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RESEARCH ARTICLE

Detection of proteolytic signatures for Parkinson’s disease

Peter Lüttge Jordal‡,1,2,3, Thomas F Dyrlund‡,2, Kristian Winge4, Martin R Larsen5, Erik H Danielsen6, James A Wells7, Daniel E Otzen2,3 & Jan J Enghild*,2,3

1Section for Medical Biotechnology, Danish Technological Institute, 8000 Aarhus C, Denmark 2Department of Molecular Biology & Genetics, Aarhus University, 8000 Aarhus C, Denmark 3iNANO, Aarhus University, Gustav Wieds Vej 14, 8000 Aarhus C, Denmark 4Bispebjerg Movement Disorders Biobank, Department of Neurology, Bispebjerg University Hospital, 2400, Copenhagen NV, Denmark 5Department of Biochemistry & Molecular Biology, University of Southern Denmark, 5230 Odense M, Denmark 6Department of Neurology, Aarhus University Hospital, 8000 Aarhus C, Denmark 7Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA

*Author for correspondence: [email protected] ‡Authors contributed equally

Aim: To investigate if idiopathic Parkinson’s disease (IPD) is associated with distinct proteolytic signatures relative to non-neurodegenerative controls (NND) and patients with multiple system atrophy (MSA). Materials & methods: A subtiligase-based N-terminomics screening method was exploited for semiquantitative comparison of protein N-termini in cerebrospinal fluid for pooled samples of IPD (n = 6) and NND (n = 8) individuals. Subsequently, targeted selected reaction monitoring mass spectrometry measured the relative concentration of the proteolytic signature peptides in individual IPD (n = 22), NND (n = 11) and MSA (n = 18) samples. Results: The discovery screen detected 300 N-termini for 156 proteins. Selected reaction monitoring analysis revealed that two of these peptides differentiate IPD from NND while three peptides differentiate IPD from MSA. Conclusion: IPD is associated with distinct proteolytic signatures.

First draft submitted: 8 December 2015; Accepted for publication: 15 January 2016; Published online: 8 February 2016

KEYWORDS • cerebrospinal fluid • N-terminomics • Parkinson’s disease

Idiopathic Parkinson’s disease (IPD) is the most common neurodegenerative movement disorder [1]. The main clinical manifestations of IPD include bradykinesia, rigidity, tremor (at rest) and in later stages postural instability. These motor symptoms result from a depletion of the neurotransmitter dopamine in the striatum because the dopamine-producing neurons in the substantia nigra degen-erate [2,3]. At present, no cure exists for IPD, but several potential disease-modifying therapies are being investigated [4]. Of these, immune therapy and neuroprotective treatments with, for example, antioxidants and neurotrophic factors have shown promise in animal models and other preclini-cal or clinical experiments, but so far the long-term clinical improvements in patients have failed to appear [5]. An explanation for the subtle effect could be that most neuroprotective trials have been performed on patients with pronounced loss of dopaminergic neurons. IPD is typically not diagnosed before the motor symptoms become apparent, but studies have suggested that nonmo-tor symptoms reflecting mainly nondopaminergic pathology like sleep and gastrointestinal (GI) symptoms may precede this by several years [6–8]. The effects of the neuroprotective therapies are likely to be stronger if the disease could be detected and treated in the initial nonmotor stages [9,10]. Another explanation for the lack of success in developing disease-modifying treatments may be that other parkinsonian disorders like multiple system atrophy (MSA) may confound the early diagno-sis [11,12]. There is, therefore, a strong clinical need for biomarkers that can provide early detection and differential diagnosis of parkinsonian disorders.

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Proteolytic stress occurs when the level of misfolded or otherwise damaged protein mate-rial exceeds the capacity of the cellular clearance mechanisms. Proteolytic stress is strongly asso-ciated with IPD and suspected of being one of the early molecular events that trigger neurode-generation in the brains of IPD subjects [13,14]. Nevertheless, no studies have investigated whether the proteolytic stress gives rise to distinct signatures that can be exploited as early biomark-ers for IPD. A functional ubiquitin-proteasome system (UPS) is the most important degrada-tion pathway in cells; however, during IPD the UPS is inhibited by aggregated protein species including misfolded α-synuclein [15,16]. This decline in the UPS activity hampers the ability to remove damaged and aggregated proteins, producing a self-perpetuating mechanism. The physiological evidence in the brain includes the appearance of large intracellular inclusions of protein aggregates termed Lewy bodies (LB). LB contains agitated forms of the synaptic protein α-synuclein. We hypothesized that protein aggre-gation along with proteolytic stress leads to the presence of aberrantly cleaved proteins in cerebro-spinal fluid (CSF). When a protein is internally cleaved, this will give rise to a novel N-terminal (neoterminal), and thus it should be possible to detect potential proteolytic signatures for IPD by exploiting N-terminomics investigations. Our N-terminomics protocol relied on selective liga-tion of affinity tags to protein N-termini medi-ated by a re-engineered version of the subtilisin BPN’ protease, subtiligase [17]. We optimized this protocol for protein-sparse samples and further developed a robust relative quantification strat-egy with early incorporation of isotopic labels to identify N-termini that differentiate IPD from non-neurodegenerative subjects (NND). Subsequently we developed selected reaction monitoring (SRM) assays to measure prototypic signature peptides specific for the protein termini in question in a panel of individual patient sam-ples, including patients with MSA as a control arm. As with IPD, MSA is a synucleinopathy associated with α-synuclein aggregation, LB and proteolytic stress [18] and, therefore, we expected that the proteolytic signatures of MSA would also differ from that of the NND controls.

Materials & methods●● Patient samples for discovery study

A total of six IPD patients and eight NND con-trols were included in the initial discovery study.

IPD patients participating in the N-terminomics discovery study were recruited from the outpa-tient clinic at Aarhus University Hospital between December 2007 and May 2010. All included patients were examined by a movement disorder specialist and underwent dopamine transporter (DAT) scan to confirm loss of dopaminergic ter-mini in the striatum as well as MRI to exclude parkinsonism caused by vascular or other struc-tural lesions. All analyzed IPD patients displayed a stage 2.0 bilateral involvement without impair-ment of balance based on a modified Hoehn and Yahr scale assessment [19]. CSF samples were only included, if the individuals were otherwise healthy with no signs of dementia. All patients were screened for cognitive dysfunction using Mini Mental State Examination or Montreal Cognitive assessment as well as Addenbrooks Cognitive Assessment. In cases with signs of cog-nitive impairment, full neuropsychological eval-uation was performed [20,21]. Samples from NND controls were obtained from healthy individuals with suspected neuroborreliosis. The inclusion criteria for the NND group required that an IgG test for borreliosis was negative and that the patients were otherwise healthy apart from idi-opathic dizziness. CSF (4–10 ml) were collected by lumbar puncture into polypropylene tubes and immediately transferred to 4°C. Within 30 min, the samples were gently centrifuged to sediment cells (10 min; 2000 g; 4°C) before the supernatants were aliquoted and stored at -80°C. Cell counts, total protein concentration, albumin concentration, immunoglobulin concentration, glucose and salts levels were all within standard values [22]. Discard criteria were blood contami-nation, infection, blood–brain barrier leakage or inflammation – these criteria were examined by CSF–albumin, red blood cell counts and total protein measurements. In the N-terminomics protocol, sample pooling was applied in order to have sufficient CSF protein. Different sex- and age-matched pools were created to allow for two different quantitative approaches. For the 4-plex iTRAQ strategy the pools were: IPD pool 1 (68 ± 5 years), IPD pool 2 (71 ± 12 years), NND pool 1 (64 ± 20 years), NND pool 2 (62 ± 10 years). For the 2-plex light/heavy strat-egy the pools were: IPD pool (70 ± 8 years) and NND pool (57 ± 7 years). Additional informa-tion about the patient samples for the discovery study is available as Supplementary Information 1. Before inclusion, written informed consent was obtained from all patients. All analyses of CSF

17

Detection of proteolytic signatures for Parkinson’s disease RESEaRch aRticlE

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samples were approved by the regional ethics committees.

●● Patient samples for targeted SRM studyA total of 22 IPD, 11 NND and 18 MSA CSF patients were part of the SRM verification study; the average age and standard deviation for these groups were 65.2 ± 4.5 (IPD), 60 ± 12.2 (NND) and 65.1 ± 7.4 (MSA). The CSF samples from Aarhus University Hospital (Noerrebrogade, 8000 Aarhus C) were included along with additional samples from Bispebjerg Movement Disorders Biobank (BMDB). All included patients at BMDB were examined by a movement disorder specialist and underwent DAT scan and MRI. Blood screening, orthostatic blood pressure measurement and urological tests were also per-formed. CSF from NND controls was obtained from patients recruited from the Department of Neurology, Bispebjerg Hospital (Copenhagen, Denmark), where they were admitted with acute onset of a headache. In all cases, a neurological examination, brain MRI scan, blood screening and lumbar puncture with routine CSF analyses were conducted. Besides a headache (<2%), no clinical or laboratory evidence of neurological disease were found. MSA patients were diag-nosed according to consensus guidelines [23]. CSF sampling at BMDB was carried out with similar procedures as at Aarhus University Hospital as described previously [24]. Before inclusion, written informed consent was obtained from all patients and all analyses of CSF samples were approved by the regional ethics committees.

●● Purification of subtiligaseThe subtiligase used in this study was a phage-display optimized subtilisin mutant [25] with eight point mutations including four mutations for increased ligase activity (S221C, P225A, M124L and S125A [25,26]) and five point mutations intro-duced to increase the stability (M50F, N76D, N109S, K213R and N218S [27]). Expression and purification were essentially conducted as described before [26] except that, ‘helper’ sub-tilisin was omitted from the culture, and a cation exchange chromatography step was used to achieve the desired purity and concentration instead of size exclusion chromatography.

●● Tobacco Etch virus proteinaseCrude tobacco Etch virus proteinase (TEVpr) was a generous gift from Xavier Gomis-Rüth (Department of Structural Biology, Proteolysis

laboratory, Barcelona Science Park, Barcelona, Spain). TEVpr was expressed in Escherichia coli and purified as described in Fang et al. 1997 [28]. The TEVpr used for N-terminomics was further purified by cation exchange chromatography.

●● Synthesis & purification of light & heavy peptide esterThe light biotinylated peptide ester (PE) has been described before [17]. In the present study, a more soluble (N-(3-(2-(2-(3-amino-propoxy)-ethoxy)-ethoxy)-propyl)succinamic acid (Ttds) linker was used, and the overall structure was thus biotin-Ttds-Thr-Glu-Asn-Leu-Tyr-Phe-Gln-Ser*-Tyr-C0-O-CH2-CO-Tyr-amide. In the light PE, the Ser* (the asterisk refers to a heavy-labeled amino acid [standard token in mass spectometry]) residue was composed of nat-ural isotopes while the +4 heavy PE, contained a Ser* composed of three 13C atoms and a single 15N atom (JPT peptides [Berlin, Germany]). The PE’s were purified by reverse-phase HPLC (μBondapak C18 125 Å 10 μM 3.9 × 150 mm, Waters Corp. [Milford, MA, USA]) using an Amersham Pharmacia Äkta Explorer HPLC sys-tem (GE Healthcare, Little Chalford, UK). The mass of the purified PEs were verified by Matrix Assisted Laser Desorption Ionization Time of Flight mass spectrometry using a Micromass Ultima Global (Micromass/Waters Corp.).

●● N-terminomics protocolThe N-terminal positive enrichment strategy using subtiligase (Figure 1A) and the workflow for the discovery study (Figure 1B) was essen-tially conducted as described in [17]. A full protocol is provided as online Supplementary Information 2.

●● LC-MS/MS analysis of N-terminomics samplesFor the light/heavy strategy, the purif ied N-terminal peptides were resuspended in 0.5 μl 100% formic acid and diluted to 20 μl in 0.05% TFA. An aliquot (5 μl) was loaded onto an EASY-nLC system (Proxeon, Denmark). Peptides were loaded directly onto a 20 mm length, 100 μm inner diameter, 360 μm outer diameter, ReproSil – Pur C18 AQ 3 μm (Dr Maisch, Ammerbuch-Entringen, Germany) reversed-phase capillary column. The peptides were eluted using a gradient from 100% phase A (0.1% formic acid) to 35% phase B (0.1% formic acid, 90% acetonitrile) over 90 min




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Figure 1. N-terminomics protocol and overview of discovery and verification study (see facing page). (A) Simplified illustration of the subtiligase method for purification of the N-terminal ends of proteins in complex mixtures. Initially (Ai) the subtiligase ligates a biotinylated peptide ester to the N-α primary amine group of a protein. Subsequently (Aii), trypsin digests at lysine and arginine residues, which leads to release of all internal peptides. Next (Aiii) the N-terminal peptides are enriched by avidin-agarose affinity capture of the biotin moieties, while the internal peptides are removed by rigorous washing procedures under denaturing conditions. Finally, tobacco etch virus protease is added in order to remove the biotin part prior to LC–MS/MS analysis (not shown here). The illustration has been simplified for clarity; for original reference see [17]. (B) Workflow for the discovery study, where N-termini from pooled IPD samples and pooled NND control samples were analyzed in a semiquantitative fashion by both 2-plex and 4-plex methodologies. The 2-plex method exploited stable isotope labeling with a natural isotope version of the peptide ester (light) and a peptide ester with a +4 heavy serine, S*. The 4-plex strategy relied on labeling of N-terminal peptides with 4-plex iTRAQ reagents. (C) Illustration of a method for relative quantification of the proteolytic signatures in single patient samples by LC–SRM MS. The chromatogram displays the retention time for each of the individual spiked-in heavy peptides used to quantify the endogenous cerebrospinal fluid-termini. For clarity, some of the peptide sequences have been omitted from the diagram. IPD: Idiopathic Parkinson’s disease; NND: Non-neurodegenerative control; MSA: Multiple system atrophy; SRM: Selected reaction monitoring.



at 200 nl/min directly into an LTQ-Orbitrap XL (Thermo Scientific). The LTQ-Orbitrap XL was operated in a data-independent mode auto-matically switching between MS and MS/MS using a threshold of 20,000 for ion selection. For each MS scan, the five most abundant precursor ions were selected for fragmentation using CID (normalized collision energy 35; isolation win-dow of 2 Da, activation time 10 ms and selection of 2+, 3+ and 4+ ions). iTRAQ labeled sample were analyzed as described above but with the following modifications: The LTQ-Orbitrap XL was operated in a data-independent mode auto-matically switching between MS and MS/MS using a threshold of 30,000 for ion selection. For each MS scan, the three most abundant precur-sor ions were selected for fragmentation using CID (normalized collision energy 35; isolation window of 3 Da, activation time 10 ms and selec-tion of 2+, 3+ and 4+ ions) and higher energy collision induced dissociation (HCD; resolution 7500 in the Orbitrap, isolation window 3 Da, normalized collision energy 48, activation time 5 ms and FT first mass value set to m/z 110).

●● Peptide identification & quantitationAll raw MS files for the 2-plex N-terminomics protocol were processed using Mascot Distiller 2.4.3.2 (Matrix Science, London, UK). After peak picking all scans, a search was performed against the human proteins in Swiss-Prot (version 2011-03) using Mascot 2.2 with search param-eters allowing one missed semiTrypsin cleavage site. Carbamidomethyl and methionine oxidation

were entered as fixed and variable modifications, respectively. The mass accuracy of the precursor and product ions were 10 ppm and 0.3 Da and the instrument setting was specified as ESI-TRAP. An identical search was done using ‘trypsin’ as the enzyme in order to provide improved identi-fication of potential short, fully tryptic peptides (i.e. in the cases where the neo-terminal was downstream to a Lys/Arg residue).

Data processing of iTRAQ searches was per-formed using Proteome Discoverer (beta version 1.2.0.198) and Mascot 2.2 against the human proteins in Swiss-Prot (version 2010-03). The enzyme was specified to semiTrypsin, and one missed cleavage was allowed. Carbamidomethyl modification of cys residues and N-terminal 4-plex iTRAQ + serine tyrosine (N-term) were entered as fixed modifications. Oxidation of methionine, and 4-plex iTRAQ modification (K) were entered as variable modifications. The mass accuracy of the precursor and product ions were 10 ppm and 0.8 Da for MSA/ETD and 0.05 Da for HCD and the instrument setting was specified as CID-TRAP/HCV-FTICR. The false discovery rate was limited to 5% in all searches.

All Mascot hits were manually verified for: like-lihood of missed cleavages; proline-directed frag-mentation in MS/MS spectra; occurrence of unin-terrupted ion series of at least four consecutive ions from the same series; and presence of ‘diagnos-tic’ a

2/b

2 ions. For example, for iTRAQ-labeled

samples the MS/MS spectra should contain the m/z peaks 367.21 and 395.21, corresponding to a 4-plex iTRAQ-label on N-terminal Ser-Tyr




residues. For the 2-plex strategy, the spectra should contain either the a

2/b

2 ion pair 223.1/251.1 (in

case of the light peptide ester) or the 227.1/255.1 ion pair (in case of the heavy peptide ester). Both 2-plex and 4-plex data were normalized for overall protein concentration by median normalization. In the 2-plex quantification, label swapping was performed to eliminate random hits due to techni-cal noise; that is, in the first experiment the IPD samples were labeled with the light peptide ester and the NND samples were labeled with the +4 heavy peptide ester. Subsequently, the labeling of samples was swapped. The quantitation was only accepted if the ratios of the light/heavy LC–MS envelopes for a given peptide returned reciprocal value in the label-swapped experiments. When available, 2-plex quantitation was preferred over 4-plex quantitation because in the 2-plex protocol the stable isotopes were introduced at the earli-est possible step. To identify protein N-termini that differ in abundance between IPD and NND samples an arbitrary IPD/NND threshold was defined. A relatively lowfold change was chosen, because the proteolytic differences would subse-quently be confirmed by the SRM method. For the 2-plex quantitation, only N-termini with a 1.2-fold difference were accepted, and due to the possible higher technical variance in the 4-plex labeling, the threshold in this case was 1.8. All peptides that exceeded the thresholds were inspected for possible technical variation that might occur due to the primary structure. Therefore, peptides were not regarded as potential biomarkers if they contained, potential trypsin missed cleavage sites, possible Met oxidations, Glu and Asp near the cleavage site and possible post-translational modifications.

●● Sample preparation for LC-SRM MSSynthetic peptides (SpikeTides_L and MaxiPrep_L, JPT Peptide Technologies, Berlin, Germany) were used for optimization of the SRM assay. The crude peptides were resuspended in 80% 0.1 M ammonium bicarbonate and 20% acetonitrile for 30 min in the 96-well plate, to a concentration of approximately 175 pmol/μl. Peptides were combined into a peptide pool by adding 10 μl aliquots from each peptide stock to 100 μl 0.1% formic acid. The peptide mixture was micropurified using Poros 50 R2 reversed phase column material (PerSeptive Biosystems, MA, USA) packed in GELoader Tips (Eppendorf, Hamburg, Germany), dried in a vacuum centrifuge, resuspended in 0.1%

formic acid and stored at 4°C until analysis. After optimization of the SRM assay, one stock peptide mixture was prepared and all CSF samples were dissolved in this mixture.

Total protein concentration for all CSF sam-ples was determined by the Bicinchoninic acid assay, BCA (Thermo Scientific, MA, USA). A total of 50 μg of each CSF sample was lyophi-lized and resuspended in 25 μl 8 M urea 0.2 M Tris-HCl pH 8.3, reduced in 15 mM DTT for 1 h and alkylated in 15 mM iodoacetamide for 1 h at 22°C. The samples were diluted five times with 0.1 M Tris-HCl pH 8.3 and digested over-night with 1:50 w/w trypsin (Sigma-Aldrich, MO, USA) at 37°C for 16 h. From each sample, 3 μg was desalted using Poros 50 R2 reverse phase column material (PerSeptive Biosystems) packed in GELoader Tips (Eppendorf), dried in a vacuum centrifuge, resuspended in 18 μl peptide mixture and stored at 4°C until analysis by LC–SRM MS.

●● SRM assay & LC-SRM MS analysisThe SRM assay was developed and optimized using Skyline v. 2.1.0.4936 [29]. Briefly, an MS discovery analysis of the synthetic peptide mix-ture was performed on a Qtrap 5500 instrument (AB Sciex, MA, USA). The peptides, associated precursor ions and fragment ions were then pro-cessed in Skyline. For peptides containing Met residues, both the oxidized and nonoxidized ver-sions were analyzed. Multiple rounds of opti-mizations were performed by running SRM analyses, importing the data into Skyline and selecting optimal transitions. Once five optimal transitions had been selected in Skyline, the col-lision energy was optimized for all transitions using four steps of 1 V on each side of the cal-culated collision energy values, and the tree most intense transitions were kept for each peptide. Peptides for which three co-eluting transitions could not be found were excluded from the final assay. Finally, the identity and elution time of all peptides was confirmed by SRM-triggered MS/MS scans.

SRM analyses were performed on an EASY-nLC system (Thermo Scientific) connected in-line to a Qtrap 5500 mass spectrometer (AB Sciex) equipped with a NanoSpray III source (AB Sciex) and operated under Analyst 1.6.2 control. The samples were injected, trapped and desalted using a isocratic elution on a Biosphere C18 precolumn (ID 100 μm × 2 cm, 5 μm, 120 Å, NanoSeparation, Nieuwkoop, The Netherlands)

21



after which the peptides were eluted from the trap column and separated on a 15 cm analytical column (75 μm i.d.), which was pulled in-house using a P2000 laser puller (Sutter Instrument Co.), and packed with ReproSil-Pur C18-AQ 3 μm resin (Dr Marisch GmbH, Ammerbuch-Entringen, Germany). Peptides were eluted at a flow rate of 250 nl/min employing a 80 min gradi-ent from 5 to 35% B (0.1% formic acid, 90% ace-tonitrile), followed by re-equilibration for 10 min back to the starting conditions. The Qtrap was operated in positive ion mode with 2500 V ion spray voltage, curtain gas setting of 30, ion source gas setting of 5 and an interface heater tempera-ture of 150°C. The eluted peptides were measured with a 90 min scheduled SRM assay using a 5 min detection window and a target scan time of 2.2 s, resulting in a minimum of 25 ms dwell time and 10 data points over the elution peak for all transi-tions (432 in total). The resolution in Q1 and Q3 was set to unit (0.7 amu FWHM). All samples were measured in triplicates. The mass spectrom-etry data have been deposited to the PeptideAtlas SRM Experiment Library (PASSEL) [30] with the dataset identifier PASS00549.

●● Statistical analysis of SRM dataAll SRM samples were imported into Skyline, the peak boarders were manually verified and, where needed, corrected for all samples. The ratios between the endogenous and exogenous (spiked) peptides were exported to Microsoft Excel, and average values were calculated from the triplicate measures. Finally, a student’s t-test was performed on the average values to identify peptides differ-ing in prevalence between the control groups (online Supplementary Information 3).

Results●● Discovery of proteolytic signatures using

N-terminomicsThe aim of the present study was to investigate if Parkinson’s disease is associated with distinct proteolytic signatures. In essence, this was ana-lyzed by a semiquantitative N-terminomics pro-tocol to determine if the protein N-termini in CSF samples varies between IPD subjects and NND controls. One of the challenging aspects of N-terminomics is that the reactivity of the ε-group of lysine residues very closely resemble the reactivity of the N-terminal amino-group of a protein, and therefore it can be difficult to obtain the desired selectivity. A solution to this problem is to exploit the re-engineered enzyme

subtiligase because the particular structure and mechanism of the enzyme mean that it is exqui-sitely specific for the N-terminal amino group. In brief, the N-terminomics protocol (Figure 1A) is based on an initial subtiligase-mediated liga-tion of a biotin-containing peptide ester to the N-terminus of a protein. Subsequently, trypsin-digestion leads to release of N-terminal peptides, internal fully tryptic peptides and C-terminal peptides. The biotin-tag – originally on the pep-tide ester and now ligated to the N-terminal pep-tides – is exploited for affinity purification, while the internal and C-terminal peptides are washed away under denaturing conditions. Finally, TEV protease is used to release the N-terminal pep-tide from the affinity resins, and LC–MS/MS is performed to identify the sequence of the peptide. A small MS signature from the peptide will reside on the N-terminal peptide after the N-terminomics protocol and thus the existence of N-terminal peptides were verified by two char-acteristics including: the presence of a doublet in the LC–MS chromatogram in the case of 2-plex light/heavy quantitation (Figure 2A); and, a spe-cific N-terminal Ser-Tyr a

2 and b

2 ion signature in

the LC–MS/MS spectrum (Figure 2B). By exploit-ing these two traits, non-N-terminal peptides can be excluded from the analysis, producing a list of 300 different N-termini in 156 proteins (online Supplementary Information 4).

Several of the detected proteins contained more than one N-terminal including ALBU (31 termini), CYTC (six termini), immuno-globulin chains (six termini) and PTGDS (eight termini). Western blots of 2D gels using a poly-clonal antibody specific for ALBU confirmed the presence of truncated versions of albumin in both IPD and NND samples (online Supplementary Information 5). ALBU, CYTC and immunoglobu-lins all have high protein concentration in CSF, for example, ALBU constitutes approximately 75% of the total CSF protein by weight [31]. The many observed termini in high-abundance pro-teins are likely due to the availability of these pro-teins as substrates for both endogenous proteo-lytic events and for subtiligase tagging during the N-terminomics protocol. Conversely, some pro-teins with low concentration in CSF were detected with several N-termini including secretogranin-1 (11 N-termini) even though the protein only con-stitutes approximately 0.09% of the total protein content in CSF [31]. In this case, the many detected N-termini are likely to present because secretogra-nin-1 is reported to be extensively processed by




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nyl g

roup

. (C)

Sum

mar

y of

the

spec

ific

sequ

ence

s as

wel

l as

the

sem

i-qua

ntita

tive

info

rmat

ion

for t

he d

etec

ted

CD99

N-t

erm

inal

pep

tides

. “IP

D N

-ter

min

al A

A” c

orre

spon

ds to

the

Uni

prot

resi

due

num

ber.

The

antic

ipat

ed

N-t

erm

inal

acc

ordi

ng to

Uni

prot

pro

tein

kno

wle

dgeb

ase

is a

lso

show

n. (D

) Tru

ncat

ion

plot

dis

play

ing

the

IPD

/NN

D ra

tio a

s a

func

tion

of th

e de

tect

ed C

D99

ant

igen

N

-ter

min

i. A

ll N

-ter

min

i are

map

ping

to th

e ex

trac

ellu

lar d

omai

n of

CD

99. N

ote

that

a s

ingl

e N

-ter

min

al d

etec

ted

(arr

ow) w

as d

etec

ted

with

a 1

:1 p

reva

lenc

e in

IPD

and

NN

D

sam

ples

whi

le th

e ot

her p

eptid

es a

ll ex

ceed

ed a

nd IP

D/N

ND

ratio

of 1

.34.

A

A: A

min

o ac

id; I

PD: I

diop

athi

c Pa

rkin

son’

s di

seas

e; N

ND

: Non

-neu

rode

gene

rativ

e co

ntro

l; TM

: Tra

nsm

embr

ane

regi

on.

51015 0

931.

093

1.5

931.

408

Total ion count (104)

Ligh

t env

elop

e: N

ND

Hea

vy e

nvel

ope:

IPD

931.

911

932.

413

932.

911

933.

412

933.

914

934.

415

934.

917

935.

420

935.

916

932.

093

2.5

933.

093

3.5

934.

093

5.0

934.

593

5.5

m/z

Total ion count (104)

200

300

400

500

600

700

800

900

1000

1100

1200

1300

m/z

1020304050

N-t

erm

SY

(a 21+

)22

7.10

4N

-ter

mS

Y (

b 21+)

251.

100

y 31+

333.

179

y 51+

447.

218

y 11+

147.

100

y 61+

534.

253

y 81+

690.

343

y 91+

805.

371

y 101+

876.

409

y 111+

989.

499

y 121+

1104

.521

y 131+

1175

.551

y 141+

1290

.585

y 151+

1377

.623

y 71+

633.

321

y 21+

204.

149

b 22+

245.

300

SY

*-S

FS

DA

DLA

DG

VG

VS

GG

EG

K

Seq

uenc

eN

-ter

min

al A

AIP

D/N

ND

Sco

re82 85 89 90 23

0.99

1.34

1.44

1.74

56 68 108

31

NH

PS

SS

GS

FD

AD

LA

DG

VS

GG

EG

KS

SS

GS

FD

AD

LA

DG

VS

GG

EG

KS

FS

DA

DL

AD

GV

SG

GE

GK

GF

SD

AD

LA

DG

VS

GG

EG

KR

egul

ar N

-ter

min

al

IPD/NND peptide ratio 0.8

1.0

1.2

1.4

1.6

1.8

Sig

nal

5110

115

1E

xtra

cellu

lar

TM

Cyt

opla

smic

1

23



limited proteolysis at basic residues by prohormone convertases, which leads to the generation of sev-eral biologically active peptides [32]. Six N-termini were also detected for A4 protein , which is simi-larly present with relatively low concentration in CSF. Thus in some cases the number of identified termini per protein is in accordance with the pro-tein abundance in CSF, while in other cases a high number of identified termini can be explained by expected biological processing and high suscep-tibility to proteolysis. Apart from these observa-tions, unexpected proteolytic activities were also observed in specific proteins. Interestingly, several of these differed in prevalence when comparing IPD patients and NND controls.

●● Proteolytic signatures for IPDIn the present study, two semiquantitative approaches were explored to detect proteolytic incidents with elevated prevalence in IPD CSF samples, namely 4-plex iTRAQ-labeling and a 2-plex light/heavy strategy. The advantage of the iTRAQ strategy is that the peptide identification yield is boosted because the isobaric tags give identical m/z value of the precursor ions and the fragments in the MS/MS spectrum. On the other hand, a drawback with the iTRAQ approach is that the quantitative labels are introduced rela-tively late in the N-terminomics protocol and therefore the quantitation will likely exhibit rela-tively high standard deviation. Conversely, the 2-plex light/heavy strategy introduces the quan-titative labels very early in the N-terminomics protocol with the natural isotopes (light) and the +4 Ser* residue (heavy) at the subtiligase bioti-nylation step. Subsequent to this step, the light- and heavy-labeled CSF protein termini from IPD and NND subjects can be pooled and thus the termini will exhibit the same technical vari-ances for the remainder of the protocol, which means that the quantitation will be more robust. Label-swap experiments were used to assess the robustness of the 2-plex method. Thus in the first experiment, the pooled CSF samples from IPD subjects were labeled with heavy peptide ester, and CSF samples from NND subjects were labeled with light peptide ester. Conversely in the second experiment, the labeling was inter-changed. These label-swap experiments shows that the technical variance is extremely low (Table 1), for example, for some peptides the standard deviation between label-swap experi-ments was ±0.09 (GASQAGAPQGR, GELS), ±0.02 (SNLDEDIIAEENIVSR, CO3 (C3b

alpha’ chain)) and ±0.00 (NILTEEPK, PON1).As observed in the truncation plot for CD99

antigen protein (Figure 2C) some of the detected N-terminal peptides differed in abundance between IPD patients and NND controls. A total of 38 N-terminal peptides in 25 different proteins were observed that exceeded the defined thresholds of 1.2-fold difference for the 2-plex analysis and 1.8-fold difference for the 4-plex analysis (Table 1). In most cases, the difference in abundance in IPD and NND pools were relatively modest, while, for example, for PON1 the incidence of N-terminal at position 309 was much higher in the NND pool (2.8-fold differ-ence). Several of the detected N-termini with higher abundance in the IPD samples showed signs of possible degradation intermediates. This was especially evident for CYTC and PTGDS where many IPD-specific N-terminal peptides were differing only by single residues (online Supplementary information 6). These N-terminal trimming events could likely be due to sequential removal of amino acids by the amino peptidases that are known to be involved in the degradation and recycling of proteins [33].

●● Verification by SRMThe purpose of the N-terminomics study was to serve as an initial, in-depth, discovery screen for detection of IPD-specific proteolytic signatures in pooled CSF samples, which subsequently could be analyzed for biological variance and validity in individual CSF samples by SRM assays. In the SRM approach, synthetic, heavy-labeled +10 Arg or +6 Lys semitryptic peptides corresponding to the proteolytic signature pep-tides from the N-terminomics study were spiked into the individual CSF samples after these have been digested with trypsin.

As illustrated in the flow chart (Figure 1C) the heavy peptides elute from a C18 reversed phase column during a long, shallow, acetonitrile gra-dient. The exogenously added heavy peptides will have the same retention time behavior as the endogenous semitryptic N-terminal CSF pep-tides. Thus, by having a fixed spike-in of heavy peptides relative to the CSF protein concentration, relative quantification of the 38 CSF N-termini in individual patient samples can be performed highly reproducibly in a single LC–MS/MS run.

25 IPD samples and 18 NND samples were included in the targeted SRM study. Furthermore, to test if the proteolytic signa-tures could differentiate IPD subjects from




Tabl

e 1.

38

N-t

erm

ini w

ith

alte

red

abun

danc

e in

Par

kins

on’s

dis

ease

pat

ient

s.

Acc

essi

onPr

otei

n na

me

Sequ

ence

Pept

ide

Scor

eIP

D/N

ND

rati

oN

-ter

min

al p

osit

ion

Regu

lar N

-ter

min

al

Q14

118

DAG

1 M

AQS.

HW

PSEP

SEAV

R.D

WEN

491.

36 ±

0.12

3030

P050

67A

4D

LQP.

WH

SFG

AD

SVPA

NTE

NEV

EPV

DA

RPA

AD

R.G

LTT

540.

56†

621

18‡

P516

93A

PLP1

GSL

A.G

GSP

GA

AEA

PGSA

QVA

GLC

GR.

LTLH

651.

44 ±

0.3

542

39Q

0648

1A

PLP2

PMKK

.GSG

VGEQ

DG

GLI

GA

EEK

.VIN

S10

41.

27 ±

0.0

762

532

P142

09CD

99PN

HP.

SSSG

SFSD

AD

LAD

GVS

GG

EGK

.GG

SD68

1.34

± 0

.02

8523

‡ /228

‡

SSSG

.SFS

DA

DLA

DG

VSG

GEG

K.G

GSD

108

1.44

± 0

.08

89

SSG

S.FS

DA

DLA

DG

VSG

GEG

K.G

GSD

311.

7490

P1

0909

CLU

SV

TTV.

ASH

TSD

SDV

PSG

VTE

VV

VK

.LFD

S61

1.41

± 0

.13

390

23‡

P010

24CO

3 (C

3b α

’ cha

in)

GLA

R.SN

LDED

IIAEE

NIV

SR.S

EFP

430.

24 ±

0.0

274

974

9‡

P0C0

L4CO

4AG

LQR.

ALE

ILQ

EED

LID

EDD

IPV

R.SF

FP73

1.44

± 0

.02

757

757‡

Q12

860

CNTN

1VA

LA.V

INSA

QD

APS

EAPT

EVG

VK

.VLS

S33

1.32

± 0

.01

800

21‡

P010

34C

YTC

VALA

.VSP

AA

GSS

PGKP

PR.L

VGG

661.

54 ±

0.1

921

27‡

AVSP

.AA

GSS

PGKP

PR.L

VGG

771.

39 ±

0.1

924

Q

9UBP

4D

KK3

LDLI

.TW

ELEP

DG

ALD

R.CP

CA39

1.22

± 0

.47

253

22‡

P082

94SO

DE

ASD

A.W

TGED

SAEP

NSD

SAEW

IR.D

MYA

981.

59 ±

0.17

1919

‡

P026

71FI

BA (fi

brin

opep

tide

A)

TAW

T.A

DSG

EGD

FLA

EGG

GV

R.G

PRV

882.

09 ±

0.0

820

20

FIBA

(fibr

inog

en α

-cha

in)

EITR

.GG

STSY

GTG

SETE

SPR.

NPS

S76

0.38

± 0

.08

272

36P0

6396

GEL

STA

SR.G

ASQ

AG

APQ

GR.

VPEA

781.

43 ±

0.0

933

28

G

LSY.

LSSH

IAN

VER

.VPF

D44

1.21

± 0

.1241

0

P007

38H

PTQ

LFA

.VD

SGN

DV

TDIA

DD

GCP

KPPE

IAH

GY

VEH

SVR.

YQCK

730.

3719

19‡

Q16

653

MO

GH

PIR.

ALV

GD

EVEL

PCR.

ISPG

821.

55 ±

0.0

743

30

PI

RA.L

VGD

EVEL

PCR.

ISPG

701.

3544

30P1

0451

OST

PD

AVA

.TW

LNPD

PSQ

K.Q

NLL

451.

3542

17

AV

AT.W

LNPD

PSQ

K.Q

NLL

361.

23 ±

0.11

43

KAIP

.VA

QD

LNA

PSD

WD

SR.G

KDS

681.

62 ±

0.0

220

7

AIP

V.A

QD

LNA

PSD

WD

SR.G

KDS

871.

54 ±

0.1

520

8

Q6U

X71

PLXD

C2TN

RA.S

VGQ

DSP

EPR.

SFTD

651.

33 ±

0.0

581

31P4

1222

PTG

DS

ALL

G.V

LGD

LQA

APE

AQ

VSVQ

PNFQ

QD

K.

FLG

R38

1.46

1623

‡

DLQ

A.A

PEA

QVS

VQPN

FQQ

DK

.FLG

R76

1.31

± 0

.09

23

QA

AP.

EAQ

VSVQ

PNFQ

QD

K.F

LGR

501.

30 ±

0.1

525

Su

mm

ary

of id

entifi

ed N

-ter

min

al p

eptid

es th

at e

xcee

d th

e re

quire

d IP

D/N

ND

thre

shol

d (s

ee ‘M

ater

ials

& m

etho

ds’ s

ectio

n). U

nipr

ot a

cces

sion

num

ber

and

the

corr

esp

ondi

ng p

rote

in n

ame

is in

dica

ted

for e

ach

iden

tified

N

-ter

min

al. ‘

Sequ

ence

’ den

otes

the

iden

tified

seq

uenc

e (b

old)

alo

ng w

ith th

e fla

nkin

g fo

ur C

- and

N-t

erm

inal

resi

dues

. ‘IP

D/N

ND

’ is

the

rela

tive

ratio

of t

he id

entifi

ed N

-ter

min

al p

eptid

e in

the

IPD

sam

ple

rela

tive

to th

e N

ND

sa

mp

le a

s de

term

ined

by

eith

er th

e 2-

ple

x or

4-p

lex

anal

ysis

. Sta

ndar

d de

viat

ions

are

rep

orte

d if

the

sam

e p

eptid

e w

as d

etec

ted

in b

oth

2ple

x la

bel

-sw

ap e

xper

imen

ts, i

n th

ese

case

s th

e av

erag

e an

d st

anda

rd d

evia

tion

are

rep

orte

d b

ased

on

the

tota

l num

ber

of a

ccep

ted

LC–M

S ob

serv

atio

ns. ‘

N-t

erm

inal

pos

ition

’ den

otes

the

resi

due

num

ber

that

the

iden

tified

N-t

erm

inal

cor

resp

onds

to, w

hile

‘Reg

ular

N-t

erm

inus

’ den

otes

the

usua

l pro

tein

N

-ter

min

al re

sidu

e, a

s in

dica

ted

in U

nipr

ot.

† Indi

cate

s th

at th

e qu

antit

atio

n w

as b

ased

on

4-p

lex

iTRA

Q q

uant

itatio

n –

all o

ther

ratio

s ar

e in

the

tab

le a

re b

ased

on

2-p

lex

light

/hea

vy q

uant

itatio

n.‡ In

dica

tes

that

the

N-t

erm

inal

ass

ignm

ent i

s b

ased

on

exp

erim

enta

l find

ings

.IP

D: I

diop

athi

c Pa

rkin

son’

s di

seas

e; N

ND

: Non

-neu

rode

gene

rativ

e co

ntro

l.

25



patients with MSA, CSF samples from 18 MSA patients were included (Supplementary Information 1). Three additional proteotypic peptides, previously observed in SRM experi-ments, were included for A4. These peptides were included because this protein is of high interest as a biomarker for differential diagnosis of neurodegenerative diseases like Alzheimer’s and Parkinsonian disorders, in particular in cases with early cognitive involvement. While the N-terminal peptide detected in the discov-ery study corresponded to proteolytic activity in the C-terminal part of the protein (residue 621), the designed proteotypic peptides corresponded to the N-terminal part (THPHFVIPYR, resi-due 107 and CLVGEFVSDALLVPDK, residue 117) and the central part (VESLEQEAANER, residue 439). The final SRM assay included 29 peptides covering 19 different proteins (Supplementary Information 7) with all peptides identified with a signal distinguishable from the background noise.

As exemplified in the SRM chromatograms for the SODE peptide WTGEDSAEPNSDSAEWIR (Figure 3), several fragments (daughter ions) were monitored over time for each endogenous (solid lines) and exogenous peptide (dashed lines). By comparing the area under the curve for the spiked-in heavy peptides with the area under the curve for the endogenous peptides, a rela-tive amount of the peptides can be calculated. Thus in the case with the patient “330 IPD”? the light/heavy ratio is 0.44 while SRM-analysis of the NND control returns the light/heavy ratio 0.37 and a final IPD/NND ratio 1.19 (0.44/0.37) when comparing these specific CSF samples.

A statistical t-test analysis (p < 0.05) of the SRM results confirmed that two termini (corresponding to the peptides WTGEDSAEPNSDSAEWIR and SFSDADLADGVSGGEGK) had a higher prev-alence in IPD compared with NND, while three termini (VESLEQEAANER, THPHFVIPYR and FTECCQAADK) were more abundant in IPD compared with MSA subjects (Table 2). In addition, a single terminal (corresponding to the peptide GGSTSYGTGSETESPR) was more frequent in MSA patients compared with NND controls.

Discussion●● N-terminomics screening for Parkinson’s

disease biomarkersProteolytic dysfunction is suspected to occur early in the disease progression of IPD. Aberrant A

cces

sion

Prot

ein

nam

e Se

quen

cePe

ptid

e Sc

ore

IPD

/NN

D ra

tio

N-t

erm

inal

pos

itio

nRe

gula

r N-t

erm

inal

P076

02SA

PD

TVK.

SLPC

DIC

K.D

VV

T33

1.24

6060

‡

Q92

743

HTR

A1RA

GR.

SAPL

AA

GCP

DR.

CPD

R62

1.20

± 0

.08

3023

P027

68A

LBU

YKA

A.F

TECC

QA

AD

K.A

ACL

520.

47†

189

25‡

LEKC

.CA

AA

DPH

ECYA

K.V

FDE

500.

18†

385

LI

KQ.N

CELF

EQLG

EYK

.FQ

NA

360.

3941

5

VTK

C.C

TESL

VN

R.RP

CF57

0.40

± 0

.10

501

P2

7169

PON

1LR

IQ.N

ILTE

EPK

.VTQ

V45

0.36

± 0

.00

309

2‡

P027

66TT

HY

DYS

Y.ST

TAV

VTN

PK.E

– (C

-ter

min

al)

331.

81†

137

21‡

Sum

mar

y of

iden

tified

N-t

erm

inal

pep

tides

that

exc

eed

the

requ

ired

IPD

/NN

D th

resh

old

(see

‘Mat

eria

ls &

met

hods

’ sec

tion)

. Uni

prot

acc

essi

on n

umb

er a

nd th

e co

rres

pon

ding

pro

tein

nam

e is

indi

cate

d fo

r eac

h id

entifi

ed

N-t

erm

inal

. ‘Se

quen

ce’ d

enot

es th

e id

entifi

ed s

eque

nce

(bol

d) a

long

with

the

flank

ing

four

C- a

nd N

-ter

min

al re

sidu

es. ‘

IPD

/NN

D’ i

s th

e re

lativ

e ra

tio o

f the

iden

tified

N-t

erm

inal

pep

tide

in th

e IP

D s

amp

le re

lativ

e to

the

NN

D

sam

ple

as

dete

rmin

ed b

y ei

ther

the

2-p

lex

or 4

-ple

x an

alys

is. S

tand

ard

devi

atio

ns a

re re

por

ted

if th

e sa

me

pep

tide

was

det

ecte

d in

bot

h 2p

lex

lab

el-s

wap

exp

erim

ents

, in

thes

e ca

ses

the

aver

age

and

stan

dard

dev

iatio

n ar

e re

por

ted

bas

ed o

n th

e to

tal n

umb

er o

f acc

epte

d LC

–MS

obse

rvat

ions

. ‘N

-ter

min

al p

ositi

on’ d

enot

es th

e re

sidu

e nu

mb

er th

at th

e id

entifi

ed N

-ter

min

al c

orre

spon

ds to

, whi

le ‘R

egul

ar N

-ter

min

us’ d

enot

es th

e us

ual p

rote

in

N-t

erm

inal

resi

due,

as

indi

cate

d in

Uni

prot

.† In

dica

tes

that

the

quan

titat

ion

was

bas

ed o

n 4-

ple

x iT

RAQ

qua

ntita

tion

– al

l oth

er ra

tios

are

in th

e ta

ble

are

bas

ed o

n 2-

ple

x lig

ht/h

eavy

qua

ntita

tion.

‡ Indi

cate

s th

at th

e N

-ter

min

al a

ssig

nmen

t is

bas

ed o

n ex

per

imen

tal fi

ndin

gs.

IPD

: Idi

opat

hic

Park

inso

n’s

dise

ase;

NN

D: N

on-n

euro

dege

nera

tive

cont

rol.

Tabl

e 1.

38

N-t

erm

ini w

ith

alte

red

abun

danc

e in

Par

kins

on’s

dis

ease

pat

ient

s (c

ont.)

.


Figu

re 3

. Tar

gete

d re

lati

ve q

uant

ifica

tion

of p

rote

olyt

ic s

igna

ture

pep

tide

s in

indi

vidu

al s

ampl

es b

y se

lect

ed re

acti

on m

onit

orin

g. (A

) Sel

ecte

d re

actio

n m

onito

ring

tran

sitio

ns u

sed

for r

elat

ive

quan

tifica

tion

of th

e SO

DE

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27



cleavage of proteins will lead to occurrence of novel protein termini, and therefore an N-terminomics protocol, was optimized to perform a semiquantitative comparison of the N-termini of IPD patients and NND controls.

An N-terminomics protocol based on subti-ligase was used because this engineered enzyme has an unrivaled specificity for Nα groups [17]. Another advantage of this protocol is that it is performed under near-native conditions, which means that the subtiligase will have similar accessibility for the CSF protein tails as the endogenous proteases. The subtiligase protocol has hitherto been limited to biological matrices with high protein content because the subtiligase has limited labeling efficiency [34], but with the sample work-up and adaptations in the present protocol it is now possible to use the N-terminomics method down to 500 μg and perform analyses on protein-scarce samples like CSF-samples. Nevertheless, 300 peptides detected is a relatively low number and since these studies were conducted, robust, selective and more sensitive N-terminomics protocols based on chemical approaches have been estab-lished, for example, terminal amine isotopic labeling of substrates (TAILS) and COmbined FR Actional Diagonal Chromatography (COFRADIC) [35,36]. Strategies like these could increase the number of N-termini detected and moreover provide identification and quantifica-tion of N-termini with post-translational modi-fications (e.g., acetylated amino groups at the N-terminus).

●● N-termini detected for amyloidogenic proteinsN-terminomics comparison of CSF from IPD and NND subjects revealed a higher prevalence of N-termini for several amyloidogenic pro-teins. In fact, nine of the 25 detected proteins with different abundance of N-terminal iso-forms (Table 1) are known to be involved in pro-tein aggregation including A4 [37], APLP1 and APLP2 [38], CYTC [39], FIBA [40], GELS [41], HTRA1 [42], TTHY [43] and CLUS [44]. It is often mutations that give rise to the amyloid diseases, although the nonmutated variants also display amyloidogenic properties and may accumulate in vivo in the nonhereditary forms of amyloid diseases [45]. During normal physi-ological conditions the baseline level of mis-folded proteins are removed but when the UPS is compromised, degradation intermediates

accumulate in the CSF. In addition, it has been shown that aggregated protein can be propa-gated from one generation by a mechanism referred to as seeding [46–49]. This process may further add to the accumulation of proteolytic fragments of amyloid-related proteins in CSF, but the validity of this hypothesis remains to be further investigated.

●● Collateral damage in high abundance proteinsApart from the above, aggregation-related pro-teins, quantitative differences between IPD patients and NND controls were observed in high-abundance proteins, for example, ALBU, FIBA (alpha chain) and complement factors. Of these, ALBU and FIBA were observed with inconsistent ratios in the discovery study and the validation study. The cause of the anomaly is not certain, but it is possible that proteolytic events acting on these proteins are not specific for IPD, but rather the consequence of “collateral damage” in high-abundance proteins.

●● Proteolytic signatures for neurodegenerative diseasesAs we have shown previously, SRM can be exploited to verify the individual proteolytic signature peptides in single patient sam-ples [50,51]. In the present study, several of the peptides from the discovery study were statis-tically confirmed by the SRM assay (Table 2), which further strengthened the reliability of the N-terminomics protocol. The majority of peptides were consistent in trend when com-paring the N-terminomics discovery study and the SRM-based targeted MS-analysis. Of the peptides with significant fold change between IPD and NND, the N-terminal SODE peptide (WTGEDSAEPNSDSAEWIR) displayed the best discriminatory power. The CD99 peptide SFSDADLADGVSGGEGK also differed sig-nificantly in prevalence when comparing IPD and NND. To our knowledge, CD99 has not previously been associated with IPD. CD99 is involved in many physiological conditions, like intracellular trafficking, cell adhesion and differentiation. Under pathological condi-tions, CD99 can induce apoptosis [52] and is pathogenically mainly associated with Ewing’s sarcoma tumors, where it can induce massive cell death [53]. The association with CD99 and Parkinson’s disease remains elusive, but a recent study has reported that CD99 is an




early marker for familial Alzheimer’s disease patients [54]. CD99 may have a similar role in IPD.

●● An SRM assay to distinguish Parkinson’s disease & multiple system atrophyBecause IPD and MSA are both synucleinopa-thies associated with proteolytic stress and protein misfolding, the proteolytic signatures of these diseases were not expected to differ substantially. In line with this assumption, the SRM study revealed only a single peptide from the IPD versus NND discovery study that differed between the MSA and IPD, namely the peptide FTECCQAADK (Table 2, Serum Albumin). Therefore, as expected, no dramatic proteolytic differences were observed between IPD and MSA. On the other hand, SRM-differences were observed that could be ascribed to differences in protein concentration, namely for the two fully tryptic proteotypic SRM pep-tides designed in silico for the Amyloid beta A4 protein, Abeta (VESLEQEAANER and THPHFVIPYR). Abeta has long been associ-ated with neurodegeneration, especially with respect to the amyloidogenic Alzheimer’s dis-ease peptide Abeta

1–42 (residue 672 to residue

713) [37]. Abeta1–42

has also been implicated in Parkinson’s disease in antibody-based meas-urements of the concentration of the peptide in CSF. For example, Hall et al. reported that Abeta

1–42 is a useful marker for distinguishing

Parkinsonian disorders [55]. Abeta1–42

has also very recently been suggested as an early prog-nostic biomarker of dementia associated with Parkinson’s disease [56]. In the present study, the proteotypic peptides that distinguish MSA and IPD do not map to the Abeta

1–42 region, but

instead belong to the N-terminal and central region of Abeta (THPHFVIPYR, N-terminal residue no. 107 and VESLEQEAANER, N-terminal residue no. 439). This could mean that differential proteolytic processing of Abeta occurs in IPD and MSA. We have previously shown that the Abeta

1–42 concentra-

tion is lower in MSA compared with IPD [57], but it is not known whether this is reflected in the present data. Discriminatory markers for Parkinsonian disorders are strongly needed for stratification and monitoring purposes in order to develop better treatments for IPD and atypical parkinsonism. This is especially the case for MSA, where the neurologist’s sensitivity can be as low as 56% in the early Ta

ble

2. P

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des

diff

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ly (p

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749

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107

–0.

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0.04

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354

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AA

DK

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450

–0.

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(α-c

hain

)G

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444

Rela

tive

quan

tifica

tion

by s

elec

ted

reac

tion

mon

itorin

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veal

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at tw

o p

eptid

es s

how

ed a

sig

nific

ant d

iffer

ence

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wee

n IP

D a

nd N

ND

, thr

ee s

how

ed a

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eren

ce b

etw

een

IPD

and

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a s

ingl

e p

eptid

e sh

owed

a d

iffer

ence

b

etw

een

MSA

and

NN

D p

atie

nts.

‘N-t

erm

inal

pos

ition

’ den

otes

the

resi

due

num

ber

that

the

iden

tified

N-t

erm

inal

cor

resp

onds

to, w

hile

‘reg

ular

N-t

erm

inus

’ den

otes

the

usua

l pro

tein

N-t

erm

inal

resi

due,

as

indi

cate

d in

Uni

prot

.IP

D: I

diop

athi

c Pa

rkin

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s di

seas

e; N

ND

: Non

-neu

rode

gene

rativ

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ntro

l; M

SA: M

ultip

le s

yste

m a

trop

hy.

29



phases, but increasing as the condition pro-gresses into phases where rational (pharmaco-)therapy has little effects [11,12]. So far the most promising markers reported for discriminat-ing IPD and MSA are neurofilament light chain (NF-L), DJ-1 and SAPPα (reviewed in Magdalinou et al. and Jiménez-Jiménez et al. [58,59]). Nevertheless, conflicting and incon-sistent results have been reported and there remains a strong need for novel, robust markers that discriminate IPD and MSA. A promising approach appears to be to combine measured biomarkers into panels that offer high power for discriminating between IPD and atypical Parkinsonism [55,60]. In this regard, it could prove fruitful to develop a multiplexed SRM assay that enables quantification of the most promising biomarkers along with proteolytic signature peptides for high-confidence disease stratification.

The present study also identified a single FIBA peptide that can distinguish MSA patients from NND controls, namely GGSTSYGTGSETESPR.

The exact mechanisms underlying the differ-ences in termini are as yet not clear. Nonetheless, the data show that SRM measurements of

specific proteolytic signature peptides may be exploited to perform differential diagnosis and prognosis of Parkinsonian disorders.

●● Future studiesThe present work has highlighted interesting findings of proteolytic signatures in a relatively restricted number of patient and control samples. A limitations of the study is that the average age of NND group is lower than that of the IPD group and MSA group both in the discovery and targeted SRM study. Although the differ-ence in average age at CSF tap is not statistically significant, there is a risk that the markers may reflect differences in age in addition to disease-specific markers. Therefore, if these results are to be translated into clinically applicable early biomarkers for differentiation of Parkinsonian disorders, SRM studies in well-validated large cohorts are needed in addition to longitudinal studies.

ConclusionThe present study has demonstrated that neu-rodegenerative diseases like IPD and MSA are associated with distinct proteolytic signatures

EXEcUtiVE SUMMaRYThe need for novel biomarkers for Parkinson’s disease

● Early and accurate diagnosis of Parkinson’s disease is important for future clinical trials aiming to develop novel disease-modifying strategies.

Proteolytic stress could be an early event in Parkinson’s disease

● Defects in the protein degradation machinery and proteolytic stress may be some of the early events in the etiopathogenesis of Parkinson’s disease. Thus, molecular indications of proteolysis-gone-wrong may be a source of future, early biomarkers.

Proteolytic signatures for neurodegenerative diseases

● Enrichment and LC–MS/MS-based identification of the N-termini of proteins (N-terminomics) can be used in combination with selected reaction monitoring (SRM) to detect distinct proteolytic signatures for neurodegenerative diseases.

Discovery of proteolytic signatures for Parkinson’s disease

● This study represents the first N-terminomics investigation of human cerebrospinal fluid from patients with idiopathic Parkinson’s disease. In total, 300 N-terminal peptides in 158 different proteins were detected.

SRM enables quantification of proteolytic signature peptides

● Analysis by SRM returned two significant peptides (p < 0.05) from SODE and CD99 that could be used to differentiate idiopathic Parkinson’s disease from non-neurodegenerative controls. In addition, three significant peptides – two from A4 and one from ALBU - could be used to differentiate idiopathic Parkinson’s disease from multiple system atrophy.

Longitudinal studies warranted

● The results must be validated in larger, longitudinal, cohorts of patients and non-neurodegenerative controls before it is known whether the proteolytic signature peptides can be exploited as early biomarkers for parkinsonian disorders.




and that these can be quantified using targeted MS-based SRM assays. More studies in larger cohorts are warranted to validate if the proteo-lytic signatures are applicable as discriminatory, early biomarkers for Parkinsonian disorders.

Future perspectiveImproved techniques for early detection and differentiation of Parkinsonian disorders are strongly needed. Quantitative mass spectrom-etry has undergone a remarkable evolution for clinical applicability in the past decade, and it has thus become feasible to complement tradi-tional ELISA assays with robust, multiplexed SRM assays. The present study serves to give an illustration of how mass spectrometry can be exploited to detect proteolytic signatures detected by N-terminomics instead of merely quantifying intact proteins as biomarkers of disease. As Overall et al. have recently sug-gested, the approach with characterization of proteolytic signatures holds promise for detec-tion of biomarkers and development of preci-sion medicine in other protease-related disease areas like inflammation and cancer [61].

Supplementary DataTo view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/full/10.2217/fnl.16.3.

AcknowledgmentsThe authors acknowledge Sami Mahrus and David Wildes for skilled assistance in optimizing experimental conditions for subtiligase-mediated biotinylation of protein N-termini.

Financial & competing interests disclosureThis study was supported by the Michael J Fox Foundation and the Danish Agency for Science, Technology and Innovation (Innovation Consortium CureND). Bispebjerg Movement Disorders Biobank is supported by The John and Birthe Meyer Foundation and the ANT Foundation. MR Larson was funded by the Lundbeck Foundation (Junior Group Leader Fellowship). The authors have no other relevant affiliations or financial involvement with any organization or entity with a finan-cial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

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45 Merlini G, Bellotti V. Molecular mechanisms of amyloidosis. N. Engl. J. Med. 349(6), 583–596 (2003).

46 Petkova AT, Leapman RD, Guo Z, Yau WM, Mattson MP, Tycko R. Self-propagating, molecular-level polymorphism in Alzheimer’s

beta-amyloid fibrils. Science 307(5707), 262–265 (2005).

47 Dzwolak W, Smirnovas V, Jansen R, Winter R. Insulin forms amyloid in a strain-dependent manner: an FT-IR spectroscopic study. Protein Sci. 13(7), 1927–1932 (2004).

48 Surmacz-Chwedoruk W, Nieznanska H, Wojcik S, Dzwolak W. Cross-seeding of fibrils from two types of insulin induces new amyloid strains. Biochemistry 51(47), 9460–9469 (2012).

49 Macchi F, Hoffmann SV, Carlsen M et al. Mechanical stress affects glucagon fibrillation kinetics and fibril structure. Langmuir. 27(20), 12539–12549 (2011).

50 Wiita AP, Hsu GW, Lu CM, Esensten JH, Wells JA. Circulating proteolytic signatures of chemotherapy-induced cell death in humans discovered by N-terminal labeling. Proc. Natl Acad. Sci. USA 111(21), 7594–7599 (2014).

• IllustrateshowN-terminomicscanbeusedincombinationwithselectedreactionmonitoringtoidentifycelldeath-associatedsignaturesovertimeinplasma.

51 Shimbo K, Hsu GW, Nguyen H et al. Quantitative profiling of caspase-cleaved substrates reveals different drug-induced and cell-type patterns in apoptosis. Proc. Natl Acad. Sci. USA 109(31), 12432–12437 (2012).

52 Bernard G, Breittmayer JP, De Matteis M et al. Apoptosis of immature thymocytes mediated by E2/CD99. J. Immunol. 158(6), 2543–2550 (1997).

53 Cerisano V, Aalto Y, Perdichizzi S et al. Molecular mechanisms of CD99-induced caspase-independent cell death and cell–cell adhesion in Ewing’s sarcoma cells: actin and zyxin as key intracellular mediators. Oncogene 23(33), 5664–5674 (2004).

54 Ringman JM, Schulman H, Becker C et al. Proteomic changes in cerebrospinal fluid of presymptomatic and affected persons carrying familial Alzheimer disease mutations. Arch. Neurol. 69(1), 96–104 (2012).

55 Hall S, Ohrfelt A, Constantinescu R et al. Accuracy of a panel of 5 cerebrospinal fluid biomarkers in the differential diagnosis of patients with dementia and/or parkinsonian disorders. Arch. Neurol. 69(11), 1445–1452 (2012).

56 Alves G, Lange J, Blennow K et al. CSF Abeta42 predicts early-onset dementia in Parkinson disease. Neurology 82(20), 1784–1790 (2014).

57 Bech S, Hjermind LE, Salvesen L et al. Amyloid-related biomarkers and axonal damage proteins in parkinsonian syndromes.




Parkinsonism Relat. Disord. 18(1), 69–72 (2012).

58 Magdalinou N, Lees AJ, Zetterberg H. Cerebrospinal fluid biomarkers in parkinsonian conditions: an update and future directions. J. Neurol. Neurosurg. Psychiatry 85(10), 1065–1075 (2014).

59 Jimenez-Jimenez FJ, Alonso-Navarro H, Garcia-Martin E, Agundez JA. Cerebrospinal fluid biochemical studies in patients with Parkinson’s disease: toward a potential search for biomarkers for this disease. Front Cell Neurosci. 8, 369 (2014).

60 Magdalinou NK, Paterson RW, Schott JM et al. A panel of nine cerebrospinal fluid biomarkers may identify patients with atypical parkinsonian syndromes. J. Neurol. Neurosurg. Psychiatry 86(11), 1240–1247 (2015).

•• Highlightsthechallengesrelatedtodiscriminatingparkinsoniandisorders.Figure 1(boxplot)providesagoodoverviewoftheconcentrationsandspansofpromisingbiomarkersfortherespectiveparkinsoniandisorders.

61 Eckhard U, Marino G, Butler GS, Overall CM. Positional proteomics in the era of the human proteome project on the doorstep of precision medicine. Biochimie. doi:10.1016/j.biochi.2015.10.018 (2015) (Epub ahead of print).

•• Excellentintroductiontoterminomicstoolsandprotocolsavailableforpositionalproteomicsandtheiradvantages.

part of

10.2217/nmt-2016-0002 © 2016 Future Medicine Ltd

SyStematic Review

Electroconvulsive therapy for depression in Parkinson’s disease: systematic review of evidence and recommendations

Anna Borisovskaya*,1,2, William Culbertson Bryson1, Jonathan Buchholz1,2, Ali Samii1,2 & Soo Borson1

1University of Washington Medical Center, Seattle, Washington, USA 2Veterans’ Affairs Medical Center, Seattle, Washington, USA

*Author for correspondence: [email protected]

Aim: We performed a systematic review of evidence regarding treatment of depression in Parkinson’s disease (PD) utilizing electroconvulsive therapy. Methods: The search led to the inclusion of 43 articles, mainly case reports or case series, with the largest number of patients totaling 19. Results: The analysis included 116 patients with depression and PD; depression improved in 93.1%. Where motor symptoms’ severity was reported, 83% of patients improved. Cognition did not worsen in the majority (94%). Many patients experienced delirium or transient confusion, sometimes necessitating discontinuation of electroconvulsive therapy (ECT). Little is known about maintenance ECT in this population. Conclusion: ECT can benefit patients suffering from PD and depression. We recommend an algorithm for treatment of depression in PD, utilizing ECT sooner rather than later.

First draft submitted: 5 January 2016; Accepted for publication: 8 February 2016; Published online: 1 April 2016

KeywoRds • electroconvulsive therapy • major depressive disorder • Parkinson’s disease

BackgroundDepression is a frequently encountered neuropsychiatric disorder in patients with Parkinson’s disease (PD). Estimates in prevalence range widely in the literature with studies reporting 2.7–70% [1]. Inconsistent measurement techniques and sampling methods may account for these discrepancies. Further, depressed patients with PD may not be a homogeneous group. A rigorous systematic review reported clinically significant depressive symptoms in 35% of patients with prevalence rates of 17%

Practice points

● All patients with Parkinson’s disease (PD) should be screened for depression. When treating depression in PD, a clinician should not be satisfied with nonresponse or partial response.

● Electroconvulsive therapy (ECT) is a safe and effective treatment for most patients with PD and depression, offering advantage in that it also may improve motor symptoms.

● Cognitive impairment at baseline should not be a contraindication for ECT, though all patients should be counseled regarding the potential for transient cognitive worsening.

● ECT should be included in the guidelines for treatment of patients with PD and depression.

● Prospective studies of ECT in patients with PD and psychiatric comorbidities will help determine what ECT parameters are most likely to lead to remission of symptoms with minimal side effects.

● We call for more publications regarding clinical experience of maintenance ECT in patients with PD, which will advance this area of scientific inquiry.

Neurodegener. Dis. Manag. (Epub ahead of print) ISSN 1758-2024


systeMAtiC Review Borisovskaya, Bryson, Buchholz, Samii & Borson


tabl

e 1.

Dep

ress

ion

seve

rity

, mot

or s

ympt

om s

ever

ity,

sid

e eff

ects

and

dur

atio

n of

trea

tmen

t eff

ect.

Stud

y (y

ear)

Num

ber

of

pati

ents

Dep

ress

ion

mea

sure

at

base

line

Dep

ress

ion

mea

sure

af

ter e

ct

mot

or s

ympt

oms

mea

sure

at b

asel

ine

mot

or s

ympt

oms

mea

sure

aft

er e

ct

Side

eff

ects

Dur

atio

n of

tr

eatm

ent e

ffec

tRe

f.

Scic

utel

la (2

015)

1Ps

ycho

tic d

epre

ssio

nIm

prov

emen

tN

AN

AD

eliri

um le

adin

g to

EC

T di

scon

tinua

tion

NA

[30]

Nis

hiok

a (2

014)

2 BD

I 31;

HA

M-D

30

BDI 8

; HA

M-D

5H

&Y 5

; UPD

RS(II

I) 47

H&Y

3; U

PDRS

(III)

23N

one

repo

rted

NA

[31]

BDI 1

0; H

AM

-D 7

BDI 8

; HA

M-D

2H

&Y 5

; UPD

RS(II

I) 46

H&Y

4; U

PDRS

(III)

30N

one

repo

rted

At l

east

1 y

ear

G

adit

(201

2)1

MD

D a

nd O

CD‘M

arke

d’

impr

ovem

ent

NA

NA

Non

e re

port

edN

A[32]

Berg

(201

1)1

BDI 2

0BD

I 14

H&Y

4-5

H&Y

2Tr

ansi

ent c

onfu

sion

NA

[33]

Bui (

2011

)1

HA

M-D

21

HA

M-D

13

UPD

RS(II

I) 22

UPD

RS(II

I) 10

Non

e re

port

edA

t lea

st 5

mon

ths

[34]

Duc

harm

e (2

011)

1M

DD

‘Ade

quat

e’ re

spon

seN

AN

AN

one

repo

rted

Dep

ress

ion

retu

rns

afte

r 2 y

ears

[35]

Nas

r (20

11)

1M

DD

with

psy

chot

ic

sym

ptom

sRe

mis

sion

NA

NA

Non

e re

port

edD

epre

ssio

n re

turn

s af

ter a

n un

spec

ified

pe

riod

of ti

me

[36]

Ued

a (2

010)

2

HA

M-D

23

HA

M-D

2H

&Y 4

H&Y

3W

ell t

oler

ated

At l

east

1 w

eek

[22]

H

AM

-D 2

5H

AM

-D 1

H&Y

5H

&Y 4

Wel

l tol

erat

edA

t lea

st 1

wee

k

Virit

(201

0)3

HA

M-D

40

HA

M-D

16

UPD

RS(II

I) 49

UPD

RS(II

I) 39

Non

e re

port

edN

A[37]

HA

M-D

28

HA

M-D

8U

PDRS

(III)

60U

PDRS

(III)

24N

one

repo

rted

NA

H

AM

-D 3

0H

AM

-D 6

UPD

RS(II

I) 39

UPD

RS(II

I) 21

Non

e re

port

edN

A

Baili

ne (2

008)

1M

DD

with

psy

chot

ic

sym

ptom

s‘S

igni

fican

t’ im

prov

emen

tN

AN

ATr

ansi

ent c

onfu

sion

NA

[38]

Belz

eaux

(200

7)1

MD

D w

ith c

atat

onic

sy

mpt

oms

Rem

issi

onN

AN

AN

one

repo

rted

MEC

T do

ne e

very

1-

2 w

eeks

with

su

stai

ned

effec

t

[39]

Chou

(200

5)1

MD

D w

ith p

sych

otic

sy

mpt

oms

Rem

issi

onN

AN

ATr

ansi

ent c

onfu

sion

At l

east

6 m

onth

s[40]

Oze

r (20

05)

1

‘Dep

ress

ion

and

psyc

hosi

s’ fo

r all

thre

e co

urse

s

Non

e re

port

ed a

fter

1s

t cou

rse

UPD

RS(to

tal)

142

UPD

RS(to

tal)

42Tr

ansi

ent c

onfu

sion

Dep

ress

ion

retu

rns

5 ye

ars

afte

r 1st

co

urse

and

4 y

ears

af

ter 2

nd c

ours

e.

Patie

nt ‘c

ured

’ aft

er

3rd

cour

se

[41]

Rem

issi

on a

fter

2nd

co

urse

NA

NA

Non

e re

port

ed

‘Cur

ed’ a

fter

3rd

co

urse

NA

NA

Non

e re

port

ed

Shul

man

(200

3)1

MD

DRe

mis

sion

NA

NA

Non

e re

port

edN

A[42]

Beni

to (2

002)

3H

AM

-D 4

2H

AM

-D 4

2N

AN

ATr

ansi

ent c

onfu

sion

No

effec

t[43]

H

AM

-D 4

6H

AM

-D 3

6H

&Y 4

NA

Non

e re

port

edN

A

H

AM

-D 4

5H

AM

-D 1

4H

&Y 3

H&Y

2.5

Non

e re

port

edN

A

AIM

S: A

bnor

mal

Invo

lunt

ary

Mov

emen

t Sca

le; B

DI:

Beck

Dep

ress

ion

Inve

ntor

y; C

ORS

D: C

ronh

om &

Ott

osso

n D

epre

ssio

n Ra

ting

Scal

e; C

RSD

: Car

roll

Ratin

g Sc

ale

for D

epre

ssio

n; D

uvoi

sin:

Col

umbi

a U

nive

rsit

y Ra

ting

Scal

e;

ECT:

ele

ctro

conv

ulsi

ve th

erap

y; E

PS: E

xtra

pyra

mid

al s

ympt

oms;

HA

M-D

: Ham

ilton

Rat

ing

Scal

e fo

r Dep

ress

ion;

H&Y

: Hoe

hn a

nd Y

ahr S

cale

; MA

DRS

: Mon

tgom

ery–

Asb

erg

Dep

ress

ion

Ratin

g Sc

ale;

MD

D: M

ajor

Dep

ress

ive

Dis

orde

r; M

ECT:

Mai

nten

ance

Ele

ctro

conv

ulsi

ve T

hera

py; M

MSE

: Min

i-Men

tal S

tatu

s Ex

am; N

A: N

ot a

pplic

able

; NO

S: N

ot o

ther

wis

e sp

ecifi

ed; O

CD

: Obs

essi

ve-c

omp

ulsi

ve d

isor

der;

SCRS

: Sho

rt C

linic

al R

atin

g Sc

ale;

SD

S: Z

ung

Self-

Ratin

g D

epre

ssio

n Sc

ale;

Sim

pson

: Sim

pson

–Ang

us S

cale

; UPD

RS: U

nifie

d Pa

rkin

son’

s D

isea

se R

atin

g Sc

ale;

UPD

RS(II

I): M

otor

Sec

tion

(Par

t 3) o

f the

UPD

RS.

10.2217/nmt-2016-0002 Neurodegener. Dis. Manag. (Epub ahead of print)

Electroconvulsive therapy for depression in Parkinson’s disease systeMAtiC Review


Stud

y (y

ear)

Num

ber

of

pati

ents

Dep

ress

ion

mea

sure

at

base

line

Dep

ress

ion

mea

sure

af

ter e

ct

mot

or s

ympt

oms

mea

sure

at b

asel

ine

mot

or s

ympt

oms

mea

sure

aft

er e

ct

Side

eff

ects

Dur

atio

n of

tr

eatm

ent e

ffec

tRe

f.

Fall

(200

0)3

MA

DRS

8M

AD

RS 4

H&Y

4; U

PDRS

(III)

67U

PDRS

(III)

62N

one

repo

rted

NA

[44]

M

AD

RS 1

2M

AD

RS 8

H&Y

2.5

; UPD

RS(II

I) 11

UPD

RS(II

I) 0

Non

e re

port

edN

A

MA

DRS

19

MA

DRS

8H

&Y 4

; UPD

RS(II

I) 26

UPD

RS(II

I) 14

Non

e re

port

edN

A

Am

ann

(199

9)1

‘Del

usio

nal d

epre

ssio

n’‘N

o su

cces

s’N

AN

AN

one

repo

rted

No

effec

t[45]

Fall

(199

9)1

MA

DRS

19

MA

DRS

8H

&Y 4

; UPD

RS(II

I) 26

UPD

RS(II

I) 14

Tran

sien

t con

fusi

onN

A[46]

Kram

er (1

999)

10

Incl

udes

DSM

-IIIR

or

DSM

-IV d

efine

d M

DD

, at

ypic

al d

epre

ssio

n,

or d

epre

ssio

n no

t ot

herw

ise

spec

ified

5 pa

tient

s ‘m

uch

impr

oved

’N

AN

AN

one

repo

rted

Follo

wed

for

4 ye

ars:

5 p

atie

nts

had

sust

aine

d eff

ect;

4 pa

tient

s ha

d so

me

depr

essi

on b

ut s

till

bett

er th

an p

re-E

CT;

1

patie

nt h

ad n

o eff

ect

[47]

4 pa

tient

s ‘p

artia

lly

impr

oved

’

1 pa

tient

no

effec

t

Avila

(199

7)

2 ‘P

sych

otic

dep

ress

ion’

Rem

issi

onU

PDRS

(III)

61U

PDRS

(III)

30N

one

repo

rted

MEC

T fo

r 2 m

onth

s fo

llow

ing

inde

x co

urse

with

sust

aine

d eff

ect

[48]

‘Psy

chot

ic d

epre

ssio

n’‘S

igni

fican

t’ im

prov

emen

tN

AN

AN

one

repo

rted

Nym

eyer

(199

7)1

MD

DIm

prov

emen

tN

AN

ATr

ansi

ent d

eliri

umN

A[49]

Fact

or (1

995)

1M

DD

with

psy

chot

ic

sym

ptom

sIm

prov

emen

tN

AN

ATr

ansi

ent c

onfu

sion

NA

[50]

Sand

yk (1

993)

1‘D

elus

iona

l uni

pola

r de

pres

sion

’‘M

odes

t’ im

prov

emen

tN

AN

AA

gita

tion

Dep

ress

ion

retu

rns

afte

r an

unsp

ecifi

ed

perio

d of

tim

e

[51]

Frie

dman

(199

2)

5

NA

Rem

issi

onH

&Y 4

H&Y

3N

one

repo

rted

At l

east

10

wee

ks[52]

‘Sev

ere

depr

essi

on’

Impr

ovem

ent

H&Y

3–4

H&Y

3D

eliri

umD

epre

ssio

n re

turn

s af

ter 1

-2 m

onth

s

NA

Impr

ovem

ent

H&Y

3N

APV

Cs d

urin

g in

duct

ion

NA

‘Sev

ere

depr

essi

on’

‘Mild

’ im

prov

emen

tH

&Y 4

NA

Non

e re

port

edD

ied

2 da

ys a

fter

last

EC

T

‘Sev

ere

depr

essi

on’

NA

H&Y

4H

&Y 3

Non

e re

port

edD

epre

ssio

n re

turn

s af

ter 8

wee

ks

Hol

zer (

1992

)1

‘Maj

or d

epre

ssiv

e ep

isod

e’Re

mis

sion

NA

NA

Stut

terin

g, s

lurr

ed

spee

chN

A[53]

AIM

S: A

bnor

mal

Invo

lunt

ary

Mov

emen

t Sca

le; B

DI:

Beck

Dep

ress

ion

Inve

ntor

y; C

ORS

D: C

ronh

om &

Ott

osso

n D

epre

ssio

n Ra

ting

Scal

e; C

RSD

: Car

roll

Ratin

g Sc

ale

for D

epre

ssio

n; D

uvoi

sin:

Col

umbi

a U

nive

rsit

y Ra

ting

Scal

e;

ECT:

ele

ctro

conv

ulsi

ve th

erap

y; E

PS: E

xtra

pyra

mid

al s

ympt

oms;

HA

M-D

: Ham

ilton

Rat

ing

Scal

e fo

r Dep

ress

ion;

H&Y

: Hoe

hn a

nd Y

ahr S

cale

; MA

DRS

: Mon

tgom

ery–

Asb

erg

Dep

ress

ion

Ratin

g Sc

ale;

MD

D: M

ajor

Dep

ress

ive

Dis

orde

r; M

ECT:

Mai

nten

ance

Ele

ctro

conv

ulsi

ve T

hera

py; M

MSE

: Min

i-Men

tal S

tatu

s Ex

am; N

A: N

ot a

pplic

able

; NO

S: N

ot o

ther

wis

e sp

ecifi

ed; O

CD

: Obs

essi

ve-c

omp

ulsi

ve d

isor

der;

SCRS

: Sho

rt C

linic

al R

atin

g Sc

ale;

SD

S: Z

ung

Self-

Ratin

g D

epre

ssio

n Sc

ale;

Sim

pson

: Sim

pson

–Ang

us S

cale

; UPD

RS: U

nifie

d Pa

rkin

son’

s D

isea

se R

atin

g Sc

ale;

UPD

RS(II

I): M

otor

Sec

tion

(Par

t 3) o

f the

UPD

RS.

tabl

e 1.

Dep

ress

ion

seve

rity

, mot

or s

ympt

om s

ever

ity,

sid

e eff

ects

and

dur

atio

n of

trea

tmen

t eff

ect (

cont

.).

10.2217/nmt-2016-0002



Stud

y (y

ear)

Num

ber

of

pati

ents

Dep

ress

ion

mea

sure

at

base

line

Dep

ress

ion

mea

sure

af

ter e

ct

mot

or s

ympt

oms

mea

sure

at b

asel

ine

mot

or s

ympt

oms

mea

sure

aft

er e

ct

Side

eff

ects

Dur

atio

n of

tr

eatm

ent e

ffec

tRe

f.

Oh

(199

2)

11

5 pa

tient

s w

ith M

DD

3 pa

tient

s ‘m

arke

d re

lief’

NA

NA

Del

irium

in 7

pa

tient

s, le

adin

g to

trea

tmen

t di

scon

tinua

tion

in

6 pa

tient

s

NA

[54]

4 pa

tient

s w

ith p

sych

otic

M

DD

6 pa

tient

s ‘so

me

relie

f’

1 pa

tient

with

de

pres

sion

NO

S2

patie

nts

no e

ffect

1 pa

tient

with

bip

olar

m

ania

Zwil

(199

2)

8

MD

D w

ith p

sych

otic

sy

mpt

oms

Impr

ovem

ent

NA

NA

‘Dis

orie

ntat

ion’

ar

ose

in 8

out

of 2

7 pa

tient

s, b

ut s

ide

effec

ts in

the

subs

et

of p

atie

nts

with

Pa

rkin

son’

s di

seas

e (n

= 8

) are

not

de

scrib

ed se

para

tely

.

NA

NA

NA

NA

NA

NA

NA

NA

[29]

MD

DIm

prov

emen

tN

AN

ACR

SD 3

1CR

SD 3

2N

AN

AM

DD

with

psy

chot

ic

sym

ptom

sIm

prov

emen

tN

AN

A

HA

M-D

22

HA

M-D

3N

AN

AH

AM

-D 3

0H

AM

-D 6

NA

NA

‘Org

anic

moo

d di

sord

er,

depr

esse

d’Im

prov

emen

tN

AN

A

MD

DIm

prov

emen

tN

AN

AFi

giel

(199

1)

7

‘Maj

or d

epre

ssiv

e ep

isod

e’‘V

ery

muc

h’

impr

ovem

ent

NA

NA

Del

irium

NA

[55]

‘Maj

or d

epre

ssiv

e ep

isod

e’N

o eff

ect

NA

NA

Del

irium

NA

MD

D‘V

ery

muc

h’

impr

ovem

ent

NA

NA

Del

irium

NA

MD

D‘V

ery

muc

h’

impr

ovem

ent

NA

NA

Del

irium

NA

Bipo

lar d

isor

der,

depr

esse

d‘V

ery

muc

h’

impr

ovem

ent

NA

NA

Del

irium

NA

‘Maj

or d

epre

ssiv

e ep

isod

e’Im

prov

emen

tN

AN

AD

eliri

umN

A

‘Maj

or d

epre

ssiv

e ep

isod

e’‘M

uch’

impr

ovem

ent

NA

NA

Del

irium

NA

Ster

n (1

991)

1‘A

nxio

us a

nd d

epre

ssed

’Im

prov

emen

tN

AN

AN

one

repo

rted

NA

[56]

Libe

rzon

(199

0)1

Dep

ress

ion

and

‘par

anoi

d id

eatio

n’‘M

arke

d’

impr

ovem

ent

NA

NA

‘No

sign

ifica

nt

conf

usio

n’N

A[57]

AIM

S: A

bnor

mal

Invo

lunt

ary

Mov

emen

t Sca

le; B

DI:

Beck

Dep

ress

ion

Inve

ntor

y; C

ORS

D: C

ronh

om &

Ott

osso

n D

epre

ssio

n Ra

ting

Scal

e; C

RSD

: Car

roll

Ratin

g Sc

ale

for D

epre

ssio

n; D

uvoi

sin:

Col

umbi

a U

nive

rsit

y Ra

ting

Scal

e;

ECT:

ele

ctro

conv

ulsi

ve th

erap

y; E

PS: E

xtra

pyra

mid

al s

ympt

oms;

HA

M-D

: Ham

ilton

Rat

ing

Scal

e fo

r Dep

ress

ion;

H&Y

: Hoe

hn a

nd Y

ahr S

cale

; MA

DRS

: Mon

tgom

ery–

Asb

erg

Dep

ress

ion

Ratin

g Sc

ale;

MD

D: M

ajor

Dep

ress

ive

Dis

orde

r; M

ECT:

Mai

nten

ance

Ele

ctro

conv

ulsi

ve T

hera

py; M

MSE

: Min

i-Men

tal S

tatu

s Ex

am; N

A: N

ot a

pplic

able

; NO

S: N

ot o

ther

wis

e sp

ecifi

ed; O

CD

: Obs

essi

ve-c

omp

ulsi

ve d

isor

der;

SCRS

: Sho

rt C

linic

al R

atin

g Sc

ale;

SD

S: Z

ung

Self-

Ratin

g D

epre

ssio

n Sc

ale;

Sim

pson

: Sim

pson

–Ang

us S

cale

; UPD

RS: U

nifie

d Pa

rkin

son’

s D

isea

se R

atin

g Sc

ale;

UPD

RS(II

I): M

otor

Sec

tion

(Par

t 3) o

f the

UPD

RS.

tabl

e 1.

Dep

ress

ion

seve

rity

, mot

or s

ympt

om s

ever

ity,

sid

e eff

ects

and

dur

atio

n of

trea

tmen

t eff

ect (

cont

.).




Stud

y (y

ear)

Num

ber

of

pati

ents

Dep

ress

ion

mea

sure

at

base

line

Dep

ress

ion

mea

sure

af

ter e

ct

mot

or s

ympt

oms

mea

sure

at b

asel

ine

mot

or s

ympt

oms

mea

sure

aft

er e

ct

Side

eff

ects

Dur

atio

n of

tr

eatm

ent e

ffec

tRe

f.

Dou

yon

(198

9)

7H

AM

-D ra

nge

21-3

3 in

th

ese

seve

n pa

tient

s; m

ean

27 w

ith s

.d. 4

Impr

ovem

ent

NYU

PD 7

9N

YUPD

46%

de

crea

seD

eliri

umN

A[58]

H

AM

-D 2

7% d

ecre

ase

NYU

PD 6

8N

YUPD

60%

de

crea

seD

eliri

umN

A

Im

prov

emen

tN

YUPD

61

NYU

PD 7

4%

decr

ease

Non

e re

port

edN

A

H

AM

-D 5

2% d

ecre

ase

NYU

PD 5

1N

YUPD

41%

de

crea

seN

one

repo

rted

NA

H

AM

-D 4

3% d

ecre

ase

NYU

PD 3

6N

YUPD

39%

de

crea

seN

one

repo

rted

NA

Im

prov

emen

tN

YUPD

95

NYU

PD 2

1%

decr

ease

Del

irium

NA

H

AM

-D 8

0% d

ecre

ase

NYU

PD 6

5N

YUPD

80%

de

crea

seN

one

repo

rted

NA

Burk

e (1

988)

3M

DD

Rem

issi

onN

AN

AN

one

repo

rted

At l

east

1 m

onth

[59]

MD

DRe

mis

sion

NA

NA

Non

e re

port

edA

t lea

st 1

mon

th

MD

DIm

prov

emen

tN

AN

AN

one

repo

rted

At l

east

2 m

onth

s

Youn

g (1

985)

1H

AM

-D 2

2H

AM

-D 2

4N

AN

ATr

ansi

ent c

onfu

sion

No

effec

t[60]

Hol

com

b (1

983)

1SC

RS 8

SCRS

2EP

S sc

ale

3; A

IMS

9EP

S sc

ale

1; A

IMS

14N

one

repo

rted

Dep

ress

ion

retu

rns

afte

r 1 w

eek

[61]

Levy

(198

3)1

‘Sev

ere

depr

essi

on’

‘Rem

arka

ble’

im

prov

emen

tN

AN

AN

one

repo

rted

NA

[62]

Balld

in (1

980)

3CO

RSD

4CO

RSD

0N

AN

AW

ell t

oler

ated

NA

[63]

CORS

D 1

CORS

D 0

NA

NA

Wel

l tol

erat

edN

A

CORS

D 3

CORS

D 0

NA

NA

Tran

sien

t con

fusi

onN

A

Yudo

fsky

(197

9)1

‘Del

usio

nal d

epre

ssio

n’Im

prov

emen

tN

AN

AN

one

repo

rted

At l

east

3 w

eeks

[64]

Barc

ia (1

978)

1M

DD

with

psy

chot

ic

sym

ptom

sRe

mis

sion

NA

NA

Non

e re

port

edN

A[65]

Asn

is (1

977)

1SD

S 70

SDS

40D

uvoi

sin

22; S

imps

on

15D

uvoi

sin

6; S

imps

on

9Tr

ansi

ent c

onfu

sion

Dep

ress

ion

retu

rns

afte

r 7 w

eeks

[66]

Dys

ken

(197

6)1

MD

DIm

prov

emen

tN

AN

AN

one

repo

rted

NA

[67]

Lebe

nsoh

n (1

975)

2M

DD

with

psy

chot

ic

sym

ptom

s‘R

emar

kabl

e’

impr

ovem

ent

NA

NA

Wel

l tol

erat

edN

A[68]

Bipo

lar d

isor

der,

depr

esse

dRe

mis

sion

NA

NA

Non

e re

port

edN

A

AIM

S: A

bnor

mal

Invo

lunt

ary

Mov

emen

t Sca

le; B

DI:

Beck

Dep

ress

ion

Inve

ntor

y; C

ORS

D: C

ronh

om &

Ott

osso

n D

epre

ssio

n Ra

ting

Scal

e; C

RSD

: Car

roll

Ratin

g Sc

ale

for D

epre

ssio

n; D

uvoi

sin:

Col

umbi

a U

nive

rsit

y Ra

ting

Scal

e;

ECT:

ele

ctro

conv

ulsi

ve th

erap

y; E

PS: E

xtra

pyra

mid

al s

ympt

oms;

HA

M-D

: Ham

ilton

Rat

ing

Scal

e fo

r Dep

ress

ion;

H&Y

: Hoe

hn a

nd Y

ahr S

cale

; MA

DRS

: Mon

tgom

ery–

Asb

erg

Dep

ress

ion

Ratin

g Sc

ale;

MD

D: M

ajor

Dep

ress

ive

Dis

orde

r; M

ECT:

Mai

nten

ance

Ele

ctro

conv

ulsi

ve T

hera

py; M

MSE

: Min

i-Men

tal S

tatu

s Ex

am; N

A: N

ot a

pplic

able

; NO

S: N

ot o

ther

wis

e sp

ecifi

ed; O

CD

: Obs

essi

ve-c

omp

ulsi

ve d

isor

der;

SCRS

: Sho

rt C

linic

al R

atin

g Sc

ale;

SD

S: Z

ung

Self-

Ratin

g D

epre

ssio

n Sc

ale;

Sim

pson

: Sim

pson

–Ang

us S

cale

; UPD

RS: U

nifie

d Pa

rkin

son’

s D

isea

se R

atin

g Sc

ale;

UPD

RS(II

I): M

otor

Sec

tion

(Par

t 3) o

f the

UPD

RS.

tabl

e 1.

Dep

ress

ion

seve

rity

, mot

or s

ympt

om s

ever

ity,

sid

e eff

ects

and

dur

atio

n of

trea

tmen

t eff

ect (

cont

.).

10.2217/nmt-2016-0002



Stud

y (y

ear)

Num

ber

of

pati

ents

Dep

ress

ion

mea

sure

at

base

line

Dep

ress

ion

mea

sure

af

ter e

ct

mot

or s

ympt

oms

mea

sure

at b

asel

ine

mot

or s

ympt

oms

mea

sure

aft

er e

ct

Side

eff

ects

Dur

atio

n of

tr

eatm

ent e

ffec

tRe

f.

Lipp

er (1

975)

1Ps

ycho

tic d

epre

ssio

n‘M

arke

d’

impr

ovem

ent

NA

NA

Wel

l tol

erat

edN

A[69]

97

A

IMS:

Abn

orm

al In

volu

ntar

y M

ovem

ent S

cale

; BD

I: Be

ck D

epre

ssio

n In

vent

ory;

CO

RSD

: Cro

nhom

& O

ttos

son

Dep

ress

ion

Ratin

g Sc

ale;

CRS

D: C

arro

ll Ra

ting

Scal

e fo

r Dep

ress

ion;

Duv

oisi

n: C

olum

bia

Uni

vers

ity

Ratin

g Sc

ale;

EC

T: e

lect

roco

nvul

sive

ther

apy;

EPS

: Ext

rapy

ram

idal

sym

ptom

s; H

AM

-D: H

amilt

on R

atin

g Sc

ale

for D

epre

ssio

n; H

&Y: H

oehn

and

Yah

r Sca

le; M

AD

RS: M

ontg

omer

y–A

sber

g D

epre

ssio

n Ra

ting

Scal

e; M

DD

: Maj

or D

epre

ssiv

e D

isor

der;

MEC

T: M

aint

enan

ce E

lect

roco

nvul

sive

The

rapy

; MM

SE: M

ini-M

enta

l Sta

tus

Exam

; NA

: Not

app

licab

le; N

OS:

Not

oth

erw

ise

spec

ified

; OC

D: O

bses

sive

-com

pul

sive

dis

orde

r; SC

RS: S

hort

Clin

ical

Rat

ing

Scal

e; S

DS:

Zun

g Se

lf-Ra

ting

Dep

ress

ion

Scal

e; S

imps

on: S

imps

on–A

ngus

Sca

le; U

PDRS

: Uni

fied

Park

inso

n’s

Dis

ease

Rat

ing

Scal

e; U

PDRS

(III):

Mot

or S

ectio

n (P

art 3

) of t

he U

PDRS

.

tabl

e 1.

Dep

ress

ion

seve

rity

, mot

or s

ympt

om s

ever

ity,

sid

e eff

ects

and

dur

atio

n of

trea

tmen

t eff

ect (

cont

.).

for major depression, 22% for minor depression and 13% for dysthymia [2]. Depression is asso-ciated with poorer quality of life and increased morbidity and mortality in patients with PD, and it is the most important risk factor for suicide in this population [3]. Despite conventional thought that suicide is uncommon in PD, recent studies suggest that suicidal ideation may be present in up to 30% of patients with PD [4]. Unfortunately, despite the high prevalence, depression in PD is often unrecognized and undertreated [5].

Overlapping symptoms may make it difficult to discern problems expected in PD, such as apa-thy, sleep disturbance, masked facies and brad-ykinesia, from symptoms of depression, such as anhedonia, depressed affect and psychomotor slowing. According to the American Academy of Neurology guidelines, screening of depression in PD should include the use of a standardized measure such as the Beck Depression Inventory or the Hamilton Depression Rating Scale. A recent taskforce from the Movement Disorders Society advocated for use of the Geriatric Depression Scale [6,7]. Once accurately detected, treatment can still be a challenge.

Antidepressant therapy is the most common treatment of depression in PD with studies showing that up to 25% of patients with PD are on an antidepressant treatment at any given time [4]. Serotonin selective reuptake inhibitors (SSRIs) are more often prescribed than tricyclic antidepressants (TCAs). A national study from the Veteran’s Administration showed rates for treatment of depression in PD at 63% for SSRIs compared with 7.5% for TCAs [8]. Despite these statistics, there are relatively few randomized trials showing efficacy of SSRIs and TCAs in the treatment of depression in PD. According to the most recent practice parameter from the American Academy of Neurology, there was insufficient evidence to support or refute effectiveness of antidepressants aside from small results indicating amitriptyline may be useful [6]. In 2011, the Movement Disorder Society Task Force on Evidence-Based Medicine published a review of the literature. They argued that desipramine and nortriptyline were likely effi-cacious and possibly clinically useful but found insufficient evidence to support SSRIs and rated them investigational [9]. Since that time a few randomized trials have been performed; the largest showed that both paroxetine and venlafaxine improved depression and did not worsen motor function [10]. Dopamine agonists




such as pramipexole have also been found use-ful [11]. A small randomized pilot study dem-onstrated efficacy for depression in PD with Omega-3 fatty-acid supplementation [12]. The latest review of evidence from UpToDate rec-ommends using SSRIs first, based on the lesser likelihood of adverse side effects with SSRIs than with TCAs, and using TCAs if SSRIs do not lead to improvement of depressive symptoms [13].

Nonmedication treatment options for depres-sion in PD have been studied to an even lesser extent. Though a growing body of evidence for cognitive behavioral therapy exists, it is limited to only a few studies [14,15].

In cases of treatment refractory depression, repetitive transcranial magnetic stimulation (rTMS) and electroconvulsive therapy (ECT) have been shown to be effective, with ECT being more effective than rTMS [16]. Unfortunately, there is no specific data regarding treatment refractory depression among depressed patients with PD. A recent meta-analysis included eight small randomized trials showing that rTMS was superior to sham-rTMS, with similar effi-cacy to SSRIs in the treatment of depression for patients with PD [17]. This study also showed that rTMS potentially improved movement in these patients.

ECT has been used in PD for decades for a variety of psychiatric and motor symptoms. The latest comprehensive review of the effect of ECT on motor symptoms in PD was pub-lished in 2005. Authors concluded that ECT may exert a significant effect on motor function in PD patients, though the number of studies included in the analysis was small and these data could only be interpreted with caution [18]. A thorough review published in 1991 concluded that ECT improved motor symptoms in over half of PD patients, regardless of psychiatric comorbidity [19]. Another, similar in scope, review was published in 2003. Although the authors pointed out the literature limitations and biases, they concluded the existence of “con-siderable evidence…that ECT improves motor symptoms of Parkinson’s disease in patients with and without mood disorders.” [20].

ECT has also been used for treatment of psy-chosis and anxiety in PD, though the body of literature is smaller than that describing treat-ment of motor symptoms and depression. Only two patients with PD who benefitted from ECT for refractory anxiety have been reported [21]. Significant improvements in psychosis were seen in five elderly patients with PD and medically refractory psychosis [22] and eight patients with PD and quetiapine-resistant psychosis [23].

Proponents of ECT such as Dr Max Fink, Dr Richard Abrams and Dr Iannis Zervas had advocated for more widespread use of ECT in PD [24–26], with Dr Abrams calling for a thera-peutic trial of ECT in all patients with intrac-table or drug-resistant PD [25]. Dr Cummings recommended TCAs or ECT for treatment of depression in PD in a review from 1992 [27]. However, these endorsements have not found their way into the most recent guidelines for treatment of PD. Further, there are no recent comprehensive reviews of ECT used specifically for depression in PD. Here, we will summarize the literature and provide recommendations based on the data available.

methods●● Search strategy

We systematically searched for all available published studies that focused on treatment of patients with documented PD and comor-bid depression. The computerized databases of PubMed, EMBASE, Cochrane and Google Scholar were searched for studies published from earliest entry date in the database through September 2015. Further, we searched through the relevant journals in the field (Movement Disorders, Journal of ECT and Psychosomatics) over the course of 6 months prior to September 2015, for any publications that were not yet available in PubMed or EMBASE. We used the search strategy with the following parameters in each database: “depression and Parkinson’s and ECT,” and “depression and Parkinson’s and electroconvulsive therapy,” and hand-searched through the reference sections of relevant articles for studies we may have missed. The identified

table 2. Depressive and motor symptoms’ response to electroconvulsive therapy.

Depression measure ↓

motor symptoms →

improved Not improved Not reported worse

Improved 39 7 62 1Not improved 0 0 8 0

10.2217/nmt-2016-0002



studies were entered into Endnote to ensure as full as possible a collection of available literature and no duplication of studies.

●● Study eligibilityInclusion criteria required that studies describe the use of ECT for treatment of depression in patients with PD, rather than any other psychi-atric or neurological problem. We included any studies that reported patient data, such as case reports, case controlled studies, clinical trials, cohort studies and randomized controlled trials, but we excluded expert opinions. We excluded studies describing patients with no firm diag-nosis of PD, studies in which patients had not been assessed for presence of depression before and after the treatment, and studies that did not report any data about changes or lack thereof in patients’ depressive illness.

●● Review process & data extractionTwo independent reviewers (A Borisovskaya and W Bryson) conducted the literature search and review of study eligibility. We examined the titles and abstracts of all studies identified through our search strategy, and those studies that were clearly not pertinent to our review were eliminated. We read the full text of articles that appeared per-tinent based on their titles and abstracts, 58 in total, and excluded studies that did not meet our inclusion criteria, 15 in total. The reasons for exclusions were as follows: four studies described patients who were not depressed prior to ECT, seven studies did not provide pertinent infor-mation regarding the patients they treated, two studies described patients with no clear PD diag-nosis, one study did not use ECT for treatment of the patient and one study turned out to be a

literature review upon translation from French without any specific patient data. The stud-ies published in languages other than English (Turkish, German, French and Spanish), but that met our inclusion criteria, were translated using the Google Translate website.

Forty-three studies met our inclusion crite-ria. Of those, 27 were case reports describing single cases, 13 were case series describing any-where from two to eleven patients, all of these reported retroactively, two were retrospective chart reviews, and one was a retrospective case control study. The latter described the largest number of patients treated with ECT for PD and depression, 19 in total [28]. The studies were published between the years of 1975–2015.

From the included studies, we extracted the fol-lowing data: the number of patients treated with ECT, the measure of PD symptoms at baseline and after treatment with ECT, the measure of depressive symptoms at baseline and after treat-ment with ECT, the measure of cognition at base-line and after treatment with ECT, description of any side effects, and duration of the treatment effect after completion of ECT. Separately, we also extracted the data regarding method of ECT administration such as type of device, right unilat-eral versus bilateral lead placement, pulse width, number of ECT procedures performed, and the total energy given during the procedures. For the purpose of this review, we accepted descriptions of treatment results such as ‘improvement’ or ‘remis-sion’ or ‘response’ or ‘relief ’ or ‘cure’ or ‘no effect’ if validated scales to assess depressive symptoms were not used or cited. From the data obtained, we were able to determine the percentage of reported cases where depressive symptoms responded to treatment with ECT, as well as the percentage of

table 3. Side effects in patients treated with electroconvulsive therapy.

Side effect Number (percentage) of cases

Delirium 31 (26.7%)‘Transient confusion’ 10 (8.6%)Agitation 1 (0.9%)Stuttering and slurred speech 1 (0.9%)PVCs during induction 1 (0.9%)MI/death 1 (0.9%)Urinary retention (two of these patients also had a UTI) 5 (4.3%)Choreiform movements 1 (0.9%)Falls 2 (1.7%)‘Disorientation’† 8 out of 27 patients, not all of whom had PDThe numbers regarding frequency of urinary retention, choreiform movements and falls may be overestimated. †Data taken from [29].MI: Myocardial infarction; PD: Parkinson’s disease; PVC: Premature ventricular contraction; UTI: Urinary tract infection.




cases where motor symptoms improved as well. We evaluated the percentage of patients who experienced significant side effects from ECT, particularly cognitive impairment.

ResultsThe included studies are organized by year of publication and presented in table 1, with exception of Moellentine et al. [28] which is summarized in the paragraph below. A total of 116 patients, described in these studies, were included in our analysis. While the majority of patients were diagnosed with major depressive disorder (MDD), some studies used descrip-tions rather than a specific diagnosis of MDD, for example: ‘delusional depression’, ‘depression and psychosis’, ‘severe depression’. One patient was diagnosed with depression not otherwise specified. Two patients were diagnosed with bipolar depression. One patient was diagnosed with bipolar mania and was not depressed at the time of ECT administration [29], however, we could not parse out the results of this patient’s treatment from those of other patients in that study. Therefore, this patient was included in our final calculations. The age of patients described in the studies ranged from 33 to 83, though the 33-year-old patient was an outlier; the rest ranged in age from 52 to 83 years.

Moellentine and colleagues compared the outcomes of ECT for psychiatric indications between 25 patients with PD and 25 patients without PD. MDD with or without psychosis constituted the psychiatric indication for ECT in the majority (78%) of patients, totaling 19 in the treatment group. They found a “significant decrease in depression” as measured with the Hamilton Depression Rating Scale (HAM-D) in both groups following ECT. The duration of the effect was not reported. Among the patients with PD, 56% reported subjective improvement in motor function, 40% reported no change and 4% reported deterioration. Average Mini Mental Status Exam scores improved after ECT in both groups, although the finding was not statisti-cally significant. A higher proportion of patients with PD experienced ECT side effects compared with those without PD (56 vs 12%). The most common side effect was “transient intertreat-ment delirium,” which necessitated postpone-ment or termination of ECT in 52% of patients with PD. Unfortunately, for any of the effects described above, it was not noted in the study which specific patients experienced them [28].

The results regarding ECT efficacy for treat-ment of depression and for motor symptoms of PD are summarized in table 2. A total of 93.1% of all patients experienced improvement in their depression. Of those cases where a change (or lack thereof) in motor symptoms was recorded, 83% of patients reported improvement in their symptoms alongside improvement of depres-sion, 15% had no improvement in motor symp-toms despite improvement of depression and 2% of patients experienced worsening of motor symptoms despite improvement of depression. Approximately 6.9% of patients had no improve-ment in depression, though we do not have information on whether their motor symptoms had changed for better or for worse.

The duration of treatment effect was not noted for 46 of the cases described. In cases where it was recorded, the duration of treatment effect ranged in magnitude from weeks to years.

Side effects of ECT treatment are summarized in table 3. Though we chose to list the relevant data from study by Moellentine et al. [28], it was unclear which of the patients had urinary reten-tion, choreiform movements or falls, and it is possible that these side effects occurred in 6 of 25 patients who were not included in our study. No side effects were noted for 43 of the stud-ied cases, though we cannot conclude from this absence of data whether the patients did not, in effect, have any side effects. The main side effects of treatment were delirium and ‘transient confusion’; several studies mentioned that these side effects necessitated postponement or discon-tinuation of ECT. ‘Disorientation’ arose in 8 out of 27 patients in the study by Zwil et al. [29] but is reported separately since it is unclear whether these patients had PD.

We paid particular attention to the reports of patients’ cognition and whether it was affected by ECT. Unfortunately, in 75 of the reported cases, there was no description of whether the patients’ cognition worsened, improved or remained unchanged – or no conclusions could be drawn based on the reported data. Improvement in cognition was described in three (9%) of the reported cases. Worsening cognition was described in two (6%) cases. Two of the patients could not complete cognitive screen-ing prior to ECT, but completed the testing thereafter. Unfortunately, no definitive conclu-sions about changes in their cognition could be made [22]. Twenty-eight (85%) of the patients had no significant change in their cognition.

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table 4. electroconvulsive therapy parameters, number of electroconvulsive therapy procedures and cognition.

Study (year) ect Parameters Number of ect sessions

cognition measure at baseline

cognition measure after ect

Ref.

Scicutella (2015) None reported 8 NA NA [30]

Nishioka (2014)

Thymatron; BL; ‘half-age method’ 7 HDS-R or MMSE 14 HDS-R or MMSE 26 [31]

Thymatron; BL; ‘half-age method’ NA HDS-R or MMSE 25 HDS-R or MMSE 21 Gadit (2012) None reported 9 NA NA [32]

Berg (2011) Thymatron; RUL; 70-100%; 0.5 ms 12 MMSE ‘normal’ NA [33]

Bui (2011) BL 12 NA NA [34]

Ducharme (2011) MECTA; BL; 40 Hz; 0.8 mA; 1 ms 8 NA NA [35]

Nasr (2011) MECTA; BL; 62-80 J; 576.4 mC 7 MMSE 21 MMSE 30 [36]

Ueda (2010) CS-1; BL; 95-105 V; 50 Hz 5 Neither could complete baseline MMSE due to PD symptoms

MMSE 24 [22]

CS-1; BL; 95-105 V; 50 Hz 10 MMSE 24

Virit (2010) BL NA NA NA [37]

BL NA NA NA BL 8 NA NA

Bailine (2008) Thymatron; BL; 40% 7 NA NA [38]

Belzeaux (2007) None reported NA NA NA [39]

Chou (2005) Thymatron; BL frontal; 45–75% 9 NA NA [40]

Ozer (2005) MECTA; BL; 39.4 J; 50 Hz; 1.4 ms (same protocol used for all 3 courses)

5 in first course

NA NA [41]

5 in second course

NA NA

6 in third course

NA MMSE 28

Shulman (2003) BL 4 NA NA [42]

Benito (2002) Thymatron 10 NA NA [43]

Thymatron 12 NA NA Thymatron 10 NA NA

Fall (2000) Thymatron; RUL for all 3 patients; mean charge 212 mC (s.d. 91 mC)

Each patient received 6–7 ECT treatments

MMSE 20 MMSE 21 [44]

MMSE 30 MMSE 29 MMSE 24 MMSE 29

Amann (1999) None reported 11 NA NA [45]

Fall (1999) Thymatron; BL 6 MMSE 24 NA [46]

Kramer (1999) MECTA SR-1; ‘most patients’ BL NA NA NA [47]

Avila (1997) BL; 36.7 J 9 NA NA [48]

BL; 50.9 J 10 NA NA Nymeyer (1997) Thymatron; RUL; 70% NA MMSE 26 NA [49]

Factor (1995) BL 6 NA NA [50]

Sandyk (1993) None reported ‘Several’ in first course

NA NA [51]

24 in second course

Friedman (1992) RUL 12 NA NA [52]

BL; ‘brief pulse’ 8 NA NA UL 10 NA NA None reported 7 MMSE 26 NA RUL 10 NA NA Holzer (1992) MECTA SR-1; UL; 80 Hz; 1.4 ms 8 NA NA [53]BL: Bilateral; ECT: Electroconvulsive therapy; HDS-R: Hierarchic dementia scale, revised; mC: milliCoulomb; MECTA/MECTA SR-1: ECT device; MMSE: Mini-mental state examination; ms: millisecond; NA: Not applicable; RUL: Right unilateral; Thymatron: ECT device; UL: Unilateral; V: Volt.




Study (year) ect Parameters Number of ect sessions

cognition measure at baseline

cognition measure after ect

Ref.

Oh (1992) Thymatron; UL for 9 patients; BL for 1 patient; UL -> BL for 1 patient

Range 3–9 (mean 5.9)

NA NA [54]

Zwil (1992) For all 8 patients: MECTA Model D; 800 mA current; variable frequency, pulse width, and stimulus duration; started UL and proceeded to BL if needed

NA NA NA [29]

NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Figiel (1991) MECTA SR-1; RUL 3 MMSE 29 MMSE 28 [55]

MECTA SR-1; RUL 6 MMSE 26 MMSE 23 MECTA SR-1; BL 10 MMSE 29 MMSE 28 MECTA SR-1; BL 6 MMSE 28 MMSE 29 MECTA SR-1; RUL 4 MMSE 26 MMSE 25 MECTA SR-1; 3 RUL ->5 BL 8 MMSE 26 MMSE 25 MECTA SR-1; 8 RUL ->3 BL 11 MMSE 28 MMSE 26 Stern (1991) None reported 8 NA NA [56]

Liberzon (1990) UL 7 NA NA [57]

Douyon (1989)

MECTA SR-1 and BL for all patientsInitial parameters: 56 ± 9 Hz; 1.5 msFinal parameters: 76 ± 9 Hz; 1.6 ± 0.2 ms

Average number of ECT treatments per patient is reported to be 7

NA NA [58]

NA NA NA NA NA NA NA NA NA NA NA NA

Burke (1988) UL 5 NA NA [59]

UL 6 NA NA RUL 8 NA NA Young (1985) ‘Modified’ UL 7 MMSE 12 MMSE 13 [60]

Holcomb (1983) BL 14 NA NA [61]

Levy (1983) None reported 10 NA NA [62]

Balldin (1980) All patients: Siemens convulsator 628; BL; 50 Hz; peak current 0.8 A

8 NA NA [63]

4 NA NA

7 NA NA Yudofsky (1979) None reported 10 NA NA [64]

Barcia (1978) None reported 3 NA NA [65]

Asnis (1977) BL 6 NA NA [66]

Dysken (1976) BL; 120 V 12 NA NA [67]

Lebensohn (1975) None reported 4 NA NA [68]

None reported 4 NA NA Lipper (1975) 140 V 7 NA NA [69]BL: Bilateral; ECT: Electroconvulsive therapy; HDS-R: Hierarchic dementia scale, revised; mC: milliCoulomb; MECTA/MECTA SR-1: ECT device; MMSE: Mini-mental state examination; ms: millisecond; NA: Not applicable; RUL: Right unilateral; Thymatron: ECT device; UL: Unilateral; V: Volt.

table 4. electroconvulsive therapy parameters, number of electroconvulsive therapy procedures and cognition (cont.).

We also collected the sparse data regarding the method of ECT administration and the total number of sessions done for each patient. Eleven cases came with no information on ECT stimu-lus, five cases came with no information on the

number of ECT sessions. Disparate and limited information was provided in the majority of the papers, with no reporting, variably, regarding ECT device, lead placement, stimulus inten-sity, pulse width or stimulus duration. Available

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data are reported in table 4. Quite a few of the studies described using bilateral lead placement, rather than right unilateral, as the first line of treatment, even studies from years 2010 to 2014, though the latter is generally recommended to prevent cognitive losses. Interestingly, in cases where bilateral lead placement was used, the cognition did not fare badly and in some cases improved [31,36].

It is noteworthy that even studies that were published within the last few years often did not have the data we were seeking, such as measure of motor symptoms, cognitive status or dura-tion of treatment effect. Objective measures of depression were often omitted, with ‘improve-ment’ or ‘remission’ of depression serving as the only descriptors of the ECT effect. Although short term memory loss is one of the more both-ersome side effects of ECT, the patients’ cogni-tion was not formally assessed or reported in the majority of the cases included in our analysis.

Finally, we were hoping to find the reasons why patients were referred to ECT rather than prescribed another medication or referred to psychotherapy, but often we did not find such information in the literature we reviewed. While we have no doubts about the necessity of refer-rals to ECT in any given case, it would have been useful to elucidate the clinicians’ thought process and/or treatment algorithm which led them to consider ECT.

DiscussionWe discovered a fair number of case reports and case series about ECT in patients with PD and

comorbid depression. These studies are heteroge-neous and do not easily lend themselves to global interpretation. Further, they are subject to report-ing bias, since the negative reports may not get published, and patients not responding to ECT may not complete their courses of treatment. Our review had other limitations. Most studies did not use validated scales for assessing depression or motor symptoms. Lack of precise information about ECT administration prevented us from making correlations among ECT parameters, number of treatments, side effects and treatment efficacy. We could not draw conclusions about duration of treatment effect, nor risks for relapse. Finally, there are no randomized controlled trials or prospective studies to compare, head-to-head, use of medications, psychotherapy, TMS or ECT in depression and PD.

Still, we were able to draw the following con-clusions: first, depression most often improved during the treatment, sometimes dramatically; second, improvement in motor symptoms com-monly accompanied improvement in depres-sion, which is relevant to those PD patients whose motor symptoms do not fully respond to pharmacological strategies.

When a patient with PD is diagnosed with depression, it behooves the clinicians to not be satisfied with a nonresponse or a partial response they might see from medication trials. In treat-ment-resistant depression (by most common defi-nition, no improvement in depressive symptoms after two full trials of antidepressants from two different classes of adequate duration), a referral to ECT may be the key. Switching antidepressants

Box 1. algorithm for treatment of depression in Parkinson’s disease.

● Follow DSM-V criteria to diagnose the depressive syndrome in question ● Use validated scale to measure depressive symptoms ● If there is a suspicion for contribution of medical illness (besides PD) and/or substance as etiologies of depression, perform workup and treat as indicated

● If depression is severe (refusal to eat, significant dysfunction in the social or occupational role, catatonic symptoms, psychotic symptoms, suicidal ideation with intent or plan), consider hospitalization and ECT sooner rather than later

● If there is evidence of cognitive impairment, particularly executive dysfunction, consider referral to problem solving therapy ● If depression is mild to moderate, consider referral to psychotherapy (interpersonal therapy, cognitive behavioral therapy, psychodynamic therapy) and starting an antidepressant

● When choosing an antidepressant, start preferentially with an SSRI (evaluate risk vs benefit for each given case since side effects may be potentially significant). If no response after an adequate trial, consider a switch to a TCA. Bupropion [83] may also be appropriate

● If an adequate trial of antidepressants (as above) results in no response or partial response only, consider augmentation with lithium, quetiapine (at a low dose), or a switch to a monoamine oxidase inhibitor since these are the most effective antidepressants at our disposal (after an evaluation of risks vs benefits for each given case) [84]

● If a combined treatment with validated psychotherapy and medication regimen results in nonresponse, consider a referral to TMS in cases of mild to moderate depression, where available. If depression is severe or worsening, consider referral to ECT

ECT: Electroconvulsive therapy; PD: Parkinson’s disease; TCA: Tricyclic antidepressants; TMS: Transcranial magnetic stimulation.




again and again does not lead to improvement or remission most of the time – and a recent review of this common strategy found no evidence to support it [70]. This finding is echoed by the STAR*D trial, which found significantly lessen-ing remission rates with repeated trials of different antidepressants [71]. Delaying the referral to ECT may be a failing strategy, since the longer depres-sive episodes and medication failure at baseline correlate with poorer response to ECT [72].

There is a high prevalence of dementia in PD patients – 24–31% [73]. Even in those without dementia syndrome, deficits in executive and visuospatial functioning and mild cognitive impairment are not uncommon. Our study was reassuring in that no significant changes in cognition after ECT were noted; some patients’ cognition improved. This finding is not unusual. Cognitive deficits often resolve with improvement in depression [74]. A recent prospective study dem-onstrated lack of cognitive decline and sometimes improvement in depressed elderly patients after ECT [75].

Still, ECT was not without side effects in this population. Reports of delirium and ‘transient confusion’ were frequent. Autonomic dysregu-lation and falls are common in PD, and ECT may have increased the patients’ susceptibility to urinary retention and falls.

Patients and clinicians should be assured that ECT side effects are temporary. Fears about the extent of cognitive losses may be one of the main reasons why so few PD patients get referred to ECT, along with the impermanence of positive effect on motor symptoms and difficulty pre-dicting how long the effect of treatment would last [76,77]. Stigma still surrounds ECT, and the general public often gets a maligned view of ECT from the media [78].

Little is known specifically about maintenance ECT for PD. Three reports are included in this

review [42,46–47]. The results are not always posi-tive. Two studies, not included in this review, suggest significant benefit from maintenance ECT for motor symptoms in PD [79] and schizo-phrenia comorbid with PD [80]; another study cites benefit in two patients, but also describes two patients who developed significant cognitive impairment and delusions during maintenance ECT [81]. Nonetheless, some authors cite inevita-ble relapse within 6 months upon completion of index ECT course [82]. More severe and chronic forms of depression, as well as history of resist-ance to medications are indicators that patients may need maintenance ECT to prevent relapse.

We suggest an algorithm for treatment of depression in patients with PD, largely based on what we know regarding the treatment of depression in the elderly and treatment-resistant depression (Box 1). Further, we make recommen-dations to minimize cognitive losses during ECT for patients with PD (Box 2).

conclusionECT is a beneficial treatment for patients suf-fering from PD and depression, often effective for mood and motor symptoms. Common side effects include delirium, transient confusion, falls, urinary retention and rarely exacerbation of abnormal movements and motor symptoms. Cognitive losses are not universal during a course of ECT. To date, little is known about the utility of maintenance ECT specifically in patients with PD and depression.

Future perspectiveWhile we realize that randomized controlled trials of ECT and medications in this popu-lation would be costly and impractical, future studies should focus on comparison of similar cohorts of patients’ response to medications, ECT and TMS. Each study should thoroughly

Box 2. Strategies to prevent or decrease cognitive losses during electroconvulsive therapy.

● Perform cognitive testing prior to ECT and during ECT to determine the patient’s baseline functioning and the impact that ECT might have had. If significant changes occur, consider taking a break from treatment or decreasing amount of energy used during the procedure.

● Start with right unilateral stimulus administration ● Use ultrabrief pulse width (0.3 ms) [85]. If using bilateral ECT, use pulse width of 0.5 ms rather than 1 ms ● Decrease frequency of treatments (perform procedures twice a week rather than three-times a week) ● Minimize concurrent medications that may compound the cognitive losses (anticholinergics, benzodiazepines) ● Keep in mind that the higher the total amount of energy is administered, the more significant the potential memory losses. Utilize the energy setting that improves depressive symptoms but minimizes side effects

● For those who have been diagnosed with dementia, start and continue a cholinesterase inhibitor, which may be protective against cognitive losses during ECT [86]

ECT: Electroconvulsive therapy.

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document the patients’ motor symptom severity with a validated scale such as UPDRS, severity of depressive symptoms with a validated scale such as HAM-D or PHQ-9, and cognitive status with a validated scale such as Montreal Cognitive Assessment (MOCA). The ECT treatment course should be documented accord-ing to recommendations in the literature [87], so as to include the stimulus waveform characteris-tics, mode of stimulus delivery, stimulus inten-sity parameters, electrode placement and sei-zure monitoring. Each patient being referred for ECT should have a thorough documentation of all prior treatment trials and response to them, as well as their response to ECT. Even then, the patients’ medications are likely to change during the course of ECT, both for motor symptoms of PD and for depression, making comparisons among treatment modalities more challenging.

A weakness of current literature is that long-term follow-up with such patients has not been sufficiently described. It is necessary to follow patients who respond to ECT over time, if at all possible, and document whether such a response has been sustained, what kind of treatments the patients have done well with, and which treatments have failed.

Financial & competing interests disclosureThe authors have no relevant affiliations or financial involvement with any organization or entity with a finan-cial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.



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55 Figiel GS, Hassen MA, Zorumski C et al. ECT-induced delirium in depressed patients with Parkinson’s disease. J. Neuropsychiatry Clin. Neurosci. 3(4), 405–411(1991).

56 Stern MB. Electroconvulsive therapy in untreated Parkinson’s disease. Mov. Disord. 6(3), 265 (1991).

57 Liberzon I, Dequardo JR, Silk KR. Bupropion and delirium. Am. J. Psychiatry 147(12), 1689–1690 (1990).

58 Douyon R, Serby M, Klutchko B, Rotrosen J. ECT and Parkinson’s disease revisited:

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a “naturalistic” study. Am. J. Psychiatry 146(11), 1451–1456 (1989).

59 Burke WJ, Peterson J, Rubin EH. Electroconvulsive therapy in the treatment of combined depression and Parkinson’s disease. Psychosomatics 29(3), 341–346 (1988).

60 Young RC, Alexopoulos GS, Shamoian CA. Dissociation of motor response from mood and cognition in a parkinsonian patient treated with ECT. Biol. Psychiatry 20(5), 566–569 (1985).

61 Holcomb HH, Sternberg DE, Heninger GR. Effects of electroconvulsive therapy on mood, parkinsonism, and tardive dyskinesia in a depressed patient: ECT and dopamine systems. Biol. Psychiatry 18(8), 865–873 (1983).

62 Levy LA, Savit JM, Hodes M. Parkinsonism: improvement by electroconvulsive therapy. Arch. Phys. Med. Rehabil. 64(9), 432–433 (1983).

63 Balldin J, Edén S, Granérus AK et al. Electroconvulsive therapy in Parkinson’s syndrome with “on-off” phenomenon. J. Neural Transm. 47(1), 11–21 (1980).

64 Yudofsky SC. Parkinson’s disease, depression, and electroconvulsive therapy: a clinical and neurobiologic synthesis. Compr. Psychiatry 20(6), 579–581 (1979).

65 Barcia D, Martinez PF. Parkinson disease with depression treated by E.C.T. Archivos de Neurobiologia 41(5), 393–398 (1978).

66 Asnis G. Parkinson’s disease, depression, and ECT: a review and case study. Am. J. Psychiatry 134(2), 191–195 (1977).

67 Dysken M, Evans HM, Chan CH, Davis JM. Improvement of depression and parkinsonism during ECT: a case study. Neuropsychobiology 2(2–3), 81–86 (1976).

68 Lebensohn ZM, Jenkins RB. Improvement of Parkinsonism in depressed patients treated with ECT. Am. J. Psychiatry 132(3), 283–285 (1975).

69 Lipper S, Bermanzohn PC. Letter: electroconvulsive therapy in patients with parkinsonism. Am. J. Psychiatry 132(4), 457 (1975).

70 Bschlor T, Baethge C. No evidence for switching the antidepressant: systematic review and meta-analysis of RCTs of a common therapeutic strategy. Acta. Psychiatr. Scand. 121(3), 174–179 (2010).

• Veryrelevantstudydemonstratingpoorefficacyofswitchinganantidepressantincaseofnonresponse.

71 Rush AJ, Trivedi MH, Wisniewski SR et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry 163(11), 1905–1917 (2006).

72 Haq AU, Sitzmann AF, Goldman ML, Maixner DF, Mickey BJ. Response of depression to electroconvulsive therapy: a meta-analysis of clinical predictors. J. Clin. Psychiatry 76 (10), 1374–1384 (2015).

73 Aarsland D, Zaccai J, Brayne C. A systematic review of prevalence studies of dementia in Parkinson’s disease. Mov. Disord. 20(10), 1255–1263 (2005).

74 Stoudemire A, Hill CD, Morris R, Dalton ST. Improvement in depression-related cognitive dysfunction following ECT. J. Neuropsychiatry Clin. Neurosci. 7(1), 31–34 (1995).

75 Verwijk E, Comijs HC, Kok RM et al. Short- and long-term neurocognitive functioning after electroconvulsive therapy in depressed elderly: a prospective naturalistic study. Int. Psychogeriatr. 26(2), 315–324 (2014).

76 Popeo D, Kellner CH. ECT for Parkinson’s disease. Med. Hypotheses 73, 468–469 (2009).

77 Narang P, Glowacki A, Lippmann S. Electroconvulsive therapy intervention for Parkinson’s disease. Innov. Clin. Neurosci. 12(9–10), 25–28 (2015).

78 McFarguhar TF, Thompson J. Knowledge and attitudes regarding electroconvulsive therapy among medical students and the general public. J. ECT 24, 244–253 (2008).

79 Aarsland D, Larsen JP, Waage O, Langeveld JH. Maintenance electroconvulsive therapy for Parkinson’s disease. Convuls. Ther. 13(4), 274–277 (1997).

80 Hoflich G, Kasper S, Burghof K-W, Scholl H-P, Moller H-J. Maintenance ECT for treatment of therapy-resistant paranoid schizophrenia and Parkinson’s disease. Biol. Psychiatry 37, 892–894 (1995).

81 Wengel SP, Burke WJ, Pfeiffer RF, Roccaforte WH, Paige SR. Maintenance electroconvulsive therapy for intractable Parkinson’s disease. Am. J. Geriatr. Psychiatry 6(3), 263–269 (1998).

82 Sackeim HA, Haskett RF, Mulsant BH et al. Continuation pharmacotherapy in the prevention of relapse following electroconvulsive therapy: a randomized controlled trial. JAMA 285(10), 1299–1307 (2001).

•• Oneofthemostimportantstudiesregardingpreventionofrelapseofdepressivesymptomsafterasuccessfulelectroconvulsivetherapycourse.

83 Raskin S, Durst R. Bupropion as the treatment of choice in depression associated with Parkinson’s disease and its various treatments. Med. Hypotheses 76(6), 544–546 (2010).

84 Bschor T, Bauer M, Adli M. Chronic and treatment resistant depression: diagnosis and stepwise therapy. Dtsch. Arztebl. Int. 111(45), 766–775 (2014).

85 Sackeim HA, Prudic J, Nobler MS et al. Effects of pulse width and electrode placement on the efficacy and cognitive effects of electroconvulsive therapy. Brain. Stimul. 1(2), 71–83 (2008).

86 Hausner L, Damian M, Sarotorius A, Frolich L. Efficacy and cognitive side effects of electroconvulsive therapy (ECT) in depressed elderly inpatients with coexisting mild cognitive impairment or dementia. J. Clin. Psychiatry 72(1), 91–97 (2011).

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425ISSN 1758-2024Neurodegener. Dis. Manag. (2015) 5(5), 425–443

part of

10.2217/nmt.15.34 © 2015 Future Medicine Ltd

Review

Mild cognitive impairment: an update in Parkinson’s disease and lessons learned from Alzheimer’s disease

Jennifer G Goldman*,1, Neelum T Aggarwal2 & Cynthia D Schroeder3

1Rush University Medical Center, Department of Neurological Sciences, Section of Parkinson Disease & Movement Disorders,

1725 W. Harrison Street, Suite 755, Chicago, IL 60612, USA 2Rush University Medical Center, Department of Neurological Sciences & Rush Alzheimer’s Disease Center, 600 South Paulina,

Suite 1038, Chicago, IL 60612, USA 3Rush University Medical Center, Department of Neurological Sciences, 1735 W. Harrison Street, Suite 306, Chicago, IL 60612, USA

*Author for correspondence: Tel.: +1 312 563 2900; Fax: +1 312 563 2024; [email protected]

Cognitive dysfunction is an important focus of research in Parkinson’s disease (PD) and Alzheimer’s disease (AD). While the concept of amnestic mild cognitive impairment (MCI) as a prodrome to AD has been recognized for many years, the construct of MCI in PD is a relative newcomer with recent development of diagnostic criteria, biomarker research programs and treatment trials. Controversies and challenges, however, regarding PD-MCI’s definition, application, heterogeneity and different trajectories have arisen. This review will highlight current research advances and challenges in PD-MCI. Furthermore, lessons from the AD field, which has witnessed an evolution in MCI/AD definitions, relevant advances in biomarker research and development of disease-modifying and targeted therapeutic trials will be discussed.

Practice points

● Cognitive deficits are frequent in Parkinson’s disease (PD) and encompass a broad spectrum of clinical features and severity. Patients may have difficulty with attention, working memory, executive function, psychomotor speed, visuospatial abilities, language and memory domains, individually or in combination.

● It is important for clinicians to inquire about cognitive changes or problems, even early in the course of PD and even when symptoms are at mild stages.

● Mild cognitive impairment (MCI) has gained recognition as a construct, an early stage of cognitive decline and a risk factor for developing dementia in PD.

● Recent advances in our understanding of PD-MCI, its variable clinical presentations and differences in progression to dementia, however, suggest that PD-MCI may not be a single, uniform entity. Differences in underlying neurobiological substrates, neuropathology, genetics, among other factors, may contribute to the clinical variability of PD-MCI.

● Research studies have investigated biomarkers such as cerebrospinal fluid markers, neuroimaging studies and genetics that may be associated with PD cognitive impairment and could potentially be used for diagnosis, prognosis or early detection of cognitive decline.

● Compared to the field of MCI and Alzheimer’s disease (AD), PD-MCI is a ‘relative newcomer’ with more recent advances in diagnostic criteria, biomarker studies and therapeutic trials.

● Many lessons can be learned from the MCI-AD field including the evolution of MCI definitions over the years, clinical trials that now incorporate biomarkers and genetics in the study design and emerging therapeutic strategies targeting specific biological mechanisms, novel compounds and delivery systems, and earlier stages of cognitive impairment with potential disease-modifying or prevention trials.


Neurodegener. Dis. Manag. (2015) 5(5)426

Review Goldman, Aggarwal & Schroeder


While the presence of cognitive deficits in Parkinson’s disease (PD) has been recognized for many years, it is only more recently that mild cognitive impairment in PD (PD-MCI) has emerged as a concept and distinct entity, with epidemiological studies, proposed diag-nostic criteria and symptomatic treatment trials. PD-MCI may represent an early stage of cog-nitive decline and a risk factor for developing dementia [1,2], and thus, an intermediate state between normal cognition and dementia, similar to amnestic MCI in the context of developing Alzheimer’s disease (AD). Recent advances in our understanding of PD-MCI, however, sug-gest that PD-MCI is rather heterogeneous with different clinical phenotypes, rates of progres-sion and perhaps underlying mechanisms. In 2012, a Movement Disorder Society (MDS) Task Force proposed diagnostic criteria for PD-MCI in order to harmonize disparate defi-nitions of PD-MCI across multiple clinical and research sites and to identify PD-MCI cohorts for future therapeutic trials (Figure 1) [3]. The MDS PD-MCI criteria have now been applied in clinical and research settings, including inter-national validation efforts and recent treatment trials, but unresolved issues and areas for further study remain [4]. This review will discuss recent findings related to PD-MCI, highlighting its heterogeneity and challenges, discussing several debates and unmet needs regarding PD-MCI, and exploring lessons that can be learned from the MCI-AD field.

PD-MCi: a heterogeneous construct●● Frequent & identifiable

MCI in nondemented PD is frequent, affecting 25–50% [5–10]. While estimates vary across stud-ies depending on the PD population (e.g., clinic or community-based, incident or prevalent PD), presence/absence of co-morbid neuropsychiat-ric disorders (e.g., depression, anxiety, apathy, sleep), severity of motor problems, potential effects of PD-related and other medications and methodological issues (e.g., diagnostic criteria, cognitive assessments, definitions of impair-ment), which will be further discussed below, they are fairly consistent across these studies and definitions. PD-MCI has gained attention as an identifiable cognitive category within the PD cognitive spectrum, a common problem and a state distinct from dementia. However, PD-MCI has emerged as a more heterogeneous entity in its clinical phenotype, timing, progression, and

pathology, perhaps even beyond what might be expected by differences in PD-MCI definitions across studies.

●● Clinical phenotypes & definitionsCognitive dysfunction in PD-MCI encompasses a broad spectrum of clinical deficits and severity with impairment in attention, working memory, executive function, psychomotor speed, visuos-patial abilities, language and memory domains, individually or in combination. In older PD studies using modified Petersen’s MCI criteria or other definitions, cognitive phenotypes were fre-quently categorized as nonamnestic and amnes-tic cognitive domains affected and as single and multiple-domain impairment [11]. Instead of classifying PD-MCI as nonamnestic or amnestic type, the MDS PD-MCI criteria recommended specification of the affected domain(s) in order to examine potential differences among cogni-tive domain subtypes and since episodic memory function, albeit impaired at times in PD, was not the main hallmark as found in AD. Subtype designation of PD-MCI nonamnestic deficits thereby captures individual domains (e.g., atten-tion/working memory vs executive function vs language vs visuospatial function). Moreover, these proposed subtype distinctions may facili-tate investigations of whether different types of cognitive impairment differ in their progression rates and neurochemical or neuropathological substrates.

Clinical phenotypes of PD-MCI, in studies of incident and prevalence cohorts and pre- and post-MDS PD-MCI criteria, are highlighted below and in Table 1 [5,7,8,10,12–23]. Newly diag-nosed PD patients across different studies dem-onstrate deficits in executive function, atten-tion, psychomotor speed and visuospatial skills, as well as memory, in some studies [5,10,17,24]. In one study of incident PD cases, PD-MCI as defined by MDS criteria level II (comprehen-sive neuropsychological battery), occurred in 42.5% with memory deficits in 15.1% [17]. In studies of prevalent, nondemented PD cohorts prior to MDS PD-MCI criteria, similar cogni-tive profiles occur with greater nonamnestic subtypes, but predominantly as single domain impairment [7–9,14,15,25]. Recent studies apply-ing MDS PD-MCI level II criteria demon-strate that PD-MCI remains frequent, ranging from 35 to 65% of PD cohorts [13,15,18,26–28]. Several studies categorize the PD-MCI cohorts as having single domain and multiple domain

KeywoRds • Alzheimer’s disease • amnestic • biomarker • cognitive • dementia • diagnostic criteria • executive function • mild cognitive impairment • nonamnestic • Parkinson’s disease

427

Figure 1. The interface of different Parkinson’s disease-mild cognitive impairment criteria. PD-MCI, diagnostic flowchart adapted from MDS task force criteria for diagnosis of PD-MCI and Petersen’s amnestic/nonamnestic mild cognitive impairment criteria MDS PD-MCI criteria features in solid dark gray; MCI criteria features (Petersen) in gray striped pattern; overlap of both of these criteria features in light gray. MCI: Mild cognitive impairment; MDS: Movement Disorder Society; NC: Normal cognition; PD: Parkinson’s disease.

PDPatients

MDS PD-MCI criteria

MCI criteria (Petersen)

Overlap of criteria

Patient has other condition explaining cognitive impairment

(e.g., co-morbidity, stroke, delirium,head trauma, severe psychiatric

illness) or meets dementia criteria

Decline in cognitive abilities overtime reported by patient orinformant, or observed by

clinician, but de�cits do not impede functional independence

Yes

Yes

Yes Yes

Excludedfrom MCIcriteria

No impairment

Neuropsychological impairment found?Level I: Global cognitive abilities scale or limited battery of neuropsychological tests

with impairment on ≥2 tests

Level II: Two tests in each of 5 cognitive domains(attention/working memory, executive, language,memory, visuospatial) with impairment ≥2 tests(either as two impaired tests in one cognitivedomain or one impaired tests in two differentcognitive domains), de�ned as 1–2 SD below

norms, or signi�cant decline on serial cognitivetesting or signi�cant decline from estimated

premorbid levelsImpairment

No

No No

>11 How many affected domains?

PD-MCIsingle-domain

amnestic

PD-MCIsingle-domain

nonamnestic (specifywhich domains)

PD-MCImultiple-domainamnestic (specifywhich domains)

PD-MCImultiple-domain

nonamnestic (specifywhich domains)

Patient does not meetcriteria for PD-MCI,patient classi�ed

as PD-NC

Memory domain affected? Memory domain affected?

No

Mild cognitive impairment: an update in Parkinson’s Disease & lessons learned from Alzheimer’s Disease Review


impairment, but details regarding individual cognitive domain subtypes are limited. One consistent, notable finding across recent studies utilizing the MDS PD-MCI level II criteria is an increased frequency of multiple domain impair-ment. PD-MCI multiple domain impairment occurred in 90, 93, 91.2 and 65% of PD-MCI, compared with single domain impairment in 5, 7, 8.5 to 35%, respectively, a feature that may relate to criteria requirements of impairment in at least one test in two or more cognitive domains [13,15,18,26]. Another schema for catego-rizing PD cognitive impairment has emerged from the CamPaIGN study with two distinct

phenotypes: frontostriatal/executive func-tion deficits and posterior cortical dysfunction (i.e., impaired language/semantic fluency and visuospatial orientation/pentagon copying) [2,29]. Executive deficits may primarily relate to dis-rupted dopaminergic frontostriatal networks, whereas posterior cortical impairment reflects nondopaminergic dysfunction, cortical Lewy body deposition and/or AD-type pathology [30]. Different neurochemical and neuropathologi-cal predispositions may underlie not only the cognitive phenotype in early PD, but also their rates of progression and conversion to dementia. This cognitive categorization of ‘frontostriatal’




Tabl

e 1.

Cro

ss-s

ecti

onal

stu

dies

of m

ild c

ogni

tive

impa

irm

ent i

n Pa

rkin

son’

s di

seas

e co

hort

s.

Popu

lati

on/

sam

ple

size

MCi

cri

teri

aD

omai

ns a

nd n

euro

psyc

holo

gica

l tes

ts u

sed

PD-M

Ci

diag

nosi

sSi

ngle

/mul

tipl

e do

mai

ns a

ffec

ted

Stud

y (y

ear)

Ref.

PD co

hort

s pre

-MD

S PD

-MCI

crit

eria

Prev

alen

t, co

mm

unit

y,

n =

103

≥2 S

D b

elow

nor

mat

ive

data

on

≥1 te

stG

ener

al: M

MSE

, DRS

; att

entio

n/ex

ecut

ive

func

tion:

Str

oop

Colo

r Wor

d Te

st; m

emor

y: B

VRT;

vis

uosp

atia

l/con

stru

ctiv

e sk

ills:

JLO

55%

57.1

%/4

2.8%

Janv

in e

t al.

(200

3)[6]

Inci

dent

, co

mm

unit

y,

n =

159

MM

SE ≥

24 a

nd im

pairm

ent o

n pa

tter

n re

cogn

ition

mem

ory

test

or

Tow

er o

f Lon

don

task

Gen

eral

: MM

SE, N

ART

; fro

ntal

lobe

: pho

nem

ic fl

uenc

y,

sem

antic

flue

ncy,

CA

NTA

B m

odifi

ed T

ower

of L

ondo

n;

tem

pora

l lob

e: C

AN

TAB

patt

ern

reco

gniti

on m

emor

y ta

sk;

fron

tal/t

empo

ral:

CAN

TAB

spat

ial r

ecog

nitio

n m

emor

y ta

sk

36%

58%

/42%

Folt

ynie

et a

l. (2

004)

(C

amPa

IGN

)

[5]

Inci

dent

, co

mm

unit

y,

n =

115

≥2 S

D b

elow

nor

mat

ive

data

on

≥3 te

sts

Gen

eral

: MM

SE, D

ART

; att

entio

n: D

igit

Span

For

war

d an

d Ba

ckw

ard,

TM

T-B,

Str

oop

Colo

r Wor

d Te

st P

art C

; exe

cutiv

e fu

nctio

n: m

odifi

ed W

CST,

CO

WAT

, sem

antic

flue

ncy,

WA

IS-II

I Si

mila

ritie

s, T

ower

of L

ondo

n-D

rexe

l Tes

t; la

ngua

ge: B

NT;

m

emor

y: R

AVLT

tria

ls (d

elay

ed fr

ee re

call)

, rec

ogni

tion,

RB

MT

Logi

cal M

emor

y Te

st im

med

iate

(del

ayed

reca

ll),

WM

S-III

Fac

e re

cogn

ition

imm

edia

te (d

elay

ed re

cogn

ition

); Vi

sual

Ass

ocia

tion

Test

; Psy

chom

otor

spe

ed: W

AIS

-R D

igit

Sym

bol t

est,

TMT-

A, S

troo

p Co

lor W

ord

Test

(Par

ts A

/B);

visu

ospa

tial/c

onst

ruct

ive

skill

s: JL

O, G

roni

ngen

Inte

llige

nce

Test

spa

tial t

est,

Cloc

k D

raw

ing

Test

23.5

%N

ot s

peci

fied

Mus

limov

ic e

t al.

(200

5)[10]

Prev

alen

t, cl

inic

, n

= 86

≥1.5

SD

bel

ow n

orm

ativ

e da

ta o

n ≥1

dom

ain

Atte

ntio

n: d

igits

forw

ard

and

back

war

d; e

xecu

tive

func

tion:

TM

T-B,

Str

oop;

lang

uage

: CO

WAT

, sem

antic

flu

ency

; mem

ory:

RAV

LT le

arni

ng, d

elay

ed re

call;

vi

suom

otor

pro

cess

ing

spee

d: T

MT-

A (T

MT-

B); v

isuo

spat

ial

(mot

or/n

onm

otor

): JL

O, c

lock

dra

win

g te

st

21%

67%

/33%

Cavi

ness

et a

l. (2

007)

[8]

Inci

dent

, co

mm

unit

y,

n =

196

>1.5

SD

bel

ow n

orm

ativ

e da

ta in

>1

dom

ain

Gen

eral

: MM

SE, I

QCO

DE;

att

entio

n/ex

ecut

ive

func

tion:

ser

ial 7

s fr

om M

MSE

, sem

antic

flue

ncy,

Str

oop;

m

emor

y: C

VLT-

II im

med

iate

reca

ll, s

hort

- and

long

-del

ay

reca

ll; v

isuo

spat

ial:

VOSP

silh

ouet

tes

18.9

%86

.5%

/13.

5%A

arsl

and

et a

l. (2

009)

(Par

kWes

t)[12]

BNT:

Bos

ton

nam

ing

test

; BVR

T: B

ento

n vi

sual

rete

ntio

n te

st; C

AN

TAB:

Cam

brid

ge n

euro

psyc

holo

gica

l tes

t aut

omat

ed b

atte

ry; C

DR:

Cog

nitiv

e dr

ug re

sear

ch; C

ERA

D: C

onso

rtiu

m to

est

ablis

h a

regi

stry

for A

lzhe

imer

’s di

seas

e;

COW

AT: C

ontr

ol w

ord

asso

ciat

ion

test

; CVL

T: C

alifo

rnia

ver

bal

lear

ning

test

; DA

RT: N

atio

nal a

dult

read

ing

test

, Dut

ch v

ersi

on; D

-KEF

S: D

elis

-Kap

lan

exec

utiv

e fu

nctio

n sy

stem

; DRS

: Dem

entia

ratin

g sc

ale;

FC

SRT:

Fre

e an

d cu

ed

sele

ctiv

e re

min

ding

test

; HVL

T: H

opki

ns v

erb

al le

arni

ng te

st; I

Q-C

OD

E: In

form

ant q

uest

ionn

aire

on

cogn

itive

dec

line

in th

e el

derly

; JLO

: Jud

gmen

t of l

ine

orie

ntat

ion

test

; MC

I: M

ild c

ogni

tive

imp

airm

ent;

MM

SE: M

ini-m

enta

l st

ate

exam

; MoC

A: M

ontr

eal c

ogni

tive

asse

ssm

ent;

NA

I: N

uern

ber

ger a

lters

inve

ntar

; NA

RT: N

atio

nal a

dult

read

ing

test

; NBI

: Neu

rob

ehav

iora

l sig

ns a

nd s

ympt

oms

Abb

revi

ated

Inve

ntor

y; P

D: P

arki

nson

’s di

seas

e; P

D-C

RS:

Park

inso

n’s

dise

ase

cogn

itive

ratin

g sc

ale;

RAV

T: R

ey a

udito

ry v

erb

al le

arni

ng te

st; R

BMT:

Riv

erm

ead

beh

avio

ral m

emor

y te

st; R

CF:

Rey

com

ple

x fig

ure

test

; SD

: Sta

ndar

d de

viat

ion;

SRT

: Sel

ectiv

e re

min

ding

test

; TA

P: T

est f

or

atte

ntio

nal p

erfo

rman

ce; V

OSP

: Vis

ual o

bjec

t sp

ace

per

cept

ion

test

; WA

IS: W

echs

ler a

dult

inte

llige

nce

scal

e; W

CST

: Wis

cons

in c

ard

sort

ing

test

; WM

S: W

echs

ler m

emor

y sc

ale;

WTA

R: W

echs

ler t

est o

f adu

lt re

adin

g.

429



Tabl

e 1.

Cro

ss-s

ecti

onal

stu

dies

of m

ild c

ogni

tive

impa

irm

ent i

n Pa

rkin

son’

s di

seas

e co

hort

s (c

ont.)

.

Popu

lati

on/

sam

ple

size

MCi

cri

teri

aD

omai

ns a

nd n

euro

psyc

holo

gica

l tes

ts u

sed

PD-M

Ci

diag

nosi

sSi

ngle

/mul

tipl

e do

mai

ns a

ffec

ted

Stud

y (y

ear)

Ref.

Inci

dent

and

pr

eval

ent,

com

mun

ity

and

clin

ic, m

ulti-

cent

er, n

= 1

346

≥-1

.5 S

D b

elow

nor

ms

on ≥

1 do

mai

nAt

tent

ion/

exec

utiv

e fu

nctio

n: D

RS a

tten

tion/

initi

atio

n,

Stro

op C

olor

Wor

d Te

st, p

hone

mic

flue

ncy,

sem

antic

flu

ency

, Tow

er o

f Lon

don,

PD

-CRS

sub

test

s (a

tten

tion)

/Se

rial 7

s fr

om M

MSE

, CD

R D

igit

Vigi

lanc

e an

d si

mpl

e/ch

oice

reac

tion

time,

Dig

it Sp

an, C

ance

llatio

n te

st,

Sim

ilarit

ies,

Cor

si b

lock

spa

n, T

MT-

A (e

xecu

tive

func

tion)

; m

emor

y: C

VLT-

II (im

med

iate

reca

ll sh

ort-

and

long

-del

ay

reca

ll), D

RS m

emor

y, C

DR

dela

yed

wor

d re

cogn

ition

, SRT

(im

med

iate

del

ayed

reca

ll re

cogn

ition

), H

VLT

(imm

edia

te

dela

yed

reca

ll), P

D-C

RS (i

mm

edia

te a

nd d

elay

ed re

call)

, RA

VLT

(imm

edia

te a

nd d

elay

ed re

call,

ver

bal)/

BVRT

, CA

NTA

B pa

tter

n an

d sp

atia

l rec

ogni

tion

mem

ory,

CD

R de

laye

d pi

ctur

e re

cogn

ition

, RCF

reca

ll (v

isua

l); v

isuo

spat

ial:

Bent

on

test

mat

chin

g, D

RS c

onst

ruct

ion,

Inte

rsec

ting

Pent

agon

s,

JLO

, RCF

, PD

-CRS

Clo

ck C

opy,

VO

SP c

ube

and

silh

ouet

tes

25.8

%76

.1%

/23.

9%A

arsl

and

et a

l. (2

010)

[7]

Prev

alen

t, re

tros

pect

ive

clin

ic, n

= 7

2

Pete

rsen

crit

eria

, SD

cut

off n

ot

spec

ified

, defi

cits

on

≥2 te

sts/

dom

ain

Atte

ntio

n: D

igit

Span

For

war

ds, T

MT-

A; e

xecu

tive

func

tion:

TM

T-B,

‘WO

RLD

’ bac

kwar

ds fr

om M

MSE

; la

ngua

ge: B

NT,

pho

nem

ic fl

uenc

y, s

eman

tic fl

uenc

y;

mem

ory:

CER

AD

or H

VLT-

R, 3

–ite

m re

call

from

MM

SE;

visu

ospa

tial:

Inte

rsec

ting

Pent

agon

s, JL

O

52.8

%60

.5%

/39.

5%So

lling

er e

t al.

(201

0)[25]

Prev

alen

t, cl

inic

, n

= 14

3 (n

= 1

19,

nond

emen

ted

PD)

≥1.5

SD

bel

ow n

orm

ativ

e da

ta o

n 2

test

s in

≥1

one

dom

ain,

or ≥

1.5

SD o

r ≥2

SD b

elow

nor

mat

ive

data

fo

r 1 te

st (m

ultip

le c

utoff

s an

d co

mbi

natio

ns e

xplo

red)

Atte

ntio

n: S

troo

p Co

lor W

ord

Test

, Dig

it sp

an F

orw

ard

and

Back

war

d, D

igit

Ord

erin

g, M

ap S

earc

h, T

MT-

A;

exec

utiv

e fu

nctio

n: a

ctio

n ve

rb fl

uenc

y, v

erba

l flue

ncy

(lett

er, c

ateg

ory)

, cat

egor

y sw

itch

from

D-K

EFS,

Str

oop

Inte

rfer

ence

, TM

T-B;

mem

ory:

CVL

T-II

acqu

isiti

on,

shor

t del

ay, l

ong

dela

y, R

CF s

hort

del

ay, l

ong

dela

y;

visu

ospa

tial:

RCF

copy

, JLO

, Fra

gmen

ted

Lett

ers

30%

(for

≥1.

5 SD

bel

ow

norm

ativ

e da

ta o

n 2

test

s/do

mai

n)

53%

/47%

Dal

rym

ple-

Alfo

rd e

t al.

(201

1)[19]

BNT:

Bos

ton

nam

ing

test

; BVR

T: B

ento

n vi

sual

rete

ntio

n te

st; C

VLT:

Cal

iforn

ia v

erb

al lL

earn

ing

test

; CA

NTA

B: C

ambr

idge

neu

rops

ycho

logi

cal t

est a

utom

ated

bat

tery

; CD

R: C

ogni

tive

drug

rese

arch

; CER

AD

: Con

sort

ium

to e

stab

lish

a re

gist

ry fo

r Alz

heim

er’s

dise

ase;

CO

WAT

: Con

trol

wor

d as

soci

atio

n te

st; C

VLT:

Cal

iforn

ia v

erb

al le

arni

ng te

st; D

ART

: Nat

iona

l adu

lt re

adin

g te

st, D

utch

ver

sion

; D-K

EFS:

Del

is-K

apla

n ex

ecut

ive

func

tion

syst

em; D

RS: D

emen

tia

ratin

g sc

ale;

FC

SRT:

Fre

e an

d cu

ed s

elec

tive

rem

indi

ng te

st; H

VLT:

Hop

kins

ver

bal

lear

ning

test

; IQ

-CO

DE:

Info

rman

t que

stio

nnai

re o

n co

gniti

ve d

eclin

e in

the

elde

rly; J

LO: J

udgm

ent o

f lin

e or

ient

atio

n te

st; M

CI:

Mild

cog

nitiv

e im

pai

rmen

t; M

MSE

: Min

i-men

tal s

tate

exa

m; M

oCA

: Mon

trea

l cog

nitiv

e as

sess

men

t; N

AI:

Nue

rnb

erge

r alte

rsin

vent

ar; N

ART

: Nat

iona

l adu

lt re

adin

g te

st; N

BI: N

euro

beh

avio

ral s

igns

and

sym

ptom

s A

bbre

viat

ed In

vent

ory;

PD

: Par

kins

on’s

dise

ase;

PD

-CRS

, Par

kins

on’s

dise

ase

cogn

itive

ratin

g sc

ale;

RAV

T: R

ey a

udito

ry v

erb

al le

arni

ng te

st; R

CF:

Rey

com

ple

x fig

ure

test

; RBM

T: R

iver

mea

d b

ehav

iora

l mem

ory

test

; SRT

: Sel

ectiv

e re

min

ding

test

; SD

: Sta

ndar

d de

viat

ion;

TA

P: T

est f

or a

tten

tiona

l per

form

ance

; VO

SP: V

isua

l obj

ect s

pac

e p

erce

ptio

n te

st; W

AIS

: Wec

hsle

r adu

lt in

telli

genc

e sc

ale;

WM

S: W

echs

ler m

emor

y sc

ale;

WTA

R: W

echs

ler t

est o

f adu

lt re

adin

g;

WC

ST: W

isco

nsin

car

d so

rtin

g te

st.




Popu

lati

on/

sam

ple

size

MCi

cri

teri

aD

omai

ns a

nd n

euro

psyc

holo

gica

l tes

ts u

sed

PD-M

Ci

diag

nosi

sSi

ngle

/mul

tipl

e do

mai

ns a

ffec

ted

Stud

y (y

ear)

Ref.

Prev

alen

t, cl

inic

, n

= 10

7≥1

SD

, ≥1.

5 SD

, or ≥

2 SD

bel

ow

norm

ativ

e da

ta o

n on

e te

st/

dom

ain

or ≥

1 SD

, ≥1.

5 SD

, or ≥

2 SD

bel

ow n

orm

ativ

e da

ta o

n ≥2

te

sts/

dom

ain

(mul

tiple

cut

offs

and

com

bina

tions

exp

lore

d)

Atte

ntio

n: T

AP

(Ale

rtne

ss, G

o-N

ogo

subt

ests

); ex

ecut

ive

func

tion:

Tow

er o

f Lon

don,

TM

T-B,

NA

I, D

igit

Span

For

war

d an

d Ba

ckw

ard;

mem

ory:

CER

AD

wor

d lis

t mem

ory,

del

ayed

re

call,

reco

gniti

on, L

ogic

al M

emor

y I a

nd II

; psy

chom

otor

sp

eed

and

nam

ing

abili

ty: T

MT-

A, B

NT,

CER

AD

sem

antic

flu

ency

; pra

xis

and

visu

al fu

nctio

n: C

ERA

D li

ne d

raw

ings

, ob

ject

dec

isio

n of

VO

SP

9.9–

92.1

%

(dep

endi

ng

on d

efini

tion

used

)

25.8

–100

%/0

–74

.2%

(dep

endi

ng

on d

efini

tion

used

)

Liep

elt-

Scar

fone

et a

l. (2

011)

[23]

Prev

alen

t, cl

inic

, n

= 61

≥1.5

SD

bel

ow n

orm

ativ

e da

ta in

≥1

one

dom

ain

Gen

eral

: MM

SE, N

ART

; exe

cutiv

e fu

nctio

n: T

MT-

B;

lang

uage

: sem

antic

flue

ncy,

pho

nem

ic fl

uenc

y;

mem

ory:

Log

ical

Mem

ory;

psy

chom

otor

spe

ed: T

MT-

A;

wor

king

mem

ory:

Dig

it Sp

an to

tal

62%

37.7

%/2

4.6%

Nai

smith

et a

l. (2

011)

[20,21]

Prev

alen

t, cl

inic

, n

= 40

≥1.5

SD

bel

ow s

tand

ardi

zed

mea

n (o

r sca

led

scor

e ≤6

or p

erce

ntile

ra

nge

≤10)

on

two

test

s in

the

sam

e do

mai

n

Gen

eral

: DRS

–2, M

MSE

; att

entio

n/ex

ecut

ive

func

tion:

Str

oop

Colo

r Wor

d Te

st, T

MT-

B, s

eman

tic fl

uenc

y,

phon

emic

flue

ncy;

mem

ory:

RAV

LT li

sts,

imm

edia

te a

nd

dela

yed

reca

ll, re

cogn

ition

; vis

uosp

atia

l: RC

F co

py, B

lock

de

sign

(WA

IS-II

I), B

ell t

est

45%

61.1

%/3

8.9%

Ville

neuv

e et

al.

(201

1)[21]

Prev

alen

t, cl

inic

, n

= 35

0≥1

.5 S

D b

elow

nor

mat

ive

data

in ≥

1 on

e do

mai

nG

ener

al: M

MSE

; att

entio

n/ex

ecut

ive

func

tion:

Dig

it Sp

an F

orw

ard

and

Back

war

d, S

ymbo

l Dig

it M

odal

ities

Te

st, s

eman

tic fl

uenc

y; la

ngua

ge: B

NT,

Sim

ilarit

ies;

mem

ory:

CER

AD

wor

d lis

t lea

rnin

g an

d de

laye

d re

call;

vi

suos

patia

l: in

ters

ectin

g pe

ntag

ons,

JLO

36.6

%67

%/3

3%G

oldm

an e

t al.

(201

2)[14]

Prev

alen

t, cl

inic

, n

= 80

≥1.5

SD

bel

ow n

orm

ativ

e da

ta in

≥1

one

dom

ain

Gen

eral

: MM

SE; a

tten

tion:

Dig

it Sp

an; e

xecu

tive

func

tion:

Str

oop

Colo

r Wor

d Te

st; m

emor

y: R

AVLT

im

med

iate

reca

ll, d

elay

ed re

call;

vis

uosp

atia

l: Cl

ock

Dra

win

g Te

st

60%

58.3

%/4

1.7%

Wu

et a

l. (2

012)

[22]

PD co

hort

s with

MD

S PD

-MCI

Lev

el II

crit

eria

(with

mod

ifica

tions

as n

oted

)

Prev

alen

t, cl

inic

, n

= 10

4≥1

.5 S

D b

elow

nor

mat

ive

data

Gen

eral

: MM

SE; a

tten

tion/

wor

king

mem

ory:

TM

T, D

igit

canc

ella

tion,

Dig

it Sp

an F

orw

ards

and

Bac

kwar

ds, S

troo

p,

Cors

i tes

t; ex

ecut

ive

func

tion:

Pho

nem

ic fl

uenc

y, F

AB,

Cl

ock

Dra

win

g Te

st; l

angu

age:

Sim

ilarit

ies,

sem

antic

flu

ency

; mem

ory:

RAV

LT im

med

iate

and

del

ayed

reca

ll, R

CF

imm

edia

te re

call;

vis

uosp

atia

l: D

raw

ing

Copy

ing

Test

, RCF

co

py

33%

Not

spe

cifie

dBi

undo

et a

l. (2

013)

[28]

BNT:

Bos

ton

nam

ing

test

; BVR

T: B

ento

n vi

sual

rete

ntio

n te

st; C

VLT:

Cal

iforn

ia v

erb

al lL

earn

ing

test

; CA

NTA

B: C

ambr

idge

neu

rops

ycho

logi

cal t

est a

utom

ated

bat

tery

; CD

R: C

ogni

tive

drug

rese

arch

; CER

AD

: Con

sort

ium

to e

stab

lish

a re

gist

ry fo

r Alz

heim

er’s

dise

ase;

CO

WAT

: Con

trol

wor

d as

soci

atio

n te

st; C

VLT:

Cal

iforn

ia v

erb

al le

arni

ng te

st; D

ART

: Nat

iona

l adu

lt re

adin

g te

st, D

utch

ver

sion

; D-K

EFS:

Del

is-K

apla

n ex

ecut

ive

func

tion

syst

em; D

RS: D

emen

tia

ratin

g sc

ale;

FC

SRT:

Fre

e an

d cu

ed s

elec

tive

rem

indi

ng te

st; H

VLT:

Hop

kins

ver

bal

lear

ning

test

; IQ

-CO

DE:

Info

rman

t que

stio

nnai

re o

n co

gniti

ve d

eclin

e in

the

elde

rly; J

LO: J

udgm

ent o

f lin

e or

ient

atio

n te

st; M

CI:

Mild

cog

nitiv

e im

pai

rmen

t; M

MSE

: Min

i-men

tal s

tate

exa

m; M

oCA

: Mon

trea

l cog

nitiv

e as

sess

men

t; N

AI:

Nue

rnb

erge

r alte

rsin

vent

ar; N

ART

: Nat

iona

l adu

lt re

adin

g te

st; N

BI: N

euro

beh

avio

ral s

igns

and

sym

ptom

s A

bbre

viat

ed In

vent

ory;

PD

: Par

kins

on’s

dise

ase;

PD

-CRS

, Par

kins

on’s

dise

ase

cogn

itive

ratin

g sc

ale;

RAV

T: R

ey a

udito

ry v

erb

al le

arni

ng te

st; R

CF:

Rey

com

ple

x fig

ure

test

; RBM

T: R

iver

mea

d b

ehav

iora

l mem

ory

test

; SRT

: Sel

ectiv

e re

min

ding

test

; SD

: Sta

ndar

d de

viat

ion;

TA

P: T

est f

or a

tten

tiona

l per

form

ance

; VO

SP: V

isua

l obj

ect s

pac

e p

erce

ptio

n te

st; W

AIS

: Wec

hsle

r adu

lt in

telli

genc

e sc

ale;

WM

S: W

echs

ler m

emor

y sc

ale;

WTA

R: W

echs

ler t

est o

f adu

lt re

adin

g;

WC

ST: W

isco

nsin

car

d so

rtin

g te

st.

Tabl

e 1.

Cro

ss-s

ecti

onal

stu

dies

of m

ild c

ogni

tive

impa

irm

ent i

n Pa

rkin

son’

s di

seas

e co

hort

s (c

ont.)

.

431



Popu

lati

on/

sam

ple

size

MCi

cri

teri

aD

omai

ns a

nd n

euro

psyc

holo

gica

l tes

ts u

sed

PD-M

Ci

diag

nosi

sSi

ngle

/mul

tipl

e do

mai

ns a

ffec

ted

Stud

y (y

ear)

Ref.

Inci

dent

, clin

ic,

n =

123

≥1.5

SD

bel

ow n

orm

ativ

e da

taG

ener

al: M

MSE

, DA

RT; a

tten

tion:

Dig

it Sy

mbo

l Tes

t, TM

T-A

; ex

ecut

ive

func

tion:

Mod

ified

WCS

T, C

OW

AT; l

angu

age:

BN

T,

WA

IS-II

I Sim

ilarit

ies;

mem

ory:

RAV

LT, R

BMT

Logi

cal M

emor

y su

btes

t; vi

suos

patia

l: Cl

ock

Dra

win

g Te

st, J

LO

35%

35%

/65%

Broe

ders

et a

l. (2

013)

[26]

Prev

alen

t, cl

inic

, n

= 76

≥2 S

D (a

lso

1–2.

5) b

elow

nor

mat

ive

data

Gen

eral

: MM

SE; a

tten

tion/

wor

king

mem

ory:

Dig

it Sp

an

Forw

ards

, LN

S, S

DM

T, T

MT-

A; e

xecu

tive

func

tion:

Clo

ck

Dra

win

g Te

st, C

OW

AT, D

igit

Span

Bac

kwar

ds, P

rogr

essi

ve

Mat

rices

, TM

T-B;

lang

uage

: BN

T, s

eman

tic fl

uenc

y,

WA

IS-II

I Sim

ilarit

ies;

mem

ory:

CER

AD

wor

d lis

t lea

rnin

g,

dela

yed

reca

ll, a

nd re

cogn

ition

, Log

ical

Mem

ory

I and

II,

FCSR

T, F

igur

al M

emor

y; v

isuo

spat

ial:

Cloc

k Co

pyin

g Te

st,

Inte

rsec

ting

Pent

agon

s, JL

O

62%

(for

≥2

SD b

elow

no

rmat

ive

data

)

8.5%

/91.

5%G

oldm

an e

t al.

(201

3)[13]

Prev

alen

t, cl

inic

, m

ultic

ente

r,

n =

139

≥1.5

SD

bel

ow n

orm

ativ

e da

taG

ener

al: M

MSE

, MoC

A, N

BI, W

TAR;

att

entio

n: L

NS,

DKE

FS

Colo

r Wor

d In

terf

eren

ce C

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ont.)

.




versus ‘posterior cortical’ deficits is reminiscent to some degree of the nonamnestic and amnestic categorization. Along with the aforementioned challenges in parsing out individual subtypes of single domain PD-MCI and identifying suf-ficient subject numbers per subtype, further research is needed regarding optimal definitions of PD-MCI subtypes and whether subtyping by individual cognitive domains will be a fruitful concept.

●● TimingBesides its clinical spectrum, PD-MCI also can be thought of in terms of its time course and rela-tionship to motor symptom onset and PD diag-nosis. Cognitive impairment in PD is no longer just a late-stage phenomenon but rather it can occur in incident PD with reports of PD-MCI in 20–40% [5,10,12,17]. Although these studies vary in definitions of PD cognitive impairment or PD-MCI used, it is apparent that cognitive dysfunction can be a symptom in PD early on and even prior to initiation of dopaminergic therapy. Furthermore, these studies support the importance of asking PD patients and caregiv-ers about cognitive symptoms even at this early disease stage.

The presence of cognitive deficits in de novo, untreated PD patients leads to several questions including: how early in the course of PD can cog-nitive deficits occur, are they present in premotor PD, is their presence related to dopaminergic deficiency (and perhaps improved by dopamin-ergic treatments) or related to other neuro-chemical, neuropathological or clinical issues (e.g., depression, anxiety, sleep disturbances), and is there a distinction between early cognitive deficits in PD or in dementia with Lewy bodies (DLB)? Indeed, there is increasing evidence for cognitive deficits in ‘premotor’ PD (e.g., persons who do not have motor features characteristic of diagnosable PD but who may have nonmo-tor features affecting smell, bowel function, mood or sleep), ‘preclinical’ PD (e.g., persons who may not have any clinical features but have abnormalities on neuroimaging measures such as [18F]-fluorodopa PET or dopamine transporter [DAT] SPECT imaging), or in cohorts ‘at risk’ or relatives of PD patients, who also may be at genetic risk [31]. Rapid eye movement behavior disorder (RBD) is associated with cognitive defi-cits in executive function, memory and visuos-patial abilities and the development of synucle-inopathies such as PD [32,33]. RBD can predate

synucleinopathies such as PD or DLB by 5 or more years [34], and about half of ‘idiopathic’ RBD patients will develop a synucleinopathy after 12 years [35]. The Parkinson Associated Risk Study found that healthy relatives of PD patients with hyposmia and decreased DAT uptake on imaging scans had worse scores on verbal fluency, attention/executive function and processing speed [36]. While a primary inclusion criterion of the MDS PD-MCI is the presence of clinically diagnosed PD, there is a current move-ment in the PD field to redefine the criteria for PD [37]. Indeed, studies of these premotor or ‘at-risk’ cohorts challenge our notions of when PD actually begins and at what stage MCI may occur within the PD diagnostic spectrum.

Another challenge in defining PD-MCI is determining how this construct fits in with DLB. Whether PD dementia and DLB are the same disorder has been debated over the years [37,38]. In the development of the MDS PD-MCI criteria, the task force recognized this issue, particularly since the onset of cognitive symptoms relative to motor symptoms can be historically vague, and in some cases, occur concurrently. The PD-MCI criteria focus on clinically diagnosed PD but acknowledge the challenge of differenti-ating PD-MCI from incipient DLB. Indeed, the concept of MCI as prodromal DLB has gained attention and support from studies documenting the progression of nonamnestic MCI to DLB and other non-AD dementias as well as clinico-pathological correlates of MCI in longitudinally followed cohorts [11,39]. Nonamnestic MCI sub-jects, compared with those with amnestic MCI, had a 10-fold greater likelihood to develop prob-able DLB; these subjects initially manifested greater attention and/or visuospatial impairment (88%) than memory deficits (25%) as well as RBD, daytime sleepiness and fluctuations [40], clinical features found in other MCI cases later confirmed by autopsy to have DLB [41]. Further studies regarding MCI as prodromal DLB, including clinical features, biomarkers and path-ological correlates, may be needed to determine the timing, phenotype, definitions and context of MCI in parkinsonian/synuclein disorders.

●● Progression, stability or reversionEmerging data from longitudinal studies of PD-MCI shed light on the progression of PD-MCI and its conversion to PD dementia, but also raise questions regarding whether PD-MCI subtypes differ in their course and whether all

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PD-MCI progresses to dementia. In a study of prevalent PD subjects, 18/29 (62%) of those with PD-MCI who completed follow-up at 4 years converted to PD dementia, whereas dementia ensued in only 6/30 (20%) with intact cogni-tion at baseline; although a small sample with a limited neuropsychological battery, the study suggested that single domain nonamnestic MCI, along with higher depression scores, were associ-ated with dementia conversion, whereas predom-inant amnestic deficits and multiple domains were not [1]. The CamPaIGN study provides over 10-year follow-up of incident PD persons and suggests divergent patterns of PD-MCI [2,29,29,2]. At 3–5 years follow-up, 13/126 (10%) converted to demented and an additional 57% had cog-nitive impairment, mainly frontostriatal defi-cits [2]. Multiple factors predicted global cogni-tive decline at 5 years including: age ≥72 years (Odds ratio [OR]: 4.81; 95% CI: 1.14–20.23), decreased semantic fluency (OR: 6.89; 95% CI: 1.30–36.55), impaired copy of intersecting pen-tagons (OR: 2.78; 95% CI: 1.001–7.73), non-tremor dominant motor phenotype (OR: 3.93; 95% CI: 0.79–19.57) and a genetic variant in the MAPT gene (H1/H1 genotype) (OR: 12.14; 95% CI: 1.26 = 117.36). Older age along with the impaired posterior cortical cognitive func-tion (i.e., semantic fluency and intersecting pen-tagons) had a combined OR of 88 for developing dementia within the first 5 years of PD diagno-sis [29]. This study suggests that not all cognitive impaired PD patients will necessarily develop dementia and proposes that PD patients with greater posterior cortical phenotypes, but not those with greater frontostriatal-based/execu-tive dysfunction, develop dementia at follow-up. Moreover, a functional polymorphism in the dopamine-regulating enzyme COMT was associated with executive dysfunction but not dementia, whereas the MAPT and APOE4 poly-morphisms were strongly associated with earlier dementia in this cohort [29,29,42].

Other longitudinal studies of PD cohorts, particularly those using MDS PD-MCI diag-nostic criteria, are in early stages but provide some estimates of PD-MCI progression. In a community-based incident PD cohort in Sweden, 37/134 (27%) of PD patients developed dementia over 5 years of follow-up [43]. Of the 49 PD patients diagnosed as MCI, 25/49 (50%) developed dementia in this timeframe. Presence of MCI and older age predicted dementia, and baseline scores on episodic memory, semantic

fluency, mental flexibility and visuospatial func-tion tests were worse in those PD-MCI who converted to dementia, compared with those who did not. A follow-up study of the incident Norwegian ParkWest PD cohort at 1 year and 3 year supports that PD patients with MCI at baseline were more likely to progress to demen-tia, with 27% of the initial group subsequently diagnosed with PD dementia [44]; similar find-ings were found in a Netherlands study with increasing rates of PD-MCI and of those with baseline PD-MCI, dementia at 3-year and 5-year follow-up [26]. These studies, however, also dem-onstrate a high rate of attrition at follow-up and thereby, an important challenge of conducting lo ngitudinal studies.

PD-MCI may also be an unstable state with reversion to normal cognitive status at follow-up in some studies. In the Swedish study, 6 (11%) patients with PD-MCI at baseline reverted to normal cognition, and 10 patients fluctuated between MCI and normal cognition [43]. Both the Dutch and Norwegian studies also demon-strate that some PD-MCI patients at follow-up revert to normal cognition, though with longer follow-up, may ultimately have MCI or demen-tia. In the ParkWest study, at 1-year follow-up approximately 20% of PD-MCI had normal cognition; however, among those patients with MCI at baseline and 1-year follow-up, only 9% reverted to normal cognition at 3 years. Thus, PD-MCI status may fluctuate, and there may be other contributing factors to consider, such as cognitive test performance, co-morbid non-motor features, medication use, underlying neu ropathology or other biomarkers.

●● Biomarkers & neuropathologyThere is a growing interest in identifying bio-markers such as cerebrospinal fluid (CSF), genet-ics, neuroimaging, among others to characterize PD-MCI and its underlying neuroanatomical, neurochemical or neuropathological substrates and that may predict conversion to dementia. While the MDS PD-MCI criteria do not incor-porate biomarkers into current definitions, there may be lessons to be learned from the MCI/AD field (as discussed below) with the inclusion of biomarkers in recent revisions of MCI research criteria and their use in AD prevention trials [45].

Proposed CSF biomarkers for PD cognitive decline include several previously associated with AD pathology but also others. Decreased CSF-αβ 1–42 levels are thought to ref lect




amyloid deposition in the brain, and increased tau or phosphorylated tau (p-tau) CSF levels, increased neuronal death. Several PD studies reveal decreased CSF-αβ 1–42 in cognitively impaired PD patients compared with healthy controls [46–48]. Lower αβ 1–42 levels cor-related with semantic fluency [47] and a more rapid cognitive decline from baseline to 1-year follow-up [48]. Levels of tau and p-tau have been variable with some, but not all, studies report-ing increased CSF levels in PD dementia; in one study, elevated tau also correlated with impaired naming and memory performance [47,48]. Newly diagnosed PD patients had decreased CSF-αβ levels, though not as reduced as in AD, and lev-els were significantly associated with memory impairment but not with attention/executive or visuospatial dysfunction; CSF total tau or p-tau levels neither differed between PD patients and controls, nor correlated with cognitive meas-ures [49]. In another incident PD study, CSF-αβ correlated with pattern recognition memory and Montreal Cognitive Assessment (MoCA) scores, with lower levels in PD-MCI patients [17]. These CSF markers may reflect pathological processes of PD cognitive impairment, including possible co-morbid AD and thereby, generate new ave-nues for diagnostic and prognostic biomarkers and intervention targets.

Several genetic biomarkers have been explored in PD cognitive impairment. As previously men-tioned, data from the CamPaIGN study suggest a genotype-phenotype dissociation regarding risk of PD dementia, increased with tau-related MAPT gene polymorphisms and posterior cor-tical deficits, but not COMT polymorphisms and frontal-executive type deficits [2,30]. Others have described similar associations between the MAPT H1 polymorphism and PD dementia [50] as well as an effect on parietal activation in spa-tial rotation tasks in early PD [51]. Although APOE ε4 is a strong risk factor for AD, con-flicting results have been found in PD demen-tia [52]. Other genetic mutations associated with PD dementia, more rapid cognitive decline and greater neuropsychiatric features include those related to alpha-synuclein triplication and car-riers of mutations in the GBA gene, encoding the lysosomal enzyme glucocerebrosidase [42,53]. In the CamPaIGN cohort, GBA mutations occurred in 3.5%, and GBA carriers exhibited greater risk for progression to dementia (hazard ratio 5.7) and worse motor function (hazard ratio 4.2). The relationship between cognitive

dysfunction and mutations in LRRK2, a com-mon genetic and sporadic cause of PD, has been variable with mixed study results, some reveal-ing lower executive function or Mini-Mental State Examination scores [54–56]. Future studies including well-defined PD-MCI cohorts and longitudinal follow-up will be needed to estab-lish links between genotype and dementia risk as well as the possibility of incorporating genotype in clinical trials and study design.

Structural and metabolic neuroimaging offer other opportunities to study biomarker corre-lates of PD-MCI [57,58]. Gray matter atrophy on brain MRI has been variably found in PD-MCI, depending on the cohort studied (incident vs prevalent), PD-MCI definitions and MRI analy-ses (voxel-based morphometry [VBM], cortical thickness, others). PD-MCI patients, defined using Petersen criteria, had reduced gray matter in the left frontal and bilateral temporal lobe regions, compared with PD without MCI; how-ever, these differences did not remain significant after corrections for multiple comparisons and patient groups were small in size [59]. Other stud-ies reveal that PD-MCI patients exhibit ante-rior caudate atrophy and posterior ventricular enlargement on MRI [60], and compared with healthy controls, multiple areas of reduced gray matter such as frontal, temporal (including the hippocampus), parietal and pre/post central gyri; however, compared with cognitively normal PD patients, PD-MCI patients have not always dem-onstrated statistically significant differences in gray matter atrophy [61]. In a comparison of PD patients with amnestic MCI (n = 41) to amnes-tic MCI patients (without PD, n = 78), the PD group had decreased gray matter in the right temporal and anterior prefrontal areas compared with amnestic MCI (without PD); when multi-ple domains were affected in PD-MCI, regional atrophy was more extensive [62]. Several stud-ies have focused on MRI correlates of PD-MCI in the incident PD cohorts. Two VBM studies of de novo PD patients, however, did not reveal differences in gray matter atrophy in PD-MCI patients compared with PD without MCI or healthy controls [17,63], though PD-MCI patients drawn from a large, de novo cohort revealed cor-tical thinning in temporal, parietal, frontal and occipital areas compared with healthy controls, and in the right inferior temporal region com-pared with cognitively normal PD patients [64]. Metabolic studies of PD-MCI reveal abnor-malities in posterior cortical regions, similar to

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regions frequently abnormal in PD dementia patients and AD. PD-MCI patients with mul-tiple domains impaired had decreased glucose metabolism on 18F-flurodeoxyglucose (FDG) PET scans in prefrontal and parietal regions, while PD-MCI patients with single domain impairment had a similar pattern but to a lesser degree [65]. A PD-MCI cohort (of whom 11 had an isolated memory deficit and 4, a mild deficit in verbal fluency) demonstrated parietal, tem-poral and occipital hypoperfusion compared with cognitively intact PD [66]. Interestingly, the PD-MCI had greater hypoperfusion in parieto-occipital regions compared with the amnestic MCI patients (without PD), whereas the amnes-tic MCI patients had greater hypoperfusion in medial temporal lobe regions, thereby, perhaps suggesting different underlying neural substrates and neuropathologies. These neuroimaging studies support regional gray matter atrophy pat-terns or altered metabolism in PD-MCI, with notable abnormalities in posterior cortical areas that may potentially reflect shared substrates with PD dementia and in some cases, AD.

To date, few studies describe the neuro-pathology of PD-MCI. Adler et al. report on 8 PD-MCI cases (of 80 PD cases), of whom 4 had amnestic single domain MCI, 3 nonam-nestic single domain MCI (executive dysfunc-tion) and 1 nonamnestic multiple domain MCI (executive/visuospatial dysfunction); the neuro-pathologies of the PD-MCI cases were hetero-geneous with varying Lewy body distributions and in 50%, moderate neuritic plaque pathology (though only 2 met AD criteria), and cerebro-vascular pathology in 3 cases [67]. Jellinger also reported a mix of Lewy bodies, AD pathology and cerebral amyloid angiopathy in 8 PD-MCI autopsy cases with amnestic and nonamnestic deficits [68]. Future clinico-pathological studies will be needed to examine the underlying neu-ropathology of PD-MCI and its relationship to MCI subtype.

PD-MCi: theory, practice & debated issues●● Conceptual usefulness

Whether PD-MCI represents a useful concept has been debated in the field [38,69]. Recognition of mild cognitive deficits in PD has brought increased awareness, education and research to an important and previously under-recognized area of PD patient care. The emergence of PD-MCI as a diagnostic entity provides a frame-work for investigating its clinical features and

pathophysiology and for identifying patients for clinical research trials for symptomatic thera-pies, and ultimately, disease-modifying or pre-ventive agents. Greater awareness of PD-MCI can lead to appropriate counseling for patients and caregivers, validation of symptoms that are sometimes ‘dismissed’ or attributed to aging, and discussions regarding safety, driving and care planning. However, there are several concerns with the concept and diagnosis of PD-MCI. As previously discussed, PD-MCI is a heterogeneous condition, with different phe-notypes and progression, and not necessarily a prodrome to dementia. In some cases, PD-MCI may be a static entity without further decline, a ‘short-term’ event with reversion to normal cognition, or a marker of impending dementia. How this information is conveyed to patients and caregivers in clinical settings and applied in research settings with symptomatic and disease-modifying treatment trials and selected target patients will need to be sorted out for the con-cept of PD-MCI to be successfully utilized in the field.

●● Diagnostic challengesThe diagnosis of PD-MCI rests upon the con-cept that MCI, in general and in PD, refers to a clinical syndrome of cognitive impairment in the absence of dementia. However, many different definitions have been used over the years and thereby, influence our understand-ing of PD-MCI. The MDS PD-MCI criteria provide an important initial step toward a uni-form diagnosis across multiple sites. Even with these criteria as a framework, there is latitude in interpretation and application with different neuropsychological tests, cut off scores and levels of assessment used.

There are a number of challenges in diag-nosing PD-MCI clinically. First, one needs to identify that a decline in cognitive abilities has occurred. Estimates of cognitive impairment by patients and their caregivers vary in their reliability, due to either over or under report-ing [8,20,70] or to difficulty separating cognitive from motor problems, and information from several sources (e.g., patient, caregiver and clinician) may be needed. PD-MCI studies vary in how preservation of activities of daily living is evaluated, and this issue is further compounded by difficulty in distinguishing cognitive and motor effects. Other motor and nonmotor features of PD may affect cognitive




function and thereby, the diagnosis of PD-MCI. Cognitive performance may differ in ‘on’ versus ‘off ’ motor states [71,72], and neuropsychologi-cal tests with timed components or significant motor demands may be difficult for PD patients. Nonmotor features such as depression, anxiety, apathy, psychosis, fatigue and sleep disturbances are common in PD, particularly alongside

impaired cognition or dementia [73,74]. Lastly, there are unresolved methodological issues regarding choices for global screening tests, neu-ropsychological test batteries and cutoffs of 1–2 standard deviation (SD) below normative data. Different research groups have interpreted these elements of the MDS PD-MCI criteria differ-ently. To date, there is no agreement regarding

Table 2. Revised Alzheimer’s disease and mild cognitive impairment criteria by clinical and biomarker evidence.

Diagnostic category Core clinical criteria met Likelihood of biomarker probability of AD etiology

Likelihood of Aβ presence (PeT or CSF)

Likelihood of neuronal injury evidence (CSF tau, FDG-PeT, structural MRi)

AD by clinical criteria

AD core clinical criteria: cognitive or behavioral symptoms that: interfere with ability to function at work or at usual activities; represent a decline from previous levels of functioning; are not explained by delirium or major psychiatric disorder; cognitive impairment is detected and diagnosed (history, objective assessment); involve at least 2 domains (impaired ability to acquire and remember new information; reasoning and handling of complex tasks or poor judgment; visuospatial abilities; language functions; or changes in personality, behavior, or comportment; insidious onset; amnestic presentation (most common), nonamnestic presentation; absence of concomitant substantial cerebrovascular disease, features of other dementias (DLB, PPA) or other neurological or medical disorders or medications that could substantially affect cognition

AD by biomarker criteria

Probable AD

Clinical criteria Yes Uninformative Unavailable, indeterminant or conflicting

Untested, indeterminant or conflicting

Evidence of AD pathophysiological process

Yes Intermediate intermediate high

Unavailable/indeterminant positive positive

Positive unavailable/indeterminant positive

Possible AD

Clinical criteria Atypical course or etiologically mixed presentation

Uninformative Unavailable, indeterminant or conflicting

Untested, indeterminant or conflicting


No, but meets non-AD dementia criteria (e.g., DLB, FTD)

High but does not exclude alternative etiology

Positive Positive

Unlikely AD

Clinical criteria No, or sufficient evidence for alternative diagnosis (e.g., HIV dementia, HD)

Low Negative Negative


No Low Negative Negative

MCI by clinical criteria

MCI core clinical criteria: concern regarding a change in cognition, impairment in one or more cognitive domains, preservation of independence in functional abilities, not demented; objective evidence of cognitive decline, preferably on cognitive testing, scores typically 1–15 SD below norms; episodic memory impairment, though other domains may be impaired

MCI due to AD by biomarker criteria

High likelihood Yes High Positive PositiveIntermediate likelihood

Yes Intermediate Positive or untested Positive

No likelihood Yes Low Negative NegativeAβ: Amyloid-beta; AD: Alzheimer’s disease; CSF: Cerebrospinal fluid; DLB: Dementia with Lewy bodies; FDG: 18fluorodeoxyglucose; FTD: Frontotemporal dementia; HD: Huntington’s disease; MRI: Magnetic resonance imaging; PET: Positron emission tomography; PPA: Primary progressive aphasia.Data taken from [45,89].

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the ideal neuropsychological battery, given the plethora of tests available to evaluate global and individual cognitive functions, the best sources of normative data, handling of normative scores and best cutoff scores to use, though data are emerging [13,28,75]. Cutoff scores used to define impairment can greatly influence frequency estimates of PD-MCI. Sensitivity and speci-ficity of PD-MCI by MDS PD-MCI level II criteria varied depending on whether 1, 1.5, 2 and 2.5 SD cutoffs below norms were used in one study, with the best sensitivity (85.4%) and specificity (78.6%) measures achieved using a cutoff of 2 SD below norms; other cutoff scores compromised either specif icity (21.4% for 1 SD below norms and 60.7% for 1.5 SD below norms) or sensitivity (58.3% for 2.5 SD below norms) [13]. Validation of the MDS PD-MCI criteria including efforts of a large international consortium may help elucidate these operation-alization issues in defining PD-MCI, particu-larly across cognitive test batteries and diverse PD populations.

Lessons from MCi & ADLooking toward the research conducted in MCI and AD, particularly regarding criteria and their revisions, biomarker studies, and clinical trials for disease-modification and symptomatic ther-apies, may be especially relevant in informing research studies and clinical trials conducted in PD cohorts. In this section, we will review pertinent lessons from AD and MCI research.

●● Defining MCi – a construct in evolutionThe past decades have witnessed considerable debate in the definition, classification and con-ceptualization of the MCI-AD state, and these debates have provided lessons, and continue to provide lessons, for the PD-MCI field. MCI has evolved from a general concept of impaired cogni-tion but with an intact ability to carry out daily living activities, to more specifically focused on memory complaints and impairment, to subtype categorization with amnestic or nonamnestic deficits and single or multiple cognitive domains affected [11,76,77]. Different MCI subtypes have

Table 3. examples of preclinical and preventive Alzheimer’s disease trials incorporating biomarkers in the study design.

Trial Population Biomarker intervention Aim Ref.

APOE e4 treatment trial Persons who are homozygous for APOE e4 alleles

APOE e4 Antiamyloid medication

Prevent or delay the emergence of AD symptoms in persons at high risk for developing AD

[108]

Alzheimer prevention initiative

Large extended Columbian family with rare presenilin (PS1) gene mutation

PS1 mutation Crenezumab Study whether a monoclonal antibody targeting Aβ precursor protein can delay the onset of AD

[109]

Dominantly inherited Alzheimer network

Persons who have a known genetic mutation that causes autosomal dominant AD or have parent, sibling with a known genetic mutation

PS1, PS2 or APP mutation

Solanezumab, gantenerumab, beta-secretase inhibitor

Examine and compare the safety, side effects and effect on imaging and biomarkers of three investigational drugs

[110]

Antiamyloid treatment of asymptomatic AD (A4 ADCS- NIA, Lilly)

Healthy population sample with positive amyloid imaging on PET scan

Positive amyloid imaging

Solanezumab Evaluate whether early treatment will slow down memory loss and cognitive decline and delay the progression of AD-related brain injury on imaging

[111]

A4 sub-study: LEARN (ADCS- NIA Alzheimer’s Association)

Older individuals who have negative amyloid imaging on PET scans performed in A4 study

Negative amyloid imaging

None Longitudinal natural history study of cognitive function outcomes in amyloid PET negative individuals from A4 study, examining differences in rates of clinical decline

[112]

Takeda/Zinfandel Trial (TOMMOROW)

Healthy population sample genetic risk of developing AD

APOE and TOMM40

Pioglitazone Study a new investigational risk algorithm to predict the genetic risk for developing MCI and test the safety and effectiveness of an investigational medication in delaying MCI due to AD

[113]

Aβ: Amyloid-beta; AD: Alzheimer’s disease; MCI: Mild cognitive impairment; MRI: Magnetic resonance imaging.




been found to progress to different types of dementia syndromes, with amnestic MCI repre-senting a potential precursor to AD, while nonam-nestic MCI subtypes may progress to other forms of dementia [24,78]. These MCI studies paved the way for many of the early studies of cognitive impairment in nondemented PD, application of MCI definitions in PD cohorts, and subsequently, the generation of MCI as a construct in PD.

The MCI-AD field has also faced its own issues regarding variable prevalence estimates, conver-sion rates and heterogeneity, and these shared chal-lenges may offer insights and support to the PD field. Prevalence estimates of MCI and its subtypes vary with respect to which diagnostic criteria were employed or what type of patient population was examined, similar to our recent experiences in the field of PD-MCI [79,80]. In addition, reversion rates of MCI to normal cognitive functioning have been reported, varying widely from 15% over a 3.6-year follow-up to 34% with 1.5-year follow-up [81–83]. These observations underscore the challenges encountered in accurately characterizing and diagnosing MCI and support the view that clini-cal classification of MCI should be considered a heterogeneous and potentially unstable d iagnostic entity, in both AD and PD [84].

At present, there are no widely accepted screening tests for MCI. In the MCI-AD field, there have been efforts to develop tests and bat-teries (e.g., MMSE enriched with delayed recall items, or the MoCA) that can be validated against the clinical diagnosis of MCI or predic-tively against the development of dementia [85,86] as well as computerized cognitive assessment sys-tems (e.g., CogState) that can discriminate MCI from cognitively healthy individuals and can be used to screen community-dwelling individuals or implemented in large scale clinical trials for AD prevention [87]. In PD-MCI, similar chal-lenges are faced, and efforts to determine the optimal cognitive batteries or tests that discrimi-nate PD-MCI from PD patients with intact cog-nition or dementia and mode of administration for clinical trials are underway [28,75].

●● incorporating biomarkers & genetics into clinical criteria & research study designWith advances in clinical and pathophysiological relationships over the years, the diagnostic defini-tions of AD and MCI have been refined to incor-porate biomarkers. In 2011, consensus reports from National Institute on Aging (NIA) and the Alzheimer’s Association (AA) working groups

described MCI-AD as three contiguous disease phases: the ‘AD pathophysiological process’, ‘MCI due to AD’ and ‘clinical AD dementia’ in an effort not only to assist physicians in diagnoses, but also to provide a platform for developing primary pre-vention therapies (Table 2) [45,88,89]. The modified definitions include biomarkers that reflect the underlying neurodegenerative processes [90,91], spanning those with potential for identifying early and subtle but measureable signs (e.g., decreased CSF-αβ–42, increased total tau or p-tau levels) and later stage evidence (e.g., MRI-derived hip-pocampal and entorhinal cortex atrophy, reduced glucose metabolism in temporoparietal and p osterior cingulate cortices) [92,93].

Other studies focus on the role of genetics such as dominantly inherited mutations for early-onset AD (APP, PSEN1 and PSEN2) and susceptibility factors for late-onset AD (APOE gene polymorphism, APOE ε4) [94–96]. Current clinical trials have now incorporated family his-tory and genetic criteria into the study design to enrich the studies with persons at risk for development of cognitive decline, MCI and AD. Thus, the application of genetics and biomarkers in therapeutic trials is a growing research area, which in due time and with research advances, may also emerge in the PD field.

●● emerging therapeutic strategies in MCi & AD researchConventional therapies to treat AD, such as cholinesterase inhibitors and NMDA receptor antagonists, have not produced a disease-modi-fying effect or impacted the progression of AD over a prolonged period of time. Lessons to be learned from the MCI-AD field include the devel-opment of novel therapies targeting pathophysi-ological mechanisms (e.g., amyloid cascade, tau production and processing or specific biological mechanisms related to inflammation, insulin, cholesterol, etc.) [97,98]. The amyloid hypothesis also has evolved in AD, from its initial focus on contributions of plaques in disease development to using specific soluble plaque components (oli-gomers, monomers) as potential drug targets. The latest antiamyloid strategies focus on facilitating amyloid’s clearance, inhibiting its production, or preventing its aggregation [99]. Metabolic factors influencing brain glucose utilization and insulin-like growth factor resistance also may play a role in cognitive function [100–102], and clinical trials of novel compounds such as intranasal insulin in MCI and AD are underway. In addition, clinical

439



trials with immunotherapy (e.g., intravenous immunoglobulin-G) and antiamyloid antibodies (e.g., bapineuzumab, solanezumab) have been, and continue to be cautiously tested in patients, with lessons to be learned regarding safety issues, heightened immune responses and optimal doses and delivery [103–107].

Recent clinical trials now focus on the preclini-cal stages of AD with the aim of preventing cogni-tive decline or AD and implementing aggressive treatment at earlier stages. Biomarker profiles play an important role in guiding study design, selecting target populations and identifying drug interventions for several large-scale trials in per-sons at risk for developing AD (Table 3) [108–113]. Furthermore, studying disease mechanisms, biomarkers and therapies longitudinally, across preclinical phases to dementia, can inform the timelines and benchmarks of progression needed for trials and identify those persons who are the most likely to decline and thereby, potentially benefit from early therapeutic intervention, prior to substantial synaptic loss and neurodegenera-tion. Thus, strategic use of biomarkers and sensi-tive cognitive tests in prevention trials, whether for cognitive decline in AD or PD, may help provide the necessary evidence of efficacy to sup-port future drug approval. The MCI-AD field has set forth informative examples for the MCI-PD field regarding the direct application of biomarker and genetic information in clinical trial design; development of novel therapeutic targets based on advances in neuroscience, animal models, neuro-imaging and molecular studies; and early identifi-cation of those at highest risk of cognitive decline.

Conclusion & future perspectiveWhether in PD or AD, the construct of MCI has taken hold over the years and has been defined, and redefined, and will likely continue to evolve as research advances. In both fields, research devoted to identifying persons at the earliest stage of cognitive symptoms has gained attention. Improved therapeutic interventions

are still needed for symptomatic benefit and disease-modification. Discovery of biomarkers that reflect disease progression and underlying pathologies associated with cognitive impair-ment may provide a path toward early detection of persons at high risk for cognitive decline and thereby, prevention and/or early intervention. However, these advances, whether for PD or AD, do not come without some risks and limitations as studies will need to reconcile the potentially negative aspects of early diagnosis, the risk–ben-efit ratios of various therapeutics, and accessibil-ity of biomarker testing and clinical resources, counseling and therapies once available. While MCI in PD is a relatively newer concept com-pared with MCI due to AD, lessons highlighted in this review may be shared by both neurologi-cal disorders and individually or collectively, advance our understanding of neurodegenerative processes and treatment interventions for both.

Author disclosuresJG Goldman: Consultancies: Acadia, Advisory Boards: Acadia, Pfizer, Teva, Employment: Rush University Medical Center, Honoraria: Movement Disorders Society, American Academy of Neurology, Michael J. Fox Foundation, Grants: NIH, Michael J. Fox Foundation, Parkinson’s Disease Foundation, Rush University, Teva (study site-PI), Biotie (study site-PI). NT Aggarwal: Consultancies: Medical Consultant – Illinois Institute of Continuing Legal Education (IICLE), Advisory Boards: Lilly Alzheimer’s Disease Environment Evolution (ADEE) Working Group, MERCK, Employment: Rush University Medical Center, Honoraria: Preventative Cardiologist Nursing Association, Grants: NIH/NIA, PCORI, Eli Lilly (study site), Lundbeck (study site). CD Schroeder: Employment: Rush University Medical Center, Governors State University, Grants: NIH T32AG000269–15. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.



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Neurology - Future Medicine...Neurodegenerative Disease Management Vol. 5 Issue 5 Neurology TOP...

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