Lowest Optimal Cutoffs to Predict Future â-Amyloid Accumulation and Cognitive Decline

Researchers have identified the lowest optimal cutoff values for detecting â-amyloid (Aâ) accumulation as assessed by positron emission tomography (PET), with these cutoff values offering sufficient power to detect progression of cognitive decline in individuals with low to moderate Aâ burden. Results from this study were published in Neurology.

The failure of existing therapies for mild cognitive impairment and Alzheimer Disease (AD) warrants earlier intervention to slow or prevent disease progression. The objective of this study was to redefine the Aâ-PET threshold based on the lowest cutoff that predicts future cognitive decline and Aâ accumulation.

The researchers of this study enrolled clinically normal participants from Harvard Aging Brain Study (HABS), the Australian Imaging, Biomarker and Lifestyle (AIBL) study of ageing, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Enrolled participants were from across the AD continuum.

The PACC5 version of the Preclinical Alzheimer Cognitive Composite (PACC) was used to assess Aâ-related cognitive decline. Sequential Aâ cut-offs were examined to determine the lowest cutoff related to future change in cognition, via the PACC, and Aâ-PET.

Study researchers observed similar optimal cutoffs associated with cognitive decline between HABS at a C-Pittsburgh Compound B (PIB) distribution volume ratios (DVR) of 1.14 (Centiloid [CL]=17.5), AIBL at a PIB standardized uptake value ratio (SUVR) of 1.24 (CL=15.0), and ADNI at an F-Florbetapir (FBP) SUVR of 1.1 (CL=18.5).

The optimal cutoff across samples fell between 0.04 and 0.05 SUVR/DVR below their respective Gaussian Mixture Model cutoff. Optimal thresholds associated with Aβ-accumulation were the same as the cognitively-derived threshold for HABS and AIBL, with minor differences only observed in ADNI (FBP SUVR 1.09; CL=16.7).

Regarding limitations, the investigators of this study noted that the separate modeling of possible cutoffs limited their “ability to estimate uncertainty in the cutoff.”

They concluded that the “threshold convergence raises the possibility of contemporaneous early changes in Aâ and cognition,” adding that the identified “optimized thresholds can help to inform future research and clinical trials targeting early Aâ.”

Disclosure: Several study authors declared affiliations with the pharmaceutical industry. Please see the original reference for a full list of authors’ disclosures.

Reference

Farrell ME, Jiang S, Schultz AP, et al. Defining the lowest threshold for amyloid-PET to predict future cognitive decline and amyloid accumulation. Neurology. Published online November 16, 2020. doi:10.1212/WNL.0000000000011214