Metabolite and Lipid Profiling of Biobank Plasma Samples Collected Prior to Onset of Rheumatoid Arthritis

Targeted Detection of 76 Carnitine Indicators Combined with a Machine Learning Algorithm Based on HPLC-MS/MS in the Diagnosis of Rheumatoid Arthritis

BACKGROUND/OBJECTIVES

Early diagnosis and treatment of rheumatoid arthritis (RA) are essential to reducing disability. However, the diagnostic criteria remain unclear, relying on clinical symptoms and blood markers.

METHODS

Using high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) targeted detection, we evaluated 76 carnitine indicators (55 carnitines and 21 corresponding ratios) in the serum of patients with RA to investigate the role of carnitine in RA. A total of 359 patients (207 patients with RA and 152 healthy controls) were included in the study. Screening involved three methods and integrated 76 carnitine indicators and 128 clinical indicators to identify candidate markers to establish a theoretical basis for RA diagnosis and new therapeutic targets. The diagnostic model derived from the screened markers was validated using three machine learning algorithms.

RESULTS

The model was refined using eight candidate indicators (C0, C10:1, LYMPH, platelet distribution width, anti-keratin antibody, glucose, urobilinogen, and erythrocyte sedimentation rate (ESR)). The receiver operating characteristic curve, sensitivity, specificity, and accuracy of the V8 model obtained from the training set were >0.948, 79.46%, 92.99%, and 89.18%, whereas those of the test set were >0.925, 78.89%, 89.22%, and 85.87%, respectively. Twenty-four carnitines were identified as risk factors of RA, with three significantly correlating with ESR, four with anti-cyclic citrullinated peptide antibody activity, two with C-reactive protein, five with immunoglobulin-G, eight with immunoglobulin-A levels, and eleven with immunoglobulin-M levels.

CONCLUSIONS

Carnitine is integral in the progression of RA. The diagnostic model developed shows excellent diagnostic capacity, improving early detection and enabling timely intervention to minimize disability associated with RA.

Evaluating treatment responsiveness in rheumatoid arthritis through predictive metabolomic profiling: A systematic review of studies examining methotrexate, TNF, and IL-6 inhibitors as therapeutic interventions

Rheumatoid arthritis (RA) is a systemic chronic autoimmune disease characterized by joint damage and systemic involvement. Despite advancements in understanding RA, early diagnosis and effective treatment remain challenging due to the complex pathogenesis and limited specificity of current biomarkers. Metabolomics, offers a promising approach for identifying new biomarkers to assess treatment responsiveness in RA. A systematic review was conducted to identify key metabolites and metabolic pathways that may reveal responsiveness to different drug therapy strategies (methotrexate, TNF, and IL-6 inhibitors) in RA treatment. The systematic search was conducted in PubMed and Google Scholar in accordance with PRISMA recommendations. The risk of bias and the quality of the final selected studies were assessed in duplicate using the Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) tool and using the QUADOMICS tool. Eighteen studies were eligible for data extraction. Metabolomic studies revealed distinct profiles for responders and non-responders to different RA treatments. For methotrexate therapy, key metabolites included for example: homocysteine, glycerol-3-phosphate, and diphosphoglyceric acid. TNF inhibitor response was associated mainly with changes in carbohydrate derivatives and amino acids. IL-6 inhibitor studies identified metabolites such as N-acetylglucosamine, N-acetylgalactosamine, and N-acetylneuraminic acid as potential predictors of response. Across studies, metabolomic profiles demonstrated high sensitivity and specificity in distinguishing responders from non-responders. These studies collectively highlight alterations in TCA cycle metabolites, amino acids, nucleotide metabolism, and lipid profiles, among others. This review supports the identification of better treatment strategies choosing methotrexate, TNF, or IL-6 inhibitors as therapeutic interventions based on metabolomics profiling.

Phospholipids and Sphingolipids in Osteoarthritis

Many studies now emphasize the intricate relationship between lipid metabolism and osteoarthritis (OA), a leading cause of disability. This narrative review examines alterations in the levels of phospholipids (PLs) and sphingolipids (SLs) in synovial fluid (SF), plasma, serum, and articular tissues; discusses their role in joint lubrication, inflammation, and cartilage degradation; and describes their potential as diagnostic markers and therapeutic targets. Key findings include stage-dependent elevated levels of specific PLs and SLs in the SF, blood, and tissue of OA patients, implicating them as possible biomarkers of disease severity and progression. Studies suggest that beyond the involvement of these lipids in joint lubrication, individual species, such as lysophosphatidylcholine (LPC) 16:0, lysophosphatidic acid (LPA), ceramide-1-phosphate (C1P), and sphingosine-1-phosphate (S1P), contribute to pain, inflammation, and degradation of joints through various signaling pathways. Cross-species comparisons suggest that dogs and mice experience similar lipidomic changes during OA as humans, rendering them valuable models for studying lipid-related mechanisms. PLs and SLs in SF appear to originate primarily from the synovial blood capillaries through diffusion. In addition, lipids that are produced locally by fibroblast-like synoviocytes (FLSs) are influenced by cytokines and growth factors that regulate the biosynthesis of PLs for joint lubrication. Emerging research has identified genes such as UGCG and ESYT1 as regulators of lipid metabolism in OA. Further, we examine the suitability of lipids as biomarkers of OA and the potential of targeting the PL and SL pathways to treat OA, emphasizing the need for further research to translate these findings into clinical applications.

Causal Effects of Rheumatoid Arthritis, Ankylosing Spondylitis, Juvenile Idiopathic Arthritis on Psoriasis: A Mendelian Randomization Study

Background

It is well-documented that rheumatoid arthritis (RA), ankylosing spondylitis (AS), and juvenile idiopathic arthritis (JIA) often exhibit skin manifestations, with psoriasis typically occurring around the time of diagnosis. Thus, it is essential to investigate the potential causal relationship between these forms of arthritis and psoriasis.

Methods

The OpenGWAS provided traitIDs for exposure factors (RA (bbj-A-74), AS (ebi-A-GCST005529), and JIA (finn-b-JUVEN-ARTHR)) and outcome (psoriasis, finn-b-L12-PSORIASIS). bbj-A-74 had 19,190 samples (9,739,303 SNPs), ebi-A-GCST005529 had 22,647 samples (99,962 SNPs), finn-b-JUVEN-ARTHR had 173,622 samples (16,380,296 SNPs), and psoriasis had 216,752 samples (16,380,464 SNPs). Initially, 57 RA SNPs, 25 AS SNPs, and 5 JIA SNPs were acquired. Causal links were explored via univariate Mendelian Randomization (UVMR) analysis, with sensitivity analyses ensuring reliability. Additionally, multivariate MR (MVMR) analysis was conducted to further estimate the effect of each exposure factor on psoriasis.

Results

Significant causal links (P < 0.05, OR > 1) were found between bbj-A-74, ebi-A-GCST005529, finn-b-JUVEN-ARTHR, and finn-b-L12-PSORIASIS, indicating associations of RA, AS, and JIA with psoriasis. Sensitivity analyses ensured the reliability of these finding, showing no heterogeneity, horizontal pleiotropy, or SNP locus oversensitivity in UVMR results. Furthermore, MVMR analysis revealed AS and JIA as psoriasis risk factors, while RA showed non-significant protective effects. This suggests AS and JIA may contribute to psoriasis onset or exacerbation when coexisting.

Conclusion

MR analyses were conducted to investigate the causal links between RA, AS, JIA, and psoriasis, enhancing our grasp of the underlying mechanisms of psoriasis.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

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.

Alteration of Gut Microbiota in Individuals at High-Risk for Rheumatoid Arthritis Associated With Disturbed Metabolome and the Initiation of Arthritis Through the Triggering of Mucosal Immunity Imbalance

OBJECTIVE

In this study, we aimed to decipher the gut microbiome (GM) and serum metabolic characteristic of individuals at high risk for rheumatoid arthritis (RA) and to investigate the causative effect of GM on the mucosal immune system and its involvement in the pathogenesis of arthritis.

METHODS

Fecal samples were collected from 38 healthy individuals and 53 high-risk RA individuals with anti-citrullinated protein antibody (ACPA) positivity (Pre-RA), 12 of 53 Pre-RA individuals developed RA within 5 years of follow-up. The differences in intestinal microbial composition between the healthy controls and Pre-RA individuals or among Pre-RA subgroups were identified by 16S ribosomal RNA sequencing. The serum metabolite profile and its correlation with GM were also explored. Moreover, antibiotic-pretreated mice that received GM from the healthy control or Pre-RA groups were then evaluated for intestinal permeability, inflammatory cytokines, and immune cell populations. Collagen-induced arthritis (CIA) was also applied to test the effect of fecal microbiota transplantation (FMT) from Pre-RA individuals on arthritis severity in mice.

RESULTS

Stool microbial diversity was lower in Pre-RA individuals than in healthy controls. The bacterial community structure and function significantly differed between healthy controls and Pre-RA individuals. Although there were differences to some extent in the bacterial abundance among the Pre-RA subgroups, no robust functional differences were observed. The metabolites in the serum of the Pre-RA group were dramatically different from those in the healthy controls group, with KEGG pathway enrichment of amino acid and lipid metabolism. Moreover, intestinal bacteria from the Pre-RA group increased intestinal permeability in FMT mice and zonula occludens-1 expression in the small intestine and Caco-2 cells. Moreover, Th17 cells in the mesenteric lymph nodes and Peyer's patches were also increased in mice receiving Pre-RA feces compared to healthy controls. The changes in intestinal permeability and Th17-cell activation prior to arthritis induction enhanced CIA severity in PreRA-FMT mice compared with HC-FMT mice.

CONCLUSION

Gut microbial dysbiosis and metabolome alterations already occur in individuals at high risk for RA. FMT from preclinical individuals triggers intestinal barrier dysfunction and changes mucosal immunity, further contributing to the development of arthritis.

Anti-Rheumatoid Arthritis Pharmacodynamic Substances Screening of Periploca forrestii Schltr.: Component Analyses In Vitro and In Vivo Combined with Multi-Technical Metabolomics

The purpose of this study was to elucidate the metabolic action patterns of P. forrestii against rheumatoid arthritis (RA) using metabolomics, and to obtain its potential effective substances for treating RA. First, the therapeutic effects of P. forrestii against RA were confirmed; second, the chemical composition of P. forrestii was analyzed, and 17 prototypes were absorbed into blood; subsequently, plasma metabolomics studies using UPLC-Triple-TOF-MS/MS and GC-MS were performed to disclose the metabolomics alterations in groups, which revealed 38 altered metabolites after drug intervention. These metabolites were all associated with the arthritis pathophysiology process (-log(p) > 1.6). Among them, sorted by variable important in projection (VIP), the metabolites affected (VIP ≥ 1.72) belonged to lipid metabolites. Finally, Pearson's analysis between endogenous metabolites and exogenous compounds was conducted to obtain potential pharmacological substances for the P. forrestii treatment of RA, which showed a high correlation between five blood-absorbed components and P. forrestii-regulated metabolites. This information provides a basis for the selection of metabolic action modes for P. forrestii clinical application dosage, and potential pharmacological substances that exerted anti-RA effects of P. forrestii were discovered. The study provided an experimental basis for further research on pharmacoequivalence, molecular mechanism validation, and even the development of new dosage forms in the future.

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.

Synovial tissue metabolomics analysis of the therapeutic effects of stir-fried Xanthii Fructus on rheumatoid arthritis in rats

Rheumatoid arthritis (RA) is a rheumatic disease that easily causes synovial hyperplasia and joint damage. Comprehensive metabolomic profiling of synovial tissue can reveal local pathological changes during RA and identify metabolites as candidate biomarkers. Detecting metabolites in synovial tissue can more directly reflect the pathological state and disease activity associated with it. stir-fried Xanthii Fructus has demonstrated efficacy in treating RA, but its pharmacodynamic property and mechanism of action are unclear. In this study, the molecular composition of the extract of stir-fried Xanthium Fructus was determined through HPLC. The major components that exert anti-inflammatory and analgesic effects were speculated to be phenolic acids. Next, the effect of stir-fried Xanthii Fructus extracts in RA treatment was comprehensively evaluated using rat body weight, foot volume, inflammatory factors, and histopathological sections of the ankle joint as evaluation indicators. The results showed that the extract of stir-fried Xanthii Fructus could significantly reduce the inflammatory response and improve the degree of joint swelling and the imbalance between pro-inflammatory and anti-inflammatory in adjuvant arthritis rats. Finally, non-targeted metabolomics based on UPLC-Q-TOF/MS and multivariate statistical analysis were used to explore the changes of endogenous metabolites in synovium tissues and to search for potential biomarkers and related metabolic pathways in stir-fried Xanthii Fructus extract-treated AA rats. The results showed that stir-fried Xanthii Fructus mainly treated RA by regulating energy metabolism, hormone metabolism, amino acid metabolism and oxidative stress response in adjuvant arthritis rats. This study provides a theoretical basis for the mechanism of action of stir-fried Xanthii Fructus extract in treating RA.

Metabolomics and molecular docking-directed antiarthritic study of the ethyl acetate extract from Celastrus orbiculatus Thunb

ETHNOPHARMACOLOGICAL RELEVANCE

Celastrus orbiculatus Thunb., an important folk medicine, has long been used for the treatment of rheumatoid arthritis and its ethyl acetate extract (COE) has been reported to possess anticancer, antiinflammation and antiarthritic effects. However, the therapeutic effect and mechanism of COE treatment in rheumatoid arthritis has been rarely studied especially from the perspective of metabolomics.

AIM OF STUDY

To reveal the therapeutic effects of COE on adjuvant-induced arthritis (AIA) rats through histopathological analysis, non-targeted metabolomics, and molecular docking study.

MATERIALS AND METHODS

Forty-three Wistar rats were randomly divided into normal group, AIA model group, methotrexate group, and COE groups (80 mg/kg, 160 mg/kg and 320 mg/kg of ethyl acetate extract). Paw swelling and arthritis score were monitored through the experiment. Serum levels of tumor necrosis factor α (TNF-α) and nitric oxide were determined and histopathological evaluation was performed. Furthermore, Ultra-high performance liquid chromatography-linear trap quadrupole-Orbitrap-based metabolomics was employed to characterize metabolic changes of AIA rats after COE treatment and molecular docking was performed to predict the potential phytochemicals of COE against TNF-α.

RESULTS

COE at three dosages could significantly relieve paw swelling and reduce arthritis scores of AIA rat. Histopathological analysis revealed remarkable decrease in synovial inflammation and bone erosion after COE treatment, especially at middle and high dosage. Additionally, COE down-regulated serum levels of TNF-α and nitric oxide. Serum metabolomics showed that 22 potential biomarkers for the COE treatment of AIA rats were identified, which were closely related to fatty acid metabolism, glycerophospholipid catabolism, and tryptophan metabolism. The molecular docking models predicted that olean-type triterpenes in COE may contribute most to therapeutic effects of rheumatoid arthritis through targeting TNF-α.

CONCLUSIONS

COE could significantly relieve the arthritic symptoms in AIA rats and the ultra-high performance liquid chromatography-mass spectrometry based metabolomics proved to be an efficient method to characterize subtle metabolic changes of AIA rats after COE treatment.

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.

Lipidomic Profiling in Synovial Tissue

The analysis of synovial tissue offers the potential for the comprehensive characterization of cell types involved in arthritis pathogenesis. The studies performed to date in synovial tissue have made it possible to define synovial pathotypes, which relate to disease severity and response to treatment. Lipidomics is the branch of metabolomics that allows the quantification and identification of lipids in different biological samples. Studies in animal models of arthritis and in serum/plasma from patients with arthritis suggest the involvement of different types of lipids (glycerophospholipids, glycerolipids, sphingolipids, oxylipins, fatty acids) in the pathogenesis of arthritis. We reviewed studies that quantified lipids in different types of tissues and their relationship with inflammation. We propose that combining lipidomics with currently used “omics” techniques can improve the information obtained from the analysis of synovial tissue, for a better understanding of pathogenesis and the development of new therapeutic strategies.

Low-Density Lipoprotein Cholesterol and the Risk of Rheumatoid Arthritis: A Prospective Study in a Chinese Cohort

Objective: This study aimed to investigate whether low-density lipoprotein cholesterol (LDL-C) concentration was associated with the risk of rheumatoid arthritis (RA) in Chinese adults. Methods: The study included the 97,411 participants in the Kailuan Study without RA, with complete baseline LDL-C data, and who did not use lipid-lowering medications at baseline or during follow-up. We used Cox proportional hazards modeling to calculate the hazard ratio (HR) and 95% confidence interval (95% CI) of RA according to baseline LDL-C tertiles, adjusting for age, sex, body mass index, HDL-C, triglycerides, diabetes, hypertension, alcohol consumption, and smoking. We also calculated the HR and 95% CI of RA using updated LDL-C measurements prior to the end of follow-up, adjusting for covariates. Results: We identified 97 incident RA cases between 2006 and 2018. After adjusting for potential confounders, updated LDL-C concentration—rather than baseline LDL-C—was inversely associated with RA risk. The adjusted HR of RA was 0.64 (95% CI: 0.38, 1.09; p-trend = 0.10) comparing the two extreme baseline LDL-C tertiles, and 0.38 (95% CI: 0.22, 0.64; p-trend < 0.01) comparing the two extreme tertiles of the updated LDL-C concentrations. Conclusions: In this prospective study, high LDL-C concentrations, when measured closest to RA diagnosis or the end of follow-up, were associated with a low risk of RA. These findings highlight the changes in LDL-C prior to RA diagnosis, and the importance of including lipid analyses into studies of the pathogenesis of RA.

Metabolomics-driven of relationships among kidney, bone marrow and bone of rats with postmenopausal osteoporosis

As a global public health problem, postmenopausal osteoporosis (PMOP) poses a great threat to old women's health. Bone is the target organ of PMOP, and the dynamic changes of bone marrow could affect the bone status. Kidney is the main organ regulating calcium and phosphorus homeostasis. Kidney, bone marrow and bone play crucial roles in PMOP, but the relationships of the three tissues in the disease have not been completely described. Here, metabolomics was employed to investigate the disease mechanism of PMOP from the perspectives of kidney, bone marrow and bone, and the relationships among the three tissues were also discussed. Six-month-old female Sprague-Dawley (SD) rats were randomly divided into ovariectomized (OVX) group (with bilateral ovariectomy) and sham group (with sham surgery). 13 weeks after surgery, gas chromatography-mass spectrometry (GC-MS) was performed to analyze the metabolic profiling of two groups. Multivariate statistical analysis revealed that the number of differential metabolites in kidney, bone marrow and bone between the two groups were 37, 16 and 17, respectively. The common differential metabolites of the three tissues were N-methyl-L-alanine. Kidney and bone marrow had common differential metabolites, including N-methyl-L-alanine, 2-hydroxybutyric acid, (R)-3-hydroxybutyric acid (β-hydroxybutyric acid, βHBA), urea and dodecanoic acid. There were three common differential metabolites between kidney and bone, including N-methyl-L-alanine, α-tocopherol and isofucostanol. The common differential metabolite of bone marrow and bone was N-methyl-L-alanine. Some common metabolic pathways were disturbed in multiple tissues of OVX rats, such as glycine, serine and threonine metabolism, purine metabolism, tryptophan metabolism, ubiquinone and other terpenoid-quinone biosynthesis and fatty acid biosynthesis. In conclusion, our study demonstrated that profound metabolic changes have taken place in the kidney, bone marrow and bone, involving common differential metabolites and metabolic pathways. The evaluation of differential metabolites strengthened the understanding of the kidney-bone axis and the metabolic relationships among the three tissues of OVX rats.

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.

Analysis of Serum Metabolomics in Rats with Osteoarthritis by Mass Spectrometry

Osteoarthritis is a common multifactorial chronic disease that occurs in articular cartilage, subchondral bone, and periarticular tissue. The pathogenesis of OA is still unclear. To investigate the differences in serum metabolites between OA and the control group, liquid chromatography/mass spectrometry (LC/MS)-based metabolomics was used. To reveal the pathogenesis of OA, 12 SD male rats were randomly divided into control and OA groups using collagenase to induce OA for modeling, and serum was collected 7 days after modeling for testing. The OA group was distinguished from the control group by principal component analysis and orthogonal partial least squares-discriminant analysis, and six biomarkers were finally identified. These biomarkers were metabolized through tryptophan metabolism, glutamate metabolism, nitrogen metabolism, spermidine metabolism, and fatty acid metabolism pathways. The study identified metabolites that may be altered in OA, suggesting a role in OA through relevant metabolic pathways. Metabolomics, as an important tool for studying disease mechanisms, provides useful information for studying the metabolic mechanisms of OA.

Circulating blood metabolite trajectories and risk of rheumatoid arthritis among military personnel in the Department of Defense Biorepository

OBJECTIVES

We sought to identify metabolic changes potentially related to rheumatoid arthritis (RA) pathogenesis occurring in the blood prior to its diagnosis.

METHODS

In a US military biorepository, serum samples collected at two timepoints prior to a diagnosis of RA were identified. These were matched to controls who did not develop RA by subject age, race and time between sample collections and RA diagnosis time to stored serum samples. Relative abundances of 380 metabolites were measured using liquid chromatography-tandem mass spectrometry. We determined whether pre-RA case versus control status predicted metabolite concentration differences and differences over time (trajectories) using linear mixed models, assessing for interactions between time, pre-RA status and metabolite concentrations. We separately examined pre-RA and pre-seropositive RA cases versus matched controls and adjusted for smoking. Multiple comparison adjustment set the false discovery rate to 0.05.

RESULTS

291 pre-RA cases (80.8% pre seropositive RA) were matched to 292 controls, all with two serum samples (2.7±1.6 years; 1.0±0.9 years before RA/matched date). 52.0% were women; 52.8% were White, 26.8% Black and 20.4% other race. Mean age was 31.2 (±8.1) years at earliest blood draw. Fourteen metabolites had statistically significant trajectory differences among pre-RA subjects versus controls, including sex steroids, amino acid/lipid metabolism and xenobiotics. Results were similar when limited to pre seropositive RA and after adjusting for smoking.

CONCLUSIONS

In this military case-control study, metabolite concentration trajectory differences in pre-RA cases versus controls implicated steroidogenesis, lipid/amino acid metabolism and xenobiotics in RA pathogenesis. Metabolites may have potential as biomarkers and/or therapeutic targets preceding RA diagnosis.

Metabolomics in Bone Research

Identifying the changes in endogenous metabolites in response to intrinsic and extrinsic factors has excellent potential to obtain an understanding of cells, biofluids, tissues, or organisms' functions and interactions with the environment. The advantages provided by the metabolomics strategy have promoted studies in bone research fields, including an understanding of bone cell behaviors, diagnosis and prognosis of diseases, and the development of treatment methods such as implanted biomaterials. This review article summarizes the metabolism changes during osteogenesis, osteoclastogenesis, and immunoregulation in hard tissue. The second section of this review is dedicated to describing and discussing metabolite changes in the most relevant bone diseases: osteoporosis, bone injuries, rheumatoid arthritis, and osteosarcoma. We consolidated the most recent finding of the metabolites and metabolite pathways affected by various bone disorders. This collection can serve as a basis for future metabolomics-driven bone research studies to select the most relevant metabolites and metabolic pathways. Additionally, we summarize recent metabolic studies on metabolomics for the development of bone disease treatment including biomaterials for bone engineering. With this article, we aim to provide a comprehensive summary of metabolomics in bone research, which can be helpful for interdisciplinary researchers, including material engineers, biologists, and clinicians.

Metabolic networks of plasma and joint fluid base on differential correlation

Whether osteoarthritis (OA) is a systemic metabolic disorder remains controversial. The aim of this study was to investigate the metabolic characteristics between plasma and knee joint fluid (JF) of patients with advanced OA using a differential correlation metabolic (DCM) networks approach. Plasma and JF were collected during the joint replacement surgery of patients with knee OA. The biological samples were pretreated with standard procedures for metabolite analysis. The metabolic profiling was conducted by means of liquid mass spectrometry coupled with a AbsoluteIDQ kit. A DCM network approach was adopted for analyzing the metabolomics data between the plasma and JF. The variation in the correlation of the pairwise metabolites was quantified across the plasma and JF samples, and networks analysis was used to characterize the difference in the correlations of the metabolites from the two sample types. Core metabolites that played an important role in the DCM networks were identified via topological analysis. One hundred advanced OA patients (50 men and 50 women) were included in this study, with an average age of 65.0 ± 7.6 years (65.6 ± 7.1 years for females and 64.4 ± 8.1 years for males) and a mean BMI of 32.6 ± 5.8 kg/m² (33.4 ± 6.3 kg/m² for females and 31.7 ± 5.3 kg/m² for males). Age and BMI matched between the male and female groups. One hundred and forty-five nodes, 567 edges, and 131 nodes, 407 edges were found in the DCM networks (p < 0.05) of the female and male groups, respectively. Six metabolites in the female group and 5 metabolites in the male group were identified as key nodes in the network. There was a significant difference in the differential correlation metabolism networks of plasma and JF that may be related to local joint metabolism. Focusing on these key metabolites may help uncover the pathogenesis of knee OA. In addition, the differential metabolic correlation between plasma and JF mostly overlapped, indicating that these common correlations of pairwise metabolites may be a reflection of systemic characteristics of JF and that most significant correlation variations were just a result of "housekeeping” biological reactions.

Circulating plasma metabolites and risk of rheumatoid arthritis in the Nurses' Health Study

OBJECTIVES

RA develops slowly over years. We tested for metabolic changes prior to RA onset using a large non-targeted metabolomics platform to identify novel pathways and advance understanding of RA development.

METHODS

Two hundred and fifty-four incident RA cases with plasma samples drawn pre-RA onset in the Nurses' Health Study (NHS) cohorts were matched 1:2 to 501 controls on age, race, menopause/post-menopausal hormone use and blood collection features. Relative abundances of 360 unique, known metabolites were measured. Conditional logistic regression analyses assessed associations between metabolites and incidence of RA, adjusted for age, smoking and BMI, accounting for multiple comparisons. Subgroup analyses investigated seropositive (sero+) RA and RA within 5 years of sample collection. Significant metabolites were then tested in a female military pre-RA case-control study (n = 290).

RESULTS

In the NHS, metabolites associated with RA and sero+RA in multivariable models included 4-acetamidobutanoate (odds ratio (OR) = 0.80/S.d., 95% CI: 0.66, 0.95), N-acetylputrescine (OR = 0.82, 95% CI: 0.69, 0.96), C5 carnitine (OR = 0.84, 95% CI: 0.71, 0.99) and C5:1 carnitine (OR = 0.81, 95% CI: 0.68, 0.95). These were involved primarily in polyamine and leucine, isoleucine and valine metabolism. Several metabolites associated with sero+RA within 5 years of diagnosis were replicated in the independent military cohort: C5 carnitine (OR = 0.55, 95% CI: 0.33, 0.92), C5:1 carnitine (OR = 0.62, 95% CI: 0.39, 0.99) and C3 carnitine (OR = 0.57, 95% CI: 0.36, 0.91).

CONCLUSION

Several metabolites were inversely associated with incidence of RA among women. Three short-chain acylcarnitines replicated in a smaller dataset and may reflect inflammation in the 5-year period prior to sero+RA diagnosis.

Metabolomics in the development and progression of rheumatoid arthritis: A systematic review

OBJECTIVE

A systematic review and analysis of data from several rheumatoid arthritis metabolomics studies attempts to determine which metabolites can be used as potential biomarkers for the diagnosis of rheumatoid arthritis and to explore the pathogenesis of rheumatoid arthritis.

METHODS

We searched all the subject-related documents published by EMBASE, PubMed, Web of Science, and Cochrane Library from the database to the September 2019 publication. Two researchers independently screened the literature and extracted the data. QUADOMICS tool was used to assess the quality of studies included in this systematic review.

RESULTS

A total of 10 studies met the inclusion criteria of systematic review, including 502 patients with rheumatoid arthritis and 373 healthy people. Among them, the biological samples utilised for metabolomic analysis include: serum (n=8), urine (n=1) and synovial fluid (n=1). Some metabolites play an important role in rheumatoid arthritis: glucose, lactic acid, citric acid, leucine, methionine, isoleucine, valine, phenylalanine, threonine, serine, proline, glutamate, histidine, alanine, cholesterol, glycerol, and ribose.

CONCLUSIONS

Metabolomics provides important new opportunities for further research in rheumatoid arthritis and is expected to elucidate the pathogenesis of rheumatoid arthritis that has not been fully understood before.

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.

Microbiota and metabolites in rheumatic diseases

As a gigantic community in the human body, the microbiota exerts pleiotropic roles in human health and disease ranging from digestion and absorption of nutrients from food, defense against infection of pathogens, to regulation of immune system development and immune homeostasis. Recent advances in "omics" studies and bioinformatics analyses have broadened our insights of the microbiota composition of the inner and other surfaces of the body and their interactions with the host. Apart from the direct contact of microbes at the mucosal barrier, metabolites produced or metabolized by the gut microbes can serve as important immune regulators or initiators in a wide variety of diseases, including gastrointestinal diseases, metabolic disorders and systemic rheumatic diseases. This review focuses on the most recent understanding of how the microbiota and metabolites shape rheumatic diseases. Studies that explore the mechanistic interplay between microbes, metabolites and the host could thereby provide clues for novel methods in the diagnosis, therapy, and prevention of rheumatic diseases.

Protein profiling and network enrichment analysis in individuals before and after the onset of rheumatoid arthritis

Background

Antibodies and upregulated cytokines and chemokines predate the onset of rheumatoid arthritis (RA) symptoms. We aimed to identify the pathways related to the early processes leading to RA development, as well as potential novel biomarkers, using multiple protein analyses.

Methods

A case-control study was conducted within the Biobank of northern Sweden. The plasma samples from 118 pre-symptomatic individuals (207 samples; median predating time 4.1 years), 79 early RA patients, and 74 matched controls were analyzed. The levels of 122 unique proteins with an acknowledged relationship to autoimmunity were analyzed using 153 antibodies and a bead-based multiplex system (FlexMap3D; Luminex Corp.). The data were analyzed using multifactorial linear regression model, random forest, and network enrichment analysis (NEA) based on the 10 most significantly differentially expressed proteins for each two-by-two group comparison, using the MSigDB collection of hallmarks.

Results

There was a high agreement between the different statistical methods to identify the most significant proteins. The adipogenesis and interferon alpha response hallmarks differentiated pre-symptomatic individuals from controls. These two hallmarks included proteins involved in innate immunity. Between pre-symptomatic individuals and RA patients, three hallmarks were identified as follows: apical junction, epithelial mesenchymal transition, and TGF-β signaling, including proteins suggestive of cell interaction, remodulation, and fibrosis. The adipogenesis and heme metabolism hallmarks differentiated RA patients from controls.

Conclusions

We confirm the importance of interferon alpha signaling and lipids in the early phases of RA development. Network enrichment analysis provides a tool for a deeper understanding of molecules involved at different phases of the disease progression.

Metabolomic profiles of induced pluripotent stem cells derived from patients with rheumatoid arthritis and osteoarthritis

Background

Metabolomics is the systemic study of the unique fingerprints of metabolites involved in cellular processes and biochemical reactions. The metabolomic approach is useful in diagnosing and predicting the development of rheumatoid arthritis (RA) and osteoarthritis (OA) and is emerging as a useful tool for identifying disease biomarkers. The aim of this study was to compare the metabolic blueprint of fibroblast-like synoviocyte (FLS) cells and induced pluripotent stem cells (iPSCs) derived from RA and OA patients.

Methods

Somatic cells of RA patients (n = 3) and OA patients (n = 3) were isolated, transduced with a lentiviral plasmid, and reprogrammed into iPSCs displaying pluripotency. Metabolic profiling of RA and OA patient–derived FLS cells and iPSCs was performed using liquid chromatography/mass spectrometry and statistical analysis. After normalization by the sum of the peak intensities through LC/MS, 37 metabolites were detected across RA and OA patients.

Results

The metabolites of RA and OA were distinguishable according to the PLS-DA analysis. LysoPC (20:4), 4-methoxychalcone, phosphorylcholine, and nicotinamide (NAM) were significantly higher in RA iPSCs than in OA iPSCs (p < 0.05). The NMNAT-3 enzyme, which catalyzes an important step in the biosynthesis of NAD⁺ from adenosine triphosphate, was also upregulated in RA iPSCs. Interestingly, the proliferation of RA iPSCs was significantly greater than OA iPSC proliferation (p < 0.05). NAM played a critical role in the proliferation of RA iPSCs but not in OA iPSCs. When iPSCs were treated with 100 nM of the NAM inhibitor tannic acid (TA), the proliferation of RA iPSCs was significantly reduced (p < 0.001).

Conclusions

The metabolites of RA and OA FLS cells and RA and OA iPSCs were all clearly distinguishable from each other. NAM played a critical role in the proliferation of RA iPSCs but not in OA iPSCs. TA effectively inhibited the expression of NAM in RA iPSCs and is a possible effective treatment for RA patients.

Lipidomics in Ulcerative Colitis Reveal Alteration in Mucosal Lipid Composition Associated With the Disease State

BACKGROUND

The onset of ulcerative colitis (UC) is associated with alterations in lipid metabolism and a disruption of the balance between pro- and anti-inflammatory molecules. Only a few studies describe the mucosal lipid biosignatures during active UC. Moreover, the dynamics of lipid metabolism in the remission state is poorly defined. Therefore, this study aims to characterize mucosal lipid profiles in treatment-naïve UC patients and deep remission UC patients compared with healthy subjects.

METHODS

Treatment-naïve UC patients (n = 21), UC patients in deep remission (n = 12), and healthy volunteers (n = 14) were recruited. The state of deep remission was defined by histological and immunological remission defined by a normalized TNF-α gene expression. Mucosa biopsies were collected by colonoscopy. Lipid analysis was performed by means of ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS-MS). In total, 220 lipids from 11 lipid classes were identified.

RESULTS

The relative concentration of 122 and 36 lipids was altered in UC treatment-naïve patients and UC remission patients, respectively, compared with healthy controls. The highest number of significant variations was in the phosphatidylcholine (PC), ceramide (Cer), and sphingomyelin (SM) composition. Multivariate analysis revealed discrimination among the study groups based on the lipid profile. Furthermore, changes in phosphatidylethanolamine(38:3), Cer(d18:1/24:0), and Cer(d18:1/24:2) were most distinctive between the groups.

CONCLUSION

This study revealed a discriminant mucosal lipid composition pattern between treatment-naïve UC patients, deep remission UC patients, and healthy controls. We report several distinctive lipids, which might be involved in the inflammatory response in UC, and could reflect the disease state.

Identification of a Shared Microbiomic and Metabolomic Profile in Systemic Autoimmune Diseases

Dysbiosis has been described in systemic autoimmune diseases (SADs), including systemic lupus erythematosus (SLE), Sjögren’s syndrome (SjS), and primary anti-phosholipid syndrome (PAPS), however the biological implications of these associations are often elusive. Stool and plasma samples from 114 subjects, including in SLE (n = 27), SjS (n = 23), PAPs (n = 11) and undifferentiated connective tissue (UCTD, n = 26) patients, and geographically-matched healthy controls (HCs, n = 27), were collected for microbiome (16s rRNA gene sequencing) and metabolome (high-performance liquid chromatography coupled to mass spectrometry) analysis to identify shared characteristics across diseases. Out of 130 identified microbial genera, a subset of 29 bacteria was able to differentiate study groups (area under receiver operating characteristics (AUROC) = 0.730 ± 0.025). A fair classification was obtained with a subset of 41 metabolic peaks out of 254 (AUROC = 0.748 ± 0.021). In both models, HCs were well separated from SADs, while UCTD largely overlapped with the other diseases. In all of the SADs pro-tolerogenic bacteria were reduced, while pathobiont genera were increased. Metabolic alterations included two clusters comprised of: (a) members of the acylcarnitine family, positively correlating with a Prevotella-enriched cluster and negatively correlating with a butyrate-producing bacteria-enriched cluster; and (b) phospholipids, negatively correlating with butyrate-producing bacteria. These findings demonstrate a strong interaction between intestinal microbiota and metabolic function in patients with SADs.

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

We aimed to investigate metabolites associated with the 28-joint disease activity score based on erythrocyte sedimentation rate (DAS28-ESR) in patients with rheumatoid arthritis (RA) using capillary electrophoresis quadrupole time-of-flight mass spectrometry. Plasma and urine samples were collected from 32 patients with active RA (DAS28-ESR≥3.2) and 17 with inactive RA (DAS28-ESR<3.2). We found 15 metabolites in plasma and 20 metabolites in urine which showed a significant but weak positive or negative correlation with DAS28-ESR. When metabolites between active and inactive patients were compared, 9 metabolites in plasma and 15 in urine were found to be significantly different. Consequently, we selected 11 metabolites in plasma and urine as biomarker candidates which significantly correlated positively or negatively with DAS28-ESR, and significantly differed between active and inactive patients. When a multiple logistic regression model was built to discriminate active and inactive cohorts, three variables-histidine and guanidoacetic acid from plasma and hypotaurine from urine-generated a high area under the receiver operating characteristic (ROC) curve value (AUC = 0.8934). Thus, this metabolomics approach appeared to be useful for investigating biomarkers of RA. Combination of plasma and urine analysis may lead to more precise and reliable understanding of the disease condition. We also considered the pathophysiological significance of the found biomarker candidates.

Can Metabolic Pathways Be Therapeutic Targets in Rheumatoid Arthritis?

The metabolic rewiring of tumor cells and immune cells has been viewed as a promising source of novel drug targets. Many of the molecular pathways implicated in rheumatoid arthritis (RA) directly modify synovium metabolism and transform the resident cells, such as the fibroblast-like synoviocytes (FLS), and the synovial tissue macrophages (STM), toward an overproduction of enzymes, which degrade cartilage and bone, and cytokines, which promote immune cell infiltration. Recent studies have shown metabolic changes in stromal and immune cells from RA patients. Metabolic disruption in the synovium provide the opportunity to use in vivo metabolism-based imaging techniques for patient stratification and to monitor treatment response. In addition, these metabolic changes may be therapeutically targetable. Thus, resetting metabolism of the synovial membrane offers additional opportunities for disease modulation and restoration of homeostasis in RA. In fact, rheumatologists already use the antimetabolite methotrexate, a chemotherapy agent, for the treatment of patients with inflammatory arthritis. Metabolic targets that do not compromise systemic homeostasis or corresponding metabolic functions in normal cells could increase the drug armamentarium in rheumatic diseases for combination therapy independent of systemic immunosuppression. This article summarizes what is known about metabolism in synovial tissue cells and highlights chemotherapies that target metabolism as potential future therapeutic strategies for RA.

Multi-omic analysis of signalling factors in inflammatory comorbidities

BACKGROUND

Inflammation is a core element of many different, systemic and chronic diseases that usually involve an important autoimmune component. The clinical phase of inflammatory diseases is often the culmination of a long series of pathologic events that started years before. The systemic characteristics and related mechanisms could be investigated through the multi-omic comparative analysis of many inflammatory diseases. Therefore, it is important to use molecular data to study the genesis of the diseases. Here we propose a new methodology to study the relationships between inflammatory diseases and signalling molecules whose dysregulation at molecular levels could lead to systemic pathological events observed in inflammatory diseases.

RESULTS

We first perform an exploratory analysis of gene expression data of a number of diseases that involve a strong inflammatory component. The comparison of gene expression between disease and healthy samples reveals the importance of members of gene families coding for signalling factors. Next, we focus on interested signalling gene families and a subset of inflammation related diseases with multi-omic features including both gene expression and DNA methylation. We introduce a phylogenetic-based multi-omic method to study the relationships between multi-omic features of inflammation related diseases by integrating gene expression, DNA methylation through sequence based phylogeny of the signalling gene families. The models of adaptations between gene expression and DNA methylation can be inferred from pre-estimated evolutionary relationship of a gene family. Members of the gene family whose expression or methylation levels significantly deviate from the model are considered as the potential disease associated genes.

CONCLUSIONS

Applying the methodology to four gene families (the chemokine receptor family, the TNF receptor family, the TGF- β gene family, the IL-17 gene family) in nine inflammation related diseases, we identify disease associated genes which exhibit significant dysregulation in gene expression or DNA methylation in the inflammation related diseases, which provides clues for functional associations between the diseases.

Hypoxia, oxidative stress and inflammation

Inflammatory Arthritis is characterized by synovial proliferation, neovascularization and leukocyte extravasation leading to joint destruction and functional disability. Efficiency of oxygen supply to the synovium is poor due to the highly dysregulated synovial microvasculature. This along with the increased energy demands of activated infiltrating immune cells and inflamed resident cells leads to an hypoxic microenvironment and mitochondrial dysfunction. This favors an increase of reactive oxygen species, leading to oxidative damage which further promotes inflammation. In this adverse microenvironment synovial cells adapt to generate energy and switch their cell metabolism from a resting regulatory state to a highly metabolically active state which allows them to produce essential building blocks to support their proliferation. This metabolic shift results in the accumulation of metabolic intermediates which act as signaling molecules that further dictate the inflammatory response. Understanding the complex interplay between hypoxia-induced signaling pathways, oxidative stress and mitochondrial function will provide better insight into the underlying mechanisms of disease pathogenesis.

Metabolic features and regulation of the healing cycle-A new model for chronic disease pathogenesis and treatment

Without healing, multicellular life on Earth would not exist. Without healing, one injury predisposes to another, leading to disability, chronic disease, accelerated aging, and death. Over 60% of adults and 30% of children and teens in the United States now live with a chronic illness. Advances in mass spectrometry and metabolomics have given scientists a new lens for studying health and disease. This study defines the healing cycle in metabolic terms and reframes the pathophysiology of chronic illness as the result of metabolic signaling abnormalities that block healing and cause the normal stages of the cell danger response (CDR) to persist abnormally. Once an injury occurs, active progress through the stages of healing is driven by sequential changes in cellular bioenergetics and the disposition of oxygen and carbon skeletons used for fuel, signaling, defense, repair, and recovery. >100 chronic illnesses can be organized into three persistent stages of the CDR. One hundred and two targetable chemosensory G-protein coupled and ionotropic receptors are presented that regulate the CDR and healing. Metabokines are signaling molecules derived from metabolism that regulate these receptors. Reframing the pathogenesis of chronic illness in this way, as a systems problem that maintains disease, rather than focusing on remote trigger(s) that caused the initial injury, permits new research to focus on novel signaling therapies to unblock the healing cycle, and restore health when other approaches have failed.

Plasma lipidomic profile signature of rheumatoid arthritis versus Lyme arthritis patients

OBJECTIVES

Distinguishing of rheumatoid arthritis (RA) and Lyme arthritis (LA) is difficult, because of similar symptoms. This presents a significant clinical problem since treatments are quite different in both diseases. We investigated the plasma phospholipid profiles of RA and LA patients versus healthy subjects to find metabolic changes responsible for differentiation of both diseases.

METHODS

Plasma was collected from 9 RA, 9 LA, and 9 healthy subjects. Extracted lipids were analyzed using LC- MS/MS to characterize phospholipid profiles of RA, LA and healthy subjects. Principal components analysis (PCA), partial least squares-discriminate analysis (PLS-DA) and variable importance in projection (VIP) scores were used to estimate the importance of each phospholipid variable.

RESULTS

We identified 114 phospholipids in plasma. Phospholipid profiles were significantly different in RA and LA patients than in healthy subjects. Principal discriminant phospholipids between RA and LA groups were LPE (14:0), LPC(14:0) PI(18:0/20:4), PI(18:2/18:0), PI(16:1/18:2), PI(18:1/18:0), and PI(18:0/20:3).

CONCLUSIONS

Our study provides insights into the alteration of the plasma phospholipid profile of LA patients, resulting from Borrelia burgdorferi infection, that may lead to improved LA diagnosis and differentiation of this disease from RA. Furthermore, LPE (14:0) was found to have a high potential to be a possible biomarker of LA.

Quantification of run order effect on chromatography - mass spectrometry profiling data

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

High erythrocyte levels of the n-6 polyunsaturated fatty acid linoleic acid are associated with lower risk of subsequent rheumatoid arthritis in a southern European nested case-control study

OBJECTIVES

Findings relating to dietary intake of n-3 polyunsaturated fatty acids (PUFA) and risk of rheumatoid arthritis (RA) are mixed. Erythrocyte membrane PUFA is an accurate objective biomarker of PUFA status; however, there are little data on erythrocyte membrane PUFA and risk of RA. The objective was therefore to compare erythrocyte membrane PUFA between pre-RA individuals and matched controls from a population-based sample, and specifically to test the hypothesis that higher levels of longer chain n-3 PUFA are associated with lower risk of RA.

METHODS

The European Prospective Investigation into Cancer and Nutrition (EPIC) is a large European prospective cohort study of apparently healthy populations. We undertook a nested case-control study by identifying RA cases with onset after enrolment (pre-RA) in four EPIC cohorts in Italy and Spain. Confirmed pre-RA cases were matched with controls by age, sex, centre, and date, time and fasting status at blood collection. Conditional logistic regression analysis was used to estimate associations of PUFA with the development of RA, adjusting for potential confounders including body mass index, waist circumference, education level, physical activity, smoking status and alcohol intake.

RESULTS

The study analysed samples from 96 pre-RA subjects and 258 matched controls. In this analysis, the median time to diagnosis (defined as time between date of blood sample and date of diagnosis) was 6.71 years (range 0.8-15). A significant inverse association was observed with n-6 PUFA linoleic acid (LA) levels and pre-RA in the fully adjusted model (highest tertile: OR 0.29; 95% CI 0.12 to 0.75; P for trend 0.01). No association was observed with any individual n-3 PUFA, total n-3 PUFA or total n-3:n-6 ratio.

CONCLUSIONS

Erythrocyte levels of the n-6 PUFA LA were inversely associated with risk of RA, whereas no associations were observed for other n-6 or n-3 PUFA. Further work is warranted to replicate these findings and to investigate if lower LA levels are a bystander or contributor to the process of RA development.

Metabolomic serum profiling after ACL injury in rats: A pilot study implicating inflammation and immune dysregulation in post-traumatic osteoarthritis

ACL rupture is a major risk factor for post-traumatic osteoarthritis (PTOA) development. Little information exists on acute systemic metabolic indicators of disease development. Thirty-six female Lewis rats were randomized to Control or noninvasive anterior cruciate ligament rupture (ACLR) and to three post-injury time points: 72 h, 4 weeks, 10 weeks (n = 6). Serum was collected and analyzed by ¹ H nuclear magnetic resonance (NMR) spectroscopy and combined direct injection and liquid chromatography (LC)-mass spectrometry (MS)/MS (DI-MS). Univariate and multivariate statistics were used to analyze metabolomic data, and predictive biomarker models were analyzed by receiver operating characteristic (ROC) analysis. Topological pathway analysis was used to identify perturbed pathways. Two hundred twenty-two metabolites were identified by ¹ H NMR and DI-MS. Differences in the serum metabolome between ACLR and Control were dominated by medium- and long-chain acylcarnitine species. Further, decreases in several tryptophan metabolites were either found to be significantly different in univariate analysis or to play important contributory roles to multivariate model separation. In addition to acylcarnitines and tryptophan metabolites, glycine, carnosine, and D-mannose were found to differentiate ACLR from Control. Glycine, 9-hexadecenoylcarnitine, trans-2-Dodecenoylcarnitine, linoelaidyl carnitine, hydroxypropionylcarnitine, and D-Mannose were identified as biomarkers with high area under ROC curve values and high predictive accuracies. Our analysis provides new information regarding the potential contribution of inflammatory processes and immune dysregulation to the onset and progression of PTOA following ACL injury. As these processes have most commonly been associated with inflammatory arthropathies, larger-scale studies elucidating their involvement in PTOA development and progression are necessary. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1969-1979, 2018.