Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression

Abstract

Background: Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear.

Objective: This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression.

Methods: This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes.

Results: Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, β = 1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS-Part Ⅱ, β = 1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, β = 1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, β = −0.02 ± 0.01, P = 0.016) and depression (GDS, β = 0.26 ± 0.083, P = 0.019) compared to subtype 1.

Conclusion: The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.

Keywords

Parkinson's disease / subtypes / structural MRI / longitudinal atrophy rate

Author contributions

Guoqing Pan (Data curation, Formal analysis, Writing - original draft), Yuchao Jiang (Validation), Wei Zhang (Validation), Xuejuan Zhang (Conceptualization, Funding acquisition, Methodology, Supervision), Linbo Wang (Conceptualization, Data curation, Methodology, Supervision, Validation), and Wei Cheng (Conceptualization, Funding acquisition, Methodology, Supervision, Validation)

Supplementary data

Supplementary data is available at PSYRAD Journal online.

Conflict of interests

The authors declare that they have no competing interests.

Acknowledgement

This study was funded by grants from the National Natural Science Foundation of China (82071997), Shanghai Rising-Star Program (21QA1408700), 111 Project (B18015), and the Natural Science Foundation of Shanghai (23ZR1406000). Further, we would like to thank the support from the Shanghai Center for Brain Science and Brain-Inspired Technology, ZHANGJIANG LAB, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of the Ministry of Education. PPMI—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson's Research funding partners 4D Pharma, Abbvie, Acurex Therapeutics, Allergan, Amathus Therapeutics, ASAP, Avid Radiopharmaceuticals, Bial Biotech, Biogen, BioLegend, Bristol-Myers Squibb, Calico, Celgene, Dacapo Brain Science, Denali, The Edmond J. Safra Foundaiton, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lilly, Lundbeck, Merck, Meso Scale Discovery, Neurocrine Biosciences, Pfizer, Piramal, Prevail, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, Verily, and Voyager Therapeutics. We want to thank all the participants in the study.

Data Availability

Data used in the preparation of this study were obtained from the PPMI database (ppmi-info.org/data). For up-to-date information on the study, visit ppmi-info.org.

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