Large-scale Observational Epidemiology
Reliable assessment of the main determinants of cardiovascular disease
If current trends continue, it is estimated that >20 million people worldwide will die from cardiovascular diseases (mainly heart attacks and stroke) in 2030, and due to population growth about three-quarters of these deaths will occur in low- and middle-income countries (LMICs). The University of Oxford’s cardiovascular epidemiological research priorities are determined both by UK and global public health priorities and hence, its large-scale research projects typically involve wide collaboration between many investigators not just in the UK but also throughout Europe, Africa, Asia and the Americas.
Reliable assessment of the main determinants of cardiovascular disease
Previous observational epidemiological studies have helped to identify a number of causative factors for cardiovascular disease and there is, perhaps, the perception that little more can be learnt from further such studies, particularly for established risk factors (such as smoking, blood pressure, blood lipids and obesity). But, populations in different parts of the world have different exposures, different genetic architectures and different outcomes, hence the effects of such factors can vary enormously from one population to another, and there is still substantial uncertainty as to how important these are in different circumstances, and how their importance is changing with time. Small prospective and case-control studies, involving just a few thousand people with cardiovascular disease, may suffice to identify a risk factor, but (due to their inherent statistical uncertainties) do not generally suffice to assess the age-specific quantitative importance of such factors or any "interactions" with other risk factors.
Development of large-scale studies in diverse populations
Consequently, Oxford research groups have established a range of large prospective studies (involving at least a few hundred thousand people and a few thousand cardiovascular events) and large case-control studies (with thousands of vascular disease "cases") that are helping to reduce the quantitative uncertainties about known vascular risk factors and to identify new causative risk factors. By involving populations that have not previously been studied extensively (e.g. very different developed and developing populations), these studies should allow more reliable assessments of the importance of various causes of vascular disease. One such study is the Kadoorie Biobank Study in China. During 2004-8, 515,000 adults aged 35-74 from 10 regions across China were enrolled, with extensive data collection for each participant by laptop-based questionnaire, physical measurement, and with storage of blood samples (including DNA) for future research. Long-term follow-up of study participants uses established linkages with death registries and health insurance claim databases. Similarly, the Mexico City Prospective Study involves 150,000 middle-aged adults with stored blood, each of whom have now been followed for ~15 years. In that study, obesity and diabetes were extremely common at recruitment, with the excess mortality associated with previously-diagnosed diabetes accounting for more than one third of all deaths between ages 35-74 years. Other studies include two prospective studies in India (total 900K individuals tracked for nearly 10 years), Russia (200K individuals tracked for 10 years) and Cuba (150K individuals tracked for nearly 15 years), while Oxford researchers are also involved with similar studies in South Korea and in the Middle East.
Much of what is discovered in the next few years, and beyond, in these uniquely large studies will be relevant to disease prediction and prevention, and to better understanding of disease mechanisms.
Oxford has also established laboratories that are especially orientated towards the development and use of methods suitable for the particular constraints of large-scale epidemiological studies involving tens, or hundreds, of thousands of blood samples.