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Indices of Systemic Arterial Health are Associated With Cerebral Perfusion and Structure in a Population of Young and Old Adults

2020· article· en· W3017140808 on OpenAlexaffabout
Rory A. Marshall, Nicole S. Coverdale, Allen A. Champagne, Matti D. Allen, Joshua C. Tremblay, Tarrah Ethier, Kaitlyn R. Liu, Kyra E. Pyke, Rebecca E. K. MacPherson, Douglas J. Cook, T. Dylan Olver

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsQueen's UniversityBrock UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsArterial stiffnessMedicineCardiologyInternal medicineIntima-media thicknessHemodynamicsPopulationPerfusion scanningBrachial arteryPulse wave velocityCerebral perfusion pressureBlood pressureCerebral blood flowPerfusionCarotid arteries

Abstract

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Age‐related decrements in systemic arterial health may be indicative of alterations in cerebral perfusion and structure that are implicated in neurocognitive impairment. Purpose To determine if indicators of systemic arterial health are related to hemodynamic and structural changes in the brain in a population of healthy young and old adults. Hypothesis Poorer markers of arterial health will be associated with aging‐related hemodynamic and structural changes in the brain. Methods Young (n=13, f=7, m=6, 24±3 y, 71±18 kg) and old (n=14, f=5, m=9, 71±4 y, 83±15 kg) participants completed two experimental visits. In visit 1, systemic arterial health was assessed using carotid‐to‐femoral pulse wave velocity (PWV; indicator of central arterial stiffness; tonometry), left common carotid artery intima media thickness (IMT; indicator of carotid health; B‐mode Doppler Ultrasound), and brachial artery flow‐mediated dilation (FMD; indicator of peripheral endothelial function; duplex ultrasound). In visit 2, structural and arterial spin‐labelling neuroimaging data were acquired for computation of grey matter volume (GMV; indicator for structural integrity of the cortical tissues) and baseline cerebral blood flow (CBF0; indicator of gray matter vascular tone and perfusion). Between group differences were assessed using a two‐tailed, unpaired t‐test. Multiple linear regression analysis was used to model the relationship between indices of peripheral arterial function (PWV, IMT and FMD) and age with hemodynamic (CBF 0 ) and structural (GMV) indices of brain health. Results PWV and IMT were greater in old vs. young adults (p<0.01). Reductions in FMD in the older adults approached significance (p=0.06). Both CBF 0 and GMV were significantly lower in old vs. young adults (p<0.01). The dependent variable CBF 0 could be predicted from a linear combination (R 2 =0.72) of the independent variables age (p<0.01), PWV (p=0.04), FMD (p=0.04) and the contribution from IMT approached significance (p=0.07). The dependent variable GMV could be predicted by age alone (R 2 =0.74, p<0.01; all other variables p≥0.49). To isolate the relationship between indices of arterial health and brain structure, the regression analysis was repeated with age excluded . The dependent variable GMV could be predicted by IMT alone (R 2 =0.56, p<0.01) with the contribution from PWV approaching significance (p=0.10; FMD, p=0.83). Conclusion These data indicate that in a population of healthy young and old adults, decreased systemic arterial health is associated with hemodynamic and structural deficits in the brain. Support or Funding Information This work was supported by an Alzheimer’s Society of Brant, Haldimand Norfolk, Hamilton Halton award to REK MacPherson. TD Olver is supported by the Saskatchewan Health Research Foundation Establishment Grant #4522.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.238
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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