Nonstatin Low-Density Lipoprotein–Lowering Therapy and Cardiovascular Risk Reduction—Statement From <i>ATVB</i> Council
Bibliographic record
Abstract
Pharmacological reduction of low-density lipoprotein (LDL) cholesterol using statin drugs is foundational therapy to reduce cardiovascular disease (CVD) risk. Here, we consider the place of nonstatin therapies that also reduce LDL cholesterol in prevention of CVD. Among conventional nonstatins, placebo-controlled randomized clinical trials showed that bile acid sequestrants, niacin, and fibrates given as monotherapy each reduce CVD end points. From trials in which patients' LDL cholesterol was already well controlled on a statin, adding ezetimibe incrementally reduced CVD end points, whereas adding a fibrate or niacin showed no incremental benefit. Among emerging nonstatins, monoclonal antibodies against proprotein convertase subtilisin kexin type 9 added to a statin and given for ≤78 weeks showed preliminary evidence of reductions in CVD outcomes. Although these promising early findings contributed to the recent approval of these agents in Europe and in North America, much larger and longer duration outcomes studies are ongoing for definitive proof of CVD benefits. Other nonstatin agents recently approved in the United States include lomitapide and mipomersen, which both act via distinctive LDL receptor independent mechanisms to substantially reduce LDL cholesterol in homozygous familial hypercholesterolemia. We also address some unanswered questions, including measuring alternative biochemical variables to LDL cholesterol, evidence for treating children with monitoring of subclinical atherosclerosis, and potential risks of extremely low LDL cholesterol. As evidence for benefit in CVD prevention accumulates, we anticipate that clinical practice will shift toward more assertive LDL-lowering treatment, using both statins and nonstatins initiated earlier in appropriately selected patients.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".