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 antirheumatic 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 1244 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-labled 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.
The aim of the current study was to explore the effects and possible mechanisms of tripterygium glycosides tablet (TGT) in the treatment of active ankylosing spondylitis (AS). Thirty‑six patients with active AS were given a 20 mg TGT treatment three times per day for 12 weeks, and 21 unrelated healthy controls were recruited as the control group. Efficacy measures included the Bath AS disease activity index (BASDAI), erythrocyte sedimentation rate (ESR) and C‑reactive protein (CRP) prior and subsequent to TGT treatment. Serum dickkopf homolog 1 (DKK1) and interleukin-17 (IL‑17) levels before and after TGT treatment were assessed using reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) and ELISA assay. The levels of several serum biomarkers were determined by ELISA, including receptor activator of nuclear factor κ‑B ligand (RANKL), osteoprotegerin (OPG), bone alkaline phosphatase (BAP), bone morphogenetic protein‑2 (BMP‑2), matrix metalloproteinase‑3 (MMP‑3), cross‑linked telopeptide of type II collagen (CTX‑II), vascular endothelial growth factor (VEGF), and prostaglandin E2 (PGE2). After 12 weeks of TGT treatment, the BASDAI score of the patients was significantly reduced (P<0.05), their levels of ESR and CRP were significantly reduced to a normal level (P<0.05, P<0.05), RT‑PCR and ELISA showed a significant increase in the level of DKK1 expression (P<0.05) and a significant decreased IL‑17 expression (P<0.05), there was a significant increase in the expression of OPG, BAP and BMP‑2 (P<0.01, P<0.01, P<0.01) and a significant reduction in the expression levels of RANKL, CTX‑II. MMP‑3, PGE2, and VEGF (P<0.01, P<0.01, P<0.01, P<0.05, P<0.01) compared with those of the controls. TGT is effective at improving the signs and symptoms of patients with AS through the regulation of serum biomarkers, and the mechanisms may be associated with the anti‑inflammatory effect, inhibition of new bone formation and potential bone‑protective effects.
The relationship between serum lipid variations in SS and healthy controls was investigated to identify potential predictive lipid biomarkers.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.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.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.
This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and to find potential underlying pathological mechanisms therefrom.Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as centre 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as centre 2 (external validation set), including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by ultra high performance liquid chromatography plus Q-Exactive mass spectrometry (UHPLC-Q Exactive MS). Differential metabolites were identifed by both variable importance in the projection ≥1 in orthogonal partial least-squares discriminant analysis mode and false discovery rate adjusted P ≤ 0.05. Biomarkers were found by logistic regression and receiver operating characteristic (ROC) analysis.In the discovery set, a total of 245 and 150 metabolites, respectively, were found for normal subjects vs HUA and normal subjects vs gout. The disturbed metabolites included diacylglycerol, triacylglycerol (TAG), phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, etc. We also found 116 differential metabolites for HUA vs gout. Among them, the biomarker panel of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 could differentiate well between HUA and gout. The area under the receiver operating characteristic ROC curve was 0.8288, the sensitivity was 82% and the specificity was 78%, at a 95% CI 0.747, 0.9106. In the internal validation set, the predictive accuracy of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 panel for differentiation of HUA and gout reached 74.38%, while it was 84.03% in external validation set.We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients.
Background: Ankylosing Spondylitis (AS) is a systemic, chronic and inflammatory rheumatic disease, which affected 0.2% of the population. Current diagnostic criteria relys on a composite of clinical and rediological changes, with an average of 14 years from AS symptoms onset to diagnosis. Here, we aimed to discover a panel of potential biomarkers for AS diagnosis.Methods: A cohort of 403 patients including 153 AS and 250 healthy subjects from 2 independent centers was studied. In the discovery set 1, TMT-based quantitative proteomics method was applied to identify serum proteins. In the discovery set 2, ELISA method was further performed to quantify and validate the levels of differential proteins from proteomics. An array of potential biomarkers were screened by the area under the receiver operating characteristic (ROC) curve. Finally, an accurate and reliable biomarkers panel was evaluated by the prediction accuracies in internal and external validation sets.Findings: A total of 762 proteins were identified from 15 pooled AS and 60 pooled healthy serum samples. Among them, 46 up-regulated and 59 down-regulated proteins were identified in AS compared to healthy subjects. The AS responsive proteins including CRP, SAA1, ORM2, FG-γ and THBS1 were validated successfully by ELISA using sera of 36 AS and 36 healthy subjects. The area under the ROC curve of the CRP and SAA1 combination was high, near by 0.9. The sensitivity was 0.970, specificity was 0.805 at 95% confidence interval from 0.811 to 0.977. The predictive values reached 92.00% in the internal validation set (62 AS vs 114 healthy subjects) and 97.50% in the external validation phase (40 AS vs 40 healthy subjects).Interpretation: A panel of two acute phase proteins (CRP and SAA1) as potential biomarkers could be useful for complementary diagnosis of AS.Funding: This work is financially supported by the National Natural Science Foundation of China,the Natural Science Foundation of Jiangsu province, Jiangsu Provincial Medical Youth Talent (No.QNRC2016642), the Young Elite Scientists Sponsorship Program by CAST (No. QNRC2-B04), Science and technology projects of Jiangsu Province Hospital of TCM (No. Y18010) and the topsix talent project of Jiangsu province 2016 (WSN-051).Declaration of Interest: There is no any conflict of interest with regard to our work.Ethical Approval: All procedures were approved by the medical ethics committee of the affiliated Hospital of Nanjing University of Chinese Medicine and followed the tenets of the Declaration of Helsinki. The experimental protocol was reviewed and approved by the Institutional Review Board of the affiliated Hospital of Nanjing University of Chinese Medicine (2018NL-106-02).
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 acid-binding 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.
Triptolide (TP) has anti-inflammatory and immunosuppressive effects. However, the effect of triptolide on Sjögren's syndrome (SS) is rarely reported. In this paper, we studied the effects of triptolide on non-obese diabetes mice model of SS. In this study, salivary flow rate was measured every two weeks, and autoantibodies levels in the serum were detected. Salivary gland index and spleen index were detected, pathological changes of salivary gland were detected by hematoxylin-eosin staining, inflammatory factors were detected by enzyme linked immunosorbent assay, lymphocytes were detected by flow cytometry, proliferation of T cells and B cells were detected, and related proteins were detected by Western blot. Triptolide increased salivary flow rate and salivary gland index, and decreased spleen gland index. Moreover, triptolide reduced the infiltration of lymphocytes to salivary glands, decreased the level of autoantibodies in serum, and reduced the inflammatory factors in salivary glands and IFN-γ induced salivary gland epithelial cells. Further, triptolide inhibited activator of JAK/STAT pathway and NF-κB pathway. In conclusion, triptolide could inhibit the infiltration of lymphocytes and the expression of inflammatory factors through JAK/STAT pathway and NF-κB pathway. Thus, triptolide may be used as a potential drug to treat SS.
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