Eicosapentaenoic acid, arachidonic acid, and triglyceride levels mediate most of the benefit of icosapent ethyl in REDUCE-IT
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».