Mediation analyses of the effect of ertugliflozin on hospitalisation for heart failure in patients with type 2 diabetes and atherosclerotic cardiovascular disease from the VERTIS CV trial
Notice bibliographique
Résumé
Abstract Introduction Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce risk of hospitalisation for heart failure (HHF) in outcome trials, but the biological mediators underlying the therapeutic benefit are not well established. Purpose To identify potential biological mediators through which ertugliflozin reduces risk of HHF. Methods In VERTIS CV, 8246 patients with type 2 diabetes and atherosclerotic cardiovascular disease were randomised to ertugliflozin 5 or 15 mg (observations pooled as prospectively planned) or placebo. Cox regression models were used to evaluate the associations between changes in 26 potential mediators with outcomes. Potential mediators were selected based on proposed mechanisms and/or differential change from baseline with SGLT2 inhibitors. Mediation criteria required 1) significant (P<0.05 for change from baseline) effects of ertugliflozin vs placebo on each potential mediator; and 2) significant (P<0.05) association of change in post-randomisation levels of the potential mediator with risk of HHF when added to an unadjusted model of randomised treatment assignment. Percent mediation was determined by comparing the unadjusted hazard ratio and hazard ratio adjusted for change in the potential mediator of interest. Each covariate was tested individually, such that percent mediation across the analyses summed to >100%. Time-dependent models were used to evaluate associations between early (change from baseline for the first post-baseline measurement) and average (weighted average of change from baseline using all post-baseline measurements) changes in covariates with clinical outcomes. Results Over a mean of 3.5 years, the incidence rate of HHF was 0.7 and 1.1 per 100 patient-years with ertugliflozin and placebo, respectively. Among 26 candidate mediators, 9 and 13 met the mediation criteria based on early and average changes, respectively. The 3 covariates with the largest mediating effects of early changes included haematocrit (40%), haemoglobin (27%) and HDL-C (23%) (Table); other significant biomarkers included urine albumin/creatinine ratio, and serum albumin, uric acid, chloride, protein and sodium. The 3 biomarkers with the largest mediating effects in average changes included haemoglobin (63%), albumin (50%) and uric acid (47%) (Table); other significant biomarkers included haematocrit, urine albumin/creatinine ratio, body weight, serum protein and chloride, systolic blood pressure, ALT, BUN, eGFR and heart rate. Conclusions In these analyses from the VERTIS CV trial, potential markers of volume status and haemoconcentration and/or haematopoiesis were the strongest mediators of the effect of ertugliflozin on reducing risk of HHF in the early and average change periods. Other potential mediators included uric acid, lipid markers and kidney parameters. These findings provide insights into potential mechanisms through which ertugliflozin, and potentially the SGLT2 inhibitor class, may prevent HHF. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Sponsored by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA, and Pfizer Inc., New York, NY, USA.
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 | 0,016 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,006 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».