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Research Highlights

Estimates of amyloid onset may predict Alzheimer’s progression

Determining how long beta-amyloid plaques have been building up in a person’s brain may help predict the course of their Alzheimer’s disease, according to recent NIA-funded research. Published in Brain, the study compared three methods of estimating amyloid onset — the age at which amyloid begins accumulating in the brains of people with preclinical dementia — and modeling the progression of amyloid accumulation. The researchers found the methods to be equally effective and used them to assess common risk factors that influence amyloid accumulation, including age, sex, and presence of the APOE e4 allele, a version of the APOE gene that increases an individual’s risk of developing Alzheimer’s. They also investigated whether these factors, along with age, affect the time between amyloid onset and when Alzheimer’s symptoms first appear.

Beta-amyloid plaques

The presence of beta-amyloid plaques along with tau-containing neurofibrillary tangles in a person’s brain is considered a hallmark of Alzheimer’s. Plaques form when clumps of beta-amyloid protein stick together in the brain. This accumulation happens gradually, and a person may not show signs of Alzheimer’s for 20 years or more after their first amyloid plaques appear. By age 70, about a third of cognitively unimpaired individuals have elevated levels of brain amyloid, which increases their risk of developing dementia later in life. Identifying the onset age of amyloid accumulation makes it possible to estimate how far the disease has progressed and when symptoms may appear. This information can help researchers better evaluate treatment options and the best timing for interventions if needed.

For this study, the research team developed and tested three different algorithms that use positron emission tomography (PET) brain scans to model the process of amyloid accumulation, estimating amyloid onset and its influence on disease course. The team applied these algorithms to data from three separate cohort studies: the Alzheimer’s Disease Neuroimaging Initiative, the Baltimore Longitudinal Study of Aging, and the Wisconsin Registry for Alzheimer’s Prevention. They tested the ability of the three methods to predict amyloid onset and, in a subset, analyzed disease progression. All three methods could predict amyloid onset in all three studies, validating an “amyloid clock” for studying disease progression.

The three different methods yielded similarly accurate results for estimating an individual’s amyloid onset. Estimates of individualized age at amyloid onset enable researchers to examine factors that may delay or accelerate the development of amyloid plaques. Analyzing this individualized amyloid onset data with the other cohort data, the researchers determined that APOE e4 is associated with lower age of amyloid onset. The presence of APOE e4 also decreased the time from amyloid onset to cognitive impairment but did not affect how fast amyloid accumulates in the brain. Being older when amyloid starts to accumulate also shortened the duration from amyloid onset to impairment onset, as did being female. However, being female did not affect when amyloid began to accumulate or how fast it accumulated.

The authors note that further work is needed to understand the impact of using different PET scanners, the demographic makeup of each cohort, and different methods of interpreting PET scans to measure amyloid accumulation. Future research should test these three methods in more diverse populations and prioritize collecting biomarkers.

This research was supported in part by NIA grants R01 AG021155, R01 AG027161, P50 AG033514, U54 HD090256, S10 OD025245, R01 AG054047, and RF1 AG059869.

These activities relate to NIA’s AD+ADRD Research Implementation Milestone 9.M, “Develop diagnostics/biomarkers in asymptomatic individuals.”


Betthauser TJ, et al. Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts. Brain. 2022. Epub Nov. 21. doi: 10.1093/brain/awac213.

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