Cuparencu Cătălina, Rinnan Åsmund, Silvestre Marta P, Poppitt Sally D, Raben Anne, Dragsted Lars O
Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958, Frederiksberg C, Denmark.
Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958, Frederiksberg C, Denmark.
Eur J Nutr. 2021 Feb;60(1):179-192. doi: 10.1007/s00394-020-02230-3. Epub 2020 Apr 3.
Biomarkers of meat intake hold promise in clarifying the health effects of meat consumption, yet the differentiation between red and white meat remains a challenge. We measure meat intake objectively in a free-living population by applying a newly developed, three-step strategy for biomarker-based assessment of dietary intakes aimed to indicate if (1) any meat was consumed, (2) what type it was and (3) the quantity consumed.
Twenty-four hour urine samples collected in a four-way crossover RCT and in a cross-sectional analysis of a longitudinal lifestyle intervention (the PREVIEW Study) were analyzed by untargeted LC-MS metabolomics. In the RCT, healthy volunteers consumed three test meals (beef, pork and chicken) and a control; in PREVIEW, overweight participants followed a diet with high or moderate protein levels. PLS-DA modeling of all possible combinations between six previously reported, partially validated, meat biomarkers was used to classify meat intake using samples from the RCT to predict consumption in PREVIEW.
Anserine best separated omnivores from vegetarians (AUROC 0.94-0.97), while the anserine to carnosine ratio best distinguished the consumption of red from white meat (AUROC 0.94). Carnosine showed a trend for dose-response between non-consumers, low consumers and high consumers for all meat categories, while in combination with other biomarkers the difference was significant.
It is possible to evaluate red meat intake by using combinations of existing biomarkers of white and general meat intake. Our results are novel and can be applied to assess qualitatively recent meat intake in nutritional studies. Further work to improve quantitation by biomarkers is needed.
肉类摄入量的生物标志物有望阐明肉类消费对健康的影响，但区分红肉和白肉仍然是一个挑战。我们通过应用一种新开发的三步策略，以基于生物标志物的饮食摄入量评估方法，在自由生活的人群中客观测量肉类摄入量，该方法旨在表明：（1）是否食用了任何肉类；（2）肉类的类型；（3）食用量。
通过非靶向液相色谱-质谱代谢组学分析在四项交叉随机对照试验中收集的24小时尿液样本，以及在一项纵向生活方式干预（PREVIEW研究）的横断面分析中收集的样本。在随机对照试验中，健康志愿者食用了三顿测试餐（牛肉、猪肉和鸡肉）和一顿对照餐；在PREVIEW研究中，超重参与者遵循高蛋白或中等蛋白水平的饮食。使用六种先前报道的、部分验证的肉类生物标志物之间的所有可能组合进行偏最小二乘判别分析（PLS-DA）建模，以利用随机对照试验的样本对肉类摄入量进行分类，从而预测PREVIEW研究中的消费量。
鹅肌肽最能将杂食者与素食者区分开来（曲线下面积为0.94 - 0.97），而鹅肌肽与肌肽的比值最能区分红肉和白肉的消费情况（曲线下面积为0.94）。肌肽在所有肉类类别中，非消费者、低消费者和高消费者之间呈现出剂量反应趋势，而与其他生物标志物结合时，差异具有显著性。
通过结合现有的白肉和一般肉类摄入量生物标志物，可以评估红肉摄入量。我们的结果具有创新性，可应用于营养研究中定性评估近期肉类摄入量。需要进一步开展工作以通过生物标志物改进定量分析。
