Have you done measurements for baseline heat shock response elements?
- I don’t think we did a heat shock response, we did some cryotherapy but haven’t got the data back. Others are doing hyperbaric chambers, so it will be interesting when they come back with data.
How can we scale the kind of science you discussed esp. data collection (many data types at healthy baseline + tracked longitudinally + extra samples in illness): ie (a) How can we get funding for a larger cohort across multiple institutions, (b) can we separate the data collection from the analysis with all the data open to any researcher who wants to analyze, (c) can we incorporate citizen science where high end people contribute but without complete data?
- It’s hard to get money for this sort of thing, because people tend to study illness and not health. We’ve struggled to get money, the way we slapped the current funding together is with a lot of philanthropy – that helped a lot. We did get one NIH grant when I packaged it under pre-diabetes, which seemed to help. It seems that there is also a rising wave of awareness about wellness and health, so I’m hopeful that will also lift our boat. There’s a lot of mini national projects, in Sweden for example – not that many make their data open though unfortunately. There is also the 100000 UK genome project which is interesting.
- As for concrete steps, we’ll obviously keep trying to plug into NIH to give us more money. In the wearables project, we’re going to put the anonymized data public – we’re actually the only group that puts wearable and CGM data public. Amazon gave us $1.3M in credits to build a data ocean to put this data out there, so that’s what we’re doing. We’ll also put some limited omics data out there as well. We love people who put their data including genome sequence open so people can use it. Actually more people have analyzed our data than we have, so the data we have is already out there and people are using it.
Q.bio seems to be mostly MRI based — is there anyone integrating all these datasets together?
- I think it is primarily because of their early days, as they wanted to stay around actionable information. There was a company called Arivale, they were trying to build these networks that were from the research standpoint pretty cool, but the information wasn’t actionable, and the company didn’t survive as a result of that. So we are pretty cognizant of that and need to start with a proper business model. So we’re doing all this research in the lab, but then use the actionable parts in the company. But they want to integrate these datasets, it’s only a matter of time.
Why doesn’t Q.bio offer the package minus the whole-body MRI? (You aren’t doing the MRI piece to the 109 person cohort in the academic work, right?)
- They are a new company so they can’t offer too many specialized packages. And even if people would like to replace the MRI with the GRAIL test for example, they are not necessarily replacing each other, rather that together they are going to be more powerful.
Do we already know which more invasive & expensive biomarkers & measurements we can cleanly and reliable cross-correlate with non-invasive, cheaper ones? (extreme example: Facial aging selfie –> DNA methylome)
- We’re very interested in that, as you saw in the presentation we are trying to correlate and predict the clinical measurements with the wearable data we can extract. We’re also doing a lot of microsampling and pricking and doing deep measurements there. We’re trying to get in that direction and see what we can learn. I am most interested in smartwatches, because there’s already a huge population and I honestly believe that we could put a smartwatch on every person on the planet, 60% of the planet has a smartphone so if we just pair that with a smartwatch, we can really improve peoples’ health.
You mentioned epigenetic methylome. Is that data for the 109 people as well as the battery of tests they’ve done open to the public as well?
- Yes and no, we did it as a pilot on me, whole genome by sulfite sequencing (the whole methylome) with a high temporal frequency. We did it as a pilot to see what we can learn and to see where we will screw up. These samples are very very precious, we have been waiting for the price of the sequencing to drop enough to the whole batch – we have like 2000 samples. We want to do it all but we want to wait for the price to drop. So you can get my data, it’s available and out there, feel free to play with it. For the rest stay tuned, I keep thinking we will do it in 2021 but now I’m guessing 2022 might be more likely.
How does the HR algorithm avoid alerts on exercise but pick up disease-associated HR spiking? And could it be used for long COVID diagnostics eg post-COVID POTS can result in major HR spiking?
- We subtract 10 minutes of heart rate after you run, we can see when you are running using steps. But walking is probably a better measurement, because it’s like a little bit of stress, and it provides a way to get larger signal from the heart, it sort of amplifies it. So we want to tune the algorithms to pull the stuff out when walking.
- As for long COVID, we are looking at those data now and we definitely see alerts and other signals going off for several months after the infection. So we’re going through the data now and trying to put it together, I don’t have a complete story yet. But we can definitely detect post COVID signals.
- Related to this is an interesting paper from United Health group that has shown that something like 4% of people get Type 2 diabetes after viral infection, so that fits very nicely with the post COVID recognition – I think this stuff is very generalizable.
What’s the willingness of payers (employers, insurance companies, CMS) to pay for prevention-oriented interventions? Some VCs are reluctant to invest in prevention companies if payback period is > 18 months – given patient churn in health plans.
- This is one of the ways in which our healthcare system is broken. We don’t pay to keep people healthy, we only pay once they get ill. It’s really backwards. How do we solve the problem? Well Q.bio right now is a concierge service, we want to get out there broader, get it cheaper, starting out expensive because of a lot of R&D costs, but as we get the scale and new technology, I hope we can get less expensive and out to the world. So I hope once it is cheap enough, they’ll take it on. In the meantime if we can make at least some parts of this, like the wearable data, show that everybody should be doing this, and if those plans could have that, I think that would help. They tend to roll it in in a different way though. Several groups that we’ve talked to want to do monitoring of at risk populations, or outpatients, and show it works with those folks. So start with at risk groups and concierge and show that it has utility, and then spread it broader.
You presented several examples of reversals in different measurements for example in response to lifestyle changes. I wonder whether this applies with aging, for example in the case of ageotypes or maybe wearables, whether you can see it going up and down.
- We are extremely interested in this. We now have about 7 or 8 years worth of data, and we’re just going through the analysis now. Maybe next time I present that, we didn’t have a clear story yet. Once we have enough data from all the folks to be able to look in a lot more detail, we’ll do that as well, there are a lot of lifestyle interventions people went through.
We have funded companies that do self-insured employment. There are two economic benefits – cost savings to your bottomline, when you make employees healthier, and then of course also the productivity increase that also adds to the bottomline. So are there health interventions that have productivity improvements that insurers should pursue if they have those types of plans?
- I am in a conflict of interest here but a no brainer would be January.ai and Q.bio. Glucose monitoring at least personally helps, since I can put myself to sleep with a slice of pizza, but now I know exactly what foods are going to do what in my body.
What can this group do to help you further your work?
- Sign up for as many studies as you want. If you are in the Bay Area and want to join our fiber study, we’d love that. If you are anywhere you can join our fiber and cognition study. You should all be wearing smartwatches, I am wearing four. By all means sign up, even in the CGM studies we have a remote version of that. All kinds of studies out there. You can find that on our website.