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Conventional strategies for prevention of cardiovascular diseases (CVD) have focused on global CVD risk assessment using cigarette smoking, elevated levels of blood pressure or LDL-cholesterol and diabetes and targeting individuals above a certain threshold for risk factor modification. Clinical guidelines advocate estimation of absolute risk (10-year risk or lifetime risk) of CVD using global CVD risk assessment tools (Framingham, European SCORE or Ideal Cardiovascular Health), but the predictive value of such approaches are uncertain. Additional clinical measurements such as grip strength or body mass index (BMI) have been advocated to supplement global CVD risk assessment. Addition of other plasma proteins (apolipoproteins, triglycerides, Lp(a) or C-reactive protein) or genetic risk scores for candidate genes should enhance the predictive value of global CVD risk scores. We propose to use data from the UK Biobank study (UKB) to evaluate conventional and novel risk scores for prediction of CVD.

The UKB is a prospective cohort study of 0.5M adults recruited during 2008-2010 from the United Kingdom, with extensive data collection on lifestyle (e.g. smoking, physical activity) and physical measurements (e.g. blood pressure, BMI and grip strength). Currently, UKB has collected over 40,000 incident CVD events recorded. Genotyping and blood biochemistry will be available on all participants in 2018.


The aims of this DPhil project are to:

  1. Review the published literature on conventional global CVD risk scores and value of other clinical measures (grip strength or BMI), novel plasma proteins (CRP or Lp[a]) or the addition of genetic risk scores for prediction of risk of CVD and sub-types of CVD.
  2. Compare performance (calibration, discrimination, internal and external validity) of conventional global CVD risk scores before and after the addition of novel risk factors for prediction of CVD and sub-types of CVD in age and sex-specific groups.
  3. Evaluate the predictive value of novel clinical measures, plasma proteins (Lp(a), CRP and other lipids) and genetic scores using candidate genes for risk of CVD  in addition to conventional risk factors for prediction of CVD.

This project will involve working within a multi-disciplinary team and the candidate will gain research experience in systematic reviews, study design and analysis of big data using modern statistical approaches. 


By the end of their DPhil, it is expected that the candidate will be able to plan, undertake, interpret, and report their findings in a clear and concise manner. The candidate will have acquired transferable skills such as the writing of project proposals and presenting the research findings at local, national, and international meetings. The candidate will be expected to publish several peer-reviewed papers as the lead author by the end of their DPhil.


The candidate should have a 2.1 or higher degree in a quantitative subject and an MSc in Global Health Science, Epidemiology, Medical Statistics or Mathematics. The candidate should also have a strong interest in cardiovascular disease epidemiology.