Now showing 1 - 4 of 4
  • Publication
    Metabolomic Based Approach to Identify Biomarkers of Apple Intake
    SCOPE:There is an increased interest in developing biomarkers of food intake to address some of the limitations associated with self-reported data. The objective was to identify biomarkers of apple intake, examine dose-response relationships and agreement with self-reported data. METHODS AND RESULTS:Metabolomic data from three studies were examined: an acute intervention, a short-term intervention and a free-living cohort study. Fasting and postprandial urine samples were collected for analysis by 1 H-NMR and LC-MS. Calibration curves were developed to determine apple intake and classify individuals into categories of intake. Multivariate analysis of data revealed that levels of multiple metabolites increased significantly post-apple consumption, compared to the control food- broccoli. In the dose-response study, urinary xylose, epicatechin sulfate and 2, 6-dimethyl-2-(2-hydroxyethyl)-3,4-dihydro-2H-1-benzopyran increased as apple intake increased. Urinary xylose concentrations in a free-living cohort performed poorly at an individual level but were capable of ranking individuals in categories of intake. CONCLUSION:Urinary xylose exhibited a dose-response relationship with apple intake and performed well as a ranking biomarker in the population study. Other potential biomarkers were identified and future work will combine these with xylose in a biomarker panel which may allow for a more objective determination of individual intake.
      484Scopus© Citations 10
  • Publication
    Metabolomic-based identification of clusters that reflect dietary patterns
    Scope: Classification of subjects into dietary patterns generally relies on self-reporting dietary data which are prone to error. The aim of the present study was to develop a model for objective classification of people into dietary patterns based on metabolomic data. Methods and results: Dietary and urinary metabolomic data from the National Adult Nutrition Survey (NANS) was used in the analysis (n=567). Two-step cluster analysis was applied to the urinary data to identify clusters. The subsequent model was used in an independent cohort to classify people into dietary patterns. Two distinct dietary patterns were identified. Cluster 1 was characterized by significantly higher intakes of breakfast cereals, low fat and skimmed milks, potatoes, fruit and fish, fish dishes (P<0.05) representing a 'healthy' cluster. Cluster 2 had significantly higher intakes of chips/processed potatoes, meat products, savory snacks and high-energy beverages (P<0.05) representing an 'unhealthy cluster'. Classification was supported by significant differences in nutrient status (P<0.05). Validation in an independent group revealed that 94% of subjects were correctly classified. Conclusion: The model developed was capable of classifying individuals into dietary patterns based on metabolomics data. Future applications of this approach could be developed for rapid and objective assignment of subjects into dietary patterns.
      705Scopus© Citations 24
  • Publication
    Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data
    Scope: The objectives are to develop a metabolomic-based model capable of classifying individuals into dietary patterns and to investigate the reproducibility of the model. Methods and Results: K-means cluster analysis is employed to derive dietary patterns using metabolomic data. Differences across the dietary patterns are examined using nutrient biomarkers. The model is used to assign individuals to a dietary pattern in an independent cohort, A-DIET Confirm (n = 175) at four time points. The stability of participants to a dietary pattern is assessed. Four dietary patterns are derived: moderately unhealthy, convenience, moderately healthy, and prudent. The moderately unhealthy and convenience patterns has lower adherence to the alternative healthy eating index (AHEI) and the alternative mediterranean diet score (AMDS) compared to the moderately healthy and prudent patterns (AHEI = 24.5 and 22.9 vs 26.7 and 28.4, p < 0.001). The dietary patterns are replicated in A-DIET Confirm, with good reproducibility across four time points. The stability of participants’ dietary pattern membership ranged from 25.0% to 61.5%. Conclusion: The multivariate model classifies individuals into dietary patterns based on metabolomic data. In an independent cohort, the model classifies individuals into dietary patterns at multiple time points furthering the potential of such an approach for nutrition research.
      75Scopus© Citations 12
  • Publication
    The Potential of Multi-Biomarker Panels in Nutrition Research: Total Fruit Intake as an Example
    Dietary and food intake biomarkers offer the potential of improving the accuracy of dietary assessment. An extensive range of putative intake biomarkers of commonly consumed foods have been identified to date. As the field of food intake biomarkers progresses toward solving the complexities of dietary habits, combining biomarkers associated with single foods or food groups may be required. The objective of this work was to examine the ability of a multi-biomarker panel to classify individuals into categories of fruit intake. Biomarker data was measured using H NMR spectroscopy in two studies: (1) An intervention study where varying amounts of fruit was consumed and (2) the National Adult Nutrition Survey (NANS). Using data from an intervention study a biomarker panel (Proline betaine, Hippurate, and Xylose) was constructed from three urinary biomarker concentrations. Biomarker cut-off values for three categories of fruit intake were developed. The biomarker sum cut-offs were ≤ 4.766, 4.766–5.976, >5.976 μM/mOsm/kg for <100, 101–160, and >160 g fruit intake. The ability of the biomarker sum to classify individuals into categories of fruit intake was examined in the cross-sectional study (NANS) (N = 565). Examination of results in the cross-sectional study revealed excellent agreement with self-reported intake: a similar number of participants were ranked into each category of fruit intake. The work illustrates the potential of multi-biomarker panels and paves the way forward for further development in the field. The use of such panels may be key to distinguishing foods and adding specificity to the predictions of food intake. 1
      89Scopus© Citations 10