Multiple System Atrophy: Phenotypic spectrum approach coupled with brain 18-FDG PET

Area of Science:

• Neurology
• Neuroimaging
• Metabolic Brain Imaging

Background:

• The 2008 diagnostic criteria for Multiple System Atrophy (MSA) define predominantly parkinsonian (MSA-P) and cerebellar (MSA-C) types.
• Clinical heterogeneity in MSA patients, including mixed-type, cognitive impairment, and atypical survival, necessitates refined classification.
• Brain 18-FDG Positron Emission Tomography (PET) can reveal metabolic patterns associated with neurological disorders.

Purpose of the Study:

• To identify distinct patient phenotypes within Multiple System Atrophy (MSA).
• To correlate identified MSA phenotypes with specific brain 18-FDG PET metabolic patterns.
• To explore the utility of these findings for diagnostic purposes in MSA.

Main Methods:

• Retrospective analysis of 85 patients with a "probable" MSA diagnosis and available 18-FDG PET scans (2006-2017).
• Latent Class Analysis (LCA) was employed to identify patient profiles based on clinical characteristics.
• Brain metabolism was assessed at rest using 18-FDG PET to analyze metabolic differences between identified profiles.

Main Results:

• Three distinct MSA patient profiles were identified: Profile 1 (54.1%) with extrapyramidal, axial, laryngeal-pharyngeal involvement (LPI), and cerebellar symptoms; Profile 2 (35.3%) with cerebellar and LPI symptoms; and Profile 3 (10.6%) with cerebellar and cognitive symptoms.
• Profile 1 showed hypometabolism in basal ganglia, frontal/prefrontal, temporal cortices, and left posterior cerebellum.
• Profile 2 exhibited hypometabolism in the medulla, prefrontal, temporal, cingular cortices, putamen, and bilateral cerebellar hemispheres.
• Profile 3 demonstrated hypometabolism in bilateral posterior cerebellar hemispheres and vermis.

Conclusions:

• A third atypical MSA profile characterized by cerebellar and cognitive symptoms without LPI involvement was identified.
• These distinct MSA phenotypes are associated with specific brain metabolic abnormalities.
• The identified metabolic patterns may serve as valuable biomarkers for MSA diagnosis.