Biology sets the table; AI serves the meal

In this inaugural podcast episode, Guru and Momo introduce the science behind personalized health, explaining how biology, the microbiome, and AI can help move us from one-size-fits-all medicine to truly individualized nutrition and prevention.

Transcript:

Guru: Hey Momo, true or false? You need to have an advanced degree to improve your health.

Momo: False.

Guru: Your genetics are the most important thing for your health.

Momo: False.

Guru: Okay, let's try this. Both cardio and weight training are important for your health span.

Momo: Yes, true. Hey Guru, I have some true or false questions for you. If your blood tests are normal, you're healthy.

Guru: No way, false.

Momo: AI and machine learning always produce trustworthy data.

Guru: False.

Momo: Oats are unhealthy for some people.

Guru: That is true.

Momo: Yeah, I agree. So if you take 100 people and feed them the exact same healthy bowl of oats, some will get a steady burn of energy, and others will experience a crazy blood glucose spike like they ate a candy bar. And as a biochemist, I know why that happens. That's because biology is complex and highly individual, and our body is a representation of 100,000 chemical reactions that are different in different people. So we will have different responses to the same oatmeal.

Guru: Yeah, you know, unfortunately, for the last hundred years, medicine has tried to solve that kind of chaos and variance by taking the average. You know what, nobody's really average, and no food is average, right? So I'm an AI and data science expert, and in my world, if you take a complex system and treat it like an average, the system just doesn't work.

Momo: Yeah, I mean, I'm sure there are a few average people, but you can't just count on being the average. That's extremely unlikely to happen.

Guru: Exactly.

Momo: I'm Momo.

Guru: I'm Guru, and we are two PhDs on a pod. All right, let's go. Yes, we have two P's in a pod, but we also have HDs, right? So we look at biology in high definition, or we think about AI and its high dimensionality, or we think about the health span, and are we driven by that? All of those things are why we are–two PhDs on a pod.

Momo: two PhDs on a pod.

Great, and I bring the messy and beautiful complexity of the human biochemistry, and I want everyone to understand from the start that the human body is basically 100,000 chemical reactions that are powering who we are and what we do. And I want everyone to understand that only half of those reactions are actually run by our own genes, and the other half are run by our microbiome. And we're going to focus heavily on both aspects of that human physiology.

Guru: Yeah, and I bring the power of the computational and data elements that currently is such a widespread and easily available resource, right? And you need that resource to make sense out of that data that comes out of biology, because when you talk about a hundred thousand reactions, that really results in tens of millions, maybe even hundreds of millions of data points that we need to make sense out of. And there's no human that can do that. You need AI and algorithms to be able to do that, right? So if you put your expertise and my expertise together, biology sets the table, and AI serves the meal.

Momo: A tasty dish.

Guru: Yeah, that's what we want to serve on this podcast, right? So let me just get started with a little bit about yourself, Momo. Tell us what was your journey in life that brought you here?

Momo: All right, let's talk about that a little bit. So I grew up in Yugoslavia, that country no longer exists. And then I moved to the United States and went to college here and got my PhD at the University of Utah. And we'll talk about that a little bit later. And then the most important part of my career, I would say, is that I spent about 10 years in academia, 10 years as a government scientist at Los Alamos National Laboratory, and now 10 years at Viome as an entrepreneur.

So that's been a really nice, balanced mix of my scientific background. And at Los Alamos National Laboratory, I was the senior scientist at the Genome Center at first, and we did microbial genomics, which is basically sequencing microbial genomes. And at that time, we were the world's leading institution for sequencing microbial genomes. Today, of course, anyone can do it. It's super easy. But back then, 15 years ago plus, that was a big challenge.

Around 2010, I reset my scientific career. I started a brand new team at Los Alamos National Laboratory called Applied Genomics. And we spent the next six years developing foundational technologies that we're using today at Viome. And those technologies were applied to many different fields such as global biosurveillance, soil ecology, toxicology, host microbiome interactions, cancer biology, and interspecies interactions in the human microbiome.

So that's kind of a very high level overview of my scientific career and a little bit about my background. What about you, Guru?

Guru: Man, you've done a lot of things, and I want to actually dive into many of those things later on. So I came from India. I was in India until my bachelor's degree, and then I came here to the United States to get my graduate degrees, and I got a PhD in computer science, where at the time we focused on large-scale distributed systems that process huge amounts of data.

One of those really interesting problems that I eventually ended up working on is a problem called spoken web. That was a project that we did at IBM. I joined IBM right after my PhD and went to India in about 2005 to look at some of the local problems that India was having. One of the local problems was that there were a lot of mobile phones, but many people were not able to access the web because not everybody was literate enough to use a standard user interface.

So we built an entire web system which you can speak to. Back 20 or 25 years ago, this was a very hard problem. We created a way for people to just speak to the web and listen to the web instead of clicking and typing. That made it possible for people like vegetable vendors and auto rickshaw drivers to use the web.

We ended up getting a presidential award for that in India. Then, about five or six years later, I was back in the United States and got pulled into a project called Smarter Cities, where I worked with the mayor of Rio de Janeiro to create a city operation center for the Olympics and the World Cup.

Later, I joined the Watson AI team and started leading the Watson AI team as the first vice president. We began applying AI to healthcare, cancer biology, financial services, manufacturing, and many other areas.

Eventually, I said I really want to apply AI to the life sciences and solve some of the big problems there. I also faced some health challenges in my personal life and decided this is the problem I want to dedicate the rest of my life to. So I joined up with Momo and Naveen Jain, and we started the company Viome.

That brings me to why Momo and I are like two sides of the same coin. Momo deals with the biology, and I deal with the AI. Momo deals with atoms and molecules, and I deal with bits and models. Momo understands biological pathways, and I look for patterns in the data.

Momo is always thinking about carbon, and I'm always thinking about silicon.

Momo understands all these pathways in biology, and I am always looking for the patterns in the data, right? And by the way, Momo is wearing his white lab coat, and I'm wearing my black shirt for this show, which is how Momo and I work almost on a daily basis to solve some of the really, really big problems in the world of health, especially in the world of chronic disease, right? So just before we get into the real substance of this show, let's just talk a little bit about our personal life model. So I want to know what's going on in your life, like today, right now, and what's exciting for you. So what's on your radar?

Momo: Man, so many things, but let's provide a couple of highlights. So on the professional side, we're submitting a scientific paper today, so pretty excited about that. And we're also very, very active on developing two new products for Viome, which is super exciting to go from an idea through all the laboratory development, all the bioinformatics developments, complemented with your teams to go to market with a product that doesn't exist or it's an improvement over the existing products. And then on the personal side, I'm a little sore from some resistance training. So that's pretty exciting. I've been able to do five clean pull-ups, which is a huge accomplishment for me. Yeah, pretty excited about that. Trying to get to 10 eventually, but it'll take probably a year or two. And then spring is here in the Seattle area. My goodness. It's warm. It's—

Guru: That is very cool.

Momo: There's lots of sun, lots of dynamic skies. So we're going to go hiking and birding, and then we're going to definitely fit in—

Guru: Well, you're talking about spring already. I mean, I'm sitting here in something like three feet of snow around my house. And I think there's more coming this weekend, I believe. Well, I'm jealous on the one hand, but actually, to be honest, I don't mind the cold, and I actually love winter sports and stuff like that. So I'm okay.

Momo: Yeah, I mean, you live in New York, which is perfect for you, and I live in Seattle, which is perfect for me. So there's something for everyone. And I'm definitely gonna fit in some table tennis this weekend, as I do all the time.

Guru: Cool. What else?

I gotta talk to you more about that table tennis stuff that you're working on, especially that new racket you got and everything. And I think I'm gonna take you up as a kind of a challenge, and not too far from now, I think you might be able to beat me in the end for the first time, maybe. I think you might already be able to do that. Let me tell you some things that are going on in my life, okay?

Momo: Maybe one day, it's a goal. Yes.

Guru: First of all, I am excited about something we're doing this evening. It is a Bulgarian Oro Teka dance. So it's actually one of those circle dances, but kind of funky and complicated. So I really enjoy that, and I get a lot of aerobic exercise from it, and I cannot wait to go to that dance studio this evening. That's one cool thing that's going on in my life. The other thing—

Momo: Wow. Are you gonna be recording that? Are we gonna be able to show some clips of that?

Guru: Why not? I actually have some recordings, and maybe I'll bring one here. Sure, sure, no problem. I love all types of dancing and music, stuff like that. The other thing that I'm excited about is that I now have this bunch of AI agents that are doing some funky things for me in the background. This new generation of large language models combined with autonomous—

Guru: Capabilities is just giving me so many ideas for what we can do. So I'm pretty excited about that. Maybe in a future episode, we can talk about that in more detail. And one more thing. I just started on a skin clinical study that one of my daughters is running. It's super exciting. It's a very nice serum that we're testing what the biological and the clinical impact is gonna be. You know, my wife and I are both doing that, and we'll see what happens.

Momo: I need that. You gotta send me the link. I need that. More than you.

Guru: I'll get you on. I'm not sure about more than me, but I'll get you the thing. And then finally, of course, I'm submitting the same paper that you mentioned earlier on, so I'm excited about that as well. So Momo, let's talk about why we're doing this podcast. So why don't you start?

Momo: Awesome. All right, yeah, this is really an important topic, and we've been talking about this for quite a long time, and we think the time is right now. Yeah, there are many reasons that we're going to talk, we started this podcast; this is our first episode, yay. And one of the reasons is that we want to explain preventive health to everyone. We want to really uncomplicate and simplify all of that conflicting and overwhelming amount of information.

More importantly, we're gonna make it fun. We're gonna make it really easy for everyone to learn and to understand because we're not going to just be pushing any one agenda. See, I think one of the main issues right now is that everyone is presenting sort of their angle, and they're very pushy and they're very assertive on that one angle. But if you have multiple people presenting their own angles in a very assertive way, people can get confused as to what is the real truth. So we don't have an angle, we don't have a bias, we're here to interpret the entire human knowledge and bring it to the masses and make it fun.

Guru: Sounds good. And you know, the idea of preventive health is so important because the current healthcare system is really not designed for preventive health. It's designed for sick care, right? So, you know, once you get sick, you go to the hospital, you go to your doctor, and they usually try to get you something to address your symptoms, you know, and yeah, and then you not only have a bad quality of life, you spend—

Momo: You'll manage your illness for the rest of your life.

Guru: Through your nose, and you're not actually helping yourself, you're not gonna have a very long health span. So preventive health is the opposite of that. You think about what you need to do today so you can avoid getting to that place. So I wanna talk about that to a large degree. The other major objective, Momo, is that we want to ground everything with data and with principles of good science, right? So one of the things we'll get to in a few minutes is that we have a ton of data that we can pull into various views that can answer many, many critical questions or claims that we are going to discuss on this podcast. It's not enough just to make a claim because you believe in something. You have to have the right experiments, you have to have the right data, you have to have the right evidence for it.

And in many cases, what people do is when they do have the right experiments and the science behind it, they kind of wrap it up with jargon and with complicated ideas and terminology, and so forth. So it doesn't hit everybody. It doesn't come across to everybody in an understandable way. So what we want to do is try to give examples. We want to try to show you simple ways of interpreting the data. We want to give more analogies. And very importantly, I think, we also want to perhaps look at multiple perspectives on the data, and sometimes there is going to be some conflict in the different perspectives, and we have to reconcile those conflicts.

And turns out when you have a standard study like is coffee good for you, some studies will come out and say coffee is good for you, some studies will come out and say coffee is bad for you, and they'll both have data. Turns out that many of these foods, like coffee, could be good for some people and maybe not good for other people. So it's not about what is good for a population or the average of a population. It's really about what is good for you as an individual. So what we want to bring it down to is not just look at the population data, but bring it down to the end of one, to the individual biology that will make a difference for you, and explain the principles behind why something complicated could actually be applied in a very rational way to an individual person, which, you know, start with yourself and then maybe the individuals in a family, right? So we can do all of those things.

Momo: Yeah, I think that's really the main takeaway and the highlight for people, for the audience, is to understand that diet and lifestyle need to be personalized, and diet especially. And we will present many, many examples, but I think I'll just go through a couple of those for now. For example, I think this alpha-gal allergy is now becoming a little more visible. There's this tick that bites people in the South.

And once that happens, some people become allergic to mammalian foods because of this molecule called alpha-gal. And they no longer can eat mammalian foods for the rest of their lives. Mammalian foods basically come from mammals, which are all red meats and all dairy. And so if there's some kind of a massive study that says cheese is great for humanity, if cheese makes you run to a hospital emergency room, cheese is not good for you. It doesn't matter that it's great for humanity overall, right?

And then the same thing, if you do a food sensitivity test and your immune system is reactive to some food and you remove that food and you feel better, it doesn't matter what studies show that that food has in terms of benefits or detriments to the general population, you're not gonna eat it anymore. And I am one of those people who has gone through this journey, having some sort of an autoimmune condition that was never diagnosed as a young man.

And then, through trial and error, I tried every single diet possible. And it turns out that I am also sensitive. My immune system is also sensitive to mammalian foods, but it's not the alpha-gal. It's not an allergy, it's a sensitivity to mammalian foods. And the point I want to make here is that I had to individualize my diet very, very clearly. Like if I eat any mammalian food, I'm sick for a month. And here's one of the highlights of this podcast. We are going to enable people to have fun with changing their dietary and lifestyle habits to become healthier, but having fun doing it.

Because I think that one of the really bothersome aspects of dieting and exercise is that people hate it. People don't want to go to the gym and work hard. People don't want to avoid foods or eat some foods that they don't like. That's just super restrictive, super hard, as if cooking isn't hard enough, right? So I was in the shoes where I basically had a very restrictive diet, and I didn't like cooking. And over a period of several years, I figured it out. And now I love cooking. I love inventing new foods. I have a highly, highly positive relationship with foods and cooking. And we will definitely be talking about that as a sort of a common thread. I'll be bringing recipes, and we'll have cooking shows and cooking challenges, and all that stuff, and really make our interactions with food fun. And the same thing with exercise and Zen.

Guru: Actually, for exercise, I want to tell you an example from my life, right? I used to try various things like going to the gym and trying out different types of exercises, both resistance training and some types of aerobic training as well, but nothing ever really stuck for very long. And then I became a runner, and I used to train very hard. And after a couple of years and a marathon and everything else, I ended up saying to myself, this is not fun because you spend hours and hours every week training. And yes, of course, I used to read audiobooks while running, which was part of the fun that I had. But over time, it became too long and too monotonous. So I decided to switch to table tennis. Actually, that's one of our common interests. Table tennis is so much fun. It's social. It is competitive if you want it to be. It is definitely–

Momo: It is weather-independent.

Guru: Rather exactly, so important in a place like New York, right? It's like, you know, it's snowing outside. I go inside my club, which is not too far from where I am right now, by the way, and I can play, and I always have people who are interested in playing, which is amazing. And then I mentioned the Bulgarian dance, right? I've taken up different types of dancing as another aerobic exercise. So, you know, you can make it fun. You can make it fun, and it becomes sustainable over the long—

Momo: Yep, that's the key. That's the key. If it's not fun, people are gonna stop doing it. So I guess as of right now, we're gonna kind of split it up, and I'll be showing people how to have fun with food and cooking, and you'll be showing people how to have fun with exercise. Maybe.

Guru: Well, yeah, and then we'll switch it around, you know, do whatever it takes. Yeah.

Momo: We'll switch it around, sure. Yeah. All right, so this brings us to the next topic, which is why we started Viome, because it's now our 10th year, and let's talk about the background of Viome. So I'll start with talking about the epidemic of chronic disease. So if you just think about, let's say the 1950s and 60s in developed countries, if you look at pictures of beaches or public events, you can see that there were very few obese people. And if you look at the statistics, there were very few people with diseases like Parkinson's, multiple sclerosis. Alzheimer's was essentially an unknown disease, inflammatory bowel disease. All these diseases were very rare. And today, those are so common that you're not surprised at all when someone tells you that they have a chronic disease. That's completely normal.

Same with allergies, you know, allergies used to be really rare, and today it's just expected that people will have allergies. And so we have this huge epidemic of chronic diseases, and there are, of course, an infinite number of speculations out there as to what is the root cause. And we could be spending the rest of our lifetime looking at those speculations and wondering what is the root cause, or we could actually solve the problem.

And so that's exactly what Viome is for. Our mission is to solve that problem of what is the root cause of chronic diseases and cancers, and how can we prevent it? And the reason that this is needed is because neither academia nor pharma, which are the two largest entities that are health focused, are actually solving the problem. Academics are super smart, super hardworking, but they're really focused on publishing papers. They're not focused on translating the knowledge that's relevant to human health into our hands. And pharma, as we discussed earlier, their business model is to manage diseases, not to prevent them. And understanding that is very important to say that, okay, we need another entity that's actually gonna focus on prevention. And that's exactly what Viome is about.

So when we were thinking about, you know, I started thinking about this platform back in about 2010, like in earnest, when I reset my scientific career, and I thought, okay, what kind of data do we need to generate on the human body in order to really understand all the different mechanisms of how it works, that we can then tune and prevent chronic disease and cancers? And when I looked at all the data streams that we could obtain from the human body, such as DNA sequencing, RNA sequencing, protein sequencing, and then metabolites, which are basically molecules, there were trade-offs with each one of those, but RNA came on top as the single best source of data that can digitize the human body, tell us exactly what's working well and what's not working well, and then identify ways to tune the human physiology. And so after about six years, that technology was developed at Los Alamos National Lab under my supervision and leadership. And then we patented all that, and now Viome continues the development of translating that data to humanity.

Guru: Amazing, Momo. This was the spark, or one of the sparks, that created the underlying foundation for Viome. That foundation needs more layers on top in order to translate that into usable clinical applications. In order to translate and transform that platform that you've built, which is the laboratory assay for RNA analysis and other kinds of analysis, too, into clinical applications, we have literally built a vertical platform. And that platform includes all of the algorithms that are required to understand the data that comes out of your lab.

Right? And it also has to do with all of the experimentation that's required, the discovery that's needed to understand the pathways underneath the known set of activities that happen within your body, right? The biology in your body. So we built all this data, and over the last almost 10 years now, it's a little bit, you know, when we started the product, I think in Miami it was April 2017 if I remember correctly. So I think it's going to be fully 10 years next year. So in that period of time, we have generated a tremendous amount of data, and I want to show you very quickly what that data looks like. Okay, so here is—

Momo: I think you need to find a better superlative than tremendous. I think it's more than tremendous.

Guru: Okay, all right, well, we'll keep looking for that. Extraordinary. How about that? This picture gives you a view.

So we have collected at this point in time an unprecedented amount of untargeted molecular data, and specifically, let's start from beginning here, right? We have people from more than 100 countries who've sent us more than, actually, well more than 1 million biological samples at this point in time. And when we say biological samples, we mean—

Guru: Stool samples, blood samples, saliva samples, mainly, but we also have other kinds of samples because we have clinical studies where we do samples that could be other tissue types in the body, liquid biopsies, and so on. But from that data, we have sequenced about a petabase, right? 10 to the 15 RNA nucleotides from that data. And now it's getting closer and closer to 10 to the 16.

And from that set of nucleotides and all of the metadata that we've collected, we have now also computed 10 to the 16, which is more than 10 peta data points. So it's all peta-scale data over here. We've computed that many data points. And this is the largest collection of metatranscriptomic data that we have ever come across currently on the planet.

If somebody is listening out there and if you know of other sources of metatranscriptomic data, please speak up, put a comment down below, and we'll take a look at it. But we think this is the largest dataset of metatranscriptomic data that's anywhere on the planet right now. And we have extracted from these nucleotide sequences more than 65 trillion molecular features, and when we say molecular features, we mean genes and organisms, meaning represented by genomes.

Equally importantly, we have collected a huge amount of phenotype metadata. And when we say phenotype metadata, we mean people telling us about their symptoms, their diseases, the medications they take, the lifestyles that they follow, the demographics that they're in, and so forth. And the combination of the molecular features that we get and the phenotype metadata that we get is tremendous because once you look at the two things together, you can start looking at disease, health, what the differences are, what are the pathways that are leading toward disease, the pathways leading toward health, and there's a huge amount of machine learning and AI that we've done to understand some of this data. I just show on this picture here about 30 different AI and machine learning models for pathways and biomarkers, but there's actually a lot more than that. And we'll go through many of those during these episodes.

But it's not just understanding what's going on in these samples. We've actually provided those insights back to the individuals who have sent us these samples. And we've given people more than 22 million such insights so far, and we've given them more than 140 million nutritional recommendations for how to optimize their molecular pathways. So we've given them foods that could be ideal for them. So we call them superfoods. We tell them which foods they should avoid so that they don't exacerbate some of their molecular pathways. We tell them what kinds of ingredients they should emphasize, potentially as supplements to enhance the nutritional quality. We've given them all kinds of data. So there is literally a treasure trove of data that we think we can ask so many questions of, and we can build so many different models and algorithms to understand better and use that to improve human health.

So along the same lines, whenever we talk about this type of data, we also look at what are the differences between different cohorts of people. And one of the very common questions that I get from people that I talk to about this dataset, which by the way also includes microbiome data and human data, so it's human gene expression, microbiome gene expression, and of course genomic activity, meaning all the species that are active in any one of these samples, so we count all of those things. So one of the common questions I get is, listen, yeah, you guys have learned a lot from all of this microbiome data, but if I'm from a completely different place, let's say I'm from somewhere in the middle of Africa, is my microbiome going to be very, very different? Right, so we have studied this question because we have data from many populations from around the world. So one particular study that we did was between populations in the US and populations in Japan. And we took, randomly, people in the US from that collection of people, which is tens of thousands of people—

And a lot of people again from Japan. And we asked the question, how similar are their microbiomes if they're from the same country versus when they're from different countries? So the really surprising result that came out, actually we kind of expected it, but it's surprising to a lot of people, is that when you look at the species of the microbiome that exists in a random person in the US and a random person in Japan, it could be only about 50% similar, meaning that they look very, very different. That's what has traditionally made people think that microbiomes are very different from different parts of the world.

But the species are not the real story. The real story is what are the biological functions that are going on in your gut as a result of the activity of these species, which is also known as the gene expression. So when you look at all these activities, meaning we call those functions, different functions for replication, for metabolism, for other immune functions, and so on and so forth, when you look at all of those functions, it turns out that a random person in the US and a random person in Japan are more than 80% similar. And it turns out that if you take two random people in the US, they're more than 80% similar as well. So it's very, very similar in terms of your microbiome functions, regardless of where you come from. You could be in the same country, you could be in different countries. As humans and as a planet which has evolved together, we have a set of functions that has allowed us to survive. We've evolved to optimize those functions, and we've evolved to thrive. So those functions are very similar across very large swaths of geography.

I've said a lot of things, and Momo has said a lot of things, and we want to have time to talk about all of these things in detail in this episode, but guess what? We're going to be talking about each one of these topics in multiple episodes that are going to come up, and I'm hoping that all of you will join us in future episodes when we dig deep, deep dive into many of these topics, not just by ourselves, but also with guests, some really great people who have studied these subjects, who have practiced these subjects, and who can give us a lot more knowledge about what it means and how to use it in real life and so forth. So, for example, we could pick a topic like the following: Why is your microbiome your best friend? And we could spend an entire episode telling you why.

In fact, those bugs are not your enemies; they are your friends. They are necessary for you to survive and thrive. So we need to understand how to take care of them, and we need to actually make them as good as we can possibly make them.

Momo: Yeah, I think that we're going to have multiple episodes. We need multiple episodes to just scratch the surface of why is microbiome our best friend? So we'll talk about that for sure. And now it's time to close for today. So let's talk about some takeaways and learnings from today. Guru, go ahead.

Guru: Yeah, so the first takeaway for me is that this podcast is about a vision to eradicate chronic disease and cancers from the planet, right? So very, very importantly, this is not magical thinking because we believe this, science and the technology needed to solve this problem exists today. And we think we can apply that, we can scale that, and we can make it work for the majority of humans.

Momo: Absolutely. No doubt in my mind. I just want to remind people that in the 13th century, Black Death caused a third of the world's population to die off. And at that time, life or death was literally a matter of luck. If you survived, you were lucky. And if you died, you were not lucky.

And that's because we had no tools and no science to actually understand the root cause of Black Death, which was the Yersinia pestis microorganism. Today, we understand that, and therefore, there's no fear of the Black Death of any kind. And unfortunately, when it comes to chronic diseases and cancers, we're literally in the 13th century because we currently do not know what causes any of the chronic diseases or cancers. And we cannot tell anyone what to do intentionally at a personalized level in order to prevent them. And so that's really what our mission is, is to answer those questions. What is the root cause of Alzheimer's? What is the root cause of Parkinson's, type 2 diabetes, every cancer, and so on? And then in a very deterministic, very intentional way, teach people how to prevent those diseases. That's our mission. We can't do that today, but we will do it one day.

And I'd like to say that one of the takeaways that people should have from this podcast, that it's going to be a common thread throughout, is that we want to make a very positive relationship with health and nutritional, and lifestyle choices and practices that support good health. There is no need to have a negative relationship and sort of dread, I have to cook something, or I have to eat something. We're gonna make it positive.

Guru: Yeah, we have a lot of exciting topics, Momo. And I cannot wait for the next episode. I think the number of ideas that are cooking right now are just amazing. And I think all of our audience members should find something very interesting in most of our episodes. So I would encourage all of you to hit the subscribe button now. So just go down there, hit the subscribe button, and we hope to see you here in the next episode.

Momo: Yeah, hit that subscribe button, and here's a little teaser. So in the next episode, we will be answering the following question. Is there a super-tasty dessert that is easy to make? I'm talking about two minutes, and that is also very healthy, at least for most people. We'll talk about personalization, of course. Yes, the answer is yes. And we will show you one. So very exciting. So that's it for today's episode. I'm Momo.

Guru: I'm Guru, and we are two PhDs on a pod.