Discovery of Biomarkers of Intake of of Highly Consumed Foods in Mexico

Diet, Healthy Clinical Trial

— BIAMEX  

Official title:

BIAMEX: Discovery of Biomarkers of Intake of Highly Consumed Foods in Mexico by Untargeted Metabolomics

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06449170
• Other study ID #: 4044
• Status: Active, not recruiting
• Phase: N/A
• Start date: January 1, 2023
• Est. completion date: December 31, 2024

Study information

• Verified date: June 2024
• Source: Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 12
• Est. completion date: December 31, 2024
• Est. primary completion date: April 5, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 40 Years

Eligibility

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

Related Conditions & MeSH terms

• Diet Habit
• Diet, Healthy
• Dietary Exposure

Intervention

Other:
• Mango Ataulfo
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.
• Avocado Hass
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.
• Nopal
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.
• 3 corn tortilla
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.
• Guava
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.
• Amaranth
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.

Dietary Supplement:
• Control Beverage (Supportan Drink ® Capuccino)
In this intervention, subjects consumed 290ml of Supportan Drink ® Capuccino to act as a control for the metabolomic profiling in urine and serum.

Locations

Country	Name	City	State
Mexico	Instituto de Ciencias Médicas y Nutrición Salvador Zubirán	Ciudad de México

Sponsors (1)

Lead Sponsor	Collaborator
Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran

Country where clinical trial is conducted

Mexico, 

References & Publications (27)

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* Note: There are 27 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.
Primary	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.

External Links

•   The Food database