COMMENTARY

What’s the Future for Genomics in Diabetes Care?

Prof Mark McCarthy MA, MB, BChir, MD, FRCP, FRSB, FMedSci.

Disclosures

March 11, 2019

I'm Professor Mark McCarthy. I'm the Robert Turner Professor of diabetic medicine at the University of Oxford in the United Kingdom. And I'm here attending the Diabetes UK Professional Conference in Liverpool.

Is there a future for genomics in diabetes?

Yes, indeed there is. In fact, there's already a lot of activity in that space in the present for individuals who have rare early onset forms of diabetes that are very strongly genetic, it's already standard clinical practice to obtain a genetic diagnosis so you can be certain of what you're dealing with and change the therapy appropriately.

The big question, which I suppose is what you're asking, is the extent to which that's going to start to apply to the more common forms of diabetes, particularly type 2 diabetes. I'm optimistic that we can now start to see ways in which that information will play out in ways that are beneficial for clinical care of type 2 diabetes, better ways of treating, better ways of preventing this disease in the coming decades.

How are you getting the genetic information?

The way that we've collected the information over the last decade is by collecting samples with diabetes and without, from populations around the world, doing large scale genetic studies to identify the specific points in the 3 billion letters of the genetic code that seemed to be most relevant to influencing differences in genetic predisposition to diabetes. Understanding what some of those do, and starting to put together a portfolio of genetic risk which we can then start to apply at the population level to tell us something about an individual's risk of developing type 2 diabetes, helping us to identify the small subset of late onset diabetes that is in fact type 1 diabetes arising later in life. And now increasingly to use genetic information to give us clues as [to] the differences between people who've got type 2 diabetes in terms of what might be driving their disease.

And of course the hope is that we can turn that information into more tailored, targeted treatments and preventions for individuals.

How do you see that precision medicine working?

If we scroll forward 10 or 20 years, I think we expect that in many countries, it will be standard for people to have some measure of their genetics already performed, pre-computed, sitting in their medical records for all sorts of uses. In the context of diabetes, we think that would be very useful for identifying people who are at particularly high-risk of future diabetes.

We're already quite good at doing that based on family history and BMI and a number of other factors. So there's an open question as to how much genetics will add on top of that, but I'm sure once it's sitting in the medical record, therefore at no real additional cost for all sorts of additional uses, it will contribute to that.

The area where we think it will be particularly useful is where the big challenging questions in diabetes care are more with people who've got diabetes, understanding which ones will progress fastest, which ones are more likely to develop the complications. Which people will respond better to which medications? Which particular interventions, such as bariatric surgery or low-calorie diets, might be most beneficial for particular people?

We don't have very many good tools for identifying that information at the moment. And that's where we hope genetics and other related technologies could be extremely powerful.

Is there evidence that patients will respond well to being told what lies in their future?

That's definitely an open question. And we all know that individuals are not necessarily great at interpreting risk in general. And maybe healthcare professionals are not very good at communicating information about risk. But I do think actually, for many of these common diseases people are already seeing [this] in their standard clinical practice. They get cardiovascular risk scores, something about the risk of developing heart disease over the next 10 years. There are clearly well established risk factors for type 2 diabetes that people may already know whether they're at particularly high risk or not, based on that information.

So putting some additional information in the form of genetics, or it could be in the form of biomarker data that comes out of the research efforts that we and others are engaged in at the moment, which we hope will turn out to have some clinical benefit, adding that in on top of the existing information I don't think represents a sea change in the type of information that we're giving to people.

It hopefully will be slightly more accurate than what we can provide at the moment, slightly more informative. And we'll continue to get more accurate and more informative over time. But I don't see particular problems in communicating that - if we do it on the top of the existing information of a quite similar type that we are already quite used to giving to people.

You presented your work up until now, what are your next steps?

We're really interested in the potential of genetics to give us a better sense of some of the individual processes that are contributing to risk of diabetes. Now we know what some of those are. We know people who are at particularly high-risk of type 2 diabetes tend to be overweight, they tend to have their fat deposited in an unfavourable place. They probably have slightly fewer insulin producing beta cells that maybe don't work quite so well, they may or may not have some islet autoimmunity sitting there on top. We think we can use genetics as a tool for understanding a little bit better what those processes are, and whether there are other processes that we should be thinking about.

And then using that to quantify, for a given individual, the contribution of each of those to their evolving diabetes.

Now, we'd love to be able to add on top of that other information. Certainly genetics is not the only factor in play at determining an individual's risk of diabetes, and the type of diabetes they'll develop. So there's other bits of information, for example, a lot of interest at the moment in the gut microbiome, the organisms in the gut and their contribution. So perhaps that's something that would be useful in the future to be able to quantify for individuals.

And we want to combine that information. Of course, we're just looking at the patient themselves at a given point in time. Is their glucose starting to rise? Do they have any biomarkers that might signify some of these processes going on? And use that totality of information to give a snapshot of where that individual sits, both in terms of a potential journey from normal to diabetes, but also, as I was saying earlier, a sense of what it is that's actually driving the diabetes in their case.

That information, of course, is only useful if we can use it in a way that's beneficial to them. And so then the next challenge is to understand how those differences in the contribution of those processes to somebody's evolving diabetes play out in terms of the risk of complications, the speed of progression of their diabetes, their response to this or that treatment, the response to this or that intervention.

If we can understand that then we can do a much better job of tailoring prevention and treatment in ways that are more effective for a given individual. And that, after all, is what we're trying to do in the context of precision medicine.

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