Medications for Lipid Control: Statins vs Newer Drugs
Bibliographic record
Abstract
In the primary and secondary prevention of atherosclerotic cardiovascular disease (ASCVD), statins are the primary pharmacologic intervention for ASCVD risk reduction. Statins have proven efficacy and safety in reducing cardiovascular events and total mortality in patients with and without clinically evident ASCVD. The purpose of this brief review is to provide a stepwise approach to lipid management, including lifestyle recommendations and medical therapy. We first review the main available approaches to lipid lowering and their mechanisms of action. We then summarise the findings of large randomised controlled trials investigating the benefit of statin therapy from 1994 to the present. The available statins are then reviewed, along with their main pharmacologic properties and potential adverse effects. Although statins are generally well tolerated, certain patients may require dose adjustments or alternative treatments because of side-effects. In patients not achieving adequate lipid control on a maximally tolerated statin, nonstatin medications, including ezetimibe and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, provide enhanced low-density lipoprotein cholesterol reduction and cardiovascular benefits, especially in high-risk patients inadequately managed with statins alone. We review the role of triglyceride-lowering agents, including fibric acid derivatives and icosapent ethyl. We then deal with special populations, including those with hepatic steatosis, chronic kidney disease, pregnancy, and heart failure. This field continues to progress, and novel therapies are under active investigation, including an oral PCSK9 inhibitor and molecular therapies targeting lipoprotein(a), angiopoietin-like protein 3, and apolipoprotein CIII. We can look forward to exciting developments that will have major impacts on patient health and management.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".