Yeah, that’s a very good question. By the way, thank you for inviting me. I should go much more backward in order to explain it. At the age of 12, someone in my family passed away. Instead of being sad about her, I was sad about myself because I realized that I was not immortal. At that time, I decided to dedicate my life to trying to understand why we die. How can we live longer, and better? How can we delay the onset of the age in preventative diseases? That’s why I decided to study biology originally. I did my undergraduate degree at Tel Aviv University, and then went on to do my Master's and PhD at the Weizmann Institute of Science, all in Biology. Then I moved to MIT and joined one of the best aging labs in the world, the Leonard Guarente Lab at MIT, and I studied aging there for around five years. During the time that I moved to MIT, Kendall Square in Cambridge, I was exposed to the biotech, pharmaceutical, and high-tech environment of Kendall Square , it’s very similar to San Francisco in a way. You have a lot of startups in a radius of one mile in and around MIT. I started to realize that I could contribute more to humanity if I started my own company than being a professor in academia.
I moved to the industry and joined a computational system biology company. Today we’ll call it a machine learning AI company. At the time , we didn’t know that you could call it like that. I worked at that company for a couple of years. They developed a platform, a very nice platform that you can use a lot of data. The platform will provide you with some network analysis of gene changes, protein changes, and so on.
During the work there I said, okay, there is a phenomenon called calorie restriction that when you cut the amount of calories that you feed the model organism, if you cut it by around 50%, the model organism can live 50% longer. But nobody understood why it’s happening. So I said, okay, let’s use all of this data, use this platform, and try to understand what’s happening under the hood of calorie restriction.
So I built a model for that and the model showed me around 18 different processes that are actually changing when you’re calorie-restricting a mouse or rat. But as a good scientist or hopefully a good scientist, I used a few controls. One of the controls was using mice that were treated with Reservatrol.
Reservatrol is a small molecule, but David Sinclair found that it can be an activator of CLT1, which is a longevity gene that I used to work on at MIT. I also used another control that is basically old mice. Basically, I tried to see what is the overlap between calorie restriction mice, then the reservatrol-treated mice, then mice that basically were old. To my surprise, the overlap between either reservatrol mice or old mice with calorie restrictive mice was only around 10%, and each of them was different by 10%.
At that time, I tried to analyze this data, and I spent some time with a couple of other scientists. We came to the idea that if the best calorie restriction pneumatic at that time was reservatrol, it would only cover 10%, the second best would cover 5%, and the next one maybe 2%. We need a lot of small molecules.
Then we said, why should we use a small molecule? Why can we use the food as a drug of choice? Why can’t we basically suggest to a specific person the right food that is right for him or her? But then we said, okay, how can we know what a specific person needs? We said , we need biomarkers. A biomarker is basically a biological entity that can mark a specific situation. For example, glucose in the blood is a biomarker.
A resting heart rate is a biomarker. It’s a physiological marker. A specific DNA score is another biomarker. It’s more of a genetic biomarker. We said let’s start with blood biomarkers, which cause blood biomarkers to be very important. Blood is something that the medical community used for the last 100 years or so. They are using it to make a decision, a medical decision, whether you are sick or healthy, what is the treatment that you will receive?
We said let’s use the best marker that we can. What we have done then, we looked at the Quest Diagnostic catalog. Quest Diagnostic is the biggest diagnostic company in the US. When I looked at it, I saw that there are around 5,000 different blood biomarkers that they’re testing. The response was, first, you will need a lot of blood for that. Second, you might not have enough money in your pocket to test all of that because that will be very expensive. We worked on the criteria to select the right biomarkers that basically will make some good recommendations for users.
We came up with a few criteria. One of them is the biomarker of health and not a disease. I don’t want to look at biomarkers related to cancer, but I would like to look at biomarkers related to metabolism, biomarkers related to inflammation, performance, and so on. The second one is biomarkers that you can modulate using simple and natural interventions. Basically should supplement exercise lifestyle changes.
The last one is the biomarker that which at least 1% of the population is out of the normal range. Based on that, we got a shortlist. Today we have around 50 blood biomarkers that we are testing. On top of that, we added some DNA scores. We have around 50 of those. And we have, I would say, around 20 physiological markers such as resting heart, deep sleep, REM sleep, VO2 Max, and so on. We are looking at all of that together.
Based on that, we are trying to provide you as a user with a holistic view of how you look from the inside, allowing you to build an action plan based on your goal. Do you want to run faster? Do you want to sleep better? Do you want to live longer?
We have around 12 goals like that. We extract the five best recommendations for you, you follow the recommendation, and then you test your blood again and actually the physiological markers, you receive the data all the time. We are providing you with a follow-up result. We published a paper in 2018 that showed that our users significantly improved the data. Now we have 100,000 users and we have very good data that we are working on publishing, showing that we can significantly improve the blood biomarkers, and all the physiological markers of our users based on follow-up.
We also, because I’m fascinated by longevity, developed a biological clock. A biological clock is basically a way for you to know how old you are from the inside. So everyone knows what is this chronological age. But biological age is basically looking from the inside and estimating how old you are. And then you can compare it to your chronological age and know whether you are older or younger than your chronological age.
We develop a biological age based on blood. Also, we have very good data that showed that our users that started with a biological age that is higher than the chronological age and the follow-up test improved significantly.
That’s basically the short explanation of what Inside Tracker is doing and my background.
Finding the first customers of Inside Tracker from triathlon events and being introduced to the Boston Red Sox’s nutritionist