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| Name | Class |
|---|---|
| King's College London | OTHER |
| Massachusetts General Hospital | OTHER |
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The foods we eat - our diet - can affect whether we develop diseases during our lives, such as diabetes or heart disease. This is because the amount and types of foods we eat can affect our weight, and because different foods are metabolised (processed) by the body in different ways.
Scientists have also found that the bacteria in our guts (the gut microbiome) affects our metabolism, weight and health and that, together with a person's diet and metabolism, could be used to predict appetite and how meals affect levels of sugar (glucose) and fats (lipids) found in blood after eating. If blood sugar and fat are too high too often, there's a greater chance of developing diseases such as diabetes.
The gut microbiome is different in different people. Only 10-20% of the types of bacteria found in our guts 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 years or over from the TwinsUK cohort to take part in a study that aims to answer the questions above. The participants will need to come in for a clinical visit where they will give blood, stool, saliva and urine samples. The participants will also be given a standardised breakfast and lunch and fitted with a glucose monitor (Abbott Freestyle Libre-CE marked) to monitor their blood sugar levels. After the visit, the participants will be asked to eat standardised meals at home for breakfast for a further 12 days. Participants will also be required to prick their fingers at regular intervals to collect small amounts of blood, and to record constantly their appetite, food, physical activity and sleep using apps and wearable devices.
Choice of design: The study is a single arm mechanistic intervention study.
Study population: Twin participants will be recruited from the TwinsUK database and non-twins will be recruited via social media platforms and advertising campaigns.
Screening Assessment: Prospective participants will be selected based on the defined inclusion and exclusion criteria by the study management team. Recruitment will be done over the phone and via the Internet and emails and prospective participants will be booked in for their initial appointment to acquire baseline measurements.
Study duration: Each participant will take part in the study for a period of up to 3 weeks.
The PREDICT study will be divided into 3 protocol cohorts, where all participants (n=2,500) complete a baseline clinical visit as described below. Of this total, Cohort 1 (n=1,150) will complete a home-based dietary intervention lasting up to 2 weeks (June 2018 - May 2019). Within this group, 100 participants will complete an additional home-based dietary intervention lasting up to 3 weeks (February 2019 - May 2019).
Following completion of this first cohort, Cohort 2 (n=900) will complete a similar home-based dietary intervention lasting up to 2 weeks (June 2019 - May 2023). Within this cohort, 50 participants will also complete deep cardiometabolic phenotyping at their clinical visit (September 2019 - February 2020).
Finally, Cohort 3 (n=450) will only complete the baseline clinical visit (June 2019 - May 2023).
Dietary intervention:
On day one (baseline visit) participants will be given standardised meals for breakfast and lunch. Participants continuing onto the home-based intervention (n=2,050) will receive a dietary intervention lasting up to 12 days following their clinical visit. Each participant will be instructed to eat standardised meals for breakfast, which must be their first meal of the day. On some of these days the participants will also be asked to eat a standardised lunch meal. Participants are free to eat whatever they wish at all other times, although we may provide a list of recommended foods. The standardised meals will be provided to all participants by the study team on the day of the visit. The foods included as part of these meals will be foods that are commonly consumed and can be made from products sold in UK supermarkets. Participants will be reassured that the amount of food will be designed to ensure a stable body weight over the course of the study. Participants will be asked to consume the entire amount of food indicated for the standardised meals and to record any left-over food via a digital app for which training will be provided at the start of the study. For the remaining 2 days post the 12 day dietary intervention period, participants are free to eat and drink whatever they wish or choose from the list of recommended foods provided to them. They will be asked to track all meals, snacks and drinks on their digital app. Participants will also be advised not to change their physical activity patterns during the course of the study.
After the baseline visit, regular contact will be made with the participants via phone, their app and text messages for the period of the intervention to encourage compliance and answer any queries.
Anthropometry: Weight, height, waist and hip circumference, blood pressure, body fat will be taken using standard procedures, in duplicates by a trained researcher at all face to face appointments. DXA scans using a Hologic machine will be used to assess body composition in all participants.
Dietary and Lifestyle: Participants will be asked to complete a simple online baseline questionnaire plus record daily dietary and activity information using digital apps. Lifestyle information (such as sleep, exercise and heart rate) will be monitored using digital wearable devices. Dietary information and psychological data (eg hunger) will be recorded in a digital mobile phone app. Training in all apps and equipment will be given at the baseline visit.
Digital devices: Participants will be asked to record daily dietary and activity information using digital apps and lifestyle information will be monitored using digital wearable devices. The continuous glucose monitor (Freestyle Libre, CGM) provides continuous glucose profiles for up to 14 days. The CGM will be inserted on the back of the upper arm at the baseline visit by a nurse. Subcutaneous interstitial fluid glucose concentrations are measured every 15 minutes by the CGM, and can be displayed on a remote device when necessary. The readings will be blinded so that the participant's behaviour is not affected by the glucose readings. The CGM will be removed at the end of the intervention period. Removal of the glucose monitor can be performed by the participants at home and detailed instructions on the removal procedure will be provided to them on day 1 of their visit. A 24-h contact number will be made available to participants for any inquiries or if any problems arose. Data from the CGM will be downloaded, and glucose profiles will be evaluated on the basis of data collected on days 1-14.
Blood samples: Postprandial blood will be collected on day one in the clinic and on additional days at home using finger-prick blood sampling.
Digital app: Participants will be asked to download an app designed specifically for this study, which provides diet & activity logging functionality similar to widely used existing apps such as MyFitnessPal.
Participants will be asked to record and monitor the following information via the digital app and wearable devices:
During their baseline visit day, participants will be assisted with installation and setup and provided with instructions on how to use the app. The app will be available in versions for both iOS and Android operating systems, and will support a wide range of mobile phone models as expected across the participant population. The app will synchronise remotely with backend database servers, over an encrypted and authenticated API, and will support offline operation for when patients wish to record an entry without network coverage. This continuous background synchronization means that it will not be necessary to explicitly download data from the phone at the end of the study.
A subgroup of participants from Cohort 2 (n=50) who continue onto the home-phase will be recruited to provide additional cardiometabolic measures, based on previously collected metabolomic and phenotyping data. This subgroup will undergo an abdominal XMR scan and cardiovascular tests at their baseline visit, before continuing onto the home-based intervention.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Dietary intervention | Experimental | 2 week dietary intervention using standardized test meals |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Dietary intervention | Other | To carry out an interventional dietary study using standardised meals to predict for an individual their metabolic response to certain foods using the gut microbiome and their metabolic profile. Responses will include post-prandial appetite, levels of satiety, circulating glucose, insulin, ketone bodies and lipid levels. |
| Measure | Description | Time Frame |
|---|---|---|
| Gut microbiome profile | Assessment of participants' gut microbiome | 1-2 days |
| Lipids | Measurement of blood lipids | 1 day to 2 weeks |
| Glucose | Measurement of blood Glucose | 2 weeks |
| Sleep | Record of sleep pattern using a wearable device (i.e. fitness watch) | 2 weeks |
| Physical activity | Record of physical activity using a wearable device (i.e. fitness watch) | 2 weeks |
| Hunger and appetite assessment | Record of hunger and appetite patterns using a digital app | 2 weeks |
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| Measure | Description | Time Frame |
|---|---|---|
| Inflammation | IL-6 | 1 day |
| Glucose metabolism | C-peptide | 2 weeks |
Inclusion Criteria:
Exclusion Criteria:
For participants continuing onto the home-based intervention (n=2,000), the additional following exclusions apply:
For participants undergoing cardiometabolic phenotyping and XMRI (n=50), the additional following exclusions apply:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Sarah Berry, PhD | Contact | 020 7848 4088 | sarah.e.berry@kcl.ac.uk |
| Name | Affiliation | Role |
|---|---|---|
| Tim Spector | King's College London | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| King's College London | Recruiting | London | England | SE1 7EH | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37709944 | Derived | Bermingham KM, May A, Asnicar F, Capdevila J, Leeming ER, Franks PW, Valdes AM, Wolf J, Hadjigeorgiou G, Delahanty LM, Segata N, Spector TD, Berry SE. Snack quality and snack timing are associated with cardiometabolic blood markers: the ZOE PREDICT study. Eur J Nutr. 2024 Feb;63(1):121-133. doi: 10.1007/s00394-023-03241-6. Epub 2023 Sep 15. | |
| 37528259 |
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| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D006331 | Heart Diseases |
| D005247 | Feeding Behavior |
| D003141 | Communicable Diseases |
| D009765 | Obesity |
| ID | Term |
|---|---|
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
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Not provided
| ID | Term |
|---|---|
| D004035 | Diet Therapy |
| ID | Term |
|---|---|
| D044623 | Nutrition Therapy |
| D013812 | Therapeutics |
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|
| Metabolomics |
NMR analysis of a panel of 220 metabolites |
| 1 day |
| Systolic and Diastolic Blood pressure | Clinic Systolic and Diastolic Blood Pressure | 6 hours |
| Body composition | Visceral fat in kg | 1 day |
| Digestive enzymes | Salivary amylase concentration | 1 day |
| Pulse wave velocity | (subgroup n=50) measure of pulse wave velocity using carotid and femoral artery | 1 day |
| Carotid intima-media thickness | (subgroup n=50) Measure of carotid intima-media thickness using ultrasound | 1 day |
| Fat quantification | (subgroup n=50) Quantification of liver, visceral and subcutaneous adipose tissue from XMR | 1 day |
| Carotid plaque | (subgroup n=50) Grading of carotid plaque using ultrasound | 1 day |
| Bermingham KM, Stensrud S, Asnicar F, Valdes AM, Franks PW, Wolf J, Hadjigeorgiou G, Davies R, Spector TD, Segata N, Berry SE, Hall WL. Exploring the relationship between social jetlag with gut microbial composition, diet and cardiometabolic health, in the ZOE PREDICT 1 cohort. Eur J Nutr. 2023 Dec;62(8):3135-3147. doi: 10.1007/s00394-023-03204-x. Epub 2023 Aug 2. |
| 36364763 | Derived | Louca P, Berry SE, Bermingham K, Franks PW, Wolf J, Spector TD, Valdes AM, Chowienczyk P, Menni C. Postprandial Responses to a Standardised Meal in Hypertension: The Mediatory Role of Visceral Fat Mass. Nutrients. 2022 Oct 26;14(21):4499. doi: 10.3390/nu14214499. |
| 35134821 | Derived | Merino J, Linenberg I, Bermingham KM, Ganesh S, Bakker E, Delahanty LM, Chan AT, Capdevila Pujol J, Wolf J, Al Khatib H, Franks PW, Spector TD, Ordovas JM, Berry SE, Valdes AM. Validity of continuous glucose monitoring for categorizing glycemic responses to diet: implications for use in personalized nutrition. Am J Clin Nutr. 2022 Jun 7;115(6):1569-1576. doi: 10.1093/ajcn/nqac026. |
| 34845532 | Derived | Tsereteli N, Vallat R, Fernandez-Tajes J, Delahanty LM, Ordovas JM, Drew DA, Valdes AM, Segata N, Chan AT, Wolf J, Berry SE, Walker MP, Spector TD, Franks PW. Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions. Diabetologia. 2022 Feb;65(2):356-365. doi: 10.1007/s00125-021-05608-y. Epub 2021 Nov 30. |
| 34100082 | Derived | Mazidi M, Valdes AM, Ordovas JM, Hall WL, Pujol JC, Wolf J, Hadjigeorgiou G, Segata N, Sattar N, Koivula R, Spector TD, Franks PW, Berry SE. Meal-induced inflammation: postprandial insights from the Personalised REsponses to DIetary Composition Trial (PREDICT) study in 1000 participants. Am J Clin Nutr. 2021 Sep 1;114(3):1028-1038. doi: 10.1093/ajcn/nqab132. |
| 33722860 | Derived | Asnicar F, Leeming ER, Dimidi E, Mazidi M, Franks PW, Al Khatib H, Valdes AM, Davies R, Bakker E, Francis L, Chan A, Gibson R, Hadjigeorgiou G, Wolf J, Spector TD, Segata N, Berry SE. Blue poo: impact of gut transit time on the gut microbiome using a novel marker. Gut. 2021 Sep;70(9):1665-1674. doi: 10.1136/gutjnl-2020-323877. Epub 2021 Mar 15. |
| 33568158 | Derived | Menni C, Louca P, Berry SE, Vijay A, Astbury S, Leeming ER, Gibson R, Asnicar F, Piccinno G, Wolf J, Davies R, Mangino M, Segata N, Spector TD, Valdes AM. High intake of vegetables is linked to lower white blood cell profile and the effect is mediated by the gut microbiome. BMC Med. 2021 Feb 11;19(1):37. doi: 10.1186/s12916-021-01913-w. |
| 32528151 | Derived | Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M, Wolf J, Capdevila J, Hadjigeorgiou G, Davies R, Al Khatib H, Bonnett C, Ganesh S, Bakker E, Hart D, Mangino M, Merino J, Linenberg I, Wyatt P, Ordovas JM, Gardner CD, Delahanty LM, Chan AT, Segata N, Franks PW, Spector TD. Human postprandial responses to food and potential for precision nutrition. Nat Med. 2020 Jun;26(6):964-973. doi: 10.1038/s41591-020-0934-0. Epub 2020 Jun 11. |
| D002318 | Cardiovascular Diseases |
| D001522 | Behavior, Animal |
| D001519 | Behavior |
| D007239 | Infections |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D050177 | Overweight |
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D001835 | Body Weight |
| D012816 | Signs and Symptoms |