Temporal Dynamics of β-Amyloid Accumulation in Aging and Alzheimer Disease
Bibliographic record
Abstract
Objective To understand the time course of β-amyloid (Aβ) deposition in the brain, which is crucial for planning therapeutic trials of Aβ-lowering therapies in Alzheimer disease (AD). Methods Two samples of participants from the Alzheimer9s Disease Neuroimaging Initiative were studied with [18F]Florbetapir (FBP) Aβ PET and followed for up to 9 years. Sample A included 475 cognitively normal (CN) older people and those with mild cognitive impairment (MCI) and AD and sample B included 220 CN Aβ− individuals. We examined the trajectory of FBP over time in sample A and the incidence rate of conversion from negative to positive Aβ PET scans in sample B. Results The relationship between time and brain Aβ was sigmoidal, taking 6.4 years to transition from amyloid negative to positive and another 13.9 years to the onset of MCI. Aβ deposition rates began to slow only 3.8 years after reaching the positivity threshold. The incidence rate for scan positivity was 38/1,000 person-years, and factors associated with conversion were age, baseline FBP, and being a female APOE ε4 carrier. Among CN Aβ− individuals, FBP slopes were associated with rates of memory decline and brain tau measured with [18F]Flortaucipir PET 5 years after baseline. Conclusions Lowering brain Aβ must be accomplished early in the evolution of AD. Transitions of PET scans from Aβ− to Aβ+ should be predictable, and it is reasonable to expect that lowering rates of Aβ even in early stages could produce clinically significant benefits.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".