Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis

• Published in: Scientific reports Vol. 14; no. 1; pp. 15108 - 11
• Main Authors: Li, Peiyu, Wang, Hui, Tian, Guo, Fan, Zhihui
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.07.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378; 639/705; Alanine transaminase; Algorithms; Biomarkers; Biomarkers - blood; Blood coagulation; Bone resorption; Bone turnover; BP neural network; Cluster Analysis; Diabetes; Diabetes mellitus; Diabetic retinopathy; Diabetic Retinopathy - blood; Diabetic Retinopathy - diagnosis; Disease; Early Diagnosis; Early warning of disease; Epidemiology; Female; Glucose metabolism; Glycated Hemoglobin - analysis; Glycated Hemoglobin - metabolism; Hemoglobin; Hierarchical cluster analysis; Hierarchies; Humanities and Social Sciences; Humans; Insulin secretion; Key markers; Lipid metabolism; Male; Metabolism; Microvasculature; Middle Aged; multidisciplinary; Neural networks; Neural Networks, Computer; Patients; Retinopathy; Science; Science (multidisciplinary); Uric acid; Diabetic retinopathy; Early warning of disease; BP neural network; Key markers; Hierarchical cluster analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-65694-x

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.