Skip to main content
Log in

Defective placentation syndromes and autism spectrum disorder in the offspring: population-based cohort and sibling-controlled studies

  • PSYCHIATRIC EPIDEMIOLOGY
  • Published:
European Journal of Epidemiology Aims and scope Submit manuscript

Abstract

Defective placentation underlies diverse syndromic manifestations that could affect brain development including: (1) placental abruption, (2) term preeclampsia with a small-for-gestational age (SGA) infant, (3) preterm preeclampsia, and (4) spontaneous preterm birth. We investigated the relations between these defective placentation syndromes and the incidence of Autism Spectrum Disorder (ASD) in offspring. We conducted a population-based cohort study of 1,645,455 non-malformed singleton infants born in Sweden 2000–2016 who were followed for up to 17 years using national registers. We compared ASD rates for children prenatally exposed and unexposed to defective placentation syndromes with use of adjusted hazard ratios (HR) with 95% confidence intervals (CI) from Cox regression. We also conducted sibling-controlled analyses among 1,092,132 full siblings. The association of the syndromes with ASD independent of preterm birth was estimated in mediation analyses. There were 23,810 cases of ASD. In both general cohort and sibling analyses, adjusted HRs (95% CI) of ASD were increased in children of mothers with term preeclampsia combined with SGA [1.5 (1.3, 1.9) and 1.9 (1.1, 3.3), respectively], preterm preeclampsia < 34 weeks [1.8 (1.4, 2.2) and 4.2 (2.1, 8.5), respectively], and spontaneous very or extremely preterm birth (≤ 31 weeks) [2.6 (2.2, 3.0) and 2.4 (1.5, 3.8), respectively]. Placental abruption was associated with increased HR of ASD in general cohort analysis only. The association between preeclampsia and ASD was not fully explained by preterm birth. In conclusion, syndromes linked to defective placentation are associated with increased incidence of ASD in the offspring.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J. Autism spectrum disorder. Lancet. 2018;392(10146):508–20. https://doi.org/10.1016/S0140-6736(18)31129-2.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Iakoucheva LM, Muotri AR, Sebat J. Getting to the cores of autism. Cell. 2019;178(6):1287–98. https://doi.org/10.1016/j.cell.2019.07.037.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bai D, Yip BHK, Windham GC, et al. Association of genetic and environmental factors with autism in a 5-country cohort. JAMA Psychiat. 2019;76(10):1035–43. https://doi.org/10.1001/jamapsychiatry.2019.1411.

    Article  Google Scholar 

  4. Persson M, Opdahl S, Risnes K, et al. Gestational age and the risk of autism spectrum disorder in Sweden, Finland, and Norway: a cohort study. PLoS Med. 2020;17(9):e1003207. https://doi.org/10.1371/journal.pmed.1003207.

    Article  PubMed  PubMed Central  Google Scholar 

  5. D’Onofrio BM, Class QA, Rickert ME, Larsson H, Langstrom N, Lichtenstein P. Preterm birth and mortality and morbidity: a population-based quasi-experimental study. JAMA Psychiat. 2013;70(11):1231–40. https://doi.org/10.1001/jamapsychiatry.2013.2107.

    Article  Google Scholar 

  6. Sun BZ, Moster D, Harmon QE, Wilcox AJ. Association of preeclampsia in term births with neurodevelopmental disorders in offspring. JAMA Psychiat. 2020. https://doi.org/10.1001/jamapsychiatry.2020.0306.

    Article  Google Scholar 

  7. Moore GS, Kneitel AW, Walker CK, Gilbert WM, Xing G. Autism risk in small- and large-for-gestational-age infants. Am J Obstet Gynecol. 2012;206(4):314-e1-314-e9. https://doi.org/10.1016/j.ajog.2012.01.044.

    Article  Google Scholar 

  8. Schieve LA, Tian LH, Baio J, et al. Population attributable fractions for three perinatal risk factors for autism spectrum disorders, 2002 and 2008 autism and developmental disabilities monitoring network. Ann Epidemiol. 2014;24(4):260–6. https://doi.org/10.1016/j.annepidem.2013.12.014.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Khong Y, Brosens I. Defective deep placentation. Best Pract Res Clin Obstet Gynaecol. 2011;25(3):301–11. https://doi.org/10.1016/j.bpobgyn.2010.10.012.

    Article  PubMed  Google Scholar 

  10. Kratimenos P, Penn AA. Placental programming of neuropsychiatric disease. Pediatr Res. 2019;86(2):157–64. https://doi.org/10.1038/s41390-019-0405-9.

    Article  PubMed  Google Scholar 

  11. Schuchter K, Metzenbauer M, Hafner E, Philipp K. Uterine artery Doppler and placental volume in the first trimester in the prediction of pregnancy complications. Ultrasound Obstet Gynecol. 2001;18(6):590–2. https://doi.org/10.1046/j.0960-7692.2001.00596.x.

    Article  CAS  PubMed  Google Scholar 

  12. Brosens IA, Robertson WB, Dixon HG. The role of the spiral arteries in the pathogenesis of preeclampsia. Obstet Gynecol Annu. 1972;1:177–91.

    CAS  PubMed  Google Scholar 

  13. Moldenhauer JS, Stanek J, Warshak C, Khoury J, Sibai B. The frequency and severity of placental findings in women with preeclampsia are gestational age dependent. Am J Obstet Gynecol. 2003;189(4):1173–7. https://doi.org/10.1067/s0002-9378(03)00576-3.

    Article  PubMed  Google Scholar 

  14. Sohlberg S, Mulic-Lutvica A, Lindgren P, Ortiz-Nieto F, Wikstrom AK, Wikstrom J. Placental perfusion in normal pregnancy and early and late preeclampsia: a magnetic resonance imaging study. Placenta. 2014;35(3):202–6. https://doi.org/10.1016/j.placenta.2014.01.008.

    Article  CAS  PubMed  Google Scholar 

  15. Nelson DB, Ziadie MS, McIntire DD, Rogers BB, Leveno KJ. Placental pathology suggesting that preeclampsia is more than one disease. Am J Obstet Gynecol. 2014;210(1):66e1–7. https://doi.org/10.1016/j.ajog.2013.09.010.

    Article  Google Scholar 

  16. Arias F, Rodriquez L, Rayne SC, Kraus FT. Maternal placental vasculopathy and infection: two distinct subgroups among patients with preterm labor and preterm ruptured membranes. Am J Obstet Gynecol. 1993;168(2):585–91. https://doi.org/10.1016/0002-9378(93)90499-9.

    Article  CAS  PubMed  Google Scholar 

  17. Raghavan R, Helfrich BB, Cerda SR, et al. Preterm birth subtypes, placental pathology findings, and risk of neurodevelopmental disabilities during childhood. Placenta. 2019;83:17–25. https://doi.org/10.1016/j.placenta.2019.06.374.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Misra VK, Hobel CJ, Sing CF. Placental blood flow and the risk of preterm delivery. Placenta. 2009;30(7):619–24. https://doi.org/10.1016/j.placenta.2009.04.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Parker SE, Werler MM, Gissler M, Tikkanen M, Ananth CV. Placental abruption and subsequent risk of pre-eclampsia: a population-based case-control study. Paediatr Perinat Epidemiol. 2015;29(3):211–9. https://doi.org/10.1111/ppe.12184.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kvalvik LG, Wilcox AJ, Skjaerven R, Ostbye T, Harmon QE. Term complications and subsequent risk of preterm birth: registry based study. BMJ. 2020;369:m1007. https://doi.org/10.1136/bmj.m1007.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Cnattingius S, Wikstrom AK, Stephansson O, Johansson K. The impact of small for gestational age births in early and late preeclamptic pregnancies for preeclampsia recurrence: a cohort study of successive pregnancies in Sweden. Paediatr Perinat Epidemiol. 2016;30(6):563–70. https://doi.org/10.1111/ppe.12317.

    Article  PubMed  Google Scholar 

  22. Maher GM, O’Keeffe GW, Dalman C, et al. Association between preeclampsia and autism spectrum disorder: a population-based study. J Child Psychol Psychiatry. 2020;61(2):131–9. https://doi.org/10.1111/jcpp.13127.

    Article  PubMed  Google Scholar 

  23. Swedish National Board of Health and Welfare. The Swedish medical birth register: a summary of content and quality. 2003. http://www.socialstyrelsen.se/Lists/Artikelkatalog/Attachments/10655/2003-112-3_20031123.pdf. Accessed 15 Feb 2016.

  24. Ludvigsson JF, Andersson E, Ekbom A, et al. External review and validation of the Swedish national inpatient register. BMC Public Health. 2011;11:450. https://doi.org/10.1186/1471-2458-11-450.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Ludvigsson JF, Almqvist C, Bonamy AK, et al. Registers of the Swedish total population and their use in medical research. Eur J Epidemiol. 2016;31(2):125–36. https://doi.org/10.1007/s10654-016-0117-y.

    Article  PubMed  Google Scholar 

  26. Statistics Sweden. Evaluation of the Swedish register of education. 2006. http://www.scb.se/statistik/_publikationer/BE9999_2006A01_BR_BE96ST0604.pdf. Accessed 15 Feb 2016.

  27. Ekbom A. The Swedish multi-generation register. Methods Mol Biol. 2011;675:215–20. https://doi.org/10.1007/978-1-59745-423-0_10.

    Article  CAS  PubMed  Google Scholar 

  28. Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, Ekbom A. The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol. 2009;24(11):659–67. https://doi.org/10.1007/s10654-009-9350-y.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Marsal K, Persson PH, Larsen T, Lilja H, Selbing A, Sultan B. Intrauterine growth curves based on ultrasonically estimated foetal weights. Acta Paediatr. 1996;85(7):843–8.

    Article  CAS  Google Scholar 

  30. Villamor E, Bosch RJ. Optimal treatment of replicate measurements in anthropometric studies. Ann Hum Biol. 2015;42(5):507–10. https://doi.org/10.3109/03014460.2014.969488.

    Article  PubMed  Google Scholar 

  31. Cnattingius S, Villamor E, Johansson S, et al. Maternal obesity and risk of preterm delivery. JAMA. 2013;309(22):2362–70. https://doi.org/10.1001/jama.2013.6295.

    Article  CAS  PubMed  Google Scholar 

  32. World Health Organization. Global database on body mass index: BMI classification. http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi. Accessed 14 Jun 2019.

  33. George L, Granath F, Johansson AL, Cnattingius S. Self-reported nicotine exposure and plasma levels of cotinine in early and late pregnancy. Acta Obstet Gynecol Scand. 2006;85(11):1331–7. https://doi.org/10.1080/00016340600935433.

    Article  CAS  PubMed  Google Scholar 

  34. Xie S, Heuvelman H, Magnusson C, et al. Prevalence of autism spectrum disorders with and without intellectual disability by gestational age at birth in the Stockholm youth cohort: a register linkage study. Paediatr Perinat Epidemiol. 2017;31(6):586–94. https://doi.org/10.1111/ppe.12413.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013;18(2):137–50. https://doi.org/10.1037/a0031034.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Valeri L, VanderWeele TJ. SAS macro for causal mediation analysis with survival data. Epidemiology. 2015;26(2):e23–4. https://doi.org/10.1097/EDE.0000000000000253.

    Article  PubMed  Google Scholar 

  37. Hernan MA, Robins JM. Causal inference. What if. Boca Raton: CRC Press; (In Press).

    Google Scholar 

  38. Lee J, Kim JS, Park JW, et al. Chronic chorioamnionitis is the most common placental lesion in late preterm birth. Placenta. 2013;34(8):681–9. https://doi.org/10.1016/j.placenta.2013.04.014.

    Article  CAS  PubMed  Google Scholar 

  39. van Vliet EO, de Kieviet JF, van der Voorn JP, Been JV, Oosterlaan J, van Elburg RM. Placental pathology and long-term neurodevelopment of very preterm infants. Am J Obstet Gynecol. 2012;206(6):489e1–7. https://doi.org/10.1016/j.ajog.2012.03.024.

    Article  Google Scholar 

  40. Barron A, McCarthy CM, O’Keeffe GW. Preeclampsia and neurodevelopmental outcomes: Potential pathogenic roles for inflammation and oxidative stress? Mol Neurobiol. 2021. https://doi.org/10.1007/s12035-021-02290-4.

    Article  PubMed  Google Scholar 

  41. Agrawal S, Rao SC, Bulsara MK, Patole SK. Prevalence of autism spectrum disorder in preterm infants: a meta-analysis. Pediatrics. 2018. https://doi.org/10.1542/peds.2018-0134.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Buchmayer S, Johansson S, Johansson A, Hultman CM, Sparen P, Cnattingius S. Can association between preterm birth and autism be explained by maternal or neonatal morbidity? Pediatrics. 2009;124(5):e817–25. https://doi.org/10.1542/peds.2008-3582.

    Article  PubMed  Google Scholar 

  43. Brumbaugh JE, Weaver AL, Myers SM, Voigt RG, Katusic SK. Gestational age, perinatal characteristics, and autism spectrum disorder: a birth cohort study. J Pediatr. 2020;220:175–83. https://doi.org/10.1016/j.jpeds.2020.01.022.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Endler M, Saltvedt S, Cnattingius S, Stephansson O, Wikstrom AK. Retained placenta is associated with pre-eclampsia, stillbirth, giving birth to a small-for-gestational-age infant, and spontaneous preterm birth: a national register-based study. BJOG. 2014;121(12):1462–70. https://doi.org/10.1111/1471-0528.12752.

    Article  CAS  PubMed  Google Scholar 

  45. Zhu Y, Mordaunt CE, Yasui DH, et al. Placental DNA methylation levels at CYP2E1 and IRS2 are associated with child outcome in a prospective autism study. Hum Mol Genet. 2019;28(16):2659–74. https://doi.org/10.1093/hmg/ddz084.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Abel KM, Dalman C, Svensson AC, et al. Deviance in fetal growth and risk of autism spectrum disorder. Am J Psychiatry. 2013;170(4):391–8. https://doi.org/10.1176/appi.ajp.2012.12040543.

    Article  PubMed  Google Scholar 

  47. Källén B, Källén K. The Swedish medical birth register. A summary of content and quality. Centre for epidemiology, national board of health and welfare. Stockolm, Sweden. 2003; https://pdfs.semanticscholar.org/4744/7a6258c2b8e4d69a8bec5861485617cae843.pdf. Accessed 23 Jul 2020.

  48. Frisell T, Oberg S, Kuja-Halkola R, Sjolander A. Sibling comparison designs: bias from non-shared confounders and measurement error. Epidemiology. 2012;23(5):713–20. https://doi.org/10.1097/EDE.0b013e31825fa230.

    Article  PubMed  Google Scholar 

  49. Saunders GRB, McGue M, Malone SM. Sibling comparison designs: addressing confounding bias with inclusion of measured confounders. Twin Res Hum Genet. 2019;22(5):290–6. https://doi.org/10.1017/thg.2019.67.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Sjolander A, Zetterqvist J. Confounders, mediators, or colliders: What types of shared covariates does a sibling comparison design control for? Epidemiology. 2017;28(4):540–7. https://doi.org/10.1097/EDE.0000000000000649.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The study was supported by the National Institutes of Health (R21 MH120824), the Swedish Research Council for Health, Working Life and Welfare (2014-0073 and 2017-00134), and the Karolinska Institutet (Unrestricted Distinguished Professor Award 2368/10-221 to SC).

Funding

The study was supported by the National Institutes of Health (R21 MH120824), the Swedish Research Council for Health, Working Life and Welfare (2014–0073 and 2017–00134), and the Karolinska Institutet (Unrestricted Distinguished Professor Award 2368/10–221 to SC).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. EV performed the analyses and wrote the first draft of the manuscript. SC obtained the data. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Eduardo Villamor.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. None of the authors has conflicts of interest to disclose.

Ethical approval

This study was approved by the Regional Ethical Review Board in Stockholm, Sweden (No. 2018/5:2). In accordance with their decision, data linkage was allowed without informed consent from participants involved in the study. All individuals’ information was anonymized and de-identified prior to analysis.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 110 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Villamor, E., Susser, E.S. & Cnattingius, S. Defective placentation syndromes and autism spectrum disorder in the offspring: population-based cohort and sibling-controlled studies. Eur J Epidemiol 37, 827–836 (2022). https://doi.org/10.1007/s10654-022-00884-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10654-022-00884-3

Keywords

Navigation