Skip to main content

Thank you for visiting 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.

Maternal anxiety during pregnancy and newborn epigenome-wide DNA methylation


Maternal anxiety during pregnancy is associated with adverse foetal, neonatal, and child outcomes, but biological mechanisms remain unclear. Altered foetal DNA methylation (DNAm) has been proposed as a potential underlying mechanism. In the current study, we performed a meta-analysis to examine the associations between maternal anxiety, measured prospectively during pregnancy, and genome-wide DNAm from umbilical cord blood. Sixteen non-overlapping cohorts from 12 independent longitudinal studies of the Pregnancy And Childhood Epigenetics Consortium participated, resulting in a combined dataset of 7243 mother-child dyads. We examined prenatal anxiety in relation to genome-wide DNAm and differentially methylated regions. We observed no association between the general symptoms of anxiety during pregnancy or pregnancy-related anxiety, and DNAm at any of the CpG sites, after multiple-testing correction. Furthermore, we identify no differentially methylated regions associated with maternal anxiety. At the cohort-level, of the 21 associations observed in individual cohorts, none replicated consistently in the other cohorts. In conclusion, contrary to some previous studies proposing cord blood DNAm as a promising potential mechanism explaining the link between maternal anxiety during pregnancy and adverse outcomes in offspring, we found no consistent evidence for any robust associations between maternal anxiety and DNAm in cord blood. Larger studies and analysis of DNAm in other tissues may be needed to establish subtle or subgroup-specific associations between maternal anxiety and the foetal epigenome.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Manhattan and quantile-quantile plot showing meta-analytic associations between general anxiety during pregnancy and cord blood DNA methylation (within 15 cohorts, maximum N = 6686 mother-child dyads).
Fig. 2: Forest plot showing associations between general anxiety during pregnancy and cord blood DNA methylation for the most significant associations (p < 5 × 10−5), across all cohorts with available data.

Data availability

Site-level meta-analytical results are publicly available ( For access to cohort-level data, requests can be sent directly to individual studies.

Code availability

Analytical codes can be requested from authors.


  1. 1.

    Dennis CL, Falah-Hassani K, Shiri R. Prevalence of antenatal and postnatal anxiety: systematic review and meta-analysis. Br J Psychiatry. 2017;210:315–23.

    PubMed  Article  PubMed Central  Google Scholar 

  2. 2.

    Grigoriadis S, Graves L, Peer M, Mamisashavili L, Tomlinson G, Vigod SN, et al. Maternal anxiety during pregnancy and the association with adverse perinatal outcomes: systematic review and meta-analysis. J Clin Psychiatry. 2018;79:17r12011.

    PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Stein A, Pearson RM, Goodman SH, Rapa E, Rahman A, McCallum M, et al. Effects of perinatal mental disorders on the fetus and child. Lancet. 2014;384:1800–19.

    Article  PubMed  Google Scholar 

  4. 4.

    Henrichs J, Schenk JJ, Roza SJ, van den Berg MP, Schmidt HG, Steegers EAP, et al. Maternal psychological distress and fetal growth trajectories: the Generation R study. Psychol Med. 2010;40:633–43.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. 5.

    Cao-Lei L, Rooij SR De, King S, Matthews SG, Metz GAS, Roseboom TJ, et al. Prenatal stress and epigenetics. Neurosci Biobehav Rev. 2020;117:198–210.

  6. 6.

    Ryan J, Mansell T, Fransquet P, Saffery R. Does maternal mental well-being in pregnancy impact the early human epigenome? Epigenomics. 2017;9:313–22.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. 7.

    Cardenas A, Faleschini S, Cortes Hidalgo A, Rifas-Shiman SL, Baccarelli AA, DeMeo DL, et al. Prenatal maternal antidepressants, anxiety, and depression and offspring DNA methylation: epigenome-wide associations at birth and persistence into early childhood. Clin Epigenetics. 2019;11:56.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  8. 8.

    Felix JF, Joubert BR, Baccarelli AA, Sharp GC, Almqvist C, Annesi-Maesano I, et al. Cohort profile: pregnancy and childhood epigenetics (PACE) consortium. Int J Epidemiol. 2018;47:22–23.

    PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, et al. Cohort profile: the Avon longitudinal study of parents and children: ALSPAC mothers cohort. Int J Epidemiol. 2013;42:97–110.

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Wright J, Small N, Raynor P, Tuffnell D, Bhopal R, Cameron N, et al. Cohort profile: the Born in Bradford multi-ethnic family cohort study. Int J Epidemiol. 2013;42:978–91.

    PubMed  Article  Google Scholar 

  11. 11.

    Stein DJ, Koen N, Donald KA, Adnams CM, Koopowitz S, Lund C, et al. Investigating the psychosocial determinants of child health in Africa: the Drakenstein Child Health Study. J Neurosci Methods. 2015;252:27–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Heude B, Forhan A, Slama R, Douhaud L, Bedel S, Saurel-Cubizolles M-J, et al. Cohort Profile: the EDEN mother-child cohort on the prenatal and early postnatal determinants of child health and development. Int J Epidemiol. 2016;45:353–63.

    PubMed  Article  Google Scholar 

  13. 13.

    Kooijman MN, Kruithof CJ, van Duijn CM, Duijts L, Franco OH, van IJzendoorn MH, et al. The generation R study: design and cohort update 2017. Eur J Epidemiol. 2016;31:1243–64.

    PubMed  Article  Google Scholar 

  14. 14.

    Paternoster L, Evans DM, Aagaard Nohr E, Holst C, Gaborieau V, Brennan P, et al. Genome-wide population-based association study of extremely overweight young adults - the GOYA study. PLoS ONE. 2011;6:e24303.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Guxens M, Ballester F, Espada M, Fernández MF, Grimalt JO, Ibarluzea J, et al. Cohort profile: the INMA-INfancia y Medio Ambiente-(environment and childhood) project. Int J Epidemiol. 2012;41:930–40.

    PubMed  Article  Google Scholar 

  16. 16.

    Magnus P, Birke C, Vejrup K, Haugan A, Alsaker E, Daltveit AK, et al. Cohort profile update: the Norwegian Mother and Child Cohort Study (MoBa). Int J Epidemiol. 2016;45:382–8.

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Girchenko P, Lahti M, Tuovinen S, Savolainen K, Lahti J, Binder EB, et al. Prediction and prevention of preeclampsia and intrauterine growth restriction (PREDO) study. Int J Epidemiol. 2017;46:1380–1.

    PubMed  Google Scholar 

  18. 18.

    Tamayo y Ortiz M, Téllez-Rojo MM, Trejo-Valdivia B, Schnaas L, Osorio-Valencia E, Coull B, et al. Maternal stress modifies the effect of exposure to lead during pregnancy and 24-month old children’s neurodevelopment. Environ Int. 2017;98:191–7.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Brunst KJ, Enlow MB, Kannan S, Carroll KN, Coull BA, Wright RJ. Effects of prenatal social stress and maternal dietary fatty acid ratio on infant temperament: Does race matter? Epidemiol. 2014;4:1000167.

    Google Scholar 

  20. 20.

    Oken E, Baccarelli AA, Gold DR, Kleinman KP, Litonjua AA, De Meo D, et al. Cohort profile: project viva. Int J Epidemiol. 2015;44:37–48.

    PubMed  Article  Google Scholar 

  21. 21.

    Brunton RJ, Dryer R, Saliba A, Kohlhoff J. Pregnancy anxiety: a systematic review of current scales. J Affect Disord. 2015;176:24–34.

    PubMed  Article  Google Scholar 

  22. 22.

    Min JL, Hemani G, Davey Smith G, Relton C, Suderman M. Meffil: efficient normalization and analysis of very large DNA methylation datasets. Bioinformatics. 2018;34:3983–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    McCartney DL, Walker RM, Morris SW, McIntosh AM, Porteous DJ, Evans KL. Identification of polymorphic and off-target probe binding sites on the Illumina Infinium MethylationEPIC BeadChip. Genomics. Data. 2016;9:22–24.

    PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinforma. 2012;13:86.

    Article  Google Scholar 

  26. 26.

    Bakulski KM, Feinberg JI, Andrews SV, Yang J, Brown S, McKenney SL, et al. DNA methylation of cord blood cell types: Applications for mixed cell birth studies. Epigenetics. 2016;11:354–62.

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    de Goede OM, Razzaghian HR, Price EM, Jones MJ, Kobor MS, Robinson WP, et al. Nucleated red blood cells impact DNA methylation and expression analyses of cord blood hematopoietic cells. Clin Epigenetics. 2015;7:95.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.

    Google Scholar 

  30. 30.

    Suderman M, Staley JR, French R, Arathimos R, Simpkin A, Tilling K Dmrff: Identifying differentially methylated regions efficiently with power and control. BioRxiv. Preprint.

  31. 31.

    Hannon E, Lunnon K, Schalkwyk L, Mill J. Interindividual methylomic variation across blood, cortex, and cerebellum: Implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics. 2015;10:1024–32.

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Edgar RD, Jones MJ, Meaney MJ, Turecki G, Kobor MS. BECon: A tool for interpreting DNA methylation findings from blood in the context of brain. Transl Psychiatry. 2017;7:e1187.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Non AL, Binder AM, Kubzansky LD, Michels KB. Genome-wide DNA methylation in neonates exposed to maternal depression, anxiety, or SSRI medication during pregnancy. Epigenetics. 2014;9:964–72.

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Conradt E, Lester BM, Appleton AA, Amstrong DA, Marsit CJ The roles of DNA methylation of NR3C1 and 11β-HSD2 and exposure to maternal mood disorder in utero on newborn neurobehavior. Epigenetics. 2013;8:1321–9.

  35. 35.

    Heron J, O’Connor TG, Evans J, Golding J, Glover V, ALSPAC Study Team. The course of anxiety and depression through pregnancy and the postpartum in a community sample. J Affect Disord. 2004;80:65–73.

    PubMed  Article  PubMed Central  Google Scholar 

  36. 36.

    Glover V, O’Connor TG, Heron J, Golding J, ALSPAC Study Team. Antenatal maternal anxiety is linked with atypical handedness in the child. Early Hum Dev. 2004;79:107–18.

    PubMed  Article  PubMed Central  Google Scholar 

  37. 37.

    Saffari A, Silver MJ, Zavattari P, Moi L, Columbano A, Meaburn EL, et al. Estimation of a significance threshold for epigenome-wide association studies. Genet Epidemiol. 2018;42:20–33.

    PubMed  Article  PubMed Central  Google Scholar 

  38. 38.

    Edgar RD, Jones MJ, Robinson WP, Kobor MS. An empirically driven data reduction method on the human 450K methylation array to remove tissue specific non-variable CpGs. Clin Epigenetics. 2017;9:11.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39.

    Solomon O, MacIsaac J, Quach H, Tindula G, Kobor MS, Huen K, et al. Comparison of DNA methylation measured by Illumina 450K and EPIC BeadChips in blood of newborns and 14-year-old children. Epigenetics. 2018;13:655–64.

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Mansell T, Vuillermin P, Ponsonby AL, Collier F, Saffery R, Ryan J. Maternal mental well-being during pregnancy and glucocorticoid receptor gene promoter methylation in the neonate. Dev Psychopathol. 2016;28:1421–30.

    PubMed  Article  Google Scholar 

  41. 41.

    Hompes T, Izzi B, Gellens E, Morreels M, Fieuws S, Pexters A, et al. Investigating the influence of maternal cortisol and emotional state during pregnancy on the DNA methylation status of the glucocorticoid receptor gene (NR3C1) promoter region in cord blood. J Psychiatr Res. 2013;47:880–91.

    PubMed  Article  Google Scholar 

  42. 42.

    Mansell T, Novakovic B, Meyer B, Rzehak P, Vuillermin P, Ponsonby A-L, et al. The effects of maternal anxiety during pregnancy on IGF2 / H19 methylation in cord blood. Transl Psychiatry. 2016;6:e765.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Chen J, Li Q, Rialdi A, Mystal E, Ly J, Finik J, et al. Influences of maternal stress during pregnancy on the epigenome: comparison of placenta and umbilical cord blood. J Depress Anxiety. 2014;3:152.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Vangeel EB, Izzi B, Hompes T, Vansteelandt K, Lambrechts D, Freson K, et al. DNA methylation in imprinted genes IGF2 and GNASXL is associated with prenatal maternal stress. Genes, Brain Behav. 2015;14:573–82.

    CAS  Article  Google Scholar 

  45. 45.

    Oberlander TF, Weinberg J, Papsdorf M, Grunau R, Misri S, Devlin AM. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics. 2008;3:97–106.

    PubMed  Article  Google Scholar 

  46. 46.

    Vangeel EB, Pishva E, Hompes T, van den Hove D, Lambrechts D, Allegaert K, et al. Newborn genome-wide DNA methylation in association with pregnancy anxiety reveals a potential role for GABBR1. Clin Epigenetics. 2017;9:107.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. 47.

    Joubert BR, Felix JF, Yousefi P, Bakulski KM, Just AC, Breton C, et al. DNA methylation in newborns and maternal smoking in pregnancy: Genome-wide consortium meta-analysis. Am J Hum Genet. 2016;98:680–96.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Hodyl NA, Roberts CT, Bianco-Miotto T. Cord blood DNA methylation biomarkers for predicting neurodevelopmental outcomes. Genes. 2016;7:117.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  49. 49.

    Spielberger CD State-trait anxiety inventory for adults. manual, instrument, and scoring guide. Palo Alto, CA: Consulting Psychologists Press; 1983.

  50. 50.

    Meades R, Ayers S. Anxiety measures validated in perinatal populations: a systematic review. J Affect Disord. 2011;133:1–15.

    PubMed  Article  Google Scholar 

  51. 51.

    Evans K, Spiby H, Morrell CJ. A psychometric systematic review of self-report instruments to identify anxiety in pregnancy. J Adv Nurs. 2015;71:1986–2001.

    PubMed  Article  Google Scholar 

  52. 52.

    O’Connor TG, Ben-Shlomo Y, Heron J, Golding J, Adams D, Glover V. Prenatal anxiety predicts individual differences in cortisol in pre-adolescent children. Biol Psychiatry. 2005;58:211–7.

    PubMed  Article  CAS  Google Scholar 

  53. 53.

    Field T, Diego M, Hernandez-Reif M, Figueiredo B, Deeds O, Ascencio A, et al. Comorbid depression and anxiety effects on pregnancy and neonatal outcome. Infant Behav Dev. 2010;33:23–29.

    PubMed  Article  Google Scholar 

  54. 54.

    Rijlaarsdam J, Pappa I, Walton E, Bakermans-Kranenburg MJ, Mileva-Seitz VR, Rippe RCA, et al. An epigenome-wide association meta-analysis of prenatal maternal stress in neonates: a model approach for replication. Epigenetics. 2016;11:140–9.

    PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Solomon O, Yousefi P, Huen K, Gunier R, Escudero-Fung M, Barcellos LF, et al. Prenatal phthalate exposure and altered patterns of DNA methylation in cord blood. Env Mol Mutagen. 2017;58:398–410.

    CAS  Article  Google Scholar 

Download references


Acknowledgements for each of the participating studies are listed in the Funding and Acknowledgements supplement.

Author information



Corresponding author

Correspondence to Henning Tiemeier.

Ethics declarations

Conflict of interest

DJS received research grants and/or consultancy honoraria from Lundbeck and Sun; the other authors confirm they have no financial relationships with commercial interests to disclose. Funding for each of the participating studies is listed in the Funding and Acknowledgements supplement. There was no editorial direction or censorship from the sponsors.


All studies acquired approval from local ethics committees and informed consent from participants.

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

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sammallahti, S., Cortes Hidalgo, A.P., Tuominen, S. et al. Maternal anxiety during pregnancy and newborn epigenome-wide DNA methylation. Mol Psychiatry (2021).

Download citation


Quick links