Faculty Opinions recommendation of Longitudinal cognitive and biomarker changes in dominantly inherited Alzheimer disease.
Bibliographic record
Abstract
ObjectiveTo assess the onset, sequence, and rate of progression of comprehensive biomarker and clinical measures across the spectrum of Alzheimer disease (AD) using the Dominantly Inherited Alzheimer Network (DIAN) study and compare these to cross-sectional estimates. MethodsWe conducted longitudinal clinical, cognitive, CSF, and neuroimaging assessments (mean of 2.7 [±1.1] visits) in 217 DIAN participants.Linear mixed effects models were used to assess changes in each measure relative to individuals' estimated years to symptom onset and to compare mutation carriers and noncarriers. ResultsLongitudinal β-amyloid measures changed first (starting 25 years before estimated symptom onset), followed by declines in measures of cortical metabolism (approximately 7-10 years later), then cognition and hippocampal atrophy (approximately 20 years later).There were significant differences in the estimates of CSF p-tau 181 and tau, with elevations from crosssectional estimates preceding longitudinal estimates by over 10 years; further, longitudinal estimates identified a significant decline in CSF p-tau 181 near symptom onset as opposed to continued elevations. ConclusionThese longitudinal estimates clarify the sequence and temporal dynamics of presymptomatic pathologic changes in autosomal dominant AD, information critical to a better understanding of the disease.The pattern of biomarker changes identified here also suggests that once β-amyloidosis begins, additional pathologies may begin to develop less than 10 years later, but more than 15 years before symptom onset, an important consideration for interventions meant to alter the disease course.
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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.510 | 0.317 |
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".