xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT06002048</url>
    </required_header>
    <id_info>
      <org_study_id>STUDY00016228</org_study_id>
      <secondary_id>3OT2OD032644-01S1</secondary_id>
      <nct_id>NCT06002048</nct_id>
    </id_info>
    <brief_title>AI Ready and Equitable Atlas for Diabetes Insights</brief_title>
    <acronym>AI-READI</acronym>
    <official_title>AI Ready and Equitable Atlas for Diabetes Insights</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>University of Washington</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>National Institutes of Health (NIH)</agency>
        <agency_class>NIH</agency_class>
      </collaborator>
    </sponsors>
    <source>University of Washington</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The study will collect a cross-sectional dataset of 4000 people across the US from diverse&#xD;
        racial/ethnic groups who are either 1) healthy, or 2) belong in one of the three stages of&#xD;
        diabetes severity (pre-diabetes/diet controlled, oral medication and/or&#xD;
        non-insulin-injectable medication controlled, or insulin dependent), forming a total of four&#xD;
        groups of patients. Clinical data (social determinants of health surveys, continuous glucose&#xD;
        monitoring data, biomarkers, genetic data, retinal imaging, cognitive testing, etc.) will be&#xD;
        collected. The purpose of this project is data generation to allow future creation of&#xD;
        artificial intelligence/machine learning (AI/ML) algorithms aimed at defining disease&#xD;
        trajectories and underlying genetic links in different racial/ethnic cohorts. A smaller&#xD;
        subgroup of participants will be invited to come for a follow-up visit in year 4 of the&#xD;
        project (longitudinal arm of the study). Data will be placed in an open-source repository and&#xD;
        samples will be sent to the study sample repository and used for future research.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        The Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI)&#xD;
        project seeks to create a flagship ethically-sourced dataset to enable future generations of&#xD;
        artificial intelligence/machine learning (AI/ML) research to provide critical insights into&#xD;
        type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health. The&#xD;
        ability to understand and affect the course of complex, multi-organ diseases such as T2DM has&#xD;
        been limited by a lack of well-designed, high quality, large, and inclusive multimodal&#xD;
        datasets. The AI-READI team of investigators will aim to collect a cross-sectional dataset of&#xD;
        4,000 people and longitudinal data from 10% of the study cohort across the US. The study&#xD;
        cohort will be balanced for self-reported race/ethnicity, gender, and diabetes disease stage.&#xD;
        Data collection will be specifically designed to permit downstream pseudo-time manifold&#xD;
        analysis, an approach used to predict disease trajectories by collecting and learning from&#xD;
        complex, multimodal data from participants with differing disease severity (normal to&#xD;
        insulin-dependent T2DM). The long-term objective for this project is to develop a&#xD;
        foundational dataset in T2DM, agnostic to existing classification criteria or biases, which&#xD;
        can be used to reconstruct a temporal atlas of T2DM development and reversal towards health&#xD;
        (i.e., salutogenesis). Six cross-disciplinary project modules involving teams located across&#xD;
        eight institutions will work together to develop this flagship dataset. Data will be&#xD;
        optimized for downstream AI/ML research and made publicly available. This project will also&#xD;
        create a roadmap for ethical and equitable research that focuses on the diversity of the&#xD;
        research participants and the workforce involved at all stages of the research process (study&#xD;
        design and data collection, curation, analysis, and sharing and collaboration).&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Enrolling by invitation</overall_status>
    <start_date type="Actual">July 19, 2023</start_date>
    <completion_date type="Anticipated">January 1, 2027</completion_date>
    <primary_completion_date type="Anticipated">January 1, 2027</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Cross-Sectional</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Best-corrected visual acuity</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
      <description>Both photopic and mesopic for right and left eyes individually</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Contrast Sensitivity</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
      <description>Both photopic and mesopic for right and left eyes individually</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Optical coherence tomography (OCT)</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
    </primary_outcome>
    <primary_outcome>
      <measure>fundus photography</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
    </primary_outcome>
    <primary_outcome>
      <measure>fluorescence lifetime imaging ophthalmoscopy (FLIO)</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
    </primary_outcome>
    <primary_outcome>
      <measure>optical coherence tomography angiography (OCTA)</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
    </primary_outcome>
    <primary_outcome>
      <measure>Continuous Glucose Monitoring</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
      <description>Participants wear the Dexcom G6 Pro for 10 days</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Home humidity</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
    </primary_outcome>
    <primary_outcome>
      <measure>Home temperature</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
      <description>measured in Fahrenheit</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Volatile Organic Compounds (VOC) in home</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
    </primary_outcome>
    <primary_outcome>
      <measure>Fine particulate matter that are 2.5 microns or less in diameter (PM2. 5) in home</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
    </primary_outcome>
    <primary_outcome>
      <measure>Montreal Cognitive Assessment (MoCA)</measure>
      <time_frame>July 19, 2023-January 1, 2027</time_frame>
      <description>Testing memory and cognitive function. Scores range from 0-30, with scores above 26 indicating normal functioning.</description>
    </primary_outcome>
    <number_of_groups>4</number_of_groups>
    <enrollment type="Anticipated">4000</enrollment>
    <condition>Type 2 Diabetes</condition>
    <arm_group>
      <arm_group_label>Healthy</arm_group_label>
      <description>Participants who do not have Type 1 or Type 2 Diabetes</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Pre-diabetes/Diet Controlled</arm_group_label>
      <description>Participants with pre-Type 2 Diabetes and those with Type 2 Diabetes whose blood sugar is controlled by diet</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Oral Medication and/or Non-insulin-injectable Medication Controlled</arm_group_label>
      <description>Participants with Type 2 Diabetes whose blood sugar is controlled by oral or injectable medications other than insulin</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Insulin Dependent</arm_group_label>
      <description>Participants with Type 2 Diabetes whose blood sugar is controlled by insulin</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          Adult patients will be recruited into one of four groups: 1) healthy/no diabetes, 2)&#xD;
          pre-diabetes/borderline diabetes/diet-controlled diabetes, 3) oral medication and/or&#xD;
          non-insulin injectable medication controlled type 2 diabetes, or 4) insulin dependent type&#xD;
          2 diabetes. The investigators aim to recruit approximately 1000 patients into each of the&#xD;
          four groups. Patients will be recruited from University of Washington (UW), University of&#xD;
          California at San Diego (UCSD), and University of Alabama at Birmingham (UAB). The study&#xD;
          aims to recruit 1,000 subjects from each of the following racial and ethnic groups: white,&#xD;
          Asian, Hispanic, and Black. Subjects will be age- and sex-matched within and between&#xD;
          groups.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Adults (≥ 40 years old)&#xD;
  &#xD;
            -  Patients with and without type 2 diabetes&#xD;
  &#xD;
            -  Able to provide consent&#xD;
  &#xD;
            -  Must be able to read and speak English&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Adults older than 85 years of age&#xD;
  &#xD;
            -  Pregnancy&#xD;
  &#xD;
            -  Gestational diabetes&#xD;
  &#xD;
            -  Type 1 diabetes&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>40 Years</minimum_age>
      <maximum_age>85 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Aaron Lee</last_name>
      <role>Principal Investigator</role>
      <affiliation>University of Washington</affiliation>
    </overall_official>
    <location>
      <facility>
        <name>University of Alabama, Birmingham</name>
        <address>
          <city>Birmingham</city>
          <state>Alabama</state>
          <zip>35233</zip>
          <country>United States</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>UC San Diego</name>
        <address>
          <city>San Diego</city>
          <state>California</state>
          <zip>92093</zip>
          <country>United States</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>University of Washington</name>
        <address>
          <city>Seattle</city>
          <state>Washington</state>
          <zip>98109</zip>
          <country>United States</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>July 24, 2023</study_first_submitted>
    <study_first_submitted_qc>August 18, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 21, 2023</study_first_posted>
    <last_update_submitted>August 18, 2023</last_update_submitted>
    <last_update_submitted_qc>August 18, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 21, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>University of Washington</investigator_affiliation>
      <investigator_full_name>Aaron Y Lee</investigator_full_name>
      <investigator_title>Associate Professor, Department of Ophthalmology</investigator_title>
    </responsible_party>
    <keyword>Machine Learning</keyword>
    <keyword>Type 2 Diabetes</keyword>
    <keyword>Retinal Imaging</keyword>
    <keyword>Data Sharing</keyword>
    <keyword>Equitable Data Collection</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Diabetes Mellitus</mesh_term>
      <mesh_term>Diabetes Mellitus, Type 2</mesh_term>
    </condition_browse>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT06002048
  org study id: STUDY00016228
  secondary id: 3OT2OD032644-01S1
  nct id: NCT06002048
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The study will collect a cross-sectional dataset of 4000 people across the US from diverse
        racial/ethnic groups who are either 1) healthy, or 2) belong in one of the three stages of
        diabetes severity (pre-diabetes/diet controlled, oral medication and/or
        non-insulin-injectable medication controlled, or insulin dependent), forming a total of four
        groups of patients. Clinical data (social determinants of health surveys, continuous glucose
        monitoring data, biomarkers, genetic data, retinal imaging, cognitive testing, etc.) will be
        collected. The purpose of this project is data generation to allow future creation of
        artificial intelligence/machine learning (AI/ML) algorithms aimed at defining disease
        trajectories and underlying genetic links in different racial/ethnic cohorts. A smaller
        subgroup of participants will be invited to come for a follow-up visit in year 4 of the
        project (longitudinal arm of the study). Data will be placed in an open-source repository and
        samples will be sent to the study sample repository and used for future research.
  The Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI)
        project seeks to create a flagship ethically-sourced dataset to enable future generations of
        artificial intelligence/machine learning (AI/ML) research to provide critical insights into
        type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health. The
        ability to understand and affect the course of complex, multi-organ diseases such as T2DM has
        been limited by a lack of well-designed, high quality, large, and inclusive multimodal
        datasets. The AI-READI team of investigators will aim to collect a cross-sectional dataset of
        4,000 people and longitudinal data from 10% of the study cohort across the US. The study
        cohort will be balanced for self-reported race/ethnicity, gender, and diabetes disease stage.
        Data collection will be specifically designed to permit downstream pseudo-time manifold
        analysis, an approach used to predict disease trajectories by collecting and learning from
        complex, multimodal data from participants with differing disease severity (normal to
        insulin-dependent T2DM). The long-term objective for this project is to develop a
        foundational dataset in T2DM, agnostic to existing classification criteria or biases, which
        can be used to reconstruct a temporal atlas of T2DM development and reversal towards health
        (i.e., salutogenesis). Six cross-disciplinary project modules involving teams located across
        eight institutions will work together to develop this flagship dataset. Data will be
        optimized for downstream AI/ML research and made publicly available. This project will also
        create a roadmap for ethical and equitable research that focuses on the diversity of the
        research participants and the workforce involved at all stages of the research process (study
        design and data collection, curation, analysis, and sharing and collaboration).
  observational model: Cohort
  time perspective: Cross-Sectional
  measure: Best-corrected visual acuity
  time frame: July 19, 2023-January 1, 2027
  description: Both photopic and mesopic for right and left eyes individually
  measure: Contrast Sensitivity
  time frame: July 19, 2023-January 1, 2027
  description: Both photopic and mesopic for right and left eyes individually
  measure: Optical coherence tomography (OCT)
  time frame: July 19, 2023-January 1, 2027
  measure: fundus photography
  time frame: July 19, 2023-January 1, 2027
  measure: fluorescence lifetime imaging ophthalmoscopy (FLIO)
  time frame: July 19, 2023-January 1, 2027
  measure: optical coherence tomography angiography (OCTA)
  time frame: July 19, 2023-January 1, 2027
  measure: Continuous Glucose Monitoring
  time frame: July 19, 2023-January 1, 2027
  description: Participants wear the Dexcom G6 Pro for 10 days
  measure: Home humidity
  time frame: July 19, 2023-January 1, 2027
  measure: Home temperature
  time frame: July 19, 2023-January 1, 2027
  description: measured in Fahrenheit
  measure: Volatile Organic Compounds (VOC) in home
  time frame: July 19, 2023-January 1, 2027
  measure: Fine particulate matter that are 2.5 microns or less in diameter (PM2. 5) in home
  time frame: July 19, 2023-January 1, 2027
  measure: Montreal Cognitive Assessment (MoCA)
  time frame: July 19, 2023-January 1, 2027
  description: Testing memory and cognitive function. Scores range from 0-30, with scores above 26 indicating normal functioning.
  arm group label: Healthy
  description: Participants who do not have Type 1 or Type 2 Diabetes
  arm group label: Pre-diabetes/Diet Controlled
  description: Participants with pre-Type 2 Diabetes and those with Type 2 Diabetes whose blood sugar is controlled by diet
  arm group label: Oral Medication and/or Non-insulin-injectable Medication Controlled
  description: Participants with Type 2 Diabetes whose blood sugar is controlled by oral or injectable medications other than insulin
  arm group label: Insulin Dependent
  description: Participants with Type 2 Diabetes whose blood sugar is controlled by insulin
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 40 Years
  maximum age: 85 Years
  healthy volunteers: No
  last name: Aaron Lee
  role: Principal Investigator
  affiliation: University of Washington
  facility: 
  facility: 
  facility: 
  country: United States
  responsible party type: Principal Investigator
  investigator affiliation: University of Washington
  investigator full name: Aaron Y Lee
  investigator title: Associate Professor, Department of Ophthalmology
  mesh term: Diabetes Mellitus
  mesh term: Diabetes Mellitus, Type 2