Parkinson's disease subtypes: Approaches and clinical implications

Area of Science:

• Neuroscience
• Neurology
• Clinical Research

Background:

• Parkinson's disease (PD) exhibits significant heterogeneity in presentation and progression.
• Subtype identification is a key priority in PD research for improved patient management.
• Existing subtypes include hypothesis-driven (e.g., motor) and emerging non-motor symptoms (NMS) subtypes like mild cognitive impairment (MCI).

Purpose of the Study:

• To review and summarize features of various PD subtypes, from hypothesis-driven to data-driven approaches.
• To highlight the current understanding and knowledge gaps in NMS and data-driven PD subtyping.
• To emphasize the need for future subtyping based on biological markers for targeted therapies and prognostication.

Main Methods:

• Review of existing literature on Parkinson's disease subtyping.
• Comparison of hypothesis-driven (pre-defined criteria) and data-driven (comprehensive multidimensional data evaluation) subtyping methods.
• Analysis of the characteristics and current understanding of different PD subtypes.

Main Results:

• Hypothesis-driven subtypes, particularly motor subtypes, are well-established.
• Non-motor symptoms (NMS) and data-driven subtypes are less understood, especially regarding biomarkers and progression.
• Current subtyping methods require further development for early-stage prognostication and personalized treatment.

Conclusions:

• Accurate PD subtyping is essential for personalized medicine, prognostication, and clinical trial design.
• Future research should focus on data-driven and biomarker-based subtyping to elucidate underlying pathophysiology.
• Developing early-stage PD subtyping algorithms will enable targeted therapies and proactive complication prevention.