<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/NCT01727349</url>
  </required_header>
  <id_info>
    <org_study_id>2011-A00686-35</org_study_id>
    <nct_id>NCT01727349</nct_id>
  </id_info>
  <brief_title>Validation of a Predictive Risk Equation for Type 2 Diabetes in Families With Risk</brief_title>
  <acronym>DESCENDANCE</acronym>
  <official_title>Validation of a Predictive Risk Equation for Type 2 Diabetes in Children With Diabetes to Achieve a Predictive Diagnostic Biochip for the Early Detection of Individuals at Risk in Families.</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Centre d'Etudes et de Recherche pour l'Intensification du Traitement du Diabète</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Centre d'Etudes et de Recherche pour l'Intensification du Traitement du Diabète</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      Considering its epidemic-like development worldwide, associated with modifications in&#xD;
      lifestyle, as well as its enormous social and economic weight, the prevention of type II&#xD;
      diabetes is certain to be a central concern of health systems within the developed countries&#xD;
      in the decades to come. However, while simple obesity concerns the entire population, type 2&#xD;
      diabetes affects only one sub-population at high genetic risk. To be effective and realistic&#xD;
      in economic terms, efforts at prevention must be thus targeted towards these subjects at high&#xD;
      risk. The key issue involves identifying such subjects early enough so that a strategy of&#xD;
      effective prevention can be organized in good time.&#xD;
&#xD;
      Until now, efforts have been concentrated on individuals at risk for diabetes readily&#xD;
      identifiable within the general population, typically subjects in the second half of&#xD;
      adulthood, presenting abdominal obesity and mild abnormalities of blood sugar. Preventive&#xD;
      lifestyle and dietary measures are proposed but are constrictive and difficult to maintain&#xD;
      over time, and the results, although they may be significant, remain disappointing, with mere&#xD;
      postponement of an outcome which at this stage appears inevitable. The reason is ascribable&#xD;
      to excessively tardy intervention, when the pathogenic process has already been ongoing for&#xD;
      some ten years and the endocrine function of the pancreas is probably already irreparably&#xD;
      impaired.&#xD;
&#xD;
      The alternative thus is earlier intervention, in childhood, adolescence or early adulthood.&#xD;
      The problem is to identify individuals at high risk of becoming diabetic at a time when they&#xD;
      are presenting no simple clinical or laboratory abnormalities allowing easy diagnosis. The&#xD;
      familial character of type 2 diabetes is now well established, and future diabetic subjects&#xD;
      are themselves above all the children of diabetic subjects. However, the prevalence of the&#xD;
      disease among the descendants of type 2 diabetic subjects is around 20-30% and predictive&#xD;
      tools are needed to combat diabetes in these high-risk families.&#xD;
&#xD;
      We propose to create a risk equation using an algorithm to reliably predict children most&#xD;
      likely to develop diabetes later in life.&#xD;
&#xD;
      The algorithm will include 3 classes of data:&#xD;
&#xD;
        -  The genotype stemming from the genetic characterization of individuals and those their&#xD;
           parents;&#xD;
&#xD;
        -  Environmental data concerning childhood, especially eating habits and physical activity;&#xD;
&#xD;
        -  Data of the mother who was eventually diabetic during pregnancy.&#xD;
&#xD;
      From a methodological standpoint, it would be rather difficult to take blood samples from&#xD;
      children and wait some 50 years to determine whether or not they develop diabetes. To&#xD;
      circumvent this difficulty, we will recruit subjects in families with a history of type II&#xD;
      diabetes:&#xD;
&#xD;
        -  Parents alive, including at least one type 2 diabetic subject&#xD;
&#xD;
        -  Adult children (aged over 35 years), some of whom are already presenting type II&#xD;
           diabetes, and healthy brothers and sisters, who form the control population. Test will&#xD;
           be done to determine whether healthy subjects are really safe from the risk of diabetes&#xD;
           (HbA1c measurement and glucose load test).&#xD;
&#xD;
      The Descendence study will include 500 families at risk involving about 3000 subjects (1000&#xD;
      subjects with diabetes and 2000 healthy subjects). It is expected to answer the following&#xD;
      question: for a child born in such families at risk, what is the probability of developing&#xD;
      diabetes later in life, so that early preventive action may be taken&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">December 14, 2011</start_date>
  <completion_date type="Actual">November 24, 2020</completion_date>
  <primary_completion_date type="Actual">November 24, 2020</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Non-Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <primary_purpose>Prevention</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Measure of risk of developing type 2 diabetes in at-risk families</measure>
    <time_frame>participants will be followed from the moment where they sign consent form and until they have sent back questionnary and done the blood test, an expected average of 4 weeks</time_frame>
    <description>Oral Glucose Tolerance Test (only for health volunteers) HbA1c assay (for type 2 diabetic subject)</description>
  </primary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">1035</enrollment>
  <condition>Type 2 Diabetes</condition>
  <arm_group>
    <arm_group_label>Type 2 diabetic subject</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>Subject with type 2 diabetes</description>
  </arm_group>
  <arm_group>
    <arm_group_label>healthy subject</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>Healthy subjet from family where there is the existence of the disease (type 2 diabetes) in two successive generations</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>HbA1c measurement</intervention_name>
    <arm_group_label>Type 2 diabetic subject</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Oral Glucoce Tolerance Test</intervention_name>
    <description>Oral Glucoce Tolerance Test</description>
    <arm_group_label>healthy subject</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Families at risk for diabetes defined by the existence of the disease in two&#xD;
             successive generations and consists with healthy subject in the two generations.&#xD;
&#xD;
          -  Subjects must be aged over 25 years&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  subject refusing to participate&#xD;
&#xD;
          -  pregnant women&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>25 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <location>
    <facility>
      <name>CHU Sart Tilman Liège</name>
      <address>
        <city>Liege</city>
        <zip>4000</zip>
        <country>Belgium</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Jean Minjoz</name>
      <address>
        <city>Besancon</city>
        <zip>25030</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Avicenne</name>
      <address>
        <city>Bobigny</city>
        <zip>93000</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de Bondy</name>
      <address>
        <city>Bondy</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de BREST</name>
      <address>
        <city>Brest</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de Caen</name>
      <address>
        <city>Caen</city>
        <zip>14000</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CH Sud Francilien</name>
      <address>
        <city>Evry</city>
        <zip>91000</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>University Hospital Grenoble</name>
      <address>
        <city>Grenoble</city>
        <zip>38043</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de Bicetre</name>
      <address>
        <city>Le Kremlin-Bicêtre</city>
        <zip>94270</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHRU Lille</name>
      <address>
        <city>Lille</city>
        <zip>59037</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Marseille Hôpitaux Sud</name>
      <address>
        <city>Marseille</city>
        <zip>13274</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de Nancy</name>
      <address>
        <city>Nancy</city>
        <zip>54500</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de Nantes</name>
      <address>
        <city>Nantes</city>
        <zip>44000</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Bichat</name>
      <address>
        <city>Paris</city>
        <zip>75877</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU de REIMS</name>
      <address>
        <city>Reims</city>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Centre Hospitalier Strasbourg</name>
      <address>
        <city>Strasbourg</city>
        <zip>67000</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>CHU Toulouse</name>
      <address>
        <city>Toulouse</city>
        <zip>31403</zip>
        <country>France</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Belgium</country>
    <country>France</country>
  </location_countries>
  <verification_date>August 2020</verification_date>
  <study_first_submitted>November 8, 2012</study_first_submitted>
  <study_first_submitted_qc>November 12, 2012</study_first_submitted_qc>
  <study_first_posted type="Estimate">November 16, 2012</study_first_posted>
  <last_update_submitted>May 7, 2021</last_update_submitted>
  <last_update_submitted_qc>May 7, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">May 11, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <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>


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/NCT01727349
org study id: 2011-A00686-35
nct id: NCT01727349
lead sponsor: 
has dmc: No
Considering its epidemic-like development worldwide, associated with modifications in
      lifestyle, as well as its enormous social and economic weight, the prevention of type II
      diabetes is certain to be a central concern of health systems within the developed countries
      in the decades to come. However, while simple obesity concerns the entire population, type 2
      diabetes affects only one sub-population at high genetic risk. To be effective and realistic
      in economic terms, efforts at prevention must be thus targeted towards these subjects at high
      risk. The key issue involves identifying such subjects early enough so that a strategy of
      effective prevention can be organized in good time.

      Until now, efforts have been concentrated on individuals at risk for diabetes readily
      identifiable within the general population, typically subjects in the second half of
      adulthood, presenting abdominal obesity and mild abnormalities of blood sugar. Preventive
      lifestyle and dietary measures are proposed but are constrictive and difficult to maintain
      over time, and the results, although they may be significant, remain disappointing, with mere
      postponement of an outcome which at this stage appears inevitable. The reason is ascribable
      to excessively tardy intervention, when the pathogenic process has already been ongoing for
      some ten years and the endocrine function of the pancreas is probably already irreparably
      impaired.

      The alternative thus is earlier intervention, in childhood, adolescence or early adulthood.
      The problem is to identify individuals at high risk of becoming diabetic at a time when they
      are presenting no simple clinical or laboratory abnormalities allowing easy diagnosis. The
      familial character of type 2 diabetes is now well established, and future diabetic subjects
      are themselves above all the children of diabetic subjects. However, the prevalence of the
      disease among the descendants of type 2 diabetic subjects is around 20-30% and predictive
      tools are needed to combat diabetes in these high-risk families.

      We propose to create a risk equation using an algorithm to reliably predict children most
      likely to develop diabetes later in life.

      The algorithm will include 3 classes of data:

        -  The genotype stemming from the genetic characterization of individuals and those their
           parents;

        -  Environmental data concerning childhood, especially eating habits and physical activity;

        -  Data of the mother who was eventually diabetic during pregnancy.

      From a methodological standpoint, it would be rather difficult to take blood samples from
      children and wait some 50 years to determine whether or not they develop diabetes. To
      circumvent this difficulty, we will recruit subjects in families with a history of type II
      diabetes:

        -  Parents alive, including at least one type 2 diabetic subject

        -  Adult children (aged over 35 years), some of whom are already presenting type II
           diabetes, and healthy brothers and sisters, who form the control population. Test will
           be done to determine whether healthy subjects are really safe from the risk of diabetes
           (HbA1c measurement and glucose load test).

      The Descendence study will include 500 families at risk involving about 3000 subjects (1000
      subjects with diabetes and 2000 healthy subjects). It is expected to answer the following
      question: for a child born in such families at risk, what is the probability of developing
      diabetes later in life, so that early preventive action may be taken
allocation: Non-Randomized
intervention model: Parallel Assignment
primary purpose: Prevention
masking: None (Open Label)
measure: Measure of risk of developing type 2 diabetes in at-risk families
time frame: participants will be followed from the moment where they sign consent form and until they have sent back questionnary and done the blood test, an expected average of 4 weeks
description: Oral Glucose Tolerance Test (only for health volunteers) HbA1c assay (for type 2 diabetic subject)
arm group label: Type 2 diabetic subject
arm group type: Other
description: Subject with type 2 diabetes
arm group label: healthy subject
arm group type: Other
description: Healthy subjet from family where there is the existence of the disease (type 2 diabetes) in two successive generations
intervention type: Other
intervention name: HbA1c measurement
arm group label: Type 2 diabetic subject
intervention type: Other
intervention name: Oral Glucoce Tolerance Test
description: Oral Glucoce Tolerance Test
arm group label: healthy subject
criteria: 
gender: All
minimum age: 25 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
facility: 
country: Belgium
country: France
responsible party type: Sponsor
mesh term: Diabetes Mellitus
mesh term: Diabetes Mellitus, Type 2