#3510 Outcomes of canagliflozin in patients with type 2 diabetes with and without progressive renal function decline: insights from the CREDENCE trial
Notice bibliographique
Résumé
Abstract Background and Aims Sodium-glucose co-transporter-2 (SGLT-2) inhibitors have become a cornerstone of management for patients with chronic kidney disease (CKD). However, pivotal clinical trials with SGLT-2 inhibitors have largely excluded individuals with an estimated glomerular filtration rate (eGFR) <20–30 ml/min/1.73 m², leaving a critical gap in understanding the risks and benefits of these agents in advanced CKD. Post-hoc analyses have attempted to address this gap by focusing on patients with an eGFR <30 ml/min/1.73 m² at baseline. Nevertheless, the limited sample size of this subgroup has reduced the statistical power and precision of these findings. Emerging evidence suggests that SGLT-2 inhibitors may retain their efficacy as kidney function declines, raising the possibility of extending their therapeutic role to patients with advanced CKD. We hypothesize that these agents will continue to protect against cardiovascular and renal events in patients whose eGFR drops below 30 ml/min/1.73 m² after treatment initiation. Method This is a post hoc analysis of the CREDENCE trial, a double-blind, randomized controlled study, investigating the effect of canagliflozin, an SGLT-2 inhibitor, at the dose of 100 mg daily on renal and cardiovascular endpoints in patients with diabetic kidney disease. The CREDENCE trial enrolled 4401 participants with an eGFR of 30–90 mL/min/1.73 m² and a urinary albumin-to-creatinine ratio of 300–5000 mg/g. The study protocol allowed for continuation of the study drug in patients with eGFR decline until the initiation of renal replacement therapy. In this analysis, we compared renal and cardiovascular outcomes between canagliflozin and placebo in two subgroups: patients whose eGFR dropped below 30 mL/min/1.73 m² during follow-up and those whose eGFR remained above this threshold. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained eGFR of <15 mL/min/1.73 m²), a doubling of the serum creatinine level, or death from renal causes. Secondary efficacy endpoints included non-fatal myocardial infarction, non-fatal stroke, cardiovascular death, hospitalization for heart failure, all-cause mortality, and the individual components of the primary endpoint. Safety endpoints included acute kidney injury and hyperkalemia. We used a time-updated Cox proportional hazards model to account for time-varying covariates. P-values for interaction were used to study the differential effect of canagliflozin on clinical endpoints in patients with and without eGFR decline. Results Among a total of 4401 patients, 665 (15.1%) experienced a deterioration of eGFR below 30 ml/min/1.73 m² during the follow-up period. Patients with a decline in eGFR had higher blood pressure at baseline, were more likely to have severely increased albuminuria, and were more frequently treated with loop diuretics and insulin. The incidence of hard clinical endpoints was higher in patients with eGFR decline with the exception of stroke. Canagliflozin was associated with a significant reduction in the primary outcome among patients who did not experience eGFR decline below the 30 mL/min/1.73 m² cutoff: hazard ratio (HR) 0.58, 95% confidence interval (CI) 0.45–0.74. In contrast, no benefit was seen in those with eGFR decline: HR 1.01, 95% CI 0.43–2.40. However, there was no significant interaction between the treatment arm and eGFR decline for this endpoint (p for interaction 0.21, Fig. 1). Similarly, point estimates for the effect of canagliflozin were comparable for most secondary endpoints, while P-values for interaction were all above the 0.20 cutoff. In addition, there was no evidence of harm from canagliflozin with respect to the safety endpoints in the subgroup of patients with eGFR decline. Conclusion In this subgroup analysis from the CREDENCE trial, there was no evidence of a differential treatment effect from canagliflozin on hard clinical endpoints in patients with and without eGFR decline below the 30 mL/min/1.73 m² cutoff. In addition, there was no evidence of harm from canagliflozin who progressed to advanced CKD during the follow-up period.
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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 | 0,009 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».