Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: a cohort study

doi:10.1016/S1474-4422(16)30328-3

The Lancet Neurology

Volume 16, Issue 1, January 2017, Pages 66-75

https://doi.org/10.1016/S1474-4422(16)30328-3 Get rights and content

Summary

Background

Parkinson's disease is associated with an increased incidence of cognitive impairment and dementia. Predicting who is at risk of cognitive decline early in the disease course has implications for clinical prognosis and for stratification of participants in clinical trials. We assessed the use of clinical information and biomarkers as predictive factors for cognitive decline in patients with newly diagnosed Parkinson's disease.

Methods

The Parkinson's Progression Markers Initiative (PPMI) study is a cohort study in patients with newly diagnosed Parkinson's disease. We evaluated cognitive performance (Montreal Cognitive Assessment [MoCA] scores), demographic and clinical data, APOE status, and biomarkers (CSF and dopamine transporter [DAT] imaging results). Using change in MoCA scores over 2 years, MoCA scores at 2 years' follow-up, and a diagnosis of cognitive impairment (combined mild cognitive impairment or dementia) at 2 years as outcome measures, we assessed the predictive values of baseline clinical variables and separate or combined additions of APOE status, DAT imaging, and CSF biomarkers. We did univariate and multivariate linear analyses with MoCA change scores between baseline and 2 years, and with MoCA scores at 2 years as dependent variables, using backwards linear regression analysis. Additionally, we constructed a prediction model for diagnosis of cognitive impairment using logistic regression analysis.

Findings

390 patients with Parkinson's disease recruited between July 1, 2010, and May 31, 2013, and for whom data on MoCA scores at baseline and 2 years were available. In multivariate analyses, baseline age, University of Pennsylvania Smell Inventory Test (UPSIT) scores, CSF amyloid — (Aβ₄₂) to t-tau ratio, and APOE status were associated with change in MoCA scores over time. Baseline age, MoCA and UPSIT scores, and CSF Aβ₄₂ to t-tau ratio were associated with MoCA score at 2 years (using a backwards p-removal threshold of 0·1). Accuracy of prediction of cognitive impairment using age alone (area under the curve 0·68, 95% CI 0·60–0·76) significantly improved by addition of clinical scores (UPSIT, Rapid Eye Movement Sleep Behaviour Disorder Screening Questionnaire [RBDSQ], Geriatric Depression Scale, and Movement Disorder Society Unified Parkinson's Disease Rating Scale motor scores; 0·76, 0·68–0·83), CSF variables (0·74, 0·68–0·81), or DAT imaging results (0·76, 0·68–0·83). In combination, the five variables showing the most significant associations with cognitive impairment (age, UPSIT, RBDSQ, CSF Aβ₄₂, and caudate uptake on DAT imaging) allowed prediction of cognitive impairment at 2 years (0·80, 0·74–0·87; p=0·0003 compared to age alone).

Interpretation

In newly diagnosed Parkinson's disease, the occurrence of cognitive impairment at 2 year follow-up can be predicted with good accuracy using a model combining information on age, non-motor assessments, DAT imaging, and CSF biomarkers.

Funding

None.

Introduction

Dementia occurs in at least 75% of patients who have had Parkinson's disease for more than 10 years, and deterioration in cognition is a substantial contributor to the disability associated with Parkinson's disease.1, 2 Mild cognitive impairment is a term used to denote cognitive impairment that does not fulfil criteria for dementia in Parkinson's disease.³ Evidence suggests that almost all patients with Parkinson's disease who have mild cognitive impairment will eventually fulfil criteria for dementia.3, 4 Early identification of individuals at risk of developing cognitive impairment could help stratify the early Parkinson's disease population for clinical trials and prognostic information, and improve understanding of the pathophysiology of cognitive decline in these patients.

There are several possible mechanisms by which cognitive impairment develops in Parkinson's disease. Findings from pathological studies show that Alzheimer's disease (amyloid — [A—] plaques and tau neurofibrillary tangles) and Parkinson's disease pathology (cortical Lewy bodies) commonly coexist.⁵ Dopaminergic deficits are suggested as a pathophysiological mechanism underlying cognitive impairment by the improvement of cognitive symptoms, especially in functions of attentional control, early in the disease course through the administration of levodopa.⁶ Besides, neuroimaging studies have shown associations between caudate and putamen dopamine transporter density with cognitive dysfunction in patients with Parkinson's disease.⁷

Research in context

Evidence before this study

Previous evidence supports the association of several clinical, genetic, CSF, and imaging markers with development of cognitive impairment in Parkinson's disease. In Alzheimer's disease research, several risk models are available to aid the prediction of dementia using clinical features and biomarker measures, but only a few studies have combined clinical features and biomarkers as predictors of cognitive decline in Parkinson's disease. We searched PubMed for reports published up to Nov 14, 2015, with the search terms “Parkinson's disease” AND “predictors” AND “dementia” as well as “Parkinson's disease” AND “predictors” AND “cognitive impairment”. There were no language restrictions. We included studies in which participants underwent longitudinal assessments that enabled assessment of predictive value of baseline markers. We found no previous studies that reported on the combination of clinical, CSF, and dopamine transporter (DAT) imaging markers, or studies that calculated the predictive value of these variables for development of cognitive impairment in Parkinson's disease.

Added value of this study

This study reports on the predictive value of clinical, genetic, CSF, and DAT imaging markers, separately and in combination, for the development of cognitive impairment in a longitudinal sample of patients with Parkinson's disease. Additionally, a risk calculation for cognitive impairment associated with each of these markers is provided. According to the results of our cohort study, the occurrence of cognitive impairment 2 years after diagnosis of Parkinson's disease can be predicted with good accuracy using a combination of age, non-motor assessments, DAT imaging, and CSF examination. To our knowledge, this the first study to report the predictive value of these combined clinical markers, imaging markers, and biomarkers for the development of cognitive impairment in Parkinson's disease.

Implications of all the available evidence

The findings of our study about the risk of cognitive impairment can support prognostic and management decisions in clinical practice, aid understanding of pathophysiological processes, and allow for planning of future trials to delay cognitive impairment in Parkinson's disease.

Older age, sex, lower education, cognitive score, higher severity of motor symptoms, hyposmia, and rapid eye movement (REM) sleep behaviour disorder (RBD) have all been suggested as predictors of cognitive decline in patients with Parkinson's disease.8, 9 Results from biomarker studies have shown that dopamine deficit on dopamine transporter (DAT)-imaging is associated with subsequent cognitive decline in patients with Parkinson's disease. Various studies have also examined the association of cognitive impairment with CSF levels of α-synuclein, Aβ₄₂, total tau (t-tau), phosphorylated tau 181p (p-tau), and ratio of Aβ₄₂ to t-tau; and with apolipoprotein (APO)E ɛ4 status.10, 11 However, the results are conflicting regarding the contribution of CSF biomarkers in the prediction of cognitive impairment in Parkinson's disease12, 13, 14, 15, 16 and, to our knowledge, no study has previously combined clinical, CSF, and DAT imaging parameters, or calculated the predictive value of these variables for development of cognitive impairment in Parkinson's disease. In this study, we investigated the extent to which the various clinical, imaging, biomarker, and genetic measures, both individually and in combination, can be used to predict the development of cognitive impairment. Specifically, we hypothesised that the addition of CSF and DAT imaging results to clinical assessments would contribute substantially to prediction of cognitive deterioration at 2 years of follow-up.

Section snippets

Study design and participants

In this cohort study, we investigated the clinical and biomarker predictors of cognitive decline in the early stage of Parkinson's disease using data from the Parkinson's Progression Marker Initiative (PPMI). PPMI started in 2010 and is an ongoing multicentre, observational clinical and biomarker study of patients with Parkinson's disease and healthy controls in 33 sites in the USA, Europe, Israel, and Australia that aims to identify biomarkers of Parkinson's disease progression and thus inform

Results

393 individuals with newly diagnosed Parkinson's disease, who were enrolled into the PPMI study between July 1, 2010, and May 31, 2013, had 2 year follow-up assessments. Three individuals did not have baseline MoCA data and were excluded. Baseline characteristics of the healthy controls and the patients with Parkinson's disease who were included in the study are shown in table 1. 318 (82%) of 390 patients had 2 year MoCA results available at the time of data download from the PPMI study

Discussion

In this study, we have identified clinical and biomarker predictors of cognitive impairment in the first 2 years after a diagnosis of Parkinson's disease. Early cognitive decline, a strong predictor of development of dementia in Parkinson's disease, was associated with a number of clinical variables, irrespective of whether the outcome at 2 years was change in MoCA scores, absolute MoCA scores, or classification of cognitive impairment (mild cognitive impairment or dementia), based on

References (36)

D Aarsland et al.
The epidemiology of dementia associated with Parkinson disease
J Neurol Sci
(2010)
T Stewart et al.
Cerebrospinal fluid α-synuclein predicts cognitive decline in Parkinson disease progression in the DATATOP cohort
Am J Pathol
(2014)
C Liu et al.
CSF tau and tau/Aβ42 predict cognitive decline in Parkinson's disease
Parkinsonism Relat Disord
(2015)
K Brockmann et al.
Neurodegenerative CSF markers in genetic and sporadic PD: classification and prediction in a longitudinal study
Parkinsonism Relat Disord
(2015)
RL Doty et al.
Development of the University of Pennsylvania Smell Identification Test: a standardized microencapsulated test of olfactory function
Physiol Behav
(1984)
JM Bland et al.
Comparing methods of measurement: why plotting difference against standard method is misleading
Lancet
(1995)
K Zhu et al.
Predictors of dementia in Parkinson's disease; findings from a 5-year prospective study using the SCOPA-COG
Parkinsonism Relat Disord
(2014)
DP Devanand et al.
Combining early markers strongly predicts conversion from mild cognitive impairment to Alzheimer's disease
Biol Psychiatry
(2008)
Y Compta et al.
Combined dementia-risk biomarkers in Parkinson's disease: a prospective longitudinal study
Parkinsonism Relat Disord
(2013)
M Coelho et al.
Dementia and severity of parkinsonism determines the handicap of patients in late-stage Parkinson's disease: the Barcelona-Lisbon cohort
Eur J Neurol
(2015)

CC Janvin et al.

Subtypes of mild cognitive impairment in Parkinson's disease: progression to dementia

Mov Disord

(2006)

K Pigott et al.

Longitudinal study of normal cognition in Parkinson disease

Neurology

(2015)

Y Compta et al.

The significance of α-synuclein, amyloid-β and tau pathologies in Parkinson's disease progression and related dementia

Neurodegener Dis

(2014)

JB1 Rowe et al.

Parkinson's disease and dopaminergic therapy–differential effects on movement, reward and cognition

Brain

(2008)

LC Silbert et al.

Neuroimaging and cognition in Parkinson's disease dementia

Brain Pathol

(2010)

MT Hu et al.

Predictors of cognitive impairment in an early stage Parkinson's disease cohort

Mov Disord

(2014)

LS Campos et al.

Clinical predictors of cognitive impairment and psychiatric complications in Parkinson's disease

Arq Neuropsiquiatr

(2015)

M Terrelonge et al.

CSF β-amyloid 1–42 predicts progression to cognitive impairment in newly diagnosed Parkinson disease

J Mol Neurosci

(2016)

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Irisin plays a neuroprotective role by decreasing the level of α-syn. Irisin is negatively correlated with the severity of motor symptoms and cognitive impairment. More importantly, irisin can improve DOPA uptake in the striatum of patients with PD, especially on the side contralateral to the affected limb.
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ArticlesClinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: a cohort study

Summary

Background

Methods

Findings

Interpretation

Funding

Introduction

Section snippets

Study design and participants

Results

Discussion

J Neurol Sci

Am J Pathol

Parkinsonism Relat Disord

Parkinsonism Relat Disord

Physiol Behav

Lancet

Parkinsonism Relat Disord

Biol Psychiatry

Parkinsonism Relat Disord

Dementia and severity of parkinsonism determines the handicap of patients in late-stage Parkinson's disease: the Barcelona-Lisbon cohort

Eur J Neurol

Subtypes of mild cognitive impairment in Parkinson's disease: progression to dementia

Mov Disord

Longitudinal study of normal cognition in Parkinson disease

Neurology

The significance of α-synuclein, amyloid-β and tau pathologies in Parkinson's disease progression and related dementia

Neurodegener Dis

Parkinson's disease and dopaminergic therapy–differential effects on movement, reward and cognition

Brain

Neuroimaging and cognition in Parkinson's disease dementia

Brain Pathol

Predictors of cognitive impairment in an early stage Parkinson's disease cohort

Mov Disord

Clinical predictors of cognitive impairment and psychiatric complications in Parkinson's disease

Arq Neuropsiquiatr

CSF β-amyloid 1–42 predicts progression to cognitive impairment in newly diagnosed Parkinson disease

J Mol Neurosci

Articles
Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: a cohort study