Disease Background

Pulmonary arterial hypertension (PAH) is a life-threatening cardiovascular disease with complex causes and a growing global burden. Idiopathic PAH (IPAH), a severe and poorly understood subtype, lacks clear etiology and remains difficult to treat effectively. Existing risk models primarily reflect cardiac function and fail to capture early pulmonary vascular changes, limiting their clinical utility.

Research Innovation

In this study, we apply Olink proteomics and machine learning to identify immune-inflammatory plasma proteins that can serve as noninvasive, dynamic biomarkers for prognosis in IPAH. Our model:

• Enhances risk stratification
• Supports personalized care
• Validated using UK Biobank data
• Confirms biological relevance through Mendelian randomization

Clinical Significance

This study has the potential to:

• Significantly improve disease management
• Transform how IPAH is treated
• Pave the way for a more precise, data-driven approach to prognosis and therapy