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MRI data-driven clustering reveals different subtypes of Dementia with Lewy bodies. | LitMetric

AI Article Synopsis

  • - The study investigates Dementia with Lewy bodies (DLB), a complex neurodegenerative disorder, by analyzing MRI data to identify different subtypes based on gray matter volumes.
  • - Researchers examined data from 165 DLB patients across multiple centers, using various clinical and biomarker assessments to characterize these subtypes and their cognitive decline over three years.
  • - Three distinct DLB subtypes were identified: an older subtype with more severe symptoms, a subtype with low gray matter in specific brain regions, and a younger subtype with higher gray matter but more cognitive fluctuations, suggesting personalized approaches for treatment and research on DLB.

Article Abstract

Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9859778PMC
http://dx.doi.org/10.1038/s41531-023-00448-6DOI Listing

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