Clinical trial NCT06657001: Enabling Microbiomics- Driven Personalized Nutrition
Official title: Enabling Microbiomics- Driven Personalized Nutrition (EMPOWER)
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Inflammatory Bowel Disease (IBD)
Irritable Bowel Syndrome (IBS)
Celiac Disease
Diarrhea
Constipation
Microbiome
Crohn's Disease
UC - Ulcerative Colitis
Microscopic Colitis
Functional Dyspepsia
Keywords: GI Disorders
Microbiomics
Diet
Microbiome
Nutrition
GI Function
Lead sponsor: Mayo Clinic
Sponsor class: OTHER
First posted: 2024-10-24
Enrollment: 4001
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adults who have consented to the Mayo Clinic Biobank with gastrointestinal disease.

Brief summary: The goal of this observational research study is to determine how diet contributes to various gastrointestinal related conditions.

The main question investigators aim to answer is:

Are host genetics, diet, and microbiome all important determinants of GI disorders, and how their relative contribution varies among individuals and populations.


Eligibility criteria: Inclusion Criteria:

* Age greater than or equal to 18 years
* Gastrointestinal patients with or without genomic sequencing results available for the following cohorts:

  * Inflammatory Bowel Disease (IBD)
  * Irritable Bowel Syndrome (IBS)
  * Crohn's Disease
  * Celiac Disease
  * Diarrhea
  * Constipation
* Healthy age and sex matched controls with or without genomic sequencing results available

  o Control is defined as patients without gastrointestinal disease or no symptoms at time of sampling
* Able to provide written, informed consent
* Participant has a smartphone device with access to internet connection

Exclusion Criteria:

* Patients with cognitive impairment
* Active use of antibiotics at the time of stool collection

Primary outcomes:
measure: To Correlate Differences in Diet and Microbiome with Different GI Symptoms and Disease States
timeFrame: From enrollment through study completion, an average of 1 year.
Secondary outcomes:
Other outcomes:
Locations:
city: Rochester
country: United States
facility: Mayo Clinic
state: Minnesota
Study officials:
affiliation: Mayo Clinic
name: Purna Kashyap, MBBS
role: PRINCIPAL_INVESTIGATOR
References:
citation: Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, Leeming E, Gibson R, Le Roy C, Khatib HA, Francis L, Mazidi M, Mompeo O, Valles-Colomer M, Tett A, Beghini F, Dubois L, Bazzani D, Thomas AM, Mirzayi C, Khleborodova A, Oh S, Hine R, Bonnett C, Capdevila J, Danzanvilliers S, Giordano F, Geistlinger L, Waldron L, Davies R, Hadjigeorgiou G, Wolf J, Ordovas JM, Gardner C, Franks PW, Chan AT, Huttenhower C, Spector TD, Segata N. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021 Feb;27(2):321-332. doi: 10.1038/s41591-020-01183-8. Epub 2021 Jan 11.
citation: 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.
citation: Thaiss CA, Itav S, Rothschild D, Meijer MT, Levy M, Moresi C, Dohnalova L, Braverman S, Rozin S, Malitsky S, Dori-Bachash M, Kuperman Y, Biton I, Gertler A, Harmelin A, Shapiro H, Halpern Z, Aharoni A, Segal E, Elinav E. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature. 2016 Dec 22;540(7634):544-551. doi: 10.1038/nature20796. Epub 2016 Nov 24.
citation: Sudharsan B, Peeples M, Shomali M. Hypoglycemia prediction using machine learning models for patients with type 2 diabetes. J Diabetes Sci Technol. 2015 Jan;9(1):86-90. doi: 10.1177/1932296814554260. Epub 2014 Oct 14.
citation: Fallaize R, Macready AL, Butler LT, Ellis JA, Lovegrove JA. An insight into the public acceptance of nutrigenomic-based personalised nutrition. Nutr Res Rev. 2013 Jun;26(1):39-48. doi: 10.1017/S0954422413000024. Epub 2013 Apr 8.
citation: 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.
citation: Albers DJ, Levine M, Gluckman B, Ginsberg H, Hripcsak G, Mamykina L. Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Comput Biol. 2017 Apr 27;13(4):e1005232. doi: 10.1371/journal.pcbi.1005232. eCollection 2017 Apr.
citation: Murayama S, Numaguchi Y, Robinson AE, Richardson DE. Magnetic resonance imaging of calvarial eosinophilic granuloma. J Comput Tomogr. 1988 Oct;12(4):251-2. doi: 10.1016/0149-936x(88)90078-1.
citation: GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019 May 11;393(10184):1958-1972. doi: 10.1016/S0140-6736(19)30041-8. Epub 2019 Apr 4.