Endotyping in Heart Failure - Identifying Mechanistically Meaningful Subtypes of Disease

Comprehensive Transcriptomics Profiling of MicroRNA Reveals Plasma Circulating Biomarkers of Hypertrophic Cardiomyopathy and Dysregulated Signaling Pathways

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is caused by mutations in genes coding for proteins essential for myocardial contraction. However, it remains unclear through which signaling pathways these gene mutations mediate HCM pathogenesis. Growing evidence indicates that microRNAs (miRNAs) play an important role in the regulation of gene expression. We hypothesized that transcriptomics profiling of plasma miRNAs would reveal circulating biomarkers and dysregulated signaling pathways in HCM.

METHODS

We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma transcriptomics profiling of miRNAs using RNA sequencing. We developed a transcriptomics-based discrimination model using samples retrieved during the first two-thirds of the study period at one institution (training set). We prospectively tested its discriminative ability in samples collected thereafter from the same institution (prospective test set). We also externally validated the model by applying it to samples collected from the other institutions (external test set). We executed pathway analysis of dysregulated miRNAs with univariable P<0.05.

RESULTS

This study included 555 patients (392 cases and 163 controls). One thousand one hundred forty-one miRNAs passed our quality control filters. The area under the receiver operating characteristic curve of the transcriptomics-based model derived from the training set was 0.86 (95% CI, 0.79-0.93) in the prospective test set and 0.94 (95% CI, 0.90-0.97) in the external test set. Pathway analysis revealed dysregulation of the Ras-MAPK (mitogen-activated protein kinase) pathway and pathways related to inflammation in HCM.

CONCLUSIONS

This study utilized comprehensive transcriptomics profiling with RNA sequencing in HCM, revealing circulating miRNA biomarkers and dysregulated pathways.

The Relationship between Angiotensin–Neprilysin Treatment, Echocardiographic Parameters, and NT-proBNP Levels in HFpEF Patients with Acute Decompensated Heart Failure

Background

The valsartan-sacubitril therapy improved the outcomes of patients with acute decompensated heart failure (ADHF) of a reduced ejection fraction (HFrEF). In ADHF patients with preserved ejection fraction (HFpEF), it is not yet clear whether the same treatment regimen may be safely used to treat ADHF.

Methods

For this study, HFpEF patients hospitalized due to ADHF were enrolled. Following hemodynamic stabilization, patients were randomized into two groups that were treated with enalapril or sacubitril-valsartan. In this trial, the primary efficacy outcomes were changes in echocardiographic parameters and NT-proBNP levels from baseline to 8 weeks treatment.

Results

ARNI treatment resulted in a significant decrease in NT-proBNP levels and an increase in LVEF in patients with HFpEF. However, HFpEF patients that underwent ARNI treatment achieved better outcomes than did patients that underwent ACEI treatment.

Conclusion

Sacubitril-valsartan treatment, which lowered NT-proBNP levels and improved cardiac function, was more effective in HFpEF patients with acute decompensated heart failure than enalapril.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.