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Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis.
Du, Qiuyao; Wang, Xiao; Chen, Junyu; Wang, Yiran; Liu, Wenlan; Wang, Liping; Liu, Huihui; Jiang, Lixia; Nie, Zongxiu.
Afiliación
  • Du Q; 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.
  • Wang X; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Chen J; 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.
  • Wang Y; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Liu W; 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.
  • Wang L; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Liu H; 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.
  • Jiang L; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Nie Z; Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518000, China.
Analyst ; 148(18): 4318-4330, 2023 Sep 11.
Article en En | MEDLINE | ID: mdl-37547947
There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Artritis Reumatoide / Enfermedades Autoinmunes / Síndrome de Sjögren / Lupus Eritematoso Sistémico Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Analyst Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Artritis Reumatoide / Enfermedades Autoinmunes / Síndrome de Sjögren / Lupus Eritematoso Sistémico Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Analyst Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido