Purpose: To determine the differences of metabolites and metabolic pathways between patients with proliferative diabetic retinopathy (PDR) and without diabetes (nondiabetic controls) in plasma and vitreous, respectively, and to characterize the relationship between plasma and vitreous metabolic profiles. Methods: Liquid chromatography/tandem mass spectrometry technology was performed to distinct metabolite profiles of plasma and vitreous. A total of 139 plasma samples from 88 patients with PDR and 51 nondiabetic controls, as well as 74 vitreous samples from 51 patients with PDR and 23 nondiabetic controls, were screened. Pathway analysis was performed using MetaboAnalyst 5.0. Pearson correlation analysis was used to investigate the correlation of metabolites in vitreous and plasma. Results: After adjusting for age, fasting blood glucose, and urea, in vitreous metabolomes, a total of 76 features distinguished patients with PDR from controls. Fifteen differential metabolites were found in plasma metabolites. Pantothenate and CoA biosynthesis was the common metabolic pathway altered in both plasma and vitreous. Aromatic amino acid metabolism pathways were dysregulated in vitreous of PDR. For four metabolic features, there were positive correlations between vitreous and plasma. Conclusions: Despite great differences between the metabolic profiles of plasma and vitreous in PDR cases, there are also similarities in the change of metabolites and metabolic pathways. Exploring the relationship of metabolomics between vitreous and plasma may help provide new understanding of the mechanism of PDR.

Retinal hard exudates (HEs) result from lipoproteins leaking from capillaries into extracellular retinal space, and are related to decreased visual acuity in diabetic retinopathy (DR). This study aims to identify differential serum lipids and metabolites associated with HEs. A cross-sectional study was conducted Jul 2017 ∼ Mar 2021. We assessed the amount of HEs using standard ETDRS photographs for comparison. HEs severity was rated as “no or questionable”, “moderate” or “severe”. Serum samples were processed via high coverage pseudotargeted lipidomics analysis, and untargeted liquid chromatography coupled with time-of-flight mass spectrometry for metabolomics study, respectively. Weighted gene co-expression network analyses, partial least squares-discriminant analysis, and multi-receiver operating characteristic analysis were applied. A total of 167 patients were included. Discovery group: 116 eyes (116 patients). Validation group: 51 eyes (51 patients). 888 lipids were detected and divided into 18 modules (MEs), ME1 ∼ ME18. Lipids in ME1 significantly increased in patients with HEs in DR (NPDR and PDR combined), NPDR, and PDR, respectively. ME1 enriched to triglycerides (29%), ceramides (17%), and N-acylethanolamines (15%). A combined model of 20 lipids was the best to discriminate HEs, area under curve = 0.804, 95% confidence interval = 0.674–0.916. For metabolomics analysis, 19 metabolites and 13 pathways associated with HEs were identified. Taurine and hypotaurine metabolism, cysteine and methionine metabolism were closely related to HEs (P < 0.01). The lipids and metabolites identified may serve as prediction biomarkers in the early stage of HEs in DR.

Background: Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) are major causes of blindness in aged people. 30% of the patients show unsatisfactory response to anti-vascular endothelial growth factor (anti-VEGF) drugs. This study aims to investigate the relationship between serum metabolome and treatment response to anti-VEGF therapy.Methods: A prospective longitudinal study was conducted between March 2017 and April 2019 in 13 clinical sites in China. The discovery group were enrolled from Shanghai General Hospital. The validation group consisted of patients from the other 12 sites. Participants received at least one intravitreal injection of 0.5 mg anti-VEGF drug, conbercept, and were divided into two groups - responders and non-responders. Serum samples of both groups were processed for UHPLC-MS/MS analysis. We constructed principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models to investigate the metabolic differences between two groups using SIMCA-P. Area under curve (AUC) was calculated to screen the biomarkers to predict treatment response. Metabolites sub-classes and enriched pathways were obtained using MetaboAnalyst5.0.Results: 219 eyes from 219 patients (nAMD = 126; PCV = 93) were enrolled. A total of 248 metabolites were detected. PCA and PLS-DA models of the discovery group demonstrated that the metabolic profiles of responders and non-responders clearly differed. Eighty-five differential metabolites were identified, including sub-classes of diacylglycerophosphocholines, lysophosphatidylcholine (LPC), fatty acids, phosphocholine, etc. Responders and non-responders differed most significantly in metabolism of LPC (p = 7.16 × 10^-19) and diacylglycerophosphocholine (p = 6.96 × 10^-17). LPC 18:0 exhibited the highest AUC, which is 0.896 with 95% confidence internal between 0.833 and 0.949, to discriminate responders. The predictive accuracy of LPC 18:0 was 72.4% in the validation group.Conclusions: This study suggests that differential metabolites may be useful for guiding treatment options for nAMD and PCV. Metabolism of LPC and diacylglycerophosphocholine were found to affect response to conbercept treatment. LPC 18:0 was a potential biomarker to discriminate responders from non-responders.

The major obstacle for the development of targeted therapies is the lack of pharmacodynamic (PD) biomarkers to provide an early readout of biological activities. As the modulation of metabolites may reflect the biological changes occurring in the targets, metabolomics is promising to be an efficient way to explore PD biomarkers. In the present study, a liver fibrosis rat model was established by intraperitoneal injection of CCl₄ twice weekly for 6 weeks, the treatment of total aglycone extracts of Scutellaria baicalensis (TAES) was begun 4 weeks after the modeling, and gas chromatography-mass spectrometry (GC-MS) based metabolomics combined with pattern recognition and network analysis were carried out for the research on PD biomarkers of TAES on liver fibrosis. After 2 weeks of treatment, TAES shows positive effects on CCl₄-induced liver fibrosis. In the metabolomics study, 63 urinary metabolites contributing to liver fibrosis were identified. Six metabolic pathways significantly enriched in metabolomics data were mapped onto a network to determine global patterns of metabolic alterations in liver fibrosis. By topological analysis, 6 metabolites with high centrality in the metabolic sub-network were selected as potential PD biomarkers. Within 24 h of the final administration, the 6 identified urine metabolic biomarkers with response to time variation of TAES were validated as PD biomarkers. This integrative study presents an attractive strategy to explore PD biomarkers, which may give insight into the actual pharmacological effect of target drugs, and the information from PD biomarkers can be combined with pharmacokinetics to select the optimal dose and a schedule of administration for the drugs.