Asymmetry at Disease Onset Is Not a Predictor of Parkinson's Disease Progression.

Asymmetry index DatSCAN Parkinson’s disease disease progression

Journal

Journal of Parkinson's disease
ISSN: 1877-718X
Titre abrégé: J Parkinsons Dis
Pays: Netherlands
ID NLM: 101567362

Informations de publication

Date de publication:
2021
Historique:
pubmed: 13 7 2021
medline: 15 12 2021
entrez: 12 7 2021
Statut: ppublish

Résumé

Even though a significant fraction of Parkinson's disease (PD) patients presents with only minor or no motor asymmetry, the motor symptoms in PD typically start on one side of the body and worse symptoms on the side of the disease onset usually persist long after the disease has become clinically bilateral. The asymmetric presentation of PD has been studied over the years, with some studies showing slower progression in PD subjects with asymmetric disease presentation. In other studies, however, it was not possible to relate the asymmetry to disease progression. The main objective of the present study was to assess the effect of asymmetry at disease onset on disease progression. Using the data available in the Parkinson's Progression Markers Initiative (PPMI) database, at baseline, 423 subjects with de-novo PD were included in the study. Instead of dichotomizing the subjects in asymmetric and symmetric, we kept the asymmetry index and the non-motor, disability, and motor progression at one-, three-, and five-year follow-up continuous. Pearson's r correlational analysis and the coefficient of determination R2 were used to correlate asymmetry indices and disease progression. There was no correlation between neither clinically, nor DatSCAN defined asymmetry and non-motor, motor, and disability progression in the de-novo PD subjects with a 5-year follow-up. Asymmetry at disease onset does not predict progression of PD. Further studies are needed to investigate whether early detection of asymmetry on clinical grounds could successfully distinguish between PD and symmetric types of atypical parkinsonism in the early stages of the disease.

Sections du résumé

BACKGROUND
Even though a significant fraction of Parkinson's disease (PD) patients presents with only minor or no motor asymmetry, the motor symptoms in PD typically start on one side of the body and worse symptoms on the side of the disease onset usually persist long after the disease has become clinically bilateral. The asymmetric presentation of PD has been studied over the years, with some studies showing slower progression in PD subjects with asymmetric disease presentation. In other studies, however, it was not possible to relate the asymmetry to disease progression.
OBJECTIVE
The main objective of the present study was to assess the effect of asymmetry at disease onset on disease progression.
METHODS
Using the data available in the Parkinson's Progression Markers Initiative (PPMI) database, at baseline, 423 subjects with de-novo PD were included in the study. Instead of dichotomizing the subjects in asymmetric and symmetric, we kept the asymmetry index and the non-motor, disability, and motor progression at one-, three-, and five-year follow-up continuous. Pearson's r correlational analysis and the coefficient of determination R2 were used to correlate asymmetry indices and disease progression.
RESULTS
There was no correlation between neither clinically, nor DatSCAN defined asymmetry and non-motor, motor, and disability progression in the de-novo PD subjects with a 5-year follow-up.
CONCLUSION
Asymmetry at disease onset does not predict progression of PD. Further studies are needed to investigate whether early detection of asymmetry on clinical grounds could successfully distinguish between PD and symmetric types of atypical parkinsonism in the early stages of the disease.

Identifiants

pubmed: 34250949
pii: JPD202525
doi: 10.3233/JPD-202525
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1689-1694

Auteurs

Marco Cotogni (M)

Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Lucia Sacchi (L)

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

Aleksander Sadikov (A)

Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.

Dejan Georgiev (D)

Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia.

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