Metabolomic approach to the exploration of biomarkers associated with disease activity in rheumatoid arthritis

Polymyalgia rheumatica shows metabolomic alterations that are further altered by glucocorticoid treatment: Identification of metabolic correlates of fatigue

OBJECTIVE

In polymyalgia rheumatica (PMR), glucocorticoids (GCs) relieve pain and stiffness, but fatigue may persist. We aimed to explore the effect of disease, GCs and PMR symptoms in the metabolite signatures of peripheral blood from patients with PMR or the related disease, giant cell arteritis (GCA).

METHODS

Nuclear magnetic resonance spectroscopy was performed on serum from 40 patients with untreated PMR, 84 with new-onset confirmed GCA, and 53 with suspected GCA who later were clinically confirmed non-GCA, and 39 age-matched controls. Further samples from PMR patients were taken one and six months into glucocorticoid therapy to explore relationship of metabolites to persistent fatigue. 100 metabolites were identified using Chenomx and statistical analysis performed in SIMCA-P to examine the relationship between metabolic profiles and, disease, GC treatment or symptoms.

RESULTS

The metabolite signature of patients with PMR and GCA differed from that of age-matched non-inflammatory controls (R2 > 0.7). There was a smaller separation between patients with clinically confirmed GCA and those with suspected GCA who later were clinically confirmed non-GCA (R2 = 0.135). In PMR, metabolite signatures were further altered with glucocorticoid treatment (R2 = 0.42) but did not return to that seen in controls. Metabolites correlated with CRP, pain, stiffness, and fatigue (R2 ≥ 0.39). CRP, pain, and stiffness declined with treatment and were associated with 3-hydroxybutyrate and acetoacetate, but fatigue did not. Metabolites differentiated patients with high and low fatigue both before and after treatment (R2 > 0.9). Low serum glutamine was predictive of high fatigue at both time points (0.79-fold change).

CONCLUSION

PMR and GCA alter the metabolite signature. In PMR, this is further altered by glucocorticoid therapy. Treatment-induced metabolite changes were linked to measures of inflammation (CRP, pain and stiffness), but not to fatigue. Furthermore, metabolite signatures distinguished patients with high or low fatigue.

Mechanism of lysine oxidase-like 1 promoting synovial inflammation mediating rheumatoid arthritis development

Rheumatoid arthritis (RA) is a chronic inflammatory joint disease that causes great distress to patients and society. Early diagnosis is the key to the successful treatment of RA. The basement membrane, one of the oldest tissue structures, is localized under the epithelium. Its complex composition and rich biological functions have made it a focus of research in recent years, while basement membrane-associated genetic variants are involved in most human disease processes. The aim of this study is to find new diagnostic biomarkers for RA and explore their role and possible mechanism in rheumatoid arthritis. The GSE12021, GSE55235 and GSE55457 datasets were downloaded from the GEO database. Their fraction associated with basement membrane genes was analyzed and differentially expressed genes between the disease and normal groups were explored. We identified two basement membrane-associated genes, lysine oxidase-like 1 (LOXL1) and discoid peptide receptor 2 (DDR2). Focusing on the more interesting LOXL1, we found that LOXL1 expression was significantly elevated in the synovium of patients with rheumatoid arthritis, and LOXL1 mRNA and protein levels were elevated in tumor necrosis factor α-stimulated human synovial sarcoma cells (SW982). And LOXL1 knockdown inhibited tumor necrosis factor α-induced inhibition in SW982 cells expression of inducible nitric oxide synthase (INOS), cyclooxygenase-2 (COX2), and interleukin-6 (IL-6). Interestingly, knockdown of LOXL1 inhibited the phosphorylation of PI3K and AKT. In summary, LOXL1 may become a novel diagnostic gene for RA, and knockdown of LoxL1 may inhibit synovial inflammation by affecting PI3K/AKT pathway.

Association Between Human Metabolomics and Rheumatoid Arthritis: A Systematic Review and Meta-analysis

OBJECTIVE

The underdiagnosis and inadequate treatment of rheumatoid arthritis (RA) can be attributed to the various clinical manifestations presented by patients. To address this concern, we conducted an extensive review and meta-analysis, focusing on RA-related metabolites.

METHODS

A comprehensive literature search was conducted in PubMed, the Cochrane Library, Web of Science, and Embase to identify relevant studies published up to October 5, 2022. The quality of the included articles was evaluated and, subsequently, a meta-analysis was conducted using Review Manager software to analyze the association between metabolites and RA.

RESULTS

Forty nine studies met the inclusion criteria for the systematic review, and six of these studies were meta-analyzed to evaluate the association between 28 reproducible metabolites and RA. The results indicated that, compared to controls, the levels of histidine (RoM = 0.83, 95% CI = 0.79-0.88, I2 = 0%), asparagine (RoM = 0.83, 95% CI = 0.75-0.91, I2 = 0%), methionine (RoM = 0.82, 95% CI = 0.69-0.98, I2 = 85%), and glycine (RoM = 0.81, 95% CI = 0.67-0.97, I2 = 68%) were significantly lower in RA patients, while hypoxanthine levels (RoM = 1.14, 95% CI = 1.09-1.19, I2 = 0%) were significantly higher.

CONCLUSION

This study identified histidine, methionine, asparagine, hypoxanthine, and glycine as significantly correlated with RA, thus offering the potential for the advancement of biomarker discovery and the elucidation of disease mechanisms in RA.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Urinary metabolomic biomarker candidates for skeletal muscle wasting in patients with rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is an autoimmune disease that affects the joints, leading to chronic synovial inflammation and local tissue destruction. Extra-articular manifestations may also occur, such as changes in body composition. Skeletal muscle wasting is often observed in patients with RA, but methods for assessing loss of muscle mass are expensive and not widely available. Metabolomic analysis has shown great potential for identifying changes in the metabolite profile of patients with autoimmune diseases. In this setting, urine metabolomic profiling in patients with RA may be a useful tool to identify skeletal muscle wasting.

METHODS

Patients aged 40-70 years with RA have been recruited according to the 2010 ACR/EULAR classification criteria. Further, the Disease Activity Score in 28 joints using the C-reactive protein level (DAS28-CRP) determined the disease activity. The muscle mass was measured by Dual X-ray absorptiometry (DXA) to generate the appendicular lean mass index (ALMI) by summing the lean mass measurements for both arms and legs and dividing them by height squared (kg/height² ). Finally, urine metabolomic analysis by ¹ H nuclear magnetic resonance (¹ H-NMR) spectroscopy was performed and the metabolomics data set analysed using the BAYESIL and MetaboAnalyst software packages. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the ¹ H-NMR data, followed by Spearman's correlation analysis. The combined receiver operating characteristic curve (ROC) was calculated, as well as the logistic regression analyses to establish a diagnostic model. The significance level at P < 0.05 was set for all analyses.

RESULTS

The total set of subjects investigated included 90 patients with RA. Most patients were women (86.7%), with a mean age of 56.5 ± 7.3 years old and a median DAS28-CRP of 3.0 (IQR 1.0-3.0). Fifteen metabolites were identified in the urine samples with high variable importance in projection (VIP scores) by MetaboAnalyst. Of these, dimethylglycine (r = 0.205; P = 0.053), oxoisovalerate (r = -0.203; P = 0.055), and isobutyric acid (r = -0.249; P = 0.018) were significantly correlated with ALMI. Based on the low muscle mass (ALMI ≤6.0 kg/m² for women and ≤8.1 kg/m² for men) a diagnostic model have been established with dimethylglycine (area under the curve [AUC] = 0.65), oxoisovalerate (AUC = 0.49), and isobutyric acid (AUC = 0.83) with significant sensitivity and specificity.

CONCLUSIONS

Isobutyric acid, oxoisovalerate, and dimethylglycine from urine samples were associated with low skeletal muscle mass in patients with RA. These findings suggest that this group of metabolites may be further tested as biomarkers for identification of skeletal muscle wasting.

Multi-omics profiling reveals potential alterations in rheumatoid arthritis with different disease activity levels

BACKGROUND

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune inflammatory disease, the pathogenesis of which is not clear. Clinical remission, or decreased disease activity, is the aim of treatment for RA. However, our understanding of disease activity is inadequate, and clinical remission rates for RA are generally poor. In this study, we used multi-omics profiling to study potential alterations in rheumatoid arthritis with different disease activity levels.

METHODS

Fecal and plasma samples from 131 rheumatoid arthritis (RA) patients and 50 healthy subjects were collected for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing, and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The PBMCS were also collected for RNA sequencing and whole exome sequencing (WES). The disease groups, based on 28 joints and ESR (DAS28), were divided into DAS28L, DAS28M, and DAS28H groups. Three random forest models were constructed and verified with an external validation cohort of 93 subjects.

RESULTS

Our findings revealed significant alterations in plasma metabolites and gut microbiota in RA patients with different disease activities. Moreover, plasma metabolites, especially lipid metabolites, demonstrated a significant correlation with the DAS28 score and also associations with gut bacteria and fungi. KEGG pathway enrichment analysis of plasma metabolites and RNA sequencing data demonstrated alterations in the lipid metabolic pathway in RA progression. Whole exome sequencing (WES) results have shown that non-synonymous single nucleotide variants (nsSNV) of the HLA-DRB1 and HLA-DRB5 gene locus were associated with the disease activity of RA. Furthermore, we developed a disease classifier based on plasma metabolites and gut microbiota that effectively discriminated RA patients with different disease activity in both the discovery cohort and the external validation cohort.

CONCLUSION

Overall, our multi-omics analysis confirmed that RA patients with different disease activity were altered in plasma metabolites, gut microbiota composition, transcript levels, and DNA. Our study identified the relationship between gut microbiota and plasma metabolites and RA disease activity, which may provide a novel therapeutic direction for improving the clinical remission rate of RA.

Dipeptidyl peptidase 4 as a potential serum biomarker for disease activity and treatment response in rheumatoid arthritis

BACKGROUND

The treatment of rheumatoid arthritis (RA) related to the disease activity. However, the lack of highly sensitive and simplified markers limits the evaluation of disease activity. We sought to explore potential biomarkers associated with disease activity and treatment response in RA.

METHODS

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomic analysis was performed to determine the differentially expressed proteins (DEPs) in serum collected from RA patients with moderate or high disease activity (determined by DAS28) before and after 24 weeks of treatment. Bioinformatic analysis were performed for DEPs and hub proteins. In the validation cohort, 15 RA patients were enrolled. Key proteins were validated by enzyme-linked immunosorbent assay (Elisa), correlation analysis and ROC curve.

RESULTS

We identified 77 DEPs. The DEPs enriched in humoral immune response, blood microparticle, and serine-type peptidase activity. KEGG enrichment analysis displayed that the DEPs were significantly enriched in cholesterol metabolism and complement and coagulation cascades. Activated CD4 + T cell, T follicular helper cell, natural killer cell, and plasmacytoid dendritic cell significantly increased after treatment. Fifteen hub proteins were screened out. Among them, dipeptidyl peptidase 4 (DPP4) was the most significant protein associated with clinical indicators and immune cells. Serum concentration of DPP4 was testified to significantly increase after treatment and inversely correlate with disease activity indicators (ESR, CRP, DAS28-ESR, DAS28-CRP, CDAI, SDAI). Significant reduction was found in the serum CXC chemokine ligand10 (CXC10) and CXC chemokine receptor 3 (CXCR3) after treatment.

CONCLUSIONS

Overall, our results suggest that serum DPP4 might be a potential biomarker for disease activity assessment and treatment response of RA.

Development and assessment of a predictive model for early diagnosis of rheumatoid arthritis in southwest China: A new nomogram

Rheumatoid arthritis (RA) is a disease complicated with inflammatory synovitis, which seriously affects the life quality of patients. Early diagnosis is important for prognosis of RA. Here, we aimed to develop and assess a model for early diagnosis of RA in southwest China. A nomogram including 44 patients with an early diagnosis of RA was developed. Variables were filtered by least absolute contraction selection operator and multiple logistic regression. The efficiency and clinical application range were evaluated. This nomogram showed that rheumatoid factor, erythrocyte sedimentation rate, RA33, facet joint and knee joint had high positive predictive value for RA. The area under curve was 0.920 [95% confidence interval (CI): 0.865-0.975]. In the validation model, area under curve was 0.942 (95% CI: 0.893-0.991). Calibration and decision curve suggested that this nomogram was helpful within the threshold probability range of 0.02 to 1.00. Using this nomogram will help clinicians in the early diagnosis of RA. Laboratory indicators such as rheumatoid factor, erythrocyte sedimentation rate, RA33, and clinical symptoms such as morning stiffness, facet joint and knee joint are very important, which deserves the attention of clinicians.

Immunometabolomics provides a new perspective for studying systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic multi-organ autoimmune disease characterized by clinical heterogeneity, unpredictable progression, and flare ups. Due to the heterogeneous nature of lupus, it has been challenging to identify sensitive and specific biomarkers for its diagnosis and monitoring. Despite the fact that the mechanism of SLE remains unknown, impressive progress has been made over the last decade towards understanding how different immune cells contribute to its pathogenesis. Research suggests that cellular metabolic programs could affect the immune response by regulating the activation, proliferation, and differentiation of innate and adaptive immune cells. Many studies have shown that the dysregulation of the immune system is associated with changes to metabolite profiles. The study of metabolite profiling may provide a means for mechanism exploration and novel biomarker discovery for disease diagnostic, classification, and monitoring. Here we review the latest advancements in understanding the role of immunometabolism in SLE, as well as the systemic metabolite profiling of this disease along with possible clinical application.

Assessment of vascular endothelial growth factor a serum level in pediatric hemophilic arthropathy

Background

Children with hemophilia have serious, recurrent joint bleeding that causes disabilities. Regular factor VIII concentrate replacements have not yet completely stopped intra-articular subclinical hemorrhages and permanent joint damage. The prevention of joint damage requires the early detection and management of this hemorrhage. Neoangiogenesis is crucial in the development of synovitis after recurrent hemophilic joint bleeding. This study assessed the level of serum Vascular endothelial growth factor A (VEGF-A) as a vascular biomarker in children with hemophilia A to determine its possible role as a diagnostic biomarker for hemophilic arthropathy.

Result

A case–control study with 30 male children who had hemophilia A and 30 healthy controls was conducted. Patients had significantly higher serum VEGF-A levels than the control group (specificity was 70.0% and sensitivity was 83.3%). Hemophilia severity and Hemophilia Joint Health Score had a significant positive correlation with VEGF-A.

Conclusion

Children with hemophilia A had significantly higher levels of VEGF-A in their serum. Additionally, VEGF-A had a significant positive correlation with Hemophilia Joint Health Score as well as the severity of the disease. In children with hemophilia A, VEGF-A can be used as a marker for early hemophilic arthropathy detection.

Metabolic Profiling of Rheumatoid Arthritis Neutrophils Reveals Altered Energy Metabolism That Is Not Affected by JAK Inhibition

Neutrophils play a key role in the pathophysiology of rheumatoid arthritis (RA) where release of ROS and proteases directly causes damage to joints and tissues. Neutrophil function can be modulated by Janus Kinase (JAK) inhibitor drugs, including tofacitinib and baricitinib, which are clinically effective treatments for RA. However, clinical trials have reported increased infection rates and transient neutropenia during therapy. The subtle differences in the mode of action, efficacy and safety of JAK inhibitors have been the primary research topic of many clinical trials and systematic reviews, to provide a more precise and targeted treatment to patients. The aim of this study was to determine both the differences in the metabolome of neutrophils from healthy controls and people with RA, and the effect of different JAK inhibitors on the metabolome of healthy and RA neutrophils. Isolated neutrophils from healthy controls (HC) (n = 6) and people with RA (n = 7) were incubated with baricitinib, tofacitinib or a pan-JAK inhibitor (all 200 ng/mL) for 2 h. Metabolites were extracted, and 1H nuclear magnetic resonance (NMR) was applied to study the metabolic changes. Multivariate analyses and machine learning models showed a divergent metabolic pattern in RA neutrophils compared to HC at 0 h (F1 score = 86.7%) driven by energy metabolites (ATP, ADP, GTP and glucose). No difference was observed in the neutrophil metabolome when treated with JAK inhibitors. However, JAK inhibitors significantly inhibited ROS production and baricitinib decreased NET production (p < 0.05). Bacterial killing was not impaired by JAK inhibitors, indicating that the effect of JAK inhibitors on neutrophils can inhibit joint damage in RA without impairing host defence. This study highlights altered energy metabolism in RA neutrophils which may explain the cause of their dysregulation in inflammatory disease.

Multi-omics profiling of collagen-induced arthritis mouse model reveals early metabolic dysregulation via SIRT1 axis

Rheumatoid arthritis (RA) is characterized by joint infiltration of immune cells and synovial inflammation which leads to progressive disability. Current treatments improve the disease outcome, but the unmet medical need is still high. New discoveries over the last decade have revealed the major impact of cellular metabolism on immune cell functions. So far, a comprehensive understanding of metabolic changes during disease development, especially in the diseased microenvironment, is still limited. Therefore, we studied the longitudinal metabolic changes during the development of murine arthritis by integrating metabolomics and transcriptomics data. We identified an early change in macrophage pathways which was accompanied by oxidative stress, a drop in NAD+ level and induction of glucose transporters. We discovered inhibition of SIRT1, a NAD-dependent histone deacetylase and confirmed its dysregulation in human macrophages and synovial tissues of RA patients. Mining this database should enable the discovery of novel metabolic targets and therapy opportunities in RA.

Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity

Immune-mediated inflammatory diseases (IMIDs), such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis (Ps), represent autoinflammatory and autoimmune disorders, as well as conditions that have an overlap of both categories. Understanding the underlying pathogeneses, making diagnoses, and choosing individualized treatments remain challenging due to heterogeneous disease phenotypes and the lack of reliable biomarkers that drive the treatment choice. In this review, we provide an overview of the low-molecular-weight metabolites that might be employed as biomarkers for various applications, e.g., early diagnosis, disease activity monitoring, and treatment-response prediction, in RA, PsA, and Ps. The literature was evaluated, and putative biomarkers in different matrices were identified, categorized, and summarized. While some of these candidate biomarkers appeared to be disease-specific, others were shared across multiple IMIDs, indicating common underlying disease mechanisms. However, there is still a long way to go for their application in a routine clinical setting. We propose that studies integrating omics analyses of large patient cohorts from different IMIDs should be performed to further elucidate their pathomechanisms and treatment options. This could lead to the identification and validation of biomarkers that might be applied in the context of precision medicine to improve the clinical outcomes of these IMID patients.

Lowering of lysophosphatidylcholines in ovariectomized rats by Curcuma comosa

Decline of ovarian function in menopausal women increases metabolic disease risk. Curcuma comosa extract and its major compound, (3R)-1,7-diphenyl-(4E,6E)-4,6-heptadien-3-ol (DPHD), improved estrogen-deficient ovariectomized (OVX) rat metabolic disturbances. However, information on their effects on metabolites is limited. Here, we investigated the impacts of C. comosa ethanol extract and DPHD on 12-week-old OVX rat metabolic disturbances, emphasizing the less hydrophobic metabolites. Metabolomics analysis of OVX rat serum showed a marked increase compared to sham-operated rat (SHAM) in levels of lysophosphatidylcholines (lysoPCs), particularly lysoPC (18:0) and lysoPC (16:0), and of arachidonic acid (AA), metabolites associated with inflammation. OVX rat elevated lysoPCs and AA levels reverted to SHAM levels following treatments with C. comosa ethanol extract and DPHD. Overall, our studies demonstrate the effect of C. comosa extract in ameliorating the metabolic disturbances caused by ovariectomy, and the elevated levels of bioactive lipid metabolites, lysoPCs and AA, may serve as potential biomarkers of menopausal metabolic disturbances.

A Review of Metabolomic Profiling in Rheumatoid Arthritis: Bringing New Insights in Disease Pathogenesis, Treatment and Comorbidities

Metabolomic analysis provides a wealth of information that can be predictive of distinctive phenotypes of pathogenic processes and has been applied to better understand disease development. Rheumatoid arthritis (RA) is an autoimmune disease with the establishment of chronic synovial inflammation that affects joints and peripheral tissues such as skeletal muscle and bone. There is a lack of useful disease biomarkers to track disease activity, drug response and follow-up in RA. In this review, we describe potential metabolic biomarkers that might be helpful in the study of RA pathogenesis, drug response and risk of comorbidities. TMAO (choline and trimethylamine oxide) and TCA (tricarboxylic acid) cycle products have been suggested to modulate metabolic profiles during the early stages of RA and are present systemically, which is a relevant characteristic for biomarkers. Moreover, the analysis of lipids such as cholesterol, FFAs and PUFAs may provide important information before disease onset to predict disease activity and treatment response. Regarding therapeutics, TNF inhibitors may increase the levels of tryptophan, valine, lysine, creatinine and alanine, whereas JAK/STAT inhibitors may modulate exclusively fatty acids. These observations indicate that different disease modifying antirheumatic drugs have specific metabolic profiles and can reveal differences between responders and non-responders. In terms of comorbidities, physical impairment represented by higher fatigue scores and muscle wasting has been associated with an increase in urea cycle, FFAs, tocopherols and BCAAs. In conclusion, synovial fluid, blood and urine samples from RA patients seem to provide critical information about the metabolic profile related to drug response, disease activity and comorbidities.

Serine, N-acetylaspartate differentiate adolescents with juvenile idiopathic arthritis compared with healthy controls: a metabolomics cross-sectional study

BACKGROUND

In comparison with the general population, adolescents with juvenile idiopathic arthritis (JIA) are at higher risk for morbidity and mortality. However, limited evidence is available about this condition's underlying metabolic profile in adolescents with JIA relative to healthy controls. In this untargeted, cross-sectional metabolomics study, we explore the plasma metabolites in this population.

METHODS

A sample of 20 adolescents with JIA and 20 controls aged 13-17 years were recruited to complete surveys, provide medical histories and biospecimens, and undergo assessments. Fasting morning plasma samples were processed with liquid chromatography-mass spectrometry. Data were centered, scaled, and analyzed using generalized linear models accounting for age, sex, and medications (p-values adjusted for multiple comparisons using the Holm method). Spearman's correlations were used to evaluate relationships among metabolites, time since diagnosis, and disease severity.

RESULTS

Of 72 metabolites identified in the samples, 55 were common to both groups. After adjustments, 6 metabolites remained significantly different between groups. Alpha-glucose, alpha-ketoglutarate, serine, and N-acetylaspartate were significantly lower in the JIA group than in controls; glycine and cystine were higher. Seven additional metabolites were detected only in the JIA group; 10 additional metabolites were detected only in the control group. Metabolites were unrelated to disease severity or time since diagnosis.

CONCLUSIONS

The metabolic signature of adolescents with JIA relative to controls reflects a disruption in oxidative stress; neurological health; and amino acid, caffeine, and energy metabolism pathways. Serine and N-acetylaspartate were promising potential biomarkers, and their metabolic pathways are linked to both JIA and cardiovascular disease risk. The pathways may be a source of new diagnostic, treatment, or prevention options. This study's findings contribute new knowledge for systems biology and precision health approaches to JIA research. Further research is warranted to confirm these findings in a larger sample.

Metabolites and metabolic pathways associated with rheumatoid arthritis and systemic lupus erythematosus

Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are chronic autoimmune diseases that result from the combined influence of genetic and environmental factors that promotes the loss of tolerance to cellular components. The complexity of these diseases converts them into a major challenge at the diagnostic and treatment level. Therefore, it is convenient to implement the use of tools for a better understanding of the physiopathology of these diseases to propose reliable biomarkers. The "omics" disciplines like metabolomics and lipidomics allow to study RA and SLE in a higher degree of detail since they evaluate the metabolites and metabolic pathways involved in disease pathogenesis. This review has compiled the information of metabolomics and lipidomics studies where samples obtained from RA and SLE patients were evaluated to find the metabolites and pathways differences between patients and healthy controls. In both diseases, there is a decrease in several amino acids and oxidative stress-related metabolites like glutathione. These findings may be useful for functional metabolomics studies aiming to reprogram the metabolism in a disease setting to recover normal immune cell homeostasis and function.

Serum metabolomic and lipidomic profiling identifies diagnostic biomarkers for seropositive and seronegative rheumatoid arthritis patients

BACKGROUND

Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum.

METHODS

We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals.

RESULTS

Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity.

CONCLUSIONS

A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

Current applications of capillary electrophoresis-mass spectrometry for the analysis of biologically important analytes in urine (2017 to mid-2021): A review

Capillary electrophoresis coupled online with mass detection is a modern tool for analyzing wide ranges of compounds in complex samples, including urine. Capillary electrophoresis with mass spectrometry allows the separation and identification of various analytes spanning from small ions to high molecular weight protein complexes. Similarly to the much more common liquid chromatography‐mass spectrometry techniques, the capillary electrophoresis separation reduces the complexity of the mixture of analytes entering the mass spectrometer resulting in reduced ion suppression and a more straightforward interpretation of the mass spectrometry data. This review summarizes capillary electrophoresis with mass spectrometry studies published between the years 2017 and 2021, aiming at the determination of various compounds excreted in urine. The properties of the urine, including its diagnostical and analytical features and chemical composition, are also discussed including general protocols for the urine sample preparation. The mechanism of the electrophoretic separation and the instrumentation for capillary electrophoresis with mass spectrometry coupling is also included. This review shows the potential of the capillary electrophoresis with mass spectrometry technique for the analyses of different kinds of analytes in a complex biological matrix. The discussed applications are divided into two main groups (capillary electrophoresis with mass spectrometry for the determination of drugs and drugs of abuse in urine and capillary electrophoresis with mass spectrometry for the studies of urinary metabolome).

Metabolomic Biomarker Candidates for Skeletal Muscle Loss in the Collagen-Induced Arthritis (CIA) Model

There is no consensus for diagnosis or treatment of RA muscle loss. We aimed to investigate metabolites in arthritic mice urine as biomarkers of muscle loss. DBA1/J mice comprised collagen-induced arthritis (CIA) and control (CO) groups. Urine samples were collected at 0, 18, 35, 45, 55, and 65 days of disease and subjected to nuclear magnetic resonance spectroscopy. Metabolites were identified using Chenomx and Birmingham Metabolite libraries. The statistical model used principal component analysis, partial least-squares discriminant analysis, and partial least-squares regression analysis. Linear regression and Fisher's exact test via the MetaboAnalyst website were performed (VIP-score). Nearly 100 identified metabolites had CIA vs. CO and disease time-dependent differences (p < 0.05). Twenty-eight metabolites were muscle-associated: carnosine (VIPs 2.8 × 10²) and succinyl acetone (VIPs 1.0 × 10) showed high importance in CIA vs. CO models at day 65; CIA pair analysis showed histidine (VIPs 1.2 × 10²) days 55 vs. 65, histamine (VIPs 1.1 × 10²) days 55 vs. 65, and L-methionine (VIPs 1.1 × 10²) days 0 vs. 18. Carnosine was fatigue- (0.039) related, creatine was food intake- (-0.177) and body weight- (-0.039) related, and both metabolites were clinical score- (0.093; 0.050) and paw edema- (0.125; 0.026) related. Therefore, muscle metabolic alterations were detected in arthritic mice urine, enabling further validation in RA patient's urine, targeting prognosis, diagnosis, and monitoring of RA-mediated muscle loss.

Metabolomic Biomarker Candidates for Skeletal Muscle Loss in the Collagen-Induced Arthritis (CIA) Model

There is no consensus for diagnosis or treatment of RA muscle loss. We aimed to investigate metabolites in arthritic mice urine as biomarkers of muscle loss. DBA1/J mice comprised collagen-induced arthritis (CIA) and control (CO) groups. Urine samples were collected at 0, 18, 35, 45, 55, and 65 days of disease and subjected to nuclear magnetic resonance spectroscopy. Metabolites were identified using Chenomx and Birmingham Metabolite libraries. The statistical model used principal component analysis, partial least-squares discriminant analysis, and partial least-squares regression analysis. Linear regression and Fisher’s exact test via the MetaboAnalyst website were performed (VIP-score). Nearly 100 identified metabolites had CIA vs. CO and disease time-dependent differences (p < 0.05). Twenty-eight metabolites were muscle-associated: carnosine (VIPs 2.8 × 102) and succinyl acetone (VIPs 1.0 × 10) showed high importance in CIA vs. CO models at day 65; CIA pair analysis showed histidine (VIPs 1.2 × 102) days 55 vs. 65, histamine (VIPs 1.1 × 102) days 55 vs. 65, and L-methionine (VIPs 1.1 × 102) days 0 vs. 18. Carnosine was fatigue- (0.039) related, creatine was food intake- (−0.177) and body weight- (−0.039) related, and both metabolites were clinical score- (0.093; 0.050) and paw edema- (0.125; 0.026) related. Therefore, muscle metabolic alterations were detected in arthritic mice urine, enabling further validation in RA patient’s urine, targeting prognosis, diagnosis, and monitoring of RA-mediated muscle loss.

Understanding the fundamentals of microbial remediation with emphasize on metabolomics

The post-genomic tool metabolomics is a great advancement in science and technology which acquires novel strategies and pathways to analyze various biological compounds. Metabolomics aids in retrieving the qualitative and quantitative data from the various biological system. The current review is focused on the application of metabolomics in bioremediation and helps to focus on the xenobiotic compounds which are discharged into the environment and have long term impact. The microbial based biodegradation can be effectively used along with the combination of metabolomic approach for a better understanding of the breakdown of certain recalcitrant. Additionally, this review also discusses the candidate gene approach which helps to comprehend the functional analysis of microbial genes in response to different contaminants. Therefore, this review intends to discuss the metabolomics in bioremediation by studying the complete set of metabolites involved during the process of degradation and their interaction with the environment.

Adenosine deaminase modulates metabolic remodeling and orchestrates joint destruction in rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic autoimmune disease associated with inflammation and joint remodeling. Adenosine deaminase (ADA), a risk factor in RA, degrades adenosine, an anti-inflammatory molecule, resulting in an inflammatory bias. We present an integrative analysis of clinical data, cytokines, serum metabolomics in RA patients and mechanistic studies on ADA-mediated effects on in vitro cell culture models. ADA activity differentiated patients into low and high ADA sets. The levels of the cytokines TNFα, IFNγ, IL-10, TGFβ and sRANKL were elevated in RA and more pronounced in high ADA sets. Serum metabolomic analysis shows altered metabolic pathways in RA which were distinct between low and high ADA sets. Comparative analysis with previous studies shows similar pathways are modulated by DMARDs and biologics. Random forest analysis distinguished RA from control by methyl-histidine and hydroxyisocaproic acid, while hexose-phosphate and fructose-6-phosphate distinguished high ADA from low ADA. The deregulated metabolic pathways of High ADA datasets significantly overlapped with high ADA expressing PBMCs GEO transcriptomics dataset. ADA induced the death of chondrocytes, synoviocyte proliferation, both inflammation in macrophages and their differentiation into osteoclasts and impaired differentiation of mesenchymal stem cells to osteoblasts and mineralization. PBMCs expressing elevated ADA had increased expression of cytokines and P2 receptors compared to synovial macrophages which has low expression of ADA. Our data demonstrates increased cytokine levels and distinct metabolic signatures of RA based on the ADA activity, suggests an important role for ADA in the pathophysiology of RA joints and as a potential marker and therapeutic target in RA patients.

Plasma metabolomic profiling in patients with rheumatoid arthritis identifies biochemical features predictive of quantitative disease activity

Background

Rheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that reflect quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA.

Methods

Ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients and on a validation cohort of 12 samples from 12 patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with RA disease activity defined by the Disease Activity Score-28 using C-reactive protein (DAS28-CRP). More specifically, mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups (“lower”, DAS28-CRP ≤ 3.2; and “higher”, DAS28-CRP > 3.2) and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was then constructed for estimating DAS28-CRP using plasma metabolite abundances. Finally, for associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups (“low-CRP”, CRP ≤ 3.0 mg/L; “high-CRP”, CRP > 3.0 mg/L) were investigated.

Results

We identified 33 metabolites differentially abundant between the lower and higher disease activity groups (P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP (P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE] ± SD: 1.51 ± 1.77) compared to a GLM without feature selection (MAE ± SD: 2.02 ± 2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted and actual DAS28-CRP (with feature selection: Spearman’s ρ = 0.69, 95% CI: [0.18, 0.90]; without feature selection: Spearman’s ρ = 0.18, 95% CI: [−0.44, 0.68]). Lastly, among all identified metabolites, the abundances of eight were significantly associated with the CRP patient groups while controlling for potential confounders (P < 0.05).

Conclusions

We demonstrate for the first time the prediction of quantitative disease activity in RA using plasma metabolomes. The metabolites identified herein provide insight into circulating pro-/anti-inflammatory metabolic signatures that reflect disease activity and inflammatory status in RA patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02537-4.

Potential clinical biomarkers in rheumatoid arthritis with an omic approach

Objective

To aid in the selection of the most suitable therapeutic option in patients with diagnosis of rheumatoid arthritis according to the phase of disease, through the review of articles that identify omics biological markers.

Methods

A systematic review in PubMed/Medline databases was performed. We searched articles from August 2014 to September 2019, in English and Spanish, filtered by title and full text; and using the terms "Biomarkers" AND “Rheumatoid arthritis".

Results

This article supplies an exhaustive review from research of objective measurement, omics biomarkers and how disease activity appraise decrease unpredictability in treatment determinations, and finally, economic, and clinical outcomes of treatment options by biomarkers’ potential influence. A total of 122 articles were included. Only 92 met the established criteria for review purposes and 17 relevant references about the topic were included as well. Therefore, it was possible to identify 196 potential clinical biomarkers: 22 non-omics, 20 epigenomics, 33 genomics, 21 transcriptomics, 78 proteomics, 4 glycomics, 1 lipidomics and 17 metabolomics.

Conclusion

A biomarker is a measurable indicator of some, biochemical, physiological, or morphological condition; evaluable at a molecular, biochemical, or cellular level. Biomarkers work as indicators of physiological or pathological processes, or as a result of a therapeutic management. In the last five years, new biomarkers have been identified, especially the omics, which are those that proceed from the investigation of genes (genomics), metabolites (metabolomics), and proteins (proteomics). These biomarkers contribute to the physician choosing the best therapeutic option in patients with rheumatoid arthritis.

Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease

The diagnosis of Vogt-Koyanagi-Harada (VKH) disease is mainly based on a complex clinical manifestation while it lacks objective laboratory biomarkers. To explore the potential molecular biomarkers for diagnosis and disease activity in VKH, we performed an untargeted urine metabolomics analysis by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Through univariate and multivariate statistical analysis, we found 9 differential metabolites when comparing VKH patients with healthy controls, and 26 differential metabolites were identified when comparing active VKH patients with inactive VKH patients. Pathway enrichment analysis showed that glycine, serine and threonine metabolism, and arginine and proline metabolism were significantly altered in VKH versus healthy controls. Lysine degradation and biotin metabolism pathways were significantly altered in active VKH versus inactive VKH. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that the combination of acetylglycine and gamma-glutamylalanine could differentiate VKH from healthy controls with an area under the curve (AUC) of 0.808. A combination of ureidopropionic acid and 5′-phosphoribosyl-5-amino-4-imidazolecarboxamide (AICAR) had an excellent AUC of 0.958 for distinguishing active VKH from inactive VKH. In summary, this study identified abnormal metabolites in urine of patients with VKH disease. Further studies are needed to confirm whether these metabolites are specific for this disease.

Quantification of endogenous aminoacids and aminoacid derivatives in urine by hydrophilic interaction liquid chromatography tandem mass spectrometry

Aminoacids and their derivatives are key biologically important metabolites and reliable, rapid and accurate, quantification for these analytes in urine remains an important analytical challenge. Here a fast and reliable HILIC-tandem MS method is presented for application in clinical or nutritional studies. The developed method was validated according to existing guidelines adapted for endogenous analytes. The validation strategy provided evidence of linearity, LOD and LOQ, accuracy, precision, matrix effect and recovery. The surrogate matrix approach was applied for calibration proving satisfactory accuracy and precision based on standard criteria over the working concentration ranges. Intra and inter day accuracy was found to range between 0.8 and 20% for the LQC (low QC) and between 0.05 and 15 % for MQC (medium QC) and HQC (high QC). Inter and intraday precision were found to be between 3 and 20 % for the LQC and between 1 and 15% for the MQC and HQC. The stability of the analytes, in both surrogate and pooled urine QC samples, was found to be within 15% over a short period at 4 °C or after a up to 3 freeze-thaw cycles. The uncertainty of the method was also assessed to provide increased confidence for the acquired measurements. The method was successfully applied to a subset of human urine samples involved in a study of amino acids dietary uptake. This method may provide a valuable tool for many applications or studies where amino acid metabolic signatures in the excreted urine are under investigation.

Metabolomic profiling reveals plasma GlycA and GlycB as a potential biomarkers for treatment efficiency in rheumatoid arthritis

In this pilot study, we carried out metabolic profiling of patients with rheumatoid arthritis (RA) starting therapy with biological disease-modifying antirheumatic drugs (bDMARDs). The main aim of the study was to assess the occurring metabolic changes associated with therapy success and metabolic pathways involved. In particular, the potential of the metabolomics profiles was evaluated as therapeutically valuable prognostic indicators of the effectiveness of bDMARD treatment to identify responders versus non-responders prior to implementing treatment. Plasma metabolomic profiles of twenty-five patients with RA prior bDMARD treatment and after three months of therapy were obtained by ¹H NMR, liquid chromatography - mass spectrometry, and gas chromatography - mass spectrometry and evaluated by statistical and multivariate analyses. In the group of responders, significant differences in their metabolic patterns were seen after three months of the bDMARD therapy compared with profiles prior to treatment. We identified 24 metabolites that differed significantly between these two-time points mainly belonging to amino acid metabolism, peptides, lipids, cofactors, and vitamins and xenobiotics. Eleven metabolites differentiated responders versus non-responders before treatment. Additionally, N-acetylglucosamine and N-acetylgalactosamine (GlycA) and N-acetylneuraminic acid (GlycB) persisted significant in comparison responders to non-responders after three months of therapy. Moreover, those two metabolites indicated prediction of response potential by results of receiver-operating characteristic (ROC) curve analysis. The applied analysis provides novel insights into the metabolic pathways involved in RA patient's response to bDMARD and therapy effectiveness. GlycA and GlycB are promising biomarkers to identify responding patients prior onset of bDMARD therapy.

Identification of Metabolic Biomarkers in Relation to Methotrexate Response in Early Rheumatoid Arthritis

This study aimed to identify baseline metabolic biomarkers for response to methotrexate (MTX) therapy in rheumatoid arthritis (RA) using an untargeted method. In total, 82 baseline plasma samples (41 insufficient responders and 41 sufficient responders to MTX) were selected from the Treatment in the Rotterdam Early Arthritis Cohort (tREACH, trial number: ISRCTN26791028) based on patients' EULAR response at 3 months. Metabolites were assessed using high-performance liquid chromatography-quadrupole time of flight mass spectrometry. Differences in metabolite concentrations between insufficient and sufficient responders were assessed using partial least square regression discriminant analysis (PLS-DA) and Welch's t-test. The predictive performance of the most significant findings was assessed in a receiver operating characteristic plot with area under the curve (AUC), sensitivity and specificity. Finally, overrepresentation analysis was performed to assess if the best discriminating metabolites were enriched in specific metabolic events. Baseline concentrations of homocystine, taurine, adenosine triphosphate, guanosine diphosphate and uric acid were significantly lower in plasma of insufficient responders versus sufficient responders, while glycolytic intermediates 1,3-/2,3-diphosphoglyceric acid, glycerol-3-phosphate and phosphoenolpyruvate were significantly higher in insufficient responders. Homocystine, glycerol-3-phosphate and 1,3-/2,3-diphosphoglyceric acid were independent predictors and together showed a high AUC of 0.81 (95% CI: 0.72-0.91) for the prediction of insufficient response, with corresponding sensitivity of 0.78 and specificity of 0.76. The Warburg effect, glycolysis and amino acid metabolism were identified as underlying metabolic events playing a role in clinical response to MTX in early RA. New metabolites and potential underlying metabolic events correlating with MTX response in early RA were identified, which warrant validation in external cohorts.

Optical Biosensors for the Detection of Rheumatoid Arthritis (RA) Biomarkers: A Comprehensive Review

A comprehensive review of optical biosensors for the detection of biomarkers associated with rheumatoid arthritis (RA) is presented here, including microRNAs (miRNAs), C-reactive protein (CRP), rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPA), interleukin-6 (IL-6) and histidine, which are biomarkers that enable RA detection and/or monitoring. An overview of the different optical biosensors (based on fluorescence, plasmon resonances, interferometry, surface-enhanced Raman spectroscopy (SERS) among other optical techniques) used to detect these biomarkers is given, describing their performance and main characteristics (limit of detection (LOD) and dynamic range), as well as the connection between the respective biomarker and rheumatoid arthritis. It has been observed that the relationship between the corresponding biomarker and rheumatoid arthritis tends to be obviated most of the time when explaining the mechanism of the optical biosensor, which forces the researcher to look for further information about the biomarker. This review work attempts to establish a clear association between optical sensors and rheumatoid arthritis biomarkers as well as to be an easy-to-use tool for the researchers working in this field.

The Progression of Hemophilic Arthropathy: The Role of Biomarkers

BACKGROUND

Hemophilia A and B are X-linked congenital bleeding disorders characterized by recurrent hemarthroses leading to specific changes in the synovium and cartilage, which finally result in the destruction of the joint: this process is called hemophilic arthropathy (HA). This review highlights the most prominent molecular biomarkers found in the literature to discuss their potential use in the clinical practice to monitor bleeding, to assess the progression of the HA and the effectiveness of treatments.

METHODS

A review of the literature was performed on PubMed and Embase, from 3 to 7 August 2020. Study selection and data extraction were achieved independently by two authors and the following inclusion criteria were determined a priori: English language, available full text and articles published in peer-reviewed journal. In addition, further articles were identified by checking the bibliography of relevant articles and searching for the studies cited in all the articles examined.

RESULTS

Eligible studies obtained at the end of the search and screen process were seventy-three (73).

CONCLUSIONS

Despite the surge of interest in the clinical use of biomarkers, current literature underlines the lack of their standardization and their potential use in the clinical practice preserving the role of physical examination and imaging in early diagnosis.

Metabolomic Profiling in the Characterization of Degenerative Bone and Joint Diseases

Osteoarthritis and inflammatory arthropathies are a cause of significant morbidity globally. New research elucidating the metabolic derangements associated with a variety of bone and joint disorders implicates various local and systemic metabolites, which further elucidate the underlying molecular mechanisms associated with these destructive disease processes. In osteoarthritis, atty acid metabolism has been implicated in disease development, both locally and systemically. Several series of rheumatoid arthritis patients have demonstrated overlapping trends related to histidine and glyceric acid, while other series showed similar results of increased cholesterol and glutamic acid. Studies comparing osteoarthritis and rheumatoid arthritis reported elevated gluconic acid and glycolytic- and tricarboxylic acid-related substrates in patients with osteoarthritis, while lysosphingolipids and cardiolipins were elevated only in patients with rheumatoid arthritis. Other bone and joint disorders, including osteonecrosis, intervertebral disc degeneration, and osteoporosis, also showed significant alterations in metabolic processes. The identification of the molecular mechanisms of osteoarthritis and inflammatory arthropathies via metabolomics-based workflows may allow for the development of new therapeutic targets to improve the quality of life in these patient populations.

Expansion of Rare and Harmful Lineages is Associated with Established Rheumatoid Arthritis

Objectives: To characterize the gut microbiota profile in rheumatoid arthritis (RA) patients and investigate its association with certain characteristics of RA. Patients and methods: A nested case–control cohort of 40 patients with RA and 40 sex-age matched controls was studied. Subjects with diabetes, with any other inflammatory disease, practicing extreme diets, taking antibiotics, probiotics or under any new treatment for at least three months prior to sampling were excluded. The microbiota composition was determined by 16S rRNA pyrosequencing and bioinformatics analysis by Quantitative Insights Into Microbial Ecology (QIIME). Other variables included clinical-laboratory variables and average Disease Activity Score 28 points during the follow-up period. Multiple linear regression models were constructed to investigate the possible risk factors for the microbiota. Results: β-diversity data showed that patients tend to differ from healthy subjects according to their microbiota (p = 0.07). The analysis showed an increase in Collinsella aerofaciens, Sedimentibacter and Enterococcus genera in patients compared to controls, as well as a decrease in Dorea formicigenerans. Likewise, an increase in the activity of arginine deiminase was observed, which was found in approximately 90% of the RA genes of the genus Collinsela. The sequence number of Collinsella aerofaciens was independently associated with age (B (95%CI), −0.347 (−21.6, −2.1)), high ACPA (0.323 (27.4–390.0)) and smoking (0.300 (8.8–256.4)) in RA patients. In addition, we observed decreases in Sarcina, 02d06 and Porphyromonas bacterial lineages. Conclusion: Patients with RA present dysbiosis, resulting from an abundance of certain bacterial lineages and a decrease in others. These alterations could influence the maintenance of autoimmunity to this disease.