Moving Toward Answers in ME/CFS

Woman in bed

Thinkstock/Katarzyna Bialasiewicz

Imagine going to work or school every day, working out at the gym, spending time with family and friends—basically, living your life in a full and vigorous way. Then one day, you wake up, feeling sick. A bad cold maybe, or perhaps the flu. A few days pass, and you think it should be over—but it’s not, you still feel achy and exhausted. Now imagine that you never get better— plagued by unrelenting fatigue not relieved by sleep. Any exertion just makes you worse. You are forced to leave your job or school and are unable to participate in any of your favorite activities; some days you can’t even get out of bed. The worst part is that your doctors don’t know what is wrong and nothing seems to help.

Unfortunately, this is not fiction, but reality for at least a million Americans—who suffer from a condition that carries the unwieldy name of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a perplexing disease that biomedical research desperately needs to unravel [1]. Very little is currently known about what causes ME/CFS or its biological basis [2]. Among the many possibilities that need to be explored are problems in cellular metabolism and changes in the immune system.

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Big Data Study Reveals Possible Subtypes of Type 2 Diabetes

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.

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