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Record W4380893969 · doi:10.1002/alz.062685

The effects of carotid artery stiffness on cerebral small vessel disease and cognition

2023· article· en· W4380893969 on OpenAlexaboutno aff
Caroline Robert, Ling Lieng‐Hsi, Eugene S.J. Tan, Bibek Gyanwali, Narayanaswamy Venketasubramanian, Shir Lynn Lim, Mark Richards, Christopher Chen, Saima Hilal

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCarotid ultrasonographyDementiaInternal medicinePulse wave velocityCardiologyHyperintensityArterial stiffnessVascular dementiaStroke (engine)Magnetic resonance imagingRadiologyBlood pressureDiseaseCarotid arteries

Abstract

fetched live from OpenAlex

Abstract Background Carotid artery stiffness is associated with cognitive impairment and dementia, however, the underlying mechanism remains unknown. We examined the association of carotid artery stiffness with cerebral small vessel disease (CSVD) markers, cognitive impairment, dementia with its subtypes, as well as cognitive performance. Method In this case‐control study, 272 subjects from a memory clinic underwent physical and medical evaluation along with carotid ultrasonography, 3T brain magnetic resonance imaging and detailed neuropsychological assessment. Carotid ultrasonography was conducted to assess arterial compliance (AC), pressure‐strain elastic modulus (Ep), β‐index, augmentation index (AI), and pulse wave velocity‐β (PWV‐β). Brain MRIs were graded for white matter hyperintensities (WMH), lacunes, and cerebral microbleeds (CMBs). Participants were classified as no cognitive impairment, cognitive impairment no dementia, as well as dementia and its subtypes; Alzheimer’s disease (AD) and vascular dementia (VaD). Cognition was assessed using NINDS–Canadian Stroke Network harmonization neuropsychological battery. Result Increased carotid β‐index (β = 0.73, P<0.001), Ep (β = 0.86, P<0.001), and PWV‐β (β = 0.83, P<0.001) were independently associated with WMH. Ep (OR = 1.42, 95%CI = 1.05–1.93), and PWV‐β (OR = 1.43, 95%CI = 1.05–1.94) were associated with presence of lacunes. Ep was associated with AD (OR = 1.59, 95%CI = 1.01‐2.73) and VaD (OR = 2.23, 95%CI = 1.04‐5.94). Additionally, PWV‐β was associated with VaD (OR = 2.24, 95%CI = 1.07–5.62). All carotid artery stiffness measurements, except AI, were associated with worse performance in global cognition, visuomotor speed and memory after adjusting for age, sex, education, hypertension, diabetes, hyperlipidaemia, cardiovascular disease, and smoking. These associations became attenuated but remained significant with global cognition after adjusting for CSVD markers (β‐index; β = ‐0.27, P = 0.04), Ep; β = ‐0.30, P = 0.03), and PWV‐β; β = ‐0.32, P = 0.02). Conclusion Carotid artery stiffening is associated with WMH, lacunes, and etiologic subtypes of dementia. Carotid artery stiffening is also associated with global cognition independent of CSVD. Elevated carotid artery stiffness may help identify patients at risk for developing CSVD and dementia.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.266
Teacher spread0.247 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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