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Ankylosing spondylitis (AS) is a systemic, chronic, and inflammatory rheumatic disease that affects 0.2% of the population. Current diagnostic criteria for disease activity rely on subjective Bath Ankylosing Spondylitis Disease Activity Index scores. Here, we aimed to discover a panel of serum protein biomarkers. First, tandem mass tag (TMT)-based quantitative proteomics was applied to identify differential proteins between 15 pooled active AS and 60 pooled healthy subjects. Second, cohort 1 of 328 humans, including 138 active AS and 190 healthy subjects from two independent centers, was used for biomarker discovery and validation. Finally, biomarker panels were applied to differentiate among active AS, stable AS, and healthy subjects from cohort 2, which enrolled 28 patients with stable AS, 26 with active AS, and 28 healthy subjects. From the proteomics study, a total of 762 proteins were identified and 46 proteins were up-regulated and 59 proteins were down-regulated in active AS patients compared to those in healthy persons. Among them, C-reactive protein (CRP), complement factor H-related protein 3 (CFHR3), α-1-acid glycoprotein 2 (ORM2), serum amyloid A1 (SAA1), fibrinogen γ (FG-γ), and fibrinogen β (FG-β) were the most significantly up-regulated inflammation-related proteins and S100A8, fatty acidbinding protein 5 (FABP5), and thrombospondin 1 (THBS1) were the most significantly down-regulated inflammation-related proteins. From the cohort 1 study, the best panel for the diagnosis of active AS vs healthy subjects is the combination of CRP and SAA1. The area under the receiver operating characteristic (ROC) curve was nearly 0.900, the sensitivity was 0.970%, and the specificity was 0.805% at a 95% confidence interval from 0.811 to 0.977. Using 0.387 as the cutoff value, the predictive values reached 92.00% in the internal validation set (62 with active AS vs 114 healthy subjects) and 97.50% in the external validation phase (40 with active AS vs 40 healthy subjects). From the cohort 2 study, a panel of CRP and SAA1 can differentiate well among active AS, stable AS, and healthy subjects. For active AS vs stable AS, the area under the ROC curve was 0.951, the sensitivity was 96.43%, the specificity was 88.46% at a 95% confidence interval from 0.891 to 1, and the coincidence rate was 92.30%. For stable AS vs healthy humans, the area under the ROC curve was 0.908, the sensitivity was 89.29%, the specificity was 78.57% at a 95% confidence interval from 0.836 to 0.980, and the coincidence rate was 83.93%. For active AS vs healthy subjects, the predictive value was 94.44%. The results indicated that the CRP and SAA1 combination can potentially diagnose disease status, especially for active or stable AS, which will be conducive to treatment recommendation for patients with AS.

Objective. 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. Conclusion. These findings enhance the informative capacity of biochemi-cal analyses through the identification of serum biomarkers and the analysis of metabolic pathways and contribute to an improved understanding of the pathogenesis of SS.