The speed limits for tau pathology progression in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Objective To examine interactive effects of modifiable factors, genetic determinants and load-dependent pathology effects on tau pathology progression. Methods Data of 162 amyloid-positive individuals were included, for whom longitudinal [18F]AV-1451-PET scans, baseline information on global amyloid load, ApoE4 status, body-mass-index (BMI), hypertension, education, neuropsychiatric symptom severity and demographic information were available in ADNI. All [18F]AV-1451 PETs were intensity-standardized (reference: inferior cerebellum), z-transformed (control sample: 147 amyloid-negative subjects) and subsequently thresholded (z-score > 1.96) and converted to volume-maps. Based on these volume-maps, tau-changes over time were assessed in terms of 1) tau-speed (i.e. newly affected volume at follow-up), and 2) tau-level-rise (i.e. tau increase in previously affected volume). These two measures were entered as dependent variables in separate linear mixed effects models including four baseline risk factors (BMI, education, hypertension, neuropsychiatric symptom severity), baseline amyloid, tau-volume or tau burden, ApoE4 status, clinical stage, sex, and age as predictors. Next, we tested the interactive effects between baseline amyloid or tau burden with the four modifiable factors on either tau-speed or tau-level-rise, respectively. Results Faster tau-speed was linked to higher BMI, female sex, ApoE4-status, and baseline tau-volume. The effect of baseline tau-volume on tau-speed was driven by greater global amyloid burden. In terms of tau-level-rise, we observed that lower hypertension and BMI were linked to a slower increase in tau burden. A load-dependent effect of baseline amyloid and tau burden was found. Higher amyloid and BMI as well as lower education and higher tau burden were linked to greater tau-level-rise. Conclusion Education, BMI and hypertension differentially influence tau speed and level rise by its interaction with initial pathological burden. Timely modification of these factors may overall slow tau’s progression.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".