No abstract

Background: The cardiovascular (CV) safety in terms of heart failure among different classes of treatment remains largely unknown. We sought to assess the comparative effect of these agents on heart failure outcomes. Methods:This study was registered in the International Prospective Register of Systematic Reviews (CRD 42016042063). MEDLINE, EMBASE, and the Cochrane Library Central Register of Controlled Trials were searched. For the primary outcomes reported previously, studies between Jan 1, 1980 and June 30, 2016 were screened, and subsequently updated till Jan 24, 2019. We performed network meta-analysis to obtain estimates for the outcomes of heart failure, in particular by rankograms for ranking of heart failure risk as well as by pairwise comparisons among all classes of anti-diabetic medications.Results: A total of 91 trials were included, among which were 171,253 participants and 4163 reported cases of heart failure events. As for rankograms, the surface under the cumulative ranking curves (SUCRA) of sodium-glucose co-transporters 2 and thiazolidinediones were 93.4% and 4.3%, respectively, signifying the lowest and highest risk of heart failure, respectively. As for pairwise comparisons in the network, sodium-glucose co-transporters 2 were significantly superior to insulin (OR: 0.75, 95% CI 0.62-0.91), dipeptidyl peptidase 4 inhibitors (OR: 0.68, 95% CI 0.59-0.78), glucagon-like peptide-1 receptor agonists (OR: 0.65, 95% CI 0.54-0.78), and thiazolidinediones (OR: 0.46, 95% CI 0.27-0.77) in terms of heart failure risk. Furthermore, in an exploratory analysis among subjects with underlying heart failure or at risk of heart failure, the superiority of sodium-glucose co-transporters 2 was still significant. Conclusions:In terms of heart failure risk, sodium-glucose co-transporters 2 were the most favorable option among all classes of anti-diabetic medications.

Background Hypertensive patients are highly heterogeneous in cardiovascular prognosis and treatment responses. A better classification system with phenomapping of clinical features would be of greater value to identify patients at higher risk of developing cardiovascular outcomes and direct individual decision-making for antihypertensive treatment. Methods An unsupervised, data-driven cluster analysis was performed for all baseline variables related to cardiovascular outcomes and treatment responses in subjects from the Systolic Blood Pressure Intervention Trial (SPRINT), in order to identify distinct subgroups with maximal within-group similarities and between-group differences. Cox regression was used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for cardiovascular outcomes and compare the effect of intensive antihypertensive treatment in different clusters. Results Four replicable clusters of patients were identified: cluster 1 (index hypertensives); cluster 2 (chronic kidney disease hypertensives); cluster 3 (obese hypertensives) and cluster 4 (extra risky hypertensives). In terms of prognosis, individuals in cluster 4 had the highest risk of developing primary outcomes. In terms of treatment responses, intensive antihypertensive treatment was shown to be beneficial only in cluster 4 (HR 0.73, 95% CI 0.55–0.98) and cluster 1 (HR 0.54, 95% CI 0.37–0.79) and was associated with an increased risk of severe adverse effects in cluster 2 (HR 1.18, 95% CI 1.05–1.32). Conclusion Using a data-driven approach, SPRINT subjects can be stratified into four phenotypically distinct subgroups with different profiles on cardiovascular prognoses and responses to intensive antihypertensive treatment. Of note, these results should be taken as hypothesis generating that warrant further validation in future prospective studies.