#800 The effect of canagliflozin dose on cardiovascular and renal outcomes in patients with type 2 diabetes and high cardiovascular risk
Notice bibliographique
Résumé
Abstract Background and Aims Sodium-glucose co-transporter-2 (SGLT-2) inhibitors have revolutionized the management of chronic kidney disease (CKD). As shown in large randomized clinical trials, they reduce the incidence of atherosclerotic cardiovascular disease and heart failure and they delay CKD progression. There is evidence from pharmacokinetic studies that their effect on surrogate endpoints, such as albuminuria or blood pressure, might be dose dependent. However, the effect of SGLT-2 inhibitor dosage on hard clinical endpoints has not been adequately studied. This analysis investigates whether the effects of canagliflozin on cardiovascular and renal endpoints are dose dependent. Methods The CANVAS trial (Canagliflozin Cardiovascular Assessment Study) randomly assigned 4,330 patients with type 2 diabetes and high cardiovascular risk to receive canagliflozin 100 mg, canagliflozin 300 mg, or placebo in a 1:1:1 ratio. Results from this trial were reported together with CANVAS-R in the CANVAS program but have not been published independently [1]. Our post-hoc analysis evaluates the impact of each dose of canagliflozin on cardiovascular and renal outcomes. Our primary endpoint was a composite of non-fatal myocardial infarction, stroke, or cardiovascular death. Secondary endpoints included the individual components of the primary outcome, hospitalization for heart failure, all-cause mortality, a composite renal endpoint (including doubling of serum creatinine, end-stage kidney disease or renal death), sustained 40% reduction in estimated glomerular filtration rate, and albuminuria progression or regression. Safety outcomes included acute kidney injury and serious hyperkalemia. Hazard ratios (HR) were estimated using Cox proportional hazards models. Kaplan-Meier survival curves were compared across groups using the log-rank test. Results The study enrolled 4330 participants with type 2 diabetes and high cardiovascular risk. Median age was 61 years and 66% of patients were males. Baseline characteristics were similar across all treatment groups. Median follow-up was 74 months. In the adjusted analysis for age, sex, and history of cardiovascular disease, canagliflozin at 300 mg significantly reduced the incidence of the composite cardiovascular endpoint, compared with placebo: hazard ratio (HR) of 0.83, 95% confidence interval (CI) of 0.68–1.00 (p = 0.049). In contrast, no difference was identified between canagliflozin 100 mg, compared with placebo, for the composite cardiovascular endpoint: HR 0.96 (95% CI 0.80–1.15, p = 0.64). Both doses of canagliflozin were associated with a significant reduction in the incidence of the composite renal endpoint, compared with placebo: HR 0.50 for canagliflozin 100 mg (95% CI 0.25–0.96, p = 0.039) and HR 0.42 for canagliflozin 300 mg (95% CI 0.21–0.86, p = 0.017). Albuminuria progression to a higher stage (A1 to A2 or A2 to A3) was less common with the 300 mg dose, compared with placebo (HR 0.83, 95% CI 0.73–0.95, p = 0.006), but not with 100 mg dose (HR 0.94, 95% CI 0.83–1.07, p = 0.326). All-cause mortality was significantly lower with the 300 mg dose compared with placebo (HR 0.78, 95% CI 0.63–0.97, p = 0.027). In addition, a trend towards lower mortality rates was seen with the 100 mg dose. The incidence of acute kidney injury was comparable across the three treatment arms. A trend towards fewer severe hyperkalemia episodes was seen with the 300 mg canagliflozin dose. Results for all secondary and safety endpoints are shown in Table. Conclusion This post-hoc analysis of the CANVAS trial demonstrates a dose-dependent effect of canagliflozin on cardiovascular endpoints. However, both doses of canagliflozin seem equally effective for the prevention of hard clinical renal endpoints. In addition, both doses of the drug had a comparable safety profile. Our findings suggest that the higher canagliflozin dose may be considered in all patients with type 2 diabetes at high cardiovascular risk.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».