Bordag Natalie, Nagy Bence Miklos, Zügner Elmar, Ludwig Helga, Foris Vasile, Nagaraj Chandran, Biasin Valentina, Bodenhofer Ulrich, Magnes Christoph, Maron Bradley A, Ulrich Silvia, Lange Tobias J, Hötzenecker Konrad, Pieber Thomas, Olschewski Horst, Olschewski Andrea
Department of Dermatology and Venereology, Medical University of Graz, Graz, Austria.
Ludwig Boltzmann Institute for Lung Vascular Research, Graz, Austria.
medRxiv. 2023 Nov 29:2023.05.17.23289772. doi: 10.1101/2023.05.17.23289772.
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.
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 .
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.
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.
肺动脉高压（PH）对健康构成重大威胁，发病率和死亡率都很高，因此需要改进诊断工具以加强管理。目前用于PH的生物标志物缺乏功能性以及全面的诊断和预后能力。因此，迫切需要开发能够填补PH诊断和预后这些空白的生物标志物。
为满足这一需求，我们对233份血液样本进行了全面的代谢组学分析，并结合机器学习分析。为了获得功能方面的见解，我们研究了特发性肺动脉高压（PAH）患者肺部的人肺动脉（PA），并测试了外源性游离脂肪酸（FFA）对人PA内皮细胞和平滑肌细胞的影响。
特发性PAH患者肺部的PA显示出脂质积累以及脂质稳态相关基因表达的改变。在PA平滑肌细胞中，外源性FFA导致PA内皮细胞过度增殖和内皮屏障功能障碍，这两者都是PAH的特征。在74例PH患者、30例无PH的疾病对照和65例健康对照的训练队列中，我们识别出了诊断和预后标志物，随后在一个独立队列中进行了验证。探索性分析显示PH患者的代谢组受到高度影响，机器学习证实了其具有很高的诊断潜力。我们得出了完全可解释的特定游离脂肪酸（FFA）/脂质比率，在训练队列中的曲线下面积（AUC）为0.89，在验证队列中为0.90，提供了出色的诊断准确性，优于机器学习结果。这些比率也具有预后价值，并补充了已确立的临床预后PAH评分（FPHR4p和COMPERA2.0），将它们的风险比（HR）分别从2.5和3.4显著提高到4.2和6.1。
总之，我们的研究证实了脂质组改变在PH中的重要性，引入了创新的诊断和预后生物标志物。这些发现可能有潜力重塑PH的管理策略。
