Amyloid Load: A More Sensitive Biomarker for Amyloid Imaging

Area of Science:

• Neuroimaging
• Biomarker Development
• Alzheimer Disease Research

Background:

• Amyloid-β (Aβ) is central to Alzheimer disease (AD) pathogenesis and is detectable via ¹⁸F-florbetapir PET imaging.
• Current composite Standardized Uptake Value Ratio (SUVr) measures for global Aβ burden have suboptimal sensitivity, limiting clinical trial power.
• Novel biomarkers are needed to improve the accuracy and sensitivity of Aβ quantification in vivo.

Purpose of the Study:

• To introduce and validate amyloid load (Aβ_L) as a novel, more sensitive biomarker for global Aβ burden.
• To develop an automated algorithm (Amyloid^IQ) for calculating Aβ_L.
• To compare the performance of Aβ_L against composite SUVr using cross-sectional and longitudinal Alzheimer's Disease Neuroimaging Initiative data.

Main Methods:

• Utilized cross-sectional data from 769 subjects (healthy controls, early MCI, late MCI, AD) from the Alzheimer's Disease Neuroimaging Initiative.
• Employed an automated algorithm (Amyloid^IQ) to calculate the novel amyloid load (Aβ_L) biomarker.
• Compared effect sizes and power between Aβ_L and composite SUVr for distinguishing diagnostic groups and detecting longitudinal changes.

Main Results:

• Aβ_L demonstrated significantly higher effect sizes (mean difference of 46%) than composite SUVr in differentiating between disease classifications across cross-sectional data.
• For longitudinal analysis, Aβ_L showed a larger effect size (0.49) compared to composite SUVr (0.36) in patients with early mild cognitive impairment over two years.
• The novel Aβ_L biomarker and its automated calculation method (Amyloid^IQ) provide increased sensitivity and power for Aβ imaging studies.

Conclusions:

• Amyloid load (Aβ_L) represents a more powerful outcome measure for quantifying global Aβ burden in Alzheimer disease.
• The Amyloid^IQ algorithm facilitates the automated calculation of Aβ_L, enhancing its utility in research and clinical settings.
• Aβ_L offers a substantial improvement in statistical power over conventional SUVr methods, promising greater sensitivity in detecting disease progression and treatment efficacy in clinical trials.