Estimation of Chicken Intake by Adults Using Metabolomics-Derived Markers.

Background: Improved assessment of meat intake with the use of metabolomics-derived markers can provide objective data and could be helpful in clarifying proposed associations between meat intake and health.Objective: The objective of this study was to identify novel markers of chicken intake using a metabolomics approach and use markers to determine intake in an independent cohort.Methods: Ten participants [age: 62 y; body mass index (in kg/m2): 28.25] in the NutriTech food intake study consumed increasing amounts of chicken, from 88 to 290 g/d, in a 3-wk span. Urine and blood samples were analyzed by nuclear magnetic resonance and mass spectrometry, respectively. A multivariate data analysis was performed to identify markers associated with chicken intake. A calibration curve was built based on dose-response association using NutriTech data. A Bland-Altman analysis evaluated the agreement between reported and calculated chicken intake in a National Adult Nutrition Survey cohort.Results: Multivariate data analysis of postprandial and fasting urine samples collected in participants in the NutriTech study revealed good discrimination between high (290 g/d) and low (88 g/d) chicken intakes. Urinary metabolite profiles showed differences in metabolite levels between low and high chicken intakes. Examining metabolite profiles revealed that guanidoacetate increased from 1.47 to 3.66 mmol/L following increasing chicken intakes from 88 to 290 g/d (P < 0.01). Using a calibration curve developed from the NutriTech study, chicken intake was calculated through the use of data from the National Adult Nutrition Survey, in which consumers of chicken had a higher guanidoacetate excretion (0.70 mmol/L) than did nonconsumers (0.47 mmol/L; P < 0.01). A Bland-Altman analysis revealed good agreement between reported and calculated intakes, with a bias of -30.2 g/d. Plasma metabolite analysis demonstrated that 3-methylhistidine was a more suitable indicator of chicken intake than 1-methylhistidine.Conclusions: Guanidoacetate was successfully identified and confirmed as a marker of chicken intake, and its measurement in fasting urine samples could be used to determine chicken intake in a free-living population. This trial was registered at clinicaltrials.gov as NCT01684917.

[1]  H. Crawley Food Portion Sizes , 1988 .

[2]  G. Mellgren,et al.  Lean-seafood intake decreases urinary markers of mitochondrial lipid and energy metabolism in healthy subjects: Metabolomics results from a randomized crossover intervention study. , 2016, Molecular nutrition & food research.

[3]  S. Bingham,et al.  Biomarkers in nutritional epidemiology: applications, needs and new horizons , 2009, Human Genetics.

[4]  Lorraine Brennan,et al.  Session 2: Personalised nutrition Metabolomic applications in nutritional research , 2008, Proceedings of the Nutrition Society.

[5]  D. Giavarina Understanding Bland Altman analysis , 2015, Biochemia medica.

[6]  R. Luben,et al.  Epidemiologic Assessment of Sugars Consumption Using Biomarkers: Comparisons of Obese and Nonobese Individuals in the European Prospective Investigation of Cancer Norfolk , 2007, Cancer Epidemiology Biomarkers & Prevention.

[7]  Joshua N Sampson,et al.  Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations. , 2014, The American journal of clinical nutrition.

[8]  A. Lemme,et al.  Supplementation of guanidinoacetic acid to broiler diets: effects on performance, carcass characteristics, meat quality, and energy metabolism. , 2012, Poultry science.

[9]  Raymond J Carroll,et al.  Bias in dietary-report instruments and its implications for nutritional epidemiology , 2002, Public Health Nutrition.

[10]  R. Sinha,et al.  Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer. , 2015, The American journal of clinical nutrition.

[11]  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.

[12]  D. Wishart Metabolomics: applications to food science and nutrition research , 2008 .

[13]  G. Ursin,et al.  Meat consumption and the risk of type 2 diabetes: a systematic review and meta-analysis of cohort studies , 2009, Diabetologia.

[14]  M. Caplin,et al.  Diet and supplements and their impact on colorectal cancer. , 2013, Journal of gastrointestinal oncology.

[15]  M. Beydoun,et al.  Meat consumption is associated with obesity and central obesity among US adults , 2009, International Journal of Obesity.

[16]  J. Manson,et al.  Fish and omega-3 fatty acid intake and risk of coronary heart disease in women. , 2002, JAMA.

[17]  Lorraine Brennan,et al.  A metabolomics approach to the identification of biomarkers of sugar-sweetened beverage intake. , 2015, The American journal of clinical nutrition.

[18]  M. Gibney,et al.  Vitamin D status of Irish adults: findings from the National Adult Nutrition Survey , 2012, British Journal of Nutrition.

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

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

[21]  R. Sinha,et al.  Heterocyclic amine content of pork products cooked by different methods and to varying degrees of doneness. , 1998, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[22]  G A Colditz,et al.  Relation of meat, fat, and fiber intake to the risk of colon cancer in a prospective study among women. , 1990, The New England journal of medicine.

[23]  Wen-Harn Pan,et al.  Meat intake and cause-specific mortality: a pooled analysis of Asian prospective cohort studies. , 2013, The American journal of clinical nutrition.

[24]  T. Myint,et al.  Urinary 1-methylhistidine is a marker of meat consumption in Black and in White California Seventh-day Adventists. , 2000, American journal of epidemiology.

[25]  C. Metz Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

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

[27]  Zerihun T. Dame,et al.  The Human Urine Metabolome , 2013, PloS one.

[28]  Dariush Mozaffarian,et al.  Red and Processed Meat Consumption and Risk of Incident Coronary Heart Disease, Stroke, and Diabetes Mellitus: A Systematic Review and Meta-Analysis , 2010, Circulation.

[29]  R. Sinha,et al.  Heterocyclic amine content in beef cooked by different methods to varying degrees of doneness and gravy made from meat drippings. , 1998, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[30]  K. Kulp,et al.  PhIP metabolites in human urine after consumption of well-cooked chicken. , 2004, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[31]  S. Ostojić Guanidinoacetic acid as a performance-enhancing agent , 2015, Amino Acids.

[32]  A. Shan,et al.  Effects of Guanidinoacetic Acid on Growth Performance, Meat Quality and Antioxidation in Growing-Finishing Pigs , 2012 .

[33]  A. Lemme,et al.  Supplemental guanidino acetic acid improved feed conversion, weight gain, and breast meat yield in male and female broilers. , 2007 .

[34]  D. English,et al.  Red meat, chicken, and fish consumption and risk of colorectal cancer. , 2004, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[35]  K. Kulp,et al.  Identification of urine metabolites of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine following consumption of a single cooked chicken meal in humans. , 2000, Carcinogenesis.

[36]  L. Dragsted Biomarkers of meat intake and the application of nutrigenomics. , 2010, Meat science.

[37]  S. Heymsfield,et al.  Urinary 3-methylhistidine excretion: association with total body skeletal muscle mass by computerized axial tomography. , 1998, JPEN. Journal of parenteral and enteral nutrition.

[38]  An Pan,et al.  Red meat consumption and mortality: results from 2 prospective cohort studies. , 2012, Archives of internal medicine.

[39]  Kazuki Saito,et al.  Potential of metabolomics as a functional genomics tool. , 2004, Trends in plant science.

[40]  I. Rutishauser Dietary intake measurements. , 2005, Public health nutrition.

[41]  L. Brennan,et al.  Metabolomics in the identification of biomarkers of dietary intake , 2013, Computational and structural biotechnology journal.