Lipidomics for Diagnosis and Prognosis of Pulmonary Hypertension

Background Pulmonary hypertension (PH) poses a significant health threat with high morbidity and mortality, necessitating improved diagnostic tools for enhanced management. Current biomarkers for PH lack functionality and comprehensive diagnostic and prognostic capabilities. Therefore, there is a critical need to develop biomarkers that address these gaps in PH diagnostics and prognosis. Methods To address this need, we employed a comprehensive metabolomics analysis in 233 blood based samples coupled with machine learning analysis. For functional insights, human pulmonary arteries (PA) of idiopathic pulmonary arterial hypertension (PAH) lungs were investigated and the effect of extrinsic FFAs on human PA endothelial and smooth muscle cells was tested in vitro . Results PA of idiopathic PAH lungs showed lipid accumulation and altered expression of lipid homeostasis-related genes. In PA smooth muscle cells, extrinsic FFAs caused excessive proliferation and endothelial barrier dysfunction in PA endothelial cells, both hallmarks of PAH. In the training cohort of 74 PH patients, 30 disease controls without PH, and 65 healthy controls, diagnostic and prognostic markers were identified and subsequently validated in an independent cohort. Exploratory analysis showed a highly impacted metabolome in PH patients and machine learning confirmed a high diagnostic potential. Fully explainable specific free fatty acid (FFA)/lipid-ratios were derived, providing exceptional diagnostic accuracy with an area under the curve (AUC) of 0.89 in the training and 0.90 in the validation cohort, outperforming machine learning results. These ratios were also prognostic and complemented established clinical prognostic PAH scores (FPHR4p and COMPERA2.0), significantly increasing their hazard ratios (HR) from 2.5 and 3.4 to 4.2 and 6.1, respectively. Conclusion In conclusion, our research confirms the significance of lipidomic alterations in PH, introducing innovative diagnostic and prognostic biomarkers. These findings may have the potential to reshape PH management strategies. ### Competing Interest Statement Several authors (NB, CM, AO, BMN, HO) are inventors of the patent: Biomarker for the diagnosis of pulmonary hypertension (PH) WO2017153472A1 (priority date 09.03.2016, granted in US, KR, JP, pending in CA, EP, AU) being jointly held by CBmed Gmbh, Joanneum Research Forschungsgesellschaft mbH, Medical University Graz and Ludwig Boltzmann Gesellschaft GmbH. The authors received no personal financial gain from the patent. During work on this publication NB was partially employed at CBmed GmbH. TP is chief scientific officer (CSO) of CBmed GmbH. EZ and CM were employed at Joanneum Research Forschungsgesellschaft mbH. The employing companies provided support in the form of salaries, materials and reagents but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. VF received honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Janssen, Chiesi, BMS, and Boehringer Ingelheim and support for attending meetings, and/or travel from Janssen, MSD, and Boehringer Ingelheim outside the submitted work. CN received support for attending meetings, and/or travel from Boehringer Ingelheim and Inventiva pharma outside the submitted work. BAM reports personal fees from Actelion Pharmaceuticals, Tenax and Regeneron, grants from Deerfield Company, NIH (5R01HL139613-03, R01HL163960, R01HL153502, R01HL155096-01), Boston Biomedical Innovation Ceter (BBIC), Brigham IGNITE award, Cardiovascular Medical research Education Foundation outside the submitted work. BAM reports patent PCT/US2019/059890 (pending), PCT/US2020/066886 (pending) and 9,605,047 (granted) not licensed and outside the submitted work. SU received grants from the Swiss National Science Foundation, Zurich and Swiss Lung League, EMDO-Foundation, Orpha-Swiss, Janssen and MSD all unrelated to the present work. SU received consultancy fees and travel support from Orpha-Swiss, Janssen, MSD and Novartis unrelated to the present work. TJL reports grants for his institution from Acceleron Pharma, Gossamer Bio, Janssen-Cilag, and United Therapeutics; personal fees and non-financial support from Acceleron Pharma, AstraZeneca, Boehringer Ingelheim, Gossamer Bio, Ferrer, Janssen-Cilag, MSD, Orphacare, and Pfizer outside the submitted work. KH is a consultant at Medtronic Oesterreich GmbH outside the submitted work. TP reports grants from AstraZeneca, Novo Nordisk, Sanofi paid to the Medical University of Graz outside the submitted work. TP reports personal fees and nonfinancial support from Novo Nordisk and Roche Diagnostics outside the submitted work. HO reports grants from Bayer, Unither, Actelion, Roche, Boehringer Ingelheim, and Pfizer. HO reports personal fees and nonfinancial support from Medupdate and Mondial, AOP, Bayer, Actelion, Pfizer, Ferrer, Novartis, Astra Zeneca, Boehringer Ingelheim, Chiesi, Menarini, MSD, and GSK outside the submitted work. AO received honoraria for presentations and support for attending meetings, and/or travel from MSD outside the submitted work. No conflict of interest, financial or otherwise, are declared by the authors HL and UB. ### Funding Statement 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 Oesterreichische Forschungsfoerderungs GmbH FFG. VB is supported by the Austrian Science Foundation (FWF, T1032-B34). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Ethics Committee of the Medical University of Graz, Austria gave ethical approval for this work for the cohort GRAPH (Graz Pulmonary Hypertension Registry, identifier: 23-408ex10/11, registered at [ClinicalTrials.gov][1] ([NCT01607502][2]). The Ethics Committee of the Medical University of Graz, Austria gave ethical approval for this work for the cohort BioPersMed (Biomarkers of Personalised Medicine, identifier: 23-408ex10/11, renewed every year 24-224 ex 11/12). The Ethics Committee of the Regensburg University, Germany gave ethical approval for this work for the validation cohort (identifier 08/090). The cantonal ethical review board Zurich, Switzerland gave ethical approval for this work for the validation cohort (identifier KEK 2010-0129; 2014-0214; 2017-0476). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as [ClinicalTrials.gov][1]. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The author declare that all data supporting the findings in this study are available in the online Supplementary Sata 1 and online repositories. Mass spectrometric data have been deposited in under doi: 10.5281/zenodo.7857706. Data is provided de-identified and is available immediately for any purpose. The provided Sample_Name in the online Supplementary Data 1 links to the file names in the online repository and are unsuitable to identify single patients. The primary key is only known to part of the study team. Machine learning code is available immediately for any purpose on Github: . * 6MWD : six minute walking distance ACh : acetylcholine ADMA : asymmetric dimethylarginine AUC : area under the curve BH : Benjamini–Hochberg BMI : body mass index BNP or NT-proBNP : natriuretic peptide levels CO : cardiac output COPD : chronic obstructive pulmonary disease CI : cardiac index CTEPH : chronic thromboembolic pulmonary hypertension DC : diseased control (non-PH) DLCOcVA : diffusing capacity for carbon monoxide per alveolar volume, hemoglobin corrected ECAR : extracellular acidification rate ECIS : electrical cell-substrate impedance sensor EDTA : ethylenediaminetetraacetic acid FEV1 : forced expiratory volume/ 1 s FVC : forced vital capacity FFA : free fatty acids H&E : hematoxylin-eosin HC : healthy control HILIC : hydrophilic interaction liquid chromatography hPAEC : human pulmonary artery endothelial cells hPASMC : human pulmonary artery smooth muscle cells HR : hazard ratio HRMS : high resolution mass spectrometry ILD : interstitial lung disease IPAH : idiopathic pulmonary arterial hypertension iPCA : independent principal component analysis IVD : in vitro diagnostics LPC : lysophosphatidylcholine LPE : lysophosphatidylethanolamine LV : left ventricle MAD : median absolute deviation mPAP : mean pulmonary arterial pressure MS : mass spectrometry OCR : oxygen consumption rate OPLS-DA : orthogonal projections to latent structures discriminant analysis PA : pulmonary arteries PAH : pulmonary arterial hypertension PAP : pulmonary arterial pressure PAWP : pulmonary arterial wedge pressure PBS : phosphate-buffered saline PC : phosphatidylcholine PDGF : platelet-derived growth factor PH : pulmonary hypertension PPP : pentose phosphate pathway PVR : pulmonary vascular resistance QC : pooled from samples for quality control RAP : right atrial pressure RDW : red cell distribution width RF : random forest RHC : right heart catheterization ROC : receiver operator curve RT : room temperature (20 – 25 °C) SEM : standard error of mean SM : sphingomyelin SMA : smooth muscle actin SvO2 : mixed venous oxygen saturation TAG : triacylglyceride TEER : transendothelial electrical resistance TLC : total lung capacity VWF : von-Willebrand Factor WHO FC : World Health Organization functional class WU : Wood unit XGBoost : eXtreme Gradient Boosting [1]: http://ClinicalTrials.gov [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01607502&atom=%2Fmedrxiv%2Fearly%2F2023%2F11%2F29%2F2023.05.17.23289772.atom