Circulating miR-30d Predicts Survival in Patients with Acute Heart Failure

Background/Aims: Identification of novel biomarkers to identify acute heart failure (AHF) patients at high risk of mortality is an area of unmet clinical need. Recently, we reported that the baseline level of circulating miR-30d was associated with left ventricular remodeling in response to cardiac resynchronization therapy in advanced chronic heart failure patients. However, the role of circulating miR-30d as a prognostic marker of survival in patients with AHF has not been explored. Methods: Patients clinically diagnosed with AHF were enrolled and followed up for 1 year. Quantitative reverse transcription polymerase chain reactions were used to determine serum miR-30d levels. The univariate logistic regression analysis and multivariate logistic regression analysis were used to determine the predictors for all-cause mortality in AHF patients. Kaplan Meier survival analysis was used to analyze the role of miR-30d in prediction of survival. Results: A total of 96 AHF patients were enrolled and followed up for 1 year. Serum miR-30d was significantly lower in AHF patients who expired in the one year follow-up period compared to those who survived. Univariate logistic regression analysis yielded 18 variables that were associated with all-cause mortality in AHF patients, while the multivariate logistic regression analysis identified 4 variables including heart rate, hemoglobin, serum sodium, and serum miR-30d level associated with mortality. ROC curve analysis showed that hemoglobin, heart rate and serum sodium displayed poor prognostic value for AHF (AUCs not higher than 0.700) compared to miR-30d level (AUC = 0.806). Kaplan Meier survival analysis confirmed that patients with higher serum miR-30d levels had significantly lower mortality (P=0.001). Conclusion: In conclusion, this study shows evidence for the predictive value of circulating miR-30d as 1-year all-cause mortality in AHF patients. Large multicentre studies are further needed to validate our findings and accelerate the transition to clinical utilization. (C) 2017 The Author(s)