A multi‐cohort study of longitudinal and cross‐sectional Alzheimer's disease biomarkers in cognitively unimpaired older adults
Bibliographic record
Abstract
INTRODUCTION: The generalizability of neuroimaging and cognitive biomarkers in their sensitivity to detect preclinical Alzheimer's disease (AD) and power to predict progression in large, multisite cohorts remains unclear. METHOD: Longitudinal demographics, T1-weighted magnetic resonance imaging (MRI), and cognitive scores of 3036 cognitively unimpaired (CU) older adults (amyloid beta [Aβ]-negative/positive [A-/A+]: 1270/1558) were included. Cross-sectional and longitudinal cognition and medial temporal lobe (MTL) structural measures were extracted. Cross-sectional MTL tau burden (T) was computed from tau positron emission tomography (N = 1095). RESULTS: We found cross-sectional tau and longitudinal structural biomarkers best separated A+ CU from A- CU. A-T+ CU had significantly faster neurodegeneration rate compared to A-T- CU. MTL tau was significantly correlated with MRI and cognitive biomarkers regardless of Aβ status. MTL tau, MRI, and cognition provided complementary information about disease progression. DISCUSSION: This large multisite study replicates prior findings in CU older adults, supporting the utility of neuroimaging and cognitive biomarkers in preclinical AD clinical trials and normal aging studies. HIGHLIGHTS: We investigated neuroimaging and cognitive biomarkers in 3036 cognitively unimpaired (CU) participants. Medial temporal lobe (MTL) tau and longitudinal MTL atrophy best separate amyloid beta positive (A+) CU from amyloid beta negative (A-) CU. A- tau positive (T+) CU had a significantly faster neurodegeneration rate compared to A-T- CU. MTL tau correlated with structural magnetic resonance imaging (MRI) and cognition regardless of amyloid beta status. Combined baseline MTL tau, MRI, and cognition best predict Alzheimer's disease 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".