The angiostatic peptide endostatin enhances mortality risk prediction in pulmonary arterial hypertension.

Abstract

Currently available noninvasive markers for assessing disease severity and mortality risk in pulmonary arterial hypertension (PAH) are unrelated to fundamental disease biology. Endostatin, an angiostatic peptide known to inhibit pulmonary artery endothelial cell migration, proliferation and survival in vitro, has been linked to adverse haemodynamics and shortened survival in small PAH cohorts. This observational cohort study sought to assess: 1) the prognostic performance of circulating endostatin levels in a large, multicentre PAH cohort; and 2) the added value gained by incorporating endostatin into existing PAH risk prediction models. Endostatin ELISAs were performed on enrolment samples collected from 2017 PAH subjects with detailed clinical data, including survival times. Endostatin associations with clinical variables, including survival, were examined using multivariable regression and Cox proportional hazards models. Extended survival models including endostatin were compared to null models based on the REVEAL risk prediction tool and European Society of Cardiology/European Respiratory Society (ESC/ERS) low-risk criteria using likelihood ratio tests, Akaike and Bayesian information criteria and C-statistics. Higher endostatin was associated with higher right atrial pressure, mean pulmonary arterial pressure and pulmonary vascular resistance, and with shorter 6-min walk distance (p<0.01). Mortality risk doubled for each log higher endostatin (hazard ratio 2.3, 95% CI 1.6-3.4, p<0.001). Endostatin remained an independent predictor of survival when incorporated into existing risk prediction models. Adding endostatin to REVEAL-based and ESC/ERS criteria-based risk assessment strategies improved mortality risk prediction. Endostatin is a robust, independent predictor of mortality in PAH. Adding endostatin to existing PAH risk prediction strategies improves PAH risk assessment.

• Authors: Catherine E Simpson, Megan Griffiths, Jun Yang, Melanie K Nies, R Dhananjay Vaidya, Stephanie Brandal, Lisa J Martin, Michael W Pauciulo, Katie A Lutz, Anna W Coleman, Eric D Austin, D Dunbar Ivy, William C Nichols, Allen D Everett, Paul M Hassoun, Rachel L Damico
• Journal: ERJ open research (ERJ Open Res) Vol. 7 Issue 4 (Oct 2021) ISSN: 2312-0541 [Print] England
• PMID: 34651041 (Publication Type: Journal Article)
• Copyright: Copyright ©The authors 2021.