Lipid Ratios for Diagnosis and Prognosis of Pulmonary Hypertension.

Abstract

Rationale: Pulmonary hypertension (PH) poses a significant health threat. Current biomarkers for PH lack specificity and have poor prognostic capabilities.

Objectives: To develop better biomarkers for PH that are useful for patient identification and management.

Methods: Explorative analysis of a broad spectrum of metabolites in PH patients, healthy controls and disease controls in a training and a validation cohort and in vitro studies on human pulmonary arteries.

Measurements: High resolution mass spectrometry in 233 subjects coupled with machine learning analysis. Histologic and gene expression analysis with focus on lipid metabolism in human pulmonary arteries (PA) of idiopathic pulmonary arterial hypertension lungs (IPAH) and assessment of the acute effects of extrinsic fatty acids (FAs).

Results: We enrolled a training cohort of 74 PH patients, 30 disease controls without PH, and 65 healthy controls, and an independent validation cohort of 64 subjects. Among other metabolites, the FAs were significantly increased. Machine learning showed a high diagnostic potential for PH. Additionally, we developed fully explainable lipid ratios with exceptional diagnostic accuracy for PH (AUC 0.89 training cohort, 0.90 external validation cohort), outperforming machine learning results. These ratios were also prognostic and complemented established clinical markers and scores, significantly increasing their hazard ratios for mortality risk. IPAH lungs showed lipid accumulation and altered expression of lipid homeostasis-related genes. In human PA smooth muscle and endothelial cells, FAs caused excessive proliferation and barrier dysfunction, respectively.

Conclusion: Our metabolomics approach suggests that lipid alterations in PH provide diagnostic and prognostic information, complementing established markers. These alterations may reflect pathologic changes in the pulmonary arteries of PH patients.