About QCBio

The Qatar Cardiovascular Biorepository (QCBio) consists of plasma and DNA of Qatari patients with coronary heart disease (CHD) and age, gender, and ethnicity-matched controls. CHD cases include patients with acute coronary syndromes (myocardial infarction, unstable angina) identified in the cardiac catheterization laboratory/coronary care unit. Controls include blood donors who have no history of CHD.

Funded by

The resource is funded by the Qatar National Research Foundation (QNRF) to enable validation of biomarkers to assess CHD risk, response to therapy, and prognosis. QCBio will also allow genomic and proteomic studies of CHD and response to drug therapy (pharmacogenetics and pharmacoproteomics).


  1. Archive plasma and DNA of 1000 Qatari patients with CHD and 1000 age-, gender-, and ethnicity-matched controls who have no history of CHD.

  2. Ascertain relevant risk factors and comorbid conditions by electronic medical record (EMR)-based electronic phenotyping algorithms that include diagnosis and procedure codes, medication use, and laboratory data.

  3. Include processes to promote use of the biorepository by Qatar investigators by facilitating access to the repository for biomarker research, while maintaining the highest ethical standards with emphases on patient confidentiality and stewardship of the biospecimens.

Overview of the Vascular Disease Biorepository. RLIMS 1⁄4 research laboratory information management systems; CAS 1⁄4 carotid artery stenosis; AAA 1⁄4 abdominal aortic aneurysm; PAD 1⁄4 peripheral arterial disease.

Leveraged Resources

  • The infrastructure of the Cardiovascular Division of the Hamad Medical Corporation (HMC), including the state-of-the-art cardiac catheterization laboratory and coronary care unit (CCU).
  • The electronic medical record (EMR) of the Hamad Hospital that contains demographics, laboratory data (lipid levels, blood sugar, serum creatinine, etc.), and clinical variables.
  • A multidisciplinary investigative team experienced in biorepository science, informatics, and mining of the EMR to annotate biospecimens with relevant clinical covariates.