factor (anti-VeGf) is the current standard of treatment for choroidal neovascularization (cnV) secondary to neovascular age-related macular degeneration (nAMD), but there are no diagnostic tools to predict response of these therapies. We hypothesize that differences in baseline metabolic profiles of patients with nAMD may influence responsiveness to anti-VeGf therapy, and thus provide prognosticating information for these patients. A prospective study was performed on 100 patients with nAMD treated with anti-VEGF therapy. We classified patients into two groups: responders (n = 54) and non-responders (n = 46). The expression levels of glycerophosphocholine,LysoPC (18:2) and PS (18:0/20:4) were higher in non-responders and these findings were verified in the validation cohort, implicating that reductions in these three metabolites can be used as predictors for responsiveness to anti-VeGf therapy during the initial loading phase for patients with nAMD. our study also provided new insights into the pathophysiological changes and molecular mechanism of anti-VeGf therapy for nAMD patients.

Metabolomics in Age-Related Macular Degeneration: A Systematic Review

“…11 The successful application of metabolomics in diseases shows its great potential in the study of complex diseases such as drug discovery, cardiovascular diseases, and the response to drug therapy for AMD. [12][13][14] The retina is a metabolically active tissue of the human body, and photoreceptors are the most numerous cells in the retina with the highest requirements for metabolism. 15,16 Retinal pigment epithelial (RPE) provide metabolic and functional (VEGF) support for photoreceptors and choroid cells.…”

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

“…Six studies developed predictive models using AI. 16 , 24 , 36 , 38 , 39 , 52 Four predictive models used metabolic or proteomic profiles to distinguish AMD cases from non-AMD controls. 24 , 36 , 38 , 39 Kersten et al developed two models using sparse partial least squares discriminant analysis (sPLS-DA), one using the complete metabolic profile and the other using the complete metabolic profile plus derived variables such as ratios of metabolites.…”

“…All of the studies characterized as "Treatment Decisions" examined patients undergoing anti-vascular endothelial growth factor therapy (anti-VEGF) for nAMD treatment. 16,22,[56][57][58][59][60] Of these studies, one examined comprehensive metabolic profiles and four examined cytokine levels or inflammatory markers. 16,22,[56][57][58][59][60] While no significant biomarkers were confirmed by multiple studies, three studies concluded that biofluid markers could be used to predict responsiveness to anti-VEGF therapy and prognosis following anti-VEGF therapy.…”

“…10,[13][14][15] In contrast, unsupervised AI techniques such as hierarchical cluster analysis and principal component analysis (PCA) are adept at determining trends in highly dimensional data as they can group unlabeled data based on similarities or differences and find associations between variables in large data sets. 16,17 Bioinformatics tools such as pathway analysis translate complex findings into interpretable information. To date, AI use in the context of AMD has been primarily for review of fundus photography, optical coherence tomography, and other ocular imaging modalities.…”

Metabolomics in Retinal Diseases: An Update

“…During the treatment course of disease, metabolomics can also be employed for evaluating therapeutic effects or to predict the response of the given regimen. Gao et al [50] conducted a serum metabolomics study of patients with nAMD in response to anti-VEGF therapy. They identified reductions in glycerophosphocholine, LysoPC (18:2), and PS (18:0/20:4) as predictors for responsiveness to anti-VEGF therapy for nAMD patients.…”