William Cheung1, Pekka Keski-Rahkonen1, Nada Assi1, Pietro Ferrari1, Heinz Freisling1, Sabina Rinaldi1, Nadia Slimani1, Raul Zamora-Ros1, Milena Rundle2, Gary Frost2, Helena Gibbons3, Eibhlin Carr3, Lorraine Brennan3, Amanda J Cross4, Valeria Pala5, Salvatore Panico6, Carlotta Sacerdote7, Domenico Palli8, Rosario Tumino9, Tilman Kühn10, Rudolf Kaaks10, Heiner Boeing11, Anna Floegel11, Francesca Mancini12,13, Marie-Christine Boutron-Ruault12,13,14, Laura Baglietto15,16, Antonia Trichopoulou17,18, Androniki Naska17,18, Philippos Orfanos17,18, Augustin Scalbert19. 1. International Agency for Research on Cancer, Lyon, France. 2. Division of Endocrinology and Metabolism, Nutrition and Dietetic Research Group, and. 3. Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Dublin, Republic of Ireland. 4. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. 5. Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. 6. Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy. 7. Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy. 8. Cancer Risk Factors and Life-Style Epidemiology Unit, Cancer Research and Prevention Institute (ISPO), Florence, Italy. 9. Cancer Registry and Histopathology Unit, "Civic-M.P.Arezzo" Hospital, Provincial Health Unit, Ragusa, Italy. 10. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 11. German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. 12. French National Institute of Health and Medical Research (INSERM), Centre for Research in Epidemiology and Population Health (CESP), Health across Generations Team, U1018, Villejuif, France. 13. Institut Gustave Roussy, Villejuif, France. 14. University Paris Sud, UMRS 1018, Villejuif, France. 15. Cancer Epidemiology Centre, Cancer Council of Victoria, Melbourne, Australia. 16. Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, Melbourne, Australia. 17. Hellenic Health Foundation, Athens, Greece; and. 18. WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology, and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece. 19. International Agency for Research on Cancer, Lyon, France; scalberta@iarc.fr.
Abstract
Background: Meat and fish intakes have been associated with various chronic diseases. The use of specific biomarkers may help to assess meat and fish intake and improve subject classification according to the amount and type of meat or fish consumed.Objective: A metabolomic approach was applied to search for biomarkers of meat and fish intake in a dietary intervention study and in free-living subjects from the European Prospective Investigation into Cancer and Nutrition (EPIC) study.Design: In the dietary intervention study, 4 groups of 10 subjects consumed increasing quantities of chicken, red meat, processed meat, and fish over 3 successive weeks. Twenty-four-hour urine samples were collected during each period and analyzed by high-resolution liquid chromatography-mass spectrometry. Signals characteristic of meat or fish intake were replicated in 50 EPIC subjects for whom a 24-h urine sample and 24-h dietary recall were available and who were selected for their exclusive intake or no intake of any of the 4 same foods. Results: A total of 249 mass spectrometric features showed a positive dose-dependent response to meat or fish intake in the intervention study. Eighteen of these features best predicted intake of the 4 food groups in the EPIC urine samples on the basis of partial receiver operator curve analyses with permutation testing (areas under the curve ranging between 0.61 and 1.0). Of these signals, 8 metabolites were identified. Anserine was found to be specific for chicken intake, whereas trimethylamine-N-oxide showed good specificity for fish. Carnosine and 3 acylcarnitines (acetylcarnitine, propionylcarnitine, and 2-methylbutyrylcarnitine) appeared to be more generic indicators of meat and meat and fish intake, respectively. Conclusion: The meat and fish biomarkers identified in this work may be used to study associations between meat and fish intake and disease risk in epidemiologic studies. This trial was registered at clinicaltrials.gov as NCT01684917.
Background: Meat and fish intakes have been associated with various chronic diseases. The use of specific biomarkers may help to assess meat and fish intake and improve subject classification according to the amount and type of meat or fish consumed.Objective: A metabolomic approach was applied to search for biomarkers of meat and fish intake in a dietary intervention study and in free-living subjects from the European Prospective Investigation into Cancer and Nutrition (EPIC) study.Design: In the dietary intervention study, 4 groups of 10 subjects consumed increasing quantities of chicken, red meat, processed meat, and fish over 3 successive weeks. Twenty-four-hour urine samples were collected during each period and analyzed by high-resolution liquid chromatography-mass spectrometry. Signals characteristic of meat or fish intake were replicated in 50 EPIC subjects for whom a 24-h urine sample and 24-h dietary recall were available and who were selected for their exclusive intake or no intake of any of the 4 same foods. Results: A total of 249 mass spectrometric features showed a positive dose-dependent response to meat or fish intake in the intervention study. Eighteen of these features best predicted intake of the 4 food groups in the EPIC urine samples on the basis of partial receiver operator curve analyses with permutation testing (areas under the curve ranging between 0.61 and 1.0). Of these signals, 8 metabolites were identified. Anserine was found to be specific for chicken intake, whereas trimethylamine-N-oxide showed good specificity for fish. Carnosine and 3 acylcarnitines (acetylcarnitine, propionylcarnitine, and 2-methylbutyrylcarnitine) appeared to be more generic indicators of meat and meat and fish intake, respectively. Conclusion: The meat and fish biomarkers identified in this work may be used to study associations between meat and fish intake and disease risk in epidemiologic studies. This trial was registered at clinicaltrials.gov as NCT01684917.
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