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

. 2023 Jan 20 ; 9 (1) : 5. [epub] 20230120

Status PubMed-not-MEDLINE Language English Country United States Media electronic

Document type Journal Article

Grant support
R01 AG041851 NIA NIH HHS - United States
C06 RR018898 NCRR NIH HHS - United States
P50 AG016574 NIA NIH HHS - United States
R01 AG040042 NIA NIH HHS - United States
R01 NS080820 NINDS NIH HHS - United States
R37 AG011378 NIA NIH HHS - United States
U01 NS100620 NINDS NIH HHS - United States
U01 AG006786 NIA NIH HHS - United States

Links

PubMed 36670121
PubMed Central PMC9859778
DOI 10.1038/s41531-023-00448-6
PII: 10.1038/s41531-023-00448-6
Knihovny.cz E-resources

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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