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

Journal: Chemical communications (Cambridge, England)
Published Date: Aug 10, 2023

Abstract

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.

Authors

  • Qiuyao Du
    School of Investigation, People's Public Security University of China, Beijing 100038, China; Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
  • Xiao Wang
    Research Centre of Basic Integrative Medicine, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
  • Junyu Chen
    Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA.
  • Caiqiao Xiong
    Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
  • Wenlan Liu
  • Jianfeng Liu
    College of Animal Science and Technology, China Agricultural University, Beijing, China.
  • Huihui Liu
    Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China. hhliu@iccas.ac.cn.
  • Lixia Jiang
    Department of Laboratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi Province 341000, China.
  • Zongxiu Nie
    Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China. hhliu@iccas.ac.cn.

Keywords

External Resources

Popular Topics
Recent Journals