The impact of amyloid‐beta and tau on prospective cognitive decline in older individuals
Bibliographic record
Abstract
OBJECTIVES: Amyloid-beta (Aβ) and tau pathologies are commonly observed among clinically normal older individuals at postmortem and can now be detected with in vivo neuroimaging. The association and interaction of these proteinopathies with prospective cognitive decline in normal aging and preclinical Alzheimer's disease (AD) remains to be fully elucidated. METHODS: F-flortaucipir) positron emission tomography (PET) with prospective neuropsychological assessments following PET imaging (mean number of cognitive visits = 2.8 ± 1.1). Tau and Aβ PET measures were assessed in regions of interest (ROIs) as well as vertex-wise map analyses. Cognitive change was evaluated with Memory and Executive Function composites. RESULTS: Higher levels of Aβ and tau were both associated with greater memory decline, but not with change in executive function. Higher cortical Aβ was associated with higher tau levels in all ROIs, independent of age, and very elevated levels of tau were observed primarily in clinically normal with elevated Aβ. A significant interaction between tau and Aβ was observed in both ROI and map-level analyses, such that rapid prospective memory decline was observed in participants who had high levels of both pathologies. INTERPRETATION: Our results are consistent with the supposition that both Aβ and tau are necessary for memory decline in the preclinical stages of AD. These findings may be relevant for disambiguating aging and early cognitive manifestations of AD, and to inform secondary prevention trials in preclinical AD. Ann Neurol 2019;00:1-3 ANN NEUROL 2019;85:181-193.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".