Study on Early pregnancy diagnosis in sows based on joint analysis of vaginal secretion metabolomics and machine learning

Abstract

ObjectiveMetabolic transformations in female mammals throughout gestation manifest in the metabolite profiles of bodily fluids, potentially serving as biomarkers for the diagnosis of pregnancy. This investigation concentrated on sows 18 days post-mating, with the objective of identifying biomarkers pertinent to early pregnancy diagnosis through the analysis of variations in metabolic molecules within vaginal secretions. MethodsVaginal secretion samples were procured from both pregnant and non-pregnant sows 18 days post-mating, subsequently subjected to untargeted metabolomic analysis utilizing liquid chromatography-mass spectrometry (LC-MS). The samples were partitioned into training and testing datasets, and machine learning algorithms--specifically, Random Forest (RF) and support vector machine (SVM)--were integrated with metabolite weight ranking to identify key biological markers. ResultsA total of 3,249 metabolic molecules were identified, among which 534 exhibited significant differential expression in the vaginal secretions of pregnant versus non-pregnant sows. Notably, three hormones that correlate with the pregnant status of the sow were discerned: Progesterone, 3-Deoxyestradiol, and Prostaglandin E1. KEGG pathway analysis revealed that the differentially expressed metabolites were predominantly enriched in nucleotide metabolic pathways. The RF model demonstrated an impressive accuracy of 1.00 in the training dataset and 0.88 in the testing dataset, while the SVM model maintained an accuracy of 1.00 across both datasets. Utilizing model weights and differential expression data, three pivotal metabolites were identified: Indolepropionic acid, cis-Aconitate, and 4-p-Coumaroylquinic acid, which exhibited ROC curve AUC values of 0.90, 0.89, and 0.79, respectively. ConclusionThis study validates the viability of utilizing vaginal secretions for the early diagnosis of pregnancy in sows, thereby establishing a foundation for the advancement of non-invasive diagnostic methodologies.