Ethnic differences in maternal dietary patterns are largely explained by socioeconomic score and integration score: a population-based study
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Copyright: 2013 christine sommer et al. this is an open access article distributed under the terms of the creative commons attribution- noncommercial 3.0 unported license (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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2013-07-08Metadata
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Sommer, C., Sletner, L., Jenum, A. K., Mørkrid, K., Andersen, L. F., Birkeland, K. I., & Mosdøl, A. (2013). Ethnic differences in maternal dietary patterns are largely explained by socio-economic score and integration score: a population-based study. Food & nutrition research, 57. http://dx.doi.org/10.3402/fnr.v57i0.21164Abstract
Background: The impact of socio-economic position and integration level on the observed ethnic differences in dietary habits has received little attention.
Objectives: To identify and describe dietary patterns in a multi-ethnic population of pregnant women, to explore ethnic differences in odds ratio (OR) for belonging to a dietary pattern, when adjusted for socioeconomic status and integration level and to examine whether the dietary patterns were reflected in levels of biomarkers related to obesity and hyperglycaemia.
Design: This cross-sectional study was a part of the STORK Groruddalen study. In total, 757 pregnant women, of whom 59% were of a non-Western origin, completed a food frequency questionnaire in gestational week 28 ± 2. Dietary patterns were extracted through cluster analysis using Ward’s method.
Results: Four robust clusters were identified where cluster 4 was considered the healthier dietary pattern and cluster 1 the least healthy. All non-European women as compared to Europeans had higher OR for belonging to the unhealthier dietary patterns 1-3 vs. cluster 4. Women from the Middle East and Africa had the highest OR, 21.5 (95% CI 10.6-43.7), of falling into cluster 1 vs. 4 as compared to Europeans. The ORs decreased substantially after adjusting for socio-economic score and integration score. A non-European ethnic origin, low socio-economic and integration scores, conduced higher OR for belonging to clusters 1, 2, and 3 as compared to cluster 4. Significant differences in fasting and 2-h glucose, fasting insulin, glycosylated haemoglobin (HbA1c), insulin resistance (HOMA-IR), and total cholesterol were observed across the dietary patterns. After adjusting for ethnicity, differences in fasting insulin (p=0.015) and HOMA-IR (p=0.040) across clusters remained significant, despite low power.
Conclusion: The results indicate that socio-economic and integration level may explain a large proportion of the ethnic differences in dietary patterns.