Objective Gout is the most common inflammatory arthritis and the worldwide incidence is increasing. By revealing the metabolic alterations in serum and urine of gout patients, the first aim of our study was to discover novel molecular biomarkers allowing for early diagnosis. We also aimed to investigate the underlying pathogenic pathways. Methods Serum and urine samples from gout patients (n = 30) and age-matched healthy controls (n = 30) were analysed by ultra-performance liquid chromatography–mass spectrometry (UPLC-MS) to screen the differential metabolites and construct a diagnostic model. Next, the model was verified and optimized in the second validation cohort (n = 100). The pathways were illustrated to understand the underlying pathogenesis of gout. Results In general, serum metabolomics demonstrated a clearer distinction than urine metabolomics. In the discovery cohort, 40 differential serum metabolites were identified that could distinguish gout patients from healthy controls. Among them, eight serum metabolites were verified in the validation cohort. Through regression analysis, the final model consisted of three serum metabolites—pyroglutamic acid, 2-methylbutyryl carnitine and Phe-Phe—that presented optimal diagnostic power. The three proposed metabolites produced an area under the curve of 0.956 (95% CI 0.911, 1.000). Additionally, the proposed metabolic pathways were primarily involved in purine metabolism, branched-chain amino acids (BCAAs) metabolism, the tricarboxylic acid cycle, synthesis and degradation of ketone bodies, bile secretion and arachidonic acid metabolism. Conclusion The metabolomics signatures could serve as an efficient tool for early diagnosis and provide novel insights into the pathogenesis of gout.
Background: Microbial involvement in ankylosing spondylitis (AS) has been suggested; however, the relationship between gut microbiome and the disease phenotypes of AS remains to be established. This study was to characterize and investigate differences in the gut microbiome between AS patients and healthy controls (HCs), and to determine whether the gut microbiome profile associated with the disease phenotypes.Methods: 16S rRNA gene V4 region sequencing was performed on fecal DNA isolated from stool samples collected from 41 patients with AS [20 axial AS (axAS) and 21 peripheral AS (pAS)] and 19 HCs. QIIME based pipeline was used to process the raw sequence data. Alpha and beta diversities were assessed using QIIME, and comparisons of gut microbiome profile were performed using linear discriminant analysis (LDA) effect size (LEfSe) to examine differences between groups and subgroups. A gut microbiota-based model for predictive diagnosis of AS was constructed using random forest algorithm and its predictive value was assessed by receiver-operating characteristic analyses.Results: Our results showed that fecal microbial communities in patients with AS differ significantly from those in HCs, driven by a higher abundance of 7 genera (Prevotella_9, Dialister, Comamonas, Collinsella, Streptococcus, Alloprevotella and Prevotella_2) and a lower abundance of 4 genera (Eubacterium_ruminantium_ group, Ruminococcus_gnavus_group, Lachnospira and Bacteroides). In addition, pAS patients were more enriched in Comamonas, Streptococcus and Collinsella, while axAS patients were more enriched in Prevotella_2. An 8 genera-based model showed high accuracy for distinguishing AS patients from HCs with an area under the curve (AUC) up to 0.950. Conclusions: Our results revealed specific alterations in the gut microbiome in patients with different phenotypes of AS, and the classification model based on gut microbial features might provide a new direction for future clinical diagnosis. Lastly, discovery of the associated microbes of AS in the gut microbiome may help us to seek more treatments for this disease.
Ankylosing spondylitis (AS) is a type of spondyloarthropathies, the diagnosis of which is often delayed. The lack of early diagnosis tools often delays the institution of appropriate therapy. This study aimed to investigate the systemic metabolic shifts associated with AS and TNF inhibitors treatment. Additionally, we aimed to define reliable serum biomarkers for the diagnosis. We employed an untargeted technique, ultra-performance liquid chromatography-mass spectroscopy (LC-MS), to analyze the serum metabolome of 32 AS individuals before and after 24-week TNF inhibitors treatment, as well as 40 health controls (HCs). Multivariate and univariate statistical analyses were used to profile the differential metabolites associated with AS and TNF inhibitors. A diagnostic panel was established with the least absolute shrinkage and selection operator (LASSO). The pathway analysis was also conducted. A total of 55 significantly differential metabolites were detected. We generated a diagnostic panel comprising five metabolites (L-glutamate, arachidonic acid, L-phenylalanine, PC (18:1(9Z)/18:1(9Z)), 1-palmitoylglycerol), capable of distinguishing HCs from AS with a high AUC of 0.998, (95%CI: 0.992–1.000). TNF inhibitors treatment could restore the equilibrium of 21 metabolites. The most involved pathways in AS were amino acid biosynthesis, glycolysis, glutaminolysis, fatty acids biosynthesis and choline metabolism. This study characterized the serum metabolomics signatures of AS and TNF inhibitor therapy. We developed a five-metabolites-based panel serving as a diagnostic tool to separate patients from HCs. This serum metabolomics study yielded new knowledge about the AS pathogenesis and the systemic effects of TNF inhibitors.
Growing evidence suggests that the gut microbiota is involved in the initiation and progression of ankylosing spondylitis (AS). In this study, we aimed to explore the gut microbiome alterations during adalimumab therapy and verify microbiome biomarkers predicting treatment response. By evaluating the gut microbial features of 30 AS patients before and after adalimumab therapy for 6 months and 24 healthy controls, we confirmed that the microbiome was restored remarkably after 6 months of adalimumab therapy in AS patients. We then compared the baseline gut microbiome of 22 adalimumab responders with 8 non-responders, a higher abundance of Comamonas was revealed in the latter, although no statistical difference was found after adjusting for the false discovery rate. These results suggested that adalimumab therapy restored the gut microbiome in AS patients and indicated the utility of gut microbiome to be potential biomarkers for therapeutic evaluation. These findings provided an insight into the development of predictive tools and the establishment of precise medical interventions for clinical practice.
Objective: To explore proteins associated with ankylosing spondylitis (AS) and to investigate potential proteins that may predict treatment response of adalimumab (ADA) in AS patients. Methods: In the discovery cohort, 39 AS patients and 20 healthy controls (HCs) were included, and 16 AS patients received ADA treatment for 24 weeks after included. In the validation cohort, 43 AS patients and 39 HCs were enrolled, and all 43 patients received ADA treatment after enrollment. Blood samples and clinical information were collected from two cohorts at baseline from all participants and week 24 from patients received ADA treatment. A human antibody array containing 1,000 proteins was used in the discovery phase, and Elisa kits were used for protein validation. Results: Compared with HCs, we identified 53 differentially expressed proteins (DEPs) in AS patients. Bioinformatics analysis revealed they were mostly enriched in coagulation function-related pathways, acute response signaling, and LXR/RXR activation. Bone metabolism pathways were also associated. Comparison between samples of pre-and post-ADA treatment revealed 42 DEPs. They were mostly associated with bone metabolism and inflammation response pathways. Significant enrichment was also found in LXR/RXR activation but not the coagulation functionrelated pathways. Upstream regulator analysis suggested that most regulators also significantly functioned under usage of ADA. Precisely, seven proteins were abnormally expressed in AS and restored after ADA treatment. Retinol-binding protein 4 (RBP4), one of the seven proteins, was validated that its baseline levels were inversely correlated with improvements in Ankylosing Spondylitis Disease Activity Score-C-reactive protein (ASDAS-CRP). Likewise, percentage changes in RBP4 levels were inversely correlated with changes in ASDAS-CRP score. Conclusion: A dysregulated serum protein profile existed in AS. ADA exerted a considerable but not entire alteration toward the dysregulation. RBP4 could be a biomarker for predicting and monitoring ADA treatment response.
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