4 Citations (Scopus)


Background: Identifying predictors of incident cognitive impairment (CI), one of the most problematic long-term outcomes, in Parkinson's disease (PD) is highly relevant for personalized medicine and prognostic counseling. The Nonmotor Symptoms Scale (NMSS) provides a global clinical assessment of a range of NMS, reflecting NMS burden (NMSB), and thus may assist in the identification of an “at-risk” CI group based on overall NMSB cutoff scores. Methods: To investigate whether specific patterns of PD NMS profiles predict incident CI, we performed a retrospective longitudinal study on a convenience sample of 541 nondemented PD patients taking part in the Nonmotor Longitudinal International Study (NILS) cohort, with Mini-Mental State Examination (MMSE), NMSS, and Scales for Outcomes in PD Motor Scale (SCOPA Motor) scores at baseline and last follow-up (mean 3.2 years) being available. Results: PD patients with incident CI (i.e., MMSE score ≤ 25) at last follow-up (n = 107) had severe overall NMSB level, significantly worse NMSS hallucinations/perceptual problems and higher NMSS attention/memory scores at baseline. Patients with CI also were older and with more advanced disease, but with no differences in disease duration, dopamine replacement therapy, sex, and comorbid depression, anxiety, and sleep disorders. Conclusions: Our findings suggest that a comprehensive baseline measure of NMS and in particular hallucinations and perceptual problems assessed with a validated single instrument can be used to predict incident CI in PD. This approach provides a simple, holistic strategy to predict future CI in this population.

Original languageEnglish
Article numbere02086
JournalBrain and Behavior
Issue number5
Publication statusPublished - May 2021


  • cognitive impairment
  • MMSE
  • Nonmotor symptom burden grading
  • Nonmotor symptoms
  • Parkinson's disease


Dive into the research topics of 'Nonmotor symptom burden grading as predictor of cognitive impairment in Parkinson’s disease'. Together they form a unique fingerprint.

Cite this