Remote Monitoring & Deep Data: Transforming Healthcare & Early Detection

•From your blood, using mass spectrometry and other methods, you can profile proteins, metabolites (substances made or used when the body breaks down food, drugs, or chemicals, or its own tissue), and lipids (the breakdown and storage of fats).

•From tissue or blood, you can sequence your genome, transcriptome, and proteome.

•From peripheral blood (blood circulating through your body), you can isolate mononuclear cells (PBMCs, blood cells with round nuclei), allowing you to measure your immune system cells (monocytes, lymphocytes, and macrophages).

•From blood plasma (the part without the cells), you can measure proteins and cytokines, which are small proteins that are important to the immune system and blood cell controllers.

•From fecal, urine, gut, nasal, tongue, and skin samples, you can follow your microbiome.

•From questionnaires, you can track your feelings, pain, nutrition, exercise, and symptoms.

•From advanced tests, such as stress echocardiograms and glucose control measures, you can track deeper clinical status.

•From wearables, you can track heart rate and rhythm, blood pressure, oxygen saturation, skin temperature, quality of sleep, total steps in a day, amount of exercise, and exercise response. With a continuous glucose monitor, you can track your glycemic response to what you eat. How often should you be taking these health measures? You should track your data longitudinally. Wearables can track you all the time. Metabolic tests can be run every month. Otherwise every six months may be enough while you’re healthy. Then if an adverse event comes along, like a viral infection, you should take more samples. (This is what the Snyder Lab does, though they do not know the real answer.) How can you use your health data? Increasing the data you gather from health monitoring tools can help you:

•Diagnose: For example, Mike Snyder is type two diabetic, which was predicted from a genome sequence and then got picked up through profiling. Mike also detected when he had Lyme disease pre-symptomatically, because his heart rate went up, and his blood oxygen dropped. This was picked up with a pulse oximeter, although you can now get it from a watch.

•Provide an early warning : Seeing things are off before people have symptoms. For example, early lymphoma, pre-cancers that can convert to aggressive cancers, and heart issues have been detected. Mike’s lab developed a COVID predictor based on raised heart rate.

•Monitor: For example, through longitudinal profiling, a case of early pancreatic cancer was detected. Other cancers can be monitored for recurrence.

•Personalize treatment : For example, your normal temperature is probably higher or lower than 98.6, which has been the generally accepted normal temperature. Everybody reacted differently to an Ensure shake. How could microsampling be used to monitor a particular disease, like prostate cancer? Microsampling is a fantastic opportunity for semi-continuous monitoring and could give you useful surrogate indicators. For example, one of the challenges metastatic prostate cancer patients face is getting tissue to support diagnostics because their lesions are typically in their bones. Today, they rely on cfDNA tests to identify oncogenic and resistant mutations that characterize their solid tumors. This is a huge step forward, but we would want to explore whether or not blood-based proteomic microsampling could detect solid tumor protein expressions that are currently limited to tissue samples. However, there are challenges to designing a microsampling and monitoring protocol for a particular disease. What to measure is as important as how often to measure it, as well as how to look for fluctuations in whatever is monitored that may be meaningful. Some commercial vendors provide monitoring, but for specific analytes or measures that may or not be the most informative. For example, there is no obvious answer to the obvious biomarker(s) for prostate cancer. To find them, we would look for the most homogeneous patient population and treatment pattern with distinct outcomes of response versus non-response. Then we would develop an observational study to see what kind of data would separate the two. The key is a simple clinical trial design to keep the study fairly small and reasonably easy to recruit patients. We are exploring convening a group to discuss this possible trial design. The study would need prostate oncologists and clinical trial statisticians. The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Cancer Patient Lab/Prostate Cancer Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health. Meeting Notes The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Prostate Cancer Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health. Discussion Outline 1.Introduction (0:00) 2.Using big data to transform healthcare and health.(3:02) 3.How do you track health and wellness? (7:30) 4.How can you tell when you’re getting an infectious disease? (12:13) 5.The most common trigger of stress. (17:31) 6.Tracking glucose regulation and inflammation. (21:30) 7. do you measure the number of monocytes? (26:55) 8.What kind of sequencing are you doing for human longevity? (33:03) 9.Micro-sampling and the cost of the test. (37:56) 10.Measuring capillary blood and heart rate. (42:59) 11.The importance of heart rate monitoring and blood pressure. (49:02) 12.Using a wearable to detect recurrence of cancer. (54:50) SUMMARY KEYWORDS people, called, question, heart rate, blood, data, brian, measure, sampling, monocytes, markers, measurements, health, smartwatch, micro, cells, genome, run, important, meaning SPEAKERS Mike Snyder (77%), Brian McCloskey (12%), Gitte Pedersen (4%), Jason Crites (2%), Richard Anders (2%), Russ Holyer (1%), Eric Hall (1%), Amit Gattani (1%), Rick Stanton (1%) Brian McCloskey Welcome, everybody to the Prostate Cancer Lab. We're very excited to have Dr. Michael Snyder with us. He is the chair of the Department of Genetics at Stanford. He is also a very well published author, most notably the genomics of personalized medicine, which everyone needs to know. His research has spanned many different areas. He was the first to perform large scale functional genomics in any organism, and has developed many technologies in genomics and proteomics. These developed the proteome chip, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites, de novo genome sequencing of genomes using high throughput technologies and RNA seek. And these technologies have been used for characterizing genomes, proteomes, and regulatory works. And so, Mike, that's coming directly from your, from your biography on your site. Some of it, I know, tell them but I don't know. But I mentioned that just because you have some patients here, and researchers, etc, that are bioinformaticians. And that's going to mean something to them. Your research has also gotten into HIIT training, and we have talked a lot about it, relative to other training. Rick Stanton, who's on the call here, the cofounder of the Prostate Cancer Lab, bioinformatician from Amgen, three years at human longevity. And he's sporting his guitar there. He's really interested in HIIT versus LDT, long distance training. Oh, anyway. So you've got a lot of really interested people. Mike Snyder 3:02 I'm not an MD. I'm 100% conflicted and everything I'm gonna tell you in terms of startups that have spun off from some of the work that we're doing here. So I will tell you a lot about 20 minutes of slides, and they give you a flavor for the kinds of work we do. And it's all revolving around big data, trying to use big data to solve biological problems. And probably our flagship work is the one I'll tell you about which is really using big data to try to transform healthcare and monitor health. And we're trying to be a bit upstream, I think of where this focus group is. But we do do some work on taking on certain cases that we do try to solve I suppose. So anyway, we think the healthcare system is broken as it currently works, it's really more sick care rather than health care. And even when you practice health care, if you think about it's kind of archaic, the way we do it. You typically get in a car to travel to a physician to show up at the doctor's office, which pretty much looks the same as it did 40 years ago, with a few new gizmos. They'll draw a very large aliquot of blood using a needle that typically hurts. From all that blood they usually don't make very many measurements, and then they'll make decisions about your health based on population averages. We think all of these steps can be dramatically improved. And so that's really been a focus of our work. And I'll tell you about some of the latest stuff, which I think you could see how it might apply to this particular group should lead to a fun discussion with regards to this last point about population health. You've probably been told since you're little that your normal temperature when you put a thermometer in your mouth is 98.6. But if you actually read the data It's out there, it's more like 97.5. But the more important point is there's a spread, this is the 25th, quartile 94.6. And this is the 75th, quartile 99.1. So that means if your normal baseline is here, 94.6, and you go to the physician's office today, and they measure 98.6, they will tell you are healthy or normal. But if you're up four degrees over your baseline, we would argue you're not healthy, something's probably off. And so that's sort of a big part of what we do, we try to actually follow people's baseline and look for these shifts . And so a number of years ago, actually 13 Little over 13 on me, and now 10 for the cohort, we got involved in the idea of trying to use big data to see if we can probe people's health. And basically, your health is influenced by your DNA, your genome, then lots of other things, your activity, the food you eat, stress, all these environmental responses, all of these impact your health, and we're in a world where we can quantify a lot of the some easily, like your genome and your activity, some is clunky, but it's quantifiable, but probably equally important, we can quantify the effects of these things by doing these deep data measurements on people. There's been a revolution in DNA sequencing and proteomics that was mentioned earlier. We are using mass spectrometry and other methods to be able to profile proteins and metabolites and lipids out of your blood. Starting about, as I say, 13 years ago, and then 10 for most of the cohort – It’s a smallish group of about 109 people – we've sequenced their genome once, but then we will basically out of their immune cells, called peripheral blood monocyte cells, measure their epigenome, transcriptome, proteome, and then out of the blood plasma, so that's the part without the cells, we will measure proteins and cytokines, which are important immune molecules. I'm sure this group knows about metabolites and lipids. On top of that we follow the microbiome. We do deep clinical testing as well in questionnaires. And then we have a number of advanced tests, stress echocardiograms and a variety of glucose control measurements. I'm actually type two diabetic myself, that was predicted from a genome sequence and then got picked up through the profiling. Anyway, we do a lot here. We do a lot with wearables , which is what I think Brian asked me to talk about, and that's what will be the emphasis of this. So we do these deep data collections on people. We also do it longitudinally. That's the second aspect. We sample people every three months while they're healthy. Then if an adverse event comes along, like a viral infection, and there have been other things as well, we'll take more samples. The goal is to try and understand what a healthy profile looks like. How does it change over time? How does it compare between different people? What happens when people first get ill? And importantly, for trying to transform the healthcare system, can these advanced technologies like genome sequencing and wearables, be better used to manage people's health? With regards to this last point, basically, just in the first three and a half years, we had 49 major health discoveries, meaning we caught some of the early lymphoma that spanned a wide range of area hematology, cardiovascular, metabolic, so on and so forth. So we caught some early lymphoma to people with these pre-cancers that can convert to aggressive cancers, to people with serious heart issues, one was picked up by genome sequencing and other by wearables, and so on, and so forth.