Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Population Study Article
  • Published:

Epigenetic associations in HPA axis genes related to bronchopulmonary dysplasia and antenatal steroids

Pediatric Research (2024)Cite this article

Abstract

Background

Bronchopulmonary dysplasia (BPD), a common morbidity among very preterm infants, is associated with chronic disease and neurodevelopmental impairments. A hypothesized mechanism for these outcomes lies in altered glucocorticoid (GC) activity. We hypothesized that BPD and its treatments may result in epigenetic differences in the hypothalamic-pituitary-adrenal (HPA) axis, which is modulated by GC, and could be ascertained using an established GC risk score and DNA methylation (DNAm) of HPA axis genes.

Methods

DNAm was quantified from buccal tissue (ECHO-NOVI) and from neonatal blood spots (ELGAN ECHO) via the EPIC microarray. Prenatal maternal characteristics, pregnancy complication, and neonatal medical complication data were collected from medical record review and maternal interviews.

Results

The GC score was not associated with steroid exposure or BPD. However, six HPA genes involved in stress response regulation demonstrated differential methylation with antenatal steroid exposure; two CpGs within FKBP5 and POMC were differentially methylated with BPD severity. These findings were sex-specific in both cohorts; males had greater magnitude of differential methylation within these genes.

Conclusions

These findings suggest that BPD severity and antenatal steroids are associated with DNAm at some HPA genes in very preterm infants and the effects appear to be sex-, tissue-, and age-specific.

Impact

  • This study addresses bronchopulmonary dysplasia (BPD), an important health outcome among preterm neonates, and interrogates a commonly studied pathway, the hypothalamic-pituitary-adrenal (HPA) axis.

  • The combination of BPD, the HPA axis, and epigenetic markers has not been previously reported.

  • In this study, we found that BPD itself was not associated with epigenetic responses in the HPA axis in infants born very preterm; however, antenatal treatment with steroids was associated with epigenetic responses.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Timeline of ECHO cohort sample collection.
Fig. 2: Forest plots of the associations between BPD severity and the polyepigenetic glucocorticoid (GC) score in ECHO NOVI and ELGAN ECHO.

Similar content being viewed by others

Data availability

Select de-identified data from the ECHO Program are available through NICHD’s Data and Specimen Hub (DASH). Information on study data not available on DASH, such as some Indigenous datasets, can be found on the ECHO study DASH webpage. The raw and processed DNAm data for NOVI are publicly accessible through NCBI Gene Expression Omnibus (GEO) via accession series GSE128821.

References

  1. Martin, J. A., Hamilton, B. E. & Osterman, M. J. Births in the United States, 2013. NCHS Data Brief 175, 1–8 (2014).

  2. Friedrich, M. J. Premature birth complications top cause of death in children younger than 5 years. JAMA 313, 235–235 (2015).

    Google Scholar 

  3. Aarnoudse-Moens, C. S., Weisglas-Kuperus, N., van Goudoever, J. B. & Oosterlaan, J. Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics 124, 717–728 (2009).

    Article  PubMed  Google Scholar 

  4. Hack, M. et al. Poor predictive validity of the Bayley Scales of Infant Development for cognitive function of extremely low birth weight children at school age. Pediatrics 116, 333–341 (2005).

    Article  PubMed  Google Scholar 

  5. Vohr, B. R., Wright, L. L., Poole, W. K. & McDonald, S. A. Neurodevelopmental outcomes of extremely low birth weight infants <32 weeks’ gestation between 1993 and 1998. Pediatrics 116, 635–643 (2005).

    Article  PubMed  Google Scholar 

  6. Aylward, G. P. Neurodevelopmental outcomes of infants born prematurely. J. Dev. Behav. Pediatr. 26, 427–440 (2005).

    Article  PubMed  Google Scholar 

  7. Grunau, R. E., Whitfield, M. F. & Davis, C. Pattern of learning disabilities in children with extremely low birth weight and broadly average intelligence. Arch. Pediatr. Adolesc. Med. 156, 615–620 (2002).

    Article  PubMed  Google Scholar 

  8. Hack, M. et al. Behavioral outcomes and evidence of psychopathology among very low birth weight infants at age 20 years. Pediatrics 114, 932–940 (2004).

    Article  PubMed  Google Scholar 

  9. Hack, M. et al. Behavioral outcomes of extremely low birth weight children at age 8 years. J. Dev. Behav. Pediatr. 30, 122–130 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hille, E. T. et al. Social lifestyle, risk-taking behavior, and psychopathology in young adults born very preterm or with a very low birthweight. J. Pediatr. 152, 793–800, 800.e1–4 (2008).

    Article  PubMed  Google Scholar 

  11. Taylor, H. G., Klein, N. & Hack, M. School-age consequences of birth weight less than 750 g: a review and update. Dev. Neuropsychol. 17, 289–321 (2000).

    Article  CAS  PubMed  Google Scholar 

  12. Stephens, B. E. & Vohr, B. R. Neurodevelopmental outcome of the premature infant. Pediatr. Clin. North Am. 56, 631–646 (2009).

    Article  PubMed  Google Scholar 

  13. Allen, M. C. Neurodevelopmental outcomes of preterm infants. Curr. Opin. Neurol. 21, 123–128 (2008).

    Article  PubMed  Google Scholar 

  14. Schmidt, B. et al. Impact of bronchopulmonary dysplasia, brain injury, and severe retinopathy on the outcome of extremely low-birth-weight infants at 18 months: results from the trial of indomethacin prophylaxis in preterms. JAMA 289, 1124–1129 (2003).

    Article  PubMed  Google Scholar 

  15. Thébaud, B. et al. Bronchopulmonary dysplasia. Nat. Rev. Dis. Prim. 5, 1–23 (2019).

    Google Scholar 

  16. O’Reilly, M., Sozo, F. & Harding, R. Impact of preterm birth and bronchopulmonary dysplasia on the developing lung: long-term consequences for respiratory health. Clin. Exp. Pharm. Physiol. 40, 765–773 (2013).

    Article  Google Scholar 

  17. Mowitz, M. E. et al. Health care burden of bronchopulmonary dysplasia among extremely preterm infants. Front. Pediatr. 7, 510 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Piyasena, C. et al. Dynamic changes in DNA methylation occur during the first year of life in preterm infants. Front. Endocrinol. 7, 158 (2016).

    Article  Google Scholar 

  19. Schuster, J. et al. Effect of prematurity on genome wide methylation in the placenta. BMC Med. Genet. 20, 116 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wang, X.-M. et al. Comparison of DNA methylation profiles associated with spontaneous preterm birth in placenta and cord blood. BMC Med. Genomics 12, 1 (2019).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  21. Breton, C. V. et al. Small-magnitude effect sizes in epigenetic end points are important in children’s environmental health studies: the Children’s Environmental Health and Disease Prevention Research Center’s Epigenetics Working Group. Environ. Health Perspect. 125, 511–526 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Everson, T. M. et al. Epigenome-wide analysis identifies genes and pathways linked to neurobehavioral variation in preterm infants. Sci. Rep. 9, 6322 (2019).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  23. Everson, T. M. et al. Serious neonatal morbidities are associated with differences in DNA methylation among very preterm infants. Clin. Epigenetics 12, 151 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Paniagua, U. et al. Epigenetic age acceleration, neonatal morbidities, and neurobehavioral profiles in infants born very preterm. Epigenetics 18, 2280738 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Giarraputo, J. et al. Medical morbidities and DNA methylation of NR3C1 in preterm infants. Pediatr. Res. 81, 68–74 (2017).

    Article  PubMed  Google Scholar 

  26. Knight, A. K. et al. Relationship between epigenetic maturity and respiratory morbidity in preterm infants. J. Pediatr. 198, 168–173.e2 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Cho, H. Y. et al. Prospective epigenome and transcriptome analyses of cord and peripheral blood from preterm infants at risk of bronchopulmonary dysplasia. Sci. Rep. 13, 12262 (2023).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  28. Jackson, W. M. et al. Differential placental CpG methylation is associated with chronic lung disease of prematurity. Pediatr. Res. 91, 1428–1435 (2022).

    Article  CAS  PubMed  Google Scholar 

  29. Wang, X. et al. Epigenome-wide association study of bronchopulmonary dysplasia in preterm infants: results from the discovery-BPD program. Clin. Epigenetics 14, 57 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Cao-Lei, L., Laplante, D. P. & King, S. Prenatal maternal stress and epigenetics: review of the human research. Curr. Mol. Biol. Rep. 2, 16–25 (2016).

    Article  Google Scholar 

  31. Parets, S. E. et al. Fetal DNA methylation associates with early spontaneous preterm birth and gestational age. PLoS ONE 8, e67489 (2013).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  32. Provençal, N. et al. Glucocorticoid exposure during hippocampal neurogenesis primes future stress response by inducing changes in DNA methylation. Proc. Natl Acad. Sci. 117, 23280 (2020).

    Article  PubMed  ADS  Google Scholar 

  33. Wiechmann, T. et al. Identification of dynamic glucocorticoid-induced methylation changes at the FKBP5 locus. Clin. Epigenetics 11, 83 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Yang, R. et al. Longitudinal genome-wide methylation study of PTSD treatment using prolonged exposure and hydrocortisone. Transl. Psychiatry 11, 398 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Braun, P. R. et al. Genome‐wide DNA methylation investigation of glucocorticoid exposure within buccal samples. Psychiatry Clin. Neurosci. 73, 323–330 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Doyle, L. W. Postnatal corticosteroids to prevent or treat bronchopulmonary dysplasia. Neonatology 118, 244–251 (2021).

    Article  CAS  PubMed  Google Scholar 

  37. Godoy, L. D., Rossignoli, M. T., Delfino-Pereira, P., Garcia-Cairasco, N. & de Lima Umeoka, E. H. A comprehensive overview on stress neurobiology: basic concepts and clinical implications. Front. Behav. Neurosci. 12, 127 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Weaver, I. C. G. et al. Epigenetic programming by maternal behavior. Nat. Neurosci. 7, 847–854 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Weaver, I. C. G., Diorio, J., Seckl, J. R., Szyf, M. & Meaney, M. J. Early environmental regulation of hippocampal glucocorticoid receptor gene expression: characterization of intracellular mediators and potential genomic target sites. Ann. N. Y. Acad. Sci. 1024, 182–212 (2004).

    Article  CAS  PubMed  ADS  Google Scholar 

  40. Blackwell, C. K., Wakschlag, L. S., Gershon, R. C. & Cella, D., with the ECHO PRO Core. Measurement framework for the Environmental influences on Child Health Outcomes research program. Curr. Opin. Pediatr. 30, 276–284 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Gillman, M. W. & Blaisdell, C. J. Environmental influences on Child Health Outcomes, a research program of the National Institutes of Health. Curr. Opin. Pediatr. 30, 260–262 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Jacobson, L. P., Lau, B., Catellier, D. & Parker, C. B. An Environmental influences on Child Health Outcomes viewpoint of data analysis centers for collaborative study designs. Curr. Opin. Pediatr. 30, 269–275 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hofheimer, J. A. et al. Psychosocial and medical adversity associated with neonatal neurobehavior in infants born before 30 weeks gestation. Pediatr. Res. 87, 721–729 (2020).

    Article  PubMed  Google Scholar 

  44. O’Shea, T. M. et al. The ELGAN study of the brain and related disorders in extremely low gestational age newborns. Early Hum. Dev. 85, 719–725 (2009).

    Article  PubMed  Google Scholar 

  45. Teschendorff, A. E. et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29, 189–196 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. Pidsley, R. et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 17, 208 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Logue, M. W. et al. The correlation of methylation levels measured using Illumina 450K and EPIC BeadChips in blood samples. Epigenomics 9, 1363–1371 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Aryee, M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363–1369 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Fortin, J. P., Fertig, E. & Hansen, K. shinyMethyl: interactive quality control of Illumina 450k DNA methylation arrays in R. F1000Res 3, 175 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  51. Martin, M. et al. Bronchopulmonary dysplasia and neurobehavioural outcomes at birth and 2 years in infants born before 30 weeks. Arch. Dis. Child. Fetal Neonatal Ed. fetalneonatal-2021-323405 https://doi.org/10.1136/archdischild-2021-323405 (2022).

  52. Jensen, E. A. et al. The diagnosis of bronchopulmonary dysplasia in very preterm infants. An evidence-based approach. Am. J. Respir. Crit. Care Med. 200, 751–759 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Natarajan, G. et al. Effect of inborn vs. outborn delivery on neurodevelopmental outcomes in infants with hypoxic-ischemic encephalopathy: secondary analyses of the NICHD whole-body cooling trial. Pediatr. Res. 72, 414–419 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Zou, Z. et al. Associations of DNA methylation of HPA axis-related genes and neuroendocrine abnormalities in panic disorder. Psychoneuroendocrinology 142, 105777 (2022).

    Article  CAS  PubMed  Google Scholar 

  55. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zheng, S. C. et al. EpiDISH web server: epigenetic dissection of intra-sample-heterogeneity with online GUI. Bioinformatics https://doi.org/10.1093/bioinformatics/btz833 (2019).

  57. Zheng, S. C. et al. A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix. Epigenomics 10, 925–940 (2018).

    Article  CAS  PubMed  Google Scholar 

  58. Gervin, K. et al. Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data. Clin. Epigenetics 11, 125 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Koestler, D. C. et al. Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL). BMC Bioinform. 17, 120 (2016).

  60. Salas, L. A. et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol. 19, 64 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Ramos-Navarro, C., Sanchez-Luna, M., Zeballos-Sarrato, S. & Pescador-Chamorro, I. Antenatal corticosteroids and the influence of sex on morbidity and mortality of preterm infants. J. Matern. Fetal Neonatal Med. 35, 3438–3445 (2022).

    Article  CAS  PubMed  Google Scholar 

  62. Suarez, A. et al. A polyepigenetic glucocorticoid exposure score at birth and childhood mental and behavioral disorders. Neurobiol. Stress 13, 100275 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. McGowan, P. O. & Matthews, S. G. Prenatal stress, glucocorticoids, and developmental programming of the stress response. Endocrinology 159, 69–82 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Talge, N. M. et al. Antenatal maternal stress and long-term effects on child neurodevelopment: how and why? J. Child Psychol. Psychiatry 48, 245–261 (2007).

    Article  PubMed  Google Scholar 

  65. Syed, S. A. & Zannas, A. S. Epigenetics in Psychiatry 2nd edn (eds Peedicayil, J., Grayson, D. R. & Avramopoulos, D.) 701–709 (Academic Press, 2021).

  66. Tsigos, C. & Chrousos, G. P. Hypothalamic–pituitary–adrenal axis, neuroendocrine factors and stress. J. Psychosom. Res. 53, 865–871 (2002).

    Article  PubMed  Google Scholar 

  67. Criado-Marrero, M. et al. Hsp90 and FKBP51: complex regulators of psychiatric diseases. Philos. Trans. R. Soc. Lond. B Biol. Sci. 373 https://doi.org/10.1098/rstb.2016.0532 (2018).

  68. Somvanshi, P. R. et al. Role of enhanced glucocorticoid receptor sensitivity in inflammation in PTSD: insights from computational model for circadian-neuroendocrine-immune interactions. Am. J. Physiol. Endocrinol. Metab. 319, E48–E66 (2020).

    Article  PubMed  Google Scholar 

  69. Plieger, T., Felten, A., Splittgerber, H., Duke, É. & Reuter, M. The role of genetic variation in the glucocorticoid receptor (NR3C1) and mineralocorticoid receptor (NR3C2) in the association between cortisol response and cognition under acute stress. Psychoneuroendocrinology 87, 173–180 (2018).

    Article  CAS  PubMed  Google Scholar 

  70. Raffin-Sanson, M. L., de Keyzer, Y. & Bertagna, X. Proopiomelanocortin, a polypeptide precursor with multiple functions: from physiology to pathological conditions. Eur. J. Endocrinol. 149, 79–90 (2003).

    Article  CAS  PubMed  Google Scholar 

  71. DiPietro, J. A. & Voegtline, K. M. The gestational foundation of sex differences in development and vulnerability. Neuroscience 342, 4–20 (2017).

    Article  CAS  PubMed  Google Scholar 

  72. De Coster, S. et al. Gender-specific transcriptomic response to environmental exposure in Flemish adults. Environ. Mol. Mutagen 54, 574–588 (2013).

    Article  CAS  PubMed  Google Scholar 

  73. Gabory, A., Roseboom, T. J., Moore, T., Moore, L. G. & Junien, C. Placental contribution to the origins of sexual dimorphism in health and diseases: sex chromosomes and epigenetics. Biol. Sex Differ. 4, 5 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  74. McCarthy, N. S. et al. Meta-analysis of human methylation data for evidence of sex-specific autosomal patterns. BMC Genomics 15, 981 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Rosenfeld, C. S. Sex-specific placental responses in fetal development. Endocrinology 156, 3422–3434 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Martin, E. et al. Sexual epigenetic dimorphism in the human placenta: implications for susceptibility during the prenatal period. Epigenomics 9, 267–278 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Clark, J. et al. Associations between placental CpG methylation of metastable epialleles and childhood body mass index across ages one, two and ten in the Extremely Low Gestational Age Newborns (ELGAN) cohort. Epigenetics 14, 1102–1111 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Watterberg, K. L., Scott, S. M. & Naeye, R. L. Chorioamnionitis, cortisol, and acute lung disease in very low birth weight infants. Pediatrics 99, E6 (1997).

    Article  CAS  PubMed  Google Scholar 

  79. Baud, O. & Watterberg, K. L. Prophylactic postnatal corticosteroids: early hydrocortisone. Semin. Fetal Neonatal Med. 24, 202–206 (2019).

    Article  PubMed  Google Scholar 

  80. Meakin, C. J. et al. Placental CpG methylation of HPA-axis genes is associated with cognitive impairment at age 10 among children born extremely preterm. Horm. Behav. 101, 29–35 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors wish to thank our ECHO Colleagues; the medical, nursing, and program staff; and the children and families participating in the ECHO cohorts. We also acknowledge the contribution of the following ECHO Program collaborators: ECHO Components—Coordinating Center: Duke Clinical Research Institute, Durham, North Carolina: Smith P.B., Newby L.K.; Data Analysis Center: Johns Hopkins University Bloomberg, School of Public Health, Baltimore, Maryland: Jacobson L.P.; Research Triangle Institute, Durham, North Carolina: Catellier D.J.; Person-Reported Outcomes Core: Northwestern University, Evanston, Illinois: Gershon R., Cella D. ECHO Awardees and Cohorts—Baystate Children’s Hospital, Springfield, MA: Vaidya R.; Beaumont Children’s Hospital, Royal Oak, MI: Obeid R.; Boston Children’s Hospital, Boston, MA: Rollins C.; East Carolina University, Brody School of Medicine, Greenville, NC: Bear K.; Michigan State University College of Human Medicine, East Lansing, MI: Lenski M.; Tufts University School of Medicine, Boston, MA: Singh R.; University of Chicago, Chicago, IL: Msall M.; University of Massachusetts Chan Medical School, Worcester, MA: Frazier J.; Atrium Health Wake Forest Baptist, Winston-Salem, NC: Gogcu S.; Yale School of Medicine, New Haven, CT: Montgomery A.; Boston Medical Center, Boston, MA: Kuban K., Douglass L., Jara H.; Boston University, Boston, MA: Joseph R.

Funding

Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) Program, Office of the Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center), U24OD023319 with co-funding from the Office of Behavioral and Social Science Research (PRO Core), UH3OD023347 (B.M.L. and C.J.M.) and UH3OD023348 (T.M.O. and R.C.F.). The Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) cohort was also supported by R01HD072267 and R01HD084515.

Author information

Authors and Affiliations

  1. Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

    Kenyaita M. Hodge, Amber A. Burt, Carmen J. Marsit & Todd M. Everson

  2. Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Vasyl Zhabotynsky

  3. Department of Pediatrics-Neonatology, Children’s Mercy Hospital, Kansas City, MO, USA

    Brian S. Carter

  4. Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Rebecca C. Fry

  5. Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC, USA

    Rebecca C. Fry, Julie A. Hofheimer & T. Michael O’Shea

  6. Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Jennifer Helderman

  7. Department of Pediatrics, Warren Alpert Medical School of Brown University and Women and Infants Hospital, Providence, RI, USA

    Elisabeth C. McGowan & Barry M. Lester

  8. Department of Pediatrics, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA

    Charles R. Neal

  9. Department of Pediatrics, Spectrum Health-Helen Devos Hospital, Grand Rapids, MI, USA

    Steven L. Pastyrnak

  10. Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA

    Lynne M. Smith

  11. Brown Center for the Study of Children at Risk, Women and Infants Hospital, Providence, RI, USA

    Sheri A. DellaGrotta, Lynne M. Dansereau & Barry M. Lester

  12. Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA

    Barry M. Lester

  13. Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA

    Carmen J. Marsit & Todd M. Everson

  14. Duke Clinical Research Institute, Durham, NC, USA

    P. B. Smith & L. K. Newby

  15. Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA

    L. P. Jacobson

  16. Research Triangle Institute, Durham, NC, USA

    D. J. Catellier

  17. Northwestern University, Evanston, IL, USA

    R. Gershon & D. Cella

  18. Baystate Children’s Hospital, Springfield, MA, USA

    R. Vaidya

  19. Beaumont Children’s Hospital, Royal Oak, MI, USA

    R. Obeid

  20. Boston Children’s Hospital, Boston, MA, USA

    C. Rollins

  21. East Carolina University, Brody School of Medicine, Greenville, NC, USA

    K. Bear

  22. Michigan State University College of Human Medicine, East Lansing, MI, USA

    M. Lenski

  23. Tufts University School of Medicine, Boston, MA, USA

    R. Singh

  24. University of Chicago, Chicago, IL, USA

    M. Msall

  25. University of Massachusetts Chan Medical School, Worcester, MA, USA

    J. Frazier

  26. Atrium Health Wake Forest Baptist, Winston-Salem, NC, USA

    S. Gogcu

  27. Yale School of Medicine, New Haven, CT, USA

    A. Montgomery

  28. Boston Medical Center, Boston, MA, USA

    K. Kuban, L. Douglass & H. Jara

  29. Boston University, Boston, MA, USA

    R. Joseph

Authors
  1. Kenyaita M. Hodge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vasyl Zhabotynsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amber A. Burt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brian S. Carter
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rebecca C. Fry
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jennifer Helderman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Julie A. Hofheimer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Elisabeth C. McGowan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Charles R. Neal
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Steven L. Pastyrnak
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lynne M. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sheri A. DellaGrotta
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Lynne M. Dansereau
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Barry M. Lester
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Carmen J. Marsit
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. T. Michael O’Shea
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Todd M. Everson
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

on behalf of program collaborators for Environmental influences on Child Health Outcomes

  • ECHO Components—Coordinating Center

    • P. B. Smith
    •  & L. K. Newby

  • Data Analysis Center

    • L. P. Jacobson
    •  & D. J. Catellier

  • Person-Reported Outcomes Core

    • R. Gershon
    •  & D. Cella

  • ECHO Awardees and Cohorts

    • R. Vaidya
    • , R. Obeid
    • , C. Rollins
    • , K. Bear
    • , M. Lenski
    • , R. Singh
    • , M. Msall
    • , J. Frazier
    • , S. Gogcu
    • , A. Montgomery
    • , K. Kuban
    • , L. Douglass
    • , H. Jara
    •  & R. Joseph

Contributions

T.M.E., C.J.M., T.M.O., R.C.F. and B.M.L. initiated, acquired the funding for, and, along with K.M.H., designed this investigation. K.M.H. and V.Z. performed the statistical analyses and interpreted the results. A.A.B., B.S.C., J.H., J.A.H., E.C.M., C.R.N., S.L.P., L.M.S., S.A.D., and L.M.D. coordinated data collection. K.M.H. and T.M.E. drafted the manuscript. All authors contributed to interpretation of the results and revisions to the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Todd M. Everson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by local Institutional Review Boards, and participants gave informed consent.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hodge, K.M., Zhabotynsky, V., Burt, A.A. et al. Epigenetic associations in HPA axis genes related to bronchopulmonary dysplasia and antenatal steroids. Pediatr Res (2024). https://doi.org/10.1038/s41390-024-03116-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41390-024-03116-4

This article is cited by

Search

Advanced search

Quick links