With the rollout of the 100 000 Genomes Project,1 NHS policymakers are working to maximise the benefits to patients of personalised medicine. In the US, this is also termed ‘precision medicine’. Many GPs consider they already offer ‘personalised medicine’, recognising that ‘one size does not fit all’ and that management requires patients’ medical histories and psychosocial issues are taken into account. While the field of genomics has been developing for many years its clinical value has, to date, largely been in the diagnosis of rare, inherited diseases. However, genomic information is increasingly offering the potential for transformed healthcare, including better prediction of potential disease, earlier and more accurate diagnosis, and prescribing tailored to an individual’s likelihood of seeing benefit.
STRATIFIED MEDICINE
Better interrogation of electronic healthcare records in primary care offers an opportunity to stratify patients into subgroups for targeted management and this is expected to lead to improved efficiency in the NHS.2 An alternative way of describing this new approach is ‘stratified medicine’, and this is already recognised in general practice, as demonstrated by the asthma and COPD clinical care pathways.3
In particular, genomic analysis, taken with the better interpretation of information in electronic health records, has the potential to improve the detection and management of common conditions earlier in life. This will allow interventions that may prolong healthy life to be started earlier than by using the current approach, which is largely dependent on a clinical event occurring before a diagnosis is made and an intervention offered.
HYPERCHOLESTEROLAEMIA
A good example of stratified medicine is the identification of hypercholesterolaemia in an individual, which is recognised to cause a higher lifetime risk of coronary heart disease (CHD). Causes of raised blood cholesterol may be stratified into those due to environmental and/or polygenic factors, with a small proportion of individuals …
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