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Background: Prediction of cardiovascular disease (CVD) is important in clinical practice. Machine learning (ML) may offer an improved alternative to current CVD risk stratification in individual patients. We aim to identify important predictors and compare ML models with traditional models according to their prediction performance in a large long-term follow-up cohort., Methods: The Atherosclerosis Risk in Communities (ARIC) study was designed to study the progression of subclinical disease to cardiovascular events over a 25-year follow-up period. All phenotypic variables at visit 1 were obtained. All-cause death, CVD, and coronary heart disease were the outcomes for analysis. The ML framework involved variable selection using the random survival forest (RSF) method, model building, and 5-fold cross-validation. Model performance was evaluated by discrimination using the Harrell concordance index (C-index), accuracy using the Brier score (BS), and interpretability using the number of variables in the model., Results: Of the 14,842 participants in ARIC, the average age was 54.2 years, with 45.2% male and 26.2% Black participants. Thirty-eight unique variables were selected in the RSF top 20 importance ranking of all 6 outcomes. Aging, hypertension, glucose metabolism, renal function, coagulation, adiposity, and sodium retention dominated the predictions of all outcomes. The ML models outperformed the regression models and established risk scores with a higher C-index, lower BS, and varied interpretability., Conclusions: The ML framework is useful for identifying important predictors of CVD and for developing models with robust performance compared with existing risk models., (Copyright © 2022 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)

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.

Brain-derived neurotrophic factor (BDNF) is one of the most abundantneurotrophins in the central nervous system. Numerous studies suggestthat BDNF has extensive roles by binding to its specific receptor, tropomyosin-related kinase receptor B (TrkB), and thereby triggering downstream signaling pathways. Recently, growing evidence highlightsthat the BDNF/TrkB pathway is expressed in the cardiovascular system andclosely associated with the development and outcome of cardiovascular diseases (CVD), including coronary artery disease, heart failure, cardiomyopathy, hypertension, and metabolic diseases. Furthermore, circulating BDNF has also been revealed as a new potential biomarker for both diagnosis and prognosis of CVD. In this review, we discuss the current evidence of the emerging role of BDNF/TrkBsignalingand address the challenges that remain in translating these discoveries to novel therapeutic strategies for CVD. [ABSTRACT FROM AUTHOR]