ArticleAging2020
Plasma cytokines for predicting diabetic retinopathy among type 2 diabetic patients via machine learning algorithms.
Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies.Frontiers in endocrinology · 2023Pooled it
- Identification and validation of biomarkers associated with lactic acid metabolism in diabetic nephropathy.PeerJ · 2026Article
- Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models.Ophthalmology and therapy · 2025Article
- Artificial intelligence in individualized retinal disease management.International journal of ophthalmology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThis study aimed to investigate changes of plasma cytokines and to develop machine learning classifiers for predicting non-proliferative diabetic retinopathy among type 2 diabetes mellitus patients.
resultsThere were 12 plasma cytokines significantly higher in the non-proliferative diabetic retinopathy group in the pilot cohort. The validation cohort showed that angiopoietin 1, platelet-derived growth factor-BB, tissue inhibitors of metalloproteinase 2 and vascular endothelial growth factor receptor 2 were significantly higher in the NPDR group. Machine learning algorithms using the random forest yielded the best performance, with sensitivity of 92.3%, specificity of 75%, PPV of 82.8%, NPV of 88.2% and area under the curve of 0.84.
conclusionsPlasma angiopoietin 1, platelet-derived growth factor-BB, and vascular endothelial growth factor receptor 2 were associated with presence of non-proliferative diabetic retinopathy and may be good biomarkers that play important roles in pathophysiology of diabetic retinopathy. MATERIALS AND
methodsIn pilot cohort, 60 plasma cytokines were simultaneously measured. In validation cohort, angiopoietin 1, CXC-chemokine ligand 16, platelet-derived growth factor-BB, tissue inhibitors of metalloproteinase 1, tissue inhibitors of metalloproteinase 2, and vascular endothelial growth factor receptor 2 were validated using ELISA kits. Machine learning algorithms were developed to build a prediction model for non-proliferative diabetic retinopathy.
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Registered trials
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 OpenQuestion graph.