ArticleEye (London, England)2024
Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy.
Article in Eye (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A machine learning model for diabetic retinopathy risk stratification using routine blood and urine parameters: insights into kidney-eye crosstalk.Frontiers in endocrinology · 2026Article
- Alteration of Tear Metabolomics Profiling in Infants With Retinopathy of Prematurity.Investigative ophthalmology & visual science · 2025Article
- Novel High-Resolution Lipidomes Could Serve as New Biomarkers for Diabetic Retinopathy: A Bidirectional and Mediated Mendelian Randomization Study.Journal of cellular and molecular medicine · 2025Article
- Stage-dependent proteomic alterations in aqueous humor of diabetic retinopathy patients based on data-independent acquisition and parallel reaction monitoring.Journal of translational medicine · 2025Article
- Tear-derived extracellular vesicles as diagnostic biomarkers for ocular and neurodegenerative diseases: opportunities and challenges.Extracellular vesicles and circulating nucleic acids · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectivesTo investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers.
methodsA cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry.
resultsAmong 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively.
conclusionsTear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR. CLINICAL
trial registrationChinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177 . Date of registration: 2021/12/12.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.