Mediators of the effect of ertugliflozin on a composite kidney outcome in patients with type 2 diabetes and atherosclerotic cardiovascular disease: analyses from VERTIS CV
Notice bibliographique
Résumé
Abstract Introduction Sodium–glucose cotransporter 2 (SGLT2) inhibitors have been shown to slow the decline of kidney function in outcome trials, but the biological mediator(s) underlying the therapeutic benefit are not well established. Purpose We performed a post-hoc analysis exploring potential mediators of the effects of the SGLT2 inhibitor ertugliflozin on the VERTIS CV exploratory kidney composite outcome (sustained 40% decrease from baseline in estimated glomerular filtration rate [eGFR], chronic kidney replacement therapy or kidney death). Methods In VERTIS CV, 8246 participants with type 2 diabetes mellitus and established atherosclerotic cardiovascular disease were randomised to placebo, ertugliflozin 5 mg or 15 mg (pooled for analyses, as prospectively planned), and were followed for a mean of 3.5 years. The hazard ratio (HR; 95% confidence interval) for the pre-specified exploratory kidney composite outcome was 0.66 (0.50, 0.88). Cox regression models were used to evaluate covariates that were significantly differentially changed from baseline with ertugliflozin treatment as candidate mediators, with a mediator identified as a covariate when added to an unadjusted model of randomised treatment assignment a) yielded a larger hazard ratio; and b) the mediator retained P<0.05 in the model (eGFR was excluded as a covariate). The percentage of mediation was determined by the proportional increase in the HR between the unadjusted and adjusted models for each post-randomisation period: early (first change from baseline measurement) and average (weighted average of change from baseline from all post-baseline measurements). Each potential mediator was tested individually, so across analyses, mediation % sums to >100%. Results Of 22 covariates significantly changed by ertugliflozin, nine were identified as potential mediators (Table). The covariates with a high percentage of mediation were those related to changes in blood erythrocytes (haemoglobin, haematocrit and red blood cell mass), with average changes in haemoglobin having the highest percentage of mediation (61.8%). Serum uric acid was associated with a mediation of 29.4% and 50.0% for the early and average post-randomisation effect periods, respectively. Early changes in glycated haemoglobin had a large mediation (50%), but the average change during the trial was not significant. Average change in serum albumin had a large mediation (29.4%). Average changes in body weight and systolic blood pressure had percentages of mediation of 41.2% and 14.7%, respectively. Conclusion Multiple factors may be involved in the reduction of the kidney composite outcome observed with ertugliflozin. In the short-term, changes in glycaemia had a high mediation effect. Over the long-term, changes suggestive of haemoconcentration and/or haematopoiesis (natriuresis-related effects), showed the highest percentage of mediation, followed by changes in serum uric acid and body weight (glucosuria-related effects). Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Sponsored by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA in collaboration with 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 | 0,012 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,007 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 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 ».