Thieleking Ronja, Schneidewind Lennard, Kanyamibwa Arsene, Hartmann Hendrik, Horstmann Annette, Witte A Veronica, Medawar Evelyn
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
BMC Nutr. 2023 Jan 13;9(1):12. doi: 10.1186/s40795-022-00636-2.
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).
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 (n = 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 (n = 134 (79 female, 1 diverse), n = 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.
In overweight adults (n = 61 (21 female), mean age 28.2 ± 6.5 years, BMI 27.4 ± 1.6 kg/m) 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 rho ≥ 0.33, rho = 0.62) and absolute agreement across two timepoints was low to high for 7 days (Pearson's rho = 0.12, rho = 0.64,) and low to moderate for 24 h (Pearson's rho = 0.11, rho = 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/m), 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.
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.
虽然对于研究饮食决策或公共卫生而言是必要的，但基于自我报告的食物摄入量来估算营养供应情况几乎无法获取或完全缺失，这在人体研究中仍然是一项挑战。特别是，关于膳食纤维的详细信息有限。在本研究中，我们引入了一种自动化的公开可用方法，用于评估一份流行且经过验证的德国食物频率问卷（FFQ）的自我报告营养摄入量，以用于研究目的。
为此，我们：i）开发并共享了一段代码，用于评估定量的、经过验证的德国DEGS1 - FFQ问卷中53个项目的营养素（碳水化合物、脂肪、蛋白质、糖、纤维等），该代码采用了多位评分者对不同来源的专家指导营养值。在一个个体样本（n = 61（21名女性），超重，杂食）中，我们：ii）对过去7天和过去24小时的营养摄入量进行交叉验证，并且：iii）计算两个时间点之间的重测信度。此外，iv）我们报告了两个具有连续体重状况和不同饮食习惯的独立横断面队列（n = 134（79名女性，1名其他），n = 76名男性）新计算的营养摄入量。探索性地，我们：v）将计算得出的、经能量调整的营养摄入量与人体测量指标和糖化血红蛋白进行关联分析，并且：vi）使用线性混合模型分析营养摄入量对BMI和腰臀比的预测能力。
在超重成年人（n = 61（21名女性），平均年龄28.2± 6.5岁，BMI 27.4± 1.6kg/m²）中，过去7天和过去24小时的营养摄入量大多在推荐的参考营养范围内。未达到推荐的纤维摄入量，且超过了糖摄入量。计算得出的过去24小时的能量摄入量显著高于过去7天，但经能量调整的营养摄入量在这两个时间段之间没有差异。每次就诊时过去7天和24小时之间营养值的信度为中等（Pearson相关系数ρ≥0.33，ρ = 0.62），两个时间点之间的绝对一致性在7天内为低到高（Pearson相关系数ρ = 0.12，ρ = 0.64），在24小时内为低到中等（Pearson相关系数ρ = 0.11，ρ = 0.45）。饮食成分与人体测量指标的关联存在明显的性别差异，总体而言男性摄入量高于女性，只有女性的BMI与纤维摄入量呈负相关。最后，在超重样本中（但将分析扩展到更广泛的BMI范围18.6 - 36.4kg/m²时未发现），我们可以确认，经能量调整的纤维摄入量较低和经能量调整的脂肪摄入量较高（在调整年龄、性别和身体活动后）可预测较高的BMI，而较高的能量摄入量可预测较高的腰臀比。
我们提供了一个公开可用的工具，用于基于一份常见的德国FFQ的自我报告系统地评估营养摄入量，包括纤维。考虑到FFQ在报告过高或过低方面的已知局限性，计算得出的营养得分总体上类似于合理且可靠的营养摄入量测量指标，并表明在调整能量摄入量后具有有效的可比性。我们的开放代码营养评分有助于在实验研究中检查饮食摄入量，包括膳食纤维，并且可以很容易地适用于其他FFQ。用血清、尿液或粪便中的生物标志物和特定营养素代谢物对计算得出的营养素进行进一步验证，将有助于解释自我报告的饮食摄入量。
