diabetic heart disease
Precision Medicine: Using Genomic Data to Predict Drug Side Effects and Benefits
Posted on by Dr. Francis Collins
People with type 2 diabetes are at increased risk for heart attacks, stroke, and other forms of cardiovascular disease, and at an earlier age than other people. Several years ago, the Food and Drug Administration (FDA) recommended that drug developers take special care to show that potential drugs to treat diabetes don’t adversely affect the cardiovascular system [1]. The challenge in implementing that laudable exhortation is that a drug’s long-term health risks may not become clear until thousands or even tens of thousands of people have received it over the course of many years, sometimes even decades.
Now, a large international study, partly funded by NIH, offers some good news: proof-of-principle that “Big Data” tools can help to identify a drug’s potential side effects much earlier in the drug development process [2]. The study, which analyzed vast troves of genomic and clinical data collected over many years from more than 50,000 people with and without diabetes, indicates that anti-diabetes therapies that lower glucose by targeting the product of a specific gene, called GLP1R, are unlikely to boost the risk of cardiovascular disease. In fact, the evidence suggests that such drugs might even offer some protection against heart disease.
Genetic approaches have increasingly been used to identify potentially promising new drug targets. In the study reported in Science Translational Medicine, researchers led by Robert Scott and Nick Wareham from the University of Cambridge, England, and Dawn Waterworth from GlaxoSmithKline, King of Prussia, PA, also wanted to explore whether genomic data could yield important clues about the potential side effects of drugs targeting particular genes.
Big Data Study Reveals Possible Subtypes of Type 2 Diabetes
Posted on by Dr. Francis Collins
Caption: Computational model showing study participants with type 2 diabetes grouped into three subtypes, based on similarities in data contained in their electronic health records. Such information included age, gender (red/orange/yellow indicates females; blue/green, males), health history, and a range of routine laboratory and medical tests.
Credit: Dudley Lab, Icahn School of Medicine at Mount Sinai, New York
In recent years, there’s been a lot of talk about how “Big Data” stands to revolutionize biomedical research. Indeed, we’ve already gained many new insights into health and disease thanks to the power of new technologies to generate astonishing amounts of molecular data—DNA sequences, epigenetic marks, and metabolic signatures, to name a few. But what’s often overlooked is the value of combining all that with a more mundane type of Big Data: the vast trove of clinical information contained in electronic health records (EHRs).
In a recent study in Science Translational Medicine [1], NIH-funded researchers demonstrated the tremendous potential of using EHRs, combined with genome-wide analysis, to learn more about a common, chronic disease—type 2 diabetes. Sifting through the EHR and genomic data of more than 11,000 volunteers, the researchers uncovered what appear to be three distinct subtypes of type 2 diabetes. Not only does this work have implications for efforts to reduce this leading cause of death and disability, it provides a sneak peek at the kind of discoveries that will be made possible by the new Precision Medicine Initiative’s national research cohort, which will enroll 1 million or more volunteers who agree to share their EHRs and genomic information.
