BrainAge moderates associations between Alzheimer’s disease biomarkers and cognitive decline: a meta-analysis across A4/LEARN, HABS and ADNI cohorts
Bibliographic record
Abstract
Abstract BrainAge delta, the difference between a person’s predicted brain age and their chronological age, is a promising marker of the accumulation of neurodegeneration that may increase vulnerability to Alzheimer’s disease (AD). In this study we use structural MRI regions vulnerable to AD, the hippocampus, the amygdala and cortical thickness measurements to build BrainAge models. We examined whether BrainAge delta moderates the relationship between AD biomarkers and longitudinal cognitive decline performing a meta-analysis across three cohorts: A4/LEARN, HABS and ADNI (2,279 cognitively unimpaired [CU]; 416 with mild cognitive impairment [MCI]). Higher BrainAge delta was linked to faster decline in CU (β = -0.13 [-0.21, - 0.06], p = 0.018, I 2 =1%) and more strongly in MCI (β = -0.31 [-0.30, -0.24], p < 1x10 -16 ). BrainAge also interacted with Aβ-PET (β = -0.09 [-0.13, -0.05], p = 0.0054, I 2 =12%) and plasma pTau 217 (β = -0.09 [-0.15, -0.03], p = 0.018, I 2 =0.1%), but not Tau-PET, to impact cognitive decline, where synergistically higher BrainAge delta and elevated AD markers resulted in faster cognitive decline. We next tested its utility for clinical trial enrichment. Sequential screening with pTau 217 and BrainAge delta reduced required sample size for prevention trials by 77%, versus 61% using pTau 217 alone. These findings support BrainAge delta as a marker of neurodegeneration and may serve as an enrichment tool for AD prevention trials.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.019 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".