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Carotid Arterial Stiffness Predicts White Matter Lesion Volume in Older Adults

2018· article· en· W3176970084 on OpenAlexaffabout
Christopher S. Balestrini, Baraa K. Al‐Khazraji, Navena R. Lingum, Jennifer L. Vording, Neville Suskin, J. Kevin Shoemaker

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineCardiologyArterial stiffnessInternal medicineBlood pressureHyperintensityPulse pressurePulse wave velocityPeripheralMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Markers of arterial wall stiffness are used commonly as indicators of cardiovascular risk. Stiff conduit vasculature transmits pulse waves with increased velocities, which may lead to end‐organ microvascular damage. However, the association of peripheral vascular stiffness to the brain's vulnerability for vascular impairment has not been established. In the brain, this microvascular damage could include breakdown of the blood‐brain barrier causing leakage of fluid through the cerebrovascular endothelial layer. Fluid‐attenuated inversion recovery (FLAIR) imaging allows the visualization of fluid‐abnormalities in subcortical tissue, which can be quantified as white matter lesions (WML). These WML have been linked to cognitive impairment, and labelled as risk factors for stroke, dementia, and cardiovascular mortality. This research tested the hypothesis that peripheral arterial stiffness predicts levels of WML volume. We examined normotensive controls (CTRL; n = 12; 6 males; age: 42–69 years) as well as normotensive ischemic heart disease patients (IHD; n = 17; 14 males; age: 40–75 years). Common carotid artery diameters were measured during systole and diastole (B‐mode ultrasound images with ECG‐gating), and time‐aligned corresponding blood pressure values were used for measures of arterial strain (strain), arterial stiffness index (β), carotid distensibility, carotid compliance, and arterial elastic modulus. All measures were taken in the supine posture. Magnetic resonance FLAIR imaging at T2 was used to assess periventricular WML volume (WMLv) using SPM software. The IHD and CTRL cohorts had similar WMLv (4.4 ± 3.2 vs. 3.2 ± 3.5 mL; p = 0.33). Also, all participants were normotensive. Therefore, the data were pooled (n = 29) to examine the association between WMLv and peripheral arterial stiffness. Following multiple linear regressions while adjusting for age and BMI, WMLv was correlated with arterial stiffness index (r 2 = 0.39, p = 0.001), strain (r 2 = 0.37, p = 0.002), carotid distensibility (r 2 = 0.36, p = 0.004), carotid compliance (r 2 = 0.26, p = 0.009), and absolute change in carotid diameter across one cardiac cycle (r 2 = 0.24, p = 0.01). Regardless of the age, BMI, and participant cohort, structural periventricular WMLv correlated with markers of arterial stiffness. These findings support the emerging hypothesis that systemic arterial stiffness affects damage in the microvasculature of the brain. Further, the resulting subcortical structural damage is apparent in older populations regardless of ageing and vascular pathology. Support or Funding Information Supported by the 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
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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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