The following is a summary of “Contribution of Global Amyloid-PET Imaging for Predicting Future Cognition in the MEMENTO Cohort,” published in the February 2024 issue of Neurology by Ackley et al.
While global amyloid-PET correlates with cognition and cognitive deterioration, most studies investigating this relationship overlook previous cognitive data.
Researchers conducted a retrospective study to evaluate the predictive utility of amyloid-PET metrics for future cognitive function in the presence of prior cognitive evaluations, thus assessing the supplementary value of amyloid measures alongside multiple historical cognitive assessments.
They examined the French MEMENTO cohort (a cohort of outpatients from French research memory centers to improve knowledge on Alzheimer’s disease and related disorders), comprising older outpatients from French research memory centers without a dementia diagnosis at enrollment and exhibiting initial cognitive changes. A subset underwent global amyloid burden assessment via positron emission tomography (amyloid-PET) and semiannual cognitive evaluations. Mini-Mental State Examination (MMSE) scores were forecasted using demographic factors (age, sex, marital status, education) alone or combined with past cognitive data. The efficacy of amyloid burden as a predictor was appraised through a percent reduction of the mean squared error (MSE) in models, separately analyzing dichotomous amyloid positivity and continuous amyloid-standardized uptake-value ratio.
The results showed that the analytic sample consisted of 510 individuals who had undergone amyloid-PET scans with at least 4 MMSE assessments. The mean age at the PET scan was 71.6 (SD 7.4) years, with 60.7% female. Median follow-up was 4.6 years (IQR: 0.9 years). When adjusting for only demographic characteristics, the addition of amyloid burden reduced the MSE of predictions by 5.08% (95% CI 0.97%–10.86%) for binary amyloid and 12.64% (95% CI 3.35%–25.28%) for continuous amyloid. If the model included 1 past MMSE measure, the MSE improvement was 3.51% (95% CI 1.01%–7.28%) when adding binary amyloid and 8.83% (95% CI 2.63%–16.37%) when adding continuous amyloid. Improvements in model fit were more minor with the addition of amyloid burden when more than 1 past cognitive assessment was included, and for all models incorporating cognitive past evaluations, differences in predictions amounted to a fraction of 1 MMSE point on average.
Investigators concluded that despite measuring amyloid buildup, past cognitive assessments still offer the best prediction of future cognitive decline in a clinical setting.