Identification of genes associated with smell dysfunction in Parkinson's disease.

Parkinson's disease (PD) predominantly affects individuals above the age of 50 and currently lacks curative treatments. Although motor symptoms classically define PD, non-motor symptoms (NMS) frequently manifest earlier. In this study, we first investigated the potential of NMS to distinguish PD patients from healthy controls (HC). Machine learning-based feature ranking applied to clinical data identified olfactory dysfunction, quantified using the University of Pennsylvania Smell Identification Test (UPSIT), as the most informative predictor distinguishing PD patients from HC. To further elucidate the biological basis of olfactory impairment, we analyzed peripheral blood transcriptomic data from the Parkinson's Progression Markers Initiative (PPMI). Weighted Gene Co-expression Network Analysis (WGCNA) identified disease and olfaction-associated gene modules, from which 32 key genes were prioritized. DNA methylation analysis identified CpG loci associated with these genes, indicating coordinated transcriptional and epigenetic regulation. Independent single-cell RNA sequencing datasets demonstrated consistent expression trends across immune cell subsets, strengthening the robustness of these findings. This integrative clinical and transcriptomic analysis highlights smell loss as both the most discriminative clinical marker and a biologically grounded feature of PD, providing systems-level insight into molecular mechanisms underlying olfactory dysfunction in PD pathogenesis.