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Published on: October 4, 2021
Dopa Responsiveness in Parkinson's Disease
Sacha E Gandhi1, Anahita Nodehi2, Michael A Lawton2
1School of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.
Dopamine response patterns in Parkinson's disease (PD) evolve over time, with definite responses increasing with disease duration. Early treatment response may not be a reliable diagnostic indicator for PD.
Area of Science:
- Neuroscience
- Neurology
- Pharmacology
Background:
- Dopaminergic responsiveness is a hallmark of Parkinson's disease (PD).
- Understanding the temporal evolution of dopaminergic response in PD is crucial.
- Limited data exists on how dopaminergic responsiveness changes over the course of PD.
Purpose of the Study:
- To investigate the longitudinal patterns of dopaminergic responsiveness in Parkinson's disease.
- To identify distinct clusters of dopaminergic response over time.
- To determine factors that predict these identified response patterns.
Main Methods:
- Analysis of repeated dopaminergic challenge tests from the Parkinson's Progression Markers Initiative (PPMI).
- Growth-mixture modeling was employed to identify distinct longitudinal response trajectories.
- Multinomial logistic regression was used to identify predictors of these response clusters.
Main Results:
- A total of 1525 dopaminergic challenge tests from 336 PD patients were analyzed.
- The proportion of definite dopaminergic responses (≥24.5%) increased with longer disease duration (48.0% at 1-2 years to 74.3% later).
- Three distinct response groups were identified: Striking (8.7%), Excellent (32.7%), and Modest (58.6%), with varying clinical and cognitive characteristics.
Conclusions:
- Three distinct longitudinal patterns of dopaminergic response were observed in Parkinson's disease.
- The increasing prevalence of definite dopaminergic responsiveness over time suggests its utility as an early diagnostic marker may be limited.
- Clinical and cognitive factors differentiate the identified response groups, offering insights into disease heterogeneity.
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