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translational medicine

Protecting Kids: Developing a Vaccine for Respiratory Syncytial Virus

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Baby at the Doctor's OfficeVaccines are one of biomedicine’s most powerful and successful tools for protecting against infectious diseases. While we currently have safe and effective vaccines to prevent measles, mumps, and a great many other common childhood diseases, we still lack a vaccine to guard against respiratory syncytial virus (RSV)—a leading cause of pneumonia among infants and young children.

Each year, more than 2 million U.S. children under the age of 5 require medical care for pneumonia and other potentially life-threatening lower respiratory infections caused by RSV [1,2]. Worldwide, the situation is even worse, with more than 30 million infections estimated to occur annually, most among kids in developing countries, where as many as 200,000 deaths may result [3]. So, I’m pleased to report some significant progress in biomedical research’s long battle against RSV: encouraging early results from a clinical trial of an experimental vaccine specifically designed to outwit the virus.

Big Data Study Reveals Possible Subtypes of Type 2 Diabetes

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Computational model

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.

An Evolving App for Genetic Tests

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We all hope for health care in the genomic era to become as easy and personal as a smartphone app. And perhaps at some point it will be. At some medical centers, electronic health records already include a list of patients’ genetic variations that might trigger harmful drug reactions and send ‘pop-up’ alerts to warn the physician or pharmacist. This is just the tip of the iceberg, but it’s a harbinger of things to come. Our big challenge is to translate all the new discoveries and data from the genome project into a format that physicians and other health care providers can use to improve health.

To bridge that transition from discovery to diagnostics and treatments, the NIH launched the Genetic Testing Registry (GTR) last year. There are hundreds of genetic testing companies, thousands of genetic tests for thousands of diseases, and some diseases have more than 20 names. What a challenge for providers to sort through! GTR is becoming a central repository of all the genetic tests available, and therefore greatly simplifies this search. It’s a vital resource, as providers can’t be expected to know all the diseases and genes or to keep tabs on the growing number of tests.

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