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Plasma cytokines for predicting diabetic retinopathy among type 2 diabetic patients via machine learning algorithms
Bin Cao1,2, Ning Zhang1,2, Yuanyuan Zhang1,2
1Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China.
Aims:
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.
Results:
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.
Conclusions:
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.
Materials And Methods:
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.
