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Record W4414516593 · doi:10.1161/atvbaha.124.322336

Multiplex Apolipoprotein Panel Improves Cardiovascular Event Prediction and Cardiovascular Outcome by Identifying Patients Who Benefit From Targeted PCSK9 Inhibitor Therapy

2025· article· en· W4414516593 on OpenAlexaff
Esther Reijnders, Patrick M. Bossuyt, J. Wouter Jukema, L. Renee Ruhaak, Fred P.H.T.M. Romijn, Michael Szarek, Stella Trompet, Deepak L. Bhatt, Vera Bittner, Rafael Dı́az, Sergio Fazio, Irena Stevanovic, Shaun G. Goodman, Robert A. Harrington, Harvey D. White, Philippe Gabríel Steg, Gregory G. Schwartz, Christa M. Cobbaert

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsCanadian VIGOUR CentreSt. Michael's Hospital
Fundersnot available
KeywordsPCSK9Cardiovascular eventMaceApolipoprotein BMultiplexLipoprotein(a)DiseaseAcute coronary syndrome

Abstract

fetched live from OpenAlex

BACKGROUND: Residual cardiovascular risk remains, despite achieving low-density lipoprotein cholesterol targets with high-intensity statins. Traditional risk scores are suboptimal. This study evaluated the prognostic utility of a 9-plex apolipoprotein panel in recent patients with acute coronary syndrome on statins and its role in predicting treatment benefit by alirocumab, a PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitor, enabling precision medicine. METHODS: Baseline serum samples from 11 843 participants in the ODYSSEY OUTCOMES trial ( https://www.clinicaltrials.gov ; Unique identifier: NCT01663402) were analyzed using mass spectrometry to measure Apo(a), ApoA-I, ApoA-II, ApoA-IV, ApoB, ApoC-I, ApoC-II, ApoC-III, and ApoE. Using logistic regression, probabilities of major adverse cardiovascular events (MACE) and all-cause death over a median follow-up of 2.9 years were estimated based on baseline apolipoproteins and lipid concentrations. Clinical performance was assessed by comparing the area under the curve (AUC) of 3 models: the apolipoprotein panel, the lipid panel (total cholesterol, high-density lipoprotein cholesterol, and triglycerides), and a combination. In addition, prediction models estimating the treatment benefit of alirocumab by the apolipoprotein panel were developed. RESULTS: The prognostic performance of the apolipoprotein panel for MACE showed an AUC (95% CI) of 0.648 (0.626–0.670), compared with 0.579 (0.557–0.602) for the lipid panel. For all-cause death, the apolipoprotein panel had an AUC of 0.699 (0.664–0.733), while the lipid panel had an AUC of 0.599 (0.564–0.635). Adding the apolipoprotein panel significantly improved the performance of the conventional lipid panel ( P <0.0001): AUC, 0.659 (0.637–0.681) for MACE and 0.724 (0.691–0.756) for all-cause death. Higher risk for MACE based on the baseline apolipoprotein panel was found to predict greater treatment benefit with alirocumab. CONCLUSIONS: A multiplex apolipoprotein panel led to better prediction of MACE and all-cause death, beyond lipids, in patients with postacute coronary syndrome on optimized statin therapy. The panel also predicts the treatment benefit of alirocumab. Further validation of this approach is now needed, and if confirmed and improved, it could lead to better disease prediction and management in the future.

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)0.003
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.277
Teacher spread0.240 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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