Has YoungAI had any discussions of partnering with Forward Health?
- Not yet, but they are as close to Longevity as a Service as you can get – a sort of McDonalds for diagnostics as I think it should be. So we would be happy to partner, please connect us if you know them.
Linear regression works really well with epigenetic clocks, almost seems to be too good to be true. But my question is about deep learning and the issue of having interpretable models. In your experience what is the breakdown between where linear regression does the trick and then when linear regression is not enough but you were able to make it work with deep learning.
- You can look at this paper and this specific figure we did where we compared different algorithms. The important thing is to see which features were prioritized in different architectures, so you can then combine a few different algorithms for where they work the best. We also learned from that work the value in combining and comparing different algorithms in different tissues.
Can you clarify how you feel about the usefulness of epigenetic clocks? You said that it is not the most actionable and interpretable – do you mean that it does not respond quick enough?
- It’s the most accurate clock we have today, but there is always a tradeoff between accuracy and sensitivity to interventions. Some clocks are extremely variable, where one person can be younger and older on the same day. But that’s exactly what you want – you want to understand what makes you younger or older on the same day as well. But also what is it that makes you older or younger over longer periods of time.
I think it is a bit naive to reset some blood markers to younger age, like cholesterol where for example middle aged women with higher cholesterol are more protected with a bit higher cholesterol.
- Yeah I agree, we don’t know the complete story with most of the things. We’re just showing what needs to be changed in blood for you to be predicted younger by the deep learning net. Think of it as wrinkles with photogenic age – you can use haut.ai for photo face age, we did a paper together where we demonstrated that you can build an age predictor from face photos that is 2.2 years accurate. And then you can use different techniques like covering different parts of the face to see where the major features that make you older or younger are. That study has shown that the area is around the corners of the eyes, so if you fix the wrinkles there, you are predicted to be younger by the neural net. Of course that doesn’t necessarily mean that you’re going to be healthier or younger, but you are going to be predicted younger by the network. We follow the same kind of philosophy with blood and other markers. I’m not saying that it is right or wrong, it might quite possibly be wrong. But we can at least show that you could be predicted younger if you change some features. I cannot say that it will necessarily make you live younger yet though, for that we need longitudinal data and nobody has done that before. And if we don’t study this area in humans, we will never know.
Are you currently partnering in any fields beyond pharma & cosmetics and/or are you planning any other cross-disciplinary applications in the future?
- Constantly thinking about it, I am very interested in partnering with anything tech. Ideal partnering would be with Amazon, because they could provide Longevity as a Service in a very true nature and the true meaning of this word. Because they help you waste a lot of time now with Amazon Prime Video, so I think it is only a matter of time before they realize that in line with ESGs, they should return some of that time back through longevity. And they collect a lot of data about people. Partnering with organizations that waste a lot of time to return some of that time would be a dream. Currently they lack that vision but I hope that I can ignite somebody to spread this longevity virus to them as well.
What are the next steps for the individual services and what can this group help with?
- We have the Deep Longevity app for which we are hiring. For chief positions in business development (if you have experience selling to insurers or clinics, let us know). But also for many other positions if you’re excellent at what you do. Or connect us with players that are in those spaces.
- Insurers will not change without medicine, medicine will not change without insurers, pharma companies will not change without medicine and insurers, academia is not going to accelerate without a huge consumer business. In order for us to drive Longevity as a Service vision, we need to ensure that all those areas are progressing very quickly and at the right pace, and we cannot progress faster than physicians and medicine, so that’s why we need to ensure that medical doctors are engaged, involved, and educated.
Do you have any predictions for machine learning or aging clocks? Interesting challenges to take on?
- The best way is to join one of the companies or academic groups that are already working in this area. I think there are many more data types that we could work with to predict age and try to understand it better. If you know just ML and don’t know much biology, go into imaging – MRI, CTs, or especially ultrasound, where there is a lot of opportunity for progress. Try to build a clock based on such an accessible data type and get it into the clinic. If I had more time, I would go to ultrasound because it is a way to get non-invasive diagnostics quickly. And then I’d try to combine the ultrasound data with something like gene expression, or mineralization levels, or cross-links. So the combination between accessible imaging biomarkers and some exotic biomarkers, to make them cheaper is the way to go. If you can substitute some of them or get the same answer by using a cheaper method, that is extremely valuable.
Have you seen ultrasound to work well for making clocks in other species?
- I don’t know, ultrasound is one of the data types where we have more from humans than animals, so not sure.
Any other challenges?
- I would also go into monkey business. Looking at experimental monkeys, it turns out that most of the monkeys being supplied to pharma are like 3 years old. Because if they are below 3 years, they are immature, and over 3 years old are too expensive to handle, so most of the experiments are done in this age range, no-one is experimenting with them longitudinally. If we had more data about monkeys from that point of view, we could learn a lot. Mice just do not live long enough to accumulate the damage humans do and therefore we miss a lot of insight we could have from monkeys. Like for example the cholesterol question, seeing that in monkeys with the ability to take a tissue sample in a little bit easier way.
If I am now a potential customer, which site should I go to?
- We’re B2B as the major segment, so from the consumer perspective, you can download the app and let us know about the UX, and report what you do and don’t like, that would be helpful. We’re trying to put the customer in the front. So you can track your blood age over time and opt out from sharing data anytime.
- If you are running an insurance company or a clinic, please do come to us, we want to partner with you.