Diabetes and cardiovascular prevention: bridging two epidemics
Notice bibliographique
Résumé
This issue of EJPC focuses on cardiovascular risk and cardioprotective treatments in the context of Type 2 diabetes (T2D). Making a meta-analysis of 82 studies (9440 patients) in New diagnosis of diabetes in patients with myocardial infarction or stroke: a systematic review and meta-analysis, Douelrachad et al.1 from Canada reported that one out of six patients hospitalized for myocardial infarction is newly diagnosed for diabetes, a proportion remaining stable over the last 30 years. This underscores the urgent need to improve population screening for diabetes. Beyond medical consequences, in The reduction of the productivity burden of cardiovascular disease by improving the risk factor control among Australians with type 2 diabetes: a 10-year dynamic analysis, Abushanab et al.2 from Australia explored the societal and economic effects of some intervention scenarios, including a 19% reduction of tobacco use, 17% decrease of systolic blood pressure (SBP), 58% lower incidence of diabetes, and a 39 mg/dL total cholesterol reduction. These ambitious targets yielded substantial gain in productivity and economic outcomes, highlighting the strong interconnection between individual health and population-level productivity. Evaluating cardiovascular risk in T2D patients remains a major challenge. Numerous risk-prediction models have been developed, but their real-life performance remains heterogenous. In External validation of cardiovascular risk scores in patients with type 2 diabetes using the Spanish population-based CARDIANA cohort, Enguita-Germán et al.3 from Spain compared the accuracy of 13 models in the T2D patient population and 5 models in the general population for cardiovascular risk prediction over a 5-year follow-up period in their cohort. The models specifically developed for T2D, particularly SCORE2-diabetes and ADVANCE, emerged as the top-performing tools. While the SCORE2-OP model was developed to estimate cardiovascular risk, it is not applicable to elderly individuals with T2D. In Development and validation of the CARE-DM model to predict the cardiovascular risk in older persons with type 2 diabetes, Aponte Ribero et al.4 from Spain addressed this limitation, using data from 12 countries (>6900 patients with T2D aged ≥65 years). The CARE-DM model enabled to predict accurately 5–10-year cardiovascular risk, based on clinical characteristics and diabetes treatment.4 Similarly, Bai et al.5 from China demonstrated the usefulness of composite clinical and biological indices to predict heart failure in elderly patients in Advanced prediction of heart failure risk in elderly diabetic and hypertensive patients using nine machine learning models and novel composite indices: insights from NHANES 2003–2016. In Circadian heart rate fluctuations predict cardiovascular and all-cause mortality in Type 2 and Type 1 diabetes: a 21-year retrospective longitudinal study, Nesti et al.6 from Italy highlight the strong association between heart rate (HR) variability and micro- and macrovascular complications in both type-1 and T2D over an impressive follow-up of 21 years. Low 24 h and nocturnal HR fluctuations, measured by ambulatory blood pressure and HR monitoring, were associated with microvascular complications and death (Figure 1). Notably, reduced 24 h HR fluctuations were associated with a twofold increase in CV mortality and a 61% increase in all-cause mortality, even after adjustment for confounders.6 Kaplan–Meier curves and 95% confidence intervals for subjects stratified by 24 h heart rate standard deviation (24 h HR SD) (A and B) and nocturnal HR dip (C and D). Among anti-diabetic drugs, sodium–glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonist (GLP-1RA) have shown their efficacy to reduce cardiovascular events. Musella et al.7 from Sweden explored treatment choice in Pharmacological treatment patterns and outcomes according to the coexistence of heart failure and type 2 diabetes: data from the Swedish Heart Failure Registry and National Diabetes Registry. They examined the bidirectional influence of heart failure and T2D regarding treatment choice, including 37 903 patients with heart failure and 16 266 patients with T2D. Patients with heart failure and concomitant T2D had a 10-fold higher use of SGLT2i compared to those without T2D. However, the same patients were less likely to use mineraloid receptor antagonists (MRA) and renin–angiotensin system inhibitors (RASi), even after adjustment for renal function and potassium levels. Conversely, among patients with T2D, heart failure was independently associated with higher SGLT2i use and lower metformin use. The widespread use of SGLT2i suggests that this drug is both clinically trusted and well-accepted by prescribers.7 Regarding GLP1-RA therapy, Krychtiuk et al.8 from the USA provided further reassuring evidence in Albiglutide and atrial fibrillation in patients with type 2 diabetes and established cardiovascular disease: insights from the Harmony Outcomes trial. Early in the albiglutide development programme, concerns were raised about the risk of atrial fibrillation/atrial flutter (AF) with this drug. Over a 1.6-year follow-up, this post hoc analysis demonstrated that albiglutide, compared with placebo, reduced the risk of major adverse cardiovascular events irrespective of AF history [history of AF: adjusted hazard ratio (aHR) 0.83 (0.58–1.19), no history of AF aHR 0.77 (0.66–0.90); Pinteraction = 0.71]. Albiglutide was not associated with an increased risk of AF during follow-up (Figure 2). Forrest plot of treatment effect for each event type. AFB, atrial fibrillation, AFLU, atrial flutter. Current clinical guidelines recommend a combination of three therapies to delay CKD progression: RASi, SGLT2i, and MRA. In Expert perspectives on incorporating glucagon-like peptide-1 receptor agonist in diabetes and chronic kidney disease: challenges and opportunities, Halimi et al.9 from France summarized the efficacy and safety profiles of each treatment class, including GLP1-RA as the newcomer, and discussed the rationale for sequential or combination therapy according to an individualized approach. To refine this individualized approach, Mori et al.10 from Japan conducted the Heterogeneous cardiovascular effects of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes: a causal forest and target trial emulation study, using a nationwide cohort of working-age Japanese with T2D. Over a 3-year follow-up, the benefit of SGLT2i was weakly correlated with CV risk estimation (r = 0.29) but better correlated with some individual characteristics, including SBP (r = 0.74), BMI (r = 0.33), and fasting plasma glucose (r = 0.35). Similarly, in Effect of sodium-glucose cotransporter-2 inhibitors on kidney outcomes of individuals with type 2 diabetes according to blood pressure levels, Jimba et al. from Japan found that the protective effect of SGLT2i on renal function was more pronounced among T2D patients with higher SBP, particularly in those with preserved renal function without antihypertensive drugs.11 In The association of haptoglobin levels and phenotype with cardiovascular disease in type 2 diabetes: a Fenofibrate Intervention and Event Lowering in Diabetes sub-study, Ong et al.12 from Australia reported that higher haptoglobin levels were associated with an increased risk of cardiovascular events, but the benefits of fenofibrate did not differ by haptoglobin phenotypes, baseline levels, or in-trial changes. Along with editorials, rapid communications, and letters, this issue highlights the need for a better understanding of the complexity of cardiometabolic health and the need for a close collaboration between the cardiovascular and diabetes specialists around our patients. Victor Aboyans (Writing—review & editing [supporting]) and Laurence Salle (Writing—original draft [lead]) No new data were generated or analysed in support of this research.
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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| 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 | 0,000 | 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 ».