Basic Science and Pathogenesis

Area of Science:

• Neuroimaging
• Biomarker Discovery
• Alzheimer's Disease Research

Background:

• Astrogliosis, a hallmark of Alzheimer's disease (AD), involves Monoamine Oxidase-B (MAO-B) dysregulation.
• SMBT-1 is a novel PET tracer for imaging MAO-B, relevant for AD diagnosis.
• This study investigates SMBT-1 PET imaging in an AD cohort, comparing quantification methods and plasma biomarkers.

Purpose of the Study:

• To compare distribution volume ratio (DVR) and standardized uptake value ratio (SUVR) quantification methods for SMBT-1 PET imaging in Alzheimer's disease spectrum subjects.
• To explore associations between SMBT-1 PET metrics (DVR and SUVR) and plasma biomarkers including GFAP, p-tau217, p-tau231, Abeta42/40, and YKL-40.
• To assess the utility of SMBT-1 PET imaging in differentiating diagnostic groups within the AD spectrum.

Main Methods:

• SMBT-1 PET imaging was performed on 22 subjects (9 cognitively normal, 5 MCI, 8 non-AD pathology) using HRRT.
• PET images were quantified using distribution volume ratio (DVR) and standardized uptake value ratio (SUVR) with cerebellar grey matter as the reference region.
• Plasma biomarkers (GFAP, p-tau-217, p-tau-231, Abeta40, Abeta42, YKL-40) were measured using NULISA, and associations with PET metrics were analyzed using VoxelStats.

Main Results:

• Mild cognitive impairment (MCI) subjects exhibited more sustained SMBT-1 tracer retention and higher DVR values compared to cognitively normal (CN) subjects.
• Both DVR and SUVR consistently captured associations between PET data and plasma biomarkers like GFAP, p-tau217, and p-tau231.
• GFAP showed a significant correlation with whole cortex average SUVR, while YKL-40 was associated with periventricular white matter; Abeta42/40 showed a marginal association with DVR.
• DVR demonstrated greater differentiation across diagnostic groups than SUVR.

Conclusions:

• SMBT-1 PET imaging reveals associations with key Alzheimer's disease fluid biomarkers, consistent between DVR and SUVR quantification methods.
• While both DVR and SUVR provide valuable information, they offer complementary insights into diagnostic associations.
• Further studies with expanded sample sizes are warranted to enhance statistical power and confirm these preliminary findings.