Untargeted metabolomics reveals the metabolic characteristics and biomarkers of obstetric antiphospholipid syndrome and undifferentiated connective tissue disease.

BACKGROUND

The clinical differentiation between obstetric antiphospholipid syndrome (OAPS) and undifferentiated connective tissue disease (UCTD) presents significant diagnostic challenges. This study employs metabolomics to investigate metabolic reprogramming patterns in OAPS and UCTD, aiming to identify potential biomarkers for early diagnosis.

METHODS

Using LC-MS-based metabolomics, we analyzed serum profiles from 40 OAPS patients (B1), 30 OAPS + UCTD patients (B2), 27 UCTD patients (B3), and 30 healthy controls (A1). Multivariate PLS-DA modeling, combined with KEGG pathway and Gene Set Enrichment Analysis (GSEA), was applied to identify disease-specific metabolic signatures.

RESULTS

Metabolomic profiling detected 1,227 metabolites, including 412 in negative ion mode and 815 in positive ion mode. The two ionization modes exhibited distinct chemical profiles, with PLS-DA analysis demonstrating superior group discrimination in positive ion mode. B1 vs B2 (Negative ion mode): nine metabolites were upregulated (notably 17(S)-HpDHA, showing the largest fold-change as a potential biomarker), and one metabolite was downregulated (5-sulfosalicylic acid). B1 vs B2 (Positive ion mode): 17 metabolites were upregulated (including 4-methyl-5-thiazoleethanol, a promising biomarker), and eight were downregulated. B1 vs B3 (Negative ion mode): 14 metabolites were upregulated (highlighted by 3-hydroxybenzoic acid, the most significantly altered candidate), and four were downregulated. B1 vs B3 (Positive ion mode): 30 metabolites were upregulated (again featuring 4-methyl-5-thiazoleethanol), and 32 were downregulated. B2 vs B3 (Negative ion mode): 15 metabolites were upregulated (e.g., chlortetracycline), and 15 were downregulated (notably 6α-prostaglandin I1). B2 vs B3 (Positive ion mode): 29 metabolites were upregulated (e.g., senecionine), and 64 were downregulated (e.g., SM 9:1 2O/16:4). These metabolites represent robust candidates for group discrimination. Enrichment analysis revealed that distinct metabolic pathways were significantly associated with different groups and ionization modes, suggesting divergent underlying metabolic mechanisms.

CONCLUSION

This study systematically characterizes the metabolic reprogramming in OAPS, UCTD, and their comorbid states, identifying potential diagnostic biomarkers. Differential metabolites and pathway analyses highlight the critical role of immunity, contributing to a theoretical framework for "metabolism-immunity-vascular" interactions.