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Abstract 4141244: RNA interference versus antibody-based PCSK9 inhibition for the prevention of cardiovascular disease: A drug-target Mendelian randomization study

2024· article· en· W4404363197 on OpenAlexaff
Éloi Gagnon, Dipender Gill, Jérôme Bourgault, Émilie Gobeil, Patricia L. Mitchell, Arnaud Girard, Audrey Paulin, Christian Couture, Yohan Bossé, Sébastien Thériault, Patrick Mathieu, Marie‐Claude Vohl, André Tchernof, Kausik K. Ray, John J.P. Kastelein, Benoît Arsenault

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMendelian randomizationMedicinePCSK9DrugDiseaseMendelian inheritanceClinical trialRNA interferencePharmacologyInternal medicineRNACholesterolGeneGeneticsGenetic variantsLDL receptor

Abstract

fetched live from OpenAlex

Background and Aims: RNA interference therapy targeting the proprotein convertase subtilisin/kexin type 9 ( PCSK9 ) gene lower low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (apoB) levels and is approved worldwide. As opposed to monoclonal antibodies neutralizing PCSK9 in the circulation, their effect on atherosclerotic cardiovascular disease (ASCVD) outcomes is unknown. We used drug-target Mendelian randomization (MR) to assess the potential impact of RNA interference therapies targeting PCSK9 on cardiometabolic traits and outcomes. Methods: We performed RNA-sequencing of 246 liver samples and genome-wide genotyping was performed to identify single-nucleotide polymorphisms (SNPs) associated with liver expression of PCSK9 . Genome-wide association study (GWAS) summary statistics of plasma protein levels of PCSK9 from the deCODE study (n=35,559) were used to instrument inhibition of circulating PCSK9 levels. A three-sample MR approach was undertaken using SNPs that influence liver PCSK9 gene expression levels (mimicking PCSK9 RNA interference) or plasma PCSK9 protein levels (mimicking PCSK9 neutralizing antibodies) as study exposures. Genetic instruments were standardized for their effect on apoB levels. Main outcomes measures included GWAS summary statistics on coronary artery disease (CAD), ischemic stroke (IS) and type 2 diabetes (T2D). Results: Each SD decrease in apoB was linked with a 55% and 56% reduction in CAD risk, respectively for genetically predicted reductions in plasma PCSK9 levels (OR [odds ratio]=0.45 [95% CI], 0.36-0.56, p=1.7e-13) and liver PCSK9 gene expression levels (OR=0.44 [95% CI], 0.22-0.88, p=0.02). Genetically predicted reductions in plasma PCSK9 levels and liver PCSK9 gene expression levels were associated with slightly lower IS risk (OR=0.82 [95% CI], 0.68-0.98, p=0.03 and OR=0.73 [95% CI], 0.51-1.04, p=0.08, respectively). Genetically predicted reductions in plasma PCSK9 levels and liver PCSK9 gene expression levels were not associated with T2D risk (OR=1.08 [95% CI], 0.92-1.28, p=0.34 and OR=1.26 [95% CI], 0.93-1.71, p=0.14, respectively). The effect of PCSK9 inhibition on CAD was entirely mediated by reductions in apoB levels. Conclusions: Genetically predicted reductions in plasma PCSK9 levels and liver PCSK9 gene expression levels were associated with lower ASCVD risk, suggesting that LDL-C/apoB reductions may provide cardiovascular benefits, regardless of how PCSK9 function is inhibited.

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)0.017
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.043
GPT teacher head0.335
Teacher spread0.291 · 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 designSimulation or modeling
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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Citations1
Published2024
Admission routes1
Has abstractyes

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