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Level I PD-MCI Using Global Cognitive Tests
and the Risk for Parkinson’s Disease
Dementia
Judith A. Boel, PhD,
1,2
Rob M.A. de Bie, MD, PhD,
1,3
Ben A. Schmand, PhD,
2,4
John C. Dalrymple-Alford, PhD,
5
Connie Marras, MD, PhD,
6
Charles H. Adler, MD, PhD,
7
Jennifer G. Goldman, MD, PhD,
8
Alexander I. Tröster, PhD,
9
David J. Burn, MD, PhD,
10
Irene Litvan, PhD,
11
and Gert J. Geurtsen PhD,
3,4,
*MDS Study Group Mild Cognitive Impairment in Parkinson’s Disease
ABSTRACT: BackgroundBackground: The criteria for PD-MCI allow the use of global cognitive tests. Their predictive value
for conversion from PD-MCI to PDD, especially compared to comprehensive neuropsychological assessment, is
unknown.
MethodsMethods: The MDS PD-MCI Study Group combined four datasets containing global cognitive tests as well as a
comprehensive neuropsychological assessment to define PD-MCI (n =467). Risk for developing PDD was
examined using a Cox model. Global cognitive tests were compared to neuropsychological test batteries (Level
I&II) in determining risk for PDD.
ResultsResults: PD-MCI based on a global cognitive test (MMSE or MoCA) increases the hazard for developing PDD
(respectively HR =2.57, P=0.001; HR =4.14, P=<0.001). The C-statistics for MMSE (0.72) and MoCA (0.70)
were lower than those based on neuropsychological tests (Level I =0.82; Level II =0.81). Sensitivity, specificity
and diagnostic accuracy balance was best in Level II.
ConclusionConclusion: MMSE and MoCA predict conversion to PDD. However, Level II neuropsychological assessment
seems the preferred assessment for PD-MCI.
The MDS PD Mild Cognitive Impairment (PD-MCI) diagnostic
criteria
1
operationalize two levels of cognitive assessment. Level I
assessment is based on a global cognitive test (Level I-GCT) or
an abbreviated neuropsychological assessment (Level I-NPA);
and Level II is based on a comprehensive neuropsychological
assessment. The use of a global cognitive test has some practical
advantages over both Level I-NPA and Level II in terms of time,
ease of administration, and costs. However, the diagnostic accu-
racy for current cognitive status (i.e. PD-MCI) when using
global cognitive tests is low compared to Level I-NPA and Level
II criteria.
2
The predictive value of global cognitive tests for pro-
gression from PD-MCI to Parkinson Disease Dementia (PDD)
needs to be determined.
Prior analyses using Level I-NPA and Level II methods
showed that PD-MCI diagnosed in these ways increases the haz-
ard of PDD and aids in the prediction of PDD (Level I-NPA
Hazard Ratio (HR) 2.02–11.25; Level II HR 2.69–14.10:
depending on cut-off used).
3,4
This study investigates the predic-
tive value of Level I-GCT, i.e. the Mini-Mental State Examina-
tion (MMSE) and Montreal Cognitive Assessment (MoCA), for
1
Department of Neurology, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands;
2
Department of Psychology, University of Amsterdam,
Amsterdam, The Netherlands;
3
Amsterdam Neuroscience, Amsterdam, The Netherlands;
4
Department of Medical Psychology, Amsterdam UMC Location University of
Amsterdam, Amsterdam, The Netherlands;
5
New Zealand Brain Research Institute and School of Psychology, Speech and Hearing, University of Canterbury,
Christchurch, New Zealand;
6
Morton and Gloria Shulman Movement Disorders Centre and the Edmond J Safra Program in Parkinson’s Disease, Toronto Western
Hospital, University of Toronto, Toronto, Canada;
7
Arizona Study of Aging and Neurodegenerative Disorders, Mayo Clinic Arizona, Scottsdale, Arizona, USA and
Banner Sun Health Research Institute, Sun City, Arizona, USA;
8
Department of Physical Medicine and Rehabilitation and Neurology, Shirley Ryan AbilityLab and
Northwestern University, Chicago, Illinois, USA;
9
Department of Clinical Neuropsychology and Center for Neuromodulation, Barrow Neurological Institute, Phoenix,
Arizona, USA;
10
Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK;
11
Parkinson and Other Movement Disorder Center, Department of
Neurosciences, University of California, San Diego, California, USA
*Correspondence to: Gert J. Geurtsen PhD, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands. E-mail: g.j.geurtsen@amsterdamumc.nl
Keywords: dementia, mild cognitive impairment, global cognitive tests, Parkinson’s disease, diagnostic accuracy.
Authors from the MDS Study Group Mild Cognitive Impairment in Parkinson’s Disease are listed in the Acknowledgments.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
Received 27 December 2021; revised 7 March 2022; accepted 24 March 2022.
Published online 29 April 2022 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/mdc3.13451
MOVEMENT DISORDERS CLINICAL PRACTICE 2022; 9(4): 479–483. doi: 10.1002/mdc3.13451 479
© 2022 AmsterdamUMC. Movement Disorders Clinical Practice published by Wiley Periodicals LLC on behalf of Movement Disorder Society.
BRIEF REPORT
CLINICAL PRACTICE
developing PDD. In addition, the predictive value of global cog-
nitive tests will be compared to Level I-NPA and Level II. This
will help to make a substantiated choice on how to assess PD-
MCI in research and clinical practice.
Methods
This study is based on data from the MDS Study Group Mild
Cognitive Impairment in Parkinson’s Disease.
5
The methods are
briefly described below (see publications for additional details).
3,4
Data Inclusion
This retrospective study combined four datasets containing global
cognitive tests and additional data suitable for both Level I and
Level II PD-MCI assessment based on neuropsychological tests.
Individual studies were included if they allowed Level II PD-
MCI assessment (i.e. at least two neuropsychological tests in each
of the five cognitive domains at baseline
1
), included ≥75 patients
at baseline, had follow-up on PDD status for ≥67% of the base-
line population and had used the MMSE and/or the MoCA to
assess the global cognitive functioning. Four studies were
included.
2,6–8
Supplementary Figure S1 displays the inclusion
flowchart and Supplementary Table S1 provides cohort details,
including PDD criteria. Demographic and clinical data were col-
lected. MDS-Unified Parkinson’s disease rating scale (UPDRS)
9
scores were converted to have a uniform measure of UPDRS-
III,
3
subsequently referred to as UPDRS-III.
Application of the PD-MCI
Criteria
To determine PD-MCI by global cognitive tests, a cut-off of
<26 for the MoCA
10
and < 29 for the MMSE
11
were used con-
sidering these match prevailing cut-offs for PD-MCI used in PD
research and practice. Measures for subjective cognitive decline
varied between studies and are described in Supplementary
Table S1. A measure for functional independence at baseline was
not included as PDD was an exclusion criterion.
In short, for the neuropsychological assessment (NPA), either
one test per cognitive domain (Level I-NPA) or two tests per
cognitive domain (Level II) were used. PD-MCI based on the
NPA was defined as scores 1.5SD below the normative data
for at least two tests (respectively out of five or ten).
3,4
Statistics
Multiple imputation (MI) was used to account for incomplete
data. Cox proportional hazards models were used to evaluate
whether Level I-CGT PD-MCI at baseline compared to no PD-
MCI adds to the risk of PDD as estimated by age, gender, level of
education, disease duration, UPDRS-III, and depression. Time
was measured from PD symptom onset until PDD or censoring.
To compare various operationalizations of the criteria, C-statistics
(bootstrap-corrected) which indicate discriminative ability between
models as a measure of goodness-of-fit were calculated. PDD risk
factors, like time since symptom onset, were taken into consider-
ation. Two patients who develop PDD can be ordered by their
time to event (PDD). If the model rightly predicts a shorter time to
PDD for the one who developed PDD first, the C-statistic
increases. Two patients, one of which develops PDD, can be
ordered as well. If the model rightly predicts a shorter time to PDD
for the one who developed PDD, the C statistic also increases.
In addition, at baseline we determined sensitivity, specificity,
positive predictive value (PPV), negative predictive value (NPV)
and diagnostic accuracy (DA) for PDD. This was done for Level
I-CGT as well as for Level I-NPA and Level II PD-MCI
assessment.
Results
A total of 467 patients were included (Table 1). Sixty-nine
patients (14.3%) developed PDD during follow-up (range 0.5–
9 years).
Frequencies of PD-MCI
MMSE scores were available in all four datasets; MoCA scores
were available in three datasets. A total of 172 out of
467 (36.8%) patients fulfilled the criteria for PD-MCI based on
the MMSE. A total of 111 out of 365 (30.4%) patients fulfilled
the criteria for PD-MCI based on the MoCA. Applying Level II
resulted in 120/467 PD-MCI patients (25.7%), while level I-
NPA based on 5 tests resulted in 46/467 PD-MCI patients
(9.9%). Compared to Level I NPA, both Level II and Level I-
GCT identified more PD-MCI cases.
Predictive Value
The Cox proportional hazards models indicated a significant
contribution of PD-MCI as defined by MMSE and MoCA cut-
off scores to the hazard of PDD (HRs respectively 2.57 and
4.14; see Supplementary Table 2). Age as well as UPDRS scores
TABLE 1 Baseline characteristics
N=467
Age, years (mean, SD) 68.7 (8.8)
Gender, male (frequency, %) 293 (62.7)
Education, years (mean, SD) 14.0 (3.1)
MMSE (median, IQR) 28.0 (27–29)
MoCA (median, IQR) 25.0 (23–28)
PD symptom duration, years (median, IQR) 4.0 (2.0–8.0)
UPDRS III (median, IQR) 20 (13–28)
IQR, InterQuartile Range.
480 MOVEMENT DISORDERS CLINICAL PRACTICE 2022; 9(4): 479–483. doi: 10.1002/mdc3.13451
BRIEF REPORT PD-MCI BASED ON GLOBAL COGNITIVE TESTS AND PDD RISK
were significant contributors in the model including the MMSE
but not in the model including the MoCA.
Comparison of PD-MCI
Assessment Methods
The sensitivity and specificity were respectively: 67.7 and 68.1% for
Level I-GCT based on the MMSE; 57.1 and 75.9% for Level I-
CGT based on the MoCA; 32.8 and 94.7% for Level I-NPA; and
66.7 and 80.0% for level II (see Table 2). The Diagnostic Accuracy
(DA) was sufficient for the MMSE (67.7), good for MoCA (73.3)
and Level II (78.4) and very good for Level I-NPA (86.1). The
results in the three datasets with both MMSE and MoCA were
comparable. The MMSE has sufficient but limited sensitivity, speci-
ficity and DA. Level I-NPA and MoCA have a low sensitivity and
miss many cases. Specificity of the Level I-NPA is very high
resulting in a very good DA. Level II seems to have the most opti-
mal balance of sensitivity and specificity and a good DA.
The C-statistics were as follows: 0.72 for Level I-CGT based
on the MMSE; 0.70 for Level I-CGT based on the MoCA; 0.82
for Level I-NPA
4
; and 0.81 for level II.
4
The lower C-statistics
for MMSE and MoCA indicate lower added value to the risk for
PDD in comparison to level I-NPA and level II PD-MCI.
Discussion
Our results show that PD-MCI based on the global cognitive tests
MMSE and MoCA have predictive value for PDD. However,
given the higher hazard ratios of Level I-NPA and Level II
3,4
the
predictive value of MMSE and MoCA are lower. The findings
were corrected for demographic and clinical characteristics known
to contribute to the hazard for PDD (age, sex, UDPRS III score).
The results are in line with previous studies indicating PD-MCI
based on neuropsychological assessment to be a risk factor for
PDD.
4,12
The significantly higher hazard ratio’s for the develop-
ment of PDD for PD-MCI patients compared to patients with
normal cognition concurs with the concept of MCI as a transi-
tional stage between normal cognition and PDD.
No difference was found in the predictive value when com-
paring the MMSE and the MoCA. However, the C-statistic is
not well suited to pick up small differences between models and
comparisons based on this statistic should be interpreted with
caution. The reason we find little difference in PDD prediction
between MMSE and MOCA in contrast to other mostly smaller
studies is unclear. The size of the studies as well as the variation
in cut-off points used for PD-MCI and PDD could have been of
influence.
13
In a study including 132 patients Hoops et al.
(2009)
13
found the % correctly diagnosed based on MMSE and
MoCA to be equal as well, when using the same cut-offs. They
advised using MoCA due to the ceiling effect on the MMSE.
13
The MMSE and MoCA resulted in the higher percentage of
patients classified as PD-MCI (36.8 and 30.4% respectively) than
did the neuropsychological assessment (9.9% for level I-NPA;
25.7% for Level II). Global cognitive tests seemed to identify
PD-MCI cases who do not develop PDD, possibly “false
positive,”leading to lower C-statistics as well as a lower balance
in sensitivity and specificity. On the other hand, Level I-NPA
seems to be too limited and strict, as scores on 2 out of 5 tests
need to be below the cut-off, and therefore may miss patients
who actually have PD-MCI.
Overall we determined the sensitivity, specificity and DA in
the same patients. For the MMSE these values are sufficient but
limited, which corresponds with Hoops et al. (2009).
13
The sen-
sitivity of Level I-NPA and MoCA is lower indicating that many
cases are missed. However, the specificity of the Level I-NPA is
very high resulting in a good DA. Level II seems to have the
most optimal balance of sensitivity and specificity and has a good
DA, thus indicating that a Level II neuropsychological assessment
is preferred over the global cognitive tests.
Strengths of our study include the use of a large multicenter,
international sample, uniform application of the MDS PD-MCI
criteria, and direct comparison of the different prevailing
operationalizations of PD-MCI. Furthermore, while the relation
between PD-MCI, demographic and clinical characteristics, and
PDD has been reported separately in previous studies,
12,14
the
current study analyzed the effects jointly in the predictive
models. Limitations included different methods between cohorts
for patient recruitment, neuropsychological assessment, assess-
ment of motor signs and clinical PDD criteria. The length of
follow-up of the studies included differed. Therefore, the PDD
frequency varies. This potentially could influence the determina-
tion of the sensitivity, specificity and diagnostic accuracy. How-
ever, as the analyses of sensitivity, specificity and diagnostic
accuracy were performed in the same groups comparability seems
TABLE 2 Sensitivity, specificity, NPV, PPV and diagnostic accuracy
MMSE MoCA Level I-NPA Level II
Sensitivity 67.7 57.1 32.8 66.7
Specificity 68.1 75.9 94.7 80.0
PPV 27.7 25.7 50.0 31.9
NPV 91.6 91.9 89.8 94.5
Diagnostic Accuracy 67.6 73.3 86.1 78.4
PPV, Positive Predictive Value; NPV, Negative Predictive Value.
MOVEMENT DISORDERS CLINICAL PRACTICE 2022; 9(4): 479–483. doi: 10.1002/mdc3.13451 481
BOEL J.A. ET AL. BRIEF REPORT
adequate. When using a one point lower cut-off the % correctly
diagnosed in Level I-CGT hardly changed in a mixed PD-MCI
and PDD sample.
13
In conclusion, PD-MCI assessed by global cognitive tests
increases the hazard ratio for the development of PDD after tak-
ing age, sex, education, PD motor symptom severity, and
depression into account. This finding supports PD-MCI being a
risk factor for PDD. PD-MCI assessed by neuropsychological
assessment (both Level I and Level II) had higher predictive value
over PD-MCI assessed by MMSE or MoCA. Given better bal-
ance of sensitivity and specificity and Diagnostic Accuracy com-
pared to the use of global cognitive tests and Level I
neuropsychological assessment, Level II neuropsychological
assessment seems the optimal method for detecting PD-MCI.
Acknowledgments
This study was conducted on behalf of the International
Parkinson and Movement Disorders Society Mild Cognitive
Impairment (MCI) Study Group, consisting of the byline authors
as well as the members listed here: Bryan Bernard PhD, Glenn
Stebbins PhD, J. Vincent Filoteo PhD, Daniel Weintraub PhD,
John N. Caviness MD, Christine Belden PhD, Cyrus
P. Zabetian MD, Brenna A. Cholerton PhD, Xuemei Huang
PhD, Paul J. Eslinger PhD, James B. Leverenz MD, Sarah Duff-
Canning PhD, Matt Farrer PhD, Tim J. Anderson FRACP,
Daniel J Myall PhD, Sharon L. Naismith PhD, Simon JG Lewis
MD, Glenda M. Halliday PhD, Ruey-Meei Wu MD PhD, Car-
oline H. Williams-Gray MRCP PhD, David P. Breen MRCP
PhD, Roger A. Barker MRCP PhD, Alison J. Yarnall MRCP
PhD, Martin Klein PhD, Brit Mollenhauer MD, Claudia
Trenkwalder MD. Jaime Kulisevsky MD PhD. Javier
Pagonabarraga MD. PhD. Carmen Gasca-Salas MD. PhD. Maria
C. Rodriguez-Oroz MD. PhD. Carme Junque PhD. Barbara
Segura PhD. Paolo Barone PhD, Gabriella Santangelo PhD,
Davide M Cammisuli PhD, Roberta Biundo PhD, Angelo
Antonini PhD, Luca Weis PhD, Kenn Freddy Pedersen PhD
and Guido Alves PhD. Additional details on these authors are
available in the Supplemental Appendix 1.
Author Roles
(1) Research project: A. Conception, B. Organization, C. Execution;
(2) Statistical Analysis: A. Design, B. Execution, C. Review and Cri-
tique; (3) Manuscript: A. Writing of the first draft, B. Review and
Critique.
JAB: 1A, 1B, 1C, 2A, 2B, 2C, 2A, 2B, 2C.
RMAB: 1A, 3A, 3B.
BAS: 1A, 1B, 2C, 3A, 3B.
JDA: 1A, 1B, 3B.
CM: 1A, 3B.
CHA: 1A, 3B.
JGG: 1A, 3B.
AIT: 1A, 3B.
DJB: 1A, 3B.
IL: 1A, 3B.
GJG: 1A, 1B, 1C, 2A, 2C, 3A, 3B.
Disclosures
Ethical Compliance Statement: The Medical Ethics Com-
mittee of the University of Amsterdam stated that the approval
of an institutional review board was not required for this work
(reference number W12_3–1 # 13.17.0003). Each study site
obtained informed consent obtained. We confirm that we have
read the Journal’s position on issues involved in ethical publica-
tion and affirm that this work is consistent with those guidelines.
Funding Sources and Conflicts of Interest: Funding was
received from the Michael J. Fox Foundation. The authors have
no conflicts of Interest for this study.
Financial Disclosures for the Previous 12 Months: Judith A.
Boel, Rob M.A. de Bie, Ben A. Schmand, Jennifer G. Goldman,
Alexander I. Tröster, David J. Burn, and Gert J. Geurtsen have no
disclosures to report. John C. Dalrymple-Alford, None, only grants
from Tertiary Education Commission, New Zealand, Health
Research Council of New Zealand, Canterbury Medical Research
Foundation and Neurological Foundation of New Zealand. Con-
nie Marras is a consultant for Gray Matter Technologies and
receives financial compensation as a steering committee member
from the Michael J Fox Foundation. She received grants from The
Michael J Fox Foundation, Canadian Institutes of Health Research,
Parkinson’s Foundation (US), International Parkinson and Move-
ment Disorders Society, Weston Brain Institute, Theravance Inc,
Centogene. Charles H. Adler has Stock Ownership in medically-
related fields: Cionic; Consultancies: Avion, CND Life Science,
Jazz Pharm, Neurocrine; Grants: NIH, Michael J. Fox Foundation,
Arizona Biomedical Research Commission. Irene Litvan’sresearch
is supported by the National Institutes of Health grants:
2R01AG038791-06A, U01NS100610, U01NS80818,
R25NS098999; U19 AG063911-1 and 1R21NS114764-01A1;
the Michael J Fox Foundation, Parkinson Foundation, Lewy Body
Association, CurePSP, Roche, Abbvie, Biogen, Centogene. EIP-
Pharma, Biohaven Pharmaceuticals, Novartis, Brain Neurotherapy
Bio and United Biopharma SRL—UCB. She was a member of
the Scientific Advisory Board of Lundbeck and is a Scientificadvi-
sor for Amydis. She receives her salary from the University of Cali-
fornia San Diego and as Chief Editor of Frontiers in Neurology.■
References
1. Litvan I, Goldman JG, Troster AI, et al. Diagnostic criteria for mild cog-
nitive impairment in Parkinson’s disease: Movement Disorder Society
task force guidelines. Mov Disord 2012;27:349–356.
2. Marras C, Armstrong MJ, Meaney CA, et al. Measuring mild cognitive
impairment in patients with Parkinson’s disease. Mov Disord 2013;28:
626–633.
3. Hoogland J, Boel JA, de Bie RMA, et al. Mild cognitive impairment as a
risk factor for Parkinson’s disease dementia. Mov Disord 2017;32:1056–1065.
482 MOVEMENT DISORDERS CLINICAL PRACTICE 2022; 9(4): 479–483. doi: 10.1002/mdc3.13451
BRIEF REPORT PD-MCI BASED ON GLOBAL COGNITIVE TESTS AND PDD RISK
4. Hoogland J, Boel JA, de Bie RMA, et al. Risk of Parkinson’sdisease
dementia related to level I MDS PD-MCI. Mov Disord 2019;34:430–435.
5. Geurtsen GJ, Hoogland J, Goldman JG, et al. Parkinson’s disease mild
cognitive impairment: Application and validation of the criteria.
J Parkinsons Dis 2014;4:131–137.
6. Beach TG, Adler CH, Sue LI, et al. Arizona study of aging and neurode-
generative disorders and brain and body donation program. Neuropathol-
ogy 2015;35:354–389.
7. Muslimovic D, Post B, Speelman JD, Schmand B. Cognitive profile of
patients with newly diagnosed Parkinson disease. Neurology 2005;65:
1239–1245.
8. Dalrymple-Alford JC, Livingston L, MacAskill MR, et al. Characterizing mild
cognitive impairment in Parkinson’sdisease.Mov Disord 2011;26:629–636.
9. Goetz CG, Tilley BC, Shaftman SR, et al. Movement Disorder Society-
sponsored revision of the unified Parkinson’s disease rating scale (MDS-
UPDRS): Scale presentation and Clinimetric testing results. Mov Disord
2008;23:2129–2170.
10. Skorvanek M, Goldman JG, Jahanshahi M, et al. Global scales for cogni-
tive screening in Parkinson’s disease: Critique and recommendations.
Mov Disord 2018;33:208–218.
11. van Steenoven I, Aarsland D, Hurtig H, et al. Conversion between mini-
mental state examination, Montreal cognitive assessment, and dementia rat-
ing scale-2 scores in Parkinson’s disease. Mov Disord 2014;29:1809–1815.
12. Litvan I, Aarsland D, Adler CH, et al. MDS task force on mild cognitive
impairment in Parkinson’s disease: Critical review of PD-MCI. Mov Dis-
ord 2011;26:1814–1824.
13. Hoops S, Nazem S, Siderowf AD, Duda JE, Xie SX, Stern MB,
Weintraub D. Validity of the MoCA and MMSE in the detection of
MCI and dementia in Parkinson disease. Neurology 2009;73:1738–1745.
14. Galtier I, Nieto A, Lorenzo JN, Barroso J. Mild cognitive impairment in
Parkinson’s disease: Diagnosis and progression to dementia. J Clin Exp
Neuropsychol 2016;38:40–50.
Supporting Information
Supporting information may be found in the online version of
this article.
Supplementary Figure S1. Flowchart showing the data
inclusion process
Supplementary Table S1. Cohort details of the included
studies.
Supplementary Table S2. Hazard ratios of models con-
taining MMSE and MoCA.
Supplementary Text S1. Members of the International
Parkinson and Movement Disorders Society Mild Cognitive
Impairment (MCI) Study Group.
MOVEMENT DISORDERS CLINICAL PRACTICE 2022; 9(4): 479–483. doi: 10.1002/mdc3.13451 483
BOEL J.A. ET AL. BRIEF REPORT