Genetic inhibition of angiopoietin-like protein-3, lipoprotein-lipid levels and cardiometabolic diseases
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".