Cardioprotective pharmacotherapy in patients with type 2 diabetes across the Middle East and Africa: the PACT-MEA study
Notice bibliographique
Résumé
Abstract Background There is limited information about the management of cardiovascular (CV) risk in patients with type 2 diabetes (T2D) in the Middle East and Africa. For patients with T2D and established atherosclerotic cardiovascular disease (eASCVD) or those at risk for ASCVD, global guidelines recommend the use of antidiabetic medications with proven cardiovascular and kidney benefit (glucagon-like peptide-1 receptor agonists [GLP-1 RAs] and sodium-glucose cotransporter-2 inhibitors [SGLT2is]), in addition to lipid lowering, blood pressure lowering, and antiplatelet therapy, independent of baseline HbA1c or metformin use. Purpose To determine the use of pharmacotherapy among a population of patients with T2D and eASCVD or high/very high ASCVD risk in seven countries across the Middle East and Africa. Methods Adult patients with T2D were enrolled in a cross-sectional, observational study in Bahrain, Egypt, Jordan, Kuwait, Qatar, South Africa, and United Arab Emirates. Pharmacotherapy data extracted from the medical charts of patients during a routine scheduled clinic visit in 2022 were analysed. Descriptive statistics were used to characterize patients with T2D and their glucose-lowering pharmacotherapy use by age, diabetes duration, body mass index, HbA1c, microvascular complications, estimated glomerular filtration rate (eGFR), and urinary albumin to creatinine ratio (UACR). Results Of the 3726 patients in the overall study sample (mean age, 58 ± 12; male, 53%), one in five had eASCVD (21%) and nearly all were classified as being at high (69%)/very high risk (30%, includes eASCVD), according to European Society of Cardiology (ESC) 2021 guidelines. About one-third (36%) of patients were taking SGLT2is (Table 1, range across countries: 20%-64%). Use of SGLT2is was similar across age and BMI but more patients with T2D for ≥10 years (40%) received these medications than those with T2D for <10 years (31%). More males than females were on SGLT2is (40% vs 32%). Use of SGLT2is was also higher among patients with HbA1c ≥7% (42% vs 33%, Table 2). SGLT2i use was similar by eGFR and UACR levels. Few (13%) of the overall sample of patients with T2D received GLP-1 RAs (Table 1, range across countries: 3%-25%), the use of which declined with age. More females than males were taking GLP1-RAs (16% vs 11%). More patients with obesity (BMI ≥30 kg/m2) received GLP-1 RAs than those without obesity (18% vs 8%); use was also higher among patients with eGFR ≥60 (14% vs 9%, Table 2). Conclusions Despite availability of cardio-renal therapy in each of the seven participating countries in the Middle East and Africa, few patients with T2D who had eASCVD or were at high/very high risk for ASCVD received SGLT2is or GLP-1 RAs, as recommended by guidelines. Active prioritization of cardio-renal protective therapies based on CV risk and renal target organ damage should be addressed given the high burden of disease and complications in the region.Table 1Table 2
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 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 ».