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Record W2934693781 · doi:10.1161/circ.135.suppl_1.p008

Abstract P008: Abnormal Ankle-Brachial Index is Inversely Associated with Improved Cardiovascular Health

2017· article· en· W2934693781 on OpenAlexaff
Sarah Singh, Courtney Pilkerton, Stephanie J. Frisbee

Bibliographic record

VenueCirculation · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineDiabetes mellitusInternal medicineLogistic regressionBody mass indexBlood pressureNational Health and Nutrition Examination SurveyOdds ratioPopulationEpidemiologyCardiovascular healthCardiologyDiseaseEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

Background/Objective: An abnormal ankle-brachial index (ABI) strongly correlates with higher mortality in patients with cardiovascular disease however, the inverse link has not been established for cardiovascular (CV) health. The American Heart Association (AHA) aims to improve CV health by 20% by 2020 and has thus proposed the use of CV health metrics (Life’s Simple 7 or LS7). This study examines the relationship of abnormally low ABI with CV health. Methods: We evaluated 5,308 men and women aged ≥40 years, without history of CVD or diabetes mellitus (DM), participating in NHANES from 1999-2004. Abnormally low ABI was defined as ABI< 1.00 which included borderline low [0.91-0.99] and low ABI [<=0.90]). LS7 was scored on a 0-14 point scale and calculated based on poor, intermediate and ideal categories of 7 health components: diet, BMI, smoking, physical activity, blood pressure, glucose and cholesterol. LS7 scores were categorized as inadequate (0-7points), average (8-11) and optimum (12-14) CV health. Ordinal logistic regression models identified associations between abnormal ABI and CV health, with adjustments for sex, age, race/ethnicity, socioeconomic status and hs-CRP. Results: The mean (95% CI) LS7 score was 7.4 (7.3-7.5), with the majority of the population (75.3%) clustered at the lower end of average CV health. Adjusted models demonstrated that, compared to those with inadequate CV health, those with average CV health experienced 28% lower odds of abnormal ABI (OR 0.72, 95% CI; 0.52-0.97). Further improving CV health from inadequate to optimum was associated with 78% lower odds of abnormal ABI (OR 0.22, 95% CI; 0.09-0.57). On examining individual components, only blood pressure was found to be significantly associated with lower odds of abnormal ABI. Those with intermediate, as compared to poor, blood pressure readings showed 32% lower odds of abnormal ABI (OR 0.68, 95% CI; 0.48-0.94) while those with ideal blood pressure showed a 61% lower odds of abnormal ABI (OR 0.39, 95% CI; 0.21-0.72). Discussion/Conclusion: Although those with average CV health experienced lower odds of abnormal ABI, improving CV health to optimum can significantly lower these odds. This suggests that optimizing cardiovascular health, particularly in those who have not yet been affected by CVD or DM, can significantly slow or prevent progression of systemic atherosclerosis.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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