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Novel insights into relationships between metabolic covariance patterns of FDG-PET data and clinical status in Parkinson's disease using partial least squares correlation analysis

C. Bevington,Sahib Dhaliwal,J. Mckenzie,A. Stoessl,Vesna Sossi

Published 2025 in Journal of Parkinson's Disease

ABSTRACT

Introduction Metabolic covariance patterns derived from imaging data help characterize disease-related physiological changes in several neurodegenerative disorders, including Parkinson's disease (PD), but their relevance to different disease stages and/or clinical variables related to disease or disease predisposition, such as age, is often unclear. Methods We incorporated clinical information in deriving metabolic covariance patterns relevant to different aspects of PD: disease initiation, disease progression, and physiological similarities in PD and healthy aging. This was achieved by combining Partial Least Squares Correlation analysis with Scaled Subprofile Modeling (SSM-PLSC). Results When combining PD and HC data, SSM-PLSC identified a spatial pattern similar to the well-known PD-related disease pattern as expected; when applied to PD-only data—thus emphasizing disease progression—the spatial pattern became characterized by expanding putaminal hypermetabolism and reduced emphasis on cerebellar hypermetabolism. Finally, when applied to PD and HC data but permitting a different dependence on clinical variables, SSM-PLSC identified a spatial pattern with relative hypermetabolism in the basal ganglia, brain stem, and white matter together with relative hypometabolism in frontal cortex; in HC this pattern solely related to age, while in PD the same pattern significantly correlated with both age and disease duration. Conclusion We identified metabolic patterns that are more closely related with different aspects of PD, directly derived from relationships between metabolic alterations and clinical variables. We also revealed metabolic signatures common to PD and aging, which may highlight age-related metabolic changes that form a predisposition to PD, as age is the single highest risk factor for PD. Plain language summary title Brain changes in Parkinson's disease and their relationship to clinical aspects of disease Plain language summary Previous research has identified patterns of changes to how the brain consumes glucose (sugar) in Parkinson's disease (PD), compared to healthy individuals of a similar age, which has an impact on how the brain generates and uses energy. However, PD is a complex disease that progresses over time, so it is important to understand how these patterns relate to different disease stages, disease severity, and to risk factors for developing PD—the most prominent being age. To achieve this, we analyzed brain imaging data of glucose in individuals with PD and healthy individuals with a technique that looks at the relationship between the imaging data and clinical information relevant to disease and age. This technique enabled us to observe how brain changes associated with having PD—that is, how the PD brain differs from the healthy brain—compared to changes that occur at different stages of disease. We found that some brain changes initially present in PD continue to worsen as disease becomes more severe, whereas others are unchanged. Additionally, by searching for common brain changes in PD and healthy individuals we identified brain regions that are affected only by aging in healthy individuals, but by aging and disease in PD. This suggests that these age-related brain changes may contribute to an increased likelihood of developing PD, since age is the highest risk factor for developing this disease. Collectively, the patterns identified in this work help increase our understanding of the specific brain changes that occur in the development and progression of PD.

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