Metabolic Disturbances in Urinary and Plasma Samples from Seven Different Systemic Autoimmune Diseases Detected by HPLC-ESI-QTOF-MS

Dissecting inflammation in the immunemetabolomic era

The role of immune metabolism, specific metabolites and cell-intrinsic and -extrinsic metabolic states across the time course of an inflammatory response are emerging knowledge. Targeted and untargeted metabolomic analysis is essential to understand how immune cells adapt their metabolic program throughout an immune response. In addition, metabolomic analysis can aid to identify pathophysiological patterns in inflammatory disease. Here, we discuss new metabolomic findings within the transition from inflammation to resolution, focusing on three key programs of immunity: Efferocytosis, IL-10 signaling and trained immunity. Particularly the tryptophan-derived metabolite kynurenine was identified as essential for efferocytosis and inflammation resolution as well as a potential biomarker in diverse inflammatory conditions. In summary, metabolomic analysis and integration with transcriptomic and proteomic data, high resolution imaging and spatial information is key to unravel metabolic drivers and dependencies during inflammation and progression to tissue-repair.

BiomiX, a user-friendly bioinformatic tool for democratized analysis and integration of multiomics data

BACKGROUND

Interpreting biological system changes requires interpreting vast amounts of multi-omics data. While user-friendly tools exist for single-omics analysis, integrating multiple omics still requires bioinformatics expertise, limiting accessibility for the broader scientific community.

RESULTS

BiomiX tackles the bottleneck in high-throughput omics data analysis, enabling efficient and integrated analysis of multiomics data obtained from two cohorts. BiomiX incorporates diverse omics data, using DESeq2/Limma packages for transcriptomics, and quantifying metabolomics peak differences, evaluated via the Wilcoxon test with the False Discovery Rate correction. The metabolomics annotation for Liquid Chromatography-Mass Spectrometry untargeted metabolomics is additionally supported using the mass-to-charge ratio in the CEU Mass Mediator database and fragmentation spectra in the TidyMass package. Methylomics analysis is performed using the ChAMP R package. Finally, Multi-Omics Factor Analysis (MOFA) integration identifies shared sources of variation across omics data. BiomiX also generates statistics, report figures and integrates EnrichR and GSEA for biological process exploration and subgroup analysis based on user-defined gene panels enhancing condition subtyping. BiomiX fine-tunes MOFA models, to optimize factors number selection, distinguishing between cohorts and providing tools to interpret discriminative MOFA factors. The interpretation relies on innovative bibliography research on Pubmed, which provides the articles most related to the discriminant factor contributors. Furthermore, discriminant MOFA factors are correlated with clinical data, and the top contributing pathways are explored, all with the aim of guiding the user in factor interpretation.

CONCLUSIONS

The analysis of single-omics and multi-omics integration in a standalone tool, along with MOFA implementation and its interpretability via literature, represents significant progress in the multi-omics field in line with the "Findable, Accessible, Interoperable, and Reusable" data principles. BiomiX offers a wide range of parameters and interactive data visualization, allowing for personalized analysis tailored to user needs. This R-based, user-friendly tool is compatible with multiple operating systems and aims to make multi-omics analysis accessible to non-experts in bioinformatics.

External and internal exposome as triggers of biological signalling in systemic sclerosis - A narrative synthesis

Systemic sclerosis (SSc) is an autoimmune chronic connective tissue disorder with a complex pathogenesis and a strong gene-environment interaction. Despite the low prevalence of SSc, with around 100-250 cases per million, the morbidity and mortality are high and disproportionately affecting women. In this context, we review the influence of the external and internal exposome on the "immunome" in SSc. While several studies have addressed aspects of exposure-induced autoimmunity in general, very few have focused on SSc-specific phenotypes. In epidemiological studies, targeted characterization of the external exposome component in relation to SSc has often been limited to a single exposure. Despite the selective characterization of exposure, such studies play an important role in providing evidence that can be used towards reduction of exposure of modifiable factors, and can lead to proper management and prevention of SSc. Additionally, there is an effort towards integration of external exposome data with health data (health records, medical imaging, diagnostic results, etc.), to significantly improve our understanding of the environmental and occupational causes of SSc. A limited number of studies have identified biological processes related to the vascular homeostasis, fibrotic processes and the immune system. The key findings of the current review show advances in our understanding of the SSc disease phenotype and associated biomarkers in relation to specific pathophysiological features, however most often such studies are not supplemented with external exposome data.

A review on the novel biomarkers of systemic lupus erythematosus discovered via metabolomic profiling

Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease affecting various body organs and systems. The diagnosis of SLE and its complications is based on evident clinical symptoms, serological marker levels, and pathological findings. Some serological markers have a low sensitivity and specificity, and biopsy procedures are invasive in nature. Hence, metabolomics has emerged as a valuable tool for SLE screening and categorization. Its application has contributed significantly to identifying SLE pathogenesis, improving clinical diagnosis, and developing treatment approaches. This review provides an overview of the utilization of metabolomics in the study of SLE, focusing on advancements in understanding the disease's pathogenesis, aiding in diagnosis, and monitoring treatment efficacy.

Genetically predicted plasma metabolites mediate the causal relationship between gut microbiota and primary immune thrombocytopenia (ITP)

Background

Primary immune thrombocytopenia (ITP) is an immune-mediated hematologic disorder characterized by a reduction in platelet count, increasing the risk of bleeding. Recent studies have indicated a close association between alterations in gut microbiota and the development of ITP. However, the mechanisms by which gut microbiota influence the occurrence and progression of ITP through plasma metabolites remain poorly understood. Evidence suggests extensive interactions between gut microbiota and plasma metabolites, implying a potential role for gut microbiota in influencing ITP through alterations in plasma metabolites, which requires further investigation.

Methods

In this study, summarized GWAS data (including 211 gut microbiota taxa, 1,400 plasma metabolites or ratios, and an ITP patient cohort) were retrieved from the MiBioGen and GWAS Catalog databases. Using a two-sample Mendelian randomization (MR) approach, we screened gut microbiota and plasma metabolites potentially causally related to ITP. We further identified plasma metabolites serving as mediators through which gut microbiota affect ITP and calculated the strength of the mediation effect. To ensure result stability, we primarily used the inverse variance weighted (IVW) method as the main judgment index. We also utilized MR Egger and inverse variance weighted methods to detect heterogeneity in the results, and employed MR-Egger and MR-PRESSO methods to assess the presence of pleiotropy.

Results

Though two-sample MR analysis, 8 gut microbiota taxa were found to have causal relationships with ITP. After excluding six plasma metabolites with pleiotropy, 39 plasma metabolites were found to be causally related to ITP (P < 0.05). Eleven plasma metabolites were identified as having causal relationships between gut microbiota and plasma metabolites. Finally, using the delta method, it was calculated that Sphingomyelin levels (8.0%, 95%CI: 0.9% to 11.5%, P = 0.047) and Glucose-to-mannose ratio (6.5%, 95%CI: 0.7% to 9.5%, P = 0.039) are intermediates for Intestinimonas influencing ITP, while Bilirubin (Z,Z) to etiocholanolone glucuronide ratio (5.6%, 95%CI: 4.7% to 6.9%, P = 0.043) is an intermediate for Senegalimassilia influencing ITP.

Conclusion

Gut microbiota can influence the development of ITP through changes in plasma metabolites. Sphingomyelin levels, Glucose-to-mannose ratio, and Bilirubin (Z,Z) to etiocholanolone glucuronide ratio are newly discovered intermediates through which gut microbiota influence ITP, providing potential indicators and targets for clinical diagnosis and treatment. This study highlights the intricate relationship between gut microbiota and plasma metabolites in the context of ITP, suggesting new avenues for clinical diagnosis and treatment.

Development of an LC-HRMS non-targeted method for comprehensive profiling of the exposome of nicotine and tobacco product users - A showcase for cigarette smokers

The global prevalence of electronic cigarettes, heated tobacco products, and other smokeless alternatives has grown significantly in the last ten years. These products have been suggested as combustion-free alternatives for conventional tobacco products like cigarettes, aiming to reduce the negative health impacts associated with smoking. However, the impact of those products on the health and safety of the general population are still unclear, as the absolute exposure from those products has not been thoroughly studied, yet. In this project, a non-targeted LC-HRMS method was developed comprising four different analytical modes for the investigation of the exposure profile in urine of the product users. The method is characterized by its high sensitivity and reproducibility, as shown during method validation. As a proof of concept, we first applied this method to detect significant differences in biomarkers of exposure (BoEs) between smokers and non-smokers. We observed a total of 171 BoEs significantly elevated in smokers, including several well-known biomarkers of smoke exposure like nicotine and its metabolites, mercapturic acid derivatives, and phenolic compounds. Some of the detected biomarkers are present at low ng/mL concentrations in urine, proving the high sensitivity needed for a holistic exploration of the exposome. Moreover, we were able to identify BoEs that have not been reported previously for smoking, such as 2,6-dimethoxyphenol and 7-methyl-1-naphthol glucuronide.

Identification of amino acids metabolomic profiling in human plasma distinguishes lupus nephritis from systemic lupus erythematosus

Lupus nephritis (LN) is an immunoinflammatory glomerulonephritis associated with renal involvement in systemic lupus erythematosus (SLE). Given the close relationship between plasma amino acids (AAs) and renal function, this study aimed to elucidate the plasma AA profiles in LN patients and identify key AAs and diagnostic patterns that distinguish LN patients from those with SLE and healthy controls. Participants were categorized into three groups: normal controls (NC), SLE, and LN. Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to quantify AA levels in human plasma. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were utilized to identify key AAs. The diagnostic capacity of the models was assessed using receiver operating characteristic (ROC) curve analysis and area under the ROC curve (AUC) values. Significant alterations in plasma AA profiles were observed in LN patients compared to the SLE and NC groups. The OPLS-DA model effectively separated LN patients from the SLE and NC groups. A joint model using histidine (His), lysine (Lys), and tryptophan (Trp) demonstrated exceptional diagnostic performance, achieving an AUC of 1.0 with 100% sensitivity, specificity, and accuracy in predicting LN. Another joint model comprising arginine (Arg), valine (Val), and Trp also exhibited robust predictive performance, with an AUC of 0.998, sensitivity of 93.80%, specificity of 100%, and accuracy of 95.78% in distinguishing between SLE and LN. The joint forecasting models showed excellent predictive capabilities in identifying LN and categorizing lupus disease status. This approach provides a novel perspective for the early identification, prevention, treatment, and management of LN based on variations in plasma AA levels.

Integration of multi-omics analysis reveals metabolic alterations of B lymphocytes in systemic lupus erythematosus

OBJECTIVE

To link changes in the B-cell transcriptome from systemic lupus erythematosus (SLE) patients with those in their macroenvironment, including cellular and fluidic components.

METHODS

Analysis was performed on 363 patients and 508 controls, encompassing transcriptomics, metabolomics, and clinical data. B-cell and whole-blood transcriptomes were analysed using DESeq and GSEA. Plasma and urine metabolomics peak changes were quantified and annotated using Ceu Mass Mediator database. Common sources of variation were identified using MOFA integration analysis.

RESULTS

Cellular macroenvironment was enriched in cytokines, stress responses, lipidic synthesis/mobility pathways and nucleotide degradation. B cells shared these pathways, except nucleotide degradation diverted to nucleotide salvage pathway, and distinct glycosylation, LPA receptors and Schlafen proteins.

CONCLUSIONS

B cells showed metabolic changes shared with their macroenvironment and unique changes directly or indirectly induced by IFN-α signalling. This study underscores the importance of understanding the interplay between B cells and their macroenvironment in SLE pathology.

Comprehensive analysis of the oral microbiota and metabolome change in patients of burning mouth syndrome with psychiatric symptoms

Background

Burning mouth syndrome (BMS) is a chronic idiopathic facial pain with intraoral burning or dysesthesia. BMS patients regularly suffer from anxiety/depression, and the association of psychiatric symptoms with BMS has received considerable attention in recent years. The aims of this study were to investigate the potential interplay between psychiatric symptoms and BMS.

Methods

Using 16S rRNA sequencing and liquid chromatography-mass spectrometry (LC/MS) to evaluate the oral microbiota and saliva metabolism of 40 BMS patients [including 29 BMS patients with depression or anxiety symptoms (DBMS)] and 40 age matched healthy control (HC).

Results

The oral microbiota composition in BMS exhibited no significant differences from HC, although DBMS manifested decreased α-diversity relative to HC. Noteworthy was the discernible elevation in the abundance of proinflammatory microorganisms within the oral microbiome of individuals with DBMS. Parallel findings in LC/MS analyses revealed discernible disparities in metabolites between DBMS and HC groups. Principal differential metabolites were notably enriched in amino acid metabolism and lipid metabolism, exhibiting associations with infectious and immunological diseases. Furthermore, the integrated analysis underscores a definitive association between the oral microbiome and metabolism in DBMS.

Conclusions

This study suggests possible future modalities for better understanding the pathogenesis and personalized treatment plans of BMS.

Probiotic Limosilactobacillus reuteri DSM 17938 Changes Foxp3 Deficiency-Induced Dyslipidemia and Chronic Hepatitis in Mice

The probiotic Limosilactobacillus reuteri DSM 17938 produces anti-inflammatory effects in scurfy (SF) mice, a model characterized by immune dysregulation, polyendocrinopathy, enteropathy, and X-linked inheritance (called IPEX syndrome in humans), caused by regulatory T cell (Treg) deficiency and is due to a Foxp3 gene mutation. Considering the pivotal role of lipids in autoimmune inflammatory processes, we investigated alterations in the relative abundance of lipid profiles in SF mice (± treatment with DSM 17938) compared to normal WT mice. We also examined the correlation between plasma lipids and gut microbiota and circulating inflammatory markers. We noted a significant upregulation of plasma lipids associated with autoimmune disease in SF mice, many of which were downregulated by DSM 17938. The upregulated lipids in SF mice demonstrated a significant correlation with gut bacteria known to be implicated in the pathogenesis of various autoimmune diseases. Chronic hepatitis in SF livers responded to DSM 17938 treatment with a reduction in hepatic inflammation. Altered gene expression associated with lipid metabolism and the positive correlation between lipids and inflammatory cytokines together suggest that autoimmunity leads to dyslipidemia with impaired fatty acid oxidation in SF mice. Probiotics are presumed to contribute to the reduction of lipids by reducing inflammatory pathways.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis

There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.

Urine and serum metabolic profiling combined with machine learning for autoimmune disease discrimination and classification

Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Herein, we performed metabolomic profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in urine and serum samples. Combined with machine learning (ML), metabolomic patterns from urine achieved the discrimination and classification of ADs with high accuracy. Furthermore, metabolic disturbances among different ADs were also investigated, and provided information of etiology. These results demonstrated that urine metabolic patterns based on MALDI-MS and ML manifest substantial potential in precision medicine.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Serum metabolite differences detected by HILIC UHPLC-Q-TOF MS in systemic sclerosis

BACKGROUND

Systemic sclerosis (SSc) is a chronic autoimmune disease characterized by extensive fibrosis and vascular damage. Vasculopathy, activation of the immune system, and diffuse fibrosis are all involved in the fatal pathogenesis of SSc. However, little metabolomic research has been conducted in SSc.

METHODS

This study included 30 SSc patients and 30 healthy individuals. The metabolite differences in serum samples were analyzed using ultra-high-pressure liquid chromatography and quadrupole-time-of-flight mass spectrometry. Meanwhile, serum metabolites were analyzed in patients with systemic involvement (lung or skin fibrosis).

RESULTS

A total of 2360 ion peaks were detected, all of which were attributable to 38 metabolites. These metabolites primarily consisted of fatty acids, amino acids, and glycerophospholipids, which were the major metabolic pathways altered in SSc patients. Glutamine metabolism was the main pathway altered in SSc patients with lung involvement, whereas amino acid metabolism and steroid hormone biosynthesis were the main pathways altered in SSc patients with skin involvement.

CONCLUSION

These findings suggested that metabolic profiles and pathways differed between SSc patients and healthy people, potentially providing new targets for SSc-directed therapeutics and diagnostics. Key Points • Metabolic profiles and pathways differed between SSc patients and healthy people. • The levels of trans-dehydroandrosterone are substantially lower in lcSSc than in dcSSc, potentially providing new targets for SSc patients with skin involvement. • L-glutamine could be used as a serum metabolic marker and a therapeutic target for SSc patients with lung involvement.

Comprehensive Analysis of the Relationships Between the Gut Microbiota and Fecal Metabolome in Individuals With Primary Sjogren's Syndrome by 16S rRNA Sequencing and LC-MS-Based Metabolomics

The gut microbiota has been associated with primary Sjogren’s syndrome (pSS), yet the biological implications of these associations are often elusive. We analyzed the fecal microbiota through 16S rRNA gene amplification and sequencing in 30 patients with pSS and 20 healthy controls (HCs); At the same time, the fecal metabolome was characterized by ultrahigh-performance liquid chromatography–mass spectrometry. In addition, correlation analyses of microbiota and metabolome data were performed to identify meaningful associations. We found that the microbiota composition of pSS patients was significantly different from that of HCs. The pSS gut microbiota is characterized by increased abundances of proinflammatory microbes, especially Escherichia-Shigella, and decreased abundances of anti-inflammatory microbes. Concerning the metabolome, a multivariate model with 33 metabolites efficiently distinguished cases from controls. Through KEGG enrichment analysis, we found that these metabolites were mainly involved in amino acid metabolism and lipid metabolism. The correlation analysis indicated that there were certain correlations between the microbiota and metabolism in pSS patients. In addition, an abundance of Escherichia-Shigella was found to be correlated with high levels of four metabolites (aflatoxin M1, glycocholic acid, L-histidine and phenylglyoxylic acid). Our research suggests that in pSS patients, the gut microbiota is characterized by a specific combination of proinflammatory changes and metabolic states. Escherichia-Shigella is a factor related to gut dysbiosis, which may promote intestinal damage and affect amino acid metabolism.

Immune-Related Urine Biomarkers for the Diagnosis of Lupus Nephritis

The kidney is one of the main organs affected by the autoimmune disease systemic lupus erythematosus. Lupus nephritis (LN) concerns 30-60% of adult SLE patients and it is significantly associated with an increase in the morbidity and mortality. The definitive diagnosis of LN can only be achieved by histological analysis of renal biopsies, but the invasiveness of this technique is an obstacle for early diagnosis of renal involvement and a proper follow-up of LN patients under treatment. The use of urine for the discovery of non-invasive biomarkers for renal disease in SLE patients is an attractive alternative to repeated renal biopsies, as several studies have described surrogate urinary cells or analytes reflecting the inflammatory state of the kidney, and/or the severity of the disease. Herein, we review the main findings in the field of urine immune-related biomarkers for LN patients, and discuss their prognostic and diagnostic value. This manuscript is focused on the complement system, antibodies and autoantibodies, chemokines, cytokines, and leukocytes, as they are the main effectors of LN pathogenesis.

Multi-Omics Approaches in Immunological Research

The immune system plays a vital role in health and disease, and is regulated through a complex interactive network of many different immune cells and mediators. To understand the complexity of the immune system, we propose to apply a multi-omics approach in immunological research. This review provides a complete overview of available methodological approaches for the different omics data layers relevant for immunological research, including genetics, epigenetics, transcriptomics, proteomics, metabolomics, and cellomics. Thereafter, we describe the various methods for data analysis as well as how to integrate different layers of omics data. Finally, we discuss the possible applications of multi-omics studies and opportunities they provide for understanding the complex regulatory networks as well as immune variation in various immune-related diseases.

New secondary metabolites from the endophytic fungus Aspergillus sp. from Tripterygium wilfordii

One new 3,5-dimethylorsellinic acid (DMOA)-based meroterpenoid (1), one prenylated tryptophan derivative (2), together with ten known compounds (3-12) were isolated from the endophytic fungus Aspergillus sp. from Tripterygium wilfordii. Their structures and absolute configurations were determined by NMR spectroscopic data, HRESIMS data, UV and IR data as well as electronic circular dichroism (ECD) calculation. In structure, compound 1 was a rare example of DMOA-based meroterpenoid with a cis-fused C/D ring system, and compound 2 possessed an unusual (E)-oxime group. In bioactivity, the lovastatin analogues 5, 6, 9 and 10 showed potential immunosuppressive activity against anti-CD3/anti-CD28 monoclonal antibodies (mAbs)-irritated murine splenocytes proliferation, with IC₅₀ values ranging from (5.30 ± 0.51) μM to (16.51 ± 1.62) μM.

Caution in studying and interpreting the lupus metabolome

Several metabolomics studies have shed substantial light on the pathophysiological pathways underlying multiple diseases including systemic lupus erythematosus (SLE). This review takes stock of our current understanding of this field. We compare, collate, and investigate the metabolites in SLE patients and healthy volunteers, as gleaned from published metabolomics studies on SLE. In the surveyed primary reports, serum or plasma samples from SLE patients and healthy controls were assayed using mass spectrometry or nuclear magnetic resonance spectroscopy, and metabolites differentiating SLE from controls were identified. Collectively, the circulating metabolome in SLE is characterized by reduced energy substrates from glycolysis, Krebs cycle, fatty acid β oxidation, and glucogenic and ketogenic amino acid metabolism; enhanced activity of the urea cycle; decreased long-chain fatty acids; increased medium-chain and free fatty acids; and augmented peroxidation and inflammation. However, these findings should be interpreted with caution because several of the same metabolic pathways are also significantly influenced by the medications commonly used in SLE patients, common co-morbidities, and other factors including smoking and diet. In particular, whereas the metabolic alterations relating to inflammation, oxidative stress, lipid peroxidation, and glutathione generation do not appear to be steroid-dependent, the other metabolic changes may in part be influenced by steroids. To conclude, metabolomics studies of SLE and other rheumatic diseases ought to factor in the potential contributions of confounders such as medications, co-morbidities, smoking, and diet.