Novel therapies for severe dyslipidemia originating from human genetics
Bibliographic record
Abstract
PURPOSE OF REVIEW: Novel therapies for severe dyslipidemia target a wide range of unmet medical needs: severe familial hypercholesterolemia, severe hypertriglyceridemia and chylomicronemia, elevated lipoprotein (a), lipodystrophies, high-density lipoprotein particle diseases, lysosomal acid lipase deficiency and storage diseases, nonalcoholic fatty liver disease and others. The purpose of this review is to describe the contribution of human genetics to the development of therapeutic approaches targeting severe dyslipidemia. RECENT FINDINGS: Recent advances in human genetics and the identification of rare genetic variants having strong effects on disease risk not only accelerated the development of therapies for severe dyslipidemia, they also revealed new pathways, genes and mechanisms of health, disease or drug response, and facilitated molecular diagnosis, which may prove essential as the authorized use of some of these novel drugs is limited to specific conditions. In addition, the dissection of the gene and cell machinery gave rise to new technologies, gene-based therapies and biodrugs covering a broad range of novel agents currently available or in clinical development to treat severe lipid disorders. SUMMARY: Several novel therapies are recently available or under development to treat severe dyslipidemia and associated risk stem directly from genetic research. Altogether, these therapies target a broad variety of severe dyslipidemia pathways or mechanisms and illustrate that clinical lipidology has now entered the era of precision medicine.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.003 |
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".