< back to all PPMI Presentations

Non-motor symptoms in early drug-naïve Parkinson's disease

Liu, R; Umbach, D; Peddada, S; Xu, Z; Tröster, A; Huang, X; Chen, H
AAN: Washington, DC
Download Presentation: Liu-AAN-2015.pdf
OBJECTIVE: To examine potential sex differences in non-motor symptoms (NMS) among drug-naïve Parkinson disease (PD) patients, and to identify NMS that can best differentiate early PD cases from controls.

DESIGN/METHODS: Cross-sectional analysis of 414 newly diagnosed, untreated PD patients (269 male and 145 female) and 188 healthy controls (121 male and 67 female) in the Parkinson's Progression Markers Initiative (PPMI) study. NMS were measured using well-validated instruments covering sleep, olfactory, neurobehavioral, autonomic, and neuropsychological domains.

RESULTS: Male and female PD patients were fairly comparable on motor presentations, however sex differences were observed for several non-motor features. Male PD patients had significantly more pronounced deficits in olfaction (p=0.02) and multiple cognitive measurements (all p<0.01) than female patients, whereas female cases experienced higher trait anxiety (p=0.02). Multiple stepwise logistic regression analysis showed that the combination of NMS measures: University of Pennsylvania Smell Identification Test (UPSIT), Montreal Cognitive Assessment (MoCA), Scales for Outcomes in Parkinson's disease - Autonomic (SCOPA-AUT), and state anxiety from the State-Trait Anxiety Inventory effectively differentiate PD patients from controls with an area under the receiver operating characteristic curve (AUC) of 0.913 (95% confidence interval [CI]: 0.89-0.94). UPSIT, MoCA, and SCOPA-AUT were the most predictive NMS in men (AUC=0.919, 95%CI: 0.89-0.95) as compared to UPSIT, MoCA, and REM Sleep Disorder Screening Questionnaire in women (AUC=0.903, 95%CI: 0.86-0.95).

CONCLUSIONS: Our analysis revealed notable sex differences in several non-motor features of de novo PD patients. Further, we found a parsimonious NMS combination that could effectively differentiate de novo cases from healthy controls.