Abstract
Pulmonary arterial hypertension is a complex disease resulting from the interplay of myriad biological and environmental processes that lead to remodeling of the pulmonary vasculature with consequent pulmonary hypertension. Despite currently available therapies, there remains significant morbidity and mortality in this disease. There is great interest in identifying and applying biomarkers to help diagnose patients with pulmonary arterial hypertension, inform prognosis, guide therapy, and serve as surrogate endpoints. An extensive literature on potential biomarker candidates is available, but barriers to the implementation of biomarkers for clinical use in pulmonary arterial hypertension are substantial. Various omic strategies have been undertaken to identify key pathways regulated in pulmonary arterial hypertension that could serve as biomarkers including genomic, transcriptomic, proteomic, and metabolomic approaches. Other biologically relevant components such as circulating cells, microRNAs, exosomes, and cell-free DNA have recently been gaining attention. Because of the size of the datasets generated by these omic approaches and their complexity, artificial intelligence methods are being increasingly applied to decipher their meaning. There is growing interest in imaging the lung with various modalities to understand and visualize processes in the lung that lead to pulmonary vascular remodeling including high resolution computed tomography, Xenon magnetic resonance imaging, and positron emission tomography. Such imaging modalities have the potential to demonstrate disease modification resulting from therapeutic interventions. Because right ventricular function is a major determinant of prognosis, imaging of the right ventricle with echocardiography or cardiac magnetic resonance imaging plays an important role in the evaluation of patients and may also be useful in clinical studies of pulmonary arterial hypertension.