With the cost to sequence a human genome dropping by the day and medical records finally going digital, public health experts are excited for a new era of personalized, or “precision,” medicine–a big data future in which there is no “average” patient, only individual patients with unique genes, environments, and lifestyles. As a measure of this excitement, this year, President Obama launched a $215 million initiative that will create a health database from 1 million volunteers that is unprecedented in detail. Breakthroughs in prevention, understanding, and treatment of disease are hoped.

Though there’s both hype and a lot of genuine promise, the field of precision medicine is still in its nascency. Genetic sequencing has helped in the diagnosis and treatment of rare genetic disease and is beginning to be important in the treatment of some cancers, such as lung cancer or brain tumors.

Now a recent study, published in the journal Science Translational Medicine, demonstrates the broader promise of precision medicine beyond genome sequencing–and in understanding an extremely common disease: type 2 diabetes.

Almost 1 in 10 Americans have type 2 diabetes, and many more are at risk. Yet it’s a poorly understood disease: Its causes, symptoms, and complications are diverse and hard for doctors to predict. By mining a database of clinical and genetic data from more than 2,500 diabetes patients, researchers Icahn School of Medicine at Mount Sinai Medical Center have now actually identified some patterns that an entire field of doctors have not: They found there are actually three distinct sub-types of type 2, each of which have very different health implications.

“This is the first tangible demonstration of precision medicine that could be applied to a more common, complex disease,” says study author Joel Dudley, director of biomedical informatics at Mount Sinai (and one of Fast Company’s Most Creative People of 2014), a major hospital in upper Manhattan.

They were able to do this with access to a still relatively rare collection of thousands of Mount Sinai patients who volunteered to give their health charts and genetic data to the hospital for researcher efforts (See “In The Hospital Of The Future, Big Data Is Your Doctor“). Usually, doctors just look at a few blood tests–such as blood sugar and insulin levels–when monitoring diabetes patients. Instead, the researchers used computer modeling to map how similar each patient was to each other (a “patient-patient similarity network”), based on every piece of health data–height, weight, blood platelet counts, and hundreds of data points that human doctors alone could never process. The result was the map, seen above, that shows Mount Sinai patients map into three distinctive clusters, or “sub-types.”

The bigger question, of course, is: Do these sub-types matter for a patient’s health? The study found that they probably do. Patients in one subtype were more likely to suffer from cancer and cardiovascular disease; in another subtype, they were at higher risk for kidney disease and eye complications; finally, in the third sub-type, allergies, neurological diseases and HIV infections were bigger problems. Even more importantly, when the researchers mapped patients’ genomes onto the network–they found unique gene variants associated with each sub-group which helped explain some of the differences between them.