Identifying Amyloid Deposits Using PET to Detect Earlier Stages of Memory Decline

by: Catherine Kim

Recent studies reported that late intervention resulted in memory decline in cognitively normal (CN) individuals who tested negative for β-amyloid (Aβ). Researchers found that therapeutic interventions typically occur when individuals test positive for Aβ, which may be too late. Exactly how to identify those Aβ negative (Aβ-) individuals with cognitive decline using cross-sectional PET is unclear. Researchers from the University of California at Berkeley discovered evidence in their 2020 paper published in Neurology by the American Academy of Neurology that examines the feasibility of using cross-sectional PET to identify cognitive decline among Aβ- CN elderly adults. 

The presence of widespread cortical Aβ into plaques in the brain is known as the initial event that leads to cognitive loss and dementia. Alzheimer disease (AD) occurs in roughly 30% of CN elderly adults of ages 70 and up, and this process may take twenty years or more, so it is important to find individuals at the early stages of amyloid deposition. Recent failures of clinical trials in patients with AD have moved therapeutic interventions to Aβ+ CN elderly adults. Studies reported that memory decline occurred even in Aβ− (negative) CN individuals who appear to be accumulating Aβ.

It is still unclear how to identify those Aβ− individuals with cognitive decline using cross-sectional PET. Researchers found the highest Aβ affected region, the banks of the superior temporal sulcus (BANKSSTS), and used this region to differentiate individuals who may have biologically significant Aβ accumulation from other Aβ− CN individuals in the COMPOSITE regions as defined by the ADNI. They investigated whether Aβ− CN individuals with high Aβ burden in the highest Aβ affected region have faster rates of cognitive decline than those with low Aβ burden.

From 355 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants who were CN and had a florbetapir-PET scan structural MRI at baseline and had 2+ subsequent longitudinal cognitive testing sessions, researchers identified the COMPOSITE region which is made up of brainstem, whole cerebellum, and eroded white matter. The standardized uptake value region (SUVR) was calculated and defined for Aβ+ individuals which differed from the value as defined by ADNI, which indicates a need for retesting and refining of the methods used to define individuals as positive or negative for Aβ.

In identifying the top Aβ-affected cortical regions, the highest baseline SUVRs in the Aβ+ participants were defined as the top Aβ affected regions, indicating the most measurement sensitivity for the detection of Aβ deposition. Random sampling tests were used to assess the generalizability of these regions. Researchers then used 50% of Aβ+ participants with baseline values as sample A, and the rest as sample B. The consistency of top Aβ affected regions between sample A and sample B were compared and did these analyses for 5,000 iterations. This analysis was repeated for Aβ- to confirm consistency.

The highest Aß affected regions (Regionhighest) were used to classify individuals into three different stages. The BANKSSTS region was identified by the researchers to be more sensitive to amyloid deposits and the COMPOSITE region was ADNI-defined. Stage 0 meant that individuals had no evidence of amyloid in either BANKSSTS or COMPOSITE regions. Stage 2 meant that individuals had amyloid in both regions. In Stage 1, individuals had evidence of amyloid in the BANKSTSS region, but not in the COMPOSITE region. In this stage, the results demonstrated that most individuals have memory decline and the BANKSSTS region has the ability to detect amyloid deposition before the traditionally used COMPOSITE region as defined by the ADNI.

Additionally, the researchers used cognitive tests to validate longitudinal memory and executive function composite scores derived from ADNI. They calculated memory and executive function composite scores by combining results of various tests. These tests included the Auditory Verbal Learning Test, Alzheimer’s Disease Assessment Scale-Cognitive Subscale, Mini-Mental State Examination, and Wechsler Memory Test-Revised. Annual rates of memory and executive function decline were calculated for each participant on the basis of longitudinal memory and executive scores using linear regression. 

These findings may help clarify the association between brain Aβ buildup and cognition in Aβ− CN cohorts and provide an approach to identify suitable early-stage patients for amyloid lowering interventions. Dr. Jagust states, “This study, along with some other studies in the lab (this isn’t the only study to make this point) shows that we can be more sensitive to amyloid than just saying amyloid throughout the whole brain is a way to classify individuals as positive or negative.” Jagust compares the results of his research to the conditions of diabetes where individuals with blood sugar over a certain level are said to have diabetes. But it is known that when one’s blood sugar is high and does not go over the level, they may be diagnosed with pre-diabetes which may put individuals at risk for other disorders such as heart disease, kidney disease, etc. Dr. Jagust says, “It’s not the idea that you have to go over a certain threshold number before you are potentially in trouble. We think that this tells us that you can detect evidence of amyloid even when the amyloid levels are fairly low, and that is indicative of a possible problem.”

In summation, the results of Guo’s study are important because they expand the traditional identification of Alzheimer’s Disease risk to an even earlier point than the typical preclinical stage of Alzheimer’s Disease.

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