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Meet the World’s Most Bio-Tracked Man

Scientist Michael Snyder tracked his own basic measurements for years. Now he’s released a study of over 100 people using similar data to make lifesaving discoveries about heart disease, cancer, and diabetes.

Illustration: Joseph Melhuish

MMichael Snyder might be the most bio-tracked man in the world. He’s tested 14 of his “-omes,” such as the standard genome and microbiome as well as the less-well-known metabolome, transcriptome, proteome, immunome, and exposome. At any given time, he has eight devices on or around his body tracking his heart rate, blood oxygen, step count, blood glucose, radiation exposure, and even the surrounding air quality.

“It’s data galore,” says the Stanford University genetics professor on a recent Saturday afternoon at his office. Snyder is an animated talker and quick to laugh, with deep smile lines around his eyes. He is a man who loves what he does, and it shows. “I’m a pretty big nerd,” he says. “Here I am on a Saturday. That probably tells you all you need to know.”

Snyder thinks the way we approach medicine is entirely wrong and that mining our personal health data could be the key to fixing it. His ambitions are two-fold: Instead of focusing on treating people when they’re sick, he wants to concentrate on keeping them well. And rather than basing treatment decisions on population studies, he believes medicine should be individualized. The idea is that if you know you have a genetic risk for a disease, you can proactively manage your health better, and awareness of your baseline measurements provides earlier insight into when you might fall ill.

“We are very focused on treating people when they’re sick,” he says. “It’s very reactive. We should obviously be focused on keeping people healthy.”

Snyder is applying his background in biology, chemistry, and big data to try to fix the field of medicine. His original claim to fame is conducting large-scale analyses of DNA, RNA, and proteins, often in yeast. When he moved his lab to Stanford from Yale 10 years ago, he also decided to change his research focus. Now he’s using the same technology to try to improve people’s health. And in the tradition of many scientists before him, he’s using himself as a guinea pig.

Thanks to his obsessive monitoring, Snyder discovered he had a genetic risk for diabetes, caught the onset of his high blood sugar, and managed to get it relatively under control through diet and exercise. He also learned that his diabetes is a special subtype that doesn’t look like either Type 1 or Type 2. His cells respond to insulin normally, and his pancreas produces the hormone just fine, but it doesn’t release insulin into the bloodstream very efficiently. This means many of the normal diabetes medications, such as metformin, don’t help him.

“The goal is to really understand what does it mean to be healthy, what does a healthy profile look like, how does it change over time, and what happens when people get sick at the earliest times.”

Snyder monitors not just what’s going on inside his body, but what’s happening outside of it as well. By measuring the particles and chemicals in his environment, Snyder learned that what he thought was an allergy to pine pollen was in fact a reaction to eucalyptus trees. He expanded the exposome study in 2018 to 15 people in San Francisco and revealed that people were regularly exposed to 3,000 chemical signatures, including bacteria found in sludge and traces of the insect repellent ingredient deet.

In a new study published May 8 in the journal Nature Medicine, Snyder extended his philosophy to 108 other people, tracking them for an average of three years. They had their whole genomes sequenced, wore fitness trackers, and came in quarterly for other -omics screenings, primarily through blood testing. If they got sick during the course of the study, even with something as simple as a cold, they came in for more samples immediately during and after.

“The goal is to really understand what does it mean to be healthy, what does a healthy profile look like, how does it change over time, and what happens when people get sick at the earliest times,” says Snyder. “Along the way, you might discover things important for people’s health, and we did.”

Out of the 109 study participants (counting Snyder), the researchers made more than 67 actionable health discoveries. These include identifying genetic risks for heart disease, cancer, and diabetes as well as catching early signs of the conditions in a few of the participants.

For example, one person learned they had early-stage lymphoma before they showed symptoms. The researchers discovered the cancer from an ultrasound scan that revealed an enlarged spleen and a test of the person’s immunome, which measures levels of immune chemicals in the blood. Thanks to clues from the frequent screens, Snyder thinks they may have identified an early biomarker for cancer, too. In blood tests a year before the person was diagnosed, one immune chemical was much higher than normal. After the person was successfully treated, the levels went back down, suggesting it may have been an indicator of the disease.

“I think this is very much what medicine in the future will look like,” says Christopher Mason, an associate professor of physiology and biophysics at Weill Cornell Medicine who was not involved in the study. “You want to build as much information about every individual so that you can look for any change in their health trajectory.”

“This is a paper that is important for ushering in the potential utility of long and deep data,” says Eric Topol, executive vice president of the Scripps Research Institute, who also was not part of the research. “So often when we collect data, we do it as a one-off. Here, they’ve done a longitudinal time series of data capture. It just shows you the power of this type of data.”

Of all the tests they administered, Snyder says the two with the biggest effects on health and behavior are ones that are commonly used today: genetic testing and continuous glucose monitoring.

Fifty-five people in the study learned they were prediabetic (the group was preselected as having a high risk for diabetes). By tracking their blood sugar with the continuous glucose monitor, the participants were able to identify which foods caused their blood sugar to spike. The culprits were often very personal (one person responded more to rice than potatoes while another person was the opposite) and surprising (someone had a large increase in blood sugar after eating lentils). However, nearly everyone spiked in response to eating cereal and milk.

“For the continuous glucose monitors, that’s where people learned the most about their diet and how it was affecting them. It served as almost a biofeedback device,” says Sophia Miryam Schüssler-Fiorenza Rose, a neurosurgery instructor at Stanford who was the first author on the paper. “It might be worthwhile for people, even before they have diabetes, to learn more about how their diet is affecting their blood sugar. I think when people see that, they’re more able to make changes.”

Participants also made changes based on their genomic screens, including switching their medications thanks to so-called pharmacogenomic discoveries. For example, one person had a heart attack during the course of the study. It turned out they had a genetic variation that placed them at a greater risk for a heart attack from an interaction with a medication they were on. Once the researchers discovered the gene variation, they were able to alert the participant and their doctor, who subsequently changed the medication before they had another heart attack.

Other -omes, like the transcriptome and metabolome, are more dynamic, assessing acute changes in proteins and other factors in the blood that can indicate the progression of a disease. Despite their potential, however, Rose says making sense of the data is still a work in progress. “They’re not quite as far along as genomic testing is at this point,” she says. Rose and Snyder hope these tests will one day help them subtype diseases better so doctors can give people the best drugs for their specific strain.

Not everyone thinks this amount of testing is necessary — or realistic. The screening is expensive (a whole genome screen still costs about $1,000), and the vast majority of preventative tests, genomic or otherwise, aren’t covered by insurance. “The American medical system would definitely need to change in order to incorporate this,” says Joanne Berghout, a research assistant professor of biomedical informatics at the University of Arizona who was not involved in the study.

There are also concerns that people will become anxious or overreact to their potential risk for a disease. “Too much testing and incidental -omics, you basically wind up inducing hypochondria,” says Topol. “You wind up not only with anxiety, but you can find things that get unnecessarily tested and all sorts of incidental findings.”

There have been past incidents of misdiagnoses by going off of genes instead of symptoms. For example, at least one healthy person got a defibrillator implanted because their doctor thought they had a genetic risk for a heart arrhythmia even though they didn’t have any symptoms. Later, the gene variation turned out to be unrelated, and the person was perfectly healthy.

Snyder agrees that this type of comprehensive testing may not be for everyone. He’s also quick to point out the new paper is a research study and is not meant to be rolled out into a health care setting just yet. As testing becomes simultaneously cheaper and more precise, though, he is adamant that personalized medicine is the way of the future.

“We’re all going to get diseases, we’re all going to die of something,” he says. But if you’re armed with the right information, “you can better manage yourself. I think what we all want is to live good health spans and then just pop away.”

Health and science writer • PhD in 🧠 • Words in Scientific American, STAT, The Atlantic, The Guardian • Award-winning Covid-19 coverage for Elemental

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