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Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.

ABSTRACT: We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.

SUBMITTER: Freedman LS

PROVIDER: S-EPMC4082341 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.

Freedman Laurence S LS Commins John M JM Moler James E JE Arab Lenore L Baer David J DJ Kipnis Victor V Midthune Douglas D Moshfegh Alanna J AJ Neuhouser Marian L ML Prentice Ross L RL Schatzkin Arthur A Spiegelman Donna D Subar Amy F AF Tinker Lesley F LF Willett Walter W

American journal of epidemiology 20140610 2

We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coeffic ...[more]

PMID: 24918187

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Project description:Purpose: Obesity is a global health issue. To investigate if protein and fat contents of the diets had effects on energy balance via the canonical hunger signaling pathways in the hypothalamus, RNAseq was performed on RNA extracted from the hypothalami of mice exposed to the different diets. A suggested mechanism by which animals may avoid obesity is by burning off excess energy via upregulation of white adipose tissue (WAT) browning. To investigate if protein and fat content of the diet had effects on energy balance via the browning related signaling pathways in the WAT, RNAseq was performed on RNA extracted from the subcutaneous WAT (sWAT) and epididymal WAT (eWAT) of mice exposed to the different diets. Methods: C57BL/6 male mice were used in this work. All mice were fed a standard diet with 10% fat and 20% protein (D12450B, Research Diets Ltd) for 2 weeks as the baseline period. Following 2 weeks of baseline monitoring (at age 12 weeks), all mice were randomly allocated to different groups and switched to the experimental diets for 12 weeks. After 12 weeks all mice were sacrificed and dissected. Methods: In total, mice were fed on 4 diet series, each series consisting of 6 different diets (total = 24 diets). In the first two series (Series 1: D14071601–D14071606 and series 2: D14071607 – D14071612) we fixed the level of fat by energy, and varied the protein content. The protein source was casein. The balance was made up by carbohydrate (roughly equal mix of corn starch and maltodextrose). The source of fat was a mix of cocoa butter, coconut oil, menhaden oil, palm oil and sunflower oil. This mix was designed to match the balance of saturated, mono-unsaturated and polyunsaturated fats (ratio 47.5: 36.8: 15.8) and the n-6: n-3 ratio (14.7: 1) in the typical western diet. The proportions of the different fat constituents and hence fatty acid distributions did not change as the total fat content changed. Sucrose and cellulose were both fixed 5% by energy and weight respectively, and all diets were supplemented with a standard vitamin and mineral mix. In the second two series of diets (series 3: D14071613 – D14071618 and series 4: D14071619 – D14071624) we fixed the level of protein by energy and then allowed the fat content to vary. In these diets the sucrose, cellulose and vitamin and mineral contents were the same as the diets in series 1 and 2. All these diets can be ordered direct from research diets (www.researchdiets.com) using the diet codes provided. Methods: From each diet group, the hypothalami of 8/20 individuals were collected. The left halves of two, and the right halves of another two, were pooled together as one sample, and the same was performed with the other 4 hypothalami, resulting in each diet group having 2 pooled samples of 4 hypothalami (n = 48 samples in total across 24 diets). From each diet group, the sWAT and eWAT of 12/20 individuals were also collected. A small piece from each of six sWAT collections were pooled together as one sample, and the same was performed with the other six eWAT collections. In this way each diet group had one pooled sWAT sample and one pooled eWAT sample (also n = 48 across 24 diets). Methods: The total RNA of the hypothalamus and WAT was isolated using the RNeasy Mini Kit (QIAGEN, 74104) according to manufacturer's protocol. All sequencing was carried out using the Illumina NextSeq 500 sequencer. RNA fragments were sequenced by 75 bp long reads from paired ends (PE 2 x 75 bp, 150 bp per fragment). Quality control checks for raw data FASTQ files were done by using FASTQC (a quality control tool for high throughput sequence data; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Paired-end reads were mapped to the Mus musculus genome (GRCm38) using Bowtie 2-2.1.0, TopHat-2.0.10, and Samtools-0.1.19; uniquely mapped reads for each gene were counted against the GTF file of GRCm38 provided by Ensembl (release 83) using HTSeq-0.6.1p1 using the strand = reverse; after read count data were obtained from the TopHat-HTSeq pipeline, counts per million (CPM) value for each gene was calculated by using the R package ‘edgeR’ (version 3.12.0, R version 3.2.2) to normalize the count data by the size of the library of each sample. Genes with the CPM value ≥ 1 in at least one of the 24 diets group were retained (Anders et al., 2013). Generalized Linear Modelling (GLM) was applied by R (version 3.2.2). The GLM model used here was: ~p+f+p:f, which regresses gene expression (CPM) against the protein (p) and fat contents (f) of diets, as well as their interaction (p:f). However, when the effect of the interaction was not significant (p value ≥ 0.05), the interaction term was dropped and a revised model (~p+f) was utilized. Results: With TopHat-HTSeq pipeline, reads of each sample were mapped to 46,078 genes. In hypothalamus there were 15,371 genes with the counts per million (CPM) value ≥ 1 in at least one of the 24 diets group; in white adipose tissue there were 18,202 genes with the CPM value ≥ 1 in at least one of the 24 diets group. No major changes in hypothalamic gene expression levels were found in relation to different dietary protein levels at fixed fat contents, however hypothalamic gene expression showed increase in expression of genes in reward pathways in relation to dietary fat, while Agrp and Npy were both downregulated in relation to dietary fat levels. WAT gene expression showed decrease in expression of general thermogenic related genes and WAT browning related genes in relation to both dietary protein and dietary fat, while Tgfb1, Pdk4 and Fgf1 were all upregulated in relation to dietary fat levels. Conclusions: Significant positive associations were evident between the fat levels of the diet and the main hedonic signaling systems linked to food intake. Significant negative associations were found between both protein and fat levels of the diet and WAT browning or general thermogenic signalings linked to energy expenditure.

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Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.

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Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.

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