Discovery of Biomarkers of Intake of of Highly Consumed Foods in Mexico
BIAMEX
BIAMEX: Discovery of Biomarkers of Intake of Highly Consumed Foods in Mexico by Untargeted Metabolomics
1 other identifier
interventional
12
1 country
1
Brief Summary
To determine diet-health associations, researchers rely on information obtained from dietary instruments, such as the 24-hour recall (R24H), food frequency questionnaires (FFQ) and food diaries, in clinical studies. However, it is widely recognized that the information provided by the different instruments is biased by different factors including recall errors and respondent burden. The impact of the variability produced by this bias decreases the robustness of diet-health associations which results in the creation of less efficient standards and recommendations for our population. To address this, the discovery of biomarkers of food intake (BFIs) is an objective tool that indicates exposure to specific foods or various dietary patterns. BFIs allow the calibration of dietary information to obtain the real consumption of the individual and thus clarify the relationship between different pathologies of interest and the intake of different foods. BIAMEX will initially focus on the discovery of BFIs of nopal, corn tortilla, mango, avocado, guava and amaranth. For this purpose, a controlled crossover intervention study is being carried out with the 6 foods to be investigated where 24h urine and plasma samples are being collected. Subsequently, the samples collected will be analyzed by mass spectrometry.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at below P25 for not_applicable
Started Jan 2023
Typical duration for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
January 1, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
April 5, 2024
CompletedFirst Submitted
Initial submission to the registry
May 22, 2024
CompletedFirst Posted
Study publicly available on registry
June 7, 2024
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2024
CompletedJune 7, 2024
June 1, 2024
1.3 years
May 22, 2024
June 6, 2024
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
Metabolic profiling of urine samples after intake of mango, amaranth, nopal, corn tortilla, avocado, and guava, detected as mass-to-charge signals (cps) by an untargeted metabolomics approach over 24 hours post-intake.
Given the absence of a priori knowledge of specific urinary biomarkers of intake for mango, nopal, amaranth, avocado, corn tortilla, and guava, an untargeted metabolomics approach will be employed to identify them. As an exploratory approach, this methodology will determine the myriad of signals (mass-to-charge ratios) present in urine samples, which correspond to metabolites that become bioavailable after the intake of the test foods, collected at 0-1, 1-2, 4-6, 6-12, and 12-24 hours after intake. The analysis of the patterns in the metabolome will facilitate the discovery of potential biomarkers of intake.
Before intake of foods 00 hours to 24 hours after intake.
Metabolic profiling of serum samples after intake of mango, amaranth, nopal, corn tortilla, avocado, and guava, detected as mass-to-charge signals (cps) by an untargeted metabolomics approach over 24 hours post-intake.
Given the absence of a priori knowledge of specific serum biomarkers of intake for mango, nopal, amaranth, avocado, corn tortilla, and guava, an untargeted metabolomics approach will be employed to identify them. As an exploratory approach, this methodology will determine the myriad of signals (mass-to-charge ratios) present in serum samples collected at baseline, 1 hour, 2 hours, 4 hours, 6 hours, and 24 hours after the intake of. The analysis of the patterns in the metabolome will facilitate the discovery of potential biomarkers of intake.
Before intake of foods 00 hours to 24 hours after intake.
Study Arms (7)
Mango Ataulfo
OTHER150g of mango Ataulfo plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 15ml of sunflower seed oil
Avocado Hass
OTHER120g of avocado hass plus 150ml of control beverage (Supportan® Drink Cappuccino)
Boiled Nopal
OTHER300g of boiled nopal plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 18ml of sunflower seed oil
Corn Tortilla
OTHER3 pieces of corn tortilla plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 2ml of sunflower seed oil
Guava
OTHER3 pieces of guava plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 16ml of sunflower seed oil
Amaranth
OTHER1/2 cup of amaranth plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 35ml of sunflower seed oil
Supportan® DKN Cappuccino
OTHER290ml of control beverage (Supportan® Drink Cappuccino)
Interventions
In this intervention, subjects consumed 150g of mango Ataulfo plus 150 ml of control beverage (Supportan® Drink Cappuccino). The addition of the control beverage has the purpose of providing energy intake and limiting the noise that the control beverage may contribute to the metabolomic profile in urine and serum.
In this intervention, subjects consumed 120g of avocado hass plus 150 ml of a control beverage (Supportan® Drink Cappuccino). The addition of the control beverage provides energy intake and limits the noise that the beverage may contribute to the metabolomic profile in urine and serum.
In this intervention, subjects consumed 300g of cooked nopal and 150 ml of control beverage (Supportan® Drink Cappuccino). The addition of the control beverage provides energy intake and limits the noise that the beverage may contribute to the metabolic profile in urine and serum.
In this intervention, subjects consumed 3 corn tortillas and 150 ml of control beverage (Supportan® Drink Cappuccino). The addition of the control beverage provides energy intake and limits the noise that the beverage may contribute to the metabolic profile in urine and serum.
In this intervention, subjects consumed 3 guavas and 150 ml of control beverage (Supportan® Drink Cappuccino). The addition of the control beverage provides energy intake and limits the noise that the beverage may contribute to the metabolic profile in urine and serum.
In this intervention, subjects consumed 1/2 cup of amaranth and 150 ml of control beverage (Supportan® Drink Cappuccino). The addition of the control beverage provides energy intake and limits the noise that the beverage may contribute to the metabolic profile in urine and serum.
In this intervention, subjects consumed 290ml of Supportan Drink ® Capuccino to act as a control for the metabolomic profiling in urine and serum.
Eligibility Criteria
You may qualify if:
- Signed informed consent
- Healthy males and females
- BMI \>18.5 and \< 25 kg/m2
- Willing/able to consume all test foods and the standardized meals
You may not qualify if:
- Smokers
- Diagnosed health condition (chronic or infectious disease)
- Taking nutritional supplements (e.g. vitamins, minerals) several times a week.
- Taking medication.
- Pregnant, lactating.
- Antibiotics treatment within 3 months prior to intervention.
- Vegetarians, as standardized meals will contain meat.
- Not willing to follow nutritional restrictions, including drinking alcohol during study days
- Allergic to foods of interest
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Instituto de Ciencias Médicas y Nutrición Salvador Zubirán
Mexico City, 14080, Mexico
Related Publications (27)
Archer E, Marlow ML, Lavie CJ. Controversy and debate: Memory-Based Methods Paper 1: the fatal flaws of food frequency questionnaires and other memory-based dietary assessment methods. J Clin Epidemiol. 2018 Dec;104:113-124. doi: 10.1016/j.jclinepi.2018.08.003. Epub 2018 Aug 17.
PMID: 30121379BACKGROUNDTinker LF, Sarto GE, Howard BV, Huang Y, Neuhouser ML, Mossavar-Rahmani Y, Beasley JM, Margolis KL, Eaton CB, Phillips LS, Prentice RL. Biomarker-calibrated dietary energy and protein intake associations with diabetes risk among postmenopausal women from the Women's Health Initiative. Am J Clin Nutr. 2011 Dec;94(6):1600-6. doi: 10.3945/ajcn.111.018648. Epub 2011 Nov 9.
PMID: 22071707BACKGROUNDVanderslice JT, Higgs DJ. Vitamin C content of foods: sample variability. Am J Clin Nutr. 1991 Dec;54(6 Suppl):1323S-1327S. doi: 10.1093/ajcn/54.6.1323s.
PMID: 1962591BACKGROUNDAndersen MB, Kristensen M, Manach C, Pujos-Guillot E, Poulsen SK, Larsen TM, Astrup A, Dragsted L. Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics. Anal Bioanal Chem. 2014 Mar;406(7):1829-44. doi: 10.1007/s00216-013-7498-5. Epub 2014 Jan 4.
PMID: 24390407BACKGROUNDGibbons H, Michielsen CJR, Rundle M, Frost G, McNulty BA, Nugent AP, Walton J, Flynn A, Gibney MJ, Brennan L. Demonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example. Mol Nutr Food Res. 2017 Oct;61(10). doi: 10.1002/mnfr.201700037. Epub 2017 Jul 20.
PMID: 28556565BACKGROUNDCuparencu C, Rinnan A, Dragsted LO. Combined Markers to Assess Meat Intake-Human Metabolomic Studies of Discovery and Validation. Mol Nutr Food Res. 2019 Sep;63(17):e1900106. doi: 10.1002/mnfr.201900106. Epub 2019 Jun 13.
PMID: 31141834BACKGROUNDVazquez-Manjarrez N, Weinert CH, Ulaszewska MM, Mack CI, Micheau P, Petera M, Durand S, Pujos-Guillot E, Egert B, Mattivi F, Bub A, Dragsted LO, Kulling SE, Manach C. Discovery and Validation of Banana Intake Biomarkers Using Untargeted Metabolomics in Human Intervention and Cross-sectional Studies. J Nutr. 2019 Oct 1;149(10):1701-1713. doi: 10.1093/jn/nxz125.
PMID: 31240312BACKGROUNDGiesbertz P, Brandl B, Lee YM, Hauner H, Daniel H, Skurk T. Specificity, Dose Dependency, and Kinetics of Markers of Chicken and Beef Intake Using Targeted Quantitative LC-MS/MS: A Human Intervention Trial. Mol Nutr Food Res. 2020 Mar;64(5):e1900921. doi: 10.1002/mnfr.201900921. Epub 2020 Jan 29.
PMID: 31916678BACKGROUNDUlaszewska MM, Weinert CH, Trimigno A, Portmann R, Andres Lacueva C, Badertscher R, Brennan L, Brunius C, Bub A, Capozzi F, Cialie Rosso M, Cordero CE, Daniel H, Durand S, Egert B, Ferrario PG, Feskens EJM, Franceschi P, Garcia-Aloy M, Giacomoni F, Giesbertz P, Gonzalez-Dominguez R, Hanhineva K, Hemeryck LY, Kopka J, Kulling SE, Llorach R, Manach C, Mattivi F, Migne C, Munger LH, Ott B, Picone G, Pimentel G, Pujos-Guillot E, Riccadonna S, Rist MJ, Rombouts C, Rubert J, Skurk T, Sri Harsha PSC, Van Meulebroek L, Vanhaecke L, Vazquez-Fresno R, Wishart D, Vergeres G. Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies. Mol Nutr Food Res. 2019 Jan;63(1):e1800384. doi: 10.1002/mnfr.201800384. Epub 2018 Oct 11.
PMID: 30176196BACKGROUNDDragsted LO, Gao Q, Scalbert A, Vergeres G, Kolehmainen M, Manach C, Brennan L, Afman LA, Wishart DS, Andres Lacueva C, Garcia-Aloy M, Verhagen H, Feskens EJM, Pratico G. Validation of biomarkers of food intake-critical assessment of candidate biomarkers. Genes Nutr. 2018 May 30;13:14. doi: 10.1186/s12263-018-0603-9. eCollection 2018.
PMID: 29861790BACKGROUNDBarnes RC, Krenek KA, Meibohm B, Mertens-Talcott SU, Talcott ST. Urinary metabolites from mango (Mangifera indica L. cv. Keitt) galloyl derivatives and in vitro hydrolysis of gallotannins in physiological conditions. Mol Nutr Food Res. 2016 Mar;60(3):542-50. doi: 10.1002/mnfr.201500706. Epub 2016 Feb 2.
PMID: 26640139BACKGROUNDKim H, Castellon-Chicas MJ, Arbizu S, Talcott ST, Drury NL, Smith S, Mertens-Talcott SU. Mango (Mangifera indica L.) Polyphenols: Anti-Inflammatory Intestinal Microbial Health Benefits, and Associated Mechanisms of Actions. Molecules. 2021 May 6;26(9):2732. doi: 10.3390/molecules26092732.
PMID: 34066494BACKGROUNDFerreira CM, Vieira AT, Vinolo MA, Oliveira FA, Curi R, Martins Fdos S. The central role of the gut microbiota in chronic inflammatory diseases. J Immunol Res. 2014;2014:689492. doi: 10.1155/2014/689492. Epub 2014 Sep 18.
PMID: 25309932BACKGROUNDDreher ML, Davenport AJ. Hass avocado composition and potential health effects. Crit Rev Food Sci Nutr. 2013;53(7):738-50. doi: 10.1080/10408398.2011.556759.
PMID: 23638933BACKGROUNDFulgoni VL 3rd, Dreher M, Davenport AJ. Avocado consumption is associated with better diet quality and nutrient intake, and lower metabolic syndrome risk in US adults: results from the National Health and Nutrition Examination Survey (NHANES) 2001-2008. Nutr J. 2013 Jan 2;12:1. doi: 10.1186/1475-2891-12-1.
PMID: 23282226BACKGROUNDVazquez-Manjarrez N, Ulaszewska M, Garcia-Aloy M, Mattivi F, Pratico G, Dragsted LO, Manach C. Biomarkers of intake for tropical fruits. Genes Nutr. 2020 Jun 19;15(1):11. doi: 10.1186/s12263-020-00670-4.
PMID: 32560627BACKGROUNDQin XJ, Yu Q, Yan H, Khan A, Feng MY, Li PP, Hao XJ, An LK, Liu HY. Meroterpenoids with Antitumor Activities from Guava (Psidium guajava). J Agric Food Chem. 2017 Jun 21;65(24):4993-4999. doi: 10.1021/acs.jafc.7b01762. Epub 2017 Jun 9.
PMID: 28578580BACKGROUNDKohlert C, van Rensen I, Marz R, Schindler G, Graefe EU, Veit M. Bioavailability and pharmacokinetics of natural volatile terpenes in animals and humans. Planta Med. 2000 Aug;66(6):495-505. doi: 10.1055/s-2000-8616.
PMID: 10985073BACKGROUNDPujos-Guillot E, Hubert J, Martin JF, Lyan B, Quintana M, Claude S, Chabanas B, Rothwell JA, Bennetau-Pelissero C, Scalbert A, Comte B, Hercberg S, Morand C, Galan P, Manach C. Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study. J Proteome Res. 2013 Apr 5;12(4):1645-59. doi: 10.1021/pr300997c. Epub 2013 Mar 5.
PMID: 23425595BACKGROUNDLopez-Romero P, Pichardo-Ontiveros E, Avila-Nava A, Vazquez-Manjarrez N, Tovar AR, Pedraza-Chaverri J, Torres N. The effect of nopal (Opuntia ficus indica) on postprandial blood glucose, incretins, and antioxidant activity in Mexican patients with type 2 diabetes after consumption of two different composition breakfasts. J Acad Nutr Diet. 2014 Nov;114(11):1811-8. doi: 10.1016/j.jand.2014.06.352. Epub 2014 Aug 12.
PMID: 25132122BACKGROUNDVazquez-Manjarrez N, Guevara-Cruz M, Flores-Lopez A, Pichardo-Ontiveros E, Tovar AR, Torres N. Effect of a dietary intervention with functional foods on LDL-C concentrations and lipoprotein subclasses in overweight subjects with hypercholesterolemia: Results of a controlled trial. Clin Nutr. 2021 May;40(5):2527-2534. doi: 10.1016/j.clnu.2021.02.048. Epub 2021 Mar 6.
PMID: 33932799BACKGROUNDNkobole N, Prinsloo G. 1H-NMR and LC-MS Based Metabolomics Analysis of Wild and Cultivated Amaranthus spp. Molecules. 2021 Feb 4;26(4):795. doi: 10.3390/molecules26040795.
PMID: 33557008BACKGROUNDWarrack BM, Hnatyshyn S, Ott KH, Reily MD, Sanders M, Zhang H, Drexler DM. Normalization strategies for metabonomic analysis of urine samples. J Chromatogr B Analyt Technol Biomed Life Sci. 2009 Feb 15;877(5-6):547-52. doi: 10.1016/j.jchromb.2009.01.007. Epub 2009 Jan 14.
PMID: 19185549BACKGROUNDKohl SM, Klein MS, Hochrein J, Oefner PJ, Spang R, Gronwald W. State-of-the art data normalization methods improve NMR-based metabolomic analysis. Metabolomics. 2012 Jun;8(Suppl 1):146-160. doi: 10.1007/s11306-011-0350-z. Epub 2011 Aug 12.
PMID: 22593726BACKGROUNDScalbert A, Brennan L, Manach C, Andres-Lacueva C, Dragsted LO, Draper J, Rappaport SM, van der Hooft JJ, Wishart DS. The food metabolome: a window over dietary exposure. Am J Clin Nutr. 2014 Jun;99(6):1286-308. doi: 10.3945/ajcn.113.076133. Epub 2014 Apr 23.
PMID: 24760973BACKGROUNDvan den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics. 2006 Jun 8;7:142. doi: 10.1186/1471-2164-7-142.
PMID: 16762068BACKGROUNDVinaixa M, Samino S, Saez I, Duran J, Guinovart JJ, Yanes O. A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data. Metabolites. 2012 Oct 18;2(4):775-95. doi: 10.3390/metabo2040775.
PMID: 24957762BACKGROUND
Related Links
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Natalia Vázquez Manjarrez, PhD
National Institute of Medical Sciences and Nutrition Salvador Zubirán
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- BASIC SCIENCE
- Intervention Model
- CROSSOVER
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Researcher in Medical Sciences
Study Record Dates
First Submitted
May 22, 2024
First Posted
June 7, 2024
Study Start
January 1, 2023
Primary Completion
April 5, 2024
Study Completion
December 31, 2024
Last Updated
June 7, 2024
Record last verified: 2024-06