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Cerebrovascular Control After Cardiac Rehabilitation in Ischemic Heart Disease Patients

2018· article· en· W3174400658 on OpenAlexaffabout
Baraa K. Al‐Khazraji, Navena R. Lingum, Jennifer L. Vording, Brad Matushewski, J. Kevin Shoemaker

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCardiologyInternal medicinePopulationCerebral blood flowCerebral autoregulationBlood pressureAutoregulation

Abstract

fetched live from OpenAlex

In this study we evaluate the impact of ischemic heart disease (IHD) and cardiac rehabilitation (CR) on cerebrovascular control and peripheral vascular health. Patients with IHD are at greater risk for stroke, a risk determined in part by cerebrovasoreactivity tests (i.e., dilation or constriction to a known stimulus). Exercise provides cerebrovascular health benefits in the aging population; yet, effects of IHD and CR (exercise) on cerebrovascular health indicators and cerebral blood flow control are not understood. We explored cerebrovascular control (i.e., vasoreactivity and autoregulation), peripheral vascular health, brain volume, and white matter hyperintensities (WMH) in a group of healthy adults (CTL), and patients with IHD after cardiac event (pre‐CR) and following 6 months CR (post‐CR) comprised of 2–3 sessions/week of 30 minutes moderate intensity aerobic exercise and 30 minutes of resistance training. In 23 participants (CTL: 11, IHD: 12; age range: 40–75 years), we measured changes in cross‐sectional area (ΔCSA) divided by changes in end‐tidal CO 2 (ΔPetCO 2 : CTL: 2.3±1.2, pre‐CR: 4.1±3.2, post‐CR: 5.2±2.2 mmHg)] during hypercapnic (5% CO 2 , 95% oxygen; ADInstruments) conditions using a 3 Tesla (Siemens) scanner and T1‐weighted pulse sequence (0.7 mm isotropic). Vasoreactivity was calculated for the basilar (BA), left (L) and right (R) internal carotid (ICA), anterior (ACA), middle (MCA), and posterior cerebral arteries (PCA) using OsiriX software. Autoregulation of the MCA was assessed by measuring relative changes in MCA flow velocity with respect to changes in mean arterial pressure during a sit‐to‐stand task, quantified by rate of regulation (ROR). Peripheral vascular health metrics included brachial artery flow‐mediated dilation (FMD; %) and common carotid artery (CCA) intima media thickness (mm) using duplex ultrasound. Compared to CTL, those with IHD had lower CSA reactivity (mm/mmHg; mean±SD; p<0.05) in the BA (CTL: 0.74±0.73, pre‐CR: 0.29±0.29, post‐CR: 0.14±0.24), LICA (CTL: 1.1±0.86, pre‐CR: 0.93±1.03, post‐CR: 0.42±0.51), RICA (CTL: 0.93±0.71, pre‐CR: 0.48±0.35, post‐CR: 0.27±0.34), LMCA (CTL: 0.67±0.53, pre‐CR: 0.37±0.48, post‐CR: 0.14±0.12), and RMCA (CTL: 0.93±0.95, pre‐CR: 0.33±0.37, post‐CR: 0.29±0.33), which were not improved with CR. Compared to CTL, those with IHD had greater CCA IMT (CTL: 0.43±0.05, pre‐CR: 0.58±0.09, post‐CR: 0.59±0.07) which was not improved with CR, as well as lower ROR (CTL: 0.18±0.02, pre‐CR: 0.14±0.03, post‐CR: 0.16±0.03) and lower FMD (CTL: 9.1±4.3, pre‐CR: 4.7±2.3, post‐CR: 6.1±1.3; p<0.05) which were improved with CR. Finally, brain volume and white matter lesions were similar across groups. Therefore, compared to healthy individuals, the IHD patients exhibited impaired cerebrovascular control and peripheral endothelial function. Peripheral vascular health, and cerebrovascular autoregulation, were improved with 6 months of CR in these IHD patients. Support or Funding Information Canadian Institute of Health Research (201503MOP‐342412‐MOV‐CEEA). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.005
GPT teacher head0.244
Teacher spread0.239 · 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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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