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Record W3137286406 · doi:10.3389/fphys.2021.643725

Study Protocol: The Heart and Brain Study

2021· article· en· W3137286406 on OpenAlexaboutno aff
Sana Suri, Daniel P. Bulte, Scott T. Chiesa, Klaus P. Ebmeier, Peter Jezzard, Sebastian W. Rieger, Jemma Pitt, Ludovica Griffanti, Thomas W. Okell, Martin Craig, Michael A. Chappell, Nicholas P. Blockley, Mika Kivimäki, Archana Singh‐Manoux, Ashraf W. Khir, Alun D. Hughes, John Deanfield, Daria E. A. Jensen, Sebastian F Green, Veronika Sigutova, Michelle G. Jansen, Enikő Zsoldos, Clare E. Mackay

Bibliographic record

VenueFrontiers in Physiology · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsnot available
FundersNational Institute on AgingEngineering and Physical Sciences Research CouncilMedical Research CouncilNational Institutes of HealthDementias Platform UKAlzheimer NederlandBritish Heart FoundationUniversity College LondonDiabetes UKEconomic and Social Research CouncilEuropean CommissionParkinson's UKNordForskRadboud UniversiteitRoyal SocietyNational Institute for Health and Care ResearchNIHR Oxford Biomedical Research CentreWellcome TrustAlzheimer's SocietyHorizon 2020 Framework ProgrammeAcademy of Medical Sciences
KeywordsMedicineCardiologyMagnetic resonance imagingNeuroimagingDementiaInternal medicineCohortStroke (engine)HyperintensityMontreal Cognitive AssessmentBlood pressureRadiologyDiseasePsychiatry

Abstract

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Background It is well-established that what is good for the heart is good for the brain. Vascular factors such as hypertension, diabetes, and high cholesterol, and genetic factors such as the apolipoprotein E4 allele increase the risk of developing both cardiovascular disease and dementia. However, the mechanisms underlying the heart–brain association remain unclear. Recent evidence suggests that impairments in vascular phenotypes and cerebrovascular reactivity (CVR) may play an important role in cognitive decline. The Heart and Brain Study combines state-of-the-art vascular ultrasound, cerebrovascular magnetic resonance imaging (MRI) and cognitive testing in participants of the long-running Whitehall II Imaging cohort to examine these processes together. This paper describes the study protocol, data pre-processing and overarching objectives. Methods and Design The 775 participants of the Whitehall II Imaging cohort, aged 65 years or older in 2019, have received clinical and vascular risk assessments at 5-year-intervals since 1985, as well as a 3T brain MRI scan and neuropsychological tests between 2012 and 2016 (Whitehall II Wave MRI-1). Approximately 25% of this cohort are selected for the Heart and Brain Study , which involves a single testing session at the University of Oxford (Wave MRI-2). Between 2019 and 2023, participants will undergo ultrasound scans of the ascending aorta and common carotid arteries, measures of central and peripheral blood pressure, and 3T MRI scans to measure CVR in response to 5% carbon dioxide in air, vessel-selective cerebral blood flow (CBF), and cerebrovascular lesions. The structural and diffusion MRI scans and neuropsychological battery conducted at Wave MRI-1 will also be repeated. Using this extensive life-course data, the Heart and Brain Study will examine how 30-year trajectories of vascular risk throughout midlife (40–70 years) affect vascular phenotypes, cerebrovascular health, longitudinal brain atrophy and cognitive decline at older ages. Discussion The study will generate one of the most comprehensive datasets to examine the longitudinal determinants of the heart–brain association. It will evaluate novel physiological processes in order to describe the optimal window for managing vascular risk in order to delay cognitive decline. Ultimately, the Heart and Brain Study will inform strategies to identify at-risk individuals for targeted interventions to prevent or delay 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)0.029
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.091
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.031
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0020.004
Science and technology studies0.0050.002
Scholarly communication0.0040.003
Open science0.0040.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0910.047

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 head0.016
GPT teacher head0.332
Teacher spread0.316 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations10
Published2021
Admission routes1
Has abstractyes

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