Sodium-glucose cotransporter 2 inhibitors in chronic kidney disease: A review of current evidence and clinical implications
Notice bibliographique
Résumé
Chronic kidney disease (CKD) is a progressive condition affecting millions worldwide, leading to substantial morbidity, mortality, and healthcare burden. While traditional treatments such as angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) have been the cornerstone of CKD management, newer therapeutic approaches are needed to slow disease progression and improve outcomes. Sodium-glucose cotransporter 2 (SGLT2) inhibitors, initially developed as antihyperglycemic agents, have demonstrated significant renoprotective and cardioprotective effects beyond glucose control. This review aims to evaluate the current evidence on the efficacy, safety, and clinical implications of SGLT2 inhibitors in CKD, highlighting their mechanisms of action, benefits, limitations, and future research directions. A comprehensive literature search was conducted in PubMed, Google Scholar, and Medline using keywords related to SGLT2 inhibitors, CKD, and renal outcomes with no time limit. Studies included randomized controlled trials, cohort studies, and case-control studies examining the effects of SGLT2 inhibitors on renal and cardiovascular outcomes in CKD patients. The risk of bias was assessed using standard tools such as the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool. Clinical trials have demonstrated that SGLT2 inhibitors, including empagliflozin, canagliflozin, dapagliflozin, and ertugliflozin, significantly reduce CKD progression, lower albuminuria, and decrease the risk of cardiovascular events and all-cause mortality. These effects are observed in both diabetic and non-diabetic populations. Additionally, SGLT2 inhibitors exhibit renoprotective mechanisms such as reducing glomerular hyperfiltration, modulating tubuloglomerular feedback, and exerting anti-inflammatory and antifibrotic properties. However, potential adverse effects, including an initial decline in estimated glomerular filtration rate (eGFR), an increased risk of euglycemic diabetic ketoacidosis, and urinary tract infections, necessitate careful patient selection and monitoring. Emerging studies also explore the role of machine learning in optimizing SGLT2 inhibitor use for personalized treatment approaches. SGLT2 inhibitors have emerged as a transformative addition to CKD management, offering substantial renal and cardiovascular benefits. Despite safety concerns, their advantages outweigh the risks, warranting broader clinical implementation. Future research should focus on refining patient selection, optimizing treatment combinations, and leveraging data science to enhance therapeutic outcomes in CKD patients. • This paper analyzes SGLT2 inhibitors’ efficacy and safety in managing chronic kidney disease (CKD). • Highlights dual benefits of SGLT2 inhibitors in slowing CKD and improving cardiovascular outcomes. • Discusses SGLT2 inhibitors’ renoprotective effects, including reduced hyperfiltration and inflammation. • Examines risks like initial eGFR decline, diabetic ketoacidosis, and urinary tract infections. • Explores machine learning to optimize SGLT2 inhibitor use through personalized treatment and drug discovery.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».