Huang Neil K, Matthan Nirupa R, Matuszek Gregory, Lichtenstein Alice H
Cardiovascular Nutrition Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA 02111, USA.
Informatics Core Unit, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA 02111, USA.
Metabolites. 2022 Jun 15;12(6):547. doi: 10.3390/metabo12060547.
Food intake data collected using subjective tools are prone to inaccuracies and biases. An objective assessment of food intake, such as metabolomic profiling, may offer a more accurate method if unique metabolites can be identified. To explore this option, we used samples generated from a randomized and controlled cross-over trial during which participants ( = 10; 65 ± 8 year, BMI, 29.8 ± 3.2 kg/m) consumed each of the three diets enriched in different types of carbohydrate. Plasma metabolite concentrations were measured at the end of each diet phase using gas chromatography/time-of-flight mass spectrometry and ultra-high pressure liquid chromatography/quadrupole time-of-flight tandem mass spectrometry. Participants were provided, in random order, with diets enriched in three carbohydrate types (simple carbohydrate (SC), refined carbohydrate (RC) and unrefined carbohydrate (URC)) for 4.5 weeks per phase and separated by two-week washout periods. Data were analyzed using partial least square-discrimination analysis, receiver operating characteristics (ROC curve) and hierarchical analysis. Among the known metabolites, 3-methylhistidine, phenylethylamine, cysteine, betaine and pipecolic acid were identified as biomarkers in the URC diet compared to the RC diet, and the later three metabolites were differentiated and compared to SC diet. Hierarchical analysis indicated that the plasma metabolites at the end of each diet phase were more strongly clustered by the participant than the carbohydrate type. Hence, although differences in plasma metabolite concentrations were observed after participants consumed diets differing in carbohydrate type, individual variation was a stronger predictor of plasma metabolite concentrations than dietary carbohydrate type. These findings limited the potential of metabolic profiling to address this variable.
使用主观工具收集的食物摄入量数据容易出现不准确和偏差的情况。如果能够识别出独特的代谢物，那么对食物摄入量进行客观评估，例如代谢组学分析，可能会提供一种更准确的方法。为了探索这一选项，我们使用了一项随机对照交叉试验产生的样本，在此试验中，参与者（n = 10；年龄65±8岁，BMI 29.8±3.2 kg/m²）食用了三种富含不同类型碳水化合物的饮食。在每个饮食阶段结束时，使用气相色谱/飞行时间质谱和超高压液相色谱/四极杆飞行时间串联质谱法测量血浆代谢物浓度。参与者被随机安排依次食用富含三种碳水化合物类型（简单碳水化合物（SC）、精制碳水化合物（RC）和未精制碳水化合物（URC））的饮食，每个阶段为期4.5周，中间间隔两周的洗脱期。使用偏最小二乘判别分析、受试者工作特征（ROC曲线）和层次分析对数据进行分析。在已知的代谢物中，与RC饮食相比，3-甲基组氨酸、苯乙胺、半胱氨酸、甜菜碱和哌啶酸被确定为URC饮食中的生物标志物，后三种代谢物与SC饮食进行了区分和比较。层次分析表明，每个饮食阶段结束时的血浆代谢物按参与者聚类的程度比按碳水化合物类型聚类的程度更强。因此，尽管在参与者食用不同碳水化合物类型的饮食后观察到了血浆代谢物浓度的差异，但个体差异比饮食碳水化合物类型更能预测血浆代谢物浓度。这些发现限制了代谢组学分析解决这一变量的潜力。
