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Record W4379228856 · doi:10.1101/2023.05.29.23290580

Genetic inhibition of angiopoietin-like protein-3, lipoprotein-lipid levels and cardiometabolic diseases

2023· preprint· en· W4379228856 on OpenAlexafffund
Émilie Gobeil, Jérôme Bourgault, Patricia L. Mitchell, Ursula Houessou, Éloi Gagnon, Arnaud Girard, Audrey Paulin, Hasanga D. Manikpurage, Christian Couture, Simon Marceau, Yohan Bossé, Sébastien Thériault, Patrick Mathieu, Marie‐Claude Vohl, André Tchernof, Benoît J. Arsenault

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersNIH Office of the DirectorFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalNational Institutes of HealthPfizerSilence TherapeuticsCommon FundBausch HealthNovo NordiskIonis Pharmaceuticals
KeywordsMendelian randomizationPCSK9Internal medicineApolipoprotein BMedicineEndocrinologySingle-nucleotide polymorphismTriglycerideMicrosomal triglyceride transfer proteinLipoproteinType 2 diabetesBiologyCholesterolDiabetes mellitusVery low-density lipoproteinGeneGeneticsGenetic variantsGenotypeLDL receptor

Abstract

fetched live from OpenAlex

Abstract Background RNA-based, antibody-based and gene editing-based therapies are currently under investigation to determine if the inhibition of angiopoietin-like protein-3 (ANGPTL3) could reduce lipoprotein-lipid levels and atherosclerotic cardiovascular diseases (ASCVD) risk. We used Mendelian randomization (MR) to determine whether genetic variations influencing ANGPTL3 liver gene expression, blood levels and protein structure could causally influence triglyceride and apolipoprotein B (apoB) levels as well as coronary artery disease (CAD), ischemic stroke (IS) and other cardiometabolic diseases. Methods We performed RNA-sequencing of 246 explanted liver samples to identify single-nucleotide polymorphisms (SNPs) associated with liver expression of ANGPTL3 . We used genome-wide summary statistics of plasma protein levels of ANGPTL3 from the deCODE study (n=35,359). We also identified 647 carriers of ANGPTL3 protein-truncating variants (PTVs) associated with lower plasma triglyceride levels in the UK Biobank. We performed two-sample MR using SNPs that influence ANGPTPL3 liver expression or ANGPTPL3 plasma protein levels as exposure and cardiometabolic diseases as outcomes (CAD, IS, heart failure, non-alcoholic fatty liver disease, acute pancreatitis and type 2 diabetes). The impact of rare PTVs influencing plasma triglyceride levels on apoB levels and CAD was also investigated in the UK Biobank. Results In two-sample MR studies, common genetic variants influencing ANGPTL3 hepatic or blood expression levels of ANGPTL3 had a very strong effect on plasma triglyceride levels, a more modest effect on LDL cholesterol, a weaker effect on apoB levels and no effect on CAD or other cardiometabolic diseases. In the UK Biobank, carriers of rare ANGPTL3 PTVs providing lifelong reductions in median plasma triglyceride levels (-0.37 [interquartile range=0.41] mmol/L) had slightly lower apoB levels (-0.06±0.32] g/L) and similar CAD event rate compared to noncarriers (10.2% versus 10.9% in carriers versus noncarriers, p=0.60). Conclusions PTVs influencing ANGPTL3 protein structure as well as common genetic variants influencing ANGPTL3 hepatic expression and/or blood protein levels exhibit a strong effect on circulating plasma triglyceride levels, a weak effect on circulating apoB levels and no effect on ASCVD. Near-complete inhibition of ANGPTL3 function in patients with very elevated apoB levels will likely be required to reduce ASCVD risk.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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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Citations2
Published2023
Admission routes2
Has abstractyes

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