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Record W2981369828 · doi:10.1093/eurheartj/ehz746.0798

P6193Dalcetrapib reduces incident diabetes in patients with recent acute coronary syndrome

2019· article· en· W2981369828 on OpenAlexaff
G.G. Schwartz, Lawrence A. Leiter, Christie M. Ballantyne, Philip J. Barter, D Black, David Kallend, Eran Leitersdorf, John J.V. McMurray, Stephen J. Nicholls, Anders Olsson, David Preiss, Prediman K. Shah, Jean‐Claude Tardif, John Kittelson

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsMontreal Heart InstituteSt. Michael's Hospital
Fundersnot available
KeywordsMedicineDiabetes mellitusInternal medicineAcute coronary syndromeType 2 diabetesCholesterylester transfer proteinCohortPlaceboMyocardial infarctionCholesterolEndocrinologyLipoprotein

Abstract

fetched live from OpenAlex

Abstract Background Among patients with acute coronary syndrome (ACS) who do not have diabetes, incident diabetes is common and associated with an adverse prognosis. Some data suggest that high density lipoprotein (HDL) has favourable effects on beta cell function and that cholesteryl ester transfer protein (CETP) inhibitors reduce incident type 2 diabetes in conjunction with increased HDL cholesterol (HDL-C) concentration. Dalcetrapib is a CETP inhibitor under ongoing evaluation as a potential cardiovascular therapy. Purpose We compared the effect of treatment with dalcetrapib or placebo on incident diabetes in patients with recent acute coronary syndrome (ACS). Methods In the dal-OUTCOMES trial, 15,871 patients were randomly assigned to treatment with dalcetrapib 600 mg or placebo daily, beginning 4–12 weeks after ACS. Absence of diabetes at baseline was based upon medical history, no use of diabetes medication, haemoglobin A1c <6.5%, and plasma glucose level <7 mmol/L (if measured under fasting conditions) or <11.1 mmol/L (if measured under non-fasting conditions). Among these patients, incident diabetes after randomization was defined by any diabetes-related adverse event, use of a diabetes medication, HbA1c ≥6.5%, or two measurements of plasma glucose ≥7 mmol/L (fasting) or ≥11.1 mmol/L (non-fasting). The association of incident diabetes with baseline and on-treatment HDL-C was determined. Results At baseline, 10621 patients (67% of the trial cohort) did not have diabetes and formed the analysis cohort. Over median follow-up of 31 months, incident diabetes was identified in 392 of 5314 patients (7.4%) assigned to dalcetrapib and 505 of 5307 (9.5%) assigned to placebo (odds ratio [OR] 0.76; 95% confidence interval [CI] 0.66–0.87; P<0.001). This corresponds to an absolute reduction in incident diabetes of 2.1%, and a need to treat 47 patients (for 31 months) to prevent 1 case of diabetes. Kaplan-Meier estimates of the cumulative incidence of diabetes are shown in the Figure. Across both treatment groups, incident diabetes was inversely associated with baseline HDL-C (OR 0.98 for 1 mg/dL increase in baseline HDL-C; 95% CI 0.97–0.98, P<0.001). In the dalcetrapib group, there was a further inverse association of incident diabetes with the change in HDL-C on assigned treatment (OR 0.98 for 1 mg/dL increase in HDL-C from baseline; 95% CI 0.97–0.99, P=0.002). Dalcetrapib was safe and generally well-tolerated in the trial. Conclusions In patients with recent ACS who do not have diabetes at baseline, incident diabetes is common. Dalcetrapib treatment reduced the relative risk of incident diabetes by 24% and the absolute risk by 2.1% over a median of 31 months. The reduction in incident diabetes with dalcetrapib was associated with increased HDL-C on treatment. Acknowledgement/Funding The dal-OUTCOMES trial was funded by F. Hoffmann LaRoche

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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