Michael Snyder Discusses How Precision Health Measurements Can Impact Diabetes Detection and Management

Transcript:

What work have you been doing with precision health measurements and diabetes, and what have you found?

Snyder: We're very interested in diabetes for a couple of reasons, and 1 is selfish. I am a type 2 diabetic. My genome predicted it. That's what put me on the alert. And then, I caught it early when I saw my glucose going out of control, actually, because I measure myself a lot. So, we think diabetes is complicated. It is complicated. Your metabolic control involves a lot of different organs in your body and things can go off and any of those like the pancreas or your liver metabolism and things like this.

We think big data is going to be very important for this because it's going to help us stratify diabetes. Right now, we talked about type 1 and type 2, but there's probably 50 types of diabetes. We lumped them into these 2 general classes and we already know already, for example, we found someone who had another less common form that's called MODY [maturity-onset diabetes of the young] in our group, and this person had been on the wrong medication for years. We would argue that by using big data to properly subtype diabetes, we could actually treat people properly.

I'm a good example of that. I'm a weirdo diabetic, that is to say, I'm actually insulin sensitive when most are insulin resistant, meaning the cells don't respond to insulin. I actually make insulin just fine to which a type 1 wouldn't do. So, what's wrong with me? Well I don't release insulin from my pancreas and that's a very special kind of drug that I would take and actually, there's some foods I can eat to help promote that as well. Basically, by knowing what was wrong with me, I actually have better control over how to manage that and now I'm a good responder. I take the right drug and I eat certain foods that help better manage my glucose. And that was really powerful in my case because the most popular drug people use is called metformin and I actually don't respond to that. But again, by knowing exactly what's wrong, you can treat people with the right treatment at the right time and get the best results. That's really the goal.

I think this should be done for diabetics worldwide. That is to say, I think we should be following people a little closer and actually stratifying them to give them the best treatment. Also, I argue that there's another area of diabetes that's underappreciated. It's a device called continuous glucose monitoring and what this does is it lets you follow your glucose. You can wear these things for 10 days or 2 weeks depending on the device. They are over the counter in Europe, but in the United States, you need a physician to prescribe them. What's powerful about these continuous glucose monitors is that they they'll follow your food and they're very eye opening.

So, it turns out, especially as people get older, they will become prediabetic or diabetic and you can see exactly what foods spike you. And it's different for different people. Bread will spike some people and rice for other people, so on and so forth. One amusing story is that 80% of people spiked the cornflakes and milk. So, that stuff's like poison for you. I think it's probably worse than smoking. It probably should be outlawed or something. Anyway, the point is that certain foods will spike different people. A lot of the planet is actually prediabetics. It's about 35% and most of them will become diabetic, so we think it's important to actually catch it early. And if you can see what foods are spiking, you can keep away from those and maybe eat a little more of the foods you like that don't spike you and that will much better manage your glucose. I think, again, big data will be powerful for stratifying patients and continuous glucose monitoring will be powerful for better managing people's health. So, there's just a lot of technologies out there that can be used to better manage diabetes.