MétaCan
Menu
Back to cohort
Record W4220911727 · doi:10.1093/eurjpc/zwac069

Medications for blood pressure, blood glucose, lipids, and anti-thrombotic medications: relationship with cardiovascular disease and death in adults from 21 high-, middle-, and low-income countries with an elevated body mass index

2022· article· en· W4220911727 on OpenAlexaff
Darryl P. Leong, Sumathy Rangarajan, Annika Rosengren, Aytekin Oğuz, Khalid F. AlHabib, Paul Poirier, Rafael Díaz, Antonio L Dans, Romaina Iqbal, Afzalhussein Yusufali, Karen Yeates, Jephat Chifamba, Pamela Serón, José López-López, Ahmad Bahonar, Li Wei, Bo Hu, Álvaro Avezum, Rajeev Gupta, Viswanathan Mohan, Herculina Kruger, P. V. M. Lakshmi, Rita Yusuf, Salim Yusuf

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecQueen's UniversityMcMaster UniversityUniversité LavalPopulation Health Research InstituteHamilton Health SciencesHamilton General Hospital
Fundersnot available
KeywordsMedicineHazard ratioMyocardial infarctionBody mass indexBlood pressureInternal medicineStroke (engine)Heart failureProportional hazards modelPopulationEpidemiologyDiseaseCause of deathCardiologyConfidence intervalEnvironmental health

Abstract

fetched live from OpenAlex

AIMS: Elevated body mass index (BMI) is an important cause of cardiovascular disease (CVD). The population-level impact of pharmacologic strategies to mitigate the risk of CVD conferred by the metabolic consequences of an elevated BMI is not well described. METHODS AND RESULTS: We conducted an analysis of 145 986 participants (mean age 50 years, 58% women) from 21 high-, middle-, and low-income countries in the Prospective Urban and Rural Epidemiology study who had no history of cancer, ischaemic heart disease, heart failure, or stroke. We evaluated whether the hazards of CVD (myocardial infarction, stroke, heart failure, or cardiovascular death) differed among those taking a cardiovascular medication (n = 29 174; including blood pressure-lowering, blood glucose-lowering, cholesterol-lowering, or anti-thrombotic medications) vs. those not taking a cardiovascular medication (n = 116 812) during 10.2 years of follow-up. Cox proportional hazard models with the community as a shared frailty were constructed by adjusting age, sex, education, geographic region, physical activity, tobacco, and alcohol use. We observed 7928 (5.4%) CVD events and 9863 (6.8%) deaths. Cardiovascular medication use was associated with different hazards of CVD (interaction P < 0.0001) and death (interaction P = 0.0020) as compared with no cardiovascular medication use. Among those not taking a cardiovascular medication, as compared with those with BMI 20 to <25 kg/m2, the hazard ratio (HR) [95% confidence interval (95% CI)] for CVD were, respectively, 1.14 (1.06-1.23); 1.45 (1.30-1.61); and 1.53 (1.28-1.82) among those with BMI 25 to <30 kg/m2; 30 to <35 kg/m2; and ≥35 kg/m2. However, among those taking a cardiovascular medication, the HR (95% CI) for CVD were, respectively, 0.79 (0.72-0.87); 0.90 (0.79-1.01); and 1.14 (0.98-1.33). Among those not taking a cardiovascular medication, the respective HR (95% CI) for death were 0.93 (0.87-1.00); 1.03 (0.93-1.15); and 1.44 (1.24-1.67) among those with BMI 25 to <30 kg/m2; 30 to <35 kg/m2; and ≥35 kg/m2. However, among those taking a cardiovascular medication, the respective HR (95% CI) for death were 0.77 (0.69-0.84); 0.88 (0.78-0.99); and 1.12 (0.96-1.30). Blood pressure-lowering medications accounted for the largest population attributable benefit of cardiovascular medications. CONCLUSION: To the extent that CVD risk among those with an elevated BMI is related to hypertension, diabetes, and an elevated thrombotic milieu, targeting these pathways pharmacologically may represent an important complementary means of reducing the CVD burden caused by an elevated BMI.

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)0.002
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 score0.055
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Preventive CardiologySame topicDiabetes, Cardiovascular Risks, and LipoproteinsFrench-language works237,207