“Omics” means less time spent sick
We all dread the idea of being sick. What if your physician could recognize and treat looming illnesses before they present as serious symptoms? Collaborative efforts by scientists and physicians are currently seeking ways to detect early deviations from health. When we get sick, our bodies undergo many changes before presenting the symptoms of an illness. The “birth of a disease” elicits changes in our bodies at a molecular level. In an effort to identify health risks and to effectively manage health, scientists from Stanford recently published a study that collected and analyzed large amounts of molecular information.
A senior scientist –Michael Snyder, Ph.D.– served as the lab rat for this study. For 14 months, Dr. Snyder allowed fellow scientists to scrutinize his genome and blood samples. A total of 20 blood samples were taken throughout the study. While healthy, blood samples were taken every 2 months; during periods of illness, blood was collected more frequently. These blood samples were analyzed for the levels of thousands of biological variables including proteins, metabolites and other molecules.
The term “omics” simply indicates the study of a body of information. For example, a metabolomics is the study of bodily metabolites, and transcritomics is the study of RNA transcripts. Dr. Snyder’s team combined genomics, transcritomics, proteomics, metabolomics, and information about autoantibodies to produce an integrative Personal Omics Profile (iPOP). This iPOP unveiled changes in biological pathways as Dr. Snyder’s body shifted between healthy and diseased states.
With this unprecedented study, the healthcare team of scientists and physicians noted how molecules in the senior scientist’s body responded to two viral infections and the development of type two-diabetes. Scientists were able to observe numerous relationships previously not observed. For example, the two copies of our genes–one from each parent–behave differently during an infection. The challenge now lies in filtering through the 40,000 biological variables (e.g. genes, proteins, RNA transcripts etc.) to identify what molecules should be observed to predict the likelihood of developing an illness.
As prices of full genome sequencing decrease, there will be an increase in the prevalence of studies similar to those led by Dr. Snyder. More studies will help scientists better understand how to integrate the massive amounts of data collected from the iPOP for predictive and health management purposes. Soon, you may see your local physician using data derived from the iPOP to diagnose your risk of developing health issues as disparate as a viral infection or a chronic illness like diabetes or cancer. An early detection allows the physician to quickly provide a treatment before illness manifests itself as serious symptoms, which means you will spend less time being sick.