ArticleFrontiers in endocrinology2021
Plasma Metabolomics Reveals Metabolic Profiling For Diabetic Retinopathy and Disease Progression.
Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Blood metabolome of cardiovascular disease, diabetic kidney disease, and diabetic retinopathy in type 2 diabetes patients: A systematic review and meta-analysis.Endocrine research · 2025Pooled it
- Implications of Diabetes-Induced Altered Metabolites on Retinal Neurodegeneration.Frontiers in neuroscience · 2022Pooled it
- [Establishment and application of a knowledge-directed pseudo-targeted analytical method for diabetic retinopathy].Se pu = Chinese journal of chromatography · 2026Article
- Biomarker signatures distinguish diabetic retinopathy and diabetic nephropathy.Diabetology international · 2025Article
- Enhanced Natamycin production in Streptomyces gilvosporeus through phosphate tolerance screening and transcriptome-based analysis of high-yielding mechanisms.Microbial cell factories · 2025Article
- New insights of potential biomarkers in diabetic retinopathy: integrated multi-omic analyses.Frontiers in endocrinology · 2025Review
- Epitranscriptomic signatures in blood: emerging biomarkers for diagnosis of diabetes and its complications.Frontiers in cell and developmental biology · 2025Review
- Metabolic Characteristics of Sclera in Lens-Induced Myopic Guinea Pigs.Investigative ophthalmology & visual science · 2024Article
- Nutritional load in post-prandial oxidative stress and the pathogeneses of diabetes mellitus.NPJ science of food · 2024Article
- Ferrous ascorbate as a potential biomarker for diabetic retinopathy: a vitreous humour metabolomics study.BMC ophthalmology · 2024Article
- Metabolomics-driven discovery of therapeutic targets for cancer cachexia.Journal of cachexia, sarcopenia and muscle · 2024Review
- Aqueous humor proteomics analyzed by bioinformatics and machine learning in PDR cases versus controls.Clinical proteomics · 2024Article
- Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review.Metabolites · 2024Review
- Transcriptome analysis combined with Mendelian randomization screening for biomarkers causally associated with diabetic retinopathy.Frontiers in endocrinology · 2024Article
- Metabolites as Risk Factors for Diabetic Retinopathy in Patients With Type 2 Diabetes: A 12-Year Follow-up Study.The Journal of clinical endocrinology and metabolism · 2023Article
- Recent Advances and Perspectives in Relation to the Metabolomics-Based Study of Diabetic Retinopathy.Metabolites · 2023Review
- Untargeted Multiomics Approach Coupling Lipidomics and Metabolomics Profiling Reveals New Insights in Diabetic Retinopathy.International journal of molecular sciences · 2023Article
- Oxidative Stress, Inflammatory, Angiogenic, and Apoptotic molecules in Proliferative Diabetic Retinopathy and Diabetic Macular Edema Patients.International journal of molecular sciences · 2023Article
- Article
- Small molecule metabolites: discovery of biomarkers and therapeutic targets.Signal transduction and targeted therapy · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Backgrounds: Diabetic retinopathy (DR), the main retinal vascular complication of DM, is the leading cause of visual impairment and blindness among working-age people worldwide. The aim of this study was to investigate the difference of plasma metabolic profiles in patients with DR to better understand the mechanism of this disease and disease progression. Methods: We used ultrahigh-performance liquid Q-Exactive mass spectrometry and multivariate statistical analyses to conduct a comprehensive analysis of plasma metabolites in a population with DR and proliferative DR (PDR). A risk score based on the level of the selected metabolite was established and evaluated using the least absolute shrinkage and selection operator regularization logistic regression (LASSO-LR) based machine learning model. Results: 22 differentially expressed metabolites which belonged to different metabolic pathway were identified and confirmed to be associated with the occurrence of DR. A risk score based on the level of the selected metabolite pseudouridine was established and evaluated to strongly associated with the occurrence of DR. Four circulating plasma metabolites (pseudouridine, glutamate, leucylleucine and N-acetyltryptophan) were identified to be differentially expressed between patients with PDR and other patients, and a risk score formula based on these plasma metabolites was developed and assessed to be significantly related to PDR. Conclusions: Our work highlights the possible use of the risk score assessment based on the plasma metabolites not only reveal in the early diagnosis of DR and PDR but also assist in enhancing current therapeutic strategies in the clinic.
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