HighlightsExercise training is beneficial to cardiovascular systemMicroRNAs are responsible for cardiac adaptions to exerciseExercise regulated microRNAs benefits heartMicroRNAs mediate protective effects of exercise in cardiovascular diseasesCirculating microRNAs mediate beneficial effects of exercise

Circulating microRNAs (miRNAs, miRs) have great potential as cardiac biomarkers and they are also being explored for their roles in intercellular communication and gene expression regulation. The analysis of circulating miRNAs in response to exercise would provide a deeper understanding of the molecular response to physical activity and valuable information for clinical practice. Here, eight male college students were recruited to participate in cardiopulmonary exercise testing (CPET) and 1 h acute exercise training (AET). Blood samples were collected and serum miRNAs involved in angiogenesis, inflammation and enriched in muscle and/or cardiac tissues were analyzed before and after cardiopulmonary exercise and acute exercise. The miRNAs we detected were miR-1, miR-20a, miR-21, miR-126, miR-133a, miR-133b, miR-146, miR155, miR-208a, miR-208b, miR-210, miR-221, miR-222, miR-328, miR-378, miR-499, and miR-940. We found that serum miR-20a was decreased significantly after CPET and serum miR-21 was increased after AET. In addition, no robust correlation was identified between the changes of these miRNAs and makers of cardiac function and exercise capacity, which indicates a distinct adaptation of these miRNAs to exercise. Future studies are highly needed to define the potential use of these circulating miRNAs as useful biomarkers of exercise training, and disclose the biological function of circulating miRNAs as physiological mediators of exercise-induced cardiovascular adaptation.

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Circulating circular RNAs (circRNAs) are emerging as novel biomarkers for cardiovascular diseases (CVDs). Machine learning can provide optimal predictions on the diagnosis of diseases. Here we performed a proof-of-concept study to determine if combining circRNAs with an artificial intelligence approach works in diagnosing CVD. We used acute myocardial infarction (AMI) as a model setup to prove the claim. We determined the expression level of five hypoxia-induced circRNAs, including cZNF292, cAFF1, cDENND4C, cTHSD1, and cSRSF4, in the whole blood of coronary angiography positive AMI and negative non-AMI patients. Based on feature selection by using lasso with 10-fold cross validation, prediction model by logistic regression, and ROC curve analysis, we found that cZNF292 combined with clinical information (CM), including age, gender, body mass index, heart rate, and diastolic blood pressure, can predict AMI effectively. In a validation cohort, CM + cZNF292 can separate AMI and non-AMI