Challenges Associated With the Design and Deployment of Food Intake Urine Biomarker Technology for Assessment of Habitual Diet in Free-Living Individuals and Populations-A Perspective.
Challenges Associated With the Design and Deployment of Food Intake Urine Biomarker Technology for Assessment of Habitual Diet in Free-Living Individuals and Populations-A Perspective.
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与食物摄入尿液生物标志物技术的设计和部署相关的挑战，以评估自由生活的个体和人口的习惯饮食。
DOI：
10.3389/fnut.2020.602515
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发表时间：
2020
影响因子：
5
通讯作者：
Draper J
中科院分区：
文献类型：
--
作者：
Beckmann M;Wilson T;Lloyd AJ;Torres D;Goios A;Willis ND;Lyons L;Phillips H;Mathers JC;Draper J
在人口一级改善饮食是国家和国际减少慢性病负担战略的基石。生成有关习惯性饮食摄入量的可靠数据的一个关键挑战是准确的暴露评估。自我报告工具（例如，食物频率调查表、饮食回忆）容易受到报告偏差和对份量的看法的影响，而加权饮食评估在大规模研究中不可行。然而，来自个体食物/食物组并存在于尿液中的次级代谢产物为开发潜在的食物摄入生物标志物（BFI）提供了机会。在人口调查中使用生物标志物进行习惯性膳食摄入评估存在若干挑战，包括需要开发负担得起的生物流体收集方法，参与者可以接受，从而收集信息样本。全面使用生物标志物监测饮食需要分析方法来量化尿液中浓度范围内的靶生物标志物的结构多样性混合物。本文章提供了一个角度的挑战与尿液生物标志物技术的发展，监测饮食暴露在自由生活的个人，以期其未来的部署在“真实的世界”的情况。作为全国饮食习惯调查的一部分，一项观察性研究（n = 95）为探索自由生活人群中的生物标志物测量提供了机会。在第二项食物干预研究（n = 15）中，个体食用了各种各样的食物，这些食物是一系列专门设计的菜单，以达到反映英国常见食物多样性的暴露，模仿正常饮食模式。显示第一晨空尿是用于生物标志物测量的合适样品。三重四极杆质谱法，加上液相色谱法，用于同时评估的行为的一组54个潜在的BFI。这组化学多样的生物标志物，报告了广泛的常见食物的摄入量，可以随着新的生物标志物线索的发现而成功地扩展。为了验证，我们证明了饮食模式和尿液中生物标志物浓度与几种食物摄入量之间的定量关系的出色区分。总之，我们相信，整合来自BFI技术和饮食自我报告工具的信息将加快对饮食选择与健康之间复杂相互作用的研究。
Improvement of diet at the population level is a cornerstone of national and international strategies for reducing chronic disease burden. A critical challenge in generating robust data on habitual dietary intake is accurate exposure assessment. Self-reporting instruments (e.g., food frequency questionnaires, dietary recall) are subject to reporting bias and serving size perceptions, while weighed dietary assessments are unfeasible in large-scale studies. However, secondary metabolites derived from individual foods/food groups and present in urine provide an opportunity to develop potential biomarkers of food intake (BFIs). Habitual dietary intake assessment in population surveys using biomarkers presents several challenges, including the need to develop affordable biofluid collection methods, acceptable to participants that allow collection of informative samples. Monitoring diet comprehensively using biomarkers requires analytical methods to quantify the structurally diverse mixture of target biomarkers, at a range of concentrations within urine. The present article provides a perspective on the challenges associated with the development of urine biomarker technology for monitoring diet exposure in free-living individuals with a view to its future deployment in “real world” situations. An observational study (n = 95), as part of a national survey on eating habits, provided an opportunity to explore biomarker measurement in a free-living population. In a second food intervention study (n = 15), individuals consumed a wide range of foods as a series of menus designed specifically to achieve exposure reflecting a diversity of foods commonly consumed in the UK, emulating normal eating patterns. First Morning Void urines were shown to be suitable samples for biomarker measurement. Triple quadrupole mass spectrometry, coupled with liquid chromatography, was used to assess simultaneously the behavior of a panel of 54 potential BFIs. This panel of chemically diverse biomarkers, reporting intake of a wide range of commonly-consumed foods, can be extended successfully as new biomarker leads are discovered. Towards validation, we demonstrate excellent discrimination of eating patterns and quantitative relationships between biomarker concentrations in urine and the intake of several foods. In conclusion, we believe that the integration of information from BFI technology and dietary self-reporting tools will expedite research on the complex interactions between dietary choices and health.
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DOI：
10.1158/1055-9965.epi-11-0048
发表时间：
2011-06
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
--
作者：
Cross AJ;Major JM;Sinha R
通讯作者：
Sinha R
影响因子：
5.2
作者：
Cuparencu, Catalina;Rinnan, Asmund;Dragsted, Lars O.
通讯作者：
Dragsted, Lars O.
影响因子：
4.4
作者：
Andersen, Maj-Britt S.;Rinnan, Asmund;Dragsted, Lars O.
通讯作者：
Dragsted, Lars O.
影响因子：
3.6
作者：
Fallaize, Rosalind;Seale, Josephine V.;Lovegrove, Julie A.
通讯作者：
Lovegrove, Julie A.
影响因子：
16.6
作者：
Collet M;de Milly X;d'Allivy Kelly O;Naletov VV;Bernard R;Bortolotti P;Ben Youssef J;Demidov VE;Demokritov SO;Prieto JL;Muñoz M;Cros V;Anane A;de Loubens G;Klein O
通讯作者：
Klein O