Advancing the early detection of canine cognitive dysfunction syndrome with machine learning-enhanced blood-based biomarkers

利用机器学习增强血液生物标志物促进犬认知功能障碍综合征的早期检测

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作者:Chae Young Kim, Jinhye Kim, Sunmi Yoon, Isaac Jinwon Yi, Hyuna Lee, Sanghyuk Seo, Dae Won Kim, Soohyun Ko, Sun-A Kim, Changhyuk Kwon, Sun Shin Yi

Abstract

Up to half of the senior dogs suffer from canine cognitive dysfunction syndrome (CCDS), the diagnosis method relies on subjective questionnaires such as canine cognitive dysfunction rating (CCDR) scores. Therefore, the necessity of objective diagnosis is emerging. Here, we developed blood-based biomarkers for CCDS early detection. Blood samples from dogs with CCDR scores above 25 were analyzed, and the biomarkers retinol-binding protein 4 (RBP4), C-X-C-motif chemokine ligand 10 (CXCL10), and NADPH oxidase 4 (NOX4) were validated against neurodegenerative models. Lower biomarker levels were correlated with higher CCDR scores, indicating cognitive decline. Machine-learning analysis revealed the highest predictive accuracy when analyzing the combination of RBP4 and NOX4 using the support vector machine algorithm and confirmed potential diagnostic biomarkers. These results suggest that blood-based biomarkers can notably improve CCDS early detection and treatment, with implications for neurodegenerative disease management in both animals and humans.

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