Targeted metabolomics combined with machine learning to identify and validate new biomarkers for early SLE diagnosis and disease activity.
Clin Immunol
; 264: 110235, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38710348
ABSTRACT
BACKGROUND:
The early diagnosis of systemic lupus erythematosus (SLE) and the assessment of disease activity progression remain a great challenge. Targeted metabolomics has great potential to identify new biomarkers of SLE.METHODS:
Serum from 44 healthy participants and 89 SLE patients were analyzed using HM400 high-throughput targeted metabolomics. Machine learning (ML) with seven learning models and trained the model several times iteratively selected the two best prediction model in a competitive way, which were independent validated by enzyme-linked immunosorbent (ELISA) with 90 SLE patients.RESULTS:
In this study, 146 differential metabolites, most of them organic acids, amino acids, and bile acids, were detected between patients with initial SLE and healthy participants, and 8 potential biomarkers were found by intersection of ML and statistics (area under the curve [AUC] > 0.95) showing a significant positive correlation with clinical indicators. In addition, we identified and validated 2 potential biomarkers for SLE classification (P < 0.05, AUC > 0.775; N-Methyl-L-glutamic acid, L-2-aminobutyric acid) showing a significant correlation with the SLE Disease Activity Index. These differential metabolites were mainly involved in metabolic pathways, amino acid biosynthesis, 2-oxocarboxylic acid metabolism and other pathways.CONCLUSION:
This study indicated that the tricarboxylic acid cycle might be associated with SLE drug therapy. We identified 8 diagnostic models biomarkers and 2 biomarkers that could be used to identify initial SLE and distinguish different activity degree, which will promote the development of new tools for the diagnosis and evaluation of SLE.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Biomarcadores
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Diagnóstico Precoce
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Metabolômica
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Aprendizado de Máquina
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Lúpus Eritematoso Sistêmico
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Clin Immunol
Assunto da revista:
ALERGIA E IMUNOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China