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Illness and Big Data

Richard Barker is director of the Center for the Advancement of Sustainable Medical Innovation, Oxford. He states, 'Illness just became another big-data problem'.

Cameron was Alissa Lundfelt's first child, so initially she didn't realize anything was wrong. He would demand milk constantly, but she thought that wasn't unusual. But when his fingers and toes started to go cold, his eyes rolled back in his head and his skin turned grey, he was taken to the emergency room and diagnosed with Type 1 diabetes. At five months old, he was the youngest person ever to receive this diagnosis in his home state of Alaska.

This meant testing his blood sugar every two to four hours and giving frequent insulin injections, since -- in Type 1 diabetics -- the insulin-producing beta cells in the pancreas are attacked by the body's own immune system, stopping the production of insulin that the body needs.

But even with this regimen, Cameron didn't thrive. When he was two years old, Ian Glass, associate professor of paediatrics and medicine at the University of Washington, and visiting Alaska from Seattle, ordered a test. This revealed that Cameron's diabetes was caused by a mutation in the KCNJ11 gene. Lundfelt went online and read about a group of scientists who had discovered that, in this rare form of diabetes, the beta cells produced plenty of insulin, but it couldn't get out of the cells. All Cameron needed was to take a single pill three times a day to restore his glucose levels to normal.

So diabetes isn't just diabetes: it's a cluster of diseases with different causes and different remedies. This story is just a glimpse of a quiet medical revolution: from defining diseases by the symptoms they cause or the part of the body affected, to the underlying molecular mechanism.

Everything from the situation in the womb to the way someone lives their life can result in a set of molecular patterns that appear as symptoms, which often hides more than they reveal
Richard Barker
The tools we have to power this revolution are being added to daily. We are testing cancers that arise in the skin, the colon and the lungs and finding that a proportion of all of them have mutations of the BRAF gene, suggesting they will all respond to the same medication. And often we can work backwards from different responses to a drug to find that superficially similar diseases have different mechanisms -- as in many autoimmune diseases, for example.

It isn't just genetics that influence the existence of a disease, or the form that it takes. Only one of a pair of identical twins may have schizophrenia, for example. Everything from the situation in the womb to the way someone lives their life can result in a set of molecular patterns that appear as symptoms, which often hides more than they reveal.

This redefinition of disease will also set us a fascinating semantic challenge. Your doctor might tell you that your swollen joints are symptomatic of a TLR and IL-1R signalling pathway imbalance: or instead perhaps she will just say you have arthritis type 13-2. Or perhaps your doctor is not aware of this new precision medicine and simply says you've got rheumatoid arthritis. So when you search for "molecular mechanisms of rheumatoid arthritis", you find over 2,270,000 results.

We'll discover a lot about ourselves and our diseases from big data -- assessing the outcomes of different therapies and finding out in retrospect what works best for who. We will then match that against our gene sequences, which may be stored confidentially at birth. If Cameron Lundfelt had been born a few years later, his parents and doctors would perhaps have known before his symptoms had even appeared that he had monogenic diabetes type KCNJ11. And they would have known immediately what to do.


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