La Barbera Giorgia, Praticò Giulia, Dragsted Lars Ove, Cuparencu Catalina
Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark.
Front Chem. 2024 Sep 24;12:1461331. doi: 10.3389/fchem.2024.1461331. eCollection 2024.
Dietary assessment is usually performed through imprecise tools, leading to error-prone associations between diet and health-related outcomes. Metabolomics has been applied in recent years to develop biomarkers of food intake (BFIs) and to study metabolites in the diet-microbiome crosstalk. Candidate BFIs exist to detect intake of meat and to a lesser extent dairy, but validation and further development of BFIs are needed. Here, we aim to identify biomarkers that differentiate between intakes of red meat and dairy, to validate previously reported BFIs for these foods, and to explore the effect of protein-matched meals on selected microbial metabolites.
We conducted a randomized, controlled, cross-over single-meal study comparing a meal with highly fermented yogurt and cheese, and a meal with beef and pork meatballs. Postprandial urine samples from 17 subjects were collected sequentially after each meal up to 24 h and analyzed by untargeted metabolomics through ultra-high-performance-liquid chromatography (UHPLC) coupled via electrospray (ESI) source to a qTOF mass spectrometer. Univariate (repeated measures ANOVA) and multivariate (PLSDA, ML-PLSDA) data analyses were used to select BFIs differentiating the two meals. 3-Indoxyl sulfate, p-cresol sulfate, and several other microbial amino acid catabolites were additionally explored within the urine profiles.
Thirty-eight markers of meat and dairy intake were selected and are presented along with their excretion kinetics. Carnosine, taurine, and creatine, as well as hydroxyproline-based dipeptides are confirmed as meat BFIs. For dairy, previously reported metabolites such as acyl-glycines are confirmed, while proline-based dipeptides are reported as novel putative BFIs. Microbial metabolites showed only marginal evidence of differential formation after the two meals.
This study allowed us to validate the postprandial kinetics of previously suggested biomarkers of meat and dairy intake and to identify new potential biomarkers. The excretion kinetics are useful to ensure that the collection of urine covers the correct time window in future dietary studies. The BFIs add to the existing body of biomarkers and may further be used in combination to provide a more reliable assessment of meat and dairy intake. Proteolytic microbial metabolites should be further investigated to assess the effect of different protein sources on health.
饮食评估通常通过不精确的工具进行,这导致饮食与健康相关结果之间的关联容易出错。近年来,代谢组学已被应用于开发食物摄入生物标志物(BFIs)以及研究饮食 - 微生物群相互作用中的代谢物。存在用于检测肉类摄入量的候选BFIs,而对于乳制品摄入量的检测能力较弱,但BFIs需要进行验证和进一步开发。在此,我们旨在识别区分红肉和乳制品摄入量的生物标志物,验证先前报道的针对这些食物的BFIs,并探索蛋白质匹配餐对选定微生物代谢物的影响。
我们进行了一项随机、对照、交叉单餐研究,比较了一顿含有高度发酵酸奶和奶酪的餐食与一顿含有牛肉和猪肉丸子的餐食。在每餐之后,依次收集17名受试者长达24小时的餐后尿液样本,并通过超高效液相色谱(UHPLC)与电喷雾(ESI)源耦合至qTOF质谱仪,采用非靶向代谢组学进行分析。使用单变量(重复测量方差分析)和多变量(PLSDA、ML - PLSDA)数据分析来选择区分两餐的BFIs。在尿液谱图中还额外探究了3 - 吲哚硫酸酯、对甲酚硫酸酯和其他几种微生物氨基酸分解代谢物。
选择了38种肉类和乳制品摄入的标志物,并展示了它们的排泄动力学。肌肽、牛磺酸、肌酸以及基于羟脯氨酸的二肽被确认为肉类BFIs。对于乳制品,先前报道的代谢物如酰基甘氨酸得到了证实,而基于脯氨酸的二肽被报道为新的假定BFIs。两餐之后,微生物代谢物仅显示出微小的差异形成证据。
本研究使我们能够验证先前提出的肉类和乳制品摄入生物标志物的餐后动力学,并识别新的潜在生物标志物。排泄动力学有助于确保在未来的饮食研究中尿液收集覆盖正确的时间窗口。这些BFIs增加了现有的生物标志物库,并且可能进一步联合使用,以提供对肉类和乳制品摄入量更可靠的评估。应进一步研究蛋白水解微生物代谢物,以评估不同蛋白质来源对健康的影响。