Circulating microRNA as predictors for exercise response in heart failure with reduced ejection fraction

Aims Exercise training is a powerful adjunctive therapy in patients with heart failure with reduced ejection fraction (HFrEF), but ca. 55% of patients fail to improve VO(2)peak. We hypothesize that circulating microRNAs (miRNAs), as epigenetic determinants of VO(2)peak, can distinguish exercise responders (ER) from exercise non-responders (ENR). Methods and results We analysed 377 miRNAs in 18 male HFrEF patients (9 ER and 9 ENR) prior to 15 weeks of exercise training using a miRNA array. ER and ENR were defined as change in VO(2)peak of >20% or <6%, respectively. First, unsupervised clustering analysis of the miRNA pattern was performed. Second, differential expression of miRNA in ER and ENR was analysed and related to percent change in VO(2)peak. Third, a gene set enrichment analysis was conducted to detect targeted genes and pathways. Baseline characteristics and training volume were similar between ER and ENR. Unsupervised clustering analysis of miRNAs distinguished ER from ENR with 83% accuracy. A total of 57 miRNAs were differentially expressed in ENR vs. ER. A panel of seven miRNAs up-regulated in ENR (Let-7b, miR-23a, miR-140, miR-146a, miR-191, miR-210, and miR-339-5p) correlated with %change VO(2)peak (all P < 0.05) and predicted ENR with area under the receiver operating characteristic curves >= 0.77. Multiple pathways involved in exercise adaptation processes were identified. Conclusion A fingerprint of seven miRNAs involved in exercise adaptation processes is highly correlated with VO(2)peak trainability in HFrEF, which holds promise for the prediction of training response and patient-targeted exercise prescription.

Automated summary

Exercise training is an effective therapy for patients with heart failure with reduced ejection fraction, however, around 55% of patients do not improve their VO(2)peak. This study identified a panel of seven miRNAs that are highly correlated with VO(2)peak trainability, which may help predict training response and guide individualized exercise prescription for patients.