diabetic heart disease
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 . 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 . 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.
Tags: anti-diabetes drugs, big data, cardiovascular disease, diabetes, diabetic heart disease, drug benefits, drug development, drug side effects, drug targets, genetic variant, genomics, GlaxoSmithKline, GLP1R, heart disease, investigational drugs, liraglutide, Mendelian randomization, PMI, precision medicine, Precision Medicine Initiative cohort, T2D, type 2 diabetes, Victoza
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 , 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.
Tags: big data, chronic disease, clinical data, cohort, diabetes, diabetes subtypes, diabetic complications, diabetic heart disease, diabetic nephropathy, diabetic neuropathy, diabetic retinopathy, electronic health records, genetic variants, genome-wide analysis, genomics, obesity, precision medicine, Precision Medicine Initiative, translational medicine, type 2 diabetes