Leveraging drug-target Mendelian randomization for tailored lipoprotein-lipid lowering
Bibliographic record
Abstract
PURPOSE OF REVIEW: The study of naturally occurring genetic variation in human populations has laid the foundation for proprotein converts subtilisin/kexin type 9 inhibitors, and more recently new classes of lipid-lowering drugs such as lipoprotein(a) inhibitors and lipoprotein lipase pathway activators. These emerging therapies lower plasma lipoprotein-lipid levels that are not adequately managed by traditional low-density lipoprotein (LDL) cholesterol-lowering medications. By targeting different risk factors, these therapies could help manage the important residual cardiovascular risk of LDL cholesterol medications. RECENT FINDINGS: We review the latest insights into the pharmacological and genetic modulation of these new therapeutic targets. We highlight that the drugs remarkably recapitulate the lipid effects observed in genetic studies. In addition to lowering lipoprotein-lipid levels, robust genetic evidence support that these drugs may prevent cardiometabolic outcomes. SUMMARY: Emerging lipid-lowering therapies could launch a new era for preventive medicine in which treatments are optimally tailored to patient's lipoprotein-lipid profiles.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".