Interpretable machine learning uncovers core seminal microbial signatures in idiopathic oligoasthenospermia

Previous studies have confirmed that changes in semen microbiota are closely related to male oligoasthenospermia(OA). However, current research only qualitatively describes the differences in the microbial community, fails to screen out core markers with independent discriminatory value, and also fails to quantitatively evaluate the diagnostic efficacy of the microbial community, resulting in insufficient research on the diagnostic value of the semen microbiota in oligoasthenospermia.

A total of 40 untreated patients with idiopathic oligoasthenospermia (IOA) and 30 fertile control (FC) were recruited for this study. The semen samples were sequenced using 16S rRNA sequencing technology to assess the differences in microbial diversity and abundance. Subsequently, three machine learning algorithms were employed to further identify the core microorganisms and to further evaluate the diagnostic performance. Then, the SHapley Additive exPlanations(SHAP)analysis method was used to explain the contribution of these core microbiota to the disease. Additionally, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) algorithm was used to predict the functions of the core microorganisms.

The analysis of the microbial community in semen revealed that there were differences in the internal composition structure of the semen microbiota between the IOA group and the FC group. Machine learning algorithms identified a total of 5 core bacterial genera. The area under the curve (AUC) of this model was 0.773, with a 95% confidence interval (CI) of 0.661-0.885, indicating that the model has strong discriminatory power. SHAP analysis further revealed the direction of the association between the abundance of the microbiota and disease prediction. Additionally, KEGG pathway prediction revealed that the 5 core genera were predominantly enriched in carbohydrate and nucleic acid metabolism pathways, most notably glycolysis/gluconeogenesis (P = 6.67 × 10^-3).

This study employed a variety of machine learning algorithms to conduct a systematic characterization study on the semen microbiota of patients with idiopathic oligoasthenospermia. This method overcomes the limitations of traditional microbiota analysis and can This approach overcame the limitations of traditional microbiota analysis and constructed a risk prediction model based on core bacterial genera. It preliminarily explored the potential of the semen microbiota as a non-invasive screening candidate marker for IOA, providing a reference for subsequent research on non-invasive diagnostic markers and mechanisms., providing a basis for non-invasive precise diagnosis of the disease and the analysis of the pathogenic mechanism of the microbiome.