Earticle

While the gut microbiota is increasingly implicated in the pathogenesis of Parkinson's Disease (PD), the majority of existing research predominantly focuses on Western populations, with limited studies addressing Eastern cohorts. This study aims to elucidate the differential composition of gut microbiota between Eastern and Western PD patients by utilizing advanced machine learning techniques. 16S ribosomal RiboNucleic Acid (rRNA) sequencing data from stool samples are obtained from the Sequence Read Archive (SRA), comprising a random selection of 70 individuals (35 healthy controls and 35 PD patients) from four countries: Korea, Japan, the United States, and Italy. Recursive Feature Elimination (RFE) is employed for feature selection, and four machine learning models— Support Vector Machine (SVM), Random Forest (RF), k- Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost)—are applied to classify PD patients by geographic origin. RFE identifies 15 key microbial taxa that distinguish between healthy controls and PD patients. Among the models trained on these taxa, the RF model exhibits the highest predictive accuracy, achieving 0.83 ± 0.048. Despite the relatively small sample size, this study underscores the necessity for larger-scale investigations and contributes to a more comprehensive understanding of gut microbiota disparities between Eastern and Western populations in the context of PD.