Pillar Risk-Based Treatment for Chronic Kidney Disease in People With Type 2 Diabetes: A Narrative Review
Notice bibliographique
Résumé
Chronic kidney disease continues to be a significant burden for people living with type 2 diabetes, despite the available guideline-directed treatment options. Traditionally, a stepwise approach has been implemented for the management of chronic kidney disease and type 2 diabetes, which involves the linear sequential initiation of one therapy after the other on the basis of an individual’s treatment outcomes. However, this approach is not beneficial for all individuals, as it can lead to treatment inertia and subsequent disease progression. Therefore, primary care practitioners should consider implementing a more proactive treatment strategy to optimize care. The pillar risk-based approach is an emerging concept with goals of glucose control and blood pressure control as well as comprising simultaneous or rapid sequential initiation of multiple therapies, such as renin–angiotensin system inhibitors (RASi), sodium–glucose cotransporter 2 inhibitors, a nonsteroidal mineralocorticoid receptor antagonist (finerenone), and glucagon-like peptide-1 receptor agonists, which target the different hemodynamic, metabolic, and fibrotic/inflammatory pathways involved in chronic kidney disease and type 2 diabetes. This approach enables earlier chronic kidney disease risk reduction, and the recently published CONFIDENCE trial reported tolerability and efficacy of simultaneous initiation of two of these therapies (finerenone and empagliflozin) in those already receiving RASi. This review article provides primary care practitioners with practical considerations, discussing current guideline-directed treatment options for chronic kidney disease in people with type 2 diabetes in the context of a historical stepwise approach versus the new patient-centric pillar risk-based approach. Chronic kidney disease is a major complication for people with type 2 diabetes. Treatments for people with type 2 diabetes and chronic kidney disease has traditionally followed a stepwise approach, wherein therapies are introduced sequentially, one by one, on the basis of how the person responds to treatment. This stepwise approach to treatment is slow and may take months or years for a person with chronic kidney disease to be on maximal therapy, thus losing crucial time and nephrons. We discuss a potentially different approach to treatment (the pillar risk-based approach) that offers a more proactive strategy and involves starting multiple therapies early and simultaneously or in a rapid stepwise fashion. This pillar risk-based approach integrates blood glucose control and blood pressure control alongside medications that slow progression of chronic kidney disease. Key treatments include renin–angiotensin system inhibitors, sodium–glucose cotransporter 2 inhibitors, a nonsteroidal mineralocorticoid receptor antagonist, and glucagon-like peptide-1 receptor agonists. By addressing multiple disease mechanisms at the same time, the pillar risk-based approach to treatment aims to reduce the risk for kidney failure requiring dialysis or kidney transplantation, as well as cardiovascular events and death. This review article discusses the current guideline-directed treatment options for chronic kidney disease in people with type 2 diabetes in the context of a historical stepwise approach versus the patient-focused pillar risk-based approach.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,004 | 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 ».