Noosheen Hashemi On January’s Personalized Tech For Controlling Blood Sugar
In a companion interview to his June 7 talk with Stanford’s Michael Snyder, Harry speaks this week with Noosheen Hashemi, who—with Snyder—co-founded the personalized health startup January.ai in 2017. The company focuses on helping users understand how their bodies respond to different foods and activities, so they can make diet and exercise choices that help them avoid unhealthy spikes in blood glucose levels.
January’s smartphone app collects blood glucose levels from disposable devices called continuous glucose monitors (CGMs), as well as heart rate data from patients’ Fitbits or Apple Watches. The app also makes it easier for users to log the food they eat, and see what impact each food has on their glucose levels. Once the app has enough data, January’s machine learning algorithms can start predicting the effects of different foods and activities on blood glucose. It can then recommend meals and exercise that’ll help users keep their blood glucose in a healthy target range.
The goal isn’t to prevent glucose spikes completely, but rather to prevent diabetes from emerging over the long term in people at risk for a cluster of serious conditions known metabolic syndrome. That could help individuals live longer, healthier lives. And at a population level it could save billions in healthcare costs.
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Harry Glorikian: I’m Harry Glorikian, and this is MoneyBall Medicine, the interview podcast where we meet researchers, entrepreneurs, and physicians who are using the power of data to improve patient health and make healthcare delivery more efficient. You can think of each episode as a new chapter in the never-ending audio version of my 2017 book, “MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market.” If you like the show, please do us a favor and leave a rating and review at Apple Podcasts.
Harry Glorikian: I’ve been making the show long enough that you can see a kind of family tree emerging, with branches that connect many of our episodes.
That’s definitely the case with today’s interview with Noosheen Hashemi, the co-founder and CEO of the precision health company January AI.
The branch leading to Hashemi started back in June of 2021 when I interviewed Professor Michael Snyder, the chair of Stanford’s Department of Genetics.
Snyder is a huge proponent of using wearable devices to help people make better decisions about their own health. In fact, the day we spoke he was wearing seven separate devices, including one called a continuous glucose monitor or CGM.
A CGM is standard equipment these days for about 3.5 million diabetics in the U.S. who need to know when their blood sugar is too high and when it’s time to take more insulin. But Snyder believes that blood glucose data could also help tens of millions of other people who don’t yet take insulin but may be on their way to developing full-blown diabetes.
Back in 2016 Snyder got a visit from Hashemi. She’s a longtime Silicon Valley tech executive and philanthropist who’d been searching for a way to use AI, wearable devices, and big data to get more people involved in medical research. Hashemi told me it took just two meetings for her and Snyder to decide to join forces to co-found January.
The company makes a smartphone app that collects blood glucose data from disposable CGMs, as well as heart rate data from patients’ existing wearable devices such as their Fitbit or Apple Watch. The app also makes it easier for users to log the food they eat, and see what impact each food has on their glucose levels. Once the app has enough data, January’s machine learning algorithms can start predicting the effects of different foods and activities on blood glucose. It can then recommend meals and exercise that’ll help users keep their blood glucose in a healthy target range.
The goal isn’t to prevent glucose spikes completely, but rather to prevent diabetes from emerging over the long term in people at risk for a cluster of serious conditions known metabolic syndrome. That could help individuals live longer, healthier lives. And at a population level it could save billions in healthcare costs.
As you’re about to hear, Hashemi and I talked about why glucose monitoring is so important and what companies like January can do in the future to make the predictive power of AI available to more people.
Harry Glorikian: Noosheen, welcome to the show.
Noosheen Hashemi: Thank you, Harry.
Harry Glorikian: So, it’s great to have you on the show. It was interesting that, you know, the minute Dr. Snyder mentioned the company, I was immediately Googling it. And I was like, oh, I have to talk to this company. I have to understand what they’re doing and, and what’s going on.
And to be quite honest, I’ve been doing my homework for the past couple of weeks. And I’m like: I think I have to call my doctor and get a ‘script to actually use the product.
Just to help everybody get up to speed on this, can you bring people up to speed on where we are with glucose monitoring and health in general? Whether they have diabetes or whether they’re just, you know, what, I, maybe someone like me who I hope is a generally a healthy person.
Noosheen Hashemi: Sure, absolutely. Yeah. So from Mike Snyder’s four-year multi-omic IPOP research, we learned that people who are so-called healthy and have healthy A1C levels could actually have huge glycemic variability. He sometimes calls these people with pre pre-diabetes. I think eight people developed diabetes during his four-year study.
There haven’t been enough longitudinal studies in healthy people with glycemic variability to suggest that they will necessarily develop diabetes. So to date, there’s really no conclusive evidence that healthy people can benefit from balancing their blood sugar. Also, not all sugar spikes are bad and a two-hour bike ride might produce a big spike, but that’s fine. It’s not the spike by itself that we worry about. It’s really how high the spike is against our baseline, against the population, whether the spike comes down quickly, the shape of the curve, the area under the curve. These are the things that are illuminating in terms of our state of metabolic health.
So at January we really view metabolic health as a spectrum. So we want to support people to figure out kind of where they are on that spectrum. And to try to really help them move up to healthier points on that spectrum. So we don’t see it as a moment in time where you are something or you are not something. You are kind of on a spectrum of metabolic health, and we continuously want you to be self-aware and, and really improve your location on that spectrum.
Now, something to keep in mind, and why I think it’s important for people to take action on this, is that 84% of the 88 million people believed to have pre-diabetes today, and 22% of the 34 million people that are believed to have diabetes today, are not diagnosed. They are undiagnosed. That’s 75 million people walking around with pre-diabetes and don’t even know. So, if we don’t measure people’s health, that doesn’t mean they’re healthy. So we really encourage people to be you know, vigilant with their health learn so that they can, they can act, you know, self-advocate. Be able to self-manage.
So we do think that wearables are an easy, useful way to kind of see where things are, but then you need companies like January to make sense of it all.
Harry Glorikian: Yeah. I mean you know, it’s interesting because you know, I’ll go to my doctor and they’ll do that one time measurement. It’s like taking your car in and you’re like, it was making a noise. It’s not making the noise right now, but, you know, try and diagnose when that event is not happening. Whereas with the wearables, I can, I can actually see, you know, my, my heart rate variability change depending on my exercise process. I can see my sleep change if I had one too many glasses of wine. I have to tell you, I hate it because I would like to have more wine than my monitor allows me to have, but you know, you see the immediate feedback, which would let you sort of course-adjust accordingly. And you know, when I, there was a paper, I believe that was published in Israel where there, I think it was 500 people that they looked at and where you could see that every person, they could eat the same foods, but their spikes would be different or how long that spike would be based on genetics, based on their microbiome. And so if you’re not monitoring, how will you know that your quote, healthy diet is actually healthy for you?
Noosheen Hashemi: You don’t. You definitely don’t. And yes, that’s study shows variability between people, but also we’ve shown glycemic variability for the same person. So we had somebody at the office have the same good sleep nine days in a row, and they had a different glycemic response to that. Mostly every single day, nine days in a row, depending on how much they had slept, how stressed they were, how much workout they had done. And most importantly, how much fiber was in there. So we are radically different person to person, and this is why we encourage people. No one is going to know you as well as you do. And no one’s going to be as interested in your health as you are as you should be, as you might be. So we really encourage people to learn, learn, be self-aware self-advocate, self-educate.
Harry Glorikian: So, help people understand this term metabolic syndrome, you know, and, and talk about how many people, maybe who are pre-diabetic go to full-blown diabetes, you know?
Noosheen Hashemi: Okay. Yeah. So I mentioned that 122 million people have either diabetes or pre-diabetes in America. 88 million plus 34 [million]. And then a larger number of people, if you believe Mike Snyder’s pre-diabetes number, that’s even a larger number. But metabolic syndrome is a cluster of conditions that leads to type 2 diabetes, heart disease, and stroke. These conditions are basically high blood sugar—which has been historically measured by A1C blood tests called hemoglobin A1C, but increasingly it’s measured by time and range using a CGM—high cholesterol and triglyceride levels, high blood pressure, high BMI, and high waist to hip ratio. So this kind of fat right in the middle.
So the 2002 diabetes prevention study showed that unless there’s an intervention, 58% of the people that have pre-diabetes could end up with diabetes. And usually they think of this prevention as weight loss.That’s what the DPP programs, diabetes prevention programs, are about.
So if you have pre-diabetes the cells in your body don’t respond normally to insulin. And insulin is a hormone that facilitates your cells taking up glucose, which is a source of energy for your body. Your pancreas basically makes more insulin to try to get the cells to take up glucose. You sort of get into this terrible vicious circle. So eventually your pancreas can’t keep up and then you have this sort of excess sugar sitting in your bloodstream, which is really a problem. And it can really lead to microvascular complications like retinopathy or neuropathy or diabetic nephropathy.
So as you know, diabetic retinopathy is the most common cause of blindness in working adults in the developed world. And in diabetic neuropathy, essentially high blood sugar can injure nerves throughout the body. And usually damages nerves in the feet, in the legs and feet, which hear about foot ulcers and amputations coming from this.
And of course diabetic kidney disease. Nephropathy is something that is the number one cause of kidney failure, actually. Almost a third of people with diabetes develop kidney disease. So you add this with the high blood pressure we can increase the force of blood through your arteries and damage arteries. And then you have excess blood pressure, you knowblood pressure and diabetes together, basically increase your risk for heart disease. So it’s really a terrible cluster of conditions to have.
And so if you have three of these conditions, three of these five, you essentially have metabolic syndrome. And if you have metabolic syndrome, you’re at a higher risk of developing these different diseases. You really don’t want to go down this path. The path itself is not great. And then the comorbidities from this path are just worse and complications of course are very painful, costly, and potentially, deadly.
Harry Glorikian: And so that’s one end of the spectrum, but in reality, even someone like me who tries to watch he eats, who goes running regularly, or tries to go running regularly. I mean, you know, I have sleep apnea because they tell me my BMI is too high. Right. So but this sort of technology, you know, I could be spiking and keeping a high glucose level, which would inhibit my ability to lose weight, et cetera. So how can more data about blood glucose, and its relationship to diet, help people avoid diabetes?
Noosheen Hashemi: Yeah. So for so long, we’ve been able, we’ve been told just to avoid refined sugar, refined flour, eat a lot of vegetables, walk 10,000 steps. You’ll be fine. Or, you know, weight loss is given as the end goal to cure all diseases. You know, why don’t you, Harry, drop 25 pounds? Or how about drop 5 to 10% of your weight?
Harry Glorikian: Just like that!
Noosheen Hashemi: It’s true, weight loss really improves biomarkers. But how many people who get this advice can actually do that? And at the timeframe that they need to. So we feel like that’s just not a practical approach to solving a problem.
A more practical approach is to really figure out what works for each individual. You know, you mentioned you’ve dialed your own wine drinking based on its impact. I’ve done the same. I was, you know, enjoying two, three sips of wine. And then I learned that it would wake me up in the middle of the night. So I stopped having even the two, three sips of wine. So don’t feel bad that you can’t have your second and third and fourth glass. But basically we offer a multitude of levers that you can dial for your lifestyle.
For example, intermittent fasting and calorie restriction together have shown benefits in clinical studies for improving insulin sensitivity, if you do them together. So you can’t just fast and then gorge yourself. But if you fast and you restrict your calories together, you can really improve insulin sensitivity. So we let you, we help you using the January program to learn to experiment with fasting and calorie restriction and figure out what works for you. How much of it you can make. You know, slowly help you essentially build it into your habits and your daily routines to fast. You know, we increase your fasting period 15 minutes at a time. So you may start with January you’re eating 16 hours a day and you’re fasting eight hours. You may end the program having reversed that.
And other thing is we, we really pro promote fiber consumption. So increased fiber intake has been associated with higher levels of bacteria-derived short chain fatty acids, which is a regulator of GLP-1 production. As you know, GLP-1 is an incretin and a recognized regulator of glycemic homeostasis and satiety. So we help you track how much fiber you’re eating. We encourage you to eat more, knowing what foods spike you, spike your blood sugar, helps you basically eliminate or reduce consumption of those foods. It tells you how much, how much of those things to eat or alternatives that kind of honor your food preferences and food tastes, but have lower glycemic index. If you can’t walk 10,000 steps a day, okay. January tells you how much you need to walk, when you need to walk to keep your blood sugar in a healthy range.
So you really need data to, to dial your lifestyle. There are many levers and there are no silver bullets and there’s too much to keep in your head. Which is why it’s nice to have AI sort of help you kind of make, you know, take it all in to a platform and then synthesize it and give you insights.
Harry Glorikian: Yeah. I mean, like, I’ve got my, my Apple Watch. I’ve got my, you know, Whoop band. Right.I don’t have as many as he [Mike Snyder] does, but I know, I think my wife would kill me if I, if I was wearing eight things, but, but it’s, you know, it’s true. Like it’s, you know, each one of these, because they’re not holistically designed, give me a different piece of data that then I can then react to. You know, one is probably more of a coach that causes me to push a little bit farther, you know, et cetera. So I mean, I hope one day we evolve to something that’s a little bit more holistic so that the average person can sort of, it becomes more digestible and more actionable. But you know, I do believe, based on my conversation with him and even all the work that I do multi-factorial biomarkers or multi biomarkers are going to be how you manage, you know, yourself much better.
But you know, tell me how January started. What is the thing that excited you about what you saw and what attracted you to this role?
Noosheen Hashemi: Yes, absolutely. So January’s origin story started with me deciding in 2016 to start my own company, essentially, after many years of running a family office, investing in, serving on boards of companies and nonprofits. I had early success at Oracle where I rose basically from the bottom of the organization in 1985 to vice-president by age 27. Along [with] Mark Benioff, who at the time was 26. It was quite the time, taking the company from $25 million to $3 billion in revenue. So you know a really, really amazing tenure there.
In 2016, I started this massive research in, into theses that were getting a lot of attention, you know, big trends over the next decade. And most importantly, what I really knew. You know, the classic kind of [inaudible]. I happened to attend a conference, a White House Stanford University conference on societal benefits of AI and how to integrate sort of ever-changing AI into everyday life and into the real world. It was a healthcare panel that took my breath away. So Faith A. Lee who had organized the conference with Russ Goldman. They suggested that interested parties run off to this machine learning and healthcare conference in LA two weeks. I immediately booked my ticket.
And there I met Larry Smarr. I don’t know if you’ve come across him or not, but he was the first quantified self, maniacal quantified self person I had come across. And he had diagnosed his own Crohn’s disease way before symptoms had manifested. And so, and then the common theme of this conference, between all of these presentations was that machine learning could essentially fill in for missing variables in research, not just going forward, but going backwards. So I was just hooked and I never looked back.
But it was a hard problem. My own husband had been investing in healthcare and warned of like an opaque sector. He was like, “Honey, this is heavily regulated incentives are aligned with acute disease, not with chronic disease, not to mention even anything or prevention. It’s just not a market economy.” And he knew how interested I am in market economies. My first love before medicine was economics. So that’s a whole different podcast. So he warned that I’d be sort of fighting this uphill battle, but I was not discouraged. I basically kept on researching.
I came across the MIT economist Andrew Lo. I don’t know if you’ve come across him, but you should definitely talk to him. He’s brilliant. His work showed that so little research had been done compared to what we really need to do in terms of medical research. And he comes up with ways of funding, medical research, he has a lot of innovative ways that we could really change the whole model of medical and scientific research, but it kind of became obvious to me that the answer was that we needed to get everyone involved in research.
So just, just putting things in perspective. After Nixon declared a war on cancer 50 years ago, we now have some therapeutics and some solutions to cancer. We have really nothing for neurological diseases. We’re spending over $300 billion just on symptoms of Alzheimer’s— don’t talk about even the cure or anything like that. We have nothing for aging, which is the ultimate killer. So it was, to me, the answer was obvious, which was, we have to get everyone contributing to research. Everyone should be looking at themselves. And then with the data, we can also learn across populations. And so deep phenotyping of the population sort of in a multi-omic way was the answer.
And that’s what led me to Mike Snyder. I actually looked for multi-omics. I went to Stanford medical school and I met with the CEO. He said, what are you interested in? I said I’m interested in multi-omics. He said, you have to talk to Mike Snyder. And so basically what Larry Smarr had done at the [San Diego Supercomputer Center] was to measure everything by himself. But Mike had essentially extended this kind of research to others, not just to himself. So not only sort of diagnosed himself with diabetes before the doctors, but he’d also run the Human Microbiome Project, the IPOP study, innumerable other research using metabolomics, proteomics, transcriptomics, wearables, and so on.
So he had spent a lifetime studying how people went from healthy to disease essentially. And he had taken a whole person approach, which is what I was interested in. And so in his role as chairman of genetics at Stanford and head of precision medicine at Stanford, he was kind of already living in the future. And that’s kind of where I thought, you know, all of us needed to go.
So our first meeting was supposed to take 45 minutes. It took 90 minutes. And in our second meeting, we agreed to join forces. It was like, it was instant. It was just instant chemistry. Like the universe just brought us together.
And then all of a sudden sort of everything fell into place for me. Looking back at my life, I been getting ready for this actually all along. Caring for my dad who had been diagnosed with cancer too late to actually give him a surviving chance. My mom had been misdiagnosed with asthma when she had heart failure. So I had to leave my family, you know, everyone get together and really intervene. Really changed her, her lifestyle in order to save her life. She is thankfully now 91 years old and living fine, but it has absolutely no salt in her life and a completely different, different life. My own health, my own health journey sitting in front of a computer for three decades, more than three decades, as we know that now they call it called sitting, you know,
Harry Glorikian: Right, the new smoking.
Noosheen Hashemi: The new smoking. My experience running a couple of hardware companies, my love of food, and my skills of kind of scaling companies. You know, all of this came together. I just basically became obsessed with prevention and I felt that, you know, food could play an outsized role.
So wearables, you know, give you signals from the body continuously, which is incredible. But you also need to understand what people are eating and, you know, we can talk about that a little bit later, but we can basically now imagine predicting chronic conditions, much like Larry and Mike had. And then, you know, postponing and potentially preventing them. And if they’ve already started, prevent them.
Harry Glorikian: Yeah, I was lucky enough to be there and help when Evidation Health was getting off the ground and, you know, once we started to see the data coming in, I remember looking at the data. Is that real, like, is that actually happening? And I was like, the first thing I was thinking of was like, how do we design a clinical trial? Like if you’re going to actually say that’s happening, that trial is not going to be trivial to set up, to make that claim, but you could see it in the data.
And, you know I actually think some of the shifts that you’re talking about, if it wasn’t for things like the Affordable Care Act, if it wasn’t for putting EMRs in place, if it wasn’t for some of these shifts that have happened, you and I would still be, you know, battling this system that pays you no matter what. Right? And I think now is technology is a way that that can empower the average person to manage their own health. I’m not going to say optimally, but boy, a hell of a lot better than no information. I mean, at least some information can maybe give you an early warning light of something that you might be able to intervene in.
And I don’t know anybody that likes being sick. I mean, I don’t do well when this thing starts to age a little bit and not function the way that I want it to. So I’ve tried to try and keep it in as good of a running condition as I can. So it lasts as long as possible. I mean, I’m one of those people that would listen if I just drop dead at 95, like just boom gone. I would be so happy. Right. As opposed to this sort of chronic dynamic.
Harry Glorikian: I want to pause the conversation for a minute to make a quick request.
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And now back to the show.
So you mentioned AI, you mentioned machine learning. Where do machine learning and other forms of AI fit into January’s service and you know, what do you do on consumer data? What kind of predictions can you make that wouldn’t otherwise be possible?
Noosheen Hashemi: Okay. I can first talk about exactly that. What did we do that hadn’t been done before. What is really unique? What are we filling? So essentially in one word, it is prediction. You said it.
So as you know, there’ve been, there have been glycemic prediction models for type 1 diabetes, but type 1, as, you know, is a serious condition, which, you know, precision really matters for type one. It’s life and death.
But there hasn’t been much done with type 2 diabetes. And so we set out to do predictions, for type 2 diabetes. And the type 1 diabetes models are pretty simple. They basically are an insulin-carb calculus, essentially. But as we dug in, we realized that you know, carbs are not all the same and that there are so many other factors besides carbs that affect glycemic response, including things like fiber fat and protein, water, and foods. We wanted to understand glycemic index and glycemic load of foods. So our major machine learning research projects, we basically did research for two and a half years before we sold anything. One of the first things that we did was to try to understand the foods themselves. So we essentially built the largest database. Essentially we licensed all the, these curated food databases, and then we labeled the foods that didn’t have food labels, because right now the only food labeling you really have is like grocery foods and chain restaurants.
So we labeled foods and then, recognizing that glycemic response was better associated with glycemic index than carbs alone, we set out to create glycemic index and glycemic load for all these foods. Then we ran a clinical trial and associated people’s glycemic response to the glycemic load of foods they were eating. And then we turned that into a prediction.
So, the prediction model. Why is it so cool? Well, why should you use your body to figure out how many glasses of wine is going to spike you? Why not have the AI tell you that? Why not do that in silico? It’s this weekend, you want to cook for your wife. You want to get her the right fried chicken recipe. Well, check those out in January, check out those recipes in January. If you know what the glycemic response of, of each one of those recipes could be, it really helps you compare foods. For kind of recipes you can comparefood items in your local cafe. You want to figure out what to eat. You don’t have to put them through your body to figure out how you’re going to respond, put them through the AI to figure out how you’re going to respond.
And then in terms of, you know, how we’re different. I mean, we essentially live in the future. We, we don’t we don’t live in blood pricks and strips and blood glucose meters. We kind of live in the CGM, HRM (heart rate monitor) precision foodworld. We’ve turned food into actionable health data, which is a necessary ingredient you need if you want to understand people’s glycemic response. And if you want to be able to predict it, and that is our huge innovation that nobody has. And we have quite a bit of IP around it.
There are a number of things that we’re using. We’re using meta-learning. We’re using neural networks. I don’t know how much I should say about what we’re using. Yeah. We have one paper that we’ve put out, which is really, really, really simple. But we, we always talk about, what kind of papers we want to put out and how much we should put out and how much should we not put out, but essentially you can look at the people that advise the company and you can see that, you know, we have a lot of expertise around essentially…
Harry Glorikian: But Noosheen, when you’re doing this right, you need to, at some point, I think you need a baseline on say me for a certain period of time before the algorithm can then respond appropriately to that. And then doesn’t that potentially change over time, time you mentioned the yogurt, the meusli, right. And how that affects. So it’s constantly gotta be in a feedback learning loop.
Noosheen Hashemi: Yes. Yes. And the beauty of January is that essentially you don’t have to wear a CGM 365 days out of the year. We think that with AI, we allow you to wear a CGM intermittently. So maybe you want to wear it every quarter to update our models just to see how things are going, but you don’t need to wear it all the time. You can wear it for a period of training and then basically run your simulations in silico rather than through your body. Let the AI do the work.
So you definitely should wear it intermittently so we can update our, our models because people do age. People do have inflection points in their health. They get pregnant, they travel, a lot of things change, but we don’t think it’s necessary for healthy people to wear CGMs all year long necessarily.
Harry Glorikian: So now we’re talking about consumer behavior, right, for a, for a tech product like this. And if, you know, if you look at some of the data that I’ve read in some of these papers, you know, the potential market is significant. It’s, you know, it’s quite large. I mean, if I just said, you know, 15% of the people have pre-diabetic levels of glucose after eating, that would translate to like 50 million people in the United States alone. But the service depends on the CGM, the app, the external heart monitor. It’s, you know, users have to be diligent about monitoring and logging food intake and activities during the introductory month. So for a quantified self junkie, I get it. They’re all over this. What’s the plan for getting everybody else on to this?
Noosheen Hashemi: Well, I think it’s all about the user experience. And I think we have a, we have a long way to go as an industry and for us as well.As a company we have, what we imagine to be the user experience is nowhere near where we are today.
I’m old enough to remember world before Starbucks. So you would see ads on TV for MJB coffee, which is something you made at home. You know, I don’t know if you remember that but Starbucks created a new experience, really a place between home and work where you would stop by for coffee.
And so the outrage around the, you know, $3, $4 latteat the time, do you remember that?Well, Starbucks continue to improve the experience. They added wi-fi, they had ethical coffee, they had kind of a diverse employee population. People’s initial wonder and worry gave way to this, you know, gigantic global brand. And I think all of that is because of the experience that people had. I think we need to make health a positive experience. We need to—we, including January—need to make health something that people….it’s going to be a little clunky in the beginning, just like the old, you know, cell phones used to be. But while we’re going through this process, the companies need to work on to improve the experience and people need to be patient with the clunkiness of everything to get us to a place where these things become much, much more pleasant to use and easier to use, and essentially AI starts reading your mind about what you were eating and what you were doing.
That is going to happen. You know, I’ve gotten so used to my Apple Watch now that I actually love it. It actually is doing a very good job training me. Just at the right time, you know, “Come on, you still have a chance. Let’s go.” You know, all the things that it’s doing I’m actually liking it. It’s it’s enjoyable. Because it Is coaching. And I feel like the answer for mass adoption lives in experience. We need to improve the experience dramatically.
Harry Glorikian: It’s interesting though, because I I’m play with a lot of these different things and I noticed that depending on how they’re designed, how they’re put together, it nudges me to do that much more or et cetera. I don’t always listen. Human beings don’t always do what they’re supposed to do for their better good. But you can see how, when the app is designed in a way to nudge someone the right in through the right mechanisms. And that’s the problem, right, is trying to—not the same mechanism works on everybody. So you may have to have multiple approaches that the system tries like AB testing for a website to, to get them to do that.
But so, if the average person like me wants to do something like this, obviously I have to get a ‘script from my doctor, which just drives me crazy that I can’t just—because I can buy a finger-prick, right, over the counter and poke myself a thousand times and then write down these numbers to see what happens. Which seems a little clunky in my opinion. But I can’t buy the CGM that does it automatically. There’s gotta be some medical person saying like, we’re gonna make more money off this if we do this or do that, or, or it just doesn’t make any sense to me. How do you, how does January come at the expense reimbursement or the insured part of it, or is this just out of pocket for everybody?
Noosheen Hashemi: Sure. So right now government insurance, companies, and private insurance companies cover CGMs for people that are intense insulin users. So people that prick themselves four times a day. And so that’s three and a half million out of 122 million people that have pre-diabetes or diabetes. So it’s a very small population. And the rest is all cash paid. And it it’s really out of pocket.
So we have an early access price of $288. And we, you know, we include the CGM, but you can also buy CGMs only from January. You can just, if you just want a CGM, you don’t want to do anything else. You’re just curious. You want an introduction to this world? You can order a CGM from January for $80 if you want to do that. So if you’re one of the 12 million people that are insured by Kaiser—and Kaiser doctors will not write you a prescription, you can go to your doctor and ask them, they won’t write you a prescription—come to January. We will give you a CGM. You can be introduced to the program and then, you know, take, take up January from there and experience the magic of CGMs alone.
I really do think they are a magical product because they they’re showing you for the first time you kind of can see inside your body, which is really phenomenal. Unfortunately by themselves, they’re not that effective and they’re not that effective by themselves longitudinally. So if you really want to keep track of how you’ve been doing, what food spiked you, how you can, you know, what kind of exercise, things like that. They don’t really have that additional intelligence, but they are magical, they are really magical tools. But, you know, you want an insightful experience on top of that. With the AI that can essentially synthesize this kind of data from your heart rate, monitor from your food, from your glucose monitor and sort of let you know how much to eat, what to eat, how to hack your food, how much to walk, how much, how much to fast, when to fast, how much fiber you’re having, not having. That’s where we come in.
Harry Glorikian: I feel like at some point I’m going to need a big monitor in my house that just tells me these things as I’m walking by. But you know, it, it’s interesting. I mean, we are entering the era of real wearables and apps and big data and, and, you know, but here’s the question though. Soyou know, Apple just announced what’s going to be the update to their iOS and, you know, pretty soon I’m going to be able to push a button and share data with my physician. Which is funny because I go in his office and I pull up my phone and I’m like, here’s my longitudinal. And here’s my longitudinal. And I’m like, look, you can take the measurement because you’re supposed to, but here’s how it looks over the last three months as opposed to the one time when I’m here. Can January’s customers export and share the data with their doctor?
Noosheen Hashemi: We have a report midstream at 14 days that you can share with, with your doctor. But of course we intend to, you know, we have features planned that are going to make things way more easily done, much more easily in the future. We really strongly believe that people should own their own health data. We are huge advocates for people owning their own health data, because there are a lot of people hanging onto your health data and they don’t want to give it to you. I’m talking about device makers and others. You’re paying for the device, which comes with the data, but they don’t want you to have the data. So they’re like, “You can have the data and study it yourself, but you can’t give that data to other people.” But that doesn’t work.
We are living in a multi-omics world. Single ‘omics by themselves, the single side node biomarkers, you know, “Harry, you just manage your cholesterol. Noosheen, you can’t keep two things in your head. Why don’t you just manage your A1C? And Mike, you should watch your blood pressure.” That just doesn’t work. There are many, many markers that you’ve just, as you just said, that we need to keep in our heads. We can’t keep them in our heads, but that’s where AI comes in. We need to feed them into something and people must have the right to own their data and share their data with whoever they want. If it’s their coach, it’s their doctor, it’s their wife or spouse or significant other, their dog. They should be able to share the data that they own.
As long as they provision it properly to whoever they want to give it to because you know, someone doesn’t want their employer to know X, Y, and Z. Somebody else wants their coach to know that is people’s rights. And coming from kind of a libertarian point of view, I really think people, you know, people should own their own data and they should be able to mix it with other data for synthesis, if they want to.
Harry Glorikian: Yeah, it’s interesting. I mean, I totally believe in that. I always, I also understand that people may not understand the implications of sharing sometimes. And that’s not clear, but I do believe that the next iteration of where we’re going to see this technology go is multifactorial software programs that can take a number of different inputs to give a much more holistic view of what’s going on with me, so I can manage myself better share that information. My biggest worry is most physicians I know are—it’s not totally like, it’s not their fault, right….
Noosheen Hashemi: They’re so busy, so they’re spending 15 minutes a year with you. And during that 15 minutes, you know, they’re taking a point in time, you know, to see a snapshot of your health. And your health is way more complicated than that. We’re talking about reverse engineering, 5 billion, years of evolution. And you know, they’re going to get, see if such an infinite small part of that. We need to be way more self-aware.
Harry Glorikian: Well, it’s funny because I do have, some of my physician friends will be like, you want me to understand that genomic marker that whatever, like, I can’t, I can’t get my patient to manage their insulin level!
Noosheen Hashemi: I have a lot of empathy for that. They just don’t have the time. I completely fully understand. Which is why I think we should carry more of the, we should have more agency over our health and we should carry the burden a little bit more.
Harry Glorikian: So what is wild success for January?
Noosheen Hashemi: Well, we want to keep on this path of developing our multi-omic platform. We want to essentially help people understand themselves deeply and figure out how to dial their lifestyles and sort of tweak and tune their health. This is non-trivial obviously because there’s not enough research in food science or enough research on prevention. You know, out of the $3.8 trillion that we spend on healthcare, 2.9% goes to prevention and 10% goes to acute care end of life care. Just think about that. More than three times as much goes to end of life acute care than goes to prevention. And I’m talking about healthcare costs, I’m not talking about research costs in terms of what NIH and USAID and all of those people spend. So there’s not enough research that’s happening.
You know, people’s health data is not organized today. I’m sure there are companies who are trying to organize the world’s data. You know, the company that tries to organize the world’s data is trying to organize your health data. So I think that’s pretty smart. I think today it’s still very opaque and it lives in silos, but I think in the future is going to be mixed. I think today people just aren’t fully empowered yet, you know, with the knowledge and with the agency and with the tools they need to really manage their health.
Wild success for us means that people, that we’re part of this revolution of consumerized healthcare. We’re part of the food-as-medicine revolution, the precision nutrition revolution. So we see ourselves coming up with tools that can essentially get amazing experiences in the hands of millions of people.
If you can think about a company like Livongo going public with 192,000 patients. Or if you think about everyone that’s playing in the metabolic health today, if you put 12 or 13 companies together, maybe they have a million users, or maybe a million and a half users. Where is that compared to 122 million people that have pre-diabetes diabetes and another a hundred million people that are optimizers? They’re either wearing a wearable, they belong to a gym, they’re on a diet. You have the entire population as your market. And we have very little that has really made a major foray into health. So wild success means having a product that becomes mainstream.
Harry Glorikian: So I think what you’re saying is January is moving beyond just CGMs and metabolic syndrome, right?
Noosheen Hashemi: Absolutely. Yeah, we, we imagine ourselves, we have built an expandable platform. Our goal is to keep doing deep phenotyping. So we will add ‘omics you will see us adding ‘omics beyond what we have today. You will see us get to other cardio-metabolic disease, you know, cardiometabolic disease, essentially going beyond metabolic disease to the rest ofmetabolic syndrome. You’ll see us be ahardware-agnostic company. We want to essentially let people wear whatever they want. Whatever works for them and, and still try to bring that data, synthesize it and make sense of it and feed it back to them so they can take action.
Harry Glorikian: Excellent. Well, that’s, that’s a great way to end the program with. We have so much more to see from the company and what it’s going to be able to do with the data and, and, and help you know, people live a healthier life. Or like I said, with me I’m constantly trying to measure what’s going on. It’s just distilling it to make it easily consumable to do what I need to do rather than have me learn statistics so that I can figure it out.
Noosheen Hashemi: We have to get, all of us need to get better than that. I remember when I first put on my Oura ring, you know, there’s, you know, most people first when they wear their Fitbits, you know, first it was like, how much did I sleep? And then they kind of learned about REM and sort of deep sleep and then slowly. And then Oura came and then it was like, oh, and Whoop had already had heart rate variability, but then, you know, Oura came in with their other markers, you know, restfulness. And efficiency, sleep efficiency and timing, et cetera. And so people are slowly wrapping their heads around this. It takes a little whil. And yes, January gives you a lot of levers. You know, there’s fasting, there’s fiber, there’s calorie management. There’s you know, the spikers. There is the activity counterfactuals—I ate this, but had I eaten this other thing, this would have been my glycemic response. Or had I walked X number of minutes after that, this would have been my glycemic response. At the beginning it’s a lot, but that’s where it goes back to the experience. We must make the experience enjoyable and better, and we must, companies like us should strive to make the experience enjoyable, make them fantastic consumer experiences like Apple products. But remember Apple’s 45 years old and we’re just getting going with this, But [Apple is] a great role model.
Harry Glorikian: Wellyou know, my doctor may not like it, but I may have to get one of these. He’s listening to this podcast. I know that he will, because he always comments on them.
Noosheen Hashemi: We’re definitely doing that. And you know what? You can have Mike Snyder, you can chat with Mike about your numbers after. That would be a lot of fun.
Harry Glorikian: Excellent. Oh, I look forward to it. So thank you so much for participating.
Noosheen Hashemi: Thank you, Harry. It was pleasure.
Harry Glorikian: That’s it for this week’s show. You can find past episodes of MoneyBall Medicine at my website, glorikian.com, under the tab “Podcast.” And you can follow me on Twitter at hglorikian. Thanks for listening, and we’ll be back soon with our next interview.