Plasma cytokines for predicting diabetic retinopathy among type 2 diabetic patients via machine learning algorithms

• Published in: Aging (Albany, NY.) Vol. 13; no. 2; pp. 1972 - 1988
• Main Authors: Cao, Bin, Zhang, Ning, Zhang, Yuanyuan, Fu, Ying, Zhao, Dong
• Format: Journal Article
• Language: English
• Published: United States Impact Journals 11.12.2020
• Subjects: Research Paper; plasma cytokines; type 2 diabetes mellitus; prediction model; diabetic retinopathy; machine learning algorithms
• ISSN: 1945-4589; 1945-4589
• DOI: 10.18632/aging.202168

This 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. There 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. Plasma 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. In 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.