Abstract
The majority of the extremely premature infants are affected by the development of chronic lung disease, i.e. bronchopulmonary dysplasia (BPD). Driven by the exposure to mechanical ventilation and oxygen treatment after birth as well as pre- and postnatal infections, disease incidence steadily increases with improved survival rates (Rivera, L et al., Front. Pediatr., 2016). In order to possibly alleviate the debilitating and lifelong consequences especially of severe BPD, treatment of at-risk premature infants is needed as early as possible. Since BPD is diagnosed late in a premature neonate’s hospital stay, we used high-end protein screening in early plasma samples (n=30 infants, sampling day of life 1-7) to identify early biomarkers.
Machine learning approaches were used to define a set of proteins associated with disease severity of later BPD. In particular, controls and mild BPD cases were separated well from moderate and severe cases by a characteristic protein signature together with the known risk factors gestational age, gender and early infections. The identified proteins were attributed to inflammation and cardiac strain as well as coagulation activation (extrinsic pathway), the latter mirroring tissue injury. The differential protein expression was driven by postnatal injury but not the degree of acute respiratory distress. Further associations of the protein signature was shown with imaging markers (lung MRI), i.e. indicators of interstitial remodeling and altered pulmonary blood flow.
In conclusion, moderate and severe BPD can be separated from mild cases and controls by characteristic protein expression, allowing for early individualized treatment and monitoring.
Footnotes
Cite this article as: European Respiratory Journal 2018 52: Suppl. 62, OA300.
This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).
- Copyright ©the authors 2018