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Abstract 4360415: Small, Dense LDL-C and Conventional LDL-C Similarly Predict Cardiovascular Risk and Benefit of Alirocumab in Statin-Treated Patients With Recent Acute Coronary Syndrome

2025· article· en· W4415790197 on OpenAlexaff
Gregory G. Schwartz, Michael Szarek, Christa M. Cobbaert, Markus Schwertfeger, Deepak L. Bhatt, Vera Bittner, Shaun G. Goodman, Robert A. Harrington, Esther Reijnders, Fred P.H.T.M. Romijn, Irena Stevanovic, Hagai Tavori, Nicolaas van Neer, Harvey D. White, J. Wouter Jukema

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsAlirocumabMaceAcute coronary syndromePlaceboRosuvastatinMyocardial infarction

Abstract

fetched live from OpenAlex

Introduction: Small, dense low-density lipoprotein (sdLDL) particles are believed to be a highly atherogenic subfraction of LDL due to prolonged residence time in circulation, greater adherence to and penetration of vascular endothelium, and higher susceptibility to oxidation. Automated biochemical measurement of sdLDL cholesterol (sdLDL-C) has demonstrated good fidelity to gold standard gradient ultracentrifugation or NMR spectroscopy. We evaluated the relationship of sdLDL-C and conventional LDL-C to risk of major adverse cardiovascular events (MACE) and treatment benefit of alirocumab in patients with recent acute coronary syndrome (ACS) receiving high-intensity or maximum-tolerated statin treatment. Methods: The analysis included 11,837 participants in the ODYSSEY OUTCOMES trial (NCT01663402) with recent ACS and LDL-C ≥70 mg/dL despite optimized statin treatment. At baseline prior to randomized treatment with the PCSK9 monoclonal antibody alirocumab (N=5917) or placebo (N=5920), sdLDL-C was measured using the Denka (Nigata, Japan) method on a Roche cobas autoanalyzer and LDL-C was calculated with the Friedewald formula. In the placebo group, natural cubic splines depicted the relationships of sdLDL-C, LDL-C, and their ratio to the risk of MACE (CV death, non-fatal myocardial infarction or ischemic stroke, hospitalization for unstable angina, and ischemia-driven coronary revascularization) and treatment hazard ratio (HR: alirocumab/placebo) as a function of sdLDL-C and LDL-C. Results: In Figure Panel A, the risk of MACE in the placebo group increased with concentrations of baseline sdLDL-C and LDL-C, with nearly superimposable splines. In Panel B, the relationship of sdLDL-C/LDL-C to risk of MACE in the placebo group (adjusted for LDL-C) showed no evidence of greater risk with greater sdLDL-C fraction. Overall, alirocumab reduced the risk of MACE (HR 0.87, 95% CI 0.79, 0.95). Panel C shows that the treatment HR did not vary significantly across the range of either LDL-C or sdLDL-C. Conclusion: In patients with recent ACS and LDL-C ≥70 mg/dL on optimized statin treatment, sdLDL-C and conventional LDL-C similarly predict risk of MACE and benefit of treatment with alirocumab. Measurement of sdLDL-C does not appear to provide additional prognostic or predictive information.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0030.000

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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
Published2025
Admission routes1
Has abstractyes

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