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Record W2818723673 · doi:10.1001/jama.2018.11498

Association of Cardiovascular Risk Factors With MRI Indices of Cerebrovascular Structure and Function and White Matter Hyperintensities in Young Adults

2018· article· en· W2818723673 on OpenAlexaff
Wilby Williamson, Adam J. Lewandowski, Nils D. Forkert, Ludovica Griffanti, Thomas W. Okell, J F Betts, Timo Siepmann, David McKean, Odaro J. Huckstep, Jane M Francis, Stefan Neubauer, Renzo Phellan, Mark Jenkinson, Aiden Doherty, Helen Dawes, Eleni Frangou, Christina Malamateniou, Charlie Foster, Paul Leeson

Bibliographic record

VenueJAMA · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Institute for Health and Care ResearchBritish Heart FoundationWellcome Trust
KeywordsMedicineInternal medicineHyperintensityBlood pressureCardiologyBody mass indexRisk factorStroke (engine)Lacunar strokePhysical therapyMagnetic resonance imagingIschemic strokeIschemia

Abstract

fetched live from OpenAlex

Importance: Risk of stroke and brain atrophy in later life relate to levels of cardiovascular risk in early adulthood. However, it is unknown whether cerebrovascular changes are present in young adults. Objective: To examine relationships between modifiable cardiovascular risk factors and cerebrovascular structure, function, and white matter integrity in young adults. Design, Setting, and Participants: A cross-sectional observational study of 125 young adults (aged 18-40 years) without clinical evidence of cerebrovascular disease. Data collection was completed between August 2014 and May 2016 at the University of Oxford, United Kingdom. Final data collection was completed on May 31, 2016. Exposures: The number of modifiable cardiovascular risk factors at recommended levels, based on the following criteria: body mass index (BMI) <25; highest tertile of cardiovascular fitness and/or physical activity; alcohol consumption <8 drinks/week; nonsmoker for >6 months; blood pressure on awake ambulatory monitoring <130/80 mm Hg; a nonhypertensive diastolic response to exercise (peak diastolic blood pressure <90 mm Hg); total cholesterol <200 mg/dL; and fasting glucose <100mg/dL. Each risk factor at the recommended level was assigned a value of 1, and participants were categorized from 0-8, according to the number of risk factors at recommended levels, with higher numbers indicating healthier risk categories. Main Outcomes and Measures: Cerebral vessel density, caliber and tortuosity, brain white matter hyperintensity lesion count. In a subgroup (n = 52), brain blood arrival time and cerebral blood flow assessed by brain magnetic resonance imaging (MRI). Results: A total of 125 participants, mean (SD) age 25 (5) years, 49% women, with a mean (SD) score of 6.0 (1.4) modifiable cardiovascular risk factors at recommended levels, completed the cardiovascular risk assessment and brain MRI protocol. Cardiovascular risk factors were correlated with cerebrovascular morphology and white matter hyperintensity count in multivariable models. For each additional modifiable risk factor categorized as healthy, vessel density was greater by 0.3 vessels/cm3 (95% CI, 0.1-0.5; P = .003), vessel caliber was greater by 8 μm (95% CI, 3-13; P = .01), and white matter hyperintensity lesions were fewer by 1.6 lesions (95% CI, -3.0 to -0.5; P = .006). Among the 52 participants with available data, cerebral blood flow varied with vessel density and was 2.5 mL/100 g/min higher for each healthier category of a modifiable risk factor (95% CI, 0.16-4.89; P = .03). Conclusions and Relevance: In this preliminary study involving young adults without clinical evidence of cerebrovascular disease, a greater number of modifiable cardiovascular risk factors at recommended levels was associated with higher cerebral vessel density and caliber, higher cerebral blood flow, and fewer white matter hyperintensities. Further research is needed to verify these findings and determine their clinical importance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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 teacher head, 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

Citations142
Published2018
Admission routes1
Has abstractyes

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