, 15:20 | Cite as

Identification of new biomarkers of bronchopulmonary dysplasia using metabolomics

  • Fiammetta Piersigilli
  • TuKiet T. Lam
  • Pamela Vernocchi
  • Andrea Quagliariello
  • Lorenza Putignani
  • Zubair H. Aghai
  • Vineet BhandariEmail author
Original Article



To identify new biomarkers of bronchopulmonary dysplasia (BPD) in preterm neonates.

Study design

Metabolomic study of prospectively collected tracheal aspirate (TA) samples from preterm neonates admitted in 2 neonatal intensive care units measured by a mass spectroscopy-based assay and analysed using partial least squares-discriminant analysis.


We evaluated 160 TA samples from 68 neonates, 44 with BPD and 24 without BPD in the first week of life. A cluster of 53 metabolites was identified as characteristic of BPD, with 18 select metabolites being highly significant in the separation of BPD versus No BPD. To control for the gestational age (GA) differences, we did a sub-group analyses, and noted that the amino acids histidine, glutamic acid, citrulline, glycine and isoleucine levels were higher in neonates with BPD. In addition, acylcarnitines C16-OH and C18:1-OH were also higher in neonates who developed BPD, but especially in the most preterm infants (neonates with GA < 27 weeks).


Metabolomics is a promising approach to identify novel specific biomarkers for BPD.


Chronic lung disease Preterm newborn Prematurity Metabolomics Mass spectrometry. 



Area under the receiver operating characteristic curve


Bronchoalveolar lavage fluid


Bronchopulmonary dysplasia


Gestational age


Liquid chromatography


Latent variables


Mass spectrometer


Neonatal intensive care unit


Number of misclassifications


Pulmonary hypertension


Partial least squares-discriminant analysis


Tracheal aspirate


Variables importance for prediction



We thank Edward Z. Voss for assistance with mass spectrometry sample preparation and data collection. The 4000 QTRAP mass spectrometer purchased through NIH CTSA Grant, UL1 RR024139.

Author Contributions

Conceived and designed the experiments—FP, VB; collected data and performed experiments—FP, TL, ZA, PV, AQ, VB; analyzed the data—FP, PV, AQ, LP, ZA, VB; drafted and edited the manuscript—FP, TL, PV, AQ, LP, ZA, VB; supervised the entire project—VB. All authors have read and approved the final version of the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11306_2019_1482_MOESM1_ESM.docx (1.3 mb)
Supplementary material 1 (DOCX 1365 KB)
11306_2019_1482_MOESM2_ESM.pdf (485 kb)
Supplementary material 2 (PDF 485 KB)


  1. Aghai, Z. H., Camacho, J., Saslow, J. G., Mody, K., Eydelman, R., Bhat, V., et al. (2012). Impact of histological chorioamnionitis on tracheal aspirate cytokines in premature infants. American Journal of Perinatology, 29, 567–572.PubMedGoogle Scholar
  2. Aghai, Z. H., Mody, S. J., Eydelman, K., Bhat, R., Stahl, V., Pyon, G., et al. (2013). IFN-γ and IP-10 in tracheal aspirates from premature infants: Relationship with bronchopulmonary dysplasia. Pediatric Pulmonology, 48, 8–13.CrossRefGoogle Scholar
  3. Baraldi, E., Giordano, G., Stocchero, M., Moschino, L., Zaramella, P., Tran, M. R., Carraro, S., Romero, R., & Gervasi, M. T. (2016). Untargeted metabolomic analysis of amniotic fluid in the prediction of preterm delivery and bronchopulmonary dysplasia. PLoS ONE, 11, e0164211.CrossRefGoogle Scholar
  4. Barker, M., & Rayens, M. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17, 166–173.CrossRefGoogle Scholar
  5. Bhandari, A., & Bhandari, V. (2013). Biomarkers in bronchopulmonary dysplasia. Paediatric Respiratory Reviews, 14, 173–179.CrossRefGoogle Scholar
  6. Bhandari, V., Bizzarro, M. J., Shetty, A., Zhong, X., Page, G. P., Zhang, H., et al. (2006). Familial and genetic susceptibility to major neonatal morbidities in preterm twins. Pediatrics, 117, 1901–1906.CrossRefGoogle Scholar
  7. Bhargava, M., Becker, T. L., Viken, K. J., Jagtap, P. D., Dey, S., Steinbach, M. S., et al. (2014). Proteomic profiles in acute respiratory distress syndrome differentiates survivors from non-survivors. PLoS ONE, 9, e109713.CrossRefGoogle Scholar
  8. Chong, I.-G., & Jun, C.-H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78, 103–112.CrossRefGoogle Scholar
  9. Csardi, G., & Nepusz, T. (2006) The igraph software package for complex network research. InterJournal, Complex Systems 1695.Google Scholar
  10. de Blic, J., Midulla, F., Barbato, A., Clement, A., Dab, I., Eber, E., et al. (2000). Bronchoalveolar lavage in children. ERS Task Force on bronchoalveolar lavage in children. European Respiratory Society. European Respiratory Journal, 15, 217–231.CrossRefGoogle Scholar
  11. de Boo, H. A., & Harding, J. E. (2007) Taurine as a marker for foetal wellbeing? Neonatology, 91, 145–154.CrossRefGoogle Scholar
  12. Fabiano, A., Gazzolo, D., Zimmermann, L. J., Gavilanes, A. W., Paolillo, P., Fanos, V., et al. (2011). Metabolomic analysis of bronchoalveolar lavage fluid in preterm infants complicated by respiratory distress syndrome: Preliminary results. The Journal of Maternal-Fetal & Neonatal Medicine, 24(Suppl 2), 55–58.CrossRefGoogle Scholar
  13. Fanos, V., Pintus, M. C., Lussu, M., Atzori, L., Noto, A., Stronati, M., et al. (2014). Urinary metabolomics of bronchopulmonary dysplasia (BPD): Preliminary data at birth suggest it is a congenital disease. The Journal of Maternal-Fetal & Neonatal Medicine, 27(Suppl 2), 39–45.CrossRefGoogle Scholar
  14. Fike, C. D., Dikalova, A., Kaplowitz, M. R., Cunningham, G., Summar, M., & Aschner, J. L. (2015). Rescue treatment with L-citrulline inhibits hypoxia-induced pulmonary hypertension in newborn pigs. American Journal of Respiratory Cell and Molecular Biology, 53, 255–264.CrossRefGoogle Scholar
  15. Fike, C. D., Summar, M., & Aschner, J. L. (2014). L-citrulline provides a novel strategy for treating chronic pulmonary hypertension in newborn infants. Acta Paediatrica, 103, 1019–1026.CrossRefGoogle Scholar
  16. Goffredo, M., Santoro, N., Trico, D., Giannini, C., D’Adamo, E., Zhao, H., et al. (2017) A branched-chain amino acid-related metabolic signature characterizes obese adolescents with non-alcoholic fatty liver disease. Nutrients. CrossRefPubMedPubMedCentralGoogle Scholar
  17. Griffiths, W. J., Koal, T., Wang, Y., Kohl, M., Enot, D. P., & Deigner, H. P. (2010). Targeted metabolomics for biomarker discovery. Angewandte Chemie International Edition, 49, 5426–5445.CrossRefGoogle Scholar
  18. Hsia, C. C., Hyde, D. M., & Weibel, E. R. (2016). Lung structure and the intrinsic challenges of gas exchange. Comprehensive Physiology, 6, 827–895.CrossRefGoogle Scholar
  19. Illsinger, S., Janzen, N., Sander, S., Schmidt, K. H., Bednarczyk, J., Mallunat, L., et al. (2010). Preeclampsia and HELLP syndrome: Impaired mitochondrial function in umbilical endothelial cells. Reproductive Sciences, 17, 219–226.CrossRefGoogle Scholar
  20. La Frano, M., Fahrmann, J., Grapov, D., Pedersen, T., Newman, J. W., Fiehn, O., et al. (2018) Umbilical cord blood metabolomics reveal distinct signatures of dyslipidemia prior to bronchopulmonary dysplasia and pulmonary hypertension. American Journal of Physiology-Lung Cellular and Molecular Physiology. Scholar
  21. Lal, C. V., Bhandari, V., & Ambalavanan, N. (2018a). Genomics, microbiomics, proteomics, and metabolomics in bronchopulmonary dysplasia. Seminars in Perinatology, 42, 425–431.CrossRefGoogle Scholar
  22. Lal, C. V., Kandasamy, J., Dolma, K., Ramani, M., Kumar, R., Wilson, L., et al. (2018b) Early airway microbial metagenomic and metabolomic signatures are associated with development of severe bronchopulmonary dysplasia. American Journal of Physiology-Lung Cellular and Molecular Physiology. Scholar
  23. Lenz, A. G., Meyer, B., Costabel, U., & Maier, K. (1993). Bronchoalveolar lavage fluid proteins in human lung disease: Analysis by two-dimensional electrophoresis. Electrophoresis, 14, 242–244.CrossRefGoogle Scholar
  24. Marzetti, E., Landi, F., Marini, F., Cesari, M., Buford, T. W., Manini, T. M., et al. (2014). Patterns of circulating inflammatory biomarkers in older persons with varying levels of physical performance: A partial least squares-discriminant analysis approach. Frontiers in Medicine (Lausanne), 1, 27.Google Scholar
  25. Mody, K., Saslow, J. G., Kathiravan, S., Eydelman, R., Bhat, V., Stahl, G. E., et al. (2012). Sirtuin1 in tracheal aspirate leukocytes: Possible role in the development of bronchopulmonary dysplasia in premature infants. The Journal of Maternal-Fetal & Neonatal Medicine, 25, 1483–1487.CrossRefGoogle Scholar
  26. Montgomery, A. M., Bazzy-Asaad, A., Asnes, J. D., Bizzarro, M. J., Ehrenkranz, R. A., & Weismann, C. G. (2016). Biochemical screening for pulmonary hypertension in preterm infants with bronchopulmonary dysplasia. Neonatology, 109, 190–194.CrossRefGoogle Scholar
  27. Pintus, M. C., Lussu, M., Dessi, A., Pintus, R., Noto, A., Masile, V., et al. (2018) Urinary (1)H-NMR metabolomics in the first week of life can anticipate BPD diagnosis. Oxidative Medicine and Cellular Longevity. Scholar
  28. Romisch-Margl, W., Prehn, C., Bogumil, R., Rohring, C., Suhre, K., & Adamski, J. (2012). Procedure for tissue sample preparation and metaboliteextraction for high-throughput targeted metabolomics. Metabolomics, 8, 133–142.CrossRefGoogle Scholar
  29. Smith, H. A., Canter, J. A., Christian, K. G., Drinkwater, D. C., Scholl, F. G., Christman, B. W., et al. (2006). Nitric oxide precursors and congenital heart surgery: A randomized controlled trial of oral citrulline. The Journal of Thoracic and Cardiovascular Surgery, 132, 58–65.CrossRefGoogle Scholar
  30. Szymanska, E., Saccenti, E., Smilde, A. K., & Westerhuis, J. A. (2012). Double-check: Validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics, 8, 3–16.CrossRefGoogle Scholar
  31. Team, R. C. (2017) R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  32. Torchin, H., Ancel, P. Y., Goffinet, F., Hascoet, J. M., Truffert, P., Tran, D., et al. (2016). Placental complications and bronchopulmonary dysplasia: EPIPAGE-2 cohort study. Pediatrics, 137, e20152163.CrossRefGoogle Scholar
  33. Tran, T. N., Afanador, T. L., Buydens, L. M. C., & Blancet, L. (2014). Interpretation of variable importance in partial least squares with significance multivariate correlation (sMC). Chemometrics and Intelligent Laboratory Systems, 138, 1453–1160.CrossRefGoogle Scholar
  34. Trittmann, J. K., Peterson, E., Rogers, L. K., Chen, B., Backes, C. H., Klebanoff, M. A., et al. (2015). Plasma asymmetric dimethylarginine levels are increased in neonates with bronchopulmonary dysplasia-associated pulmonary hypertension. The Journal of Pediatrics, 166, 230–233.CrossRefGoogle Scholar
  35. von Bredow, C., Birrer, P., & Griese, M. (2001). Surfactant protein A and other bronchoalveolar lavage fluid proteins are altered in cystic fibrosis. European Respiratory Journal, 17, 716–722.CrossRefGoogle Scholar
  36. Walsh, M. C., Yao, Q., Gettner, P., Hale, E., Collins, M., Hensman, A., et al. (2004) Impact of a physiologic definition on bronchopulmonary dysplasia rates. Pediatrics, 114, 1305–1311.CrossRefGoogle Scholar
  37. Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D., Smilde, A. K., van Velzen, E. J. J., et al. (2008). Assessment of PLS-DA cross-validation. Metabolomics, 4, 81–89.CrossRefGoogle Scholar
  38. Wolak, J. E., Esther, C. R. Jr., & O’Connell, T. M. (2009). Metabolomic analysis of bronchoalveolar lavage fluid from cystic fibrosis patients. Biomarkers, 14, 55–60.CrossRefGoogle Scholar
  39. Wold, S., Johansson, E., & Cocchi, M. (1993). PLS: Partial least squares projections to latent structures. In H. Kubinyi (Ed.), 3D QSAR in drug design: Theory, methods, and applications (pp. 523–550). Leiden: ESCOM Science Publishers.Google Scholar
  40. Wold, S., Martens, H., & Wold, H. (1982). The multivariate calibration problem in chemistry solved by the PLS method. Pite Havsbad: Matrix Pencils, pp. 286–293.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Division of Perinatal Medicine, and Yale Child Health Research Center, Department of PediatricsYale University School of MedicineNew HavenUSA
  2. 2.Division of NeonatologyBambino Gesù Children’s HospitalRomeItaly
  3. 3.Department of Molecular Biophysics & BiochemistryYale UniversityNew HavenUSA
  4. 4.Keck MS & Proteomics ResourceWM Keck Foundation Biotechnology Resource LaboratoryNew HavenUSA
  5. 5.Unit of Human Microbiome, Genetic and Rare Diseases AreaBambino Gesù Children’s HospitalRomeItaly
  6. 6.Unit of Parasitology, Department of Laboratory and Immunological DiagnosticsBambino Gesù Children’s HospitalRomeItaly
  7. 7.Section of Neonatology, Department of PediatricsThomas Jefferson UniversityPhiladelphiaUSA
  8. 8.Section of Neonatal-Perinatal Medicine, Department of Pediatrics, St. Christopher’s Hospital for ChildrenDrexel University College of MedicinePhiladelphiaUSA

Personalised recommendations