Predicting dementia in Parkinson disease by combining neurophysiologic and cognitive markers

Objective: To assess the ability of neurophysiologic markers in conjunction with cognitive assessment to improve prediction of progression to dementia in Parkinson disease (PD).

Methods: Baseline cognitive assessments and magnetoencephalographic recordings from 63 prospectively included PD patients without dementia were analyzed in relation to PD-related dementia (PDD) conversion over a 7-year period. We computed Cox proportional hazard models to assess the risk of converting to dementia conveyed by cognitive and neurophysiologic markers in individual as well as combined risk factor analyses.

Results: Nineteen patients (30.2%) developed dementia. Baseline cognitive performance and neurophysiologic markers each individually predicted conversion to PDD. Of the cognitive test battery, performance on a posterior (pattern recognition memory score < median; hazard ratio (HR) 6.80; p = 0.001) and a fronto-executive (spatial span score < median; HR 4.41; p = 0.006) task most strongly predicted dementia conversion. Of the neurophysiologic markers, beta power < median was the strongest PDD predictor (HR 5.21; p = 0.004), followed by peak frequency < median (HR 3.97; p = 0.016) and theta power > median (HR 2.82; p = 0.037). In combination, baseline cognitive performance and neurophysiologic measures had even stronger predictive value, with the combination of impaired fronto-executive task performance and low beta power being associated with the highest dementia risk (both risk factors vs none: HR 27.3; p < 0.001).

Conclusions: Combining neurophysiologic markers with cognitive assessment can substantially improve dementia risk profiling in PD, providing potential benefits for clinical care as well as for the future development of therapeutic strategies.