Cognitive Trajectories and Alzheimer Disease Biomarkers: From Successful Cognitive Aging to Clinical Impairment
Bibliographic record
Abstract
OBJECTIVE: Cross-sectional definitions of successful cognitive aging have been widely utilized, but longitudinal measurements can identify people who do not decline. We performed this study to contrast maintenance with declining trajectories, including clinical conversion. METHODS: We included baseline cognitively unimpaired Alzheimer's Disease Neuroimaging Initiative participants with 3 or more cognitive testing sessions (n = 539, follow-up 6.1 ± 3.5 years) and calculated slopes of an episodic memory composite (MEM) to classify them into two groups: maintainers (slope ≥ 0) and decliners (slope < 0). Within decliners, we examined a subgroup of individuals who became clinically impaired during follow-up. These groups were compared on baseline characteristics and cognitive performance, as well as both cross-sectional and longitudinal Alzheimer disease (AD) biomarker measures (beta-amyloid [Aβ], tau, and hippocampal volume). RESULTS: Forty-one percent (n = 221) of the cohort were MEM maintainers, and 33% (n = 105) of decliners converted to clinical impairment during follow-up. Compared to those with superior baseline scores, maintainers had lower education and were more likely to be male. Maintainers and decliners did not differ on baseline MEM scores, but maintainers did have higher non-MEM cognitive scores. Maintainers had lower baseline global Aβ, lower tau pathology, and larger hippocampal volumes than decliners, even after removing converters. There were no differences in rates of change of any AD biomarkers between any cognitive trajectory groups except for a higher rate of hippocampal atrophy in clinical converters compared to maintainers. INTERPRETATION: Using longitudinal data to define cognitive trajectory groups reduces education and sex bias and reveals the prognostic importance of early onset of accumulation of AD pathology. ANN NEUROL 2024;96:378-389.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".