Unveiling Endotypes in Systemic Lupus Erythematosus Through Multiomic Analysis: Insights Into Cardiovascular and...

Systemic lupus erythematosus (SLE) shows clinical and molecular heterogeneity, and cardiovascular (CV) complications and lupus nephritis (LN) remain leading causes of morbidity and mortality. This study investigated whether omic profiling can reveal molecular endotypes linked to these outcomes.

Serum from 199 patients with SLE underwent proximity extension assay-based proteomics and targeted nuclear magnetic resonance metabolomics. Unsupervised clustering was performed on proteomic data, followed by integrative multiomic factor analysis, logistic regression, and machine learning models. Cohorts with SLE from the University College London, a subset with expanded Olink Reveal panel and untargeted metabolomics, and in vitro (human umbilical vein endothelial cell and HK2) and ex vivo (rat kidneys) models exposed to patient sera were used for validation and mechanistic exploration.

Proteomic clustering identified two molecular subgroups (cluster 1 and cluster 2). Compared with cluster 2, cluster 1 showed damage burden and increased rates of LN (1.8-fold), hypertension (3-fold), dyslipidemia (2-fold), obesity (8-fold), and abnormal C-reactive protein/erythrocyte sedimentation rate (4-fold), consistent with CV risk. A total of 47 inflammatory and organ-damage proteins and multiple metabolites were increased in cluster 1. Neural network models based on metabolites and clinical variables discriminated clusters (area under the curve = 0.77), highlighting citrate and lipoproteins as key features. Multiomic analyses and external cohorts confirmed reproducibility and enrichment in pathways related to leukocyte trafficking, endothelial stress, and nephritis. In vitro, serum from patients with SLE induced NF-κB activation in rat kidneys compared with controls, supporting a proinflammatory effect.

Multiomic profiling delineates molecular endotypes in SLE, integrating immune, vascular, and metabolic pathways associated with CV risk and LN, supporting their potential for precision risk stratification.