ArticleFrontiers in endocrinology2025
Metabolomics profiling identifies diagnostic metabolic signatures for pregnancy loss: a cross-sectional study from northwestern China.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Linking the metals to metabolism in recurrent pregnancy loss through untargeted metabolomics and machine learning.Frontiers in endocrinology · 2025Article
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4 authors.
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Abstract
Objective: To identify potential diagnostic metabolic biomarkers for pregnancy loss (PL) by performing untargeted metabolomics analysis. Methods: The present study performed untargeted metabolomics analysis on plasma samples from PL patients (n=70) and control subjects (n=122) using liquid chromatography‒mass spectrometry (LC‒MS). Metabolic profiles were evaluated using orthogonal partial least squares discriminant analysis (OPLS-DA), and pathway enrichment analysis was conducted via the KEGG database. LASSO regression was employed to identify significant metabolites, and their diagnostic performance was evaluated through receiver operating characteristic (ROC) curves. Pearson correlation analysis was used to explore the relationships between differentially abundant metabolites and clinical parameters. Results: In total, 359 metabolites were identified, 57 of which were significantly altered between the control and PL group through OPLS-DA. Differential metabolites were significantly enriched in caffeine metabolism, tryptophan metabolism, and riboflavin metabolism pathways. Key metabolites, such as testosterone glucuronide, 6-hydroxymelatonin, and (S)-leucic acid, exhibited strong diagnostic potential, with AUC values of 0.991, 0.936 and 0.952, respectively, and the combined AUC was 0.993. Furthermore, Pearson correlation analysis revealed a significant negative correlation between the waist‒to‒hip ratio (WHR) and the abundance of testosterone glucuronide (r = -0.291, p = 0.0146), and a significant positive correlation between WHR and (S)-leucic acid (r = 0.248, p = 0.0381) in the PL group. Conclusion: We identified a panel of plasma metabolites with significant diagnostic potential for PL. These biomarkers may facilitate early, noninvasive diagnosis and offer insights into metabolic dysregulation associated with pregnancy loss.
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