Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients

• Published in: Journal of translational medicine Vol. 19; no. 1; pp. 500 - 10
• Main Authors: Luan, Hemi, Gu, Wanjian, Li, Hua, Wang, Zi, Lu, Lu, Ke, Mengying, Lu, Jiawei, Chen, Wenjun, Lan, Zhangzhang, Xiao, Yanlin, Xu, Jinyue, Zhang, Yi, Cai, Zongwei, Liu, Shijia, Zhang, Wenyong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.12.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Amino acids; Arthritis, Rheumatoid; Biological markers; Biomarkers; Biomedical and Life Sciences; Biomedicine; Blood lipids; Chromatography; Chronic illnesses; Development and progression; Diagnosis; Discriminant analysis; Disease; Disease Biomarkers; Energy metabolism; Health aspects; Histidine; Humans; Identification and classification; Learning algorithms; Lipid metabolism; Lipidomic; Lipidomics; Lipids; Medicine/Public Health; Metabolism; Metabolites; Metabolomic; Metabolomics; Phenylalanine; Phosphatidic acid; Phosphatidylethanolamine; Physiological aspects; Prediction models; Rheumatoid arthritis; Seronegative; Serum; China; United States > US; Metabolomic; Rheumatoid arthritis; Seronegative; Lipidomic
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-021-03169-7

Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.