Eicosapentaenoic acid, arachidonic acid, and triglyceride levels mediate most of the benefit of icosapent ethyl in REDUCE-IT
Bibliographic record
Abstract
Abstract Background In REDUCE-IT, icosapent ethyl (IPE) reduced major adverse cardiovascular (CV) events (MACE) relative to placebo (PBO) in 8179 statin-treated patients with residual hypertriglyceridemia and high CV risk. Questions have been raised about the mechanisms of benefit of IPE and the potential effects of the pharmaceutical grade mineral oil PBO on the results. Purpose The contributions of eicosapentaenoic acid (EPA), arachidonic acid (AA), triglycerides (TG), and other biomarkers (listed in Table 1) to MACE reduction by IPE relative to PBO were quantified via mediation analyses to illustrate the mechanisms of IPE. Methods Patients were randomised 1:1 to IPE 4 g/day or PBO and followed for a median 4.9 years. For a biomarker to be a mediator, there had to be both a treatment group difference on the biomarker and an association between the biomarker and risk of MACE. For the first condition, treatment group differences in change from baseline in each biomarker were analysed by mixed effects repeated measures models. For the second condition, time-varying values of each biomarker were related to the risk of MACE by calculating the time-weighted moving average (TWMA) for each variable, using all values for a given patient. Each was analysed in a Cox regression model with time to MACE as the outcome and TWMA values as time-varying covariates. The individual and joint mediation of those biomarkers determined to be mediators were assessed in Cox models that included treatment assignment. Biomarkers individually found to be ³10% mediators in absolute terms were included in the multivariable models. If biomarkers that would otherwise be included in multivariable models were strongly correlated (baseline values R2>0.5), the marker with the greatest univariate mediation was included. All analyses were intention-to-treat. Results IPE reduced MACE by 25% (HR (95% CI) = 0.75 (0.68, 0.83), p<0.0001). Treatment group differences on all potential mediators had p<0.05, and all but oxLDL were significantly related to MACE (p<0.05). Analyses of individual biomarkers showed EPA to be the strongest single mediator (Table 1). EPA, AA, and TG jointly mediated 78.9% of the IPE treatment effect, with EPA driving most of the mediation (57% as a single mediator). The marginal mediation by the remaining 3 biomarkers was 4.5% of the treatment effect, for total joint mediation of 83.4% (Figure 1). Conclusion In this mediation analysis of REDUCE-IT, most of the IPE benefit on MACE reduction was attributable to the two major mechanisms of the drug: (1) increasing EPA while reducing AA and, to a lesser extent, (2) reducing TG. The remainder of measured biomarker changes accounted for a minority of the benefit.Table 1Figure 1
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".