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But Snyder had no symptoms. He had just had a nasty cold,
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, which he now thinks may have triggered the onset of diabetes because he was already genetically at risk.
Because he spotted the changes early and his doctor confirmed he had diabetes, Snyder was able to fend off symptoms by changing his diet and exercise habits — which was reflected in the follow-up data he collected.
He monitored how other biological measurements (molecules that standard medical diagnostic tests might not even look at) changed with the onset of his diabetes and the lifestyle changes he made in response. That information might be valuable for doctors hoping to detect the onset of diabetes in patients before symptoms appear. So now his lab is tracking omic data from 100 people, most of whom have elevated blood sugar levels, but have not yet been diagnosed with diabetes. Snyder wants to see whether viral infections trigger diabetes in other people, or whether his case is an isolated incident.
Snyder is still tracking fluctuations in his own data over time,
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, too. He thinks it’s a powerful tool for personalized medicine because it shows in real time how an individual responds to disease and other stressors.
“You’ll be able to start linking disease to treatment to outcomes,
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,” Snyder says. “It becomes what I call data-driven medicine, instead of hunch-driven medicine.”


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