Developing a Model for Predicting Cognitive Impairment in Parkinson’s

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black man depression
A combination of age and clinical scores better predicted cognitive impairment than age alone.

A model combining clinical, biological and imaging markers accurately predicts cognitive impairment in patients with newly-diagnosed Parkinson’s disease, according to a study published in Lancet Neurology.1

Mild cognitive impairment is fairly common in Parkinson’s disease, occurring in 20 to 50%2 of the population. It is also associated with a high rate of progression to dementia.3,4

To better predict who is at risk for cognitive decline early in the course of the disease, Anette Schrag, MD, from the University College London, and colleagues evaluated the predictive capacity of clinical, biological, genetic, and imaging biomarkers.

Dr Schrag and colleagues used data from the Parkinson’s Progression Marker Initiative (PPMI) and enrolled 390 newly-diagnosed, drug-naïve Parkinson’s patients and 178 controls and followed them for 2 years.

Results showed that age alone had a poor to fair predictive accuracy (area under the curve (AUC): 0.68; 95% CI, 0.60-0.76). The accuracy was significantly improved with the addition of clinical scores, including the University of Pennsylvania Smell Inventory Test (UPSIT), Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Geriatric Depression Scale, and Movement Disorder Society-Unified Parkinson’s Disease Rating Scale motor scores  (AUC: 0.76; 95% CI, 0.68-0.83). Cerebrospinal fluid variables (AUC: 0.74; 95% CI, 0.68-0.81) and dopamine-transporter (DAT) imaging (AUC: 0.76; 95% CI,  0.68-0.83) also significantly improved accuracy. However, the greatest improvement came with a logistic regression model of age, UPSIT, RBDSQ, CSF Aβ42 (CSF Amyloid β to t-tau ratio), and caudate uptake on DAT imaging (AUC: 0.80; 95% CI, 0.74-0.87; P =.0003).

“Combining these clinical and biomarker variables could be helpful in clinical practice, but most importantly, in clinical trials aiming to identify people at risk of cognitive decline; being able to estimate a 5% risk, compared with a 13% or a 34% risk, is likely to be clinically useful,” the authors wrote.

In a related commentary,5 Omar El-Agnaf, PhD and Nour Majbour, MSc said the “findings suggest that cognitive symptoms should be given equal attention as motor symptoms, and can be better tackled if recognized at the early stage of Parkinson’s disease.”

The researchers concluded by saying that this important study may help “establish strategies to delay and perhaps halt cognitive impairment.”


  1. Schrag A, Siddiqui UF, Anastasiou Z, et al. Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson’s disease: a cohort study. Lancet Neurol. 2017;16:66-75.
  2. Goldman JG, Litvan I. Mild cognitive impairment in Parkinson’s disease. Minerva Med. 2011;102:441–459.
  3. Pigott K, Rick J, Xie SX, et al. Longitudinal study of normal cognition in Parkinson disease. Neurology. 2015;85:1276-82.
  4. Aarsland D, Kurz MW. The epidemiology of dementia associated with Parkinson’s disease. Brain Pathol. 2010;20:633-9.
  5. El-Agnaf O, Majbour N. Cognitive impairment in Parkinson’s disease. Lancet Neurol. 2017;16:23-24.