Objective: To delineate subtype‑specific metabolic networks in Multiple system atrophy (MSA) using FDG‑PET, and to examine how these patterns relate to striatal dopamine transporter (DAT) loss, patient demographics, and disease duration.

Background: MSA presents as parkinsonian (MSA‑P) or cerebellar (MSA‑C) variants, yet robust biomarkers capable of distinguishing these subtypes and tracking progression remain limited. Spatial covariance analysis of FDG‑PET enables detection of network‑level metabolic abnormalities characteristic of neurodegenerative conditions.

Method: We analyzed FDG‑PET and FP‑CIT PET data from 271 individuals with probable MSA (140 MSA‑P; 131 MSA‑C) alongside healthy controls. Scaled subprofile modeling/principal component analysis (SSM/PCA) [1] identified disease‑related metabolic patterns, which were validated in independent test sets. Pattern expression was correlated with striatal DAT binding and clinical variables, with additional assessment of sex‑specific effects.

Results: MSA‑P expression was best described by two orthogonal metabolic components, whereas MSA‑C was associated with a single robust pattern. MSA‑P and MSA‑C demonstrated clearly separable topographies, with both subtypes showing putaminal hypometabolism but differential involvement of cerebellar and cortical territories. DAT binding was markedly reduced in MSA‑P—particularly in the putamen—while MSA‑C exhibited relative preservation. In MSA‑P, age and disease duration were associated with greater metabolic pattern expression and lower DAT binding. In females with MSA‑P, expression of the cerebellar‑related metabolic component correlated with longer disease duration. In MSA‑C, metabolic pattern expression increased with disease duration and demonstrated age‑related effects in females only.

Conclusion: FDG‑PET–derived metabolic networks reliably distinguish MSA subtypes and reveal differential associations with dopaminergic degeneration, age, and sex. These subtype‑specific metabolic signatures may serve as stable biomarkers to support diagnosis, monitoring, and future trial design in MSA.

References: 1. Spetsieris P, Ma Y, Peng S, Ko JH, Dhawan V, Tang CC, et al. Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data. J Vis Exp. 2013;(76). doi: 10.3791/50319. PubMed PMID: 23851955.

« Back to 2026 International Congress

MDS Abstracts - https://www.mdsabstracts.org/abstract/distinct-fdg-pet-metabolic-signatures-and-dopaminergic-loss-across-multiple-system-atrophy-subtypes/