Annals of the rheumatic diseases · 2026
An immunometabolic signature of major depressive disorder in systemic lupus erythematosus.
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Ellul Pierre, Tchitchek Nicolas, Kvedaraviciute Grete, Lorenzon Roberta, Melki Isabelle, Delorme Richard, Rosenzwajg Michelle, Klatzmann David
Аннотация
OBJECTIVES: Systemic lupus erythematosus (SLE) frequently affects the central nervous system, leading to neuropsychiatric SLE (NPSLE). Major depressive disorder in SLE (SLEMDD) is the most frequent manifestation of NPSLE and is believed to arise from an immune-mediated process. However, biomarkers for SLEMDD remain lacking. The aim of this study was to identify candidate immunometabolic biomarkers associated with SLEMDD.
METHODS: We analysed deep flow cytometry immune phenotyping, gut microbiota profiling, and targeted mass spectrometry-based metabolomics from 99 patients from the LUPIL-2 study (NCT02955615). Biological signatures were identified using unsupervised principal component analysis and supervised decision tree classification. They were then validated in an independent cohort from the TRANSIMMUNOM study (NCT02466217).
RESULTS: SLEMDD patients exhibited a distinct immune profile with decreased naïve CD4⁺ T cells and naïve regulatory T cells (Tregs), alongside increased ICOS⁺ effector memory Tregs (94% classification accuracy). Gut microbiota diversity was reduced with depletion of Akkermansia muciniphila and enrichment of Faecalibacterium prausnitzii. Metabolomic analyses revealed disruptions in kynurenine and short-chain fatty acid pathways, including decreased butyrate levels. Integrative analyses demonstrated coordinated alterations linking Treg activation, microbial metabolites, and immune pathways, distinguishing SLEMDD from SLEnon-MDD with up to 85% accuracy.
CONCLUSIONS: SLEMDD is associated with an immunometabolic signature involving alterations in Treg phenotype, gut microbiota composition, and metabolic pathways. These findings provide a rationale for future immunoregulatory or microbiota-targeted therapeutic strategies in SLEMDD.
METHODS: We analysed deep flow cytometry immune phenotyping, gut microbiota profiling, and targeted mass spectrometry-based metabolomics from 99 patients from the LUPIL-2 study (NCT02955615). Biological signatures were identified using unsupervised principal component analysis and supervised decision tree classification. They were then validated in an independent cohort from the TRANSIMMUNOM study (NCT02466217).
RESULTS: SLEMDD patients exhibited a distinct immune profile with decreased naïve CD4⁺ T cells and naïve regulatory T cells (Tregs), alongside increased ICOS⁺ effector memory Tregs (94% classification accuracy). Gut microbiota diversity was reduced with depletion of Akkermansia muciniphila and enrichment of Faecalibacterium prausnitzii. Metabolomic analyses revealed disruptions in kynurenine and short-chain fatty acid pathways, including decreased butyrate levels. Integrative analyses demonstrated coordinated alterations linking Treg activation, microbial metabolites, and immune pathways, distinguishing SLEMDD from SLEnon-MDD with up to 85% accuracy.
CONCLUSIONS: SLEMDD is associated with an immunometabolic signature involving alterations in Treg phenotype, gut microbiota composition, and metabolic pathways. These findings provide a rationale for future immunoregulatory or microbiota-targeted therapeutic strategies in SLEMDD.