Open Access Research article

Dietary energy density and adiposity: Employing bias adjustments in a meta-analysis of prospective studies

Désirée C Wilks1, Adrian P Mander12, Susan A Jebb1, Simon G Thompson2, Stephen J Sharp3, Rebecca M Turner2 and Anna Karin Lindroos1*

Author Affiliations

1 Medical Research Council Human Nutrition Research, Cambridge, UK

2 Medical Research Council Biostatistics Unit, Cambridge, UK

3 Medical Research Council Epidemiology Unit, Cambridge, UK

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BMC Public Health 2011, 11:48  doi:10.1186/1471-2458-11-48

Published: 22 January 2011



Dietary studies differ in design and quality making it difficult to compare results. This study quantifies the prospective association between dietary energy density (DED) and adiposity in children using a meta-analysis method that adjusts for differences in design and quality through eliciting and incorporating expert opinion on the biases and their uncertainty.


Six prospective studies identified by a previous systematic literature search were included. Differences in study quality and design were considered respectively as internal and external biases and captured in bias checklists. Study results were converted to correlation coefficients; biases were considered either additive or proportional on this scale. The extent and uncertainty of the internal and external biases in each study were elicited in a formal process by five quantitatively-trained assessors and five subject-matter specialists. Biases for each study were combined across assessors using median pooling and results combined across studies by random-effects meta-analysis.


The unadjusted combined correlation between DED and adiposity change was 0.06 (95%CI 0.01, 0.11; p = 0.013), but with considerable heterogeneity (I2 = 52%). After bias-adjustment the pooled correlation was 0.17 (95%CI - 0.11, 0.45; p = 0.24), and the studies were apparently compatible (I2 = 0%).


This method allowed quantitative synthesis of the prospective association between DED and adiposity change in children, which is important for the development of evidence-informed policy. Bias adjustment increased the magnitude of the positive association but the widening confidence interval reflects the uncertainty of the assessed biases and implies that higher quality studies are required.