Skip to content
DraconiticResearch
Search Sources API
186,086 studies31 sourcesUpdated 27 min ago
Peer-reviewedCC BY 4.0Frontiers in Immunology

Untargeted serum metabolomics in systemic lupus erythematosus reveals candidate metabolomic signatures associated with clinical phenotypes and glucocorticoid use

  • Laura E. Eades1
  • Christopher K. Barlow2
  • Katherine J. Jeppe2
  • Hossein Valipour Kahrood2
  • Ralf B. Schittenhelm2
  • Joanna R. Kent3
  • Arthur Richard Kitching1,3
  • Artemis Papadaki4
  • William A. Figgett5
  • Rangi Kandane-Rathnayake1
  • Alberta Y. Hoi1
  • Eric F. Morand1
1 more authors
  • Fabien B. Vincent1
5 affiliations
  1. 1Centre for Inflammatory Diseases, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University
  2. 2Monash Proteomics and Metabolomics Platform, Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University
  3. 3Department of Nephrology, Monash Health
  4. 4National Heart and Lung Institute, Faculty of Medicine, Imperial College London
  5. 5Data Science Platform, Garvan Institute of Medical Research
Read full textPDFDOICite

Abstract

Objectives: Despite promising advances in metabolomics for biomarker discovery in systemic lupus erythematosus (SLE), studies examining associations with detailed disease phenotypes, including organ damage and validated outcome measures, remain limited. We aimed to explore associations between serum metabolomic profiles with SLE phenotypes.

Methods: Untargeted serum metabolomics was performed in 60 SLE patients using liquid chromatography-mass spectrometry. Metabolites with missing values or low-confidence assignments were excluded. Differential abundance analysis (DA) using regression models and supervised learning using least absolute shrinkage and selection operator (LASSO) were performed to investigate relationships between metabolites and clinical features. Covariates in multivariable models included age, sex, ethnicity and renal impairment (eGFR < 60 mL/min/m2). Disease activity was assessed using the SLE Disease Activity Index-2000 (SLEDAI-2K), with renal activity defined as the presence of SLEDAI-2K proteinuria.

Results: 364 metabolites were studied in 58 patients (median (IQR) age 42 (34-52) years, 91% female, 50% Asian ethnicity, 50% European ethnicity, 50% active disease). Using DA, 20 metabolites were significantly associated with glucocorticoid use, although no predictive LASSO model for this feature was identified. A metabolomic feature putatively identified as 5β-Cholestane-3α, 7α, 24, 25-tetrol was significantly negatively associated with biopsy-proven lupus nephritis (LN) and active LN. Glutamic acid was significantly positively associated with glucocorticoid-free remission. Homocitrulline and N6, N6, N6-trimethyl-L-lysine were significantly positively associated with active LN. A feature putatively identified as 3-Hydroxy-N6, N6, N6-trimethyl-L-lysine was significantly positively associated with biopsy-proven LN. These metabolite associations were also predictive on LASSO modelling. Pathway-level enrichment revealed associations with active disease (Valine, Leucine and Isoleucine Degradation, FDR = 0.0015), organ damage (Citric Acid Cycle and Gluconeogenesis, FDR = 0.043 each), DORIS remission (Phenylalanine and Tyrosine Metabolism, FDR = 0.028; Valine, Leucine and Isoleucine Degradation, FDR = 0.041), and remission off prednisolone (Urea Cycle, Tryptophan Metabolism, Purine Metabolism, and Glutamate Metabolism; FDR = 0.044 each).

Conclusions: Associations between glucocorticoid use and serum metabolites highlight the importance of accounting for this in SLE metabolomics studies. Significant associations between serum metabolites and SLE phenotypes, particularly previously unreported associations between methylated and carbamylated lysine derivatives with LN, support the promise of serum metabolomics to identify prognostic SLE biomarkers.

Related

  1. Untargeted Metabolomics Reveals Regional and Cultivar-Associated Metabolomic Variation in Soybeans Across Northeast China
    Jinghui Wang, Lupeng Gou, Haohai You +6
    International Journal of Molecular SciencesPeer-reviewedCC BY 4.0Full textPlant biologyCrossrefEurope PMC
  2. Sensitive untargeted metabolomics via 3-nitrophenylhydrazine derivatization maps gut microbiota-related metabolites across multiple mouse biological compartments
    Q. Wu, L. Zhu, Y. Chi +1
    bioRxivPreprintAll rights reservedFull textSystems & synthetic biologybioRxiv
  3. MetaboCensoR: A Shiny Application for Data Filtering in Untargeted LC-MS Metabolomics to Enhance Interpretability
    I. V. Plyushchenko, T. Luzzatto-Knaan
    bioRxivPreprintCC BY-NC-NDFull textBioinformatics & computational biologybioRxiv
  4. Shotgun metagenomic and untargeted metabolomic analysis of gut microbiome associated with peripheral artery disease
    S. Niraula, J. W. Jang, J. Jung +7
    bioRxivPreprintCC BYFull textMicrobiologybioRxiv
  5. Analysis of Metabolic Differences Between Marselan and Cabernet Sauvignon Grape Berries from the Manas Wine-Producing Region of Xinjiang Based on Untargeted Metabolomics.
    Guiyin Wei, Yun Wu, Na Xu +6
    Foods (Basel, Switzerland)Peer-reviewedCC BYFull textPlant biologyEurope PMC

Cite

BibTeXRIS
BibTeX
@article{eades2026untargeted,
  title = {{Untargeted serum metabolomics in systemic lupus erythematosus reveals candidate metabolomic signatures associated with clinical phenotypes and glucocorticoid use}},
  author = {Laura E. Eades and Christopher K. Barlow and Katherine J. Jeppe and Hossein Valipour Kahrood and Ralf B. Schittenhelm and Joanna R. Kent and Arthur Richard Kitching and Artemis Papadaki and William A. Figgett and Rangi Kandane-Rathnayake and Alberta Y. Hoi and Eric F. Morand and Fabien B. Vincent},
  journal = {Frontiers in Immunology},
  year = {2026},
  month = oct,
  doi = {10.3389/fimmu.2026.1925827},
  url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1925827/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Untargeted serum metabolomics in systemic lupus erythematosus reveals candidate metabolomic signatures associated with clinical phenotypes and glucocorticoid use
AU  - Laura E. Eades
AU  - Christopher K. Barlow
AU  - Katherine J. Jeppe
AU  - Hossein Valipour Kahrood
AU  - Ralf B. Schittenhelm
AU  - Joanna R. Kent
AU  - Arthur Richard Kitching
AU  - Artemis Papadaki
AU  - William A. Figgett
AU  - Rangi Kandane-Rathnayake
AU  - Alberta Y. Hoi
AU  - Eric F. Morand
AU  - Fabien B. Vincent
PY  - 2026
DA  - 2026/10/06
JO  - Frontiers in Immunology
DO  - 10.3389/fimmu.2026.1925827
UR  - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1925827/full
AB  - Objectives: Despite promising advances in metabolomics for biomarker discovery in systemic lupus erythematosus (SLE), studies examining associations with detailed disease phenotypes, including organ damage and validated outcome measures, remain limited. We aimed to explore associations between serum metabolomic profiles with SLE phenotypes.  Methods: Untargeted serum metabolomics was performed in 60 SLE patients using liquid chromatography-mass spectrometry. Metabolites with missing values or low-confidence assignments were excluded. Differential abundance analysis (DA) using regression models and supervised learning using least absolute shrinkage and selection operator (LASSO) were performed to investigate relationships between metabolites and clinical features. Covariates in multivariable models included age, sex, ethnicity and renal impairment (eGFR < 60 mL/min/m2). Disease activity was assessed using the SLE Disease Activity Index-2000 (SLEDAI-2K), with renal activity defined as the presence of SLEDAI-2K proteinuria.  Results: 364 metabolites were studied in 58 patients (median (IQR) age 42 (34-52) years, 91% female, 50% Asian ethnicity, 50% European ethnicity, 50% active disease). Using DA, 20 metabolites were significantly associated with glucocorticoid use, although no predictive LASSO model for this feature was identified. A metabolomic feature putatively identified as 5β-Cholestane-3α, 7α, 24, 25-tetrol was significantly negatively associated with biopsy-proven lupus nephritis (LN) and active LN. Glutamic acid was significantly positively associated with glucocorticoid-free remission. Homocitrulline and N6, N6, N6-trimethyl-L-lysine were significantly positively associated with active LN. A feature putatively identified as 3-Hydroxy-N6, N6, N6-trimethyl-L-lysine was significantly positively associated with biopsy-proven LN. These metabolite associations were also predictive on LASSO modelling. Pathway-level enrichment revealed associations with active disease (Valine, Leucine and Isoleucine Degradation, FDR = 0.0015), organ damage (Citric Acid Cycle and Gluconeogenesis, FDR = 0.043 each), DORIS remission (Phenylalanine and Tyrosine Metabolism, FDR = 0.028; Valine, Leucine and Isoleucine Degradation, FDR = 0.041), and remission off prednisolone (Urea Cycle, Tryptophan Metabolism, Purine Metabolism, and Glutamate Metabolism; FDR = 0.044 each).  Conclusions: Associations between glucocorticoid use and serum metabolites highlight the importance of accounting for this in SLE metabolomics studies. Significant associations between serum metabolites and SLE phenotypes, particularly previously unreported associations between methylated and carbamylated lysine derivatives with LN, support the promise of serum metabolomics to identify prognostic SLE biomarkers.
SN  - 1664-3224
ER  -