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InTechnology Podcast

What That Means with Camille: Genetics and Personalized Medicine (153)

In this episode of What That Means, Camille gets into genetics with Dr. Michael Snyder, Chairman of Stanford University’s Department of Genetics. The conversation covers how big data is helping personalized medicine, the potential for reverse aging, and the latest advancements in genetics.

To find the transcription of this podcast, scroll to the bottom of the page.

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How Big Data Is Helping Personalized Medicine

Michael sees big data as the most exciting development going on in the fields of genetics and medicine. Through genome sequencing, for example, doctors can better predict genetic risks for disease in patients and treat patients for diseases before symptoms start to appear. Doctors can also make more precise measurements than ever before with the help of big data and artificial intelligence. This all leads to improved personalized medicine, helping doctors create care plans specifically tailored to a patient’s unique needs like prescribing medication known to work better with other people who have similar genetic, lifestyle, and environmental factors.

There are a number of ways for healthcare providers and patients themselves to collect this big data. This includes genome sequencing, microbiome sampling, and wearable devices (smartwatches, glucose monitors, hearing aids, etc.). When it comes to the idea of implantable devices, Michael sees them as overall valuable tools for closely monitoring one’s health.

The Potential for Longevity and Reverse Aging

Is it possible to reverse or at least stop the aging process? Michael believes it’s definitely possible. We already have the technology to assist with longevity on a smaller scale, including senolytics, stem cell research, and mechanical devices like pacemakers. Stem cells seem to be one of the most promising avenues for reverse aging. The main scientific concern on rejuvenating stem cells is whether or not they will grow beyond our control and cause cancer.

There are also many ethical and social concerns about people living longer. Science cannot yet stop mental health decline like dementia, for example, even if people are living well beyond 100 years old. Longer lives additionally put more pressure on the Earth’s resources.

What Are the Latest Advancements in Genetics?

Health monitoring, including for genetics, is making waves in how people think about healthcare. Michael predicts that increased access to health monitoring will shift the view of healthcare to include more preventative treatments. micro sampling is one example already in use to measure thousands of molecules in only a few drops of blood.

The most prominent advancement in genetics is CRISPR and other DNA editing techniques. These are used to alter genes in patients with diseases such as sickle cell anemia and muscular dystrophy. Michael explains that blood diseases are the easiest to use gene editing for right now, but there is still much more for scientists to learn.

Dr. Michael Snyder, Chairman of the Department of Genetics at Stanford University

Michael Snyder genetics personalized medicine health monitoring

Michael Snyder is a Professor, Chairman of the Department of Genetics, and Director of the Center for Genomics and Personalized Medicine at Stanford University. He earned a Ph.D. in Biology from The California Institute of Technology. For over two decades, he has been a leader in the field of genetics, pioneering research at both Yale University and Stanford University.

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[00:00:37]  Camille Morhardt: Hi, and welcome to today’s InTechnology episode of What That Means: Genetics. I’m talking with Michael Snyder, who is chairman of the Department of Genetics at Stanford University. He’s also director of the Center for Genomics and Personalized Medicine at Stanford University. We are going to talk about genetics and its intersection with compute, including artificial intelligence, big data. We’re going to talk about longevity and anti-aging. We’re going to talk about implants and micro samples and ethics when it comes to genetics and what’s going on in that space. Welcome to the podcast, Mike.

[00:01:20]  Michael Snyder:  Oh, it’s great to be here.

[00:01:22]  Camille Morhardt:  So Michael, can you just level set us as we kick off this enormously complex conversation by defining what is genetics?

[00:01:30]  Michael Snyder: Yeah, genetics is from your DNA and it’s really your instruction manual for making you a human being. How that instruction manual plays out counts on environmental factors and lifestyle. The food you eat, exercise, all that will impact your health. So again, genetics is just the blueprint. Everything else actually does play into what makes you you.

[00:01:53]  Camille Morhardt: Right. Makes sense. There’s the blueprint, but then there’s actually the building of the blueprint. Can you tell us what is most exciting to you in this field right now?

[00:02:04]  Michael Snyder: Well, for me it’s big data. We can really change the way healthcare is done. We can now sequence people’s DNA and predict your genetic risk for disease. That was not possible even 10 years ago. We can also make lots and lots of measurements on you to see what’s going on with your health at a level that’s never been possible. So backing up a little bit, I think the healthcare system’s broken. If you think about, it’s very bizarre. We treat people when they’re sick rather than keep them healthy. And in fact, the whole way we practice it is kind of crazy. Show up at an office that looks pretty much the same as it’s looked for the last 40 years. They draw a large amount of blood with a needle that hurts and then make very few measurements from that blood. And then they’ll make a decision about your health based on population-based measurements.

But I think on this whole idea about population averages is really a, well, to be honest, Neanderthal idea. We should really be working on people based on individual measurements, meaning we can see exactly what your health profile looks like and detect changes pre-symptomatically, before any symptoms appear. And that’s an area we’re very excited about. We can bring in lots and lots of data to better manage your health.

[00:03:16]  Camille Morhardt: I remember listening to one of the things that you said in a talk you gave that of the top 10 grossing drugs from pharmaceuticals in the United States, only one in 10 or one in 24 actually work on a patient.

[00:03:31]  Michael Snyder: Yeah. Meaning most of them don’t work. That’s absolutely right. So most people are taking drugs that have no chance of working. So we really need to get much more precise about it. That’s just from the treatment side, but even catching disease, again, this is an area that we work hard on to try and keep people healthy. Catching disease before the symptoms appear, it’s totally doable, but we don’t do it. And how can we do it? Well, we use big data. So we’re actually sequencing people’s genomes to predict disease risk, we’ll make measurements from their blood. There are new technologies out there that you can measure literally thousands and thousands of molecules from people’s blood, meaning all their RNA and proteins and metabolites and lipids. These are important molecules in your blood and we measure them out of urine as well.

We even measure something called the microbiome, which is really important for your health. You may or may not know you have more cells in and on you that aren’t you than are you, meaning a human being is 37 trillion cells, but we have about a hundred trillion microbes living on us, mostly in our gut that digests our food and makes essential vitamins, things like that, and we can measure that. And so we’re in a world where you can now make measurements on people and catch disease before symptoms appear.

It turns out just from this proof of principle we’ve been doing as a research study… We actually have been following 109 people, and in the first three and a half years… We’ve been following them for 10 years, but in the first three and a half years, nearly half, 49, had major health discoveries, meaning we caught some with early lymphoma, two people with pre-cancerous, two people with serious heart issues. And we caught them all pre-symptomatically, meaning we would see shifts in their profiles that said something wasn’t right. And then that indicator basically led the follow-ups that caught the disease early and then basically in some cases we think totally avoided these people from getting any serious complications. Like the lymphoma was caught so early. That person was treated and they’ve been disease free ever since we caught that. It’s like a jigsaw puzzle.

So the way we practice health now, they’ll measure maybe five pieces and we are trying to take more five or 600 pieces out of a thousand piece jigsaw puzzles so we get a much clearer picture of your health and we can catch these things pre-symptomatically. And we’ve gone on to spinoff companies around this to try and scale it to get it out to the world. And then to your earlier point, we also think this is going to be powerful for treating disease. We can see quickly how people are responding to treatments and not wait months and months. We can see by this high resolution measurement, if you will, exactly how they’re responding. So we think these technologies are going to be very, very powerful.

[00:06:14]  Camille Morhardt:  So you’re talking about a lot of different data collection methods. You’re talking about genome sequencing, you’re talking about microbiome sampling, you’re talking about wearables. You probably have more than just the one on your wrist.

[00:06:28]  Michael Snyder: Yeah, I’m wearing four right now and believe it or not, these hearing aids, they are for hearing, but they actually do measurements as well. They’ll measure my interactions throughout the day. They can also measure activity and things like that, physiological measurements. And I’m wearing the continuous glucose monitor, so I use about eight of these devices every day.

[00:06:49]  Camille Morhardt: So are you looking forward to a future where these kinds of devices can exist inside your body as opposed to something that you have to wear or does that kind of a future scare you?

[00:07:00]  Michael Snyder: Well, I look for a future where there’ll be one device instead of me wearing… I use eight or nine of these things every day. I like the idea of them being inside of me, quite frankly. That scares a lot of people because of the privacy side, but I think for me, I like it because I think you’ll get better measurements and I think that’ll be valuable for monitoring health.

[00:07:20]  Camille Morhardt:  Obviously we already have some computers that exist inside of our bodies. We have pacemakers and insulin pumps, but generally speaking, you’re talking much smaller scale. So can you help us understand, would this be like a chip that’s implanted in a specific place in your body or would it be swimming through your bloodstream? How would that work?

[00:07:42]  Michael Snyder:  Well, I guess what I would envision, they’d probably be chipped in a fixed location for the most part so you get very consistent measurement. And what do we want to measure? Well, I think we want to measure standard physiology, heart rate, heart rate variability, your temperature, your blood oxygen. These are things you can measure right now with a smartwatch, but they would probably get more accurate inside of you. But I think measuring your glucose, your cortisol… Cortisol by the way has evolved in stress and things like that. So that would be useful. There’s a lot of other markers that might be specifically useful for people who might have issues like some inflammation markers could be very, very valuable. Imagine people on drugs. We talked before about taking drugs. You can measure people’s levels of drugs, and people metabolize drugs very differently one person to the next. You could actually follow that with implantables that would be inside you.

[00:08:35]  Camille Morhardt:  So you’re talking about combining a whole bunch of different pieces of information to try to identify which drug might match make with which person. And I’m wondering what is the end goal here? Is it just that you would tell somebody in advance, “Okay, this particular drug is not going to work for you, so don’t bother taking it.” Are we going to now design drugs differently on a more personal scale?

[00:09:00]  Michael Snyder: I think ultimately we want to get to both taking the right drug for the right people. Most conditions that people have are complex and they’re probably not one simple solution. Diabetes is a good example. There’s at least five subtypes of Type 2 Diabetes and the different drugs work on the different subtypes. You don’t really have one drug that works on all. So what we need to do first, and I think big data will be powerful for this, is that as we take data on people, we better understand what people will respond to what drugs. We should be able to get predictive markers of that, meaning we can tell, for example, from a continuous glucose monitor which follows people’s glucose levels after something they eat. We believe you’ll be able to tell what Type 2 Diabetes you have, what subtype you have, and what drug you should take.

And I think that’s going to be true for all these what are called complex diseases, whether it’s mental health like depression, bipolar, what have you, or whether it’s metabolic diseases like Type 2 Diabetes or other things, even rheumatoid arthritis, coronary artery disease. There are actually probably again multiple diseases, and if we can get the right drug, we should be able to make profiles and make predictions on them, what drugs they’ll respond to, and then we can monitor the response.

Now, what we’re not very good at in the mental health space, we don’t have good biomarkers for complex disease like for depression and things like that. It’s generally run by surveys and how people feel. And I just think it’s not a good way to measure people, if you will. So I think we need good measurements for many conditions still, but you have to be able to measure it to know how to treat people.

[00:10:39]  Camille Morhardt:  I feel like I just need to play devil’s advocate and ask the cynical question of do we have a mismatch of incentives? Because if you know that, let’s say, you’re the number 10 top grossing drug in the US and it’s only working on one out of every 24 people, then if I’ve suddenly figured out that 23 of those people are better off not taking the drug, have I now just dropped my income level on that? Is it better for me if people don’t know whether or not it’s going to work or am I looking at this incorrectly?

[00:11:12]  Michael Snyder:  Well, when you’re first drug to market, what you want to do is of course give it to everybody. And so from the pharmaceutical standpoint, sure it’s in their best interest to give it to everyone and not know who will respond or not respond. Some of these drugs are really real expensive. So from the insurance company side, from the provider side, you actually do want to know who’s going to respond. And I think we’re going to get better and better biomarkers, whether it be genetics, whether it be some of these molecular markers that we measure out of blood or digital markers from your smartwatch, what have you. I think we’re going to get better at predicting who will respond to what. And I’m a good example of this.

I’m Type 2 diabetic and it was predicted from my genome and I initially got it under control through a lifestyle change, but it came back. I did some things that improved it, but I never got it down to fully under control. So what I wound up doing was taking the obvious frontline drug, metformin, and guess what? I’m a non-responder. It turns out my cells respond to insulin, which is what used normally to control diabetes and I make insulin, I just don’t release it from the pancreas. We used big data to figure that out. And it turns out then at the end there’s a certain drug for that that helps me release insulin from my pancreas. And had I known that from the get-go, I could have gone on that right from the start and been effective instead of spending a year and a half taking drugs that never had a chance. So I think this is how the world’s going to move. We can make the right measurements on people, we can give them the right drug so they get the right response at the right time and be much more effective.

[00:12:48]  Camille Morhardt:  I want to talk about longevity and I would be shocked if somebody hasn’t heard about age reversing or longevity by now. It’s all over the news, it’s clickbait, it’s on every podcast. So a lot of the work that you’re doing seems to me like its goal is early detection or prevention by knowing what you’re up against from a genetic perspective. And I’m wondering your thoughts on actually reverse aging. Do you think that this is possible or will be possible?

[00:13:20]  Michael Snyder:  Yeah, I do. And to back up a little bit, I think most of what we do and what most people do is extending health span, trying to keep people healthy so they’ll live longer, healthier lives. And in an ideal world, you would live a long healthy life and then just die. But I actually do believe you could reverse aging as we learn how to rejuvenate stem cells and replace organ parts. And some of it may be mechanical. We do have pacemakers and hearts and things like that.

So I do think most organs, we will be able to keep going and improve. The one area that’s going to be tricky is the brain, but I actually think even that we should be able to rejuvenate and keep going. So I’m one of the few people who believes we should be able to reverse aging or at least prevent aging. Right now a lot of people say, well, 120 is the limit. That’s about what people can hit and then they’ll die, but I actually believe it could go on forever. That creates all kinds of social implications as you might imagine and we can talk about that if you like.

[00:14:23]  Camille Morhardt:  I do want to get into the social and ethical implications, but before that, I want to understand from you, how do you see the slowing down or reversal of aging actually happening? How does it work?

[00:14:36]  Michael Snyder: We can actually see how people are aging. People age very differently it turns out. Some people are cardio agers, other people are kidney agers. I myself am a metabolic ager. And when you see what’s going on, you can actually then design strategies to try and mitigate that.

We now know a lot of the hallmarks of aging and some of that, I think we do know how to slow down. Anti-inflammatories, antioxidants, things like that. I think if you really want to reverse aging, you’ve probably… There are these things called senolytics. These are drugs that kill your senescent cells, which are associated with aging. As well as rejuvenating, your stem cells can replace organs. A good example is your liver. You may or may not know that your liver cells will divide once a year on average. And so they gradually regenerate your liver and you have actually some of these cells in all parts of your body, including your brain. So they have the potential to actually replace some of these things. They’re just very few of them and they’re kind of quiescent for the most part. That may be one avenue for replacing them.

Other people are working on ways of trying to take cells that aren’t stem cells that are already differentiated and reverse them. There are tricks out there that people are using, genetic tricks and other means to try and actually reverse cells from their, what’s called a more differentiated state. Say a cell that is a liver cell, turn it into one of these stem cells, actually let it keep dividing and replenish your organ. So I think there’s some very fascinating technologies out there. It’s obviously early days and this is… I wouldn’t recommend any of the listeners go out and grab one of these experimental treatments because I think safety has not been worked out at all. And the biggest concern when you’re rejuvenating stem cells and things like that is are you going to cause cancer? Are you going to make cells that grow out of control and that then cause cancer? That’s not good for you either.

So I feel like genetics is really exploding for understanding genetic basis of disease. It’s not all genetics. I think we do need to bring in lifestyle and environmental factors and get personalized predictive models.

I think artificial intelligence is going to be very, very powerful for pulling in all these different kinds of data, your genetics, your environmental exposures, your other aspects, and then build predictive models about what’s likely to cause disease. And we can even do this on a personalized fashion. For example, me, I follow myself a lot, as you could tell with all my smartwatches. So I can actually see what’s going on with my various health measurements and I can make predictive models about when things are growing to go off the rails and when they start happening pretty early. And I think that’s going to be very mainstream. We’ll be able to build personalized models to see, “Oh, oh, you’re heading this way,” and I think this is going to be powerful.

We’ll need all kinds of tricks to make this to happen, but the field is moving very, very quickly and I’m optimistic that… It may be too late for me. I may not be able to live forever and that’s okay, but I don’t think it’s too late for people born today. Everybody says everybody born today is going to live to at least be a hundred. And I think many of us think that they may live forever possibly.

[00:17:56]  Camille Morhardt:  I’m sure we can all brainstorm all kinds of social and ethical implications to living forever or living indefinitely. And I imagine some of them might be similar to the cloning conversations that people have had. But rather than just ask your opinion, I wonder if you can offer some insight into where should humanity begin to even address these questions or establish some standards or regulations or frameworks or thinking around them?

[00:18:29]  Michael Snyder:  Well, that’s a good question. I don’t know that there are any laws or guidelines to stop people from doing that. So I think right now it’s totally open. I think anyone who can get themselves to live forever probably could. I don’t think you would do it at the expense of anyone else.

On the social side, what do we do? People will obviously keep reproducing. We will have more and more people. The plan only holds so much resources, and I think we will need alternatives, meaning going to other planets, other things to be able to expand. I mean, we’re capable of increasing the capacity of this planet up to a point, but there is still is a question of how much we can really. How much can earth hold for the number of people. And that number goes up over time. But I think may be space exploration and using other planets that… You probably know NASA’s already talking about trying to put people on Mars by 2030 and some of the private programs want to do it well in advance of that. And again, it’s not going to be terribly useful. People live long lives but still hit dementia at age 80. That’s not going to be a very productive society. It’s going to create all kinds of issues that we’ll have to deal with. So we really have to extend all aspects of people’s health span, including their mental health.

[00:19:51]  Camille Morhardt:  What kinds of things do you think regular people should know about genetics and some of the advancements in the field right now? Not talking about medical students or people in biology departments, but what should everybody be aware of?

[00:20:04]  Michael Snyder: Well, I think people should be more attuned to their health. I think they get more attuned to their health when problems arise, especially when they hit the 50s or later. I think we can start that process much, much earlier, get people attuned to the health right from the get go. You may or may not know. We can tell when people are getting a viral infection in advance of symptoms. So we can tell, for example, if you’re getting COVID on average three days before symptoms onset from a simple smartwatch. So one of these watches, we have alerting system. Your heart rate jumps up, we can follow that. So we think health monitoring will take that kind of stage, if you will.

The analogy I like to use is a car. A car has lots of sensors, racecars have 400 sensors on them and they relay all this information back to a dashboard that you see and you use that to manage the health of your car. If something goes off, a check engine light goes on. And I think that’s what’s going to happen with human health monitoring. I think we can have these devices that are passively monitoring our health or we’ll do checkup, so to speak from home. We can talk about micro sampling if you want, where you do a little prick of blood, mail it in. You’d get back a detailed report of your metabolic and other health, and then that would say, “Whoa, something might be off.” And we don’t do that today. I think this what’s called longitudinal monitoring, following people over time is very, very powerful for following shifts in people’s health. And that’s because we’re all different. We all have different baselines. We’re trying to understand people’s baselines with this frequent monitoring and then see these shifts. And I think that’s going to be a paradigm change in keeping people healthy and living long, healthy lives.

[00:21:46]  Camille Morhardt:  Can you tell us a little bit more about micro sampling and how that works in personalized medicine? I had read a paper that you were part of about weight loss and taking microbiome samples to understand how people are digesting either carbohydrates or fats.

[00:22:04]  Michael Snyder:  Yeah, what you’re referring to is a study we had out recently that showed everybody’s different. That is to say people respond differently in weight loss. And it goes back to what we talked about earlier. We can predict from some early markers who will respond and who won’t respond to these different kinds of diets. And again, it’s because we’re all different. We have a very cool study we just did with this micro-sampling that you referred to where again, you take small drops of blood, you mail it in, and we do a detailed analysis. We can measure 2,200 molecules in that blood, and some of them are very, very important health molecules.

One thing we discovered is that we had people drink a very simple shake. It’s called Ensure shake. You may have seen it in CVS or your grocery store. And what’ll happen is we had 32 people drink this and everybody responded differently. Some people, their carbs plummeted, others went up. Some people’s inflammatory markers went way down, others went up to the exact same shake. And that makes sense. People when they eat different foods, some people get indigestion, other people don’t. And we have different biologies. Probably a lot of it’s our immune system and our microbiome, we referred to earlier, that actually probably play into this. And so I think we can actually figure this out by first of all making predictions, knowing what molecular markers will predict these things. And the other is by measuring. By this kind of micro-sampling and other means, we can tell how you’re responding to that piece of bread you just ate at a level that’s never been possible. And so I think this is really going to be great for managing people’s diet and lifestyles.

[00:23:42]  Camille Morhardt:  I don’t want to let you go without actually having you define and describe how CRISPR works. I think we’ve all heard of it, but I’d love to have a true geneticist give us that explanation.

[00:23:54]  Michael Snyder:  Sure. CRISPR is a new method, if you will, for editing DNA. And it’s obviously being used a lot in the research setting. It’s being used to change DNA to see how genes work and this sort of thing. And it’s being used to mouse models of disease and it’s just starting to play into humans. And a good example where there’s some trials is something called sickle cell anemia where folks have a mutation in one of their blood genes called hemoglobin. And you can actually make a change there that improves, it keeps their hemoglobin up, if you will, keeps it functional. And so there are now trials to do this where you can make these changes and some of the results look promising.

And so the hope is that you can change these severe genetic diseases with this and it’s going to be easiest to do on blood diseases initially because we have ways of getting your blood cells out, changing it and putting them back in. They’ll go into your bone marrow and they’ll repopulate your blood system, if you will, your immune system and your blood cells.  So I think the bloods kinds of cures with this CRISPR technology for correcting these kinds of, they’re generally errors or so, it’s thought, in your DNA will be powerful. It’ll be harder to do it in non-blood cells, but people are working on that. There are ways of adding genes to people that don’t involve CRISPR for things like muscular dystrophy and some eye diseases, things like that. There’s now actually ways of adding genes to cells in your eye to help correct vision. It’s pretty powerful.

What scares a lot of people is, well, what about behavioral genes and are we going to try and make people smarter and all these sorts of things? And that’s still a ways off. We don’t first of all understand what makes people smart and all that stuff at any reasonable level. But I think for correcting these severe diseases, that’s starting to happen now, especially on the blood diseases.

[00:25:50]  Camille Morhardt:  So once you make a change in the DNA via something like CRISPR, how long does it take to roll out the benefits of that change? For example, if it takes your liver an entire year to replicate itself, would it take an entire year to see that benefit or do some things happen faster?

[00:26:09]  Michael Snyder:  Well, for the blood one, it could all probably be done in a few weeks to a few months, depends on the particular situation. Now most cells in your body don’t divide that quickly and replace very fast. So you can’t really do that kind of manipulation. Liver is one of the few actually where you do get cells dividing pretty slowly about once a year. So that may be possible too. It’s not going to be easy to fix cells and muscle and things like that, but it’s easy to add genes to cells or easier to add genes than it is to try and make those precise changes. But we will be in a world where you can fix a lot of genetic mutations. The ones that are going to be particularly trickier are brain, for example, like fixing Parkinson’s. How do you do that? That’s not going to be easy. And some of the genetic changes there, you can’t just add a new gene and get the desired result. There’s a problem there that’s kind of dominant and it’s hard to get rid of.

[00:27:13]  Camille Morhardt:  Well, super fascinating. So what are we missing? What topic have we failed to cover in this conversation? I don’t want to miss something gigantic in genetics.

[00:27:23]  Michael Snyder:  Well, I think we’re getting better at understanding complex disease, and I hope we’ll get better at predicting who’s at risk for them. Things like ALS, muscular dystrophy, things like that. And for ALS, it’s a complex disease, meaning probably many genes are involved and we can actually go in now and have a good idea about many of those genes that are involved. And we’re I think on the verge of getting predictive models, who’s likely to get the disease and who’s not, which is not well done now. And I think if we can start there and then start understanding then how to prevent that, I think that could be very, very powerful.

[00:28:07]  Camille Morhardt: Michael Snyder, thank you so much for joining today. Again, chair of the Department of Genetics at Stanford University and director of the Center for Genomics and Personalized Medicine. Really appreciate your time.

[00:28:20]  Michael Snyder:  No, it’s been my pleasure.

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