<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/NCT02973412</url>
  </required_header>
  <id_info>
    <org_study_id>0590</org_study_id>
    <nct_id>NCT02973412</nct_id>
  </id_info>
  <brief_title>Chronotype of Patients With Type 2 Diabetes and Effect on Glycaemic Control</brief_title>
  <acronym>CODEC</acronym>
  <official_title>Chronotype of Patients With Type 2 Diabetes and Effect on Glycaemic Control: The CODEC Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Leicester</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University Hospitals, Leicester</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University of Leicester</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      The aim of this study is to explore the associations between chronotype and glycaemic&#xD;
      control, cardiometabolic health and other lifestyle factors.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The incidence and prevalence of diabetes mellitus has now reached over 4 million in the UK.&#xD;
      Type 2 diabetes mellitus (T2DM) accounts for approximately 90% of the UK population with&#xD;
      diabetes and confers significant morbidity and mortality on patients and healthcare systems.&#xD;
      Despite new therapies and management tools to improve quality of life and outcomes for&#xD;
      patients with T2DM, diabetic complications with cardiovascular disease (CVD) remains the&#xD;
      greatest cause of mortality. New paradigms for characterising and treating these patients&#xD;
      could enhance current T2DM management.&#xD;
&#xD;
      Recently, there has been considerable interest in the association between quantity and&#xD;
      quality of sleep and circadian rhythms and the development of cardiometabolic disease&#xD;
      especially metabolic syndrome, T2DM and CVD. A &quot;U&quot;-shaped relationship related to both short&#xD;
      and long sleep duration exists between sleep duration and T2DM, obesity, CVD, hypertension&#xD;
      and stroke. A meta-analysis of nearly 500,000 individuals (~4% T2DM) identified a relative&#xD;
      risk (RR) of 1.14 (95% CI 1.03-1.26) for every additional hour of sleep and RR 1.09 (95% CI&#xD;
      1.04-1.15) with each hour of shorter sleep compared to 7-hours sleep per day for the&#xD;
      development of T2DM. Despite this many individuals do not consider sleep essential for good&#xD;
      health but instead consider it to be rather more of an inconvenience. Subsequently, lifestyle&#xD;
      choices, societal pressures and shift-work render the population chronically sleep deprived&#xD;
      and thus at increased risk of metabolic dysfunction.&#xD;
&#xD;
      Sleep is regulated, in part, by a homeostatic drive and is therefore unavoidable in humans&#xD;
      (without sleep disorders). The circadian system, our internal clock, is also responsible for&#xD;
      the regulation of sleep. Sleep is a multidimensional behaviour (and biological process) where&#xD;
      the investigators need to not only consider duration and quality but timing also. A person's&#xD;
      sleep pattern, in relation to the 24-hour clock, i.e. timing, is individual to them and&#xD;
      referred to as their chronotype. The investigators can quantitatively characterize these&#xD;
      individual differences in daily timing using a number of questionnaire based tools.&#xD;
&#xD;
      Five different chronotypes have been identified using the 'Morningness-Eveningness'&#xD;
      Questionnaire i.e. definite evening type, moderate evening type, intermediate, moderate&#xD;
      morning type and definite morning type.The identification of these different chronotypes,&#xD;
      which describes preferred circadian phases, into, at the two extremes, &quot;morning type&quot; and&#xD;
      &quot;evening type&quot; has led to further research confirming that &quot;evening types&quot; are at greater&#xD;
      risk of cardiometabolic disease. The underlying causes have not been clearly defined but&#xD;
      appear to be related to circadian mal-alignment causing chronic sleep deprivation and leading&#xD;
      to dysregulation of metabolic, immune and hormonal processes that govern energy regulation&#xD;
      and glycaemic control.&#xD;
&#xD;
      Several clock genes have been identified in the control of circadian rhythms including Clock&#xD;
      (Circadian locomotor output cycles protein kaput), Npas2 (Neuronal PAS domain protein2),&#xD;
      Bmal1 (Brain and muscle ARNT-like protein), Per1 (Period), Per2, Per3, Cry1 (Cryptochrome),&#xD;
      Cry2, Rev-Erbs (Reverse erythroblastosis virus) and CkI (Casein kinase). However their role&#xD;
      if any, in prevention and progression in T2DM remains to be elucidated.&#xD;
&#xD;
      The concept of &quot;social jetlag&quot; has been developed to describe the deleterious effects of&#xD;
      chronic sleep deprivation related to weekday occupational obligations on &quot;evening types&quot; and&#xD;
      weekend social demands on &quot;morning types&quot;. For example, a large epidemiological study in&#xD;
      Germany has shown that social jetlag is associated with obesity. Several public health&#xD;
      questions are raised by these associations, not least whether chronotyping of all individuals&#xD;
      should be considered and whether the individual chronotype can be adjusted by sleep hygiene&#xD;
      and training (which requires discipline for maintenance) and/or exogenous treatment with&#xD;
      melatonin.&#xD;
&#xD;
      In this cross-sectional observational study, the investigators therefore propose to&#xD;
      extensively chronotype a sample of patients with T2DM, and to determine the impact of&#xD;
      chronotype on glycaemic control, insulin resistance, biochemical profile, and inflammatory,&#xD;
      adipocytokine and genetic markers using a validated questionnaire and blood tests.&#xD;
&#xD;
      In an optional sub-study we will explore the association between chronotype and objectively&#xD;
      measured physical activity, energy intake and clock genes.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">December 7, 2016</start_date>
  <completion_date type="Anticipated">November 30, 2022</completion_date>
  <primary_completion_date type="Anticipated">November 30, 2022</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Only</observational_model>
    <time_perspective>Cross-Sectional</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Choronotype (As defined by the MEQ chronotype categories and a HBA1C level (mmol/mnol) measured from a blood sample.)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </primary_outcome>
  <secondary_outcome>
    <measure>Mid-Sleep Time (MSF) - on both free and work days</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Glucose (mmol/L)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Insulin (mmol/L)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>C-Peptide (ng/mL (conventional units), or nmol/L (SI))</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Total cholesterol levels (mmol/L)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>HDL-cholesterol levels (mmol/L)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>LDL-cholesterol levels (mmol/L)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Trigylceride levels(mmol/L)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Liver function test (including AST, ALT, ALP and albumin)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Weight (Kg)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Body composition via bioimpedance</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Height (cm)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Blood pressure (mmHg)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>hsCRP (mg/L)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
    <description>Biomarker of inflammation</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Levels of physical activity (Recall Physical Activity Questionnaire,RPAQ)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Duration of diabetes</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Consumption of Pathogen Associated Molecular Patterns (PAMPs)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Sleep duration (self-report)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Physical function (self - report)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Physical performance (Short Physical Performance Battery (SPPB) plus hand grip)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Objective measures of physical activity and sleep duration (GENEActiv)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Energy intake (24-hour dietary recall (DR))</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Clock genes (whole blood sample)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Temporal distribution of calorie intake (determined by 24-hr food recall)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Prevalence of each chronotype category</measure>
    <time_frame>Baseline (1 time point)</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>IL-6 (pg/ml)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
    <description>Biomarker of inflammation</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Leptin (ng/L)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
    <description>Biomarker of inflammation</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Adiponectin (pg/ml)</measure>
    <time_frame>Baseline (1 time point)</time_frame>
    <description>Biomarker of inflammation</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Age of onset</measure>
    <time_frame>Baseline (1 time point)</time_frame>
    <description>Age at which the participant was diagnosed with Type 2 Diabetes</description>
  </secondary_outcome>
  <enrollment type="Anticipated">2247</enrollment>
  <condition>Type2 Diabetes</condition>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      Collection of whole blood for genetic analysis&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        Participants with established (&gt;6months since diagnosis) T2DM, between the age of 18-75&#xD;
        years inclusive, who do not currently have a known sleep disorder (excluding Obstructive&#xD;
        Sleep Apnoea (OSA)).&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Participant is willing and able to give informed consent for participation in the&#xD;
             study&#xD;
&#xD;
          2. Established T2DM (&gt;6months since diagnosis)&#xD;
&#xD;
          3. Male or Female&#xD;
&#xD;
          4. Aged 18-75 years inclusive&#xD;
&#xD;
          5. BMI 23-45kg/m² inclusive&#xD;
&#xD;
          6. No known sleep disorders except OSA&#xD;
&#xD;
          7. HbA1c up to and below 10% (86mmol/mol)&#xD;
&#xD;
          8. On any glucose-lowering therapy or lifestyle modification for management of T2DM&#xD;
&#xD;
          9. Good command of the English language&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Participant is unwilling or unable to give informed consent&#xD;
&#xD;
          2. Anyone without a good command of the English language&#xD;
&#xD;
          3. Anyone &lt;18 years of age and &gt;75 years of age&#xD;
&#xD;
          4. HbA1c above 10% (86mmol/mol)&#xD;
&#xD;
          5. BMI&gt;45 or &lt;23 kg/m²&#xD;
&#xD;
          6. A cannabis user&#xD;
&#xD;
          7. Have a terminal illness&#xD;
&#xD;
          8. A known sleep disorder that is not OSA&#xD;
&#xD;
          9. Taking wakefulness promoting medication i.e. Modafinil as an adjunct to the management&#xD;
             of OSA-related sleepiness&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>75 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Andrew Hall</last_name>
    <role>Principal Investigator</role>
    <affiliation>Sleep Consultant</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Lois J Daniels</last_name>
    <phone>0116258</phone>
    <phone_ext>8070</phone_ext>
    <email>Lois.J.Daniels@uhl-tr.nhs.uk</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Emer Brady, Msc</last_name>
    <phone>0116258</phone>
    <phone_ext>8959</phone_ext>
    <email>Emer.Brady@uhl-tr.nhs.uk</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Leicester Diabetes Centre, Leicester General Hospital</name>
      <address>
        <city>Leicester</city>
        <zip>LE5 4PW</zip>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Lois Daniels</last_name>
      <phone>0116 258 8070</phone>
      <email>Lois.J.Daniels@uhl-tr.nhs.uk</email>
    </contact>
    <contact_backup>
      <last_name>Emer Brady</last_name>
      <phone>0116 258 8959</phone>
      <email>Emer.Brady@uhl-tr.nhs.uk</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>March 2021</verification_date>
  <study_first_submitted>October 26, 2016</study_first_submitted>
  <study_first_submitted_qc>November 21, 2016</study_first_submitted_qc>
  <study_first_posted type="Estimate">November 25, 2016</study_first_posted>
  <last_update_submitted>February 25, 2022</last_update_submitted>
  <last_update_submitted_qc>February 25, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">February 28, 2022</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>
  <patient_data>
    <sharing_ipd>Undecided</sharing_ipd>
  </patient_data>
  <!-- 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/NCT02973412
org study id: 0590
nct id: NCT02973412
lead sponsor: 
collaborator: 
has dmc: No
The aim of this study is to explore the associations between chronotype and glycaemic
      control, cardiometabolic health and other lifestyle factors.
The incidence and prevalence of diabetes mellitus has now reached over 4 million in the UK.
      Type 2 diabetes mellitus (T2DM) accounts for approximately 90% of the UK population with
      diabetes and confers significant morbidity and mortality on patients and healthcare systems.
      Despite new therapies and management tools to improve quality of life and outcomes for
      patients with T2DM, diabetic complications with cardiovascular disease (CVD) remains the
      greatest cause of mortality. New paradigms for characterising and treating these patients
      could enhance current T2DM management.

      Recently, there has been considerable interest in the association between quantity and
      quality of sleep and circadian rhythms and the development of cardiometabolic disease
      especially metabolic syndrome, T2DM and CVD. A "U"-shaped relationship related to both short
      and long sleep duration exists between sleep duration and T2DM, obesity, CVD, hypertension
      and stroke. A meta-analysis of nearly 500,000 individuals (~4% T2DM) identified a relative
      risk (RR) of 1.14 (95% CI 1.03-1.26) for every additional hour of sleep and RR 1.09 (95% CI
      1.04-1.15) with each hour of shorter sleep compared to 7-hours sleep per day for the
      development of T2DM. Despite this many individuals do not consider sleep essential for good
      health but instead consider it to be rather more of an inconvenience. Subsequently, lifestyle
      choices, societal pressures and shift-work render the population chronically sleep deprived
      and thus at increased risk of metabolic dysfunction.

      Sleep is regulated, in part, by a homeostatic drive and is therefore unavoidable in humans
      (without sleep disorders). The circadian system, our internal clock, is also responsible for
      the regulation of sleep. Sleep is a multidimensional behaviour (and biological process) where
      the investigators need to not only consider duration and quality but timing also. A person's
      sleep pattern, in relation to the 24-hour clock, i.e. timing, is individual to them and
      referred to as their chronotype. The investigators can quantitatively characterize these
      individual differences in daily timing using a number of questionnaire based tools.

      Five different chronotypes have been identified using the 'Morningness-Eveningness'
      Questionnaire i.e. definite evening type, moderate evening type, intermediate, moderate
      morning type and definite morning type.The identification of these different chronotypes,
      which describes preferred circadian phases, into, at the two extremes, "morning type" and
      "evening type" has led to further research confirming that "evening types" are at greater
      risk of cardiometabolic disease. The underlying causes have not been clearly defined but
      appear to be related to circadian mal-alignment causing chronic sleep deprivation and leading
      to dysregulation of metabolic, immune and hormonal processes that govern energy regulation
      and glycaemic control.

      Several clock genes have been identified in the control of circadian rhythms including Clock
      (Circadian locomotor output cycles protein kaput), Npas2 (Neuronal PAS domain protein2),
      Bmal1 (Brain and muscle ARNT-like protein), Per1 (Period), Per2, Per3, Cry1 (Cryptochrome),
      Cry2, Rev-Erbs (Reverse erythroblastosis virus) and CkI (Casein kinase). However their role
      if any, in prevention and progression in T2DM remains to be elucidated.

      The concept of "social jetlag" has been developed to describe the deleterious effects of
      chronic sleep deprivation related to weekday occupational obligations on "evening types" and
      weekend social demands on "morning types". For example, a large epidemiological study in
      Germany has shown that social jetlag is associated with obesity. Several public health
      questions are raised by these associations, not least whether chronotyping of all individuals
      should be considered and whether the individual chronotype can be adjusted by sleep hygiene
      and training (which requires discipline for maintenance) and/or exogenous treatment with
      melatonin.

      In this cross-sectional observational study, the investigators therefore propose to
      extensively chronotype a sample of patients with T2DM, and to determine the impact of
      chronotype on glycaemic control, insulin resistance, biochemical profile, and inflammatory,
      adipocytokine and genetic markers using a validated questionnaire and blood tests.

      In an optional sub-study we will explore the association between chronotype and objectively
      measured physical activity, energy intake and clock genes.
observational model: Case-Only
time perspective: Cross-Sectional
measure: Choronotype (As defined by the MEQ chronotype categories and a HBA1C level (mmol/mnol) measured from a blood sample.)
time frame: Baseline (1 time point)
measure: Mid-Sleep Time (MSF) - on both free and work days
time frame: Baseline (1 time point)
measure: Glucose (mmol/L)
time frame: Baseline (1 time point)
measure: Insulin (mmol/L)
time frame: Baseline (1 time point)
measure: C-Peptide (ng/mL (conventional units), or nmol/L (SI))
time frame: Baseline (1 time point)
measure: Total cholesterol levels (mmol/L)
time frame: Baseline (1 time point)
measure: HDL-cholesterol levels (mmol/L)
time frame: Baseline (1 time point)
measure: LDL-cholesterol levels (mmol/L)
time frame: Baseline (1 time point)
measure: Trigylceride levels(mmol/L)
time frame: Baseline (1 time point)
measure: Liver function test (including AST, ALT, ALP and albumin)
time frame: Baseline (1 time point)
measure: Weight (Kg)
time frame: Baseline (1 time point)
measure: Body composition via bioimpedance
time frame: Baseline (1 time point)
measure: Height (cm)
time frame: Baseline (1 time point)
measure: Blood pressure (mmHg)
time frame: Baseline (1 time point)
measure: hsCRP (mg/L)
time frame: Baseline (1 time point)
description: Biomarker of inflammation
measure: Levels of physical activity (Recall Physical Activity Questionnaire,RPAQ)
time frame: Baseline (1 time point)
measure: Duration of diabetes
time frame: Baseline (1 time point)
measure: Consumption of Pathogen Associated Molecular Patterns (PAMPs)
time frame: Baseline (1 time point)
measure: Sleep duration (self-report)
time frame: Baseline (1 time point)
measure: Physical function (self - report)
time frame: Baseline (1 time point)
measure: Physical performance (Short Physical Performance Battery (SPPB) plus hand grip)
time frame: Baseline (1 time point)
measure: Objective measures of physical activity and sleep duration (GENEActiv)
time frame: Baseline (1 time point)
measure: Energy intake (24-hour dietary recall (DR))
time frame: Baseline (1 time point)
measure: Clock genes (whole blood sample)
time frame: Baseline (1 time point)
measure: Temporal distribution of calorie intake (determined by 24-hr food recall)
time frame: Baseline (1 time point)
measure: Prevalence of each chronotype category
time frame: Baseline (1 time point)
measure: IL-6 (pg/ml)
time frame: Baseline (1 time point)
description: Biomarker of inflammation
measure: Leptin (ng/L)
time frame: Baseline (1 time point)
description: Biomarker of inflammation
measure: Adiponectin (pg/ml)
time frame: Baseline (1 time point)
description: Biomarker of inflammation
measure: Age of onset
time frame: Baseline (1 time point)
description: Age at which the participant was diagnosed with Type 2 Diabetes
Collection of whole blood for genetic analysis
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 75 Years
healthy volunteers: No
last name: Andrew Hall
role: Principal Investigator
affiliation: Sleep Consultant
last name: Lois J Daniels
phone: 0116258
phone ext: 8070
email: Lois.J.Daniels@uhl-tr.nhs.uk
last name: Emer Brady, Msc
phone: 0116258
phone ext: 8959
email: Emer.Brady@uhl-tr.nhs.uk
facility: 
status: Recruiting
contact: 
contact backup: 
country: United Kingdom
responsible party type: Sponsor
mesh term: Diabetes Mellitus
mesh term: Diabetes Mellitus, Type 2
sharing ipd: Undecided