Objective To analyze the differential metabolites in the seminal plasma of different types of male infertility based on untargeted metabolomics and explore new biomarkers. Methods Seminal plasma samples were collected from 85 men,and divided into oligospermia (n=20),azoospermia (n=20),asthenozoospermia (n=10),high sperm DNA fragmentation index (n=15),and healthy control (n=20) groups. Seminal plasma metabolites were detected using liquid chromatography-mass spectrometry (LC-MS). Differential metabolites were identified after data processing and statistical analyses. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to identify key metabolic pathways. Area under the curve (AUC) was used to evaluate the diag-nostic efficacy of differential metabolites. Results We identified 1431 differential metabolites. KEGG enrichment analysis showed that differential metabolites mainly participated in pathways,such as amino acid biosynthesis,protein digestion and absorption,and nucleotide metabolism. The AUC of the top 10 upregulated and downregulated metabolites in each group were>0.7,indicating good diagnostic effi-cacy. Conclusion Untargeted metabolomics of seminal plasma can detect differential metabolites between different types of male infer-tility and healthy men,providing new ideas for exploring biomarkers and metabolomics-related mechanisms of male infertility.

Objective To analyze the differential metabolites in the seminal plasma of different types of male infertility based on untargeted metabolomics and explore new biomarkers. Methods Seminal plasma samples were collected from 85 men,and divided into oligospermia (n=20),azoospermia (n=20),asthenozoospermia (n=10),high sperm DNA fragmentation index (n=15),and healthy control (n=20) groups. Seminal plasma metabolites were detected using liquid chromatography-mass spectrometry (LC-MS). Differential metabolites were identified after data processing and statistical analyses. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to identify key metabolic pathways. Area under the curve (AUC) was used to evaluate the diag-nostic efficacy of differential metabolites. Results We identified 1431 differential metabolites. KEGG enrichment analysis showed that differential metabolites mainly participated in pathways,such as amino acid biosynthesis,protein digestion and absorption,and nucleotide metabolism. The AUC of the top 10 upregulated and downregulated metabolites in each group were>0.7,indicating good diagnostic effi-cacy. Conclusion Untargeted metabolomics of seminal plasma can detect differential metabolites between different types of male infer-tility and healthy men,providing new ideas for exploring biomarkers and metabolomics-related mechanisms of male infertility.