Persistent lipid abnormalities in diabetes; are we approaching the treatment optimally?
Notice bibliographique
Résumé
There is overwhelming evidence that reducing LDL cholesterol with the use of statins leads to reductions in cardiovascular disease in diabetes. This has resulted in international and country-specific guidelines of the treatment of dyslipidaemia in diabetes, with recommendations of target LDL cholesterol to be achieved. Consequently, in many countries the majority of people with Type 2 diabetes are now being treated with a statin. Nonetheless, there is a growing body of evidence that, in clinical practice, target LDL cholesterol levels are not achieved and therapy is not targeted at the dyslipidaemia associated with diabetes, namely low HDL cholesterol and high triglycerides. In this edition of Diabetic Medicine, Leiter and colleagues on behalf of the Dyslipidaemia International Study (DYSIS) investigators (page 1349) have performed a cross-sectional study of 22 063 statin-treated outpatients consecutively recruited by clinicians in Canada and 11 European countries. Forty-one per cent of outpatients had diabetes and, in those patients, only 48% were at cholesterol target and many had persistent lipid abnormalities. Simvastatin was the most common statin, being prescribed at a dose of 20–40 mg per day; 14% patients were taking ezetimibe alone or in combination with a statin. The possible barriers to effective lipid interventions are manyfold and need tackling at all levels. In some parts of the world this is related to finance and limits set by insurance companies or health providers. Whilst it is reasonable to always prescribe the cheapest statin in the majority of people with diabetes in the first instance, if the targets are not achieved or there is evidence of dyslipidaemia it is important to consider the use of a more potent or appropriate statin. Physician attitudes are another important contributor to the problem and aggressive marketing of some of the non-statin lipid-lowering drugs can influence prescribing patterns. It is likely that the other most important contributor to less effective lipid lowering is at the level of the person with diabetes themselves. We place many demands on the person with diabetes, who has to take many medications, several of which have side effects. It is well documented that even in the setting of clinical trials not all patients take their tablets; indeed, in the Collaborative Atorvastatin Diabetes Study (CARDS) trial, on average 20% of patients on statin were not taking their medication over the course of the 4-year study. In clinical practice, it seems likely that the most common reason not to achieve target cholesterol is therefore going to relate to the underuse of medication by people with diabetes. It is therefore of the highest importance that the person with diabetes has the locus of control compared with health professional-focused approaches. It seems highly likely that in addition to LDL cholesterol we should be considering optimizing HDL cholesterol and triglycerides. Currently, the evidence base for second-line therapy to treat diabetic dyslipdaemia is not strong, but nonetheless suggests that further benefits may be achieved using either fibrate- and nicotinic acid-containing medications. Despite their relatively high uptake, the current evidence for the use of ezetimibe to reduce cardiovascular disease in diabetes remains comparatively weak. We therefore eagerly await the results of several ongoing trials addressing these issues, including AIM-HIGH, HPS2-THRIVE, dal-OUTCOME, IMPROVE-IT and others. In the meanwhile, it is important that the majority of people with diabetes have the opportunity to be treated with the best statin and at the most appropriate dose to prevent cardiovascular disease, whilst at the same time attention is paid to all other reversible cardiovascular risk factors.
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 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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 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 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».