(1)Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, CA.
(2)Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, University of Southern California, Los Angeles, CA; (3)First Moscow State Medical University, Moscow, Russia; (4)Population Health Department, Nutrition and Health Research Group, Luxembourg Institute of Health, Strassen, Luxembourg, Sweden.
J Acad Nutr Diet. 2021 May;121(5):931-941.e2. doi: 10.1016/j.jand.2020.10.024. Epub 2020 Dec 3.
Due to the challenges associated with accurate monitoring of dietary intake in humans, nutritional metabolomics (including food intake biomarkers) analysis as a complementary tool to traditional dietary assessment methods has been explored. Food intake biomarker assessment using postprandial dried blood spot (DBS) collection can be a convenient and accurate means of monitoring dietary intake vs 24-hour urine collection.
The objective of this study was to use nutritional metabolomics analysis to differentiate a high-fat, high-protein meat (HFPM) diet from a high-carbohydrate vegan (HCV) diet in postprandial DBS and 24-hour urine.
This was a randomized controlled crossover feeding trial.
PARTICIPANTS/SETTING: Participants were healthy young adult volunteers (n = 8) in California. The study was completed in August 2019.
The standardized isocaloric diet interventions included an HFPM and an HCV diet. Participants attended 2 intervention days, separated by a 2-week washout.
During each intervention day, a finger-prick blood sample was collected in the fasting state, 3 hours post breakfast, and 3 hours post lunch. Participants also collected their urine for 24 hours. DBS and urine samples were analyzed by ultra-performance liquid chromatography mass spectrometry to identify potential food intake biomarkers.
Principal component analysis for discriminatory analysis and univariate analysis using paired t tests were performed.
Principal component analysis found no discrimination of baseline DBS samples. In both the postprandial DBS and 24-hour urine, post-HFPM consumption had higher (P < 0.05) levels of acylcarnitines, creatine, and cis-trans hydroxyproline, and the HCV diet was associated with elevated sorbitol (P < 0.05). The HFPM diet had higher concentrations of triacylglycerols with fewer than 54 total carbons in DBS, and 24-hour urine had higher nucleoside mono- and di-phosphates (P < 0.05).
Nutritional metabolomics profiles of postprandial DBS and 24-hour urine collections were capable of differentiating the HFPM and HCV diets. The potential use of postprandial DBS-based metabolomic analysis deserves further investigation for dietary intake monitoring.
由于准确监测人体饮食摄入存在挑战，因此作为传统饮食评估方法的补充工具，营养代谢组学（包括饮食摄入生物标志物）分析已被探索。与 24 小时尿液收集相比，餐后干血斑（DBS）采集的饮食摄入生物标志物评估可以更方便、更准确地监测饮食摄入。
本研究旨在使用营养代谢组学分析方法，区分高脂肪、高蛋白肉类（HFPM）饮食和高碳水化合物素食（HCV）饮食在餐后 DBS 和 24 小时尿液中的差异。
这是一项随机对照交叉喂养试验。
参与者/设置：参与者为加利福尼亚州的健康年轻成年志愿者（n=8）。该研究于 2019 年 8 月完成。
标准化等热量饮食干预包括 HFPM 和 HCV 饮食。参与者参加 2 天干预，中间间隔 2 周洗脱期。
在每次干预日，参与者在空腹状态、早餐后 3 小时和午餐后 3 小时采集指血样。参与者还收集 24 小时尿液。使用超高效液相色谱-质谱联用技术分析 DBS 和尿液样本，以鉴定潜在的饮食摄入生物标志物。
采用主成分分析进行判别分析，采用配对 t 检验进行单变量分析。
主成分分析发现基线 DBS 样本无差异。在餐后 DBS 和 24 小时尿液中，HFPM 饮食后酰基肉碱、肌酸和反式-4-羟基脯氨酸水平升高（P<0.05），而 HCV 饮食后山梨醇水平升高（P<0.05）。HFPM 饮食的 DBS 中三酰甘油浓度更高，少于 54 个总碳原子，24 小时尿液中核苷单磷酸和二磷酸浓度更高（P<0.05）。
餐后 DBS 和 24 小时尿液采集的营养代谢组学特征能够区分 HFPM 和 HCV 饮食。餐后 DBS 代谢组学分析的潜在应用值得进一步研究，以用于饮食摄入监测。
