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Body mass index and clinical outcomes in patients with diabetes and stable coronary artery disease in THEMIS

2024· article· en· W4403822105 on OpenAlexaff
Manan Pareek, Deepak L. Bhatt, L.A. Leiter, Léon Zheng, J J Lee, Tabassome Simon, Shamir R. Mehta, Kim Fox, Anders Himmelmänn, W Ridderstraale, Robert A. Harrington, Philippe Gabríel Steg

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsMcMaster University
FundersAstraZeneca
KeywordsMedicineBody mass indexCoronary artery diseaseDiabetes mellitusCardiologyInternal medicineDiseaseIndex (typography)Endocrinology

Abstract

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Abstract Background Physiologic changes due to extremes of body weight may affect the efficacy and tolerability of antiplatelet treatment. However, it remains unclear whether the risks and benefits of intensified antiplatelet therapy among patients with diabetes and stable coronary artery disease (CAD) are affected by body mass index (BMI). Purpose To assess the relationship between baseline BMI and major adverse cardiovascular (CV) events, and to determine if the effects of ticagrelor vs. placebo varied across the BMI spectrum, in patients with diabetes and stable CAD. Methods THEMIS was a randomized, controlled trial of 19,220 individuals aged ≥50 years with type 2 diabetes and stable CAD, randomly allocated to ticagrelor or placebo on top of aspirin. Patients with a prior myocardial infarction (MI) or stroke, or already on dual antiplatelet therapy, were excluded. The primary efficacy outcome was a composite of CV death, MI, or stroke. The primary safety outcome was TIMI major bleeding. We examined prognostic implications of BMI using 1) restricted cubic splines for the overall trends with outcomes; 2) Cox regression models with predefined BMI intervals (<25 kg/m2, 25-29.9 kg/m2, and ≥30 kg/m2) adjusted for demographic, clinical, and laboratory variables; and 3) Cox regression models for the effects of ticagrelor vs. placebo on outcomes across the spectrum of BMI values (test for interaction). A two-sided P-value <0.01 was considered statistically significant to control the overall chance of type I errors. Results BMI was available in 19,202 (99.9%) participants, with a median of 29 kg/m2 (interquartile range: 26-33). A total of 3352 (17.5%) individuals had a BMI <25 kg/m2, 7644 (39.8%) had a BMI 25-29.9 kg/m2, and 8206 (42.7%) had a BMI ≥30 kg/m2. Median follow-up was 39.9 months (range 0-57), with 1554 primary efficacy events and 306 primary safety events occurring over the course of the study. BMI was not associated with the primary efficacy or safety outcome, or with their individual components. However, BMI was significantly associated with hospitalization for heart failure (HHF) (BMI <25 kg/m2: reference; hazard ratio [HR] for BMI 25-29.9 kg/m2: 1.07, 95% confidence interval [CI], 0.82 to 1.40; HR for BMI ≥30 kg/m2: 1.41, 95% CI, 1.07 to 1.84) and with the composite of HHF or CV death (comparable estimates) after multivariable adjustment (Figure). BMI was also significantly, positively associated with serious adverse events (BMI <25 kg/m2: reference; HR for BMI 25-29.9 kg/m2: 0.99, 95% CI, 0.93 to 1.06; HR for BMI ≥30 kg/m2: 1.20, 95% CI, 1.12 to 1.29). BMI did not modify the risks and benefits of ticagrelor vs. placebo. Conclusions BMI was independently associated with the risk of HHF and the composite of HHF or CV death, but not with other efficacy or safety outcomes, in patients with stable CAD and type 2 diabetes. Ticagrelor was of similar benefit on the primary efficacy outcome vs. placebo across the full range of 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 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)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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
Published2024
Admission routes1
Has abstractyes

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