PREDICT 2: Personalized Responses to Dietary Composition Trial 2
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|ClinicalTrials.gov Identifier: NCT03983733|
Recruitment Status : Recruiting
First Posted : June 12, 2019
Last Update Posted : June 25, 2019
Foods in the human diet can affect the development of diseases over time, such as diabetes or heart disease. This is because the amount and types of foods in the diet eat can affect a person's weight, and because different foods are metabolised (processed) by the body in different ways.
Scientists have also found that the bacteria in the human gut (the gut microbiome) affect their metabolism, weight and health and that, together with a person's diet and metabolism, could be used to predict appetite and how meals affect the levels of sugar (glucose) and fats (lipids) found in blood after eating. If blood sugar and fat are too high too often for too long, there is a greater chance of developing diseases such as diabetes and cardiovascular disease.
The gut microbiome is different in different people. Only 10-20% of the types of bacteria found in the human gut are found in everyone. This might mean that the best diet to prevent disease needs matching to a person's gut microbiome and it might be possible to find personalised foods or diets that will help reduce the chance of developing chronic disease as well as metabolic syndrome.
The study investigators are recruiting volunteers aged 18-65 years to take part in a study that aims to answer the questions above. Participants will be asked to consume standardised meals over 8 days while wearing glucose monitors (Abbott Freestyle Libre) to measure their blood sugar levels. Participants will also be required to prick their fingers at regular intervals to collect small amounts of blood, and to record their appetite, food, physical activity and sleep using apps and wearable devices. They will be asked to collect a fecal and saliva sample before consuming the standardised meals, and to provide a fasted blood sample at the end of the study period.
|Condition or disease||Intervention/treatment||Phase|
|Diabetes Heart Diseases Diet Habit Diet Modification Microbial Colonization Healthy Obesity Metabolism||Other: Dietary Intervention||Not Applicable|
Show Detailed Description
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||1200 participants|
|Intervention Model:||Single Group Assignment|
|Masking:||None (Open Label)|
|Primary Purpose:||Basic Science|
|Official Title:||PREDICT 2: Personalized Responses to Dietary Composition Trial 2|
|Actual Study Start Date :||June 10, 2019|
|Estimated Primary Completion Date :||December 31, 2019|
|Estimated Study Completion Date :||December 31, 2019|
Experimental: Dietary Intervention
8 day dietary intervention using standardized test meals.
Other: Dietary Intervention
To carry out an interventional dietary study using standardised meals to predict an individual's metabolic response to certain foods, using their gut microbiome, metabolic profile, and other measurable characteristics such as weight, sleep and exercise.
- Gut microbiome species richness [ Time Frame: 1 Day ]Species count in fecal sample
- Lipids [ Time Frame: 3 days ]Measurement of blood lipids
- Glucose [ Time Frame: 11 days ]Measurement of blood glucose
- Sleep [ Time Frame: 10 days ]Record of sleep pattern using a wearable device (i.e. fitness watch)
- Physical activity [ Time Frame: 10 days ]Record of physical activity using a wearable device (i.e. fitness watch)
- Hunger and appetite assessment [ Time Frame: 10 days ]Record of hunger and appetite patterns using a digital app
- Glucose metabolism [ Time Frame: 3 days ]C-peptide
- Dietary assessment [ Time Frame: 10 days ]Weighed food log
- Anthropometry [ Time Frame: 1 day ]Weight (kg)
- Anthropometry [ Time Frame: 1 day ]Height (cm)
- Anthropometry [ Time Frame: 1 day ]Hip and waist circumference (cm)
- Metabolomics by NMR analysis [ Time Frame: 1 day ]Lipoprotein concentration (mol/L), lipoprotein composition (mol/L), glycoprotein acetyl concentration (mol/L), ketone bodies concentration (mol/L)
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03983733
|Contact: Sarah Berry, PhD||+44 207 848 email@example.com|
|Contact: Inbar Linenberg, MScfirstname.lastname@example.org|
|United States, Massachusetts|
|Zoe US Inc.||Recruiting|
|Boston, Massachusetts, United States, 02118|
|Contact: Jeffrey Brady 888-255-6904 email@example.com|
|Principal Investigator:||Tim Spector, Pr.||Zoe Global Limited|