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Abstract

Background:

In patients with early parkinsonism, misdiagnosis may occur in >30% of cases. This can have detrimental consequences clinically and in clinical trials. Dopamine transporter (DAT) SPECT imaging can help improve diagnostic accuracy.

Objective:

To describe characteristics of individuals initially diagnosed with idiopathic Parkinson’s disease (iPD) and with abnormal DAT SPECT imaging who had a change in diagnosis on follow-up.

Methods:

Data were obtained from the biomarker study Parkinson’s Progression Markers Initiative (PPMI). PPMI is a multicenter, observational study that enrolled 423 individuals with a diagnosis of iPD of ≤2 years duration and with abnormal DAT SPECT imaging. Participants were assessed at least annually, and diagnosis was documented by the site neurologist. Characteristics of those that had a change in diagnosis were compared to those with stable diagnosis.

Results:

390 subjects were included. Eight (2%) had a change in diagnosis. The diagnosis was changed to multiple system atrophy in 5 cases, dementia with Lewy bodies in 2, and corticobasal degeneration in 1. Revision of diagnosis occurred 2–5.2 years from enrollment. Mean motor score was higher (26.9 vs 20.6; p = 0.01), DAT binding lower (1.056 vs 1.406; p = 0.01), genetic risk score lower (–0.016 vs –0.022; p = 0.0470), and olfaction score higher (28.75 vs 22.05; p = 0.03) in those whose diagnosis changed compared to those who did not.

Conclusion:

Diagnosis remained stable in most individuals with early parkinsonism diagnosed with iPD and with abnormal DAT imaging. A small number had a revision in diagnosis. Clinical and biomarker abnormalities were greater at baseline in those whose diagnosis changed.

Keywords

Parkinson’s disease parkinsonism movement disorders SPECT DAT scan multiple system atrophy atypical parkinsonism

INTRODUCTION

Several neurodegenerative disorders feature the presence of parkinsonism. Identifying clinical and biomarker characteristics that distinguish idiopathic Parkinson’s disease (iPD) from other etiologies of parkinsonism early on in the disease course is essential. Greater diagnostic accuracy is important for management and counseling of patients and caregivers. It will be increasingly important when disease-specific therapies emerge. As well, diagnostic accuracy is essential for the fidelity of iPD clinical trials. The inclusion of misdiagnosed patients in PD clinical trials could have detrimental effects [1]. Use of dopamine transporter (DAT) SPECT has been advocated for as a way to reduce enrollment of patients in iPD clinical trials who do not have iPD [2]. While use of DAT SPECT will minimize enrolment of patients who do not have striatal denervation, which is largely caused by neurodegenerative parkinsonisms, current DAT SPECT analytic techniques may not distinguish between iPD and other etiologies of parkinsonism with reduced DAT binding [3]. The Parkinson’s Progression Markers Initiative (PPMI) study offers a unique opportunity to examine a large cohort of patients with early parkinsonism who had both a clinical diagnosis of iPD and evidence of reduced DAT binding on SPECT at enrollment. These PPMI patients had close follow-up and extensive biomarker and clinical characterization. We herein describe the characteristics of patients whose diagnosis changed and contrast them to those whose diagnosis remained stable.

METHODS

Study participants

The PPMI PD cohort includes 423 participants with newly diagnosed PD. Study aims, methodology, and details of study assessments have been published elsewhere [4, 5] and are available on the PPMI website [6]. Briefly, inclusion criteria included: (i) a diagnosis of PD with disease duration ≤2 years (ii) presence of asymmetric resting tremor or bradykinesia, or two of bradykinesia, resting tremor and rigidity (iii) no atypical parkinsonian features (iv) no PD treatment at baseline and no anticipated need for treatment within 6 months of baseline (v) no dementia (vi) DAT deficit on SPECT. These criteria allowed for the inclusion of “possible” iPD in addition to “probable” iPD, as defined by the Movement Disorders Society criteria for diagnosis of PD [7].

Assessments

In the first 5 years of the study, PPMI PD participants were assessed every 3 months in the 1st year of diagnosis and every 6 months thereafter. The PPMI protocol includes extensive baseline clinical, imaging and biofluid biomarker assessments. Based on existing literature, a subset of candidate measures were selected which could potentially be associated with revision in diagnosis. Assessments selected and the features they assess were as follows:

Demographics/other: age, sex, family history of PD.

PD disease characteristics/motor severity: age at diagnosis, disease duration at baseline, time to initiation of dopaminergic medications, time to diagnostic revision, MDS-UPDRS score and subscores, and motor asymmetry index. The motor asymmetry index was defined as follows [8]: Motor asymmetry index = (MDS-UPDRS Part III right – left score)/(right + left score).

Non-motor assessments: Depression—15-item Geriatric Depression Scale (GDS-15); Anxiety—State-Trait Anxiety Inventory (STAI); Daytime sleepiness—Epworth Sleepiness Scale(ESS); Autonomic dysfunction—Scales for Outcomes in Parkinson’s disease—Autonomic (SCOPA-AUT); Cognitive dysfunction—Montreal Cognitive Assessment (MoCA).

Genotype: single nucleotide polymorphism (SNP) genotyping was performed on DNA samples from PPMI participants as described (PPMI project #106 methods [6]) and a genetic risk score (GRS) has been calculated, based on 28 risk variants implicated in PD based on genome-wide association studies [9 –11]. This score distinguishes between participants in the PD PPMI cohort vs controls (GRS is lower in PD compared to controls) [9 –11].

Imaging: DATscan SPECT was acquired at the screening PPMI visit as described [4, 5]; a binary determination of DAT deficit was made when DAT binding was ≤65th percentile expected for age and sex. The following continuous measures of DAT binding were examined: (i) mean striatal specific binding ratio (SBR; average of putamen and caudate SBR on right and left) (ii) mean putamen binding (average of right and left putamen binding) (iii) mean caudate binding (average of right and left caudate binding) (iv) caudate SBR/putamen SBR ratio (v) Striatum DAT binding asymmetry index = (right – left striatum SBR)/right + left striatum SBR) [8].

Biofluid biomarkers: cerebrospinal fluid (CSF) was collected via lumbar puncture at baseline, 6 months, and annually thereafter. β-amyloid1-42 [Aβ1-42], total tau [T-tau], tau phosphorylated at threonine 181 [P-tau181], and total α-synuclein were measured as previously described [5, 12].

Ascertainment of diagnosis

As per PPMI study protocol, at each study visit the site investigator indicated their diagnosis of the participant’s parkinsonism by assigning a diagnostic code on the “PD Diagnosis” case report form [6]. This case report form consisted of a comprehensive list of etiologies of parkinsonism as well as an “other, specify” field. Any subject for whom the diagnostic code changed from iPD at the baseline visit to any other code at subsequent visit was considered to have a revision of diagnosis. Subjects who had at least 1 follow-up assessment were included in this analysis.

Analysis

Descriptive statistics were used to summarize data on the subjects who had a diagnostic revision. Characteristics of the diagnostic revision group compared to those with stable diagnosis were compared using Wilcoxon rank-sum test, chi-squared or fisher’s exact test as appropriate.

Statistical significance was set at p < 0.05. Analyses were conducted with StataIC 15.1 (Stata-Corp, Tx).

Standard protocol approvals, registrations, and patient consents

Participating PPMI sites received approval from the ethical standards committee on human experimentation. Written informed consent was obtained from all participants. The clinical trial identifier of the PPMI is NCT01141023.

Data availability policy

The data used in this study were obtained from the publicly available PPMI dataset, which can be accessed online [6]. The data used for all presented analyses were downloaded from the PPMI website on December 11, 2017, except where otherwise specified.

RESULTS

Mean duration of follow-up was 1,856 days (SD 389.87.) Characteristics of the patients with revised diagnoses are shown in Table 1. There were 8 patients in total that had a revision of their diagnosis from iPD. The majority of these patients had their diagnosis revised to Multiple System Atrophy (MSA). Additionally, there were two dementia with Lewy body (DLB) cases and one case coded with a diagnosis of corticobasal degeneration. Diagnosis at each annual visit up until last follow-up, and neuropathologic diagnosis where available, is shown in Table 2.

Table 1

Baseline characteristics of the cases whose diagnosis was revised

Patient ID	1	2	3	4	5	6	7	8
Final Diagnosis	MSA	MSA	MSA	DLB	CBD	DLB	MSA	MSA
Age at study enrolment	62	68	56	69	65	72	52	66
Sex	Male	Male	Male	Male	Male	Female	Female	Male
Family History of PD in 1° Relative	No	No	No	No	No	No	No	No
Disease Duration at enrollment (months)	11.5	2.3	6.5	3.5	1	0.9	2.4	7.2
Time to Diagnostic Revision (days)	790	1127	1461	1887	1095	730	1187	1492
Time to PD meds (days)	214	184	215	335	242	92	549	242
MDS-UPDRS 1.1–1.6	4	0	2	3	0	3	0	1
MDS-UPDRS I	12	1	6	14	4	8	6	6
MDS-UPDRS II	20	13	15	12	12	9	6	15
MDS-UPDRS III	36	30	18	28	29	22	25	27
MDS-UPDRS Total	68	44	39	54	45	39	37	48
Motor Asymmetry Index	0.3	0.36	–0.4	–0.25	–0.4	–0.06	0.55	0.2
GDS-15	1	2	3	5	2	2	0	1
UPSIT	23	36	35	22	36	5	35	38
Epworth sleepiness scale	2	11	12	12	4	7	5	13
RBDSQ	8	4	10	8	7	1	2	3
MOCA	28	30	23	29	26	29	26	28
SCOPA-AUT	12	11	15	16	13	5	5	11
Orthostatic SBP	1	–10	–12	–16	–13	–5	–10	6
Orthostatic HR	6	8	0	4	4	12	2	25
Mean Striatum Binding Coefficient	0.75	1.3	1.07	0.73	1.22	0.53	1.94	0.91
Contralateral Putamen Binding Coefficient	0.28	0.44	0.42	0.53	0.40	0.26	1.01	0.52
Contralateral Putamen/Caudate Ratio	0.29	0.27	0.30	0.65	0.22	0.35	0.46	0.46
Ipsilateral Putamen/Caudate Ratio	0.80	0.39	0.43	0.49	0.37	0.49	0.56	0.51
Striatum Asymmetry Index	–0.141	0.196	–0.141	0.082	–0.091	–0.038	0.174	0.093
CSF Aβ_1 - 42 (pg/ml)	1063	568	623.7	384.2	847.8	701.3	585.4	1352
CSF T-tau (pg/ml)	146.4	124.3	127.7	141.1	153.8	327.1	124.1	189.6
CSF P-tau181 (pg/ml)	11.14	8.89	9.39	12.72	11.49	25.93	9.56	16.5
CSF α-synuclein (pg/ml)	806.9	766.2	978.3	939.1	1213.5	2069.7	912	1951.6
Genetic risk score	–0.030	–0.029	–0.030	–0.029	–0.017	–0.014	–0.008	–0.023

Table 2

Diagnosis at baseline and each follow-up visit for the cases whose diagnosis was revised (blank cells indicate no data available)

Patient number	Baseline visit	Year 1	Year 2	Year 3	Year 4	Year 5	Neuropathologic diagnosis^*
1	PD	PD	MSA	MSA	MSA	MSA
2	PD	PD	PD	MSA	MSA
3	PD	PD	PD	PD	MSA	MSA	MSA
4	PD	PD	PD	PD	PD	DLB
5	PD	PD	PD	CBD
6	PD	PD	DLB	DLB	DLB	DLB
7	PD	PD	PD	MSA
8	PD	PD	PD	PD	MSA	MSA

^*Neuropathologic diagnosis was made available in 12/2018.

In comparing the group of patients with revision in diagnosis (n = 8) to those whose diagnosis remained stable (n = 382), there were significant differences in several baseline characteristics, as shown in Table 3. At baseline, the patients whose diagnosis was later revised had higher MDS-UPDRS part II and part III scores. SCOPA-AUT scores were higher in those whose diagnosis changed but this did not reach significance. Motor asymmetry index values were not significantly different between groups. Olfactory loss, as measured by UPSIT, was less severe in those whose diagnosis was revised. Also, they had significantly lower mean striatum DAT binding, as well as lower DAT binding in the putamen contralateral to the side more affected by PD. CSF Aβ_1 - 42, T-tau, P-tau181, and total α-synuclein levels were not different between the groups.

Table 3

Baseline differences between patients with early parkinsonism who had a stable diagnosis on follow-up compared to those whose diagnosis was revised

	Stable Diagnosis (n = 382)	Diagnosis Revised (n = 8)	p-value^*,i
Average Age, mean (SD) (years)	61.4 (9.9)	64.3 (6.8)	0.407
Sex (% male)	65.97%	75.00%	0.454
Family History of PD in 1° Relative	329	0	0.605
Disease Duration at enrollment, mean (SD) (months)	6.73 (6.61)	4.41 (3.70)	0.274
Time to PD meds, mean (SD) (days)	385.94 (331.2)	259.13 (135.2)	0.633
MDS-UPDRS Ia (items 1.1–1.6), mean (SD)	1.23 (1.58)	1.63 (1.60)	0.417
MDS-UPDRS Ib, mean (SD)	5.45 (4.07)	7.13 (4.19)	0.184
MDS-UPDRS II, mean (SD)	5.60 (4.00)	12.75 (4.20)	^*0.001
MDS-UPDRS III, mean (SD)	20.56 (8.81)	26.88 (5.41)	^*0.015
MDS-UPDRS Total	31.59 (12.91)	46.75 (10.22)	^*0.001
GDS-15, mean (SD)	2.34 (2.51)	2.00 (1.51)	0.915
UPSIT, mean (SD)	22.05 (8.14)	28.75 (11.41)	0.027
Epworth, mean (SD)	5.67 (3.30)	8.25 (4.27)	0.085
RBDSQ, mean (SD)	4.06 (2.64)	5.38 (3.29)	0.256
MOCA, mean (SD)	27.1 (2.34)	17.4 (2.26)	0.744
SCOPA-AUT, mean (SD)	9.38 (6.08)	10.88 (3.94)	0.202
Orthostatic SBP Change, mean (SD)	(+)4.70 (12.88)	(–)7.38 (7.52)	0.282
Orthostatic HR Change, mean (SD)	(+)7.70 (7.16)	(+)7.63 (2.80)	0.717
Mean Striatum Binding Coefficient, mean (SD)^†	1.406 (0.385)	1.056 (0.443)	^*0.013
Contralateral Putamen Binding Coefficient, mean (SD)^†	0.685 (0.243)	0.483 (0.235)	^*0.007
Contralateral Putamen/Caudate Ratio, mean (SD)^†	0.383 (0.105)	0.375 (0.142)	0.524
Ipsilateral Putamen/Caudate Ratio, mean (SD)^†	0.442 (0.115)	0.505 (0.136)	0.205
Striatum Asymmetry Index, mean (SD)^†	0.056 (0.130)	0.007 (0.138)	0.296
CSF Aβ_1 - 42, mean (SD) (pg/ml)	862.68 (327.18)	765.66 (311.75)	0.365
CSF T-tau, mean (SD) (pg/ml)	168.88 (55.93)	166.76 (68.26)	0.617
CSF P-tau181, mean (SD) (pg/ml)	14.86 (5.19)	13.20 (5.69)	0.167
CSF α-synuclein, mean (SD) (pg/ml)	1507.60 (647.86)	1204.66 (516.05)	0.112
Genetic risk score, mean (SD)^**	0.016 (0.010)	0.022 (0.008)	*0.047

^*denotes a significant p-value; ⁱFisher’s exact test was used for comparisons of sex, first degree family history, and genotypes; all other comparisons utilized a two-sample Wilcoxon rank-sum test. †- there were three patients, all in the stable diagnosis group, who did not have baseline dopamine transporter imaging. ^**31 patients in the stable diagnosis group did not have genetic information available.

DISCUSSION

While diagnostic accuracy in parkinsonian patients can be high in the hands of a neurologist with subspecialty training in Movement Disorders [13], a relatively large number of patients with early parkinsonism initially diagnosed with iPD will later have their diagnosis changed to another etiology of parkinsonism, especially among older patients with parkinsonism [14]. Certain “red flag” characteristics have been delineated, such as vertical oculomotor abnormalities, early severe postural instability, autonomic dysfunction, or cerebellar abnormalities, that when present, may suggest an alternative diagnosis to iPD [15]. However, these well-described “atypical” features are often not detectable or easily recognized early on in the course of neurodegenerative parkinsonism [16]. While longitudinal follow-up often improves diagnostic accuracy of parkinsonism tremendously [13 , 18], awaiting longitudinal follow-up can be detrimental for both clinical care and clinical trials, emphasizing the need for biomarkers to improve diagnostic accuracy, which was the main impetus for the PPMI study [5].

In prior trials of patients with early parkinsonism referred for enrollment in several iPD clinical trials, which did not include DAT binding deficit as an inclusionary criterion, between 6–13% percent had a change of diagnosis during the study period [19, 20]. In this PPMI cohort of individuals with early parkinsonism diagnosed with possible or probable iPD, and with abnormal DAT imaging, a high degree of stability in diagnosis was observed longitudinally, with only 2% of cases having a change in diagnosis. This speaks to the utility of DAT imaging in detecting striatal denervation and thus supporting the diagnosis of neurodegenerative parkinsonian syndromes.

We compared clinical, biofluid biomarker, genotype, and DAT imaging measures in those who had a change in diagnosis vs. those who did not. We found several significant differences in physical examination scores and patient reported outcomes which intuitively reflect more severe disease in those whose diagnosis changed. Because MSA and DLB both are marked by significant autonomic dysfunction, we would have expected significant differences in autonomic function in those whose diagnosis was revised to these disorders. Questionnaire-based self-report of autonomic symptoms and orthostatic change in SBP were both greater in the group that had diagnostic revision but this did not reach significance. While this could be due to the small sample size among the diagnostic revision group, or ongoing diagnostic inaccuracy, it could also be evidence that in the earliest stages of parkinsonism, the “red flags” that help distinguish iPD from other neurodegenerative parkinsonisms may not be obvious [16].

Regarding differences in candidate PD biomarkers, we found higher (better) scores on the University of Pennsylvania Smell Identification Test (UPSIT), a well validated assessment of olfactory function [21, 22], in those whose diagnosis was revised. Previous studies have shown that iPD patients typically have greater olfactory impairment than patients with atypical parkinsonism [23]. In particular, while olfactory impairment is known to occur in MSA, it has been observed to be more mild than in iPD patients [24]. As for differences in other candidate PD biomarkers, we found greater striatal denervation, as measured with DAT binding, in those whose diagnosis changed, similar to other studies [25, 26]. Regarding CSF biomarkers, total CSF α-synuclein was not significantly different in those who had a change in diagnosis. This is not surprising considering that to date, total CSF α-synuclein has not been demonstrated to be of definitive utility in distinguishing between neurodegenerative parkinsonian disorders [27], though levels may be lower in MSA compared to iPD. If and when iPD-specific strains of pathologic α-synuclein can be measured in biofluids, they will be of great interest to test in this cohort. As for genetic differences, the diagnostic revision group had lower PD genetic risk scores as compared to those whose diagnosis remains stable. This genetic risk score has been shown to distinguish PD from healthy controls (with lower scores in PD compared to controls) [9 –11], and our results suggest that it may also be useful in distinguishing among etiologies of early parkinsonism, with abnormal DAT binding, though further studies in larger samples will be required to confirm this.

We did not find differences in clinical or imaging asymmetry indices in our cohort. This may be further evidence that the symmetry of motor and imaging abnormalities often cited as suggestive of the atypical parkinsonian syndromes [21] may not be present in the early stages of disease. Indeed, MSA, like iPD, can be quite asymmetric early in its course [28, 29].

The conclusions drawn in this study are limited by the small sample size in the group whose diagnosis was revised. Furthermore, there remains the possibility that some of the patients whose diagnosis was changed in fact had iPD or some other disorder. Indeed, diagnostic inaccuracy of atypical neurodegenerative parkinsonian disorders can be high, especially in cases presenting with corticobasal syndrome [30]. PPMI does include a brain bank and the ultimate gold-standard for clinical diagnosis, as well as for biomarker validation, will be clinico-pathologic correlation. Thus far, only 1 case whose diagnosis was revised had a neuropathologic diagnosis, and in that 1 case this was concordant with clinical diagnosis.

Our results indicate a low rate of revision in diagnosis in early parkinsonism when DAT imaging is utilized. In the relatively few cases of diagnostic revision, there were measurable differences in clinical, olfactory, DAT imaging, and genotype characteristics compared to those whose diagnosis remained stable. There is, however, significant overlap in most measures among those who have a change in diagnosis versus those who do not. It is possible that combining clinical, imaging, genotype, and novel biofluid biomarkers will be of utility to optimize diagnostic accuracy in early PD. This premise requires further study in PPMI and other large cohorts followed longitudinally. Ultimately, biomarkers that enhance diagnostic accuracy would be of value in a clinical setting to help anticipate the unique challenges of the atypical parkinsonian syndromes for practitioners, caregivers and patients. This is also of importance in clinical trials, where diagnostic accuracy is essential—particularly as disease-specific therapies are developed.

DISCLOSURES

Jason Massa – Reports no disclosures

Lana Chahine – No conflicts of interest related to this work. Receives research support from the Michael J Fox Foundation, including for the PPMI study, has received travel payment from MJFF to MJFF conferences, is a paid consultant to MJFF, and receives royalties from Wolters Kluwel (for book authorship).

AUTHOR CONTRIBUTIONS

Jason Massa directly participated in conceptualization and analysis of this study and the original drafting of this manuscript.

Lana Chahine directly participated in conceptualization and analysis of this study and the review and revision of this manuscript.

Footnotes

ACKNOWLEDGMENTS

Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (http://www.ppmi-info.org/data). For up-to-date information on the study, visit .

PPMI, a public-private partnership, is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbvie, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Eli Lilly & Co., F. Hoffman-La Roche, Ltd., GE Healthcare, Genentech, GlaxoSmithKline, Lundbeck, Merck, MesoScale, Piramal, Pfizer and UCB.

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Revision of Diagnosis in Early Parkinsonism with Abnormal Dopamine Transporter Imaging

Abstract

Background:

Objective:

Methods:

Results:

Conclusion:

Keywords

INTRODUCTION

METHODS

Study participants

Assessments

Ascertainment of diagnosis

Analysis

Standard protocol approvals, registrations, and patient consents

Data availability policy

RESULTS

DISCUSSION

DISCLOSURES

AUTHOR CONTRIBUTIONS

Footnotes

ACKNOWLEDGMENTS

References