Objective:

To create a clinical-pathological algorithm to distinguish MSA from PD with diagnostic certainty.  

Background:

Multiple system atrophy (MSA) and Parkinson’s disease (PD) have overlapping clinical phenotypes that create diagnostic dilemmas, particularly early in the disease course.

Design/Methods:

After consent, patients with MSA and PD completed neurologic examinations, medical history review, cognitive evaluation, orthostatic vital signs and neurodegenerative disease questionnaires.  Skin biopsies at the distal leg, distal thigh and posterior cervical sites were performed on all participants with quantitation of P-SYN by location, amount and nerve fiber subtype involvement.  Clinical, demographic and pathological data were combined using unbiased weighted cluster analysis into an algorithm to differentiate the two disease states based on a training data set, with blinded prospective testing in a validation cohort.

Results:

The training data set included 25 patients each with MSA and PD.  The validation cohort included an independent group of 30 patients each with MSA and PD.  Patients with MSA tended to be younger (67±6.8 vs 71±7.3, P<0.05), with greater synuclein deposition (8.6±4.1 vs 4.5±2.8, P<0.01).  The training set identified demographic, clinical and pathological data that could be integrated into a predictive algorithm for diagnosis.  The algorithm correctly identified 24/25 PD and MSA in the training set.  In the validation data set, 33/35 PD and 32/35 MSA cases were correctly identified providing 92.8% accuracy in diagnosing MSA and PD.    

Conclusions:

Differentiation between neurodegenerative diseases, such as MSA and PD, remains challenging.  The overlapping phenotypes lead to high rates of misdiagnosis in the absence of accurate diagnostic tools.  These results, using a clinical-pathological scoring system, suggest that is possible  to differentiate between major synucleinopathy subtypes with high diagnostic accuracy.