Demonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example.

SCOPE There is a dearth of studies demonstrating the use of dietary biomarkers for determination of food intake. The objective of this study was to develop calibration curves for use in quantifying citrus intakes in an independent cohort. METHODS AND RESULTS Participants (n = 50) from the NutriTech food-intake study consumed standardized breakfasts for three consecutive days over three consecutive weeks. Orange juice intake decreased over the weeks. Urine samples were analyzed by NMR-spectroscopy and proline betaine was quantified and normalized to osmolality. Calibration curves were developed and used to predict citrus intake in an independent cohort; the Irish National Adult Nutrition Survey (NANS) (n = 565). Proline betaine displayed a dose-response relationship to orange juice intake in 24 h and fasting samples (p < 0.001). In a test set, predicted orange juice intakes displayed excellent agreement with true intake. There were significant associations between predicted intake measured in 24 h and fasting samples and true intake (r = 0.710-0.919). Citrus intakes predicted for the NANS cohort demonstrated good agreement with self-reported intake and this agreement improved following normalization to osmolality. CONCLUSION The developed calibration curves successfully predicted citrus intakes in an independent cohort. Expansion of this approach to other foods will be important for the development of objective intake measurements.

[1]  Estelle Pujos-Guillot,et al.  Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics , 2014, Analytical and Bioanalytical Chemistry.

[2]  S A Jebb,et al.  Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. , 1991, European journal of clinical nutrition.

[3]  Estelle Pujos-Guillot,et al.  Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern. , 2014, Journal of proteome research.

[4]  J. Mathers,et al.  Use of mass spectrometry fingerprinting to identify urinary metabolites after consumption of specific foods. , 2011, The American journal of clinical nutrition.

[5]  J. Mathers,et al.  Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption. , 2011, The British journal of nutrition.

[6]  B. Lake,et al.  Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling. , 2011, Journal of proteome research.

[7]  J. Nicholson,et al.  An Analytical Pipeline for Quantitative Characterization of Dietary Intake: Application To Assess Grape Intake. , 2016, Journal of agricultural and food chemistry.

[8]  Victor Kipnis,et al.  Can we use biomarkers in combination with self-reports to strengthen the analysis of nutritional epidemiologic studies? , 2010, Epidemiologic perspectives & innovations : EP+I.

[9]  Wanchang Lin,et al.  Original Article , 1995 .

[10]  Blandine Comte,et al.  Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study. , 2013, Journal of proteome research.

[11]  B. Warrack,et al.  Normalization strategies for metabonomic analysis of urine samples. , 2009, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[12]  H. Adlercreutz,et al.  Dose response of whole-grain biomarkers: alkylresorcinols in human plasma and their metabolites in urine in relation to intake. , 2009, The American journal of clinical nutrition.

[13]  W. Atkinson,et al.  Effects of orange juice and proline betaine on glycine betaine and homocysteine in healthy male subjects , 2007, European journal of nutrition.

[14]  Ingo Ruczinski,et al.  Metabolomic profiling of urine: response to a randomised, controlled feeding study of select fruits and vegetables, and application to an observational study , 2013, British Journal of Nutrition.

[15]  S. Jebb,et al.  Plasma alkylresorcinols as a biomarker of whole-grain food consumption in a large population: results from the WHOLEheart Intervention Study. , 2012, The American journal of clinical nutrition.

[16]  J. Nicholson,et al.  2-Furoylglycine as a Candidate Biomarker of Coffee Consumption. , 2015, Journal of agricultural and food chemistry.

[17]  A. Ross,et al.  Herring and Beef Meals Lead to Differences in Plasma 2-Aminoadipic Acid, β-Alanine, 4-Hydroxyproline, Cetoleic Acid, and Docosahexaenoic Acid Concentrations in Overweight Men. , 2015, The Journal of nutrition.

[18]  T. Hofmann,et al.  Urinary N-methylpyridinium and trigonelline as candidate dietary biomarkers of coffee consumption. , 2011, Molecular nutrition & food research.

[19]  J M Bland,et al.  Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .

[20]  S. Bingham,et al.  Biomarkers in nutritional epidemiology , 2002, Public Health Nutrition.

[21]  Jeremiah Stamler,et al.  Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. , 2010, The American journal of clinical nutrition.

[22]  Åsmund Rinnan,et al.  Discovery of exposure markers in urine for Brassica-containing meals served with different protein sources by UPLC-qTOF-MS untargeted metabolomics , 2013, Metabolomics.

[23]  R. Sinha,et al.  Urinary Biomarkers of Meat Consumption , 2011, Cancer Epidemiology, Biomarkers & Prevention.

[24]  Elaine Holmes,et al.  Susceptibility of human metabolic phenotypes to dietary modulation. , 2006, Journal of proteome research.

[25]  A. Lloyd,et al.  Data-driven strategy for the discovery of potential urinary biomarkers of habitual dietary exposure. , 2013, The American journal of clinical nutrition.