Providing Personally Tailored Dietary Suggestions Using Individual Microbiome and Glycemic Index Response.

NCT ID: NCT02945514

Last Updated: 2018-02-28

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

327 participants

Study Classification

OBSERVATIONAL

Study Start Date

2016-09-30

Study Completion Date

2018-02-28

Brief Summary

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Provide personally tailored dietary suggestions to participants based on the Personalized Prediction Engine (patent pending) owned by DayTwo.

Detailed Description

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* Answer personal questions including such things as health information, eating habits and preferences, activity level and intensity.
* Submit a first stool sample via a self-addressed, postage-paid kit that is mailed to you.
* Attend one of the 2 hour connection meetings based on available dates.
* The study team will collect vitals: measurements of height, weight, blood pressure, pulse, waist and hip circumference.
* You will have a Blood collection for study (approximately 35 ml of blood/5 tubes)
* You will be provided a FitBit wrist band that you will wear
* You will be provided a manual blood glucose monitor you will use for pricking finger and measuring blood sugar level at least 4 times per day.
* You will have a continuous blood glucose sensor and monitor inserted by study staff.
* You will use DayTwo's mobile application where you will input food eaten, activity, measurements, medications, and sleep.
* You will be given a second stool kit and instructions for collection.
* You will be asked to eat 4 breakfasts consisting of either bagels and cream cheese or cereal. These foods will be provided for you. Other than these four breakfasts, we would like you to follow your normal eating habits during the collection week.

Conditions

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Healthy Volunteers

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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DayTwo machine learning algorithm

Personally tailored nutrition insights based on DayTwo machine learning algorithm when combining facets of your postprandial glycemic response to food consumed during collection week and your individual gut microbiome composition

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* 18 years of age or older
* Comprehension of the study objectives and requirements
* Ability to download the DayTwo proprietary mobile application
* Ability to access the DayTwo website for registration and nutrition report

Exclusion Criteria

* Under 18 years of age
* Pre-diagnosed type I or type II diabetes mellitus
* Pregnancy
* Use of antibiotics or fertility treatments within 3 months prior to participation
* Bariatric weight loss surgery
* Chronic Anemia (hemoglobin of 10g per deciliter or less
* Chronic gastrointestinal disorder (IBD, Celiac, etc.)
* Active cancer or chemotherapy or radiation within 2 years prior to participation
* Condition not allowing to follow the dietary recommendation during the study
* 4 or more alcoholic drinks per day on a regular basis or use of recreational drugs
* Chronic medical condition, treatment, or medication that may affect glucose metabolism (HIV diagnosis, use of steroids or immunosuppressive drugs, etc.)
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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DayTwo

INDUSTRY

Sponsor Role collaborator

Mayo Clinic

OTHER

Sponsor Role lead

Responsible Party

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Heidi Nelson

M.D.

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Heidi Nelson, M.D.

Role: PRINCIPAL_INVESTIGATOR

Mayo Clinic

Locations

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Mayo Clinic

Rochester, Minnesota, United States

Site Status

Countries

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United States

References

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Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.

Reference Type BACKGROUND
PMID: 26590418 (View on PubMed)

Mendes-Soares H, Raveh-Sadka T, Azulay S, Ben-Shlomo Y, Cohen Y, Ofek T, Stevens J, Bachrach D, Kashyap P, Segal L, Nelson H. Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals. Am J Clin Nutr. 2019 Jul 1;110(1):63-75. doi: 10.1093/ajcn/nqz028.

Reference Type DERIVED
PMID: 31095300 (View on PubMed)

Other Identifiers

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16-005208

Identifier Type: -

Identifier Source: org_study_id

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