To determine plasma metabolite and metabolic pathway differences between patients with type 2 diabetes with diabetic retinopathy (DR) and without retinopathy (diabetic controls), and between patients with proliferative DR (PDR) and nonproliferative DR (NPDR). Using high-resolution mass spectrometry with liquid chromatography, untargeted metabolomics was performed on plasma samples from 83 DR patients and 90 diabetic controls. Discriminatory metabolic features were identified through partial least squares discriminant analysis, and linear regression was used to adjust for age, sex, diabetes duration, and hemoglobin A1c. Pathway analysis was performed using Mummichog 2.0. In the adjusted analysis, 126 metabolic features differed significantly between DR patients and diabetic controls. Pathway analysis revealed alterations in the metabolism of amino acids, leukotrienes, niacin, pyrimidine, and purine. Arginine, citrulline, glutamic γ-semialdehyde, and dehydroxycarnitine were key contributors to these pathway differences. A total of 151 features distinguished PDR patients from NPDR patients, and pathway analysis revealed alterations in the β-oxidation of saturated fatty acids, fatty acid metabolism, and vitamin D3 metabolism. Carnitine was a major contributor to the pathway differences. This study demonstrates that arginine and citrulline-related pathways are dysregulated in DR, and fatty acid metabolism is altered in PDR patients compared with NPDR patients.

Purpose: To identify metabolites and metabolic pathways altered in neovascular age-related macular degeneration (NVAMD). Methods: We performed metabolomics analysis using high-resolution C18 liquid chromatography-mass spectrometry on plasma samples from 100 NVAMD patients and 192 controls. Data for mass/charge ratio ranging from 85 to 850 were captured, and metabolic features were extracted using xMSanalyzer. Nested feature selection was used to identify metabolites that discriminated between NVAMD patients and controls. Pathway analysis was performed with Mummichog 2.0. Hierarchical clustering was used to examine the relationship between the discriminating metabolites and NVAMD patients and controls. Results: Of the 10,917 metabolic features analyzed, a set of 159 was identified that distinguished NVAMD patients from controls (area under the curve of 0.83). Of these features, 39 were annotated with confidence and included multiple carnitine metabolites. Pathway analysis revealed that the carnitine shuttle pathway was significantly altered in NVAMD patients (P = 0.0001). Tandem mass spectrometry confirmed the molecular identity of five carnitine shuttle pathway acylcarnitine intermediates that were increased in NVAMD patients. Hierarchical cluster analysis revealed that 51% of the NVAMD patients had similar metabolic profiles, whereas the remaining 49% displayed greater variability in their metabolic profiles. Conclusions: Multiple long-chain acylcarnitines that are part of the carnitine shuttle pathway were significantly increased in NVAMD patients compared to controls, suggesting that fatty acid metabolism may be involved in NVAMD pathophysiology. Cluster analysis suggested that clinically indistinguishable NVAMD patients can be separated into distinct subgroups based on metabolic profiles.

We previously demonstrated that the bile acid taurocholic acid (TCA) inhibits features of age-related macular degeneration (AMD) in vitro. The purpose of this study was to determine if the glycine-conjugated bile acids glycocholic acid (GCA), glycodeoxycholic acid (GDCA), and glycoursodeoxycholic acid (GUDCA) can protect retinal pigment epithelial (RPE) cells against oxidative damage and inhibit vascular endothelial growth factor (VEGF)-induced angiogenesis in choroidal endothelial cells (CECs). Paraquat was used to induce oxidative stress and disrupt tight junctions in HRPEpiC primary human RPE cells. Tight junctions were assessed via transepithelial electrical resistance and ZO-1 immunofluorescence. GCA and GUDCA protected RPE tight junctions against oxidative damage at concentrations of 100–500 µM, and GDCA protected tight junctions at 10–500 µM. Angiogenesis was induced with VEGF in RF/6A macaque CECs and evaluated with cell proliferation, cell migration, and tube formation assays. GCA inhibited VEGF-induced CEC migration at 50–500 µM and tube formation at 10–500 µM. GUDCA inhibited VEGF-induced CEC migration at 100–500 µM and tube formation at 50–500 µM. GDCA had no effect on VEGF-induced angiogenesis. None of the three bile acids significantly inhibited VEGF-induced CEC proliferation. These results suggest glycine-conjugated bile acids may be protective against both atrophic and neovascular AMD.

To characterize metabolites and metabolic pathways altered in intermediate and neovascular age-related macular degeneration (IAMD and NVAMD), high resolution untargeted metabolomics was performed via liquid chromatography-mass spectrometry on plasma samples obtained from 91 IAMD patients, 100 NVAMD patients, and 195 controls. Plasma metabolite levels were compared between: AMD patients and controls, IAMD patients and controls, and NVAMD and IAMD patients. Partial least-squares discriminant analysis and linear regression were used to identify discriminatory metabolites. Pathway analysis was performed to determine metabolic pathways altered in AMD. Among the comparisons, we identified 435 unique discriminatory metabolic features. Using computational methods and tandem mass spectrometry, we identified 11 metabolic features whose molecular identities had been previously verified and confirmed the molecular identities of three additional discriminatory features. Included among the discriminatory metabolites were acylcarnitines, phospholipids, amino acids, and steroid metabolites. Pathway analysis revealed that lipid, amino acid, and vitamin metabolism pathways were altered in NVAMD, IAMD, or AMD in general, including the carnitine shuttle pathway which was significantly altered in all comparisons. Finally, few discriminatory features were identified between IAMD patients and controls, suggesting that plasma metabolic profiles of IAMD patients are more similar to controls than to NVAMD patients.