Lipoprotein(a) Blood Levels and Cardiovascular Risk Reduction With Icosapent Ethyl
Bibliographic record
Abstract
BACKGROUND: Elevated lipoprotein(a) (Lp[a]) concentrations are associated with increased cardiovascular event risk even in the presence of well-controlled low-density lipoprotein cholesterol levels, but few treatments are documented to reduce this residual risk. OBJECTIVES: The aim of this post hoc analysis of REDUCE-IT (Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial) was to explore the cardiovascular benefit of icosapent ethyl (IPE) across a range of Lp(a) levels. METHODS: A total of 8,179 participants receiving statin therapy with established cardiovascular disease or age ≥50 years with diabetes and ≥1 additional risk factor, fasting triglyceride 1.69 to 5.63 mmol/L, and low-density lipoprotein cholesterol 1.06 to 2.59 mmol/L were randomized to receive 2 g twice daily of IPE or matching placebo. Relationships between continuous baseline Lp(a) mass concentration and risk for first and total (first and subsequent) major adverse cardiovascular events (MACE) were analyzed, along with the effects of IPE on first MACE among those with Lp(a) concentrations ≥50 or <50 mg/dL. RESULTS: Among 7,026 participants (86% of those randomized) with baseline Lp(a) assessments, the median concentration was 11.6 mg/dL (Q1-Q3: 5.0-37.4 mg/dL). Lp(a) had significant relationships with first and total MACE (P < 0.0001), while event reductions with IPE did not vary across the range of Lp(a) (interaction P > 0.10). IPE significantly reduced first MACE in subgroups with concentrations ≥50 and <50 mg/dL. CONCLUSIONS: Baseline Lp(a) concentration was prognostic for MACE among participants with elevated triglyceride levels receiving statin therapy. Importantly, IPE consistently reduced MACE across a range of Lp(a) levels, including among those with clinically relevant elevations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".