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Maternal preconception weight trajectories, pregnancy complications and offspring’s childhood physical and cognitive development

Published online by Cambridge University Press:  14 August 2018

A. A. Adane Open the ORCID record for A. A. Adane [Opens in a new window] ,
G. D. Mishra  and
L. R. Tooth
A. A. Adane*
Affiliation:
School of Public Health, Centre for Longitudinal and Life Course Research, The University of Queensland, Herston, QLD 4006, Australia
G. D. Mishra
Affiliation:
School of Public Health, Centre for Longitudinal and Life Course Research, The University of Queensland, Herston, QLD 4006, Australia
L. R. Tooth
Affiliation:
School of Public Health, Centre for Longitudinal and Life Course Research, The University of Queensland, Herston, QLD 4006, Australia
*
*Address for correspondence: A. A. Adane, School of Public Health, Centre for Longitudinal and Life Course Research, The University of Queensland, Herston, QLD 4006, Australia. E-mail: akilew.adane@uq.net.au
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Abstract

There is limited evidence on the association between maternal preconception body mass index (BMI) trajectories and pregnancy complications and child development. This study examined the relationships of maternal BMI trajectories, diabetes and hypertensive disorders during pregnancy and offspring’s childhood physical and cognitive development. Data were from the Australian Longitudinal Study on Women’s Health and the Mothers and their Children’s Health study (n=771). Women’s preconception BMI trajectories were identified using group-based trajectory modelling. Children’s physical and cognitive development (up to the average age of 5 years) were obtained from the Ages and Stages Questionnaire (suspected gross motor delay) and the Australian Early Development Census (AEDC). Generalized estimating equation models, adjusted for maternal sociodemographic and lifestyle factors, were used for analyses. Three distinct BMI trajectories were identified (normative, chronically overweight and chronically obese). Children born to chronically obese women were more likely to be classified as developmentally vulnerable/at-risk on AEDC domains; gross and fine motor skills [risk ratio (RR)=1.64, 95% confidence interval (CI): 1.04, 2.61] and communication skills and general knowledge (RR=1.71, 95% CI: 1.09, 2.68). They also had an elevated risk of suspected gross motor delay (RR=2.62, 95% CI: 1.26, 5.44) compared with children born to women with a normative BMI trajectory. Maternal diabetes or hypertensive disorders during pregnancy were not associated with child outcomes. Maternal preconception BMI trajectories were associated with poorer childhood development. This study finding underscores the importance of excessive weight gain prevention throughout the reproductive stage of life.

Keywords

body mass indexchild developmentpregnancy complicationstrajectory analysis
Type
Original Article
Information
Journal of Developmental Origins of Health and Disease , Volume 9 , Issue 6 , December 2018 , pp. 653 - 660
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2018 

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References

1. Catalano, PM, McIntyre, HD, Cruickshank, JK, et al. The hyperglycemia and adverse pregnancy outcome study: associations of GDM and obesity with pregnancy outcomes. Diabetes Care. 2012; 35, 780–786.Google Scholar
2. Sibai, B, Dekker, G, Kupferminc, M. Pre-eclampsia. The Lancet. 2005; 365, 785–799.Google Scholar
3. Davis, EF, Lazdam, M, Lewandowski, AJ, et al. Cardiovascular risk factors in children and young adults born to preeclamptic pregnancies: a systematic review. Pediatrics. 2012; 129, e1552–e1561.Google Scholar
4. Dabelea, D. The predisposition to obesity and diabetes in offspring of diabetic mothers. Diabetes Care. 2007; 30(Suppl. 2), S169–S174.Google Scholar
5. Drake, AJ, Reynolds, RM. Impact of maternal obesity on offspring obesity and cardiometabolic disease risk. Reproduction. 2010; 140, 387–398.Google Scholar
6. Alvarez-Bueno, C, Cavero-Redondo, I, Lucas-de la Cruz, L, Notario-Pacheco, B, Martinez-Vizcaino, V. Association between pre-pregnancy overweight and obesity and children’s neurocognitive development: a systematic review and meta-analysis of observational studies. Int J Epidemiol. 2017; 46, 1653–1666.Google Scholar
7. Adane, AA, Mishra, GD, Tooth, LR. Maternal pre-pregnancy obesity and childhood physical and cognitive development of children: a systematic review. Int J Obes. 2016; 40, 1608–1618.Google Scholar
8. Yeung, EH, Sundaram, R, Ghassabian, A, Xie, Y, Buck Louis, G. Parental obesity and early childhood development. Pediatrics. 2017; 139, e20161459.Google Scholar
9. Thompson, ML, Ananth, CV, Jaddoe, VW, Miller, RS, Williams, MA. The association of maternal adult weight trajectory with preeclampsia and gestational diabetes mellitus. Paediatr Perinatal Epidemiol. 2014; 28, 287–296.Google Scholar
10. Strutz, KL, Richardson, LJ, Hussey, JM. Preconception health trajectories and birth weight in a national prospective cohort. J Adolesc Health. 2012; 51, 629–636.Google Scholar
11. Li, L, Law, C, Lo Conte, R, Power, C. Intergenerational influences on childhood body mass index: the effect of parental body mass index trajectories. Am J Clin Nutr. 2009; 89, 551–557.Google Scholar
12. Adane, AA, Mishra, GD, Tooth, LR. Diabetes in pregnancy and childhood cognitive development: a systematic review. Pediatrics. 2016; 137, e20154234.Google Scholar
13. Tuovinen, S, Eriksson, JG, Kajantie, E, Raikkonen, K. Maternal hypertensive pregnancy disorders and cognitive functioning of the offspring: a systematic review. J Am Soc Hypertens. 2014; 8, 832–847.e831.Google Scholar
14. Dionne, G, Boivin, M, Seguin, JR, Perusse, D, Tremblay, RE. Gestational diabetes hinders language development in offspring. Pediatrics. 2008; 122, e1073–e1079.Google Scholar
15. Nomura, Y, Marks, DJ, Grossman, B, et al. Exposure to gestational diabetes mellitus and low socioeconomic status: effects on neurocognitive development and risk of attention-deficit/hyperactivity disorder in offspring. Arch Pediatr Adolesc Med. 2012; 166, 337–343.Google Scholar
16. Veena, SR, Krishnaveni, GV, Srinivasan, K, et al. Childhood cognitive ability: relationship to gestational diabetes mellitus in India. Diabetologia. 2010; 53, 2134–2138.Google Scholar
17. Ornoy, A, Wolf, A, Ratzon, N, Greenbaum, C, Dulitzky, M. Neurodevelopmental outcome at early school age of children born to mothers with gestational diabetes. Arch Dis Child Fetal Neonatal Ed. 1999; 81, F10–F14.Google Scholar
18. Grace, T, Bulsara, M, Pennell, C, Hands, B. Maternal hypertensive diseases negatively affect offspring motor development. Pregnancy Hypertens. 2014; 4, 209–214.Google Scholar
19. Ghassabian, A, Sundaram, R, Wylie, A, Bell, E, Bello, SC, Yeung, E. Maternal medical conditions during pregnancy and gross motor development up to age 24 months in the Upstate KIDS study. Dev Med Child Neurol. 2016; 58, 728–734.Google Scholar
20. Schlapbach, LJ, Ersch, J, Adams, M, Bernet, V, Bucher, HU, Latal, B. Impact of chorioamnionitis and preeclampsia on neurodevelopmental outcome in preterm infants below 32 weeks gestational age. Acta Paediatr. 2010; 99, 1504–1509.Google Scholar
21. Silveira, RC, Procianoy, RS, Koch, MS, Benjamin, AC, Schlindwein, CF. Growth and neurodevelopment outcome of very low birth weight infants delivered by preeclamptic mothers. Acta Paediatr. 2007; 96, 1738–1742.Google Scholar
22. Poston, L, Caleyachetty, R, Cnattingius, S, et al. Preconceptional and maternal obesity: epidemiology and health consequences. Lancet Diabetes Endocrinol. 2016; 4, 1025–1036.Google Scholar
23. Lo, JO, Mission, JF, Caughey, AB. Hypertensive disease of pregnancy and maternal mortality. Curr Opin Obstet Gynecol. 2013; 25, 124–132.Google Scholar
24. Zhu, Y, Zhang, C. Prevalence of gestational diabetes and risk of progression to type 2 diabetes: a global perspective. Curr Diab Rep. 2016; 16, 7.Google Scholar
25. Dobson, AJ, Hockey, R, Brown, WJ. et al. Cohort profile update: Australian Longitudinal Study on Women’s Health. Int J Epidemiol. 2015; 44, 1547, 1547a–1547f.Google Scholar
26. Mishra, GD, Moss, K, Loos, C, et al. MatCH (Mothers and their Children’s Health) Profile: offspring of the 1973-78 cohort of the Australian Longitudinal Study on Women’s Health. Longitud Life Course Stud. 2018; 9, 351–375.Google Scholar
27. Hoffman, L, Nolan, C, Wilson, JD, Oats, JJ, Simmons, D. Gestational diabetes mellitus–management guidelines. The Australasian Diabetes in Pregnancy Society. Med J Aust. 1998; 169, 93–97.Google Scholar
28. Lowe, SA, Brown, MA, Dekker, GA, et al. Guidelines for the management of hypertensive disorders of pregnancy 2008. Aust NZ J Obstet Gynaecol. 2009; 49, 242–246.Google Scholar
29. Squires, J, Bricker, D. Ages & Stages Questionnaires, Third Edition (ASQ-3), 2009. Brookes: Baltimore, MD.Google Scholar
30. Janus, M, Brinkman, SA, Duku, EK. Validity and psychometric properties of the early development instrument in Canada, Australia, United States, and Jamaica. Soc Indic Res. 2011; 103, 283.Google Scholar
31. Centre for Community Child Health and Telethon Institute for Child Health Research. A Snapshot of Early Childhood Development in Australia – AEDI National Report 2009, 2009. Australian Government: Canberra.Google Scholar
32. Department of Health and Aged Care (GISCA). Measuring Remoteness: Accessibility/Remoteness Index of Australia (ARIA), Revised edn, 2001. Canberra.Google Scholar
33. Brown, WJ, Ford, JH, Burton, NW, Marshall, AL, Dobson, AJ. Prospective study of physical activity and depressive symptoms in middle-aged women. Am J Prev Med. 2005; 29, 265–272.Google Scholar
34. Jones, BL, Nagin, DS. Advances in group-based trajectory modeling and an SAS procedure for estimating them. Sociol Methods Res. 2007; 35, 542–571.Google Scholar
35. Nagin, DS, Odgers, CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010; 6, 109–138.Google Scholar
36. Hanley, JA, Negassa, A, Edwardes, MD, Forrester, JE. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol. 2003; 157, 364–375.Google Scholar
37. McNutt, LA, Wu, C, Xue, X, Hafner, JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003; 157, 940–943.Google Scholar
38. Godfrey, KM, Reynolds, RM, Prescott, SL. et al. Influence of maternal obesity on the long-term health of offspring. Lancet Diabetes Endocrinol. 2017; 5, 53–64.Google Scholar
39. Bolton, JL, Bilbo, SD. Developmental programming of brain and behavior by perinatal diet: focus on inflammatory mechanisms. Dialogues Clin Neurosci. 2014; 16, 307–320.Google Scholar
40. Leversen, KT, Sommerfelt, K, Ronnestad, A, et al. Prediction of neurodevelopmental and sensory outcome at 5 years in Norwegian children born extremely preterm. Pediatrics. 2011; 127, e630–e638.Google Scholar
41. Morsing, E, Marsal, K. Pre-eclampsia–an additional risk factor for cognitive impairment at school age after intrauterine growth restriction and very preterm birth. Early Hum Dev. 2014; 90, 99–101.Google Scholar
42. Camprubi Robles, M, Campoy, C, Garcia Fernandez, L, Lopez-Pedrosa, JM, Rueda, R, Martin, MJ. Maternal diabetes and cognitive performance in the offspring: a systematic review and meta-analysis. PLoS One. 2015; 10, e0142583.Google Scholar
43. Daraki, V, Roumeliotaki, T, Koutra, K, et al. Effect of parental obesity and gestational diabetes on child neuropsychological and behavioral development at 4 years of age: the Rhea mother-child cohort, Crete, Greece. Eur Child Adolesc Psychiatry. 2017; 26, 703–714.Google Scholar
44. Craig, BM, Adams, AK. Accuracy of body mass index categories based on self-reported height and weight among women in the United States. Matern Child Health J. 2009; 13, 489–496.Google Scholar
45. Gresham, E, Forder, P, Chojenta, CL, Byles, JE, Loxton, DJ, Hure, AJ. Agreement between self-reported perinatal outcomes and administrative data in New South Wales, Australia. BMC Pregnancy Childbirth. 2015; 15, 161.Google Scholar
46. Pugh, SJ, Richardson, GA, Hutcheon, JA, et al. Maternal obesity and excessive gestational weight gain are associated with components of child cognition. J Nutr. 2015; 145, 2562–2569.Google Scholar
47. Stephenson, J, Heslehurst, N, Hall, J, et al. Before the beginning: nutrition and lifestyle in the preconception period and its importance for future health. Lancet. 2018; 391, 1830–1841.Google Scholar
48. Johns, DJ, Hartmann-Boyce, J, Jebb, SA, Aveyard, P, Behavioural Weight Management Review Group. Diet or exercise interventions vs combined behavioral weight management programs: a systematic review and meta-analysis of direct comparisons. J Acad Nutr Dietetics. 2014; 114, 1557–1568.Google Scholar
49. Piek, JP, Dawson, L, Smith, LM, Gasson, N. The role of early fine and gross motor development on later motor and cognitive ability. Hum Mov Sci. 2008; 27, 668–681.Google Scholar
50. Hua, J, Duan, T, Gu, G, et al. Effects of home and education environments on children’s motor performance in China. Dev Med Child Neurol. 2016; 58, 868–876.Google Scholar
51. Miquelote, AF, Santos, DC, Cacola, PM, Montebelo, MI, Gabbard, C. Effect of the home environment on motor and cognitive behavior of infants. Infant Behav Dev. 2012; 35, 329–334.Google Scholar
52. Williams, HG, Pfeiffer, KA, O'Neill, JR, et al. Motor skill performance and physical activity in preschool children. Obesity (Silver Spring). 2008; 16, 1421–1426.Google Scholar
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