#216 – The value of biosensing for preventive medicine | Michael Dubrovsky & Josh Clemente

Biosensing offers people real-time insight into their health status. Regular insight, rather than one-time results at an annual checkup, helps people assess how lifestyle factors are impacting their health, whether they need to make adjustments, and which adjustments might have the most impact. Michael Dubrovsky and Josh Clemente discuss the value of biosensing and how it has the potential to disrupt the healthcare industry, shifting from a diagnose-and-treat approach to a preventive and actionable approach.

Helpful Links:

Michael Dubrovsky on Twitter: https://twitter.com/MikeDubrovsky

SiPhox Health: https://siphoxhealth.com

SiPhox Health on Instagram: https://www.instagram.com/siphoxhealth/

SiPhox Health on Twitter: https://twitter.com/SiPhoxhealth

5:27 — Why biosensing is important

Biosensing can help change how we approach health, moving from a reactive approach to determining underlying issues before they manifest as symptomatic disease.

I think medicine is historically symptomatic. So if nothing feels wrong, then everything’s fine. But as we’ve started collecting—let’s say over the last hundred years—much more information about biomarkers in general, including blood biomarkers, but other ones as well, we’ve found that you can see things in the data long before they manifest themselves as symptoms. And also things are labile, and you can see this. So you can see incredible things happening.

9:03 — Biosensing is beneficial for turning info into action

Healthcare issues often lack universal solutions, so biosensing can help people tweak their lifestyle to get the best solution for them.

People used to get nutrient deficiencies that are now completely solved just because the food system has been—I mean, in many ways it’s worse, but in some ways it’s better, right? And so people aren’t getting goiters because they have a deficiency of iodine. That doesn’t happen. So you don’t need to monitor that. But on the other hand, more subtle things haven’t been solved, and there is no solution. That’s where you have to monitor and tweak and do all these things. So it’s really at the kind of gray area where we have solutions, but they’re not perfect—that’s where you have benefits for monitoring. If there’s no solution, knowing that something’s going wrong doesn’t really help you. But if there is a solution but it’s kind of subtle—there’s no one-size-fits all solution, for example—that’s where monitoring is really beneficial.

14:39 — Annual blood tests are levels from one snapshot in time

Our biomarkers fluctuate, so the annual blood draw generally does not provide enough data beyond whether someone is or isn’t within the broad range of what’s considered normal at one particular moment.

The yearly blood tests for many markers—because they fluctuate frequently—if you’re getting them once a year, you don’t know if you’re looking at the noise from the fluctuation, like depending on what happened to you that week or what time of day it is that you ended up coming in for the blood draw—that type of thing. Or are you looking at a real change? So usually doctors, what they do is they look at—unless you have somebody that’s a functional medicine doctor or you’re paying very high on longevity care or something—they’re just looking for: Are you way out of range? Have you gone completely off the normal human range and do we need to start basically doing a bunch of tests on you to figure out what’s going on? Or do you need to completely change your life because this is like really high risk? One of the reasons is that the frequency is so low that if they were responding to small changes, they might just be responding to the normal variation. There are variations even related to the seasons.

24:05 — Measuring a few biomarkers can provide a high amount of data

Key biomarkers can help create a better picture of what’s going on in the body and why.

Why would you measure more than one thing? And the answer is right there, where you can start to take a few properly selected molecules and use them to interpret and act as proxies for a whole network of things. I think you explained that well, which is that you don’t have to measure everything in the body to know kind of what’s going on. You can find the high leverage elements like CRP, which can then be used to interpret T-cell activity and cytokine activity. And there are these specific molecules—hormones are a really good example—which are really information dense. If you zoom in on hormones, they are basically the messenger molecules to the distributed network of our cells. And if you can interpret that signal, what specific hormone concentrations are, you can kind of approximate what is going on in a complex system, and then you can even remeasure a byproduct of the cells to see: Did they follow the instructions set? And that can tell you whether you have a central nervous system issue (where you’re not sending the right signals to produce the right hormones) or do you have a cellular issue (where the cells are just not responding to the hormone signal). And that’s a really powerful high amount of data or high-quality amount of data that that is interpreted from a few results.

38:404— Focusing on prevention is helping to disrupt the US healthcare industry

Biosensing can help prevent chronic disease, which is in sharp contrast to the traditional healthcare approach of diagnosing and treating diseases once they’ve already manifested.

I think the chances of doing something super fundamental to fix the existing system are very low. But there are all these other things happening that are trying to circumvent it or just improve little parts of it. And I think that’s working out great. So basically, just telemedicine, gray-area services that are like wellness bleeding into healthcare. I think all of those things seem to be working. People are actually getting results from them. They’re really low cost because they’re self-paid. So I think there’s a lot of progress there and that can all eventually move into being used by the healthcare industry, and it eventually does. So they adopt things that work. So actually it benefits the main healthcare industry over time anyway. It is just hard for it to start there. But I guess to answer your question directly: What do I think is the next thing? I think the big thing is we don’t really know how to cure chronic diseases. So if we did know how to cure them, it would be a moot point, but we don’t know how to cure them. So, probably prevention is the most interesting thing because of the difficulty of curing and managing chronic disease. Management is interesting also—because probably we can do much better at managing them. But I think prevention is where a lot of the value is. And that’s also where there’s a lot of room for innovation because it just hasn’t been touched.

41:47 — Biosensing can empower people to take control of their health

Regularly monitoring biomarkers helps people see how lifestyle choices—like diet, exercise, sleep, and stress management—change those biomarkers over time and whether those choices are impacting their health or if they need to make further adjustments.

The question is, What can be enabled? I think that broadly falls into the categories of issues with hormones, issues with like metabolic health. Weight gain is the symptom within whatever’s going on, like metabolic dysfunction. There are all the inflammatory diseases, and then cardiovascular, which kind of sits at the nexus of in inflammatory and metabolic. So I think if you look at those buckets, that’s where most of the value is, trying to help people manage those things, improve them, prevent disease and so on. And most of that, in terms of technology, actually benefits enormously from just giving the power to the person to do the test. If they can test themselves and have the context around that data, have some kind of support—probably that support doesn’t even need to come from a person (maybe it does in some cases, maybe it doesn’t)—but just having the ground truth data already makes so much difference in figuring out where the person’s at, how they can make an improvement. Is whatever they’re trying to do actually working for them. All these questions—none of that is answered at a yearly visit with the complete metabolic panel.

43:03 — Successfully addressing a health issue involves more than the annual blood draw

Polycystic ovarian syndrome (PCOS) offers an example where continuous or regular biosensing would be more beneficial than getting a metabolic panel once a year.

There are all these circumstances people find themselves in day to day that they’re trying to manage. And today they do not have the abilities. They wait till the yearly checkup to get a test, or a litany of tests, some of which may be adjacent to the problem you’re trying to solve today. If my problem is I want to lose weight or if my problem is I want to have a baby—let’s say—and I go to the doctor and I get my cholesterol panel and I get a CBC and all these other things, and I’m told “you’re healthy” but I’m still having trouble conceiving… The overlap is not what I need it to be. And so I think where you’re going with this is: we need to expand what we’re detecting and the rationale for it. The traditional blood tests were developed for a specific reason, and they have some information value, but there are all these other real-world examples that understanding the health state and the underlying molecules that are driving it can be really powerful. But I think what you’re describing there is if you were to give the person the technology that they could measure this themselves—and more frequently so they can see changes specifically—I would imagine changes due to an intervention, which, in the fertility case, maybe that’s managing PCOS through different diet. PCOS is polycystic ovarian syndrome. It’s very closely related to insulin resistance and perhaps it’s trying different dietary approaches to see if, for example, insulin relative to glucose starts to adjust.

46:00 — Biosensing can help elucidate valuable associations

When people can see the connections between their symptoms and the underlying causes, they can become evangelists of the solution.

I can very clearly in my own data see when I screw up my circadian rhythm or other things of that flavor. You can see the connection between circadian rhythm and inflammation, circadian rhythm and testosterone, and things like that. So that’s not true for everybody. So when we actually do correlations across a lot of blood tests—let’s say inflammation is connected to sleep for 50% of people. So for the other 50%, it’s actually not connected. So you can improve your sleep, but your inflammation will not drop. And so the cause is just something else. And every engineer that’s built anything has had to do a hundred rounds of debugging. And so if you’re not able to measure anything, you’re not able to debug what’s going on. It is just very unlikely that, you’re going to try one of these 10 things and it’s going to fix it.

49:58 — Why the future of medicine will focus on the individual

The traditional healthcare approach is to scale solutions to the general public, but those solutions aren’t always applicable to every individual—because of so much individual variation.

The next generation that you describe is where the person is in the driver’s seat, so to speak. We’ve sort of shifted the locus of control—about what we’re measuring and how we’re measuring and when—to the individual whose health is at stake and they’re measuring things that relate to their quality of life and their health, healthspan, and the avoidance of conditions they don’t want or quality of life they don’t want. That is a value statement to that individual. It’s not a general population, sort of health mission, which is what the healthcare system is currently thinking about. They’re thinking about: How do we lower the rates of stroke acutely? If you look at daylight savings examples, every single year, when the clocks shift backwards and you lose an hour of sleep, heart attacks and strokes go up like clockwork. That’s really interesting. And the opposite happens when we shift back in the other direction and you gain an hour of sleep. So I think what we’re trying to do is sort of extricate ourselves from a situation where we are only thinking at the scale of everyone and shift to a condition where I can only think about me and you can only think about you.

52:14 — Biosensing data from individuals may lead to better solutions for the public

Biosensing data from millions of people adds to research that can then be used to solve global health issues.

At some point, there’s enough data on enough people that you don’t need to do all of these experiments on yourself. So basically at some points, you should be able to pool data from millions of people and actually start getting real insights that are forward compatible. So you bring in the millionth-and-one person, you make a measurement on them, and you just tell them, “This is going to work for you.” And that’s already true in some cases, right? They’re trying to do this for cancer therapy. Where it really matters, the medical system is pretty good. So they’ll sequence your cancer, tell you this immunotherapy is not going to work on you. That type of stuff. So I think there is an optimistic case for, over time, this transitioning from “you’re on your own, you have to fix this” to it being much more solved. Then something where you can leverage the experience of millions of other people. But for that to happen, this all has to be built up. But I do think there’s like an optimistic world where that happens.

1:07:06 — Biosensing can increase the value of healthcare

The healthcare system as it functions now, doesn’t always translate to value for the patient. Biosensing, however, can help up the value for the individual by offering insight on how to improve quality of life.

I ostensibly get value from my healthcare visits. My doctor’s the one that delivers it to me, and somebody else—like an insurance provider or some unknown third party—is responsible for paying for it. And so what I consider to be valuable and what the insurance company considers to be valuable are very different. And what I’m willing to pay and what they’re paying and the value I’m getting are totally disconnected. And so you have this broken three-party system, where what I end up with is an experience not intended to make me happy; it’s intended to make the insurance company pay. And I think going back to the realization that you don’t have to force people to participate in healthcare. People want to participate in healthcare, but they want to get value from it. And so shifting to a model where it’s a really direct consumerized version of this sort of thing where people just buy products that are relevant to their quality-of-life concerns and they then have to get value from them. Otherwise you don’t get a return customer and your business suffers. That’s what we need in healthcare, in my opinion. I won’t say all of healthcare—that’s what we need in preventative and sort of health optimization.

Michael Dubrovsky (00:00:06):
People used to get nutrient deficiencies that are now completely solved just because the food system has been … In many ways, it’s worse, but in some ways, it’s better. Right? So, people aren’t getting goiters because they have a deficiency of iodine. That doesn’t happen. So, you don’t need to monitor that. But on the other hand, more subtle things that haven’t been solved and there is no solution, that’s where you have to monitor and tweak and do all these things. Right? So, it’s really the gray area where we have solutions, but they’re not perfect, that’s where you have benefits for monitoring. If there’s no solution, knowing that something’s going wrong doesn’t really help you. But if there is a solution, but it’s kind of subtle, like there’s no one size fits all solution, for example, that’s where monitoring is really beneficial.
Ben Grynol (00:00:52):
I’m Ben Grynol, part of the early startup team here at Levels. We’re building tech that helps people to understand their metabolic health, and along the way, we have conversations with thought leaders about research-backed information so you can take your health into your own hands. This is A Whole New Level.
(00:01:21):
Glucose is not a panacea. It’s something that Josh Clemente, co-founder of Levels, often says as well. Well, he sat down with Michael Dubrovsky, co-founder of SiPhox Health. SiPhox Health is another health tech company in the space. They recently went through YC, or Y Combinator, in the summer of ’20. SiPhox offers an at-home product where people can measure 17 different biomarkers at a much lower cost than competitors. So, we know glucose is a very important molecule when it comes to measuring and monitoring metabolic health. But in isolation, it is only one molecule. Our bodies are in constant orchestration, oscillation with all these biomarkers doing different things at different times. Some move in parallel. Some move in sequence. Others have very inverse patterns in the way that they move, which might be good, or maybe it’s something that people want to look into and learn more about why the implications of that might not be so good.
(00:02:20):
So, Michael and Josh sat down and discussed this idea of why we want to monitor all these different biomarkers. What’s the point of biosensing? Which molecules should we be monitoring? What are the implications of diet, sleep, exercise, stress, environmental factors, all these things that can make biomarkers move in constant fluctuation in our bodies? Bodies are extremely complex and they’re very much this closed system that we have very little insight about in this day and age. Seems absurd, but we’ve got more insight about complex systems like airplanes and rockets than we do with the data that is in fluctuation in our own bodies. Anyway, no need to wait. Here’s Josh and Michael.
Josh Clemente (00:03:03):
I really want to first introduce the problem, which is one that I care a lot about, I know you care a lot about it as well, and the question is what is so important about biosensing is the first part of it, and the second one is what should we be measuring and why is the additional element of multiple molecules of monitoring so interesting? This is going to lead us into a question of how we get to a future technology. But first, I think the question is why should we be measuring things about the human body and does it matter if you can measure more than one thing, for example?
Michael Dubrovsky (00:03:39):
That’s a big question. So, originally, it’s maybe worth going back and originally, why did people start doing this? I think it’s probably pretty recent that people seriously decided that it’s useful to start measuring things in blood. I’m actually not sure what the first thing was. Maybe blood typing for blood transfusions, things like that. They used to look at how long it takes your cells to … I think they actually still do this, the erythrocyte sedimentation rate. It’s like if you take the blood out of the patient, how long does it take for it to settle out, and that’s proportionate to your inflammation a little bit. So, they used to do these types of things.
(00:04:19):
So, ideally, you wouldn’t really have to pay attention to your body at all. I think that would probably be the ideal case. Right? There are some people that are doing it for entertainment and I’m one of those people. I like to do it for entertainment. But I think for most people, they probably don’t want to pay attention to it at all. But it’s being operated very far outside of its design specs or design specs. Right?
(00:04:43):
So, you’re taking this ape that’s supposed to sleep in a cave and probably wake up at sunrise and walk around all day and then fall asleep exhausted or something like that, you’re taking this ape and sitting them down in a chair in indoors and having them do, whatever, digital marketing for 12 hours a day. Right? So, I saw this really funny … I don’t remember exactly what the quote was, but it’s like this person listed all the things they were doing to improve their sleep, and they’re like, “Yeah. Finally, I’m sleeping like I would if I was sleeping on a rock and waking up-”
Josh Clemente (00:05:23):
A cold, damp rock.
Michael Dubrovsky (00:05:24):
… “yeah, on a damp rock naked.” That kind of thing. So, I think, first of all, because we’re operating completely out of the range that the bodies are designed for, but also because we’re trying to get much more out of them maybe than what you would typically get, so maybe we’re all trying to get the ideal outcome, not the average outcome, it starts to make sense to make measurements.
Josh Clemente (00:05:49):
Why does it make sense to make measurements? I think that’s something that it may actually be a leap for some people to know. What is it about measuring specifically what’s inside the body that gives us any additional information that might be useful?
Michael Dubrovsky (00:06:04):
Yeah. That’s a good question. So, the typical approach is more symptomatic. So, I think medicine, historically symptomatic. So, if nothing feels wrong, then everything’s fine. But as we’ve started collecting, let’s say, over the last hundred years, started collecting much more information about biomarkers in general, including blood biomarkers, but other ones as well, we’ve found that you can see things in the data long before they manifest themselves as symptoms and also, things are labile, and you can see this. Right? So, you can see incredible things happening.
(00:06:43):
So, obviously, it might not be a good idea, but you can take hormones and become enormous. Right? So, people are putting on, whatever, 50 pounds of muscle by taking basically a tiny catalyst that completely changes their body. So, obviously, the circulating markers, it’s not just bulk things like weight gain, but it’s also these small things that are present in very small quantities that can catalyze enormous changes in your body. Being able to monitor that can, first of all, allow you to know what’s going on and know how your body’s responding to what’s happening, but also allow you to make changes that you want effectively.
Josh Clemente (00:07:21):
Yeah. I think that’s a really nice summary of the principle, which is that we’re playing with these levers that are manipulating these molecules circulating our bodies every day hundreds of times. Right? We’re making all these decisions about what to eat, how to sleep, we’re exercising, we’re eating, we’re working, and the goal is to achieve some outcome. Meanwhile, we’re kind of ignoring the complexity of the human body, which is we’re flicking all these switches, but we aren’t actually measuring the state of our bodies, which, as far as we can tell, is the most complex system in the universe. So, I think there’s something there, which is that by simply assuming that we can control our health over long time periods, but not measuring our current state or how it’s changing, we’re really missing the forest for the trees, in my opinion, around what it means to be healthy and what optimal might look like.
(00:08:18):
I think your point about we’re outside of our comfort zone as a species is absolutely right. Our environment has transformed 100x over the last hundred years, and yet honestly, many of our health monitoring strategies have not. So, I think most people’s health strategy is wake up in the morning and look in the mirror and figure out if something’s totally wrong and we’re using eyes and ears to see if we’re really going off the rail. So, we can improve, we can take the same complex system sensing that we have in our cars and in our airplanes and in our diagnostic machinery and apply them to the body, I think, is maybe where we’re heading.
Michael Dubrovsky (00:09:05):
Yeah. So, I would say that that’s one side of the argument. So, if you take the devil’s advocate side, things that work, you don’t really need to measure. So, for example, if you take a building, parts of the building that just never fail don’t really get tested. So, you put sensors where you expect to have problems. So, like wind turbine, you’re putting a sensor on the motor because that fails, it catches fire. There’s all these epic videos of wind turbines spinning on fire. Right? So, I think for certain things, ideally anything that can be solved completely, so for example, people used to get nutrient deficiencies that are now completely solved just because the food system has been … In many ways, it’s worse, but in some ways, it’s better. Right?
(00:09:57):
So, people aren’t getting goiters because they have a deficiency of iodine. That doesn’t happen. So, you don’t need to monitor that. But on the other hand, more subtle things that haven’t been solved and there is no solution, that’s where you have to monitor and tweak and do all these things. Right? So, it’s really the gray area where we have solutions, but they’re not perfect, that’s where you have benefits for monitoring. If there’s no solution, knowing that something’s going wrong doesn’t really help you. But if there is a solution, but it’s kind of subtle, like there’s no one size fits all solution, for example, that’s where monitoring is really beneficial.
(00:10:33):
That’s actually most of medicine. So, because it’s mostly not that effective. That’s the problem. There are certain things that are super effective, like if you break your leg, they can put it back together basically. But there’s many, many things, especially long-term things, that are effective, kind of like if you look at the studies, it works for 50% of the people or whatever, things like that. That’s where monitoring can really help you see and tweak and actually get the 50% result that’s actually good rather than being in the 50% that got no value out of something.
Josh Clemente (00:11:09):
Yeah. I think even in cases where there isn’t a solution necessarily, I think we have to reframe our opinion around whether it’s worth knowing that because there’s a situation where somebody doesn’t necessarily have a cure for a condition. For example, there are a number of hormonal deficiencies where the body just does not produce certain hormones. There isn’t necessarily a cure, but there are solutions that can manage those conditions or see the onset of them and make better preparations, I think, and set life up to be capable of a better quality of life if you understand what’s going on. So, I think that’s another element of this, which is that there’s a large number of individual cases that we’re talking about in generalities here. But what I think it comes down to is just the reason that we would want to measure what’s going on in the body is so that we can understand the health state and how it’s changing over time.
(00:12:13):
It’s that setting a sort of baseline and understanding it in relatively high resolution and then, like you said, the areas that are likely to fail, looking at markers there and watching how those progress is a really powerful tool to know maintenance interval and or potential failure modes. For some of us, the cardiovascular system might be the first to go and we can start to see the indicators of that relatively early. This is one area that current medical system actually looks at, whereas for cognitive decline, we don’t really have markers of how that system starts to degrade. We use things like basically cognitive function as opposed to the biochemical signature of what underlies that to know what’s happening. So, I think that it’s generally an argument for increasing the amount of information. But not just overall volume. Data’s not data in this case. Actually looking in the areas where we know failure is possible and that we’re playing with the levers already maybe with our decisions.
(00:13:15):
So, I wanted to lead into the next part of the question, which was, okay, measuring makes sense, but what about the frequency and how do you choose what to measure, and also, how do you choose the frequency? Today, I think most people who would be listening to this care a bit more about this maybe than the average person out there. But let’s just say in general in the US, most people maybe get blood work once a year, maybe once every other year, get a panel of results and you get a PDF with some numbers printed on it. That is a frequency that you can measure. So, you get blood drawn and then you get a piece of paper once every two years, let’s say. Why should one measure more frequently than that and what additional information does it unlock to do so? What’s the consideration around frequency?
Michael Dubrovsky (00:14:05):
So, I think it’s useful, actually, to give some context. So, I myself before starting this company maybe hadn’t had a blood test in five years, maybe six years or something. At the beginning, we weren’t sure we’d be building a blood testing device. So, after we realized that we’d be building it and we got past the first technical hurdle, so we knew that it could work, it can be built, I started thinking what can even be the biggest impact of something like this? Because typically, okay, let’s make it a little bit easier to do the same blood test that’s normally done. Let’s do that once a year. So, okay. This is actually an interesting point. So, if you ask a hundred doctors, “What do you want from a blood testing tool?” they’ll say, “I want to do the yearly blood test while the person’s in my office so that I can just get the data and discuss it with them while they’re there because people just do that blood test, they disappear or whatever.” Right?
(00:15:05):
So, that’s considered the burning issue in healthcare in terms of blood testing. But when I started doing my own research and looking like is this really the biggest impact we can make, going from two visits to one visit or whatever, and I kind of found that what’s actually happening, first of all, the yearly blood tests for many markers, because they fluctuate frequently, if you’re getting them once a year, you don’t know if you’re looking at the noise from the fluctuation, depending on what happened to you that week or what time of day it is that you ended up coming in for the blood draw, that type of thing, or are you looking at a real change?
(00:15:43):
So, usually, doctors, what they do, unless you have somebody that’s a functional medicine doctor or you’re paying for very high on longevity care or something, they’re just looking, are you way out of range? Have you gone completely off the normal human range and do we need to start basically doing a bunch of tests on you to figure out what’s going on? Or do you need to completely change your life because this is really high risk? Right? The reason they’re operating that way, that’s one of the reasons is that the frequency is so low that if they were responding to small changes, they might just be responding to the normal variation. There’s variations even related to the seasons. You’re going to have variations based on season. You’re going to have variations based on if you take a testosterone or cortisol test that the peak is one hour after you wake up. So, if you show up, it’s like half hour here, half hour there could make a 10% difference. They can’t respond to that because they can’t even time your blood test perfectly.
(00:16:46):
So, for all of these reasons, what they’re doing is just taking a very blunt approach, which is okay, you’re within this general range, which is the 95% two standard deviation interval for the population, whatever. Maybe the population’s very unhealthy, but at least you’re within that two standard deviations and come back, we’ll check you again, and we’ll see what happens. I’m not the first person to say this. This is a common complaint that they just wait for you to become very unhealthy before doing anything. But the question is also which tests does this matter for? I think for many of the tests that they do yearly, you don’t really expect to see changes. So, blood counts and other things, not all of them it actually makes sense to test more than once a year.
(00:17:31):
But the complete metabolic panel, which is what happens every year, the history of that test, as far as I understand it, is it really started with what they could test. So, you can take the person’s blood, put it on a microscope, slide, and count the cells. People used to do this visually. There were technicians that would look into a microscope and just count your cells and write them down on a piece of paper. So, that’s the type of blood test they could do. They could measure very high concentration stuff. A good example is CRP. So, CRP is C-reactive proteins and inflammation marker. It goes up when you have a bacterial infection, but normally it’s supposed to be very low. Until recently, all they had was the CRP test, which was actually, they could only measure it at very high levels.
(00:18:13):
Then about 10, 20 years ago, a higher sensitivity test came out. It’s called hs-CRP. It’s really the same marker, but at a lower range. That’s when they started seeing that, okay, actually, people are chronically elevated on this marker for no reason. So, they don’t have a bacterial infection, but this marker’s chronically elevated. It’s not as high as it would be if they were super sick, but it’s much higher than it’s supposed to be in a healthy person. So, even to this day, they don’t really address that part of the range. But it’s marker to marker dependent which ones it really makes sense to measure frequently or even continuously. But a lot of the ones in the yearly panel actually are relatively stable and that might be why they’ve evolutionarily ended up there.
Josh Clemente (00:18:59):
We should contrast the annual stable markers. You raised good points about most of the molecules that are circulating in our bodies, they’re a kind of combination of a half-life, meaning the molecule’s actually changing over time and being replaced. The molecule is degrading and being replaced. You’ve got circadian rhythms, so you’ve got day and night cycle. Let’s say people work second shift. They might have two circadian rhythms that are overlapped. So, there can be this dysfunctional double circadian rhythm thing and other variations on it. Then you’ve got the impact of decisions. So, my cortisol spike may be naturally circadian driven with the peak at one hour after I wake up. But if I happen to do CrossFit 45 minutes after I wake up, maybe my unnatural cortisol peak ends up being an hour and 30 minutes after I wake up, meaning I have stacked two peaks on top of each other with this unnatural elevation.
(00:20:01):
So, anyway, the point being where if I want to understand how cortisol levels impact my quality of life, my mental health, my sleep, that sort of marker that is fluctuating inside of a 24 hour basis, we know and are able to measure this, that’s the type of thing which, yes, we measure it on our annual panel occasionally. But what I think many of our listeners would be interested in is what do we unlock by measuring those more labile markers on a more continuous basis that we don’t get otherwise? One thing I specifically want to talk about is load conditions. The body, it’s a machine. It’s a dynamic machine. We’re moving around, we’re making all these decisions, we’re putting it under stress. As such, the annual panel is designed to be taken in the lowest load condition. It’s like you’ve fasted, you’ve slept well. Right after you wake up, go in, no coffee, no nothing, get your blood work. In many cases, that’s by far the best case scenario. So, why do we test that way and what would a continuous, or maybe not continuous, but higher frequency detection unlock for us?
Michael Dubrovsky (00:21:15):
That’s an interesting question. I think more broadly, I would make the claim that if we’re going to make progress on the 120 year lifespan in general or whatever the health span is now, 80 years or something like that at best, we have to start figuring out what’s really going on. So, for example, if a drug or a set of drugs or whatever, procedures is going to get us to 150 year lifespan, for sure, that’s not going to happen without measuring the effects of that drug because any longevity drug, you won’t see any symptomatic effects for decades. Right? So, if you need to start taking a drug at 30 to live to 150, you won’t see the effects of that for several decades. So, it has to be in the biomarkers. I don’t think there’s any way around it.
(00:22:02):
That’s one of the conclusions I came to doing my research for what’s the biggest impact you can have with being able to measure biomarkers more cheaply and conveniently, right? But I think to answer your question more directly, so I think for most people, it’s probably the end use. They have an end goal, so they want to sleep better or they want to lose weight or whatever. It’s typical health goals. They want to prevent diseases X, Y, Z. Six out of 10 Americans have a chronic disease at this point. So, it’s basically if you don’t have one now, there’s a good chance you’ll have one in the next 20 years, let’s say. So, just trying to avoid that or optimize something locally, like sports performance or something. So, there are 3,000 proteins in blood and then there are also a bunch of metabolites. I don’t know the total numbers. Maybe you have this number in your head. It might be like 11,000 molecules or something that are circulating.
Josh Clemente (00:23:00):
It’s tens of thousands. Yeah.
Michael Dubrovsky (00:23:01):
Yeah. If you start counting metabolites, it goes really high. But I think it’s also, the good news is that it’s also over-determined. So, a lot of them are parts of chains. So, basically, one induces the other. So, if you’re measuring one, so I’ll just use CRP again because I mentioned it. So, CRP is the inflammation protein, but it’s actually induced by what are called cytokines, which are these signaling molecules that will tell your body that it needs to have higher inflammation. So, just to give an example, if you get poison ivy, what’s really happening is the oil from poison ivy is binding to proteins in your skin, which your body starts to recognize as foreign proteins because they change shape from the binding of the oil. Then your T cells, which is like an immune system cell, releases cytokines into your blood or into your interstitial fluid locally and it just explodes, basically. So, a bunch of inflammation is triggered this way.
(00:23:54):
But anyway, so if you’re measuring CRP, what you’re actually measuring is the cytokine levels one day ago. So, dynamically, you’re a day delayed. What you’re actually measuring, the other molecules from a day ago. So, in a way, you don’t need to measure all 3,000 to get the data. In many cases, the systems are over-determined and you can get a lot also just from looking at time series data. So, you can look at one marker over time and it tells you something about what’s going on in the more complicated systems. So, I think that’s the good news in terms of there’s this huge zoo of things in your body, but actually, they’re very connected and we’ve learned a lot about them. So, taking all that information, you can actually zero in on couple of markers and get a lot out of that-
Josh Clemente (00:24:42):
For a specific [inaudible 00:24:44].
Michael Dubrovsky (00:24:43):
… in that use case.
Josh Clemente (00:24:45):
That’s a great callback towards the beginning when we started talking, which was why would you measure more than one thing? The answer is right there where you can start to take a few properly selected molecules and use them to interpret and act as proxies for a whole network of things. So, I think you explained that well, which is that you don’t have to measure everything in the body to know what’s going on. You can find the high leverage elements like CRP, which can then be used to interpret T cell activity and cytokine activity. There are these specific molecules, hormones are a really good example, which are really information-dense. If you zoom in on hormones, you know they are basically the messenger molecules to the distributed network of our cells. If you can interpret that signal, what a specific hormone’s concentrations are, you can kind of approximate what is going on in a complex system.
(00:25:47):
Then you can even remeasure a byproduct of the cells to see did they follow the instruction set? That can tell you whether you have a central nervous system issue where you’re not sending the right signals to produce the right hormones, or do you have a cellular issue where the cells are just not responding to the hormone signal, and that’s a really powerful high amount of data or high quality amount of data that is interpreted from a few results. So, I think what I’d like to go dig into a little bit more from there is essentially, how do we decide what to measure today, why do we have the panels that we have, and if we can measure this stuff now, why are we not just measuring it in higher quantities already? Are we measuring the right things, and then if so, why don’t we just go get more blood work? What’s holding back a huge industry here?
Michael Dubrovsky (00:26:40):
Yeah. That’s a good question. So, the big progress in blood testing happened probably in the late ’90s and maybe early 2000s. So, if you talk to somebody who’s older, who was working in blood testing 30 years ago, it’s not a precise science. Right? So, one of the issues is that people are very heterogeneous. So, if you take a thousand people and you test them all on the same test, it’s actually a massive battle to have that test give the same result, actually a correct result. The reason is that people are very heterogeneous. Again, 11,000 things in the blood. They have all kinds of things can be going on.
(00:27:23):
So, the industry has gotten a lot better at dealing with that. So, there’s been all these waves of things that people have figured out. The FDA then pushes down a decree, okay, we have to be dealing with this now and then that improves all the tests. But even to this day, for the best instruments, they don’t really perfectly agree with each other. So, it’s nothing like if you come from electrical engineering or whatever where somebody sells you a product that says five volts and you measure it with a volt meter, it says five volts, that doesn’t exist. But within some error, that’s starting to be the case, especially for the more well established targets.
Josh Clemente (00:27:57):
When somebody goes to get a lab panel and let’s say they measure their total cholesterol, what should they expect that the realistic accuracy of that test is?
Michael Dubrovsky (00:28:06):
So, we’ve done a ton of testing on this ourselves because we’re in the business, right? This is my intuition. I don’t have perfect proof of this. But the longer a test has been on the market, so for example, total cholesterol has been on the market for, whatever, I actually don’t know how long, but certainly decades, and the longer it’s been on the market, the lower the coefficient of variability. So, if you send in your sample 10 times, you’re going to have a 5% difference.
(00:28:35):
But if you take a vitamin D test or you take a testosterone test or whatever, those tests have really not been offered for that long and at that high volume. So, you have to definitely take those with more of a grain of salt in terms of if you have a change of 5%, you shouldn’t see that as a real change, especially if you’re making one, two measurements and you just see that they’re essentially equivalent. That’s my personal belief. This is not based on a big study or anything like that.
Josh Clemente (00:29:01):
I think people would find that kind of counterintuitive. They would think, okay, newer tests are going to be made with better technology. This is going to be much higher quality. So, what do you attribute that error?
Michael Dubrovsky (00:29:11):
Oh. What is it attributed? That’s very interesting. So, I didn’t really finish the story of what happened is, people have done an amazing job of automating and scaling up relatively complex assays in what are called clinical analyzers. So, it’s these basically giant robots that take in tubes of blood, pull out fixed volumes, and they all run pretty … All the competitors have almost the same thing. So, they’re all running the same types of assays. There’s three types. It’s blood counts, chemistry, and immunoassays. So, chemistry assays are more like you just mix a bunch of things up and shine a light through it and look at the change, typically in just the absorbance or scattering and things like that, and there’s a chemical reaction going on inside that you’re monitoring. Immunoassays actually require proteins that recognize the target and that starts to create all types of differences.
(00:30:08):
So, for example, that protein that recognizes the target, Roche, which sells an analyzer, they validated their assay for vitamin D against their previous model. So, they were basically, they sent the FDA document that said, “Our Roche Analyzer 1000 works exactly like the Roche Analyzer 900,” but they never validated against the Abbott. So, if you send your sample in into two labs and one is using the Abbott, the other one’s using the Roche, apparently I’ve been told that doctors that are sophisticated doctors will send your sample always to the same lab because otherwise, if they’re serious, they want to know the differences, they want it to be tested always on the same instrument. It’s these types of things, these subtleties that aren’t completely harmonized, especially, it takes years and years to find the differences because, again, people are heterogeneous. So, it might only happen in 1% of the population that there’s a small difference between these two machines, and these are the types of things that get ferreted out over the course of a decade.
Josh Clemente (00:31:06):
Just process improvement and technology improvement. Yeah. So, I think you’ve introduced the concept of the actual underlying equipment. There’s this whole industry and machinery behind the simple process of going into the doctor’s office, getting blood pulled. That stuff vanishes somewhere. We don’t really know where it goes. Then a week or so later, you get a phone call and you have a PDF in your hand. You had mentioned at the beginning of the conversation that doctors want to be able to provide results in real time, or at least in the same visit. Explain for people why there is currently such a long lag. What’s going on there? Why is a blood tube being sent across the country to get a result and what’s actually happening to it?
Michael Dubrovsky (00:31:49):
Yeah. That’s a good question. So, I should preface this with saying I’m by no means an industry veteran. It’s very fun to talk to the blood testing industry veterans. It’s kind of they’re like sailors or something. I don’t know. It’s actually a pretty down to earth industry, I would say, because in general, in biotech, therapeutics are really what make money because you can sell the same drug to a billion people. It’s like software. It’s incredibly profitable. Diagnostics and instruments in general are considered, they’re pretty low status because it’s all like the margins are super thin and the equipment equipment’s just difficult. But anyway, so I think it’s important to note that it’s a pretty down to earth industry and they’re definitely doing their best. So, the history of the industry is basically that people realized that they wanted to have the whole test done in the doctor’s office.
(00:32:44):
Everybody knew that. It’s kind of obvious. It’s like saying I want my iPhone to run ChatGPT locally. It was obvious to everybody and there was a vacuum for somebody just to say that they could do it. So, that’s what Theranos did. They were like, “All right. We’re just going to say we’re doing this.” Right? That was the minimum viable product is just to say you’re doing it. Of course, nobody in the industry eventually believed them because they couldn’t show anything. But that was enough to do five or 10 years or whatever, I guess five or seven years of fundraising and everything. That killed a lot of other companies that were working on real solutions. But some of them survived. Again, I got into this only in early 2020, so I wasn’t around for the whole fallout of that. But of course, it killed a lot of companies because investors got very scared of the space and everything.
(00:33:36):
That’s one of the reasons really that we don’t have these things. The other reason is that doctors, if they need a single result that you can’t produce on your device, they’re going to send a tube of blood to the central lab because at the central lab, they have three different tools, the blood count, the chemistry, and the immunoassay, that do all the blood tests they want. So, basically, they’re just sending the tubes there, they don’t care, they just want the results. They don’t care what machine it’s run on. So, people have struggled to build a machine that will do everything they want so that they don’t have to send a tube because as soon as they want one result that is not possible, that’s it. But there are a few companies like Truvian, Genalyte is doing this, there’s also Vital Bio. There are a few companies that are trying to package up the complete blood panel and put it into the doctor’s office.
(00:34:22):
This might be a hot take or something. My opinion is that it’s taking so long to deliver this that you might as well just go to the next thing. I think by the time that they deliver this, it’s going to make money. I’m sure whoever cracks, this is going to make a lot of money. But I don’t think it’s going to have as big of an impact as people were hoping because medicine is just changing. So, this whole doctor’s visit thing, the yearly blood test, it’s starting to look stale. But that’s the situation. It’s really delayed partially because of Theranos, partially because it’s a poorly funded, low status industry in general, I would say. Those are probably the two reasons. It’s not technical fully.
Josh Clemente (00:35:01):
So, I want to dig into two things you brought up there. Firstly, just for background context, Theranos, of course, is the company that claimed that they could do an entire litany of tests with a single drop of blood right there in a microwave size box. What they were trying to replicate and what this whole industry you just described is trying to replicate is the central laboratory industry. So, those three giant analyzers in a room that takes blood tubes and can break them down and get the whole menu of tests that a doctor’s interested in, trying to replicate that in the office.
(00:35:29):
So, what you’re saying, which I think is really interesting, is that you might as well go to the next thing because it’s taking so long to replicate that entire central laboratory process in a small box that can be put in a doctor’s office, that things have evolved, and I want to talk about two specific elements of that. The first is how are things evolving? What is the next thing? Secondly, it’s pretty crazy that a single example like Theranos can have as much of an effect as you indicated that it has. Maybe first, let’s talk about that. What did go wrong there and what lessons are we going to have to take from the Theranos story when we’re talking about, for example, what you’re working on next?
Michael Dubrovsky (00:36:15):
I don’t really know what went wrong. What’s interesting is that once you spend some time in the industry, lots of people worked at Theranos. You start meeting people that worked there. The engineers that I’ve talked to, like I’ve interviewed people who worked there at relatively senior positions like scientists and stuff, I don’t know. So, basically, it seems that they were doing some kind of work in a silo that was going fine. So, basically, many people were doing work that was going fine, but there was some kind of integration problem.
(00:36:48):
This is something my co-founder says, but I think it’s a really good point, is that every hard tech industry has a Theranos, but Theranos got very famous because they gave incorrect results to actual pe- … If they had never delivered anything, probably they wouldn’t be very famous. But they were actually giving critical results to people that were wrong and they did all these things. But if they had just raised a billion dollars, spent it, and disappeared, there are lidar companies that did that. You can give a bunch of examples.
Josh Clemente (00:37:18):
Just another Juicero.
Michael Dubrovsky (00:37:22):
The Juicero. Yeah. The Juicero founder is not being covered in the New York Times every year, right? So, it’s any deep tech product, it’s just so difficult for investors to diligence. But I think in general, what they did wrong fundamentally is just trying to chase the obvious problem that people want, the impossible thing that everybody wants vs. being realistic and saying, “Okay. It takes three enormous instruments today to do this. What can be done with the technology we have or can develop in five years while people are still paying attention?” You collect a good team, they have five years of runway where they’re really focused. What can be done in that time?I think that that’s really when people do deliver is when they do that.
(00:38:02):
There’s a good example of a company that does a one drop at home test. It’s Athelas. So, they also went through YC and they’re FDA cleared now on the market with a blood count. So, for cancer patients and some other use cases, you really need to keep track of blood counts. So, they have an FDA cleared device that does that at home. So, this has been unbundled. Right? Basically, instead of doing the whole thing having no idea how to do it, it’s people looking focused like, okay, we’re going to solve this piece of it for a particular niche where it actually works.
Josh Clemente (00:38:30):
So, what is that next thing that you were hinting at that the industry should start to shift its focus towards?
Michael Dubrovsky (00:38:39):
I think this is going to happen in different parts. But fundamentally, so my family’s from Soviet Union where healthcare was socialized. I don’t know how to put it. Both my grandparents were doctors, so I’m pretty familiar. My grandmother ran a pediatric hospital. I’m pretty familiar with the system. It’s interesting that you can spend a third or whatever it is of American GDP on healthcare and get what you get. What we get is really bad for that amount of money, considering what the Soviet Union was able to achieve and many other countries today are able to achieve with very small, really not that much resources.
(00:39:20):
So, I think if you look at it from that perspective, the chances of fixing the existing system, I might be rambling here, but I think the chances of doing something super fundamental to fix the existing system are very low, but there are all these other things happening that are trying to circumvent it or just improve little parts of it, and I think that’s working out great. So, basically, just telemedicine, all kinds of gray area services that are wellness bleeding into healthcare, I think all of those things seem to be working. People are actually getting results from them. They’re really low cost because they’re self-paid. So, I think there’s a lot of progress there and that can all eventually move into being used by the healthcare industry, and it eventually does. So, they adopt things at work. So, actually, it benefits the main healthcare industry over time anyway. It’s just hard for it to start there.
(00:40:15):
But I guess to answer your question directly, what do I think is the next thing? I think the big thing is we don’t really know how to cure chronic diseases. So, if we did know how to cure them, it would be a moot point, but we don’t know how to cure them. So, probably prevention is the most interesting thing because of the difficulty of curing and managing chronic disease. Management is interesting also because probably, we can do much better at managing them. But I think prevention is where a lot of the value is, and that’s also where there’s a lot of room for innovation because it just hasn’t been touched.
Josh Clemente (00:40:50):
Specific to the next, on the detection element, rather than trying to solve the central lab box-
Michael Dubrovsky (00:40:59):
Ah. Okay. You’re asking about that.
Josh Clemente (00:41:00):
Yeah. What’s the next thing there?
Michael Dubrovsky (00:41:03):
Yeah. So, I think first of all, focusing on doing the complete metabolic panel, that’s a strategic error in my opinion. If you’re starting a company today, again, because that panel is designed for that yearly visit and the yearly visit in it of itself is not that valuable, it’s just not high value, nothing happens at that visit. Right? At the doctor, I went to a visit recently just to try it out because I usually don’t do the yearly visit. I didn’t even see a doctor. I saw a nurse. She never talked to me. Basically, she gave me a form that asked me if I was depressed. Then she was like, “All right. I’ll see you next year.” It’s basically, maybe there’s a lot of innovation that can be done around that yearly visit, but then that innovation would also change what the blood test looks like or what they’re doing.
(00:41:48):
They should probably be reviewing a year’s worth of blood data that’s taken weekly or monthly rather than looking at that point measurement. So, it all needs to be reimagined. So, if you’re building something that takes five or 10 years to build to a spec that’s not working anyway, financially, you could make money from it, but it’s not going to change people’s lives that much. So, anyway. But I think in terms of what’s next, I think just looking at actually solving problems. So, people have problems that are directly labile. Right? Let’s actually look at what can be done about that. Rather than asking just how do we slightly improve the cost of doing something that’s already being done, the question is what can be enabled? So, I think it broadly falls into the categories of issues with hormones, issues with metabolic health, like weight loss, slash … Weight loss, I think, is the symptom, or weight gain is the symptom within whatever’s going on, like the metabolic dysfunction.
(00:42:50):
There’s all the inflammatory diseases and then cardiovascular, which sits at the nexus of inflammatory and metabolic. Right? So, I think if you look at those buckets, that’s where most of the value is is trying to help people manage those things, improve them, prevent disease and so on. Most of that in terms of technology actually requires, or not requires, but it benefits enormously from just giving the power to the person to do the test. So, if they can test themselves and have the context around that data, have some kind of support, probably that support doesn’t even need to come from a person. Maybe it doesn’t. In some cases, maybe it doesn’t. But just having the ground truth data already makes so much difference in figuring out where the person’s at, how they can make an improvement, is whatever they’re trying to do actually working for them, all these questions. None of that is answered at a yearly visit with a complete metabolic panel. So, that’s my take.
Josh Clemente (00:43:45):
To unpack that a little bit more, there are all these circumstances people find themselves in day to day that they’re trying to manage and today, they do not have the abilities, sort of wait until the yearly checkup to get a test or a litany of tests, some of which may be adjacent to the problem you’re trying to solve today. It’s like if my problem is I want to lose weight, or if my problem is I want to have a baby, let’s say, and I go to the doctor and I get my cholesterol panel and I get CBC and all these other things and I’m told, “You’re healthy,” but I’m still having trouble conceiving, those two things, the overlap is not what I need it to be. So, I think where you’re going with this is we need to expand what we’re detecting and the rationale for it.
(00:44:32):
The traditional blood tests were developed for a specific reason and they have some information value, but there are all these other real world examples that understanding the health state and the underlying molecules that are driving it can be really powerful. But I think what you’re describing there is if you were to give the person the technology that they could measure this themselves and more frequently so they can see changes, specifically I would imagine changes due to an intervention, which in the fertility case, maybe that’s managing PCOS through different diet, which is, PCOS is polycystic ovarian syndrome, it’s very closely related to insulin resistance, and perhaps it’s trying different dietary approaches to see if, for example, insulin relative to glucose starts to adjust. Can you give some more examples of these sorts of real world levers that you could build through testing to give people this kind of Healthcare 3.0 world where you’re not just focusing on symptoms and you’re not just doing an annual blood test?
Michael Dubrovsky (00:45:37):
Yeah. I think for many of the things people care about, you can find a menu of five or 10 things to try typically. So, if your sleep is very dysregulated, there are all these tools that … So, you can go as far as going to a concierge service that will completely analyze your sleep and do all this, and as simple as taking magnesium before you go to sleep, which is, whatever, $5 a week. But I think the reason you would do a measurement, so you can just try these things, and you can even look at sleep scores or whatever. But the reason you would do a measurement is to really see if there’s a hormonal or metabolic reason where you’re getting bad sleep. That kind of ground truth information can be a lot more valuable than symptomatic things. So, even the amount of hours you sleep, that’s a symptom of something, of an under underlying process.
(00:46:35):
So, for me personally, I’ve been doing a ton of blood tests because we sell blood tests and test blood tests and so on, I can actually very clearly in my own data just see when I screw up my circadian rhythm or other things of that flavor. You can see the connection between circadian rhythm and inflammation, circadian rhythm and testosterone, and things like that. So, that’s not true for everybody. So, when we actually do correlations across a lot of blood tests, let’s say inflammation is connected to sleep for 50% of people. So, for the other 50%, it’s actually not connected. So, you can improve your sleep, but your inflammation will not drop. So, the cause is just something else. Every engineer that’s built anything has had to do a hundred rounds of debugging. So, if you’re not able to measure anything, you’re not able to debug what’s going on, it’s just very unlikely that you’re going to try one of these 10 things and it’s going to fix it. What happens is when that happens, people become evangelists of that solution. All you have to do is fix your sleep. But these things are so complicated and interconnected that that’s not actually true.
Josh Clemente (00:47:48):
Yeah. That anecdotal, but also trended data that you’re pointing to there is really interesting and I think opens a bunch of questions about how we can even start to tackle a problem like that where you have interpersonal variability, for example, when you’re trying to develop a new technology or test that has to show good results over a very wide swath of the general population, and yet you have these interpersonal variations happening. Some of that goes to the intervention, meaning sleep doesn’t necessarily change inflammation for everyone.
(00:48:21):
But I do think that it points to the challenge of getting a new technology to market when there isn’t necessarily an intervention associated with it. We’re not tracking a symptom. You’re actually measuring for the measurement’s sake. You’re measuring to describe the person’s health state better. The system really isn’t set up for that. The regulatory bodies don’t think about measuring all the molecular milieu inside the body just because you want to understand it and see how sleep affects inflammation. So, how do you make the case that this should be measured and then produce compelling evidence that it’s worth measuring, for example, inflammation or the underlying sleep molecules or cortisol or something when you aren’t actually doing so to prove the effectiveness of an intervention?
Michael Dubrovsky (00:49:06):
That’s a good question. So, I think historically, the way people have gotten things approved for these home use cases or even wearables and things like that is they never really achieve the same performance as the central lab instrument. But what they do is they make the argument, look, people need pregnancy tests at home. They’re not going to come in for this test. They just need it at home. For some things, that argument is very easy and over time, they get approved. But for many markers, that’s not the case. I think the best strategy, at least the strategy that we’re taking, is just to develop things that are the same quality as the central lab instrument. If you can hit the same quality as the central lab instrument, regulatory is actually not as hard.
(00:49:54):
So, I think the difficulty with the regulations has really been that home testing, because it’s typically paper script based, has not really been able to hit the same level of quality as central labs. Because of that, it would essentially have to get exceptions from the FDA like, okay, this isn’t very accurate, but we’re going to put it out because there’s a great medical need or a public health need. But how do you make that argument for sleep? They might not care about sleep at all. What does it matter? In their framework, like you’re saying, that’s something that’s way upstream of anything that they care about.
Josh Clemente (00:50:32):
Yeah. I’d love to do an entire conversation just about the regulatory framework and proof of necessity, proof of value, that the next generation that you describe is where the person is in the driver’s seat, so to speak. We’ve shifted the locus of control about what we’re measuring and how we’re measuring and when to the individual whose health is at stake and they’re measuring things that relate to their quality of life and their health span and the avoidance of conditions they don’t want or quality of life they don’t want, and that is a value statement to that individual. It’s not a public health at scale, general population sort of health mission, which is what the healthcare system is currently thinking about. They’re thinking about how do we lower the rates of stroke acutely? That may be connected and if you look at the Daylight Savings examples, every single year when the clocks shift backwards and you lose an hour of sleep, heart attacks and strokes go up like clockwork-
Michael Dubrovsky (00:51:33):
That’s really interesting.
Josh Clemente (00:51:34):
… and the opposite happens when we shift back in the other direction and you gain an hour of sleep. So, I think what we’re trying to do is extricate ourselves from a situation where we are only thinking at the scale of everyone and shift to a condition where we’re thinking about where I can only think about me and you can only think about you and we are each looking at many data points about ourselves and how we’re changing and kind of adjusting our lifestyle levers along the way. So, there’s a lot there. I think the whole system is not currently structurally set up for the kind of proof of value that I as a consumer want when it comes to the healthcare or really the diagnostics that we’re talking about.
Michael Dubrovsky (00:52:11):
So, it’s interesting. So, we generally are in heated agreement about this, but I think I just like to play devil’s advocate where it’s possible. I think today, you don’t really have a choice. If you want to optimize your sleep or whatever, there’s no metaobject that’s optimizing your sleep. Nobody is on a national level trying to figure out how to get people to sleep better, how do we figure out what’s happening, this type of thing. That’s not going on really. It is in some small way, but people are tracking it, but it’s not aggressively being managed. If you want to fix your sleep, you have to do it yourself, make the measurements, just get the trial, the hacks, whatever. But I do think that there might be an optimistic world where at some point, there’s enough data on enough people that you don’t need to do all of these experiments on yourself.
(00:53:05):
So, basically, at some point, you should be able to pool data from millions of people and actually start getting real insights that are forward compatible. So, you bring in the millionth and one person, you make a measurement on them, and you just tell them, “Look, this is going to work for you,” and that’s already true in some cases, right? They’re trying to do this for cancer therapy. Where it really matters, the medical system is pretty good. So, they’ll sequence your cancer, tell you this immunotherapy is not going to work on you, that type of stuff. So, I think there is an optimistic case for over time this transitioning from you’re on your own, you have to fix this to it being much more solved and something where you can leverage the experience of millions of other people. But for that to happen, this all has to be built up. But I do think there’s an optimistic world where that happens.
Josh Clemente (00:53:52):
Yeah, yeah. I would argue that that’s not even a devil’s advocate condition for this situation. It’s a both and where I can both focus on my own selfish concerns and simultaneously, my data could be aggregated and pooled into our general population understanding of how inflammation and cortisol are connected or how fertility is affected by insulin resistance. Then we can start to understand potentially phenotype people based on a few very selective tests and quickly understand what intervention is most likely to work.
(00:54:28):
Today, you just don’t use data in that way. There are obviously exceptions, but at least not in a wellness context where I’m trying to optimize my diet for weight loss, for example. We generally do not understand what levers to pull for me vs. for you and that can certainly change. I want to kind of wrap, but mostly focus the last few minutes here on what you’re building. So, this isn’t just a conversation in the abstract. You at SiPhox with your co-founder, Diedrik, are working on a technology. I’ll let you introduce the tech and what you guys are most focused on right now and the major challenges. But yeah. Tell us about what you’re doing.
Michael Dubrovsky (00:55:11):
Sure. So, the main thing that we’re doing is we’re miniaturizing one of the … So, there’s the three pillars of blood testing that I talked about, which is the blood counts, the chemistry tests, which are your lipids, your ions, and then immunoassays, which is proteins and hormones. So, we’re really focused on proteins and hormones. What we’re doing is we’re taking all the optics that go into … I’m looking at my desk whether I have a device here, and it’s amazing I don’t. But anyway. I guess I don’t know if you’re using the video.
(00:55:44):
So, basically, we’re taking all the optics that go into one of these instruments, and it’s mostly an optics instrument, so it’s a lot of lasers and lenses and things like that inside, and we’ve already done it. So, we miniaturized all the optics onto silicon chips. So, there’s a whole industry out there that most people are not aware of, which is taking the technology that makes chips in your phones, so the electronic chips that drive phones or computers, etc., taking that technology and manufacturing optical systems with it, so miniaturizing optics, and that’s been used mostly for the internet communication. So, this call we’re having is being converted to light when it goes to a data center and that light goes through fiber and that has to be converted back into electricity to talk to other electronics and computers. That’s being done by chips with miniaturized optics on them.
Josh Clemente (00:56:36):
Just to clarify for people, the electronics is the electricity, but then the optics, it’s the light. It’s moving photons instead of electrons [inaudible 00:56:44].
Michael Dubrovsky (00:56:44):
Exactly. Yeah. It’s moving units of light that goes through fibers. That’s how communication is done between. The reason for that is that light is very fast and travels very quickly and doesn’t interact much. So, you can put a little bit of light into a glass tube that spans the ocean and it actually doesn’t make it all the way to the other end. You have to amplify it as you go. But basically, it can travel a very long way without diminishing and you can also pack a lot of data into it. But anyway, so that industry has been built up and it’s enabled the miniaturization of other optical systems. So, we’re leveraging that, especially my co-founder, Diedrik, has a ton of experience in it. He was part of the team that commercialized the most successful optical chip for data center communication. So, there are millions of them out there. They’re transmitting maybe 50% of internet traffic or something like that.
(00:57:33):
But we’re leveraging that technology and we use it to miniaturize all the optics in the blood analyzer. So, what that’s allowed us to do is cut the cost by a factor of, let’s say, a thousand, a hundred to a thousand, and cut the size by a factor of a hundred to a thousand. So, it’s something that you can have Alexa speaker-size device that you’ve actually, you’ve seen it, that you can have on your kitchen counter or wherever that will give you five or 10 results out of a very small sample of blood. What we’re focused on is really things that it makes sense to measure at home. So, proteins and hormones that change relatively frequently that are associated with either health goals people have or chronic disease management, chronic disease prevention, things like that. So, that might be a panel of all the female hormones for IVF, or it could be a cardiometabolic panel that covers what’s going on with your insulin, your inflammation, your lipids, things like that. So, those kinds of panels that actually cover an area of interest for people.
Josh Clemente (00:58:32):
That’s really exciting. For people who are getting jazzed up about this, what’s the kind of timeframe where you feel like you’ll have something, and also describe a little bit more about the user experience. Is this thing envisioned to be used in place of a continuous wearable? Is this something that you would just be measuring once every few months? What’s the interaction pattern that you’re thinking about here?
Michael Dubrovsky (00:58:57):
Yeah. So, the holy grail would be a wearable that’ll do the metabolites like glucose and also proteins and hormones, and we’ve talked about this a lot. But I think in our case, what we’re building now and what we’re really driving to get to market, so our goal is to run a pretty large study towards the end of this year or early next year where we recruit lots of people that are interested in measuring their markers, mostly for wellness, into the study. Then we can actually show both the efficacy of the device, but also the value of it so that people are actually able to use the data to improve their lives. So, looking at a study of maybe up to 10,000 people with a device over the next year or two, starting with late this year or early next year. Then after that, we’ll submit it to the FDA for general use. That’s the current plan and it’s really focusing on what we’ve been discussing, all of these types of use cases and building panels around those.
Josh Clemente (00:59:59):
It’s basically making it it more likely that someone can measure something that’s specifically useful for them and on a more continuous, or at least a more regular basis.
Michael Dubrovsky (01:00:09):
Yeah. I think the best use case, or one of the really great use cases for it, there are a lot of telemedicine use cases where you make a point measurement and you need to have, let’s say, a doctor’s visit that’s associated with it. But I think one of the best use cases is really giving more data to companies like Levels, honestly, because somebody has to take all this data, interpret it, and give the person actionable insights. Right? So, it’s really about giving very reliable data that’s also really low barrier to entry for people to collect.
(01:00:40):
So, if today, you have to send a phlebotomist to someone’s house to take two tubes of blood and bring it to a lab or whatever the workflow might be, so just cutting that down to something where they can do it as, “Oh, okay. I think I’ll do this today,” for 10 minutes before they make their coffee or whatever. So, getting that barrier so low that it actually becomes part of normal life. We take blood tests all the time and we have our prototypes, so this is something that we’re actually experiencing and it’s very interesting and fun. So, we’re actually pretty excited to get other people using it outside the company.
Josh Clemente (01:01:14):
Well, I can tell you that competing with myself and with other people on optimizing blood metrics is one of my goals in life. I think that when we get to that point, I think we’ve reached a really important milestone for healthcare and for individual health outcomes. When I care enough and there’s a competitive element of doing better with something that has such long-term leverage on my health, that’s a really good sign because today, it’s really hard to connect the dots between that PDF printout of my total cholesterol and anything negative in my life. I can walk through life and have no symptom associated with that, but that could be the thing that puts me on my deathbed.
(01:01:50):
So, I’m really excited about it. I love what you guys are working on. We’ve been gone deep on the technical side for a few years now as you guys have worked on it. So, excited to be able to make that sort of technology available to people listening to this podcast and Levels and the general population. Mike, before we jump off, is there anything you recommend people follow as you guys work on this problem? Do you have social you want to want to drop or is there a newsletter?
Michael Dubrovsky (01:02:17):
Not really, actually. We have a website, but we’re … That’s a good question.
Josh Clemente (01:02:26):
Well, we can dump one in the show notes later if you want. But otherwise, I would recommend check out your website. Right? You guys have a good breakdown of the technology.
Michael Dubrovsky (01:02:34):
Yeah. Just siphoxhealth.com. Yeah.
Josh Clemente (01:02:36):
Yeah. Awesome. Well, this is a great one. I think I expect more to come as you guys continue and as we continue to build in this next gen space. So, yeah. Anything else you wanted to touch on before we jump off?
Michael Dubrovsky (01:02:51):
Yeah. I was actually curious. You don’t have to add this to podcast, but I think this is good podcast material in case you ever need a clip is, maybe you’ve talked about this many times, but what was the transition like for you going from working at SpaceX and doing engineering on machines, very complicated machines, to dealing with the human body biomarkers and everything related to that? I’m just curious how much has carried over now that it’s been a couple of years of really doing it at scale, actually. What’s your experience? And what do you miss, actually?
Josh Clemente (01:03:29):
It’s very different.
Michael Dubrovsky (01:03:30):
What do you miss about machines?
Josh Clemente (01:03:33):
Oh, a lot. Machines are pretty easy to fully characterize. You can have a closed form solution for a structure or a mechanism. It can just be really well understood and designed, and the analog messiness of biology is something that I don’t even know what I don’t know yet, but I know that it’s difficult. It’s very different. A lot of me, I’ve got this excitement to start to unlock that black box and learn more about what’s going on by attaching better sensing to our day to day lives and really starting to describe what’s going on in the body better because it’s actually while I was at SpaceX that I got really frustrated by the complete lack of information I had about my health, and then to find out that there really isn’t any technology that can make this better. I can’t just pay more money to really describe the hormone patterns in my body. It just basically doesn’t exist.
(01:04:31):
So, I look forward to us really leaning in on that and new technologies, like what you’re working on. But relative to working on machines, it’s quite a bit more frustrating, I have to say. There’s a lot of hurdles to be able to build a technology and test it. If there’s anything I miss at SpaceX, first of all, it was an awesome experience and definitely a lot of great people, friends and family that are still there. But the thing that I miss most, I think, is being able to move really, really fast and as recently as a few weeks ago, blow things up, the Starship example. Some people look at that as a total failure and everyone inside of SpaceX is celebrating that because that’s how they build. Once you get to greater than 50/50 chance of success, test it. No further than that because you’re just investing even more in something that might fail and you want to learn quickly why.
(01:05:25):
So, I think it’s harder to have that mode of operations, especially when you’re dealing with people’s real health, and as Theranos found out, you just can’t play with that. You can’t be taking huge risks. So, there’s something there that is a little bit … I certainly miss being able to work with just aluminum because there’s less risk of that sort of thing. But that’s it. It’s also a really exciting space and I’d love for Levels to eventually be the SpaceX kind of innovator in healthcare. I think that taking a completely different angle, lowering the cost of access, and increasing the actionability and usability and scale of this sort of tech, that’s what I want to do. So, yeah. Anyway, I think there’s carryover in a philosophical sense in terms of how we’re going about things and the scrappy nature and we’re definitely in a small upstart, and especially relative to the healthcare industry. But I think day to day, quite a different work experience.
Michael Dubrovsky (01:06:29):
What’s the, if reusability of … Or I guess, I don’t know. The original insight at SpaceX was that you could use off the shelf electronics and things like that. Right? I don’t know. What was the original insight that made them successful and what do you think is the one for Levels? What is the insight that drives the ability to get to the next level where you’re an established player in the industry? Or maybe that’s already happening now.
Josh Clemente (01:06:53):
I think the insight at SpaceX is like if you just take a spreadsheet and calculate how much in raw material and manpower it takes to build a rocket, it’s orders of magnitude different than how much it would cost you to buy a launch on a rocket-
Michael Dubrovsky (01:07:10):
I see. I see.
Josh Clemente (01:07:10):
… at the time that SpaceX was founded. Elon looking at that was like, “This is fundamentally a blockade to us being able to increase access to space and eventually become multi-planetary.” The fact that the industry has manifested this totally bad solution where it wasn’t necessarily the off-shelf electronics, it’s like not only are you paying orders of magnitude more than it costs, but you are then throwing that thing in single use fashion into the ocean. So, both of those can be solved by just building it for what it costs and then also reusing it, like an airplane.
(01:07:39):
For levels, I think the insight that I might correlate there is that right now, the person receiving value and the person paying for value are totally different in healthcare. I ostensibly get value from my healthcare visits, my doctor’s the one that delivers it to me, and somebody else, like an insurance provider or some unknown third party, is responsible for paying for it. So, what I consider to be valuable and what the insurance company considers to be valuable are very different and what I’m willing to pay and what they’re paying and the value I’m getting are totally disconnected. So, you have this broken three party system where what I end up with is product experience is not intended to make me happy. It’s intended to make the insurance company pay, and I think going back to the realization that you don’t have to force people to participate in healthcare. People want to participate in healthcare, but they want to get value from it.
(01:08:31):
So, shifting to a model where it’s a really direct consumerized version of this sort of thing where people just buy products that are relevant to their quality of life concerns and they then have to get value from them, otherwise you don’t get a return customer and your business suffers, that’s what we need in healthcare, in my opinion. Certainly, I won’t say all of healthcare. That’s what we need in preventative and health optimization. Then there’s terminal illnesses and there are accidents and the things that healthcare does super well. That’s a different question. But for me, that’s how we’ll really, I think, improve the feedback loop between products that are being produced and a market existing for them. Then I think that then opening up access to really unlock traditional supply and demand is the other part that we’re doing differently, which is that we aren’t going for captive markets where there’s a moat built around it and a guaranteed long-term customer. It’s actually that we want to go to the area where the most potential users exist, and that’s actually people who aren’t yet sick.
(01:09:46):
Even though rates of illness are really high, there are still more people who aren’t sick than are, and if you build a product that is exceptional to use and you get viral distribution because people are like, “This is the best thing ever,” and they tell all their friends about it, your healthcare product will be less like a thermometer and more like an Apple Watch, and that’s what you want. It’s the vitamins vs. pills thing. People, they don’t take vitamins. They take pills because it solves a problem for them. That’s why pills are so valuable and vitamins are … There’s millions supplement providers. You want to build the pill because it provides value for people and they share that and they rave about it. So, anyway, those are my rambles. TBD on whether or not it’s the same degree of unlock that SpaceX has in aerospace. But for sure, there are orders of magnitude in the healthcare system that are not being seen on the value side.
Michael Dubrovsky (01:10:46):
It’s very similar in that sense, for sure, in terms of just the lack of effort to cut … The semiconductor industry is the exact opposite of healthcare, or what the rocket industry looked like in 2000, right? It’s like every year and a half, even though what’s interesting is in the semiconductor industry, you would think it’s software type, the Silicon Valley type people, but it’s not. The semiconductor people are pretty, I don’t know, this might be an old-timey expression, but they’re kind of hard-boiled. They’re not all techno-optimists or pretending to work 24 hours a day or anything. They’re just forced to go fast because of competition. I think they would go potentially as slow as the healthcare industry if there was one semiconductor company, there was one rocket company. Right? If there was one semiconductor company, they would maybe still be putting out a hundred nanometer transistors. Right? Why not?
(01:11:49):
It’s very hard to make transistors smaller. It takes enormous gambles. They have to gamble. Companies that are 50 years old have to gamble half the company value to get to the next note or whatever, and why would they ever do that? Right? So, it’s like that element of competition that forces these people. They’re not at all the type of people that you encounter in startups or anything like that. They’re actually the same, I would say, and that shows that it’s not good people or bad people. It’s really the dynamics of the market that make people … It actually brings out the best in you, competition and this steady march of progress that they’ve had. If there was a Moore’s Law of healthcare, who knows what would be happening at this point?
Josh Clemente (01:12:33):
Totally. It’s a great point, and I think it’s very similar to the aerospace industry where the semiconductor example, if there was the national semiconductor company, that’s exactly what you would see. It’s what you saw with aerospace. There were a few countries that could put something in orbit and none of them had innovated past Apollo, with the exception of the shuttle, which we put in a museum, and it’s arguable that was an innovation in terms of costs. So, that’s what we have with healthcare. We have this gigantic healthcare system. It’s heavily regulated. It solves certain things really well, but you do not have competition at the system scale. You have competition, you have a couple blessed providers that work inside Medicare for the same thing, but there there’s a lot of price fixing, unfortunately, because of the way the thing works. Medicare sets the price and then everybody gets that price.