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Record W4414397699 · doi:10.3389/fphys.2025.1606267

Resisting decline: the neuroprotective role of resistance exercise in supporting cerebrovascular function and brain health in aging

2025· review· en· W4414397699 on OpenAlexafffund
Elric Y. Allison, Aedan J. Rourke, Vanessa Mizzi, Jeremy J. Walsh, Jennifer J. Heisz, Baraa K. Al‐Khazraji

Bibliographic record

VenueFrontiers in Physiology · 2025
Typereview
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBrain agingContext (archaeology)Brain functionAging brainDiseaseResistance trainingNeuroprotectionDemographicsPsychological intervention

Abstract

fetched live from OpenAlex

Reduced cerebral blood flow (CBF) and cerebrovascular function are critical early-stage biomarkers preceding changes in brain function and structure observed in normal aging and during the onset and progression of Alzheimer's Disease and related dementias (ADRD). Though several interventions attempt to curb the effects of aging and brain neurodegeneration, exercise and lifestyle habits remain one of the most impactful and easily modifiable factors for preserving brain health. Although the effects of aerobic exercise on cerebrovascular function and brain health are well established, resistance training (RT) is rapidly increasing in popularity across all age demographics due to its numerous health benefits. Despite the clear physiological benefits of resistance exercise, its potential efficacy for preserving or improving cerebrovascular and overall brain health remains understudied to date. The aim of this review is to examine the literature pertaining to ways in which resistance exercise may reduce the risk of ADRD and slow age-related decline of brain structures and functions. Additionally, this review seeks to highlight key considerations and challenges regarding the feasibility, adoption, and adherence to resistance exercise in the context of normal aging, mild cognitive impairment, and ADRD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.306
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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