<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/NCT04417387</url>
  </required_header>
  <id_info>
    <org_study_id>0330/107325</org_study_id>
    <nct_id>NCT04417387</nct_id>
  </id_info>
  <brief_title>The Genetics and Vascular Health Check Study (GENVASC) Aims to Help Determine Whether Gathering Genetic Information Can Improve the Prediction of Risk of Coronary Artery Disease (CAD)</brief_title>
  <acronym>GENVASC</acronym>
  <official_title>Genetics and Vascular Health Check Study (GENVASC)</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>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The Genetics and Vascular Health Check study (GENVASC) is a large study run in conjunction&#xD;
      with Clinical Commissioning Groups and Primary Care practices across Leicester,&#xD;
      Leicestershire and Northamptonshire. The purpose of GENVASC is to help determine whether&#xD;
      gathering genetic information can improve the prediction of risk of Coronary Artery Disease&#xD;
      (CAD).&#xD;
&#xD;
      Currently, coronary risk scores are used to put individuals into low (&lt;10%), medium (10-20%)&#xD;
      and high (&gt;20%) risk groups to help target prevention in individuals at the highest risk of&#xD;
      developing CAD. While this approach has merit, since the majority of individuals fall into&#xD;
      low or medium risk groups, in absolute terms more people develop CAD in these groups than in&#xD;
      the high risk group (despite their proportional risk being lower). Therefore, improving the&#xD;
      accuracy of risk categorisation for CAD has important public health and clinical benefits.&#xD;
&#xD;
      In the last 5 years there has been remarkable progress in identifying genetic variants that&#xD;
      affect risk of CAD, with much of this work being co-led from Leicester. These discoveries&#xD;
      provide a framework for testing whether the addition of genetic information in the form of a&#xD;
      genetic risk score can improve current risk prediction of CAD.&#xD;
&#xD;
      The GENVASC study capitalises on the unique opportunity provided by the NHS Health Check&#xD;
      Programme, which is being widely promoted and specifically targets all individuals aged 40-74&#xD;
      years who are free of cardiovascular disease. Consenting participants taking part in the&#xD;
      health check programme are asked to provide an additional sample of blood to subsequently&#xD;
      determine whether the addition of genetic information would have improved prediction of risk&#xD;
      for coronary disease in individuals at low/medium risk.&#xD;
&#xD;
      To date more than 100 GP surgeries in Leicester and Leicestershire are involved in the study,&#xD;
      and recruitment has recently commenced in Northamptonshire . We aim to recruit and follow-up&#xD;
      over 30,000 participants over the course of the study.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Coronary Artery Disease (CAD) is the commonest cause of premature death and disability in the&#xD;
      UK. Several demographic and lifestyle factors, such as age, gender, smoking, hypertension,&#xD;
      diabetes and dyslipidaemia contribute to risk of CAD. A number of CAD prediction risk&#xD;
      algorithms based on these factors, such as the Framingham and the QRisk2 scores have been&#xD;
      developed and allow classification of individuals into low (&lt; 10%), medium (10-20%) and high&#xD;
      (&gt; 20%) 10-year CAD risk. These risk scores have been used to identify and target primary&#xD;
      prevention measures to those at highest risk. While targeting such individuals is clearly&#xD;
      beneficial, because many more subjects are located in the intermediate or low risk&#xD;
      categories, although their proportional risk is lower, in absolute terms more events actually&#xD;
      occur in these groups. Improving the accuracy of risk categorisation for CAD is therefore a&#xD;
      high public health and clinical priority.&#xD;
&#xD;
      Inheritance plays an important role in the aetiology of CAD. The risk to an individual is 4-8&#xD;
      fold higher if a first degree relative has died prematurely of CAD. The heritability of CAD&#xD;
      is estimated at around 50%. In some, especially more recent risk scores, a &quot;family history&quot;&#xD;
      of CAD is included in the algorithm. However, identifying a positive family history due to&#xD;
      inheritance has significant limitations. Family history based on recall can be notoriously&#xD;
      inaccurate. Algorithms vary in the age cut-off used to define a positive family history.&#xD;
      Furthermore, an individual's family may not be sufficiently large (e.g. no siblings) to&#xD;
      assess familial risk, family members could have died from competing causes (e.g. cancer or&#xD;
      road traffic accidents) before manifesting CAD, or could have developed CAD but due to a&#xD;
      strong lifestyle factor such as heavy smoking. In short, although a family history of CAD can&#xD;
      be useful it is neither a sufficient or accurate surrogate for an individual's genetic risk.&#xD;
&#xD;
      Recently, significant progress has been made in directly dissecting the genetic basis of CAD.&#xD;
      In work led by the Principal Investigator in collaboration with national and international&#xD;
      collaborators, over 30 common genetic variants (carried by between 10-80% of the population)&#xD;
      have been identified that increase risk of CAD by between 5-30% per copy of each variant.&#xD;
      Further variants are likely to emerge in on-going work and especially lower frequency&#xD;
      variants that have more powerful effects. Individually, the genetic variants do not have&#xD;
      sufficient discrimination to individually change risk prediction sufficiently. However, a&#xD;
      Genetic Risk Score (GRS) based on combining the variants (adjusted for their individual&#xD;
      effects) could be more powerful. Indeed, in a recent study we showed that there was a &gt;&#xD;
      3-fold difference in odds ratio for CAD between those subjects in the highest quintile&#xD;
      compared with those in the lowest quintile for a GRS score based on 25 of the initially&#xD;
      identified CAD-associated variants. This is similar to or greater than the strength of&#xD;
      association seen with other established risk factors such as blood pressure and cholesterol.&#xD;
      Addition of further variants as they are discovered to the GRS is likely to further improve&#xD;
      its risk prediction potential. Therefore, recent discoveries on the basis of CAD now provide&#xD;
      a framework for testing whether adding genetic information in the form of a genetic risk&#xD;
      score can improve current risk prediction of CAD.&#xD;
&#xD;
      To test whether a GRS for CAD can improve risk prediction requires assembling a large cohort&#xD;
      of individuals representative of the general population who are: (i) free of overt CAD at&#xD;
      recruitment (ii) who are assessed in a uniform fashion for their CAD risk (iii) and who can&#xD;
      provide blood samples for genetic analysis and (iv) who can be followed up systematically for&#xD;
      Cardiovascular Disease (CVD) outcomes. Such cohorts are rare nationally and internationally.&#xD;
      Biobank UK (at a cost of &gt; £30 million) has been established to address this deficiency&#xD;
      although one of its limitations is that it is not representative of the general population.&#xD;
      One of the key factors that inhibits the assembly of such cohorts solely for research&#xD;
      purposes is the huge initial cost of setting up the infrastructure required to recruit a&#xD;
      sufficiently large sample size. In this context, the recently initiated Department of Health&#xD;
      NHS Vascular Health Check Programme provides a unique opportunity to establish such a cohort&#xD;
      as all individuals in the appropriate age range (40-74 years) free of CVD are being invited&#xD;
      to their general practices for a Vascular Health Check. The large number of subjects that&#xD;
      will be assessed in a systematic manner for cardiovascular risk and who will all have blood&#xD;
      samples routinely collected for lipid profiling provides an ideal scenario to add a research&#xD;
      project that can help determine whether a GRS will be useful in predicting CAD risk. In&#xD;
      short, the Vascular Health Check programme provides a unique opportunity to test, in a major&#xD;
      way and at a marginal cost, whether in the future adding genetic information will improve CAD&#xD;
      risk prediction.&#xD;
&#xD;
      Hypothesis Addition of genetic information in the form of a Genetic Risk Score based on&#xD;
      recent discoveries of genetic variants that associate with CAD will improve risk prediction&#xD;
      of coronary artery disease.&#xD;
&#xD;
      Objectives&#xD;
&#xD;
      The objective of the GENVASC Study is to recruit (with informed consent) subjects who attend&#xD;
      their GP practices to have a Vascular Health Check. Subjects will be asked to consent to:&#xD;
&#xD;
        -  Providing blood samples for research (in most cases taken at the same time as clinical&#xD;
           samples for their vascular check).&#xD;
&#xD;
        -  Allow the GENVASC research database to hold semi-anonymised (no identifiable names) data&#xD;
           on their CVD risk score calculated using conventional algorithms.&#xD;
&#xD;
        -  To allow the database to be periodically updated with any CVD outcomes via GP practice&#xD;
           databases as well as from appropriate national registries.&#xD;
&#xD;
        -  To allow their stored samples be used for DNA and other analyses (again anonymised).&#xD;
&#xD;
      The GENVASC Study will not interfere with the primary clinical imperative of the Vascular&#xD;
      Health Check Programme. It will retrospectively analyse whether addition of a GRS would have&#xD;
      improved CAD risk prediction.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">September 9, 2012</start_date>
  <completion_date type="Anticipated">March 31, 2027</completion_date>
  <primary_completion_date type="Anticipated">March 31, 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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>To retrospectively analyse whether the addition of a genetic risk score would have improved risk prediction of Coronary Artery Disease (CAD) in an otherwise healthy population.</measure>
    <time_frame>10 years</time_frame>
  </primary_outcome>
  <enrollment type="Anticipated">30000</enrollment>
  <condition>Cardiovascular Diseases</condition>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Observational</intervention_name>
  </intervention>
  <biospec_retention>Samples With DNA</biospec_retention>
  <biospec_descr>
    <textblock>
      EDTA sample for DNA extraction and plasma.&#xD;
    </textblock>
  </biospec_descr>
  <eligibility>
    <study_pop>
      <textblock>
        All patients in Leicestershire and Northamptonshire who are eligible for the NHS Health&#xD;
        Check.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Male and female patients who are eligible for an NHS Health Check&#xD;
&#xD;
          -  Aged between 40 and 74 years of age&#xD;
&#xD;
          -  Able to provide informed consent&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  History of preexisting cardiovascular disease&#xD;
&#xD;
          -  History of blood borne infection (such as HIV, Hepatitis B)&#xD;
&#xD;
          -  Unable to provide informed consent&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>40 Years</minimum_age>
    <maximum_age>74 Years</maximum_age>
  </eligibility>
  <overall_official>
    <last_name>Nilesh J Samani, Professor</last_name>
    <role>Study Director</role>
    <affiliation>NIHR Leicester Biomedical Research Centre-Cardiovascular Theme</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Emma P Beeston</last_name>
    <phone>0116 204 4738</phone>
    <email>epb2@leicester.ac.uk</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Christopher Greengrass</last_name>
    <phone>0116 204 4771</phone>
    <email>cg166@le.ac.uk</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Primary Care, LLR and Northamptonshire</name>
      <address>
        <city>Leicester</city>
        <country>United Kingdom</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Emma P Beeston</last_name>
      <phone>0116 204 4738</phone>
      <email>epb2@leicester.ac.uk</email>
    </contact>
    <contact_backup>
      <last_name>Christopher Greengrass</last_name>
      <phone>0116 204 4771</phone>
      <email>cg166@leicester.ac.uk</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>United Kingdom</country>
  </location_countries>
  <verification_date>May 2022</verification_date>
  <study_first_submitted>June 1, 2020</study_first_submitted>
  <study_first_submitted_qc>June 3, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 4, 2020</study_first_posted>
  <last_update_submitted>May 23, 2022</last_update_submitted>
  <last_update_submitted_qc>May 23, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">May 24, 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>Cardiovascular Diseases</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>Anonymised data to be shared with partner organisations</ipd_description>
  </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/NCT04417387
org study id: 0330/107325
nct id: NCT04417387
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The Genetics and Vascular Health Check study (GENVASC) is a large study run in conjunction
      with Clinical Commissioning Groups and Primary Care practices across Leicester,
      Leicestershire and Northamptonshire. The purpose of GENVASC is to help determine whether
      gathering genetic information can improve the prediction of risk of Coronary Artery Disease
      (CAD).

      Currently, coronary risk scores are used to put individuals into low (<10%), medium (10-20%)
      and high (>20%) risk groups to help target prevention in individuals at the highest risk of
      developing CAD. While this approach has merit, since the majority of individuals fall into
      low or medium risk groups, in absolute terms more people develop CAD in these groups than in
      the high risk group (despite their proportional risk being lower). Therefore, improving the
      accuracy of risk categorisation for CAD has important public health and clinical benefits.

      In the last 5 years there has been remarkable progress in identifying genetic variants that
      affect risk of CAD, with much of this work being co-led from Leicester. These discoveries
      provide a framework for testing whether the addition of genetic information in the form of a
      genetic risk score can improve current risk prediction of CAD.

      The GENVASC study capitalises on the unique opportunity provided by the NHS Health Check
      Programme, which is being widely promoted and specifically targets all individuals aged 40-74
      years who are free of cardiovascular disease. Consenting participants taking part in the
      health check programme are asked to provide an additional sample of blood to subsequently
      determine whether the addition of genetic information would have improved prediction of risk
      for coronary disease in individuals at low/medium risk.

      To date more than 100 GP surgeries in Leicester and Leicestershire are involved in the study,
      and recruitment has recently commenced in Northamptonshire . We aim to recruit and follow-up
      over 30,000 participants over the course of the study.
Coronary Artery Disease (CAD) is the commonest cause of premature death and disability in the
      UK. Several demographic and lifestyle factors, such as age, gender, smoking, hypertension,
      diabetes and dyslipidaemia contribute to risk of CAD. A number of CAD prediction risk
      algorithms based on these factors, such as the Framingham and the QRisk2 scores have been
      developed and allow classification of individuals into low (< 10%), medium (10-20%) and high
      (> 20%) 10-year CAD risk. These risk scores have been used to identify and target primary
      prevention measures to those at highest risk. While targeting such individuals is clearly
      beneficial, because many more subjects are located in the intermediate or low risk
      categories, although their proportional risk is lower, in absolute terms more events actually
      occur in these groups. Improving the accuracy of risk categorisation for CAD is therefore a
      high public health and clinical priority.

      Inheritance plays an important role in the aetiology of CAD. The risk to an individual is 4-8
      fold higher if a first degree relative has died prematurely of CAD. The heritability of CAD
      is estimated at around 50%. In some, especially more recent risk scores, a "family history"
      of CAD is included in the algorithm. However, identifying a positive family history due to
      inheritance has significant limitations. Family history based on recall can be notoriously
      inaccurate. Algorithms vary in the age cut-off used to define a positive family history.
      Furthermore, an individual's family may not be sufficiently large (e.g. no siblings) to
      assess familial risk, family members could have died from competing causes (e.g. cancer or
      road traffic accidents) before manifesting CAD, or could have developed CAD but due to a
      strong lifestyle factor such as heavy smoking. In short, although a family history of CAD can
      be useful it is neither a sufficient or accurate surrogate for an individual's genetic risk.

      Recently, significant progress has been made in directly dissecting the genetic basis of CAD.
      In work led by the Principal Investigator in collaboration with national and international
      collaborators, over 30 common genetic variants (carried by between 10-80% of the population)
      have been identified that increase risk of CAD by between 5-30% per copy of each variant.
      Further variants are likely to emerge in on-going work and especially lower frequency
      variants that have more powerful effects. Individually, the genetic variants do not have
      sufficient discrimination to individually change risk prediction sufficiently. However, a
      Genetic Risk Score (GRS) based on combining the variants (adjusted for their individual
      effects) could be more powerful. Indeed, in a recent study we showed that there was a >
      3-fold difference in odds ratio for CAD between those subjects in the highest quintile
      compared with those in the lowest quintile for a GRS score based on 25 of the initially
      identified CAD-associated variants. This is similar to or greater than the strength of
      association seen with other established risk factors such as blood pressure and cholesterol.
      Addition of further variants as they are discovered to the GRS is likely to further improve
      its risk prediction potential. Therefore, recent discoveries on the basis of CAD now provide
      a framework for testing whether adding genetic information in the form of a genetic risk
      score can improve current risk prediction of CAD.

      To test whether a GRS for CAD can improve risk prediction requires assembling a large cohort
      of individuals representative of the general population who are: (i) free of overt CAD at
      recruitment (ii) who are assessed in a uniform fashion for their CAD risk (iii) and who can
      provide blood samples for genetic analysis and (iv) who can be followed up systematically for
      Cardiovascular Disease (CVD) outcomes. Such cohorts are rare nationally and internationally.
      Biobank UK (at a cost of > £30 million) has been established to address this deficiency
      although one of its limitations is that it is not representative of the general population.
      One of the key factors that inhibits the assembly of such cohorts solely for research
      purposes is the huge initial cost of setting up the infrastructure required to recruit a
      sufficiently large sample size. In this context, the recently initiated Department of Health
      NHS Vascular Health Check Programme provides a unique opportunity to establish such a cohort
      as all individuals in the appropriate age range (40-74 years) free of CVD are being invited
      to their general practices for a Vascular Health Check. The large number of subjects that
      will be assessed in a systematic manner for cardiovascular risk and who will all have blood
      samples routinely collected for lipid profiling provides an ideal scenario to add a research
      project that can help determine whether a GRS will be useful in predicting CAD risk. In
      short, the Vascular Health Check programme provides a unique opportunity to test, in a major
      way and at a marginal cost, whether in the future adding genetic information will improve CAD
      risk prediction.

      Hypothesis Addition of genetic information in the form of a Genetic Risk Score based on
      recent discoveries of genetic variants that associate with CAD will improve risk prediction
      of coronary artery disease.

      Objectives

      The objective of the GENVASC Study is to recruit (with informed consent) subjects who attend
      their GP practices to have a Vascular Health Check. Subjects will be asked to consent to:

        -  Providing blood samples for research (in most cases taken at the same time as clinical
           samples for their vascular check).

        -  Allow the GENVASC research database to hold semi-anonymised (no identifiable names) data
           on their CVD risk score calculated using conventional algorithms.

        -  To allow the database to be periodically updated with any CVD outcomes via GP practice
           databases as well as from appropriate national registries.

        -  To allow their stored samples be used for DNA and other analyses (again anonymised).

      The GENVASC Study will not interfere with the primary clinical imperative of the Vascular
      Health Check Programme. It will retrospectively analyse whether addition of a GRS would have
      improved CAD risk prediction.
observational model: Cohort
time perspective: Prospective
measure: To retrospectively analyse whether the addition of a genetic risk score would have improved risk prediction of Coronary Artery Disease (CAD) in an otherwise healthy population.
time frame: 10 years
intervention type: Other
intervention name: Observational
EDTA sample for DNA extraction and plasma.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 40 Years
maximum age: 74 Years
last name: Nilesh J Samani, Professor
role: Study Director
affiliation: NIHR Leicester Biomedical Research Centre-Cardiovascular Theme
last name: Emma P Beeston
phone: 0116 204 4738
email: epb2@leicester.ac.uk
last name: Christopher Greengrass
phone: 0116 204 4771
email: cg166@le.ac.uk
facility: 
status: Recruiting
contact: 
contact backup: 
country: United Kingdom
responsible party type: Sponsor
mesh term: Cardiovascular Diseases
sharing ipd: Yes
ipd description: Anonymised data to be shared with partner organisations