- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06449170
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
Study Overview
Status
Detailed Description
The BIAMEX study aims to address the challenge of improving the accuracy of dietary assessment, a critical factor in misunderstanding the relationship between diet and disease. Traditional dietary assessment tools, such as 24-hour recalls and food frequency questionnaires, are susceptible to biases related to their retrospective nature, such as memory errors and respondent burden. To overcome these limitations, BIAMEX focuses on discovering biomarkers on food intake (BFIs) for foods that originate in our country and are highly consumed by the population. This project will investigate the BFIs for nopal, corn tortilla, mango, avocado, guava, and amaranth.
This exploratory study employs a randomized, open, crossover, controlled design to investigate the metabolomic changes in urine and serum samples from healthy volunteers following the consumption of the selected foods. The interventions aim to assess the impact of each food intake on the metabolomic profile of the participants using an untargeted approach with liquid chromatography-mass spectrometry.
Participants were briefed at the Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán" on the study's aims, procedures, and benefits before providing informed consent. Subsequent steps included clinical history documentation and blood sampling for eligibility assessment, focusing on fasting glucose, cholesterol levels, and other health indicators. Volunteers underwent seven distinct food interventions in a randomized manner, including mango, avocado, nopal, corn tortillas, guavas, amaranth, and Supportan® drink Cappuccino as the control. This beverage was chosen to avoid metabolomic overlap with the different foods, ensuring distinct biomarker detection. Preceding the intervention days, subjects followed a low-polyphenol diet, excluding test foods and phytochemicals-rich items such as tea, coffee, or chocolate, culminating in a standardized dinner. On the intervention day, subjects arrived fasting at the institution and provided a baseline serum and urine samples. Then, subjects were provided with the test food, after which urine and serum samples were collected at 1h, 2h,4h, 6h postprandially on site. After the six-hour timepoint, the catheter was removed, and a standardized lunch was provided. Subjects continued to collect urine samples at home, corresponding to the 12h and 24h urine collection, using materials provided by the investigation team. Additionally, subjects received dietary instructions and menus to follow for the rest of the day. On the day after the intervention, subjects returned to the institution to deliver the urine collections and to provide the last serum sample corresponding to the 24-hour timepoint. Once the sample was collected, subjects were provided with a complimentary breakfast, and their habitual diet was resumed. This experimental procedure was repeated for each food separated by a 7-day wash-out period.
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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-
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Ciudad de México, Mexico, 14080
- Instituto de Ciencias Médicas y Nutrición Salvador Zubirán
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- 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
Exclusion Criteria:
- 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
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Basic Science
- Allocation: Randomized
- Interventional Model: Crossover Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Other: Mango Ataulfo
150g of mango Ataulfo plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 15ml of sunflower seed oil
|
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.
|
|
Other: Avocado Hass
120g of avocado hass plus 150ml of control beverage (Supportan® Drink Cappuccino)
|
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.
|
|
Other: Boiled Nopal
300g of boiled nopal plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 18ml of sunflower seed oil
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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.
|
|
Other: Corn Tortilla
3 pieces of corn tortilla plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 2ml of sunflower seed oil
|
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.
|
|
Other: Guava
3 pieces of guava plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 16ml of sunflower seed oil
|
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.
|
|
Other: Amaranth
1/2 cup of amaranth plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 35ml of sunflower seed oil
|
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.
|
|
Other: Supportan® DKN Cappuccino
290ml of control beverage (Supportan® Drink Cappuccino)
|
In this intervention, subjects consumed 290ml of Supportan Drink ® Capuccino to act as a control for the metabolomic profiling in urine and serum.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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.
Time Frame: Before intake of foods 00 hours to 24 hours after 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.
Time Frame: Before intake of foods 00 hours to 24 hours after 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.
|
Collaborators and Investigators
Investigators
- Principal Investigator: Natalia Vázquez Manjarrez, PhD, National Institute of Medical Sciences and Nutrition Salvador Zubiran
Publications and helpful links
General Publications
- Tinker 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.
- Fulgoni 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.
- Vinaixa 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.
- Andersen 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.
- Scalbert 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.
- Pujos-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.
- Lopez-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.
- 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.
- Vanderslice 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.
- Gibbons 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.
- Cuparencu 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.
- Vazquez-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.
- Giesbertz 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.
- Ulaszewska 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.
- Dragsted 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.
- Barnes 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.
- Kim 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.
- Ferreira 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.
- Dreher 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.
- Vazquez-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.
- Qin 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.
- Kohlert 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.
- Vazquez-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.
- Nkobole 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.
- Warrack 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.
- Kohl 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.
- van 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.
Helpful Links
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 4044
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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