Abstract
Parkinson's disease (PD) is a prevalent neurodegenerative disorder with complex pathogenesis and limited treatment options. The current reliance on clinical evaluation for diagnosis, due to the absence of reliable non-invasive methods, presents significant challenges. Traditional diagnostic approaches, including cerebrospinal fluid or blood sampling, are invasive, pose risks of infection, are costly, and often require complex procedures. Tongue coating sampling presents a non-invasive, cost-effective, and repeatable alternative, indicating that it could be a valuable tool for early detection and monitoring of PD, warranting further investigation. This study explores the feasibility of using tongue coating samples as a diagnostic tool for PD through microbiome analysis, with metabolomics data providing additional context and validation via machine learning models. A cohort of 36 PD patients and 31 controls was recruited. 16S rRNA sequencing was used for microbiome analysis, revealing significant alterations in the relative abundances of various microbial taxa, including Firmicutes, Bacteroidetes, and Actinobacteria. Concurrent metabolomics analysis using UPLC-Q/TOF-MS revealed a decrease in palmitoylethanolamide (PEA) levels in Parkinson's disease (PD) patients, and also showed reduced carnitine levels specifically in the severe Hoehn-Yahr (H-Y) stage and mild cognitive impairment (MCI) subgroups. These findings provide preliminary evidence suggesting a potential link between specific microbial alterations and PD progression, which may warrant further investigation. Additionally, the analysis indicates a correlation between certain microbial and metabolomic changes and the advancement of PD. Our results also suggest that tongue coating may serve as a potential non-invasive tool for PD diagnosis, with a particular emphasis on the combined role of the microbiome and metabolome in the pathogenesis of the disease.