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  Nutrient scoring for the DEGS1-FFQ: From food intake to nutrient intake

Thieleking, R., Schneidewind, L., Kanyamibwa, A., Hartmann, H., Horstmann, A., Witte, A. V., et al. (2023). Nutrient scoring for the DEGS1-FFQ: From food intake to nutrient intake. BMC Nutrition, 9(1): 12. doi:10.1186/s40795-022-00636-2.

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Thieleking, Ronja1, Author                 
Schneidewind, Lennard1, Author
Kanyamibwa, Arsene1, 2, Author           
Hartmann, Hendrik1, 2, Author
Horstmann, Annette1, 2, Author                 
Witte, A. Veronica1, 3, Author                 
Medawar, Evelyn1, Author                 
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland, ou_persistent22              
3Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              


Free keywords: BMI; Dietary assessment; FFQ; Fiber intake; Nutrient scoring
 Abstract: Background: While necessary for studying dietary decision-making or public health, estimates of nutrient supply based on self-reported food intake are barely accessible or fully lacking and remain a challenge in human research. In particular, detailed information on dietary fiber is limited. In this study we introduce an automated openly available approach to assess self-reported nutrient intake for research purposes for a popular, validated German food frequency questionnaire (FFQ).

Methods: To this end, we i) developed and shared a code for assessing nutrients (carbohydrates, fat, protein, sugar, fiber, etc.) for 53 items of the quantitative, validated German DEGS1-FFQ questionnaire implementing expert-guided nutritional values of diverse sources with several raters. In a sample of individuals (nGUT-BRAIN = 61 (21 female) overweight, omnivorous), we ii) cross-validated nutrient intake of the last 7 days and the last 24 h and iii) computed test-retest reliability across two timepoints. Further, iv) we reported newly computed nutrient intake for two independent cross-sectional cohorts with continuous weight status and different dietary habits (nMensa = 134 (79 female, 1 diverse), nGREADT = 76 male). Exploratively, we v) correlated computed, energy-adjusted nutrient intake with anthropometric markers and HbA1c and vi) used linear mixed models to analyse the predictability of BMI and WHR by nutrient intake.

Results: In overweight adults (n = 61 (21 female), mean age 28.2 ± 6.5 years, BMI 27.4 ± 1.6 kg/m2) nutrient intakes were mostly within recommended reference nutrient ranges for both last 7 days and last 24 h. Recommended fiber intake was not reached and sugar intake was surpassed. Calculated energy intake was significantly higher from last 24 h than from last 7 days but energy-adjusted nutrient intakes did not differ between those timeframes. Reliability of nutrient values between last 7 days and 24 h per visit was moderate (Pearson's rhoall ≥ 0.33, rhomax = 0.62) and absolute agreement across two timepoints was low to high for 7 days (Pearson's rhomin = 0.12, rhomax = 0.64,) and low to moderate for 24 h (Pearson's rhomin = 0.11, rhomax = 0.45). Associations of dietary components to anthropometric markers showed distinct sex differences, with overall higher intake by males compared to females and only females presenting a negative association of BMI with fiber intake. Lastly, in the overweight sample (but not when extending the analysis to a wider BMI range of 18.6-36.4 kg/m2), we could confirm that higher BMI was predicted by lower energy-adjusted fiber intake and higher energy-adjusted fat intake (when adjusting for age, sex and physical activity) while higher WHR was predicted by higher energy intake.

Conclusion: We provide an openly available tool to systematically assess nutrient intake, including fiber, based on self-report by a common German FFQ. The computed nutrient scores resembled overall plausible and reliable measures of nutrient intake given the known limitations of FFQs regarding over- or underreporting and suggest valid comparability when adjusting for energy intake. Our open code nutrient scoring can help to examine dietary intake in experimental studies, including dietary fiber, and can be readily adapted to other FFQs. Further validation of computed nutrients with biomarkers and nutrient-specific metabolites in serum, urine or feces will help to interpret self-reported dietary intake.


Language(s): eng - English
 Dates: 2022-04-182022-11-102023-01-13
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1186/s40795-022-00636-2
PMID: 36639712
PMC: PMC9837986
 Degree: -



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Funding organization : Projekt DEAL
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Funding organization : Max Planck Society
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Funding organization : German Environmental Foundation (DBU)

Source 1

Title: BMC Nutrition
Source Genre: Journal
Publ. Info: London, United Kingdom : BioMed Central
Pages: - Volume / Issue: 9 (1) Sequence Number: 12 Start / End Page: - Identifier: ISSN: 2055-0928
CoNE: https://pure.mpg.de/cone/journals/resource/2055-0928