Abstract
BACKGROUND: Excessive daytime sleepiness (EDS) is common and disabling in Parkinson's disease (PD). Predictors of EDS are unclear, and data on biological correlates of EDS in PD are limited. We investigated clinical, imaging and biological variables associated with longitudinal changes in sleepiness in early PD. METHODS: The Parkinson's Progression Markers Initiative is a prospective cohort study evaluating progression markers in participants with PD who are unmedicated at baseline (n=423) and healthy controls (HC; n=196). EDS was measured with the Epworth Sleepiness Scale (ESS). Clinical, biological and imaging variables were assessed for associations with EDS for up to 3 years. A machine learning approach (random survival forests) was used to investigate baseline predictors of incident EDS. RESULTS: ESS increased in PD from baseline to year 3 (mean±SD 5.8±3.5 to 7.55±4.6, p<0.0001), with no change in HC. Longitudinally, EDS in PD was associated with non-tremor dominant phenotype, autonomic dysfunction, depression, anxiety and probable behaviour disorder, but not cognitive dysfunction or motor severity. Dopaminergic therapy was associated with EDS at years 2 and 3, as dose increased. EDS was also associated with presynaptic dopaminergic dysfunction, whereas biofluid markers at year 1 showed no significant associations with EDS. A predictive index for EDS was generated, which included seven baseline characteristics, including non-motor symptoms and cerebrospinal fluid phosphorylated-tau/total-tau ratio. CONCLUSIONS: In early PD, EDS increases significantly over time and is associated with several clinical variables. The influence of dopaminergic therapy on EDS is dose dependent. Further longitudinal analyses will better characterise associations with imaging and biomarkers.