Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension.
Christopher J RhodesAndrew J SweattBradley A MaronPublished in: Circulation research (2022)
Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.
Keyphrases
- big data
- pulmonary hypertension
- machine learning
- artificial intelligence
- pulmonary artery
- single cell
- pulmonary arterial hypertension
- healthcare
- public health
- high resolution
- stem cells
- bariatric surgery
- electronic health record
- mental health
- coronary artery
- mesenchymal stem cells
- single molecule
- gene expression
- mass spectrometry
- weight loss
- health information
- health promotion
- adverse drug
- obese patients