Diagnosis of other diseases using NMR-based Metabonomics
Of course, clinical metabonomics has potential applications that are much broader than identifying patients with CAD. Any disease which has a characteristic metabolic profile could, in principle, be diagnosed by application of this technology. Pilot studies have already been completed for hypertension (Analyst, in the press), diabetes, osteoporosis, osteoarthritis and Alzheimer's Disease. The results from these studies will be published shortly and are likely to demonstrate the widespread applicability of this technology.
At present, the studies we have completed have focused on diagnosing the presence of an existing clinical condition. There is no reason, in principle, why there should not be characteristic metabolic signatures that could be detected with metabonomic approaches that occur long before any clinical symptoms are evident. Indeed, such metabolic perturbations might even cause the symptoms in some diseases. David Mosedale in the group has recently completed a substantial study examining the factors which control the metabolic signature in different individuals, and he has demonstrated that components of the metabolic profile are stable over years or even decades, and that such components could, in principle, be used to estimate future risk of disease incidence among people who do not yet have the disease under study. A pilot trial with 400 individuals from the EPIC cohort is now being conducted, as a collaboration between the EPIC team in Cambridge, led by Dr. Nick Wareham, the Inflammation Research & Therapy Laboratory, and the laboratory of Prof. Nicholson at Imperial College.
Clinical Metabonomics is a newly born discipline - not yet even in its infancy. Only pilot data yet exists on the application of this technology to complex, prevalent human diseases. Yet, even at this very early stage, the signs are encouraging. If this technology delivers even a small fraction of its potential in the field of clinical diagnosis, it will revolutionise the way we identify people for treatment in the coming years.