Amyloid Load: A More Sensitive Biomarker for Amyloid Imaging

Abstract

Amyloid-β (Aβ) plays a key role in the pathogenesis of Alzheimer disease (AD) and can be imaged in vivo using ¹⁸F-florbetapir PET. A composite SUV ratio (SUVr) is a commonly used outcome measure for quantifying the global Aβ burden; however, sensitivity is suboptimal and can lead to low power in clinical trials. We introduce amyloid load, Aβ_L, as a novel biomarker to quantify the global Aβ burden along with an automated algorithm for its calculation (Amyloid^IQ). Aβ_L is evaluated on cross-sectional and longitudinal data obtained from the Alzheimer's Disease Neuroimaging Initiative. The cross-sectional data consisted of 769 subjects across the disease spectrum (211 healthy controls, 223 patients with early mild cognitive impairment, 204 with late mild cognitive impairment, and 132 with AD). The distributions of Aβ_L in the 4 different classifications were compared, and the same analyses were applied to a composite SUVr outcome measure. The effect sizes (Hedges g) between all but one classification were higher for Aβ_L than for composite SUVr, with the mean difference being 46%. Of the patients with early mild cognitive impairment, 147 had a 2-y follow-up scan, and the effect size between baseline and follow-up for Aβ_L was 0.49, compared with 0.36 for a composite SUVr, demonstrating an equivalent increase in power for longitudinal data. These results offer evidence that Aβ_L will be a valuable outcome measure in future Aβ imaging studies, providing a substantial increase in power over currently used SUVr methods.

Keywords: Alzheimer disease; PET; amyloid; florbetapir; mathematic modeling; neurology.

FIGURE 1.

Amyloid^IQ algorithm for calculating Aβ_L from single SUVr image with cerebellar gray matter used as reference region.

FIGURE 2.

Evaluation of fit of SUVr_fit images. (Top) Percentage of voxels in brain with $\left|\mathrm{SUVr}-{\mathrm{SUVr}}_{\text{fit}}\right|>0.3$ for each subject. (Bottom) SUVr image, SUVr_fit image, and difference image for 3 example subjects (A–C). SUVr_fit image accurately fits SUVr image in all 3 examples.

FIGURE 3.

Box plots for composite SUVr (left) and Aβ_L (right) for each of 4 diagnosis groups in cross-sectional data. Effect sizes calculated among all groups are larger for Aβ_L than for composite SUVr, apart from EMCI to HC, for which outcome measures are equivalent (Tables 1 and 2).

FIGURE 4.

Box plots showing distributions of ns for each of 4 diagnosis groups in cross-sectional data. ns is statistically lower in AD and LMCI groups when compared with HCs but same in EMCI group.

FIGURE 5.

Box plots showing distributions of percentage change in composite SUVr (left) and in Aβ_L (right). A larger effect is seen with Aβ_L.

FIGURE 6.

Power curves for composite SUVr and Aβ_L in simulated clinical trial (50 placebo, 50 drug) of anti-Aβ therapeutic calculated using effect sizes found in longitudinal ADNI data.