Disease timeline modelling of amyloid and tau PET reveals dynamic timescales of amyloid and tau accumulation in Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Amyloid and tau accumulation in Alzheimer's disease is known to be dynamic, with expected rates of accumulation varying depending on disease stage. Establishing the precise timeline of amyloid and tau accumulation and quantifying their dynamic progression is important for identifying an optimal intervention window and predicting treatment response. METHOD: 960 individuals were selected from the Swedish BioFINDER-2 study with at least two tau-PET scans (Table 1; follow-ups were at 1 year (N = 66), 2 years (N = 924), 4 years (N = 335); 6 years (N = 60)). Two intersecting data subsets were selected: 773 individuals having at least two amyloid-PET scans for estimating amyloid duration, and 434 CSF-amyloid-positive individuals for comparison with timelines across the whole population. Regional tau-PET SUVR abnormality was computed in five established data-driven regions using mixture modelling. A novel explicit-duration version of the temporal event-based model (T-EBM) was used to determine the order and timeline of global amyloid-PET and regional tau-PET abnormality. The explicit duration approach accounts for censoring of an individual's first and last visit and handles arbitrary time intervals. RESULT: The T-EBM inferred that tau accumulates in a Braak-like pattern (Figure 1a), estimating an average timeline of global amyloid and regional tau accumulation (Figure 1b) of around 20 years. Progression from stage 1 (amyloid) to stage 2 (entorhinal tau) was estimated to take 8 years on average, from stage 2 to stage 3 (temporal lobe tau) 5.5 years, and 2-3 years between each subsequent stage. The timeline was consistent in amyloid-positive individuals (most amyloid-negative individuals were stage 0 and did not influence the timeline). Figure 1c shows the number of individuals progressing between stages at follow-up. Individuals who progressed in stage (progressors) were older, had more advanced symptoms (diagnosis), more APOE4 alleles, worse MMSE scores, and were more frequently amyloid-positive compared to non-progressors (Table 2). CONCLUSION: Amyloid accumulates slowly, after which tau spreads from the entorhinal cortex to the temporal lobe, initially at a slower pace before accelerating to a faster rate across the cortex. This data indicates that slower rates of accumulation would be expected at earlier stages. Work is ongoing validating these timelines in additional datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".