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Rheumatoid arthritis (RA), afflicting over 1% of the population, is an inflammatory joint disease leading to cartilage damage and ultimately impaired joint function. Disease-modifying anti-rheumatic drugs are considered as the first-line treatment to inhibit the progression of RA, and the treatment depends on the disease status assessment. The disease activity score 28 as clinical gold standard is extensively used for RA assessment, but it has the limitations of delayed assessment and the need for specialized expertise. It is necessary to discover biomarkers that can precisely monitor disease activity, and provide optimized treatment for RA patients. A total of 1,244 participants from two independent centers were divided into five cohorts. Cohorts 1–4 constituted sera samples of moderate to high active RA, low active RA, RA in remission and healthy subjects. Cohort 5 consisted of sera of RA, osteoarthritis (OA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), primary Sjogren's syndrome (pSS) and healthy subjects. Biomarkers were found from cohorts 1–2 (screening sets), cohort 3 (discovery and external validation sets), cohort 4 (drug intervention set) and cohort 5 (biomarker-specific evaluation set). We found 68 upregulated and 74 downregulated proteins by TMT-labeled proteomics in cohort 1, and fibrinogen-like protein 1 (FGL1) had the highest area under the receiver operating characteristic curve (AUC) values in cohort 2. In cohort 3, in cross-comparison among moderate/high active RA, low active RA, RA in remission and healthy subjects, FGL1 had AUC values of approximately 0.9000 and predictive values of 90%. Additionally, FGL1 had a predictive value of 91.46% for moderate/high active RA vs. remission/low active RA and 80.77% for RA in remission vs. low active RA in cohort 4. Importantly, FGL1 levels had no significant difference in OA and AS compared with healthy persons. The concentrations in SLE and pSS were improved, but approximately 3-fold lower than that in active RA in cohort 5. In summary, FGL1 is a novel and specific biomarker that could be clinically useful for predicting progression of RA. [ABSTRACT FROM AUTHOR]

[Display omitted] • The biomarkers of rheumatoid arthritis (RA) were screened by large cohort of samples. • 36 lipid metabolites related to RA progression were identified by clinical lipidomics. • PE 16:0-18:2, TG 18:0-18:1-18:2 and PE 18:2-18:2 were identified as biomarkers of RA. • 3 biomarkers were target for lipid aldehydes produced from the lipid peroxidation. Rheumatoid arthritis (RA) is a common chronic inflammatory disorder characterized by inflammation of the synovium which causes joint damage, chronic pain, and disability. Changes in lipid profile are seen before overt RA disease manifestations suggesting that lipids contribute to the inflammation-driven metabolic changes in tumor necrosis factor. Currently, the pathogenesis of RA is unclear. Moreover, it is important to search for lipids biomarkers for early diagnosis of the disease. Herein, we compared the lipid profile of healthy individuals and RA patients to provide evidence for the diagnosis and management of persons with RA. Serum samples were collected from 511 RA patients and 396 health controls (HC). The samples were analyzed by ultra-performance liquid chromatography Q-Exactive mass spectrometry. Potential lipid biomarkers were screened and validated using the partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), random forest, binary logistic regression (BLR), receiver operating characteristic (ROC) and counter propagation artificial neural network (CP-ANN) analysis. A total of 36 differential lipid metabolites were identified, including phosphatidylethanolamine (PE), triglyceride (TG), acylcarnitine (ACar) and phosphatidylcholine (PC). Among them, PE 16:0-18:2, TG 18:0-18:1-18:2 and PE 18:2-18:2 were identified as specific biomarkers for differential diagnosis of RA. ROC analysis showed that the biomarkers had a sensitivity of 68% with a specificity of 63% in discriminating RA from HC individuals. The resulting CP-ANN can be used to identify unknown RA samples with predictive accuracy of 97% as determined through cross validation. The external validation accuracy of CP-ANN was 100% in all evaluated cases. The three lipid serum biomarkers were associated with the disease activity in RA. These lipid biomarkers showed that lipid peroxidation is disrupted in RA. [ABSTRACT FROM AUTHOR]