The application of gene therapy in lipid disorders: where are we now?
Bibliographic record
Abstract
Lipid disorders or dyslipidemias result from perturbations within the biochemical pathways that regulate lipid metabolism. The dramatically altered lipid profiles that sometimes result from defects in these pathways can greatly enhance the risk of cardiovascular disease or other complications, with an attendant negative impact on morbidity and mortality. Patients with rare monogenic forms of dyslipidemia often live highly restrictive lifestyles, and options among existing pharmaceutical treatments are limited. Gene therapy provides a potential treatment option for monogenic dyslipidemias and perhaps even a long-term stable cure. In this article, we review the gene therapies applied to two types of monogenic dyslipidemias: homozygous familial hypercholesterolemia and familial LPL deficiency. We discuss the limitations of this approach, consider some future directions of gene therapy for monogenic dyslipidemias and possibilities for polygenic dyslipidemias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".