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Metabolomics

, 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
First Online:

Abstract

Objective

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.

Results

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).

Conclusion

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

Keywords

Chronic lung disease Preterm newborn Prematurity Metabolomics Mass spectrometry. 

Abbreviations

AUROC

Area under the receiver operating characteristic curve

BALF

Bronchoalveolar lavage fluid

BPD

Bronchopulmonary dysplasia

GA

Gestational age

LC

Liquid chromatography

LV

Latent variables

MS

Mass spectrometer

NICU

Neonatal intensive care unit

NMC

Number of misclassifications

PH

Pulmonary hypertension

PLS-DA

Partial least squares-discriminant analysis

TA

Tracheal aspirate

VIP

Variables importance for prediction

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Notes

Acknowledgements

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)

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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

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