A Stanford University geneticist and members of his lab that have gathered and analyzed his DNA, RNA, and proteins made by his cells first uncovered that he had a genetic predisposition for developing type-2 diabetes, and then watched the disease unfold at a molecular level over the next two years as his research continued.

The unfolding of the disease within Michael Snyder, chairman of the Department of Genetics of Stanford University School of Medicine has been called the “first eyewitness account” of the development of the disease at a molecular level. Snyder, who said he was unaware of any family history or significant risk factors he had for the disease, was able to intervene early in the onset of the disease and modify his diet and exercise to bring his blood sugar levels down to a normal range without the use of drugs.

“We learned through genomic sequencing that I have a genetic predisposition to the condition,” he says. “Therefore, we measured my blood glucose levels and were able to watch them shoot up after a nasty viral infection during the course of the study.”

The researchers say that Snyder’s experience will soon become commonplace within medicine as dynamic monitoring will establish personalized medicine as standard of care where healthcare can be tailored to each individual’s circumstance.

The researchers call their collection and analysis of Snyder’s biological data integrative Personal “Omics” Profile, or iPOP. Diabetes, they say, is just one of the diseases that iPOP can identify and predict. Snyder’s iPOP, in addition to DNA and RNA sequencing, also included his metabolome (metabolites), his transcriptome (RNA transcripts) and autoantibody profiles, among other things.

“This is the first time that anyone has used such detailed information to proactively manage their own health,” says Snyder. “It’s a level of understanding of health at the molecular level that has never before been achieved.”

The study, reported in the March 16 issue of the journal Cell, went far beyond the typical office visit to the doctor. Snyder provided about 20 blood samples once every two months and that increased when he became ill. Each sample was subjected to testing for tens of thousands of biological variables, generating mounds of data. Typical blood tests during a routine physical exam will look at less than 20 variables.

In Snyder’s case, the researchers began with his complete genome sequence and then built up his iPOP by crafting a dynamic picture of how his body responded to illness and disease by comparing thousands of variables captured in the frequent molecular snapshots of his body.

The researchers call Snyder’s iPOP a proof of concept. Though such analysis is not economically feasible today, he believes simplifying the tests and finding new ways to perform them could make such testing a reality in the doctor’s office of the future.

“In the future, we may not need to follow 40,000 variables. It’s possible that only a subset of them will be truly predictive of future health. But studies like these are important to know which are important and which don’t add much to our understanding,” he says. “Right now, this type of analysis is very expensive. But we have to expect that, like whole-genome sequencing, it will get much cheaper. And we also have to consider the savings to society from preventing disease.”