Dietary energy density in young children across Europe.
OBJECTIVE
To describe energy density (ED; kcal g(-1)) of dietary intake of European children.


METHODS
From 16, 228 children who participated in the IDEFICS (Identification and prevention of Dietary- and lifestyle-induced health EFfects In Children and infantS) baseline examination, 8551 children with 24-h dietary recalls (24-HDR), with plausible reported energy intakes and complete covariate information were included in the present analysis. ED was calculated using two methods: (1) ED including solid foods (EDF) and (2) ED including solid foods and energy-containing beverages (EDF&B). Beverage energy was calculated in kcal per day. Dietary characteristics and body mass index (BMI) z-score of children aged 2 to <6 years and 6 to <10 years were compared between children with an overall EDF below the <25th percentile, between the 25th and 75th percentile as well as above the >75th percentile. Standardised regression coefficients were estimated to assess the association between dietary characteristics, BMI z-score and ED of the diet.


RESULTS
Children with low EDF and EDF&B diets consumed less energy but higher quantity of food and beverages than children with high EDF and EDF&B diets. Consumption of caloric beverages decreased with increasing EDF&B of the diet owing to the relatively low ED of the beverages, in relation to solid foods. Generally, children with low EDF and EDF&B diets showed healthier food choices than peers with higher EDF and EDF&B diets. In this sample, EDF and EDF&B were not associated with BMI z-score.


CONCLUSIONS
Health promotion strategies should proclaim lower ED diets by means of foods with high water and low fat content and mainly fruit and vegetable components. Excluding caloric beverages from EDF calculation is a useful method to avoid misinterpretation of true exposure to a high energy dense diet. We recommend excluding caloric beverages from EDF calculation when investigating the effect of ED on a certain (health) outcome.
Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
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A
Hebestreit
Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
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C
Börnhorst
Epidemiology and Prevention Unit, Department of Preventive & Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
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V
Pala
Epidemology & Population Genetics, Institute of Food Sciences, National Research Council, Avellino, Italy.
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G
Barba
Department of Public Health and Community Medicine, University of Gothenburg, Sweden.
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G
Eiben
Department of Chronic Diseases, National Institute for Health Development, Tallinn, Estonia.
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T
Veidebaum
Research and Education Institute of Child Health, Strovolos, Cyprus.
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C
Hadjigergiou
Department of Pediatrics, University of Pécs, Pécs, Hungary.
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D
Molnár
Department of Public Health, Ghent University, Ghent, Belgium.
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M
Claessens
GENUD (Growth, Exercise, Nutrition and Development) Research Group, Faculty of Health Sciences, University of Zaragoza, Zaragoza, Spain.
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JM
Fernández-Alvira
1] Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany [2] Department of Mathematics and Computer Science, University of Bremen, Bremen, Germany.
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Pigeot
D001823:Body Composition; D015992:Body Mass Index; D002648:Child; D002664:Child Nutritional Physiological Phenomena; D002675:Child, Preschool; D015331:Cohort Studies; D004032:Diet; D002149:Energy Intake; D005060:Europe / Q000453:epidemiology; D005247:Feeding Behavior; D005260:Female; D005502:Food; D006293:Health Promotion; D006801:Humans; D008019:Life Style; D008297:Male; D044465:White People / Q000706:statistics & numerical data