Approaches to treatment 1: How is type 2 diabetes actually treated?
Notice bibliographique
Résumé
Given the multiple organ systems playing roles in glucose production and utilization, with the gut, islets, liver, kidney, fat, muscle, and brain of particular importance, it is not surprising that the treatment of type 2 diabetes (T2D) is complex, but it is fascinating to review actual practice patterns. In the US, from 1999 to 2010, the use of glucose-lowering medication increased from 74% to 82% of individuals with diabetes, with metformin increasing from 35% to 55%, sulfonylureas decreasing from just over to just under 40%, thiazolidinediones increasing from 12% to 28%, the proportion receiving insulin increasing from 17% to 21%, and, in 2010, 8% receiving a dipeptidyl peptidase (DPP) 4 inhibitor (Fig. 1).1 Looked a differently, among some 20 million people diagnosed as having diabetes in the US, the number of prescriptions for non-insulin diabetes medicines issued annually increased from approximately 90 to 120 million from 2003 to 2012, with metformin increasing from 30 to 50 million, sulfonylureas around 30 million, thiazolidinediones decreasing from approximately 15 to 5 million, and both DPP-4 inhibitors and glucagon-like peptide-1 (GLP-1) analogs reaching approximately 8 million.2 In that study, only 45% of metformin was used as monotherapy; 22% was administered with sulfonylureas, 22% with a DPP-4 inhibitor, 10% with long-acting insulin analogs, 8% with a thiazolidinedione, and 4% with a GLP-1 analog; approximately two-thirds of use of sulfonylureas, DPP-4 inhibitors, and thiazolidinediones, and half of use of GLP-1 analogs, was with metformin.2 In Germany, in 2010, just 63.1% of people diagnosed as having diabetes received antihyperglycemic medication, of whom 40% received metformin, 20% sulfonylureas, 6% long-acting human insulin, 7% long acting analog insulin, 8% short-acting human insulin, 4% short-acting analog insulin, 6% human mixed insulin, 1% analog mixed insulin, 3% a DPP-4 inhibitor, and 1% a GLP-1 analog.3 In Canada, during the period from 1994 t o2006, less than half of individuals age 66 years and over diagnosed as having diabetes were treated during the first year after diagnosis, with the likelihood of treatment actually decreasing somewhat over the decade.4 The use of metformin as initial treatment increased from 20% to 80%, whereas the use of sulfonylureas decreased from 70% to 10%; thiazolidinediones, insulin, acarbose, and combinations were infrequently used.4 In the large US Kaiser Permanente database, from 2005 to 2010 there was an increase in treatment initiation during the first year after diabetes diagnosis from 36% to 44% (Fig. 2a), with metformin increasing from 36% to 44%, sulfonylureas decreasing from 31% to 10%, and the combination increasing from 5% to 10%; approximately 6% received insulin (Fig. 2b).5 What are we to make of these statistics? First, physicians appear to have concerns about initiating treatment at the time of diabetes diagnosis, in particular with newer diagnostic guidelines, either positively, because of understanding of the importance of emphasizing lifestyle modification, or negatively, because of skepticism about the value of such treatment at relatively modest levels of hyperglycemia, despite evidence from epidemiologic studies and randomized controlled trials that such treatment is appropriate.6 Second, there have been major changes in therapeutic approach over the past decade, with a marked reduction in the use of sulfonylureas, particularly as initial therapy, with an equally marked increase in the use of metformin, with increasing and then decreasing use of thiazolidinediones, and with the introduction of DPP-4 inhibitors and, to a lesser extent, GLP-1 analogs. The use of insulin is increasing somewhat, and comprises a major treatment approach, and combination treatment appears increasingly to be the rule rather than the exception in the management of hyperglycemia. With these perspectives, it will be interesting to consider current T2D treatment guidelines. This topic will be addressed in the next issue's Editorial.
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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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».