Is iron intake during early pregnancy associated with size at birth? Insights revealed through structural equation modelling

Alwan, N., Greenwood, D., Simpson, N. and Cade, J. (2011) Is iron intake during early pregnancy associated with size at birth? Insights revealed through structural equation modelling. Journal of Epidemiology & Community Health, 65, supplement 1, A87. (doi:10.1136/jech.2011.142976c.67).

Abstract

Introduction: iron deficiency during early pregnancy is associated with adverse birth outcomes. Results of studies investigating the relationship between dietary iron intake during pregnancy and birth size are conflicting.

Methods: we aimed to investigate the association between iron intake during pregnancy and birth size in a prospective cohort of 1274 pregnant women (18–45?years) in Leeds, UK, where iron supplements are not routinely recommended during pregnancy. Dietary intake was reported in a 24?h recall administered by a midwife at 12?weeks gestation. Dietary supplement intake was ascertained using dietary recall and three questionnaires throughout pregnancy.

Results: 80% of women reported dietary iron intake below the UK Reference Nutrient Intake of 14.8?mg/day. 24%, 15% and 8% reported taking iron-containing supplements in the first, second and third trimesters respectively. Women with dietary iron intake >14.8?mg/day were more likely to be older, have a university degree and take daily supplements during the first trimester. They were less likely to be smokers and live in a deprived area. Structural equation modelling was used to analyse the relationship between iron, vitamin C intakes and birth size taking into account socioeconomic status and smoking using Mplus software. The model showed excellent fit (?2=2.7, p=0.8, df=5, RMSEA<0.001). The directions of the causal paths were the same as the apriori model.

Conclusion: the positive effect of iron status on customised birth size is influenced by both iron and vitamin C intakes. Using SEM describes the relevant relationships in a more holistic way than traditional regression modelling

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Contributors

Author: N. Alwan

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