Advances in the pharmacological management of hyperlipidemia through the use of combination therapies
Bibliographic record
Abstract
INTRODUCTION: Lipid-lowering therapies are well established for the treatment of cardiovascular disease (CVD). Historically monotherapy studies have been performed, but the introduction of statins has led to these drugs being recognized as baseline therapies and to the investigation of combination therapy of both older and newer medications with them. AREAS COVERED: Surrogate marker studies have shown additive effects on LDL-C, triglycerides and HDL-C of combination therapies with statins and these have extended to lipoprotein (a). Imaging studies have often shown benefits paralleling lipid studies. However, outcome studies have failed to show added benefits with niacin or fibrates while confirming the benefits of ezetimibe, bempedoic acid and proprotein convertase subtilisin kexin-9 (PCSK-9) inhibitors and icosapent ethyl. EXPERT OPINION: Combination therapy for LDL-C in dual combinations is well validated. Data for intervention on triglycerides is limited to icosapent ethyl, but this may exert effects independent of lipids. New drugs targeting triglycerides through apolipoprotein C3 and angiopoietin-like peptides are in development. Studies on combination therapy raising HDL-C have generally disappointed, though cholesterol ester transfer protein (CETP) inhibition remains a target. Lipoprotein (a) is recognized as a CVD risk factor and effective therapies are in development but results on CVD events are lacking.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.006 | 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".