Published April 24, 2023 | Version v1
Dataset Open

Lipidomics for diagnosis and prognosis of pulmonary hypertension

  • 1. Medical University of Graz, Department of Dermatology and Venereology, Graz, Austria
  • 2. Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria
  • 3. Institute for Biomedical Research and Technologies (HEALTH), Joanneum Research Forschungsgesellschaft m.b.H, Graz, Austria
  • 4. School of Informatics, Communications, and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria
  • 5. Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
  • 6. Clinic of Pulmonology, University and University Hospital of Zurich, Switzerland
  • 7. Department of Internal Medicine II, Pulmonology and Critical Care, Kreisklinik Bad Reichenhall, Bad Reichenhall, Germany
  • 8. Department of Thoracic Surgery, Medical University of Vienna, Austria
  • 9. Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz

Description

Pulmonary hypertension (PH) is associated with high morbidity and mortality with an urgent need for diagnostic and prognostic biomarkers.

A training cohort of PH patients, disease controls without PH, and healthy controls was investigated using metabolomics and machine learning. Specific free fatty acid (FFA)/lipid-ratio biomarkers were diagnostic and predictive for PH survival with an area under the curve (AUC) of 0.89. FFA/lipid-ratio performance was independently validated in PH patients from other centers(AUC 0.90). Survival could be predicted in an age-independent manner and a combination with established clinical scores (FPHR4p, COMPERA 2.0) increased the scores hazard risk.

Our mechanistic studies in healthy and diseased pulmonary artery endothelial and smooth muscle cells indicate a functional involvement of increased FFA levels in pathophysiology of PH. In conclusion, lipidomic changes in PH can be used as a novel diagnostic and prognostic approach and may help the discovery of new therapeutic targets.

Notes

NB, TP disclose that part of this work has been carried out with the K1 COMET Competence Center CBmed, which is funded by the Federal Ministry of Transport, Innovation and Technology; the Federal Ministry of Science, Research and Economy; Land Steiermark (Department 12, Business and Innovation); the Styrian Business Promotion Agency; and the Vienna Business Agency. The COMET program is executed by the Österreichische Forschungsförderungs GmbH FFG. VB is supported by the Austrian Science Foundation (FWF, T1032-B34).

Files

run1.zip

Files (22.6 GB)

Name Size Download all
md5:42310fc3af1be5857b17d445fb21f35f
811.9 MB Preview Download
md5:42b410e66c62ba37a4f66eeb0fbcf861
1.8 GB Preview Download
md5:e1c4c400a8d2f570adda448b53eb51bc
9.6 GB Preview Download
md5:d01527fc81087f22a9239874384f429e
10.4 GB Preview Download

Additional details

Related works

Is published in
Preprint: 10.1101/2023.05.17.23289772 (DOI)