Differential Cognitive Decline in Alzheimer’s Disease Is Predicted by Changes in Ventricular Size but Moderated by Apolipoprotein E and Pulse Pressure
Bibliographic record
Abstract
BACKGROUND: Differential cognitive trajectories in Alzheimer's disease (AD) may be predicted by biomarkers from multiple domains. OBJECTIVE: In a longitudinal sample of AD and AD-related dementias patients (n = 312), we tested whether 1) change in brain morphometry (ventricular enlargement) predicts differential cognitive trajectories, 2) further risk is contributed by genetic (Apolipoprotein E [APOE] ɛ4+) and vascular (pulse pressure [PP]) factors separately, and 3) the genetic + vascular risk moderates this pattern. METHODS: We applied a dynamic computational approach (parallel process models) to test both concurrent and change-related associations between predictor (ventricular size) and cognition (executive function [EF]/attention). We then tested these associations as stratified by APOE (ɛ4-/ɛ4+), PP (low/high), and APOE+ PP (low/intermediate/high) risk. RESULTS: First, concurrently, higher ventricular size predicted lower EF/attention performance and, longitudinally, increasing ventricular size predicted steeper EF/attention decline. Second, concurrently, higher ventricular size predicted lower EF/attention performance selectively in APOEɛ4+ carriers, and longitudinally, increasing ventricular size predicted steeper EF/attention decline selectively in the low PP group. Third, ventricular size and EF/attention associations were absent in the high APOE+ PP risk group both concurrently and longitudinally. CONCLUSION: As AD progresses, a threshold effect may be present in which ventricular enlargement in the context of exacerbated APOE+ PP risk does not produce further cognitive decline.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".