Ground Truths Eric Topol

After finishing her training in neurology at Mayo Clinic, Dr. Svetlana Blitshteyn started a Dysautonomia Clinic in 2009. Little did she know what was in store many years later when Covid hit!
Ground Truths podcasts are on Apple and Spotify. The video interviews are on YouTube

Transcript with audio and external links
Eric Topol (00:07):
Well, hello, it's Eric Topol from Ground Truths, and I have with me a really great authority on dysautonomia and POTS. We will get into what that is for those who aren't following this closely. And it's Svetlana Blitshteyn who is a faculty member at University of Buffalo and a neurologist who long before there was such a thing as Covid was already onto one of the most important pathways of the body, the autonomic nervous system and how it can go off track. So welcome, Svetlana.
Svetlana Blitshteyn (00:40):
Thank you so much, Eric for having me. And I want to say it's a great honor for me to be here and just to be on the list with your other guests. It's remarkable and I'm very grateful and congratulations on being on the TIME100 Health list for influential people in 2024. And I am grateful for everything that you've done. As I mentioned earlier, I'm a big fan of your work before the pandemic and of course with Covid I followed your podcast and posts because you became the best science communicator and I'm very happy to see you being a strong advocate and thank you for everything you've done.
Eric Topol (01:27):
Well, that's so kind to you. And I think talking about getting things going before the pandemic, back in 2011, you published a book with Jodi Epstein Rhum called POTS - Together We Stand: Riding the Waves of Dysautonomia. And you probably didn't have an idea that there would be an epidemic of that more than a decade later, I guess, right?

Svetlana Blitshteyn (01:54):
Yeah, absolutely. Of course, SARS-CoV-2 is a new virus and we can technically say that Long Covid and post Covid complications could be viewed as a new entity. But practically speaking, we know that post-infectious syndromes have been happening for many decades. And so, the most common trigger for POTS happened to be infection, whether it was influenza or mononucleosis or Lyme or enterovirus. We knew this was happening. So I think it didn't take long for me and my colleagues to realize that we're going to be seeing a lot of patients with autonomic dysfunction after Covid.
On the Front Line
Eric Topol (02:40):
Well, one of the things that's important for having you on is you're in the front lines taking care of lots of patients with Long Covid and this postural orthostatic tachycardia syndrome (POTS). And I wonder if you could tell us what it's care for these patients because so many of them are incapacitated. As a cardiologist, I see of course some because of the cardiovascular aspects, but you are dealing with this on a day-to-day basis.
Svetlana Blitshteyn (03:14):
Yeah, absolutely. As early as April 2020 when everything was closed, I got a call from a young doctor in New York City saying that he had Covid and he couldn't recover, he couldn't return to the hospital. And his colleagues and cardiology attendants also had the same symptoms and the symptoms were palpitations, orthostatic intolerance, tachycardia, fatigue. Now, how he knew to contact me is that his sister was my patient with POTS before Covid pandemic. So he kind of figured this looked like my sister, let me check this out. And it didn't take long for me to have a lot of patience from the early wave. And then fairly soon, I think within months I was thinking, we have to write this up because this is important. And to some of us it was not news, but I was sure that to many physicians and public health officials, this would be something new.
Svetlana Blitshteyn (04:18):
So because I'm a busy clinician and don't have a lot of time for publications, I had to recruit a graduate student from McMasters and together we had this paper out, which was the first and la

“We haven't invested this much money into an infrastructure like this really until you go back to the pyramids”—Kate Crawford

Transcript with links to audio and external links. Ground Truths podcasts are on Apple and Spotify. The video interviews are on YouTube
Eric Topol (00:06):
Well, hello, this is Eric Topol with Ground Truths, and I'm really delighted today to welcome Kate Crawford, who we're very lucky to have as an Australian here in the United States. And she's multidimensional, as I've learned, not just a scholar of AI, all the dimensions of AI, but also an artist, a musician. We're going to get into all this today, so welcome Kate.
Kate Crawford (00:31):
Thank you so much, Eric. It's a pleasure to be here.
Eric Topol (00:34):
Well, I knew of your work coming out of the University of Southern California (USC) as a professor there and at Microsoft Research, and I'm only now learning about all these other things that you've been up to including being recognized in TIME 2023 as one of 100 most influential people in AI and it's really fascinating to see all the things that you've been doing. But I guess I'd start off with one of your recent publications in Nature. It was a world view, and it was about generative AI is guzzling water and energy. And in that you wrote about how these large AI systems, which are getting larger seemingly every day are needing as much energy as entire nations and the water consumption is rampant. So maybe we can just start off with that. You wrote a really compelling piece expressing concerns, and obviously this is not just the beginning of all the different aspects you've been tackling with AI.
Exponential Growth, Exponential Concerns
Kate Crawford (01:39):
Well, we're in a really interesting moment. What I've done as a researcher in this space for a very long time now is really introduce a material analysis of artificial intelligence. So we are often told that AI is a very immaterial technology. It's algorithms in the cloud, it's objective mathematics, but in actual fact, it comes with an enormous material infrastructure. And this is something that I took five years to research for my last book, Atlas of AI. It meant going to the mines where lithium and cobalt are being extracted. It meant going into the Amazon fulfillment warehouses to see how humans collaborate with robotic and AI systems. And it also meant looking at the large-scale labs where training data is being gathered and then labeled by crowd workers. And for me, this really changed my thinking. It meant that going from being a professor for 15 years focusing on AI from a very traditional perspective where we write papers, we're sitting in our offices behind desks, that I really had to go and do these journeys, these field trips, to understand that full extractive infrastructure that is needed to run AI at a planetary scale.
(02:58):
So I've been keeping a very close eye on what would change with generative AI and what we've seen particularly in the last two years has been an extraordinary expansion of the three core elements that I really write about in Atlas, so the extraction of data of non-renewable resources, and of course hidden labor. So what we've seen, particularly on the resources side, is a gigantic spike both in terms of energy and water and that's often the story that we don't hear. We're not aware that when we're told about the fact that there gigantic hundred billion computers that are now being developed for the next stage of generative AI that has an enormous energy and water footprint. So I've been researching that along with many others who are now increasingly concerned about how we might think about AI more holistically.
Eric Topol (03:52):
Well, let's go back to your book, which is an extraordinary book, the AI Atlas and how you dissected not just the well power of politics and planetary costs, but that has won awards and it was a few years back, and I wonder so much has changed since then. I mean

If there’s one person you’d want to talk to about immunology, the immune system and Covid, holes in our knowledge base about the complex immune system, and where the field is headed, it would be Professor Iwasaki. And add to that the topic of Women in Science. Here’s our wide-ranging conversation.

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Transcript with many external link and links to the audio, recorded 30 April 2024
Eric Topol (00:06):
Hello, it's Eric Topol and I'm really thrilled to have my friend Akiko Iwasaki from Yale, and before I start talking with Akiko, I just want to mention there aren't too many silver linings of the pandemic, but one for me was getting to know Professor Iwasaki. She is my go-to immunologist. I've learned so much from her over the last four years and she's amazing. She just, as you may know, she was just recently named one of the most influential people in the world by TIME100. [and also recognized this week in TIME 100 Health]. And besides that, she's been elected to the National Academy of Medicine, National Academy of Sciences. She's the president of the American Association of Immunologists and she's a Howard Hughes principal investigator. So Akiko, it's wonderful to have you to join into an extended discussion of things that we have of mutual interest.
Akiko Iwasaki (01:04):
Thank you so much, Eric, for having me. I equally appreciate all of what you do, and I follow your blog and tweets and everything. So thank you Eric.
Eric Topol (01:14):
Well, you are a phenom. I mean just, that's all I can say because I think it was so appropriate that TIME recognize your contributions, not just over the pandemic, but of course throughout your career, a brilliant career in immunology. I thought we'd start out with our topic of great interest on Long Covid. You've done seminal work here and this is an evolving topic obviously. I wonder what your latest thoughts are on the pathogenesis and where things are headed.
Long Covid
Akiko Iwasaki (01:55):
Yeah, so as I have been saying throughout the pandemic, I think that Long Covid is not one disease. It's a collection of multiple diseases and that are sort of ending up in similar sets of symptoms. Obviously, there are over 200 symptoms and not everyone has the same set of symptoms, but what we are going for is trying to understand the disease drivers, so persistent viral infection is one of them. There are overwhelming evidence for that theory now, all the way from autopsy and biopsy studies to looking at peripheral blood RNA signatures as well as circulating spike protein and nucleocapsid proteins that are detected in people with Long Covid. Now whether that persistent virus or remnants of virus is driving the disease itself is unclear still. And that's why trials like the one that we are engaging with Harlan Krumholz on Paxlovid should tell us what percentage of the people are suffering from that type of driver and whether antivirals like Paxlovid might be able to mitigate those. If I may, I'd like to talk about three other hypotheses.
Eric Topol (03:15):
Yeah, I'd love for you to do that.
Akiko Iwasaki (03:18):
Okay, great. So the second hypothesis that we've been working on is autoimmune disease. And so, this is clearly happening in a subset of people, again, it's a heterogeneous disease, but we can actually not only look at reactogenicity of antibodies from people with Long Covid where we can transfer IgG from patients with Long Covid into an animal, a healthy animal, and really measure outcomes of a pathogenesis. So that's a functional evidence that antibodies in some people with Long Covid is really actually causing some of the damages that are occurring in vivo. And the third hypothesis is the reactivation of herpes viruses. So many of us adults have

“Where do I think the next amazing revolution is going to come? … There’s no question that digital biology is going to be it. For the very first time in our history, in human history, biology has the opportunity to be engineering, not science.” —Jensen Huang, NVIDIA CEO

Aviv Regev is one of the leading life scientists of our time. In this conversation, we cover the ongoing revolution in digital biology that has been enabled by new deep knowledge on cells, proteins and genes, and the use of generative A.I .
Transcript with audio and external links
Eric Topol (00:05):
Hello, it's Eric Topol with Ground Truths and with me today I've really got the pleasure of welcoming Aviv Regev, who is the Executive Vice President of Research and Early Development at Genentech, having been 14 years a leader at the Broad Institute and who I view as one of the leading life scientists in the world. So Aviv, thanks so much for joining.
Aviv Regev (00:33):
Thank you for having me and for the very kind introduction.
The Human Cell Atlas
Eric Topol (00:36):
Well, it is no question in my view that is the truth and I wanted to have a chance to visit a few of the principal areas that you have been nurturing over many years. First of all, the Human Cell Atlas (HCA), the 37 trillion cells in our body approximately a little affected by size and gender and whatnot, but you founded the human cell atlas and maybe you can give us a little background on what you were thinking forward thinking of course when you and your colleagues initiated that big, big project.
Aviv Regev (01:18):
Thanks. Co-founded together with my very good friend and colleague, Sarah Teichmann, who was at the Sanger and just moved to Cambridge. I think our community at the time, which was still small at the time, really had the vision that has been playing out in the last several years, which is a huge gratification that if we had a systematic map of the cells of the body, we would be able both to understand biology better as well as to provide insight that would be meaningful in trying to diagnose and to treat disease. The basic idea behind that was that cells are the basic unit of life. They're often the first level at which you understand disease as well as in which you understand health and that in the human body, given the very large number of individual cells, 37.2 trillion give or take, and there are many different characteristics.
(02:16):
Even though biologists have been spending decades and centuries trying to characterize cells, they still had a haphazard view of them and that the advancing technology at the time – it was mostly single cell genomics, it was the beginnings also of spatial genomics – suggested that now there would be a systematic way, like a shared way of doing it across all cells in the human body rather than in ways that were niche and bespoke and as a result didn't unify together. I will also say, and if you go back to our old white paper, you will see some of it that we had this feeling because many of us were computational scientists by training, including both myself and Sarah Teichmann, that having a map like this, an atlas as we call it, a data set of this magnitude and scale, would really allow us to build a model to understand cells. Today, we call them foundational models or foundation models. We knew that machine learning is hungry for these kinds of data and that once you give it to machine learning, you get amazing things in return. We didn't know exactly what those things would be, and that has been playing out in front of our eyes as well in the last couple of years.
Spatial Omics
Eric Topol (03:30):
Well, that gets us to the topic you touched on the second area I wanted to get into, which is extraordinary, which is the spatial omics, which is related to the ability to the single cell sequencing of cells and nuclei and not just RNA and DNA and methylation and chromatin. I mean, this is incredible that you can track the evolution

Professor Doudna was awarded the 2020 Nobel Prize in Chemistry with Professor Emmanuelle Charpentier for their pioneering work in CRISPR genome editing. The first genome editing therapy (Casgevy) was just FDA approved, only a decade after the CRISPR-Cas9 editing system discovery. But It’s just the beginning of a much bigger impact story for medicine and life science.
Ground Truths podcasts are now on Apple and Spotify.
And if you prefer videos, they are posted on YouTube

Transcript with links to audio and relevant external links
Eric Topol (00:06):
This is Eric Topol with Ground Truths, and I'm really excited today to have with me Professor Jennifer Doudna, who heads up the Innovative Genomics Institute (IGI) at UC Berkeley, along with other academic appointments, and as everybody knows, was the Nobel laureate for her extraordinary discovery efforts with CRISPR genome editing. So welcome, Jennifer.
Jennifer Doudna (00:31):
Hello, Eric. Great to be here.
Eric Topol (00:34):
Well, you know we hadn't met before, but I felt like I know you so well because this is one of my favorite books, The Code Breaker. And Walter Isaacson did such a wonderful job to tell your story. What did you think of the book?
My interview with Walter Isaacson on The Code Breaker, a book I highly recommend
Jennifer Doudna (00:48):
I thought Walter did a great job. He's a good storyteller, and as you know from probably from reading it or maybe talking to others about it, he wrote a page turner. He actually really dug into the science and all the different aspects of it that I think created a great tale.
Eric Topol (01:07):
Yeah, I recommended highly. It was my favorite book when it came out a couple years ago, and it is a page turner. In fact, I just want to read one, there's so many quotes out of it, but in the early part of the book, he says, “the invention of CRISPR and the plague of Covid will hasten our transition to the third great revolution of modern times. These revolutions arose from the discovery beginning just over a century ago, of the three fundamental kernels of our existence, the atom, the bit, and the gene.” That kind of tells a big story just in one sentence, but I thought I’d start with the IGI, the institute that you have set up at Berkeley and what its overall goals are.
Jennifer Doudna (01:58):
Right. Well, let's just go back a few years maybe to the origins of this institute and my thinking around it, because in the early days of CRISPR, it was clear that we were really at a moment that was quite unique in the sense that there was a transformative technology. It was going to intersect with lots of other discoveries and technologies. And I work at a public institution and my question to myself was, how can I make sure that this powerful tool is first of all used responsibly and secondly, that it's used in a way that benefits as many people as possible, and it's a tall order, but clearly we needed to have some kind of a structure that would allow people to work together towards those goals. And that was really the mission behind the IGI, which was started as a partnership between UC Berkeley and UCSF and now actually includes UC Davis as well.
The First FDA Approved Genome Editing
Eric Topol (02:57):
I didn't realize that. That's terrific. Well, this is a pretty big time because 10 years or so, I guess starting to be 11 when you got this thing going, now we're starting to see, well, hundreds of patients have been treated and in December the FDA approved the first CRISPR therapy for sickle cell disease, Casgevy. Is that the way you say it?
Jennifer Doudna (03:23):
Casgevy, yeah.
Eric Topol (03:24):
That must have felt pretty good to see if you go from the molecules to the bench all the way now to actually treating diseases and getting approval, which is no easy task.
Jennifer Doudna (03:39):
Well, Eric, for me, I'm a biochemist and somebody who has always worked on the fundamentals of biology, and so it's really been extraor

Note: This podcast is a companion to the Ground Truths newsletter “A Big Week for GLP-1 Drugs”
Eric Topol (00:06):
It is Eric Topol with Ground Truths, and with me today is Dr. Daniel Drucker from the University of Toronto, who is one of the leading endocrinologists in the world, and he along with Joel Habener and Jens Juul Holst from the University of Copenhagen and Denmark, have been credited with numerous prizes of their discovery work of glucagon-like peptide-1 (GLP-1) as we get to know these family of drugs and he's a true pioneer. He's been working on this for decades. So welcome, Daniel.
Daniel Drucker (00:43):
Thank you.
Eric Topol (00:45):
Yeah, it's great to have you and to get the perspective, one of the true pioneers in this field, because to say it's blossom would be an understatement, don't you think?
Daniel Drucker (00:57):
Yeah, it's been a bit of a hectic three years. We had a good quiet 30 plus years of solid science and then it's just exploded over the last few years.
Eric Topol (01:06):
Yeah, back in 30 years ago, did you have any sense that this was coming?
Daniel Drucker (01:14):
Not what we're experiencing today, I think there was a vision for the diabetes story. The first experiments were demonstrating insulin secretion and patents were followed around the use for the treatment of GLP-1 for diabetes. The food intake story was much more gradual and the weight loss story was quite slow. And in fact, as you know, we've had a GLP-1 drug approved for people with obesity since 2014, so it's 10 years since liraglutide was approved, but it didn't really catch the public's attention. The weight loss was good, but it wasn't as spectacular as what we're seeing today. So this really has taken off just over the last three, four years.
Eric Topol (01:58):
Yeah, no, it's actually, I've never seen a drug class like this in my life, Daniel. I mean, I've obviously witnessed the statins, but this one in terms of pleiotropy of having diverse effects, and I want to get to the brain here in just a minute because that seems to be quite a big factor. But one thing just before we get too deep into this, I think you have been great to recognize one of your colleagues who you work with at Harvard, Svetlana Mojsov. And the question I guess is over the years, as you said, there was a real kind of incremental path and I guess was in 1996 when you said, well, this drug likely will inhibit food intake, but then there were gaps of many years since then, as you mentioned about getting into the obesity side. Was that because there wasn't much weight loss in the people with diabetes or was it related to the dose of the drugs that were being tested?
Why Did It Take So Long to Get to Obesity?
Daniel Drucker (03:11):
Well, really both. So the initial doses we tested for type 2 diabetes did not produce a lot of weight loss, maybe 2-3%. And then when we got semaglutide for type 2 diabetes, maybe we were getting 4-5% mean weight loss. And so that was really good and that was much better than we achieved before with any glucose lowering drug. But a lot of credit goes to Novo Nordisk because they looked at the dose for liraglutide and diabetes, which was 1.8 milligrams once daily for people with type 2 diabetes. And they asked a simple question, what if we increase the dose for weight loss? And the answer was, we get better weight loss with 3 milligrams once a day. So they learn that. And when they introduced semaglutide for type 2 diabetes, the doses were 0.5 and 1 milligrams. But in the back of their minds was the same question, what if we increased the dose and they landed on 2.4 milligrams once a week. And that's when we really started to see that the unexpected spectacular weight loss that we're now quite familiar with.
Eric Topol (04:16):
Was there also something too that diabetics don't lose as much weight if you were to have match dose?
Daniel Drucker (04:22):
Yeah, that's a general phenomenon. If one goes from either diet to baria