Motor dysfunction in Parkinson's patients: depression differences in a latent growth model

Area of Science:

• Neurology
• Psychiatry
• Biostatistics

Background:

• Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms.
• Motor dysfunction progression and its relationship with psychological factors like depression are critical for understanding PD.
• Latent growth modeling (LGM) offers a robust framework for analyzing longitudinal changes in complex diseases.

Purpose of the Study:

• To elucidate the developmental trajectory of motor dysfunction in Parkinson's disease patients using LGM.
• To investigate the longitudinal relationship between depression and motor dysfunction in PD.
• To determine if depression influences the rate and pattern of motor symptom progression.

Main Methods:

• Utilized four-year follow-up data from 389 Parkinson's Progression Marker Initiative (PPMI) participants.
• Employed univariate and parallel growth latent growth models (LGM) to analyze motor dysfunction and depression.
• Examined motor dysfunction trajectory and its association with depression as a covariate and parallel growth variable.

Main Results:

• Motor dysfunction in PD patients demonstrated a significant quadratic growth trajectory, indicating accelerating severity with decelerating growth rate.
• Depression levels were positively correlated with motor dysfunction severity at multiple time points.
• Significant associations were found between depression and motor dysfunction intercepts, slopes, and quadratic factors, highlighting a complex interplay.

Conclusions:

• Motor dysfunction in Parkinson's disease follows a non-linear, quadratic developmental path.
• Depression is intricately linked to motor dysfunction, influencing its initial severity, progression rate, and quadratic changes.
• Managing depressive symptoms may be crucial for mitigating motor symptom deterioration in PD patients.