Association of SGLT2 Inhibitors With Cardiovascular and Kidney Outcomes in Patients With Type 2 Diabetes
Notice bibliographique
Résumé
Importance: Sodium-glucose cotransporter 2 (SGLT2) inhibitors favorably affect cardiovascular (CV) and kidney outcomes; however, the consistency of outcomes across the class remains uncertain. Objective: To perform meta-analyses that assess the CV and kidney outcomes of all 4 available SGLT2 inhibitors in patients with type 2 diabetes. Data Sources: A systematic literature search was conducted in PubMed from January 1, 2015, to January 31, 2020. Study Selection: One hundred forty-five records were initially identified; 137 were excluded because of study design or topic of interest. As a result, a total of 6 randomized, placebo-controlled CV and kidney outcomes trials of SGLT2 inhibitors in patients with type 2 diabetes were identified, with contributory data from 9 publications. All analyses were conducted on the total patient population of these trials. Data Extraction and Synthesis: Standardized data search and abstraction were performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement. Data were analyzed using a fixed-effect model. Main Outcomes and Measures: Outcomes included time to the first event of (1) the composite of major adverse CV events of myocardial infarction, stroke, or CV death, and each component, (2) the composite of hospitalization for heart failure (HHF) or CV death (HHF/CV death) and each component, and (3) kidney composite outcomes. For outcomes in the overall trial populations and in selected subgroups, hazard ratios (HRs) and 95% CIs were pooled and meta-analyzed across trials. Results: Data from 6 trials comprised 46 969 unique patients with type 2 diabetes, including 31 116 (66.2%) with atherosclerotic CV disease. The mean (SD) age of all trial participants was 63.7 (7.9) years; 30 939 (65.9%) were men, and 36 849 (78.5%) were White. The median number of participants per trial was 8246 (range, 4401-17 160). Overall, SGLT2 inhibitors were associated with a reduced risk of major adverse CV events (HR, 0.90; 95% CI, 0.85-0.95; Q statistic, P = .27), HHF/CV death (HR, 0.78; 95% CI, 0.73-0.84; Q statistic, P = .09), and kidney outcomes (HR, 0.62; 95% CI, 0.56-0.70; Q statistic, P = .09), with no significant heterogeneity of associations with outcome. Associated risk reduction for HHF was consistent across the trials (HR, 0.68; 95% CI, 0.61-0.76; I2 = 0.0%), whereas significant heterogeneity of associations with outcome was observed for CV death (HR, 0.85; 95% CI, 0.78-0.93; Q statistic, P = .02; I2 = 64.3%). The presence or absence of atherosclerotic CV disease did not modify the association with outcomes for major adverse CV events (HR, 0.89; 95% CI, 0.84-0.95 and HR, 0.94; 95% CI, 0.83-1.07, respectively; P = .63 for interaction), with similar absence of associations with outcome modification by prevalent atherosclerotic CV disease for HHF/CV death (P = .62 for interaction), HHF (P = .26 for interaction), or kidney outcomes (P = .73 for interaction). Conclusions and Relevance: In this meta-analysis, SGLT2 inhibitors were associated with a reduced risk of major adverse CV events; in addition, results suggest significant heterogeneity in associations with CV death. The largest benefit across the class was for an associated reduction in risk for HHF and kidney outcomes, with benefits for HHF risk being the most consistent observation across the trials.
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 | 0,021 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,018 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,003 | 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) | 0,002 | 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 ».