An update on trials of novel lipid-lowering drugs
Bibliographic record
Abstract
PURPOSE OF REVIEW: A number of novel trials have assessed the efficacy of new lipid-lowering therapies in cardiovascular disease (CVD). RECENT FINDINGS: Proprotein convertase subtilisin kexin-9 inhibitors reduce low-density lipoprotein cholesterol (LDL-C) by 50-55%. A CVD outcome trial in patients with acute coronary syndromes with evolocumab achieved a LDL-C of 0.8 mmol/l (31 mg/dl) and a 20% relative risk reduction in CVD events in 2.2 years. Cholesterol ester transfer protein inhibitors raise high-density lipoprotein cholesterol and can lower LDL-C. Anacetrapib reduced coronary artery disease events by 7%, but not wider composite CVD outcomes, in a population with chronic CVD with pretreatment LDL-C of 1.6 mmol/l (62 mg/dl). The conflicting outcomes of cholesterol ester transfer protein inhibitor trials means these compounds are not being developed further. Trials using lipid drugs targeting inflammation have previously been generally unsuccessful, but recent data on the interleukin-1B receptor antagonist canakinumab has proven the concept of intervention on inflammation in atherosclerosis by showing a reduction in acute coronary interventions, but at the predictable cost of increased infections. SUMMARY: Despite the success of proprotein convertase subtilisin kexin-9 inhibition, the ability to achieve low LDL-C with off-patent medications and the costs of novel therapies will limit their use even in high-risk patients and confine them to the highest-risk sub-groups of patients.
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".