Cerebrovascular adaptations to habitual resistance exercise with aging
Bibliographic record
Abstract
Resistance training (RT) is associated with improved metabolism, bone density, muscular strength, and lower risk of osteoporosis, sarcopenia, and cardiovascular disease. Although RT imparts many physiological benefits, cerebrovascular adaptations to chronic RT are not well defined. Participation in RT is associated with greater resting peripheral arterial diameters, improved endothelial function, and general cardiovascular health, whereas simultaneously linked to reductions in central arterial compliance. Rapid blood pressure fluctuations during resistance exercise, combined with reduced arterial compliance, could lead to cerebral microvasculature damage and subsequent cerebral hypoperfusion. Reductions in cerebral blood flow (CBF) accompany normal aging, where chronic reductions in CBF are associated with changes in brain structure and function, and increased risk of neurodegeneration. It remains unclear whether reductions in arterial compliance with RT relate to subclinical cerebrovascular pathology, or if such adaptations require interpretation in the context of RT specifically. The purpose of this narrative review is to synthesize literature pertaining to cerebrovascular adaptations to RT at different stages of the life span. This review also aims to identify gaps in the current understanding of the long-term impacts of RT on cerebral hemodynamics and provide a mechanistic rationale for these adaptations as they relate to aging, cerebral vasculature, and overall brain health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".