The goal of this observational study is to validate a non-invasive, urine-based diagnostic technology for the detection and differentiation of various gastrointestinal (GI) diseases.
This research study intends to enroll participants across a range of demographics and GI disease states including colorectal cancer, small intestinal bacterial overgrowth (SIBO), Crohn\'s disease, and Celiac disease, collect urine samples and clinical data, and use artificial intelligence and machine learning to build disease-specific models which can identify and differentiate a participants' specific GI disease.
The main questions it aims to answer are:
1. Does the platform identify a disease signal within each disease cohort, compared to normal controls? 2. How well does the test perform (e.g. sensitivity and specificity/false-positive rate)?
Age ≥ 18 years of age at time of enrollment.
Able and willing to provide a one-time urine sample and comply with all study procedures for the study.
Able to understand the study procedures, able to provide consent to participate in the study, and willing to authorize release of relevant protected health information by consenting to a HIPAA medical release form.
Known to be pregnant.
A medical condition which, in the opinion of the Investigator and/or Sponsor, should preclude enrollment in the study.
Participants are recruited in the United States and/or internationally, with the following order of priority and preference: · 1. multi-center recruitment and eligibility screen at approximately 5-10 clinical sites, and/or 2. clinical research organization (CRO) recruitment and eligibility screen, which may include internationally recruited patients and/or 3. decentralized recruitment via social media sites directing to an online eligibility screen and/or 4. Biobank/biorepository sourced frozen samples
Cohort
Prospective
Not listed
Disease signal detection
Disease signal detection quantification within each disease cohort, compared to normal controls.
Test performance measures
Sensitivity and specificity/false-positive rate
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