Diagnostic MicroRNA Signatures to Support Classification of Pulmonary Hypertension.

BACKGROUND

Patients with pulmonary hypertension (PH) are classified based on disease pathogenesis and hemodynamic drivers. Classification informs treatment. The heart failure biomarker NT-proBNP (N-terminal pro-B-type natriuretic peptide) is used to help inform risk but is not specific to PH or sub-classification groups. There are currently no other biomarkers in clinical use to help guide diagnosis or risk.

METHODS

We profiled a retrospective cohort of 1150 patients from 3 expert centers with PH and 334 non-PH symptomatic controls (disease controls) from the United Kingdom to measure circulating levels of 650 microRNAs (miRNAs) in serum. NT-proBNP (ELISA) and 326 well-detected miRNAs (polymerase chain reaction) were prioritized by feature selection using multiple machine learning models. From the selected miRNAs, generalized linear models were used to describe miRNA signatures to differentiate PH and pulmonary arterial hypertension from the disease controls, and pulmonary arterial hypertension, PH due to left heart disease, PH due to lung disease, and chronic thromboembolic pulmonary hypertension from other forms of PH. These signatures were validated on a UK test cohort and independently validated in the prospective CIPHER study (A Prospective, Multicenter, Noninterventional Study for the Identification of Biomarker Signatures for the Early Detection of Pulmonary Hypertension) comprising 349 patients with PH and 93 disease controls.

RESULTS

NT-proBNP achieved a balanced accuracy of 0.74 and 0.75 at identifying PH and pulmonary arterial hypertension from disease controls with a threshold of 254 and 362 pg/mL, respectively but was unable to sub-categorize PH subgroups. In the UK cohort, miRNA signatures performed similarly to NT-proBNP in distinguishing PH (area under the curve of 0.7 versus 0.78), and pulmonary arterial hypertension (area under the curve of 0.73 versus 0.79) from disease controls. MicroRNA signatures outperformed NT-proBNP in distinguishing PH classification groups. External testing in the CIPHER cohort demonstrated that miRNA signatures, in conjunction with NT-proBNP, age, and sex, performed better than either NT-proBNP or miRNAs alone in sub-classifying PH.

CONCLUSIONS

We suggest a threshold for NT-proBNP to identify patients with a high probability of PH, and the subsequent use of circulating miRNA signatures to help differentiate PH subgroups.