Li Siyin, Shi Juan, Shu Xiaofang, Jian Xuemin, Zou Jinmei, Yang Jing
Department of Rheumatology and Immunology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Department of Obstetrics and Gynecology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, Sichuan, China.
Front Mol Biosci. 2025 Aug 22;12:1632244. doi: 10.3389/fmolb.2025.1632244. eCollection 2025.
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.
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.
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.
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.
产科抗磷脂综合征（OAPS）与未分化结缔组织病（UCTD）的临床鉴别存在重大诊断挑战。本研究采用代谢组学方法研究OAPS和UCTD中的代谢重编程模式，旨在识别早期诊断的潜在生物标志物。
使用基于液相色谱-质谱联用的代谢组学技术，我们分析了40例OAPS患者（B1组）、30例OAPS+UCTD患者（B2组）、27例UCTD患者（B3组）和30例健康对照者（A1组）的血清谱。应用多变量偏最小二乘判别分析（PLS-DA）模型，结合京都基因与基因组百科全书（KEGG）通路和基因集富集分析（GSEA），以识别疾病特异性代谢特征。
代谢组学分析检测到1227种代谢物，其中负离子模式下有412种，正离子模式下有815种。两种电离模式呈现出不同的化学特征，PLS-DA分析表明正离子模式下的组间区分效果更佳。B1组与B2组（负离子模式）：9种代谢物上调（特别是17(S)-HpDHA，其倍数变化最大，为潜在生物标志物），1种代谢物下调（5-磺基水杨酸）。B1组与B2组（正离子模式）：17种代谢物上调（包括4-甲基-5-噻唑乙醇，一种有前景的生物标志物），8种下调。B1组与B3组（负离子模式）：14种代谢物上调（以3-羟基苯甲酸最为显著，为变化最明显的候选物），4种下调。B1组与B3组（正离子模式）：30种代谢物上调（再次以4-甲基-5-噻唑乙醇为特征），32种下调。B2组与B3组（负离子模式）：15种代谢物上调（如金霉素），15种下调（特别是6α-前列腺素I1）。B2组与B3组（正离子模式）：29种代谢物上调（如千里光碱），64种下调（如SM 9:1 2O/16:4）。这些代谢物是组间区分的有力候选物。富集分析表明，不同的代谢途径与不同组和电离模式显著相关，提示潜在的代谢机制存在差异。
本研究系统地描述了OAPS, UCTD及其共病状态下的代谢重编程，识别了潜在的诊断生物标志物。差异代谢物和途径分析突出了免疫的关键作用，为“代谢-免疫-血管”相互作用提供了理论框架。
