Metabolic signatures in systemic autoimmune diseases: A state-of-the-art overview of systemic lupus erythematosus, primary Sjögren's syndrome, and systemic sclerosis.

Systemic lupus erythematosus (SLE), primary Sjögren's syndrome (pSS), and systemic sclerosis (SSc) are systemic autoimmune diseases characterized by marked heterogeneity in clinical presentation, organ involvement, and disease course. Their management remains challenging, particularly in patients with multi-organ involvement or overlapping autoimmune conditions. Emerging metabolomic studies indicate that distinct molecular signatures are closely linked to disease phenotypes and can reliably distinguish patients from healthy controls, underscoring their potential clinical relevance. This review aims to synthesize current metabolomics research on SLE, pSS, and SSc. We first consolidate knowledge on metabolic alterations by identifying pathways that differentiate patients from controls. We then highlight metabolites associated with organ involvement and disease activity and compare shared and disease-specific metabolic features across the three conditions. Metabolites reported in published metabolomics studies of SLE, pSS, and SSc were systematically mapped to the KEGG database (MetaboAnalyst 6.0) to perform pathway enrichment analysis, providing an integrated overview of disease-associated metabolic pathways. Analysis revealed substantial overlap in metabolic alterations across the three diseases. Amino acid metabolism emerged as a core feature of systemic autoimmunity, whereas lipid-related pathways showed greater disease- and organ-specificity. SLE and pSS are metabolically similar, reflecting their clinical overlap, yet glycerophospholipid metabolism may distinguish pSS. SLE was the most extensively studied disease, with glycine, serine, and threonine metabolism showing particularly pronounced alterations. Altered metabolomic profiles provide insight into shared and disease-specific mechanisms underlying SLE, pSS, and SSc. Systematic integration of metabolomics data may reduce study-specific variability and identify robust metabolic pathways relevant for diagnosis and personalized management. However, further analytical validation and clinical translation are required before these metabolites can be implemented in routine diagnostic and stratification strategies.