Management of Type 2 Diabetic Kidney Disease in 2022: A Narrative Review for Specialists and Primary Care
Notice bibliographique
Résumé
Purpose of review: Kidney disease is present in almost half of Canadian patients with type 2 diabetes (T2D), and it is also the most common first cardiorenal manifestation of T2D. Despite clear guidelines for testing, opportunities are being missed to identify kidney diseases, and many Canadians are therefore not receiving the best available treatments. This has become even more important given recent clinical trials demonstrating improvements in both kidney and cardiovascular (CV) endpoints with sodium-glucose cotransporter 2 (SGLT2) inhibitors and a nonsteroidal mineralocorticoid receptor antagonist, finerenone. The goal of this document is to provide a narrative review of the current evidence for the treatment of diabetic kidney disease (DKD) that supports this new standard of care and to provide practice points. Sources of information: An expert panel of Canadian clinicians was assembled, including 9 nephrologists, an endocrinologist, and a primary care practitioner. The information the authors used for this review consisted of published clinical trials and guidelines, selected by the authors based on their assessment of their relevance to the questions being answered. Methods: Panelists met virtually to discuss potential questions to be answered in the review and agreed on 10 key questions. Two panel members volunteered as co-leads to write the summaries and practice points for each of the identified questions. Summaries and practice points were distributed to the entire author list by email. Through 2 rounds of online voting, a second virtual meeting, and subsequent email correspondence, the authors reached consensus on the contents of the review, including all the practice points. Key findings: It is critical that DKD be identified as early as possible in the course of the disease to optimally prevent disease progression and associated complications. Patients with diabetes should be routinely screened for DKD with assessments of both urinary albumin and kidney function. Treatment decisions should be individualized based on the risks and benefits, patients' needs and preferences, medication access and cost, and the degree of glucose lowering needed. Patients with DKD should be treated to achieve targets for A1C and blood pressure. Renin-angiotensin-aldosterone system blockade and treatment with SGLT2 inhibitors are also key components of the standard of care to reduce the risk of kidney and CV events for these patients. Finerenone should also be considered to further reduce the risk of CV events and chronic kidney disease progression. Education of patients with diabetes prescribed SGLT2 inhibitors and/or finerenone is an important component of treatment. Limitations: No formal guideline process was used. The practice points are not graded and are not intended to be viewed as having the weight of a clinical practice guideline or formal consensus statement. However, most practice points are well aligned with current clinical practice guidelines.
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 | 0,004 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».