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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Mango Ataulfo | Other | 150g of mango Ataulfo plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 15ml of sunflower seed oil |
|
| Avocado Hass | Other | 120g of avocado hass plus 150ml of control beverage (Supportan® Drink Cappuccino) |
|
| Boiled Nopal | Other | 300g of boiled nopal plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 18ml of sunflower seed oil |
|
| Corn Tortilla | Other | 3 pieces of corn tortilla plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 2ml of sunflower seed oil |
|
| Guava | Other | 3 pieces of guava plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 16ml of sunflower seed oil |
|
| Amaranth | Other |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Mango Ataulfo | Other | 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. |
| 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. | 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Natalia Vázquez Manjarrez, PhD | National Institute of Medical Sciences and Nutrition Salvador Zubirán | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Instituto de Ciencias Médicas y Nutrición Salvador Zubirán | Mexico City | 14080 | Mexico |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30121379 | Background | 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. | |
| 22071707 | Background | 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. |
| Label | URL |
|---|---|
| The Food database | View source |
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This is a randomized, open, crossover, and controlled design study. A total of 12 healthy subjects (six males and six females) aged 18-40 years with a BMI of 18.5-24.9 kg/m^2 were recruited. Participants underwent seven interventions (each featuring one of the selected foods or a control beverage), with a washout period of 7 days between interventions.
Each intervention is as follows:
150g of mango + 150ml of control beverage, 120g of avocado + 150ml of control beverage, 300g of cooked nopal + 150ml of control beverage, 3 corn tortillas + 150ml of control beverage, 3 guavas + 150ml of control beverage,
½ cup of amaranth + 150ml of control beverage, 290ml of control beverage
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1/2 cup of amaranth plus 150 ml of control beverage (Supportan® Drink Cappuccino) plus 35ml of sunflower seed oil
|
| Supportan® DKN Cappuccino | Other | 290ml of control beverage (Supportan® Drink Cappuccino) |
|
|
| Avocado Hass | Other | 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 | Other | 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 | Other | 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 | Other | 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 | Other | 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. |
|
| Control Beverage (Supportan Drink ® Capuccino) | Dietary Supplement | In this intervention, subjects consumed 290ml of Supportan Drink ® Capuccino to act as a control for the metabolomic profiling in urine and serum. |
|
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| ID | Term |
|---|---|
| D005247 | Feeding Behavior |
| ID | Term |
|---|---|
| D001522 | Behavior, Animal |
| D001519 | Behavior |
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| ID | Term |
|---|---|
| D000548 | Amaranth Dye |
| ID | Term |
|---|---|
| D001391 | Azo Compounds |
| D009930 | Organic Chemicals |
| D009282 | Naphthalenesulfonates |
| D009281 | Naphthalenes |
| D011084 | Polycyclic Aromatic Hydrocarbons |
| D006841 | Hydrocarbons, Aromatic |
| D006844 | Hydrocarbons, Cyclic |
| D006838 | Hydrocarbons |
| D001190 | Arylsulfonates |
| D017739 | Arylsulfonic Acids |
| D013451 | Sulfonic Acids |
| D013456 | Sulfur Acids |
| D013457 | Sulfur Compounds |
| D011083 | Polycyclic Compounds |
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