Associations Between Changes in Levels of Phosphorylated Tau and Severity of Cognitive Impairment in Early Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Aligning biomarker evidence with clinical presentation in early Alzheimer disease (AD) is essential for improving diagnosis, prognosis, and interventions. This study evaluates the relationship between cognitive impairment, future decline, and phosphorylated tau levels in plasma and CSF in predementia AD. METHODS: This longitudinal observational study included predementia cases and controls from 2 independent cohorts: the Norwegian Dementia Disease Initiation (DDI) and Canadian Pre-Symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD). In DDI, cognitively normal (CN) and mild cognitive impairment (MCI) cases were classified using CSF Aβ42/40 ratio (A) and p-tau181 (T), whereas classification in PREVENT-AD (A) was based on amyloid PET scans. In DDI, we assessed CSF-plasma correlations for p-tau181, p-tau217, and p-tau231. Diagnostic accuracies were evaluated through receiver operating characteristic analyses. Linear mixed models evaluated p-tau associations with future memory decline. Between-group differences in plasma p-tau217 were assessed in both cohorts. RESULTS: < 0.05). DISCUSSION: Our findings suggest that, unlike p-tau181 and p-tau231, plasma p-tau217 consistently aligns with cognitive status in A+ individuals and better reflects CSF biomarker abnormalities, reducing discrepancies between clinical and biochemical findings. Its association with baseline and future memory decline highlights its diagnostic and prognostic value, particularly when CSF analysis or PET is unavailable.
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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 | 0.000 |
| 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".