Biological brain age and resilience in cognitively unimpaired 70‐year‐old individuals
Bibliographic record
Abstract
INTRODUCTION: This study investigated the associations of brain age gap (BAG)-a biological marker of brain resilience-with life exposures, neuroimaging measures, biological processes, and cognitive function. METHODS: We derived BAG by subtracting predicted brain age from chronological age in 739 septuagenarians without dementia or neurological disorders. Robust linear regression models assessed BAG associations with life exposures, plasma inflammatory and metabolic biomarkers, magnetic resonance imaging, and cerebrospinal fluid biomarkers of neurodegeneration and vascular brain injury, and cognitive performance. RESULTS: Greater BAG (older-looking brains) was associated with physical inactivity, diabetes, and stroke, while prediabetes was related to lower BAG, that is, younger-looking brains. Physical activity mitigated the link between obesity and BAG. Greater BAG was associated with greater small vessel disease burden, white-matter alterations, inflammation, high glucose, poorer vascular-related cognitive domains. Sex-specific associations were identified. DISCUSSION: Vascular-related lifestyles and health shape brain appearance. Inflammation and insulin-related processes may be keys to understanding vascular cognitive disorders. HIGHLIGHTS: BAG, reflecting deviations from CA, can indicate resilience. Diabetes, stroke, and low physical activity link to "older" brains (greater BAG). Physical activity yielded to "younger" brains in septuagenarians with obesity. High cerebrovascular burden, inflammation, and glucose associate with "older" brains. Sex differences were detected in all BAG-associated factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".