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Pregnancy risk factors related to autism: an Italian case–control study in mothers of children with autism spectrum disorders (ASD), their siblings and of typically developing children

Part of: French DOHaD

Published online by Cambridge University Press:  23 April 2018

E. Grossi Open the ORCID record for E. Grossi [Opens in a new window] ,
L. Migliore  and
F. Muratori
E. Grossi*
Affiliation:
Department of Autism Research, Villa Santa Maria Foundation, Tavernerio, Italy
L. Migliore
Affiliation:
Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
F. Muratori
Affiliation:
Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, Calambrone, Italy Department of Developmental Neuroscience, University of Pisa, Calambrone, Italy
*
Address for correspondence: E. Grossi, Autism Research Unit, Villa Santa Maria Institute, Via IV Novembre, 22038, Tavernerio (CO), Italy. E-mail: [email protected]
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Abstract

This study, carried out in two Italian Institutions, assesses the frequency of 27 potential autism risk factors related to pregnancy and peri- and postnatal periods by interviewing mothers who had children with autism, children with autism and one or two typically developing siblings, or only typically developing children. The clinical sample included three case groups: 73 children and adolescents with autism (Group A), 35 children and adolescents with autism (Group A1) having 45 siblings (Group B) and 96 typically developing children (Group C) matched for gender and age. Twenty-five out of 27 of risk factors presented a higher frequency in Group A in comparison with Group C and for nine of them a statistically significant difference was found. Twenty-one out of 27 of risk factors presented a higher frequency in Group A in comparison with Group B. A higher prevalence of environmental risk factors was observed in 11 risk factors in the Group A1 in comparison with Group B and for nine of them an odds ratio higher than 1.5 was found. For 13 factors there was a progressive increase in frequency going from Group C, B and A and a statistically higher prevalence of the mean number of stressful events per pregnancy was recorded in Group A when compared with Groups B and C. The results suggest that environmental, incidental phenomena and stressful life events can influence pregnancy outcome in predisposed subjects, pointing out a possible threshold effect in women who are predisposed to have suboptimal pregnancies.

Keywords

developmental stagehumannewborn/infantpregnancy
Type
Original Article
Information
Journal of Developmental Origins of Health and Disease , Volume 9 , Issue 4: Themed Issue: French DOHaD , August 2018 , pp. 442 - 449
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2018 

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References

1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th edn, 2013. American Psychiatric Publications: Washington, DC.Google Scholar
2. Stefanatos, GA. Regression in autistic spectrum disorders. Neuropsychol Rev. 2008; 18, 305–319.Google Scholar
3. Ji, L, Chauhan, V, Flory, MJ, Chauhan, A. Brain region-specific decrease in the activity and expression of protein kinase a in the frontal cortex of regressive autism. PLoS One. 2011; 6, e23751.Google Scholar
4. Pagnamenta, AT, Khan, H, Walker, S, et al. Rare familial 16q21 microdeletions under a linkage peak implicate cadherin 8 (CDH8) in susceptibility to autism and learning disability. J Med Genet. 2011; 48, 48–54.Google Scholar
5. Volkmar, F, Siegel, M, Woodbury-Smith, M, et al. Practice parameter for the assessment and treatment of children and adolescents with autism spectrum disorder. J Am Acad Child Adolesc Psychiatry. 2014; 53, 237–257.Google Scholar
6. Hertz-Picciotto, I, Croen, LA, Hansen, R, et al. The CHARGE study: an epidemiologic investigation of genetic and environmental factors contributing to autism. Environ Health Perspect. 2006; 114, 1119–1125.Google Scholar
7. Baird, G, Simonoff, E, Pickles, A, et al. Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: The Special Needs and Autism Project (SNAP). Lancet. 2006; 368, 210–215.Google Scholar
8. Saemundsen, E, Magnússon, P, Georgsdóttir, I, Egilsson, E, Rafnsson, V. Prevalence of autism spectrum disorders in an Icelandic birth cohort. BMJ Open. 2013; 3, e002748.Google Scholar
9. Christensen, DL. Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network. J Dev Behav Pediatr. 2016; 37, 1–8.Google Scholar
10. Kim, YS, Leventhal, BL, Koh, Y-J, et al. Prevalence of autism spectrum disorders in a total population sample. Am J Psychiatry. 2011; 168, 904–912.Google Scholar
11. Hill, AP, Zuckerman, K, Fombonne, E. Epidemiology of autism spectrum disorders. In Handbook of Autism and Pervasive Developmental Disorders Diagnosis, Development, and Brain Mechanisms, 4th edn (eds. Volkmar FR, Rogers SJ, Paul R, Pelphrey KA), 2014; pp. 57–96. Wiley: New York, NY, USA.Google Scholar
12. Lai, MC, Lombardo, MV, Baron-Cohen, S. Autism. Lancet. 2014; 383, 896–910.Google Scholar
13. Wang, C, Geng, H, Liu, W, Zhang, G. Prenatal, perinatal, and postnatal factors associated with autism: a meta-analysis. Medicine (Baltimore). 2017; 96, e6696.Google Scholar
14. Modabbernia, A, Velthorst, E, Reichenberg, A. Environmental risk factors for autism: an evidence-based review of systematic reviews and meta-analyses. Mol Autism. 2017; 8, 13.Google Scholar
15. Buscema, M, Grossi, E. The semantic connectivity map: an adapting self-organising knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease. Int J Data Min Bioinform. 2008; 2, 362–404.Google Scholar
16. Lurie, S, Ribenzaft, S, Boaz, M, Golan, A, Sadan, O. The effect of cigarette smoking during pregnancy on mode of delivery in uncomplicated term singleton pregnancies. J Matern Fetal Neonatal Med. 2014; 27, 812–815.Google Scholar
17. Wallenborn, JT, Masho, SW. The interrelationship between repeat cesarean section, smoking status, and breastfeeding duration. Breastfeed Med. 2016; 11, 440–447.Google Scholar
18. Sekirov, I, Russell, SL, Antunes, LCM, Finlay, BB. Gut microbiota in health and disease. Physiol Rev. 2010; 90, 859–904.Google Scholar
19. Mc Canlies, E, Fekedulegn, D, Mnatsakanova, A, et al. Parental occupational exposure and autism spectrum disorder. J Autism Dev Disord. 2012; 42, 2323–2334.Google Scholar
20. Weinstock, M. The long-term behavioural consequences of prenatal stress. Neurosci Biobehav Rev. 2008; 32, 1073–1086.Google Scholar
21. Angelidou, A, Asadi, S, Alysandratos, KD, et al. Perinatal stress, brain inflammation and risk of autism. BMC Pediatr. 2012; 12, 89.Google Scholar
22. Varcin, KJ, Alvares, GA, Uljarević, M, Whitehouse, AJO. Prenatal maternal stress events and phenotypic outcomes in autism spectrum disorder. Autism Res. 2017; 10, 1866–1877.Google Scholar