Ethnic differences in maternal dietary patterns are largely explained by socioeconomic score and integration score: a population-based study
Journal article, Peer reviewed
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.
MetadataVis full innførsel
OriginalversjonSommer, 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.v57i0.21164
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.