Self-reported energy intake by FFQ compared with actual energy intake to maintain body weight in 516 adults

Siebelink, Els; Geelen, Anouk; Vries, J.H.M. de

doi:10.1017/s0007114511000067

Cited by 203 publications

(212 citation statements)

References 34 publications

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“…23,24 Dietary intakes were calculated from the 2011 version of the Dutch food composition table. 25 Intakes of fatty acids from major dietary sources were categorized into dairy, meat, plant and other food groups (see Supplementary Table S1 for a detailed list of dietary sources and Supplementary Information S1 for details on dietary assessment).…”

Section: Dietary Assessmentmentioning

confidence: 99%

Fatty acid intake and its dietary sources in relation with markers of type 2 diabetes risk: The NEO study

et al. 2016

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OBJECTIVE:The aim of this study was to examine the relations between intakes of total, saturated, mono-unsaturated, poly-unsaturated and trans fatty acids (SFA, MUFA, PUFA and TFA), and their dietary sources (dairy, meat and plant) with markers of type 2 diabetes risk. SUBJECTS/METHODS: This was a cross-sectional analysis of baseline data of 5675 non-diabetic, middle-aged participants of the Netherlands Epidemiology of Obesity (NEO) study. Associations between habitual dietary intake and fasting and postprandial blood glucose and insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), HOMA of β-cell function (HOMA-B) and Disposition Index were assessed through multivariable linear regression models with adjustments for demographic, lifestyle and dietary factors. RESULTS: Mean (s.d.) intakes in percent of energy (En%) were 34.4 (5.8) for total fatty acids, 12.4 (2.9) for SFA, 12.2 (2.4) for MUFA, 6.9 (1.9) for PUFA and 0.6 (0.2) for TFA. As compared with carbohydrates, only SFA was weakly inversely associated with fasting insulin, HOMA-IR and HOMA-B. When stratified by dietary source, all fatty acids from meat were positively associated with fasting insulin -total fatty acids meat (per 5 En%: 10.0%; 95% confidence interval: 4.0, 16.3), SFA meat (per 1 En%: 3.7%; 0.4, 7.2), MUFA meat (per 1 En%: 5.0%; 2.0, 8.1), PUFA meat (per 1 En%: 17.3%; 6.0, 29.7) and TFA meat (per 0.1 En%: 10.5%; 3.2, 18.3). Similarly, all fatty acids from meat were positively associated with HOMA-IR and HOMA-B and inversely with Disposition Index. CONCLUSIONS: Our study suggests that the relations between fatty acid intakes and markers of type 2 diabetes risk may depend on the dietary sources of the fatty acids. More epidemiological studies on diet and cardiometabolic disease are needed, addressing possible interactions between nutrients and their dietary sources.

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Section: Dietary Assessmentmentioning

confidence: 99%

Fatty acid intake and its dietary sources in relation with markers of type 2 diabetes risk: The NEO study

et al. 2016

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“…Detailed methods have been described before (8,9) . We collected two 24hR, one by telephone and one face-to-face, with at least 1 month in between, using EPIC-Soft software (10,11) .…”

Section: The European Food Consumption Validation Studymentioning

confidence: 99%

Comparison of approaches to correct intake–health associations for FFQ measurement error using a duplicate recovery biomarker and a duplicate 24 h dietary recall as reference method

Geelen

Souverein

Busstra

et al. 2014

Public Health Nutr.

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Objective: To illustrate the impact of intake-related bias in FFQ and 24 h recall (24hR), and correlated errors between these methods, on intake-health associations. Design: Dietary intake was assessed by a 180-item semi-quantitative FFQ and two 24hR. Urinary N and urinary K were estimated from two 24 h urine samples. We compared four scenarios to correct associations for errors in an FFQ estimating protein and K intakes. Setting: Wageningen, The Netherlands. Subjects: Fifty-nine men and fifty-eight women aged 45-65 years. Results: For this FFQ, measurement error weakened a true relative risk of 2?0 to 1?4 for protein and 1?5 for K. As compared with calibration to duplicate recovery biomarkers (i.e. the preferred scenario 1), estimating a validity coefficient using this duplicate biomarker resulted in overcorrected associations, caused by intakerelated bias in the FFQ (scenario 2). The correction factor based on a triad using biomarkers and 24hR was hampered by this intake-related bias and by correlated errors between FFQ and 24hR, and in this population resulted in a nearly perfect correction for protein but an overcorrection for K (scenario 3). When the 24hR was used for calibration, only a small correction was done, due to correlated errors between the methods and intake-related bias in the 24hR (scenario 4). Conclusions: Calibration to a gold standard reference method is the preferred approach to correct intake-health associations for FFQ measurement error. If it is not possible to do so, using the 24hR as reference method only partly removes the errors, but may result in improved intake-health associations.

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“…12 (3) 12 (3) 12 (3) 12 (3) 12 (3) 13 (11)(12)(13)(14) 13 (11-14) Monounsaturated fatty acids (energy%), mean (SD)…”

Section: Allmentioning

confidence: 99%

A National Dietary Assessment Reference Database (NDARD) for the Dutch population: rationale behind the design

Brouwer‐Brolsma

Streppel

Lee

et al. 2017

Preprint

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Abstract:The development of reliable Food Frequency Questionnaires (FFQs) requires detailed information about the level and variation of dietary intake of the target population. However, these data are often limited. To facilitate the development of new high quality FFQs and validation of existing FFQs, we developed a comprehensive National Dietary Assessment Reference Database (NDARD) detailing information about the level and variation in dietary intake of people 20-70 years old in the general Dutch population. This paper describes the methods and characteristics of the population included in the NDARD database. A total of 1063 men and 985 women agreed to participate in this study. Dietary intake data were collected using different FFQs, web-based and telephone-based 24-h recalls, as well as blood and urine-based biomarkers. The baseline FFQ was completed by 1647 participants with a mean age of 51 ± 12 years, BMI of 26 ± 4 kg/m 2 , and energy intake of 2051 ± 605 kcal/day. The percentage of total energy intake from proteins, carbohydrates, and fats were 15 ± 2, 43 ± 6, and 36 ± 5 En%, respectively. A total of 1113 participants completed telephone-based recalls and 1783 participants completed web-based recalls. This database will enable researchers to validate existing national FFQs and to develop new high quality dietary assessment methods.

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Self-reported energy intake by FFQ compared with actual energy intake to maintain body weight in 516 adults

Cited by 203 publications

References 34 publications

Fatty acid intake and its dietary sources in relation with markers of type 2 diabetes risk: The NEO study

Fatty acid intake and its dietary sources in relation with markers of type 2 diabetes risk: The NEO study

Comparison of approaches to correct intake–health associations for FFQ measurement error using a duplicate recovery biomarker and a duplicate 24 h dietary recall as reference method

A National Dietary Assessment Reference Database (NDARD) for the Dutch population: rationale behind the design

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