Data-intensive wellness companies
- explicitly focus on individual’s health and wellness (wellness monitoring),
- generate molecular and other data using many different platforms (multi-omics), resulting in tens or hundreds of thousands of measurements for each individual data point,
- use or claim to use artificial intelligence/machine learning to reach their goals.
So the heading of this blog post could just as well have been for instance “AI wellness companies” or “Molecular wellness monitoring companies”. The point with using “data-intensive” is that they all generate much more extensive molecular data on their users (DNA sequencing, RNA sequencing, proteomics, metagenomics, …) than, say, WellnessFX, LifeSum or more niche wellness solutions.
I associate these three companies with three big names in genomics.
Arivale was founded by Leroy Hood, who is president of the Institute for Systems Biology and was involved in developing the automatization of DNA sequencing. In connection with Arivale, Hood as talked about dense dynamic data clouds that will allow individuals to track their health status and make better lifestyle decisions. Arivale’s web page also talks a lot about scientific wellness. They have different plans, including a 3,500 USD one-time plan. They sample blood, saliva and the gut microbiome and have special coaches who give feedback on findings, including genetic variants and how well you have done with your FitBit.
Q, or q.bio, (podcast about them here) seems to have grown out of Michael Snyder‘s work on iPOPs, “individual personal omics profiles“, which he first developed on himself as the first person to do both DNA sequencing, repeated RNA sequencing, metagenomics etc. on himself. (He has also been involved in a large number of other pioneering genomics projects.) Q’s web site and blog talks about quantified health and the importance of measuring your physiological variables regularly to get a “positive feedback loop”. In one of their blog posts, they talk about dentistry as a model system where we get regular feedback, have lots and lots of longitudinal data on people’s dental health, and therefore get continuously improving dental status at cheaper prices. They also make the following point: We live in a world where we use millions of variables to predict what ad you will click on, what movie you might watch, whether you are creditworthy, the price of commodities, and even what the weather will be like next week. Yet, we continue to conduct limited clinical studies where we try and reduce our understanding of human health and pathology to single variable differences in groups of people, when we have enormous evidence that the results of these studies are not necessarily relevant for each and every one of us.
iCarbonX, a Chinese company, was founded by (and is headed by) Wang Jun, the former wunderkid-CEO of Beijing Genomics Institute/BGI. A couple of years ago, he gave an interview to Nature where he talked about why he was stepping down as BGI’s CEO to “devote himself to a new “lifetime project” of creating an AI health-monitoring system that would identify relationships between individual human genomic data, physiological traits (phenotypes) and lifestyle choices in order to provide advice on healthier living and to predict, and prevent, disease.” iCarbonX seems to be the company embodying that idea. Their website mentions “holographic health data” and talks a lot about artificial intelligence and machine learning, more so than the two other companies I highlight here. They also mention plans to profile millions of Chinese customers and to create an “intelligent robot” for personal health management. iCarbonX has just announced a collaboration with PatientsLikeMe, in which iCarbonX will provide “multi-omics characterization services.”
What to make of these companies? They are certainly intriguing and exciting. Regarding the multi-omics part, I know from personal experience that it is very difficult to integrate omics data sets in a meaningful way (that leads to some sort of actionable results), mostly for purely conceptual/mathematical reasons but also because of technical quality issues that impact each platform in a different way. I have seen presentations by Snyder and Hood and while they were interesting, I did not really see any examples of a result that had come through integrating multiple levels of omics (although it is of course useful to have results from “single-level omics” too!).
Similarly, with respect to AI/ML, I expect that a larger number of samples than what these companies have will be needed before, for instance, good deep learning models can be trained. On the other hand, the multi-omics aspect may prove helpful in a deep learning scenario if it turns out that information from different experiments can be combined some sort of transfer learning setting.
As for the wellness benefits, it will likely be several years before we get good statistics on how large an improvement one can get by monitoring one’s molecular profiles (although it is certainly likely that it will be beneficial to some extent.)
There are some related companies or projects that I do not discuss above. For example, Craig Venter’s Human Longevity Inc is not dissimilar to these companies but I perceive it as more genome-sequencing focused and explicitly targeting various diseases and aging (rather than wellness monitoring.) Google’s/Verily’s Baseline study has some similarities with respect to multi-omics but is anonymized and not focused on monitoring health. There are several academic projects along similar lines (including one to which I am currently affiliated) but this blog post is about commercial versions of molecular wellness monitoring.