Metabolomics Profiling Identifies Diagnostic Metabolic Signatures for Pregnancy Loss: A Cross-sectional Study from Northwestern China

Nan Ding Xin Yang Ruifang Wang Fang Wang ^*

• Lanzhou University Second Hospital, Lanzhou, China

Pregnancy loss (PL) presents a significant clinical challenge with complex aetiologies, including metabolic dysregulation. Noninvasive diagnostic metabolic biomarkers for early diagnosis may improve clinical outcomes. The present study performed untargeted metabolomics analysis on plasma samples from PL patients (n=70) and control subjects (n=122) using liquid chromatographymass 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. 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. The present study identified a panel of metabolites with potential clinical applications for diagnosing PL.