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Objectives: At present, the pathogenesis of Sjögren's syndrome (SS) remains unclear. This research aimed to identify differential metabolites that contribute to SS diagnosis and discover the disturbed metabolic pathways., Methods: Recent advances in mass spectrometry have allowed the identification of hundreds of unique metabolic signatures and the exploration of altered metabolite profiles in disease. In this study, 505 candidates including healthy controls (HCs) and SS patients were recruited and the serum samples were collected. A non-targeted gas chromatography-mass spectrometry (GC-MS) serum metabolomics method was used to explore the changes in serum metabolites., Results: We found SS patients and HCs can be distinguished by 21 significant metabolites. The levels of alanine, tryptophan, glycolic acid, pelargonic acid, cis-1-2-dihydro-1-2-naphthalenediol, diglycerol, capric acid, turanose, behenic acid, dehydroabietic acid, stearic acid, linoleic acid, heptadecanoic acid, valine, and lactic acid were increased in serum samples from SS patients, whereas levels of catechol, anabasine, 3-6-anhydro-D-galactose, beta-gentiobiose, 2-ketoisocaproic acid and ethanolamine were decreased. The significantly changed pathways included the following: Linoleic acid metabolism; unsaturated fatty acid biosynthesis; aminoacyl-tRNA biosynthesis; valine, leucine, and isoleucine biosynthesis; glycerolipid metabolism; selenocompound metabolism; galactose metabolism; alanine, aspartate and glutamate metabolism; glyoxylate and dicarboxylate metabolism; glycerophospholipid metabolism; and valine, leucine and isoleucine degradation., Conclusions: These findings enhance the informative capacity of biochemical analyses through the identification of serum biomarkers and the analysis of metabolic pathways and contribute to an improved understanding of the pathogenesis of SS.

Objective: The relationship between serum lipid variations in SS and healthy controls was investigated to identify potential predictive lipid biomarkers., Methods: Serum samples from 230 SS patients and 240 healthy controls were collected. The samples were analysed by ultrahigh-performance liquid chromatography coupled with Q Exactive™ spectrometry. Potential lipid biomarkers were screened through orthogonal projection to latent structures discriminant analysis and further evaluated by receiver operating characteristic analysis., Results: A panel of three metabolites [phosphatidylcholine (18:0/22:5), triglyceride (16:0/18:0/18:1) and acylcarnitine (12:0)] was identified as a specific biomarker of SS. The receiver operating characteristic analysis showed that the panel had a sensitivity of 84.3% with a specificity of 74.8% in discriminating patients with SS from healthy controls., Conclusion: Our approach successfully identified serum biomarkers associated with SS patients. The potential lipid biomarkers indicated that SS metabolic disturbance might be associated with oxidized lipids, fatty acid oxidation and energy metabolism., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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]