Clustering Algorithm Reveals Dopamine-Motor Mismatch in Cognitively Preserved Parkinson's Disease

Area of Science:

• Neuroscience
• Neurology
• Biomarkers

Background:

• Parkinson's disease (PD) is characterized by dopaminergic denervation and motor impairment.
• Understanding the interplay between these factors is crucial for early diagnosis and prognosis.
• Previous studies have not fully elucidated the distinct clinical trajectories associated with varying levels of denervation and motor deficits in de novo PD patients.

Purpose of the Study:

• To investigate the relationship between dopaminergic denervation and motor impairment in early-stage Parkinson's disease.
• To stratify de novo PD patients into distinct subgroups based on imaging and clinical features.
• To assess the impact of these subgroups on disease progression and cognitive decline.

Main Methods:

• Analysis of 249 de novo Parkinson's disease (PD) patients from the Parkinson's Progression Markers Initiative (PPMI) and 84 from an external cohort.
• ¹²³I-FP-CIT-SPECT imaging to measure dopaminergic denervation and clustering analysis to stratify motor impairment.
• Causal mediation analysis to evaluate the role of co-pathology (Aβ_1-42) in cognitive decline across subgroups.

Main Results:

• Four distinct subgroups emerged: concordant severe [D+/M+], concordant mild [D/M], mild denervation/severe motor [D/M+], and mild motor/severe denervation [D+/M].
• The [D+/M+] subgroup showed poorer memory, pathological Aβ_1-42, higher Levodopa Equivalent Daily Dose (LEDD), and faster motor progression.
• The [D/M+] subgroup exhibited severe, rapidly progressive rigidity. CSF Aβ_1-42 mediated cognitive decline in cognitively preserved [D+/M+] patients.

Conclusions:

• Concordant severe dopaminergic denervation and motor impairment define a malignant PD profile associated with Aβ-related cognitive decline.
• Mild concordant cases exhibit stable disease progression, while mismatch subgroups present unique clinical patterns.
• Integrating imaging and motor features enables early and accurate Parkinson's disease stratification.