Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-23T19:26:05.984Z Has data issue: false hasContentIssue false
  • English
  • Français

Longitudinal changes in maternal and neonatal anthropometrics: a case study of the Helsinki Birth Cohort, 1934–1944

Published online by Cambridge University Press:  25 February 2015

E. Moltchanova  and
J. G. Eriksson
E. Moltchanova*
Affiliation:
School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
J. G. Eriksson
Affiliation:
Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland Folkhälsan Research Centre, University of Helsinki, Helsinki, Finland Unit of General Practice, Helsinki University Central Hospital, Helsinki, Finland
*
*Address for correspondence: E. Moltchanova, School of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurh, New Zealand. (Email elena.moltchanova@canterbury.ac.nz)
Get access Rights & Permissions [Opens in a new window]

Abstract

Changes in anthropometrics often reflect changes in living conditions, and one’s characteristics at birth may be associated with future health. The aim of this study was to investigate the secular trends in maternal and neonatal anthropometrics in the Helsinki Birth Cohort Study. The study participants, thus, comprised all 13,345 live births recorded in Helsinki, Finland, between 1934 and 1944. Adult characteristics of the clinical subsample comprised of 2003 individuals, alive during 2003, were also analyzed. Linear Regression analysis with seasonal terms was applied to see whether clinically and statistically significant trends can be found in maternal age, height and body mass index (BMI) at pregnancy; gestational age, birth weight, ponderal index and sex ratio; and adult height, BMI and fat percentage. Statistically significant trends were found in maternal age and maternal BMI with abrupt changes between 1941 and 1944. Gestational age increased by an average of 0.11% per year (P<0.0001), and the proportion of premature births dropped from 7.9% in 1934 to 4.5% in 1944 (P<0.0001). In the clinical sample, a statistically significant, although small, average annual increase of 0.1% in adult heights was detected (P=0.0012 for men and P=0.0035 for women). In conclusion, although no significant changes were found in either neonatal or adult anthropometrics of babies born in Helsinki between 1934 and 1944, there were abrupt changes in the characteristics of their mothers.

Keywords

longitudinal trendsmaternal anthropometricsmaternal obesityneonatal anthropometrics
Type
Original Article
Information
Journal of Developmental Origins of Health and Disease , Volume 6 , Issue 4 , August 2015 , pp. 285 - 290
Check for updates
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Floud, R, Wachter, K, Grogory, A. Height, Health and History. Nutritional Status in the United Kingdom 1750–1980. 1990. Cambridge University Press: Cambridge.Google Scholar
2. Steckel, RH. Stature and the standard of living. J Econ Lit. 1995; 23, 1903–1940.Google Scholar
3. Cinnirella, F. On the road to industrialization: nutritional status in Saxony, 1690–1850. Cliometrica. 2008; 2, 229–257.Google Scholar
4. Penttinen, A, Moltchanova, E, Nummela, I. Bayesian modeling of the evolution of male height in 18th century Finland from incomplete data. Econ Hum Biol. 2013; 11, 405–415.Google Scholar
5. Barker, DJP. Developmental origins of chronic disease. Public Health. 2012; 126, 185–189.Google Scholar
6. Emanuel, I, Filakti, H, Alberman, E, Evans, SJ. Intergenerational studies of human birthweight from the 1958 birth cohort. 1. Evidence for a multigenerational effect. Br J Obstet Gynaecol. 1992; 99, 67–74.CrossRefGoogle ScholarPubMed
7. Hatton, TJ, Bray, BE. Long run trends in the heights of European men, 19th–20th centuries. Econ Hum Biol. 2010; 8, 405–413.CrossRefGoogle Scholar
8. Komlos, J, Kuechenhoff, H. The diminution of the physical stature of the English male population in the eighteenth century. Cliometrica. 2012; 6, 45–62.Google Scholar
9. Thompson, WD, Janerich, DT. Maternal age at birth and risk of breast cancer in daughters. Epidemiology. 1990; 1, 101–106.CrossRefGoogle ScholarPubMed
10. Sandin, S, Hultman, CM, Kolevzon, A, et al. Advancing maternal age is associated with increasing risk for autism: a review and meta-analysis. J Am Acad Child Adolesc Psychiatry. 2012; 51, 477–486.Google Scholar
11. Cardwell, CR, Stene, LC, Joner, G, et al. Maternal age at birth and childhood type 1 diabetes: a pooled analysis of 30 observational studies. Diabetes. 2010; 59, 486–494.Google Scholar
12. Niji, R, Arita, K, Abe, Y, et al. Maternal age at birth and other risk factors in early childhood caries. Pediatr Den. 2010; 32, 493–498.Google Scholar
13. Thomas, F, Teriokhin, AT, Budilova, EV, et al. Human birthweight evolution across contrasting environments. J Evol Biol. 2004; 17, 542–553.CrossRefGoogle ScholarPubMed
14. Barker, DJ. The developmental origins of insulin resistance. Horm Res. 2005; 64(Suppl. 3), 2–7.Google Scholar
15. Wadhwa, PD, Buss, C, Entringer, S, Swanson, JM. Developmental origins of health and disease: brief history of the approach and current focus on epigenetic mechanisms. Semin Reprod Med. 2009; 27, 358–368.Google Scholar
16. Alexander, BT, Dasinger, J, Intapad, S. Effect of low birth weight on women’s health. Clin Ther. 2014; 36, 1913–1923. [E-pub ahead of print].Google Scholar
17. Barker, DJ, Osmond, C, Forsén, TJ, Kajantie, E, Eriksson, JG. Trajectories of growth among children who have coronary events as adults. N Engl J Med. 2005; 353, 1802–1809.Google Scholar
18. Wagenmakers, EJ, Farrell, S. AIC model selection using Akaike weights. Psychon Bull Rev. 2004; 11, 192–196.Google Scholar
19. R Core Team. R: a language and environment for statistical computing. 2014. R Foundation for Statistical Computing: Vienna, Austria. http://www.R-project.org/.Google Scholar
20. Graffelman, J, Hoekstra, RF. A statistical analysis of the effect of warfare on the human secondary sex ratio. Hum Biol. 2000; 72, 433–445.Google Scholar
21. D’Alfonso, A, Patacchiola, F, Colagrande, I, et al. A decrease in sex ratio at birth nine months after the earthquake in L’Aquila. ScientificWorldJournal. 2012; 2012, 1–3. [E-pub].Google Scholar
22. Zorn, B, Veselin, S, Stare, J, Meden-Vrtovec, H. Decline in sex ratio at birth after 10-day war in Slovenia. Hum Reprod. 2002; 17, 3173–3177.Google Scholar
23. Roseboom, TJ, Painter, RC, van Abeelen, AFM, Veenendaal, MVE, de Rooij, SR. Hungry in the womb: what are the consequences? Lessons from the Dutch famine. Maturitas. 2011; 70, 141–145.Google Scholar
24. Lumey, LH, Stein, AD, Ravelli, AC. Timing of prenatal starvation in women and birth weight in their first and second born offspring: the Dutch famine birth cohort study. Eur J Obstet Gynecol Reprod Biol. 1995; 61, 23–30.Google Scholar
25. Morgan, DK, Whitelaw, E. The case for transgenerational epigenetic inheritance in humans. Mamm Genome. 2008; 19, 394–397.Google Scholar