Speaker 1:
Coming up on this episode of The Doctor’s Farmacy.
Noosheen Hashemi:
Generic advice doesn’t work, personalized advice works. People are infinitely interested in what works for them, not what works for everyone else.
Dr. Mark Hyman:
Welcome to The Doctor’s Farmacy. I’m Dr. Mark Hyman, that’s Farmacy with an F, a place for conversations that matter. And today, if you ever wondered about what’s happening inside your body, this is a conversation you’re going to listen to because most of us have no clue of how to map, track, find out what actually is happening day to day, minute to minute in our biology in ways that help us make decisions about how to create help for ourselves. And that’s why I’m so excited to talk to our guest, Noosheen Hashemi, who is an incredible woman. She’s a Silicon Valley tech veteran, an entrepreneur investor philanthropist. She’s the CEO and founder of an incredible new company called January AI, which stands for artificial intelligence. I think January, because it’s about new beginnings, I don’t know. It’s a precision health tech company. We’re going to talk about what precision health is and it harnesses the power of artificial intelligence to prevent, predict, postpone, and even manage chronic disease.
Dr. Mark Hyman:
It’s an answer to a very screwed up healthcare system and industry that only addresses our decline in disease, but doesn’t address the creation of health or prevention or how we really can learn about our bodies in ways that allow us to make real time choices, to create wellbeing. And they partner with incredible folks to understand how to optimize the body for health and longevity. They were awarded the World Economic Forum Technology Pioneer Award, which is pretty awesome.
Dr. Mark Hyman:
She also works in the family office and invest in over 100 companies in venture capital and the founder of The HAND Foundation, folks, on supporting scholars and organizations that promote discourse and socioeconomic growth among the disenfranchised. She’s also the Harold Pratt Associate Council on Foreign Relations and serves on the advisory board of Stanford Graduate School of Business, Stanford Institute for Economic Policy and Research and Tufts Friedman School of Nutrition, Science and Policy, which is how I got connected to her through my friend, Dariush Mozaffarian. So, welcome. How are you, Noosheen?
Noosheen Hashemi:
I’m good, thank you so much. It’s really a pleasure to meet you.
Dr. Mark Hyman:
Well, it’s great to meet you too. And I think we were chatting a little bit earlier, there’s so much going on in the healthcare space now that’s disruptive outside the normal channels of healthcare, but are really transforming the way we approach diagnosing and treating disease. And it’s I think where everything is going and unfortunately healthcare is slow to change, it’s like a dinosaur, it moves at a glacial pace. And what I’m so excited about is companies like yours that are driving changes in the way we start to think about our relationship with our own bodies by getting more data information and real time feedback about how they work.
Dr. Mark Hyman:
And your company, just for people listening, is essentially a company that’s using artificial intelligence to map what’s going on inside your body, starting with blood sugar, but it’s I think a lot more than that. So let me start by asking you how you got into this because particularly, traditional healthcare is very dogmatic and very structured and rigid and you’re coming off left field with an idea that has the potential to transform how doctors address disease and people themselves can be empowered to transform their health. So how did you start to go, oh, this is something I want to do?
Noosheen Hashemi:
Absolutely. How I got started is, I decided in 2016 to start my own company after many years of like you said, running a family office, investing and serving on boards of companies and nonprofits. I had early success at Oracle where I rose from the bottom of the organization, 1985, to vice president at age 27, taking the company from 25 million to 3 billion. So it was quite a ride. I decided I was an operator and I was going to go hands on and bet on myself as good old Larry Ellison has always taught all of us to do. And so I started a massive search-
Dr. Mark Hyman:
Bet on yourself.
Noosheen Hashemi:
Bet on yourself, he always bets on himself, absolutely.
Dr. Mark Hyman:
That’s a good one.
Noosheen Hashemi:
Yeah. So I started a massive search into the thesis that were getting a lot of attention, got a big trends over the next decade and most importantly, what I knew and loved, the classic kind of iky guy? So I happened to attend this White House Stanford University conference on societal benefit of AI and how to integrate the ever evolving AI into the real world. There was a healthcare panel that was phenomenal. Fei-Fei Li who had organized this conference with Russ Altman suggested that those of us who were interested in health and machine learning go to this conference in LA in two weeks. So, my flight that night with a friend who was doing research with me one after this conference. There, we met Larry Smarr, I don’t know if you know him or not, but he’s one of the-
Dr. Mark Hyman:
Yes.
Noosheen Hashemi:
… most self quantified people on earth. And he was keynoting that conference and he had diagnosed his own Crohn’s disease way before symptoms had manifested. And it’s the common theme between all the presentations at the conference were that machine learning could essentially fill in the gaps for missing variables in research and not just going forward, but going backwards. So you could go back and look at research that been done before and fill in with machine learning for missing variables. And this really lit a light in my head because I’d been talking to Andrew Lowe at MIT about how little research there was in medical research. And ever since Nixon had-
Dr. Mark Hyman:
How little research there was on AI, you mean, or?
Noosheen Hashemi:
No, how little research there was on human body. Basically, 50 years ago, Nixon declared a war on cancer and after 50 years, we have a few therapies and now we think of cancer as chronic condition. We have nothing for neurological diseases, we have nothing for aging. And it became obvious that A, we needed everyone to contribute data, everyone needed to be part of research doing 25 people in this university and 85 people in that university and 1,000 people here and 2,000 people there, wasn’t going to solve for health in America, it wasn’t going to. In fact, China’s taking this with gumption, that’s a whole other story if we want to get to it.
Noosheen Hashemi:
So the answer was to get everyone contributing to research, like 40 million people have taken genetic tests now, we have that information, we can use that information. But secondly, it was to apply AI and machine learning to be able to see patterns and to be able to see how people are different. And I became obsessed with this idea of this kind of collecting multiomic data and I don’t know if you remember Human Longevity at the time, a company called Arivale, these companies who were trying to collect data, but not a lot of that data was actionable. So, full genome sequence, your full microbiome and all that wasn’t immediately actionable.
Dr. Mark Hyman:
No, you’re right. I saw a lot of the patients who went through those programs, the Human Longevity Institute and Arivale. And there was a lot of actionable data, the problem is that that the data was not in the realm of traditional medicine, and so a traditional doctor would look at it and go, “I don’t know what to do with this.”
Noosheen Hashemi:
I don’t know what to do with this.
Dr. Mark Hyman:
And they’d send him to me as a functional medicine doctor who knows how to understand systems and networks and patterns in the data. And that was really insightful to me because you can generate all this data but it’s not knowledge, it’s just exactly information. It’s just a bunch of noise, but within the noise, there’s patterns and there’s data that actually help you decide to do.
Noosheen Hashemi:
100%.
Dr. Mark Hyman:
That’s the missing piece in medicine right now. And I think just to piggyback on what you were saying, you’re talking about deviating from a medical system that has been focused on a reductionist approach to disease that eliminates all on the certain variables. It basically ignores all the important data that’s in the “noise” which is actually signal-
Noosheen Hashemi:
Absolutely.
Dr. Mark Hyman:
… and this is the opportunity to understand the complexity of human biology in real time. And that’s what functional medicine for me is, it’s really a way of having container for the complexity of human biology, understanding the body as this complex adaptive system, a network of networks. And when you just think about this, I’m just going to talk about something that I think is going to be relevant for people to understand why what you’re doing is so important and why it’s so unique. And that is that when you go to the doctor, you get a lab test and a checkup and they’ll do I don’t know, 30, 40, 50 different analytes. That’s nothing. You have literally hundreds of thousands of molecules in your body, you have billions and billions of chemical reactions happening every second.
Noosheen Hashemi:
That’s right.
Dr. Mark Hyman:
And we to think that we actually understand what’s going on by just looking at a few numbers is just so silly, and yet it is what we do. And so what you’re talking about with your company is to take massive amounts of data from massive amounts of people and try to look for the relevant data that teaches us stuff that we never even saw before.
Noosheen Hashemi:
Absolutely. So you are describing what we call in our company, the multiomic approach. I was looking for exactly everything you just said, which was a more nuanced, more complex answer, as opposed to just reducing Dr. Hyman to his cholesterol and Noosheen to her A1C and the other person to their blood pressure, and that’s how I found Mike Snyder, the ultimate multiomic man. So-
Dr. Mark Hyman:
Wait, just what is multiomics? People don’t know what that is.
Noosheen Hashemi:
Multiomics is exactly what you were saying, it’s multivariate decision making. So you take in the A1C as one data point, you take the cholesterol as a data point, but you also take wearables as a data point. You also take how much the person’s sleeping as a data point. You also take how active they are as a data point. There’s no classic medical biomarker for activity. So you take novel markers, you take classic markers, you take essentially all the data that you can get your hands on. You can go to genomics, microbiome data, you can go to metabolomics, we are just beginning to understand proteomics. There are a few very promising companies too that just went public, Sierra and Nautilus who are going to be understanding the entire human proteome which is new.
Noosheen Hashemi:
It will be revolutionizing biology as genomics revolutionized health for us. So we understand so little so far, but anyway, the interesting thing is that Mike had diagnosed his own diabetes and he has Type 2 diabetes and while Larry had used super computers to understand his own data, Mike has applied this to other people. So he had to run this multiomics study four years long at iPOP at Stanford University, where he had taken genomics and microbiome and people’s wearables data and their food data, and a number of other data and to try to understand how people go from health to disease.
Noosheen Hashemi:
So it was love at first sight, we met and we decided to start us through. So I think that what you just said about systems biology, we call it my multiomics because I think it’s a better representation of what we’re trying to do. So Mike and I looked at what was possible and we said, “How can we bring this multivariate understanding of human health to everyone, to the masses?” Not just to the few people that can afford Human Longevity Inc or could have afforded some of these other solutions.
Noosheen Hashemi:
So we said, what’s the least expensive way that we can get started? And we said the basic was basically heart rate monitors who were readily available, like Apple watch and Fitbit, and then continuous glucose monitors which he had been using for eight years before we even got started. And he felt like it was an inflection point in health. We were going from using wearables from fitness to actually for health. And so we started our work and we said, what can we learn from heart rate and CGM? And we quickly learned that-
Dr. Mark Hyman:
Wait, wait, CGM is continuous glucose monitor.
Noosheen Hashemi:
Continuous glucose monitor. So what we realized is that we actually had a missing piece of data, that missing piece of data was food. We needed to deeply understand food.
Dr. Mark Hyman:
What! Food is relevant to health, what do you mean?
Noosheen Hashemi:
Yes.
Dr. Mark Hyman:
Really?
Noosheen Hashemi:
Yes. So we thought in order for us-
Dr. Mark Hyman:
What an epiphany! Like Eureka, right?
Noosheen Hashemi:
Exactly. So I was obsessed with prevention, use of AI to help us in this new field. And I thought okay, continuous modalities of data like heart rate and glucose are going to be superbly interesting for doing machine learning because continuous data, opportunity for machine learning, great, but food is missing. And so we started with trying to understand food and of course the issues that you have with food is as you know, food data is imprecise, even U.S. data can be 20% wrong. Food labels are lacking, only grocery items and chain restaurants have food labeling. Food journaling is really full of friction, hand entering stuff, there’s no passive way, like just wearing something, it just automatically can track your food, crowdsource data, like data from my fitness pal and those are faulty, they have low integrity. So it’s hard to machine learn off crowdsourced data because it’s so imprecise.
Noosheen Hashemi:
And then there’s a lot of generic advice there just like, just don’t eat refined sugar, no refined flour, lots of vegetables, walk 10,000 steps. That will take care of everything. So we realized we need to bring precision to a very imprecise world. So that’s how with machine learning, we set out to tackle these gigantic problems. Problem number one, we aggregated-
Dr. Mark Hyman:
Before you go into problem, can you distinguish between artificial intelligence and machine learning for people? Because I’m not sure everybody understands that.
Noosheen Hashemi:
Well, just machine learning is the act of developing more models and training models with the data that you get. And that’s really what we were trying to do, is essentially create a model that could guesstimate the nutritional values of foods for example, that’s a model. You get to artificial intelligence when things start working on their own to some degree and you create this loop where the machine is learning by itself, the model, you’re no longer… you have the model and it is going collecting data, it brings it back, goes back into the model, retrains and relearns and uses it. So they’re used interchangeably.
Noosheen Hashemi:
But for example, machine learning and data science, you hear a lot about data science, those are completely different. Data science is basically looking at data and looking for patterns while machine learning is actually model development. You actually develop a model and you continue to refine your model for accuracy, for-
Dr. Mark Hyman:
It’s literally learning from the data.
Noosheen Hashemi:
It’s literally learning. Yeah, exactly right. So the problems we set out to solve were first, food labeling. So we aggregated the most gigantic 16 million foods, the most biggest database of curated databases. So this mega database of databases of recipes and menu items from local restaurants, not chain restaurants, local restaurants, mom-and-pop shops. And grocery items, everything, everything that was possible to get in America, we aggregate into one database.
Dr. Mark Hyman:
Really? That’s incredible.
Noosheen Hashemi:
It is. It is incredible, it’s a lot of work, it’s expensive. And then, we used machine learning to guesstimate its nutritional values. Anything that didn’t have nutritional values, and that is a very complicated process we went through, we have a lot of intellectual property, files around our abilities, pending patents around how we did this. Then we realized that glycemic response was better associated with glycemic index and glycemic load than with just carbs because you don’t just eat pure carbs, you eat carbs with other stuff.
Dr. Mark Hyman:
Of course.
Noosheen Hashemi:
With fiber, there’s water in the food, there’s… So we felt that it was really important that we understand GI, GL foods. So our next machine learning project was to estimate glycemic index and glycemic lows for these 60 million foods.
Dr. Mark Hyman:
Which is very interesting because when you start to understand about personalized medicine and nutrition, you realize that even the same food, different people can have profoundly different effects because of their microbiome, their immune system, food sensitivities, their genetics, there’s so many factors. So glycemic load is a glycemic load, it’s like it really depends on the person.
Noosheen Hashemi:
Which is why we ran a trial. So we ran an observational trial, put it out there, 23,000 people applied to it, took 1,022 people, including healthy individuals, people with pre-diabetes and people with Type 2 diabetes, 250 of them were those and associated people’s glycemic response to the GI, GL of the foods they were eating. We turned that into a prediction model and that prediction model is what we presented at the American Diabetes Association in June of 2020 as a poster. This was Mike Snyder and our scientists together presenting that poster. That poster really described how we had done our work in silico, using AI first, using machine learning and then had put it into humans to vet it and then how our models were accurate 33 hours into the future with a high level of accuracy.
Noosheen Hashemi:
So what are the uses of these model? What does it do? What’s it good for? Well, when you can predict someone’s glycemic response, you can let them compare foods like any two grocery items, any two restaurant menus, they can compare any two recipes, say you want to make pancakes for your kids this weekend but you don’t want it to be very high glycemic index, you want it to be something lower. You can compare recipes, you can decide which one you want to make.
Noosheen Hashemi:
Maybe you want to know essentially the price of what you’re eating in minutes of walking, like hey, if you eat this fried chicken, you’re going to have to walk 36 minutes. Do you have 36 minutes to walk to put your blood sugar back in a healthy blood glucose range? You don’t have 36 minutes, maybe save this for the weekend. So it essentially tells people that truly closes the human behavior loop because then, you know what this is going to do. And then also it allows us [crosstalk 00:19:08]-
Dr. Mark Hyman:
Oh, you have to track it. So part of you do is you help people track their blood sugar.
Noosheen Hashemi:
Yes.
Dr. Mark Hyman:
And so, that gives them real time feedback, so while you may have determined through your machine learning model that this or that food has this or that glycemic index or load, when they eat it, it might be different for them, right?
Noosheen Hashemi:
Actually the prediction is for them, the prediction is for them.
Dr. Mark Hyman:
Based on what though? Based on their… is that based on the actual food?
Noosheen Hashemi:
It’s based on the actual food but after four days of training, that’s what the poster presented, after four days of training with January using food data, glucose data, and heart rate data, January can predict your glycemic response to any food in the 16 million strong.
Dr. Mark Hyman:
Wow, that’s unbelievable!
Noosheen Hashemi:
So you don’t have to put foods through your body, you can put them through the AI, let the machine tell you how you’re going to respond.
Dr. Mark Hyman:
So if you eat a plum, it might be fine, if I had a plum, it might not be fine. Right?
Noosheen Hashemi:
Exactly. But your prediction or my prediction are going to be different, they’re not going to be the same.
Dr. Mark Hyman:
Although it’s complicated because this gets even to a meta level of complexity with what the power of AI may be able to do when you think about the 16 million foods, they’re not all the same. So a carrot grown on a regenerative farm is different than a carrot grown in a commercial farm. There was just a study published today in nature, looked at the metabolic analysis with literally the metabolic features on the molecules in grass-fed meat versus plant-based meat. And even though the nutrition fax label was identical, they were profoundly different in so many different qualities.
Noosheen Hashemi:
Absolutely.
Dr. Mark Hyman:
And I think it’s something that people really have trouble with because if you say well, yes, meat, you’re going to do meat. But I wonder if you do grass-fed meat or kangaroo meat for example, we talked on the podcast, lowers inflammation or wild meat, grass-fed meat increases inflammation, same amount of volume of protein or grams of protein but profoundly different biological effects. How do you account for all that? That seems like a nightmare.
Noosheen Hashemi:
That’s our dream to be able to… What you just said is what Christopher Gardner told us four years ago. He said, “Look, tomatoes grown in Sonoma are going to be different than tomatoes grown in Chile.” But we are heading to that level of precision. We will be able to tell you your carbon footprint eventually of where your foods came from and what kind of nutrients were in the soil, where they were grown, the method of cooking and everything else you just said. Precision, we mean precision. So some of that can come from-
Dr. Mark Hyman:
That’s incredible.
Noosheen Hashemi:
… companies like us, but some of it has to come from government and government policy, which is why support Tufts School of Nutrition because it has such a strong policy program. Government policy don’t underestimate that power, government can outspend private sector as you know tremendously, it can outspend philanthropy. Government policy is critically important, they’re part of the solution and we need to bring in to the table but you’re right, that’s the precision we’re headed for. And January has just started this but we dream of greater precision.
Dr. Mark Hyman:
So what you’re describing, Noosheen, is a radical transformation in our thinking about medicine, which has pretty much been one size fits all. You have diabetes, you get the diabetes drugs, you have rheumatoid arthritis, you get the rheumatoid arthritis drugs. And it’s so uniform and there’s really very little accounting for the massive complexity of human biology. And not only the complexity of human biology, but the inter-individual differences in our biology. So no two people are the same. So you’re talking about personalized medicine, personalized and precision nutrition, precision health, precision medicine, all these terms are floating around and they’re speaking to the fact that we’ve just really barely begun to scratch the surface of understanding how our bodies work and also we’re so focused on the end stage diseases that we haven’t really taken a step back to go, what is happening in our biology like decades before our first symptom?
Dr. Mark Hyman:
Now we know for example, on an MRI, through functional MRIs, we can see inflammation and changes in brains in Alzheimer’s patients 20 or 30 years before they get a problem. Or we can start to look at patterns in your blood test that as a child that predict your risk of getting diabetes in the future.
Noosheen Hashemi:
100%.
Dr. Mark Hyman:
[inaudible 00:23:33] years ago who came in to see me, a canyon rancher walked in, their blood sugar was like 115 which is on the way to diabetes, it’s like 126 is diabetes. And I said, “Gee, your sugar’s pretty high, have you seen your doctor about it?” He said, “Oh yeah.” I said, “What did your doctor say?” “Well, the doctor said, ‘we’re going to wait and watch it.'” And I said, “Watch for what?” And said, “Wait until I get diabetes and then I’ll give you a drug.”
Noosheen Hashemi:
Exactly.
Dr. Mark Hyman:
And I’m like, “Whoa!” So I think what you’re talking about is a real revolution or thinking about medicine. So talk to me about how January AI, your company, is trying to enter this field of AI medicine which is really not anywhere near clinical application in traditional healthcare right now, but it’s happening on the margins, which is where you’re working and usually the changes come from the outside and they go on the inside. So I think I’d love to hear your vision of what you’re doing in your company and how can start to address some of these challenges we’re seeing with healthcare.
Noosheen Hashemi:
Sure. Well, first of all, let’s make sure we’re not a clinical solution, so we don’t diagnose, we don’t treat, we don’t manage, we help people understand their bodies and become fiercely self-aware and be able to act on the data that they are provided with in conjunction with their clinical care. But let’s talk about a few things. The current standard of care as you said, is that doctors visiting patients 15 minutes a year, testing people when they complain about symptoms not before then, use classic markers to manage problems which are very, very limited.
Noosheen Hashemi:
They give them like you said, reductive approach, give them one marker at a time. They don’t think that Mark can handle a high cholesterol and high A1C at the same time. So they give you one, like the biggest problem to deal with and like okay, let’s just get them working on this. They suggest weight loss is the end all to everything. So like you know what, if you just lose 25 pounds, all your markers are going to improve. And by the way, it’s really easy. Just go home, no refined sugar, no refined flowers I said, lots of vegetables, walk 10,000 steps.
Dr. Mark Hyman:
Just eat less and exercise more.
Noosheen Hashemi:
Yeah, that’s all, eat less an exercise more, it’s all going to work out. The future is looking very, very different. We are looking at continuous health monitoring, continuous health monitoring. And wearables play a role in that, but so frequent testing, and some of the unconventional tests you mentioned, like functional MRIs and you can see testing companies are just growing crazy. So the amount of consumer health data outside of traditional clinical setting is growing tremendously, whether it’s genomics, microbiomes tests, whether it’s food sensitivity tests from let’s say Everly Well, other companies such as ixLayer and other that are now food testing companies. So that’s all grown, wearables data exploding. CGM data will explode, absolutely. You know about 12 CGMs out there today, they’re going to be 47 new CGMs coming on online in the next three to five years.
Dr. Mark Hyman:
That’s the continuous glucose monitors, right?
Noosheen Hashemi:
That’s right. So you will have an explosion of wearables. Proteomics will go, be transformed completely and we will understand the human proteome for the first time in the next several years. So we are going to be-
Dr. Mark Hyman:
What is a proteome? Tell people, what is a proteome? What is that?
Noosheen Hashemi:
Well, the proteome is basically our full protein profile of humans. And currently, we can understand a few proteins. We can essentially understand a few proteins for tens of thousands of dollars. And I think in the future, we’re going to be able to understand the whole proteome for not very many dollars, kind of what happened to genomics. When we started looking at genomic sequencing, you remember it was $100,000 per person and it went down to $100,000 per person. The whole genome sequencing went down quite a bit and we have different levels of it. And that’s been very informative for us. But anyway, so we’re-
Dr. Mark Hyman:
The real issue is… before you get going there, I wanted to just go a little bit more into the proteome. So for people listening, why do I care about my proteins and who cares? You have 20,000 genes. You probably have five to 7 million variations in those genes and they’re all producing proteins, that’s their job. So your body, literally your genes are expressing when you’re being expressed, are creating proteins and those proteins are the messenger molecules in your body. And what you’re saying is that we have very little insight into all these proteins and these protein networks and how they work and what they do and how they influence things and in a very reductionist way, we might understand it but we really don’t understand the complexity of their direction. And that’s what you’re talking about, figuring out.
Noosheen Hashemi:
Yeah, we don’t understand basically the function of the… in order for us to understand the function of the cells, we need to understand the proteome, we need to basically understand it’s going to inform disease identification, it’s going to tell us so much about our bodies that we don’t understand today because frankly, it’s too expensive and we know so few. And there’s quite a few companies that attempt this, but there are new methodologies coming in that are going to be transformative. So I’m really excited about those.
Dr. Mark Hyman:
So beyond the glucose part, I think everybody can understand there’s many companies doing the glucose monitoring. Seems to me what January AI is doing is a little bit different in that it’s really trying to look at the holistic view of human biology with CGM as a start, but not the whole story. And then [crosstalk 00:29:18]-
Noosheen Hashemi:
CGM, heart rate and food to start, and then hopefully adding other omics over time. So definitely, you’re right, future, I think people will go to see the doctors for acute diseases in the future, probably most everyday things will be handled by AI at some point, but people will go when they have liver disease or something like that, when they have acute disease. And of course we will have personalized health. And you mentioned medicine personalized health, personalized nutrition. So maybe we can talk about that a little bit if you’re up for it.
Dr. Mark Hyman:
Yeah, what does that mean? For sure.
Noosheen Hashemi:
Yeah, exactly. So personalized medicine came about when people were looking at specific therapies for specific cancer. So they started sequencing for example the tumor to see what kind of chemotherapy would impact it the best that could address it the best? So it was like matching medicine to disease, specifically chemotherapy or immunotherapy with a particular tumor. Precision health is upstream from that, it’s way more preventative minded. So we think of okay, let’s collect a lot of data, understand this person’s physiology and frankly psychology. And to then help them know, predict, help them see patterns, basically collect a lot of data and see blips and trends over time.
Noosheen Hashemi:
So that hopefully we could say, oh, this person’s headed for diabetes, this is what we can do about it. Or this person has hypertension, they don’t know yet. Much like Larry Smarr and Mike Snyder self-diagnosed themselves, understand okay, this is where I’m headed, this is what I can do. Precision nutrition is about foods specifically to keep us healthy but also address certain diseases. So if you have ESRD or if you have heart failure, you may want to be on a particular diet.
Dr. Mark Hyman:
End-Stage Renal Disease, you mean?
Noosheen Hashemi:
Renal disease or… exactly. Or if you have IBS, maybe you’re on a low fo map diet, so you want to have precise nutrition for your situation if you are in disease, if you are healthy and you want to prevent disease. So the future looks to us, future is multiomic, the future is personalized, in the future, people have a lot more or data and companies like January will help them make sense out of that data, synthesize the data, analyze the data and then turn it into simple things like walk this much, eat that. Why? Because generic advice doesn’t work, personalized advice works. People are infinitely interested in what works for them, not what works for everyone else.
Dr. Mark Hyman:
Absolutely. It reminds me of Leroy Hood and the Institute for Assistance Biology and his P4 medicine concept, which is preventive, predictive, personalized, and participatory, meaning you have to participate in your wellbeing. And it’s really breaking down the old ideas of disease as we know them. And I think for me, what’s really exciting, and I’d love to hear how you see AI playing a role and who’s doing the work because to me, the holy grail is applying the technology of AI and big data analytics to all the new framework we have for medicine, which is let’s call it functional medicine or systems thinking or whatever you want to call it, network medicine, which understands the body as this network as opposed to diseases.
Dr. Mark Hyman:
And that, combined with the omics revolution, all the data from omics and all the data from quantified itself provides an enormous potential to revolutionize how we diagnose and treat disease and a potential for providers as well as patients or people to have support for decisions of what they do for their health.
Dr. Mark Hyman:
And we’re not there yet, but to me, there is no way given the complex of human biology that we can continue to actually do the medicine the way we’ve been doing it. And I think the work that you’re doing, AI and the technology and the understanding of the complexity of human biology really helps us to reframe how we’re going to do things. Talking to my daughter, who’s about to start medical school in a month, and she’s going into what I would say is a dark ages view of biology. And it’s frightening to me because I know she’s going to get indoctrinated in a paradigm that is really outdated. And it’s not that it’s irrelevant, it’s relevant but it’s just a surface of what’s happening.
Dr. Mark Hyman:
And so I’d love to hear from you how with general AI and AI in general and medicine, you see the change happening where all this data gets put into machine learning system and AI system and how that will change medical care, how it will change what people do, how it will change what doctors do, because as an individual doctor, I’m lucky, I basically had the privilege of seeing tens of thousands of patients literally doing millions and millions of data points. So I have a pattern recognition machine on my head, but it’s certainly not good as a machine learning.
Dr. Mark Hyman:
But I can tell you, after doing this for so many decades, that in my patients, I see patterns over and over that no one’s ascribes. I’ll give you an example. Let’s say someone’s got high levels of mercury. Well, often, they have oxidative stress, they have low minimal acids, they have problems with methylation B vitamins, they might have low zinc and minerals and they have low glutathione because their body tried to detoxify. And I see this whole pattern and I’m like, I know what’s going on. But that’s never in a textbook, it’s just something that I notice. And what’s true about what you’re doing is that there are millions of these kinds of examples of undiscovered archeological excavation of human biology in ways we’ve never done before, that is only now beginning to be possible.
Noosheen Hashemi:
So basically, what AI has done, I think most people, your listeners are familiar with what AI has done for say driving. A lot of people have some self-driving features in their cars. Some folks do, that warn you when a car is getting close to you for example, or if you’re getting out of your lane or you’re getting too close to the car in front of you. We know that AI is working in fraud protection. Google’s correcting your spelling, people, they they’re using that all the time. You mentioned radiology. I think AI can democratize medicine. Today, the difference between an expert, absolute first rate radiologist with not great expertise, not many years of expertise, essentially there is a difference right now, there is a difference in the care that people get right now.
Noosheen Hashemi:
But I think with AI, we can democratize it, how? Well, AI is fantastic for pattern recognition. So it can bring the genius of the finest doctor to all, much like we were able to bring the best classroom professors to the world by democratizing education. I think AI can democratize medicine by… to give you an example, 122 million people are believed to have pre-diabetes or diabetes in America. There are 7,000 endocrinologists in this whole country. So if medical doctors… Good luck with that. So if people are not taught to look at novel markers like CGM data and heart rate data, if they are not taught, if doctors are not taught nutrition in school, you tell me how well equipped are they for observing metabolic dysfunction early and for detecting it and pointing it out to people?
Noosheen Hashemi:
So I think what does AI do for us? Well, AI can interpret huge amounts of data that is not human interpretable by… it’s just not human interpretable. Let’s use the simple example of CGM. If you’re wearing a CGM, maybe you remember the food you ate yesterday or the day before or maybe you use a product, it tells you what you were eating last Tuesday. But can you keep in mind where your heart rate was then? Do you remember how many hours you were fasting then? Do you remember what you were eating that gave you the spike? Now, some people can give you a picture of what you were eating, January gives you the fiber, the amount of fiber you had in your stomach. It tells you the macros of your food, that sort of thing.
Noosheen Hashemi:
So the data, this collection of data we’re talking about, that’s growing, which is classic markers from doctors, new consumer health data, synthesis of this massive amount of data cannot be done without AI. It just can’t, it’s not human interpretable, it just isn’t. So AI can give us that.
Noosheen Hashemi:
The other thing AI can do is can predict for us. Once it learns patterns, it can predict, Mark works out every Tuesday and Thursday and when he does, he has his protein shake that he always has. Let me suggest a better time for the protein shake, or I can see that when he runs, before the protein shake, he has a better response than when he runs after the protein shake for Mark. Or I notice that the time window between his last meal and his sleep is too close.
Noosheen Hashemi:
It’s like one hour, I’m going to over time. This is what January does today. So we use these interventions to give you examples. One intervention we use is fasting. We help people understand their eating period and their fasting period. And the ultimate test of metabolic fitness is how fast, how well you go from fast to fed and all of that, but that will be in the future. But at this moment, we can tell you what’s your fasting period, what’s your eating period. Sometimes people come into our program and they’ve been eating 16 hours a day, fasting eight hours. Over time, we slowly change this 15 minutes at a time. We get them to fast say 12 hours, eat 12 hours. And over time, we encourage them like myself. Now I eat over eight hour period, I fast for 16 hours, no problem at all. I’ve done it over a long period of time. So that’s one lever.
Noosheen Hashemi:
The second lever, as you know, glycemic response is highly impacted by the amount of fiber in your body, how much fiber you have in your body from last 24 hours impacts how you might spike on the next thing that you eat. January measures the amount of fiber that’s in your body that you’ve had in your diet. So that’s another one. Another one is to just understand glycemic load and glycemic index of where you’re eating, offering you alternatives to those foods that taste the same but have lower GI, GL. The other one is to essentially help you figure out how much to work out, when to work out to keep your blood sugar in a healthy range. It also helps you manage your calories because as you know, intermittent fasting in combination with calorie management has shown to improve insulin sensitivity. And the two are very powerful together.
Noosheen Hashemi:
Darryl always reminds me, not intermittent fasting alone, not calorie management alone, together, they are very powerful. So these are things that we can do with AI that we could not do before. Your doctor can’t follow you around, they’re not in your body, they’re not a sensor, no coach or no one can see inside your body but a combination of wearables… by the way, wearables are going to be so exciting. You have seen very little so far. Wait till we get to continuous lactate sensors-
Dr. Mark Hyman:
And tell us about the future because with the wearables, they’re interesting and their novelty for a little while people get tired of them. I’ve had different wearables over the time and it’s interesting and entertaining with them, I kind of get bored. So how do you address that problem?
Noosheen Hashemi:
What are they telling us today? Sensors are telling us a lot today. They’re telling us as you know, about VO2 max, which some people say is the ultimate measure of longevity, they’re telling us about gait, they’re telling us about blood pressure, caloric burn, they’re telling us about quality of sleep, the amount of sleep, our resting, heart rate, or heart rate variability, they’re telling us a lot today.
Noosheen Hashemi:
But what’s coming? First of all, as I said, that’s not enough because we need to understand food to make sense out of all of these things, but what’s coming down the road? Continuous ketone monitors, they’re going to be fantastic. You’ve seen the announcement from Abbot where potentially the same sensor that tells you your blood glucose can also tell you your ketone level, which is great because today, we have invasive ketone monitors today in the hospital end of life in the ER. We are using very invasive but… actually that’s for lactate, but for ketone, it will warn of impending ketone acidosis for Type 1, this is really, really important if their blood sugar is going too high and they need insulin, that would be really useful.
Noosheen Hashemi:
Also people that are on super low calorie, low carb diets can see how their metabolism is faring once they have continuous ketone monitoring. Another thing that’s coming in is the continuous lactate monitors. Essentially, this is what’s used like I was saying earlier in hospitals end of life, they use invasive lactate monitors to see for example, severity of sepsis, like how bad it is when someone is close to death. But also athletes, we will love to have continuous lactate monitors for athletic performance.
Noosheen Hashemi:
Another thing of course, and the ultimate is the continuous cortisol monitors. Once we get cortisol monitors, we will understand chronic stress which impacts of course immune and inflammatory systems, which as you know, increases susceptibility to diseases like autoimmune disease, cardiometabolic disease, mental diseases and cancer. So, really looking forward to ketone and lactate and cortisol monitors coming down the road. This will also be transformative-
Dr. Mark Hyman:
What about insulin? Is there any insulin monitors coming?
Noosheen Hashemi:
We need continuous insulin monitors. The fact that we look at glucose and we don’t look at insulin is insane.
Dr. Mark Hyman:
Yeah, that’s right.
Noosheen Hashemi:
It’s insane.
Dr. Mark Hyman:
It’s true. Glucose is the big thing but it’s maybe hard to measure insulin, but it’s such a more relevant marker.
Noosheen Hashemi:
It is so relevant, it is utterly relevant. And so there are some technologies way more relevant, way more relevant. I think there are some technologies that I know of in some labs that maybe approaching that, but nothing that is going to be commercially available as far as I know today.
Dr. Mark Hyman:
Tell us why insulin is more important than sugar.
Noosheen Hashemi:
There are two sides of a coin. Insulin is a hormone that lets your cells take up glucose. People with Type 1 diabetes are born without the ability to even produce insulin. So they are lacking insulin. Some people like my co-founder, Mike Snyder, produce insulin but produce it slowly. So he’s a person with Type 2 diabetes. So if you don’t produce enough insulin or insulin is lacking, you have all this extra glucose sitting up in your bloodstream that then can damage your body in a number of ways. Excess glucose can create microvascular complications as you know, you can essentially develop retinopathy, neuropathy, you can have diabetic nephropathy, so you can have liver disease essentially when… so let’s go through it one by one. Basically-
Dr. Mark Hyman:
But the insulin is the first thing that goes wrong.
Noosheen Hashemi:
Insulin is the first thing.
Dr. Mark Hyman:
And actually your sugar can be perfectly normal and your insulin can be all over the place to try to regulate your sugar and it’s the first clue that something’s wrong.
Noosheen Hashemi:
Absolutely.
Dr. Mark Hyman:
And it’s just one of those things that’s stunning to me, that one doctors don’t measure because it’s cheap and easy to do. And two, that we haven’t developed a continuous monitor for that because that to me is a game changer because when people see that number, that’s going to be more predictive than even their blood sugar in what’s going on their metabolic health.
Noosheen Hashemi:
100%, yes. You need to understand your pancreas function, we don’t know how much someone’s pancreas capacity is right now. That would be hugely useful because like diabetic retinopathy is the most common cause of blindness right now for working adults in developed world. As you know, in diabetic neuropathy, basically high blood sugar can injure nerves throughout the body and damage nerves and legs and feet, which can lead to foot ulcers and amputation, you know that, and then of course the kidney disease coming from diabetes is the number one cause of kidney failure, almost a third of people with diabetes develop nephropathy. So these are pretty serious and you would think that if we’re solving for continuous glucose, we would solve for continuous insulin to understand the whole picture. And for some reason, we are not, it’s badly lacking.
Dr. Mark Hyman:
So really what you’re talking about is that the quantified self movement combined with the omics revolution and the superimposition of AI and machine learning and big data on top of it is just going to revolutionary everything, how we take care of people.
Noosheen Hashemi:
Absolutely. As we develop the omics better like proteomics and others, as the wearables become more telling, these continuous modalities enable us to do machine learning. And when we do machine learning, we see patterns. So I do think that China has been very smart about measuring health and trying to understand what we do-
Dr. Mark Hyman:
What are they doing?
Noosheen Hashemi:
… and they’re able to apply it to a billion three people and they can solve for health. I believe one of the greatest consequences of China being more advanced in AI than we are, is solving for health. And that’s excellent, I’m very, very happy as a global citizen, I want everyone to be healthy. I think that’s phenomenal. I just want us to also be running after health in the same way. They’re doing the right thing, we need to do the right thing by our population as well.
Dr. Mark Hyman:
And so what are they doing differently than we are?
Noosheen Hashemi:
Well, they have a lot of state sponsored research, they approach a lot of the best scientists in the world, they can set up a lab for you in 48 hours, give you a dozen, most intelligent fellows to work for you and to do the research. They pick winners as you know, when they know some private secretary, a very close public private sector partnership. If they see a company succeeding, they quickly put their finger on the scale and the company is propelled into the whole population. So as you know, they record everything, they have videos recording the whole population. So technically, you should be able to look at how often people are sleeping, getting up, when are they eating? How many times are they eating?
Noosheen Hashemi:
They have, I don’t want to say reverse engineered or genome sequencing, but I think that is a belief, they are basically all the advances we’re making an omic by omic. I think they are picking those up very quickly and taking a much faster path to it than we are. So they’re definitely going to have continuous life monitoring, they will have omics, they will have wearables, they will know more about their population. I mean, just look at… Mark, they built a hospital for 1,000 people in 10 days. Wow.
Dr. Mark Hyman:
It’s kind of crazy, yeah, it’s true.
Noosheen Hashemi:
And the virus tracing, like I said, one of the consequences of AI supremacy of China over U.S. will be solving for health and we should hustle. We should run fast, we should solve, we should innovate.
Dr. Mark Hyman:
My biggest, honestly, I’ve been struggling with this for decades literally because it’s been clear to me that given the massive shift in the paradigm of medicine from a reductionist disease oriented approach to a systems network approach, a functional medicine that there is no way that this can scale unless we have the AI machine learning help and support because the complexity of human biology is just staggering. There’s 37 billion, billion chemical reactions every second in your body.
Noosheen Hashemi:
Five billion years of evolution, absolutely.
Dr. Mark Hyman:
How can anybody think through that as a provider in a way that makes a huge difference for people? So I feel like we’re in this moment where I’m waiting around for somebody who’s got the money, the resources, the science to actually start to do this. And it seems like you’re heading on that track. It’s not as grand as I would like to see it, which is essentially encompassing this really new paradigm, and it’s starting to collect data based on this new kind of network theories of medicine because Watson is interesting. Well, that’s the IBM computer that basically went through medical school and can digest all the literature but it’s all from the old paradigm. So it’s like putting a rocket on a horse and buggy, it’s like well, we don’t want [inaudible 00:51:26] we want a Tesla.
Noosheen Hashemi:
And we need to understand exactly how two people with diabetes are different. For example, someone could have diabetes in their musculature, somebody, it’s in their liver. We will be able to understand these differences with AI. I think I agree with you, we’re in an inflection point. One, wearables are going exploding as I mentioned. We’re in the golden age of machine learning, we can learn a lot from sparse data. COVID accelerated telemedicine, I think telemedicine is here to stay, that is not going away. It unmasked chronic disease, it showed how many people have underlying conditions because people started dying, 40% of people who died had diabetes or other chronic condition. The cost of healthcare is just not sustainable.
Noosheen Hashemi:
So we’re spending $3.8 trillion on healthcare today, it’s supposed to go to almost $12 trillion by 2040, that’s in 19 years. In 19 years, we’re supposed to go to 11.8 trillion according to this report that just came out and only 2.9% of this is going to prevention. Only 2.9% of what we’re spending is going to prevention. 10% of it is going to end up like there.
Dr. Mark Hyman:
It’s insane.
Noosheen Hashemi:
It’s insane. Don’t you think those need to be swapped, 2.9% for in prevention, 10% for end of life care? I think food as medicine is gaining traction definitely. I think if people like Darryl should be minister of nutrition in this country or sector of nutrition seriously.
Dr. Mark Hyman:
I told him but he doesn’t want the job.
Noosheen Hashemi:
Oh my God, seriously. Food is medicine, food is medicine, is the medicine we take three to five times a day, and we need a lot more medically tailored foods, functional foods, both obviously natural but also bred for added health. So I hope to see government taking a much, much stronger step. And also I think we see a tipping point in consumerization of health, right? Just look at the testing companies, Everly Well, look at retail clinics. You’re going to get your healthcare at where you are at, you’re not going to go to the doctor’s office. You’re going to go to Walmart, you’re going to go to Target, to CVS, you’re going to go to Ford and Parsley and One Medical-
Dr. Mark Hyman:
And maybe they’ll come to you.
Noosheen Hashemi:
… and you’re going to corporate clinics. Yes, or they’ll come to you or you’ll go to the doctor at your work. I think people will get care where and where they want it.
Dr. Mark Hyman:
Let’s talk more about January AI and what distinguishes it from other CGM glucose monitoring companies? And you have an interesting platform called the Season of Me which is actually a personalized approach to understanding your body and blood sugar. So tell us, what is the technology using? How is it different and what is the framework of how you work with people as in drive behavior change? Because that’s the hardest part. We all know what to do, but most of us don’t do it. So tell us about those two pieces.
Noosheen Hashemi:
I would say the most defining difference between us and everyone else in this sector is our ability to predict, it’s our AI, that’s the biggest differentiator. The fact that we can produce glycemic predictions for people. Like I said, you want to know if you can drink three glasses of Chardonnay or you want to drink three glasses of Chardonnay. Ask the AI, what happens to my blood sugar, five, three glass of Chardonnay? Don’t drink three glasses of Chardonnay to find out how it impacts your body, don’t use your body as the place of experiment, do it in silico. Don’t do it in vivo, do it in silico. So let the machine tell you. Why put it through your body? You want to eat this thing you’re sitting at your pizzeria, and you want to have this pizza, you want to see how it’s going to impact you. Look at it ahead of time.
Noosheen Hashemi:
Don’t look at your glycemic response after it has already happened, look at it before it can happen. Use the machines, not your body to run experiments, make smart choices. So that’s the single most important differentiator between us and everyone else, our ability to predict our AI is our super power. And in terms of how like I mentioned, the Season of Me program is a curated program that we also have other products in the works that are going to come out. But Season of Me is a curated program that over 30 days, teaches people how to increase their fasting period, how to be cognizant of how much fiber they have in their body to increase fiber, how to manage their calories, how to dial their activity to keep their blood sugar in a healthy range, to become aware of lower glycemic index foods.
Noosheen Hashemi:
So these are levers, we offer them, some people take up some of those, some people take up all of those, some people pick up fasting and nothing else, some people do all of those. They lose weight, they manage their blood sugar, their timing range improves. As you recall, A1C has been the kind of golden measure of blood sugar for a while, but that’s an average number over 90 days. It’s not so actionable, you don’t wake up and say, I want to improve my A1C this morning. But timing ranch, you can manage that. You’re like, okay, how am I doing? And that’s what January helps you do.
Dr. Mark Hyman:
And what are you seeing from the clients that you’re using the product and how are their outcomes different and what are the changes they’re seeing and how successful have they been at changing their behavior?
Noosheen Hashemi:
They’re reporting everything from a lower A1C, lower weight, more energy fasting. If you don’t eat so close to your bedtime, you’re able to have better sleep, more restfulness. Somebody told us that their focus improved so much, they actually sent us a video and they said, “Feel free to share this with other people completely on prompted.” They said, “I’m keeping my job because of January because I lacked focus so much.” So we’re getting very positive feedback from them. I think we still want to dial our user experience a lot more, we’re a science first company, we focused on science first and our research first and we worried about developing the app and the app user experience and all of that second, we’re trying to make sure that we also create a magical user experience for people and we’re working on that now. But very, very positive feedback.
Dr. Mark Hyman:
That’s so exciting. And how does it work? How do people get connected to January AI and how do they learn how to use it?
Noosheen Hashemi:
Sure, sure. They go to our website, they go through a brief questionnaire to go through telemedicine. If they qualify, they are given a prescription for CGMs and they pay and they conclude their transaction on the website, then they download the app. We ship them what they need for the program and then they download the app, they hook up their heart rate monitor, whether it’s Apple watch or Fitbit, they hook up their CGM and they start logging their food. And within four days, Walla, they start getting predictions of their glycemic response. We tell them their spiking foods, what foods are spiking them, what foods are like those foods they could have that wouldn’t spike them. We give them what we call activity counterfactuals like you ate fish and chips. Had you walked 10 minutes, this would have been your response. Had you walked 20 minutes, this would’ve been your response.
Noosheen Hashemi:
So we give them food counterfactuals. You ate fried chicken, had you had salmon, this would’ve been your response. We tell them, this is your fasting period and we start increasing their fasting period. We give them education through content about what is insulin resistance? What is intermittent fasting? Why is it effective? How does it work on your body? So it’s a combination of education, small nudges and specific behavior or levers such as fasting, calorie management, eating lower GL foods, increasing activity to try to get them to a better metabolic state.
Dr. Mark Hyman:
And is it something that they’re able to adhere to that people find sticky and they’re willing to do the work around it?
Noosheen Hashemi:
Yes. People come to January, we’ve tried to simplify certain things over time, they absolutely don’t want that. They’re like, “No, I want to know how much.” We’re like, “Do you want to just give us a picture and a few words?” They’re like, “No, I want to know exactly what macros are in my food, I want to know exactly how much fiber is in my food. We’re coming to you for precision, don’t remove the precision.” So it’s an interesting balance, with precision comes some friction. So a lot of people are willing to do that because they are optimizers, they really want to optimize.
Dr. Mark Hyman:
And I think we’re all going to have to take control of our own health and realize that help doesn’t happen in the hospital or the doctor’s office, it happens at home and that all of us being able to be more empowered with these tools, technologies, the centralized access to our health data and superimposition of AI and machine learning to help people make sense of it and act on it is just huge, it’s a game changer in healthcare. I’m just thrilled that you’re doing this work and I’m thrilled that you’re putting the science behind it because a lot of times, it’s easy to do something fun and goofy, but the science part is hard. The 16 million foods, you’ve tracked and analyze the ways in which you’re integrating all these different data sets the way you’re looking at other analytes and things to do. So to me, it seems like January AI is not just a blood sugar monitoring company, it’s really more looking at how do we use quantified self metrics and AI to solve our big health crises?
Noosheen Hashemi:
Absolutely. I felt like I had the capacity, the mental space and the opportunity to look at some really hard problems. And when you have that opportunity, you must take it. So I feel very privileged to actually be working on these frontiers of health today. Combining medicine and machine learning and science because what we do in a lab takes 15 years to get to the doctor’s office. So how do we combine, how do we close the gap, how do we help people become really aware of their state? Those who want to know? Some people don’t want to know, that’s okay, not everyone needs to know. But the ones who want to know, how can we empower them, equip them to know, and then how can we help them synthesize the data because it’s not human interpretable?
Dr. Mark Hyman:
I think that’s a key message for people to understand that when we go to the doctor, we think the doctor knows everything, measures everything, tests everything. So I would tell the doctor that you might check out, they know my labs, everything’s fine, I don’t know what’s wrong. Well, that’s just like skimming the surface of what’s really going on. And that’s what excites me so much about medicine today, is that we’re in this really revolutionary paradigm of one, the science shifting around what disease is and how health happens in the science of health and two, the technologies that allow us to, like yours, like January AI to dive deep into our own biology and create personalized approaches to creating health for ourselves. So it’s super exciting.
Noosheen Hashemi:
Absolutely. So our dream is to mathematically model all the human functions if we can, figure out what’s happening, gastric emptying, figure out how is food moving through your body? We have a lot of dreams still. We have a 10 year roadmap ahead of us with very interesting research.
Dr. Mark Hyman:
I just interviewed a guy for the podcast, Fred Provenza, talking about this work that’s being started up looking at metabolome with analysis of people eating different foods not just in vitro, but actually in their bodies. And say, what happens when you feed somebody a grass-fed steak or a feed-lot steak? Because they’re different. What happens when you eat this or that? It’s great and fascinating and we’ve really not done that even work, which you think we would have done by now, but it’s really… then the question, how does it affect different people, all that’s going to come into focus with the work that you’re doing with AI and understanding how to really describe healthcare. So I’m super excited anyway, I can help you guys, I think it’s just a tremendous moment in healthcare. And for those listening, a lot of this is still abstract for people, but the truth is with these new metrics, we’re able to learn a lot about our bodies.
Dr. Mark Hyman:
I had the aura ring for a while I was wearing, it’s fancy. When I drank some wine or had some alcohol, I noticed my sleepless screwed up. And if I wasn’t aware of it or my heart rate variability changed, I noticed that when I stopped traveling and being on airplanes three or four times a week, my heart rate variability improved. So my stress response, even though I didn’t feel stressed in my head, my body was registering the experience as stress. And that was such an incredible piece of feedback.
Dr. Mark Hyman:
So I think we really have really just begun to scratch the surface of what we’re going to be doing in medicine. And unfortunately it doesn’t really come from the center. I work in healthcare institutions and they do their best and there are a lot of progressive people that really open minds, but it’s often challenging with the inertia of the system in terms of reimbursement and what gets paid for what people are doing. We know that food is the best treatment for diabetes and yet it’s not paid for. So doctors do imputations, they do heart surgery, they do lots of medications, the zillion dollars, diabetes is the most expensive condition in healthcare today. And costing America the most money, it’s one in three Medicare dollars and probably two out of three, if you count pre-diabetes and all its complications. And yet we don’t even treat them with the right treatment that we know works. And we were talking about it just whole next level.
Noosheen Hashemi:
And CGMs are covered today by insurance companies only for insulin intense use population, that’s three and a half million people out of 122 million people who are believed to have diabetes or pre-diabetes, but the beauty of AI, again with AI, you don’t have to wear a CGM 365 days out of the year. Payers, employers put CGMs, use January for your populations with intermittent use, use it four times a year, use it two times a year. We build the models, from there on, we predict. They don’t need to wear it in order to get the benefit, they just need to wear it a few times because their bodies change, they age, they travel, they get pregnant, et cetera. But again, AI solves, AI democratizes medicine because we can predict. Our ability to predict reduces the need for these devices and makes them available and accessible by a much larger population.
Dr. Mark Hyman:
Well, thank you so much for the work you’re doing and the advance you’re creating in healthcare and medicine and the thinking behind this, it’s not easy when you take it on. And for those listening, I think this whole conversation about AI and medicine and science, it’s a lot to take in but I think the bottom line is that the body is extraordinary complex. We’ve just been scratching the surface and with new tools and technologies of assessment of our biology, both within traditional healthcare and also within the disruptive models like January AI, we’re going to be having insights that we never had before, we’re going to be able to provide tools for people that they’ve never had before to optimize and enhance for health and reverse disease. And I’m super excited about it. And I just want to really thank you for your work. People can learn more about what you’re doing at januaryai.com I think, right?
Noosheen Hashemi:
January.ai.
Dr. Mark Hyman:
Oh, January.ai. Okay, there you go, january.ai and learn more about it and stay tuned because this is just the beginning. Probably we’ll hear more and more as you start to collect data and analyze all the metrics from your client base. So I’m super excited about it. And check it out and see what happens. I want to try it [inaudible 01:07:33] system.
Noosheen Hashemi:
Thank you, Dr. Hyman, and this is such a pleasure to be chatting with you. You’ve been a pioneer for so long, you’ve been saying all of this for decades now, so-
Dr. Mark Hyman:
It’s so fun, now [crosstalk 01:07:45]-
Noosheen Hashemi:
… thank you.
Dr. Mark Hyman:
… company, now I have a company and I can talk to people and care about this stuff, it’s like I’m not shouting in the desert. It’s so fun.
Noosheen Hashemi:
Absolutely.
Dr. Mark Hyman:
So thank you so much. And everybody listening, if you love this podcast, please share with your friends and family on social media, leave a comment, tell us how your health has been changed by one, by self metrics and what you’ve learned about your own body and subscribe wherever you get your podcasts. And we’ll see you next time on The Doctor’s Farmacy.
Speaker 1:
Hi, everyone. I hope you enjoyed this week’s episode. Just a reminder that this podcast is for educational purposes only. This podcast is not a substitute for professional care by a doctor or other qualified medical professional. This podcast is provided on the understanding that it does not constitute medical or other professional advice or services. If you’re looking for help on your journey, seek out a qualified medical practitioner. If you’re looking for a functional medicine practitioner, you can visit ifm.org and search their Find A Practitioner database. It’s important that you have someone in the corner who’s trained, who’s a licensed healthcare practitioner and can help you make changes especially when it comes to your health.
