Urine and serum metabolic profiling combined with machine learning for autoimmune disease discrimination and classification

Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Here...

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Bibliographic Details
Published in:Chemical communications (Cambridge, England) England), 2023-08, Vol.59 (65), p.9852-9855
Main Authors: Du, Qiuyao, Wang, Xiao, Chen, Junyu, Xiong, Caiqiao, Liu, Wenlan, Liu, Jianfeng, Liu, Huihui, Jiang, Lixia, Nie, Zongxiu
Format: Article
Language:English
Subjects:
Autoimmune diseases
Classification
Machine learning
Mass spectrometry
Signs and symptoms
Urine
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Summary:Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Herein, we performed metabolomic profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in urine and serum samples. Combined with machine learning (ML), metabolomic patterns from urine achieved the discrimination and classification of ADs with high accuracy. Furthermore, metabolic disturbances among different ADs were also investigated, and provided information of etiology. These results demonstrated that urine metabolic patterns based on MALDI-MS and ML manifest substantial potential in precision medicine. MALDI-MS metabolic patterns of urine and serum combined with machine learning achieved the rapid discrimination and classification of autoimmune diseases, and the metabolic disorders caused by the diseases were also investigated.
ISSN:1359-7345
1364-548X
DOI:10.1039/d3cc01861j