Depression is a huge problem which often blights the lives of sufferers and their families. Suicide  is now the leading cause of death among young adults worldwide. However, when one starts trying to define what depression actually is and it’s causes, it gets complicated. The label is seemingly a catch-all term for a set of symptoms rather than one illness. These symptoms can, for instance, be triggered by life events – such as trauma or stress – but it’s never been very clear why certain individuals are more vulnerable or why some medications or other treatments such as electrical stimulation of the brain work for some but not others.

Although environmental and social factors play a major role in many cases of depression, genetics are also now known to be crucially important.  Researchers are now looking at our genes to get some answers. However, depression has proven largely resistant to this approach. A recent analysis of the DNA of 300,000 people in the UK, the biggest of its kind, identified almost 80 genes that could be involved in depression. Some particular genetic mutations have been identified as definitely increasing the risk of a disorder.

For instance, a Dutch and Russian research team recently showed that the NKPD1 gene accounted for a 4 per cent rise in the risk of experiencing depressive symptoms. However, what researchers have come to understand is that they are not going to find a smoking gun – or rather that they are – but that there are perhaps hundreds of such guns.

There is another way of looking at it; a group of researchers in Israel have come at the problem from a different direction – one that is much more practically applicable to use in treating depression. They have added the use of one of the other huge research developments of our time – the number crunching wonder that is AI (Artificial Intelligence). By using a large database of information on age, gender, education, origin, occupation, and anything else that they find relevant, along with someone’s DNA , they can predict who will respond positively to particular treatments.

Let that sink in for a moment.

This is a great application of data. When your psychiatrist tries an antidepressant with you it can often takes weeks or months to kick-in. If you don’t respond to the first line treatment offered or if you find you are suffering side effects, the doctor you may have to try other medication –  and that too can take time to take effect. The process is one of trial and error, and, of course, some trials end in failure – which all costs time and money. At present, 65% of patients fail to achieve remission following their first time antidepressant treatment and 30% of patients quit their first-time treatment when ineffective, followed by another 30%, with their second-course of treatment. All in all, only 60% of people get positive effects from antidepressants within two months at present.

By using Artificial Intelligence to test health information on perhaps millions of people to match patterns in DNA against clinical outcomes, clinicians can more effectively target treatment, without needing to pick out which individual or combinations of genes are the blue ones. This will speed up treatment, reduce dropout rates and increase the efficacy of the medication.

The researchers have built their algorithms on data from the STAR*D  study by the National Institute of Health (NIH) in the USA, the largest collection of information on depression in the world. It is already proving a very effective tool in Israel, where thousands of sufferers from depression have had their DNA tested to improve their chances of rapid recovery. As the program develops and more people’s DNA is tested against the information, so it will continue to refine and develop new insights into the genetics of depression – and its treatment.

We are happy to say that the Israeli researchers are now working with Psychiatry-UK to bring this genetic screening technology to the UK. If you would like to find out more, take a look here.

Dr Elin Davies is a Psychiatrist with Psychiatry-UK.