Arteriosclerotic cardiovascular diseases (ASCVDs) are the leading cause of morbidity and mortality worldwide and its risk can be independently decreased by regular physical activity. Recently, ASCVD and its risk factors were found to be impacted by the gut microbiota through its diversity, distribution and metabolites. Meanwhile, several experiments demonstrated the relationship between physical exercise and diversity, distribution, metabolite of the gut microbiota as well as its functions on the lipid metabolism and chronic systematic inflammation. In this review, we summarize the current knowledge on the effects of physical exercise on ASCVD through modulation of the gut microbiota and intestinal function.

(1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognostic value of machine learning (ML) for the prediction of MACEs. (2) Methods: Five-hundred patients diagnosed with AMI and who had undergone successful percutaneous coronary intervention were included in the study. Logistic regression (LR) analysis was used to assess the relevance of MACEs and 24 selected clinical variables. Six ML models were developed with five-fold cross-validation in the training dataset and their ability to predict MACEs was compared to LR with the testing dataset. (3) Results: The MACE rate was calculated as 30.6% after a mean follow-up of 1.42 years. Killip classification (Killip IV vs. I class, odds ratio 4.386, 95% confidence interval 1.943–9.904), drug compliance (irregular vs. regular compliance, 3.06, 1.721–5.438), age (per year, 1.025, 1.006–1.044), and creatinine (1 µmol/L, 1.007, 1.002–1.012) and cholesterol levels (1 mmol/L, 0.708, 0.556–0.903) were independent predictors of MACEs. In the training dataset, the best performing model was the random forest (RDF) model with an area under the curve of (0.749, 0.644–0.853) and accuracy of (0.734, 0.647–0.820). In the testing dataset, the RDF showed the most significant survival difference (log-rank p = 0.017) in distinguishing patients with and without MACEs. (4) Conclusions: The RDF model has been identified as superior to other models for MACE prediction in this study. ML methods can be promising for improving optimal predictor selection and clinical outcomes in patients with AMI.

Arrhythmia, the basis of which is cardiomyocyte ion channel abnormalities, poses a serious threat to human health. A large number of studies have demonstrated that miRNA-1(miR-1) is involved in the occurrence of arrhythmia in many myocardial pathological conditions by post-transcriptionally regulating a variety of ion channels and proteins related to cardiac electrical activity. We aim at emphasizing the relationship between miR-1 and ion channels and proteins involved in the process of arrhythmia. In addition, we will pay attention to its future therapeutic prospects.

Estrogen had been found to be negatively associated with serum triglyceride (TG) levels. Apolipoprotein A5 (APOA5), a novel member of apolipoprotein family, was reported to have a strong ability to decrease serum concentrations of TG. Clinical data found concentrations of APOA5 were higher in woman than that in men, and the negative relationship between APOA5 and TG levels was more significant in woman. These suggests APOA5 may involve in estrogen actions. Therefore, we hypothesize estrogen up-regulates serum concentrations of APOA5 and subsequently decreases serum TG levels. We will design the following experiments to test this hypothesis. (1) We will treat wild and APOA5-defeted ovariectomized hamster with or without estrogen to examine if estrogen could up-regulate concentrations of APOA5 and decrease TG levels. (2) We will treat HepG2 cells with estrogen and investigate the possible mechanisms.