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Cardiometabolic Disease: Big Data Tackles a Big Health Problem

  • Sep 16th, 2016
  • National Institues of Health

Cardiometabolic Disease: Big Data Tackles a Big Health Problem

Posted on September 8, 2016 

More and more studies are popping up that demonstrate the power of Big Data analyses to get at the underlying molecular pathology of some of our most common diseases. A great example, which may have flown a bit under the radar during the summer holidays, involves cardiometabolic disease. It’s an umbrella term for common vascular and metabolic conditions, including hypertension, impaired glucose and lipid metabolism, excess belly fat, and inflammation. All of these components of cardiometabolic disease can increase a person’s risk for a heart attack or stroke.

In the study, an international research team tapped into the power of genomic data to develop clearer pictures of the complex biocircuitry in seven types of vascular and metabolic tissue known to be affected by cardiometabolic disease: the liver, the heart’s aortic root, visceral abdominal fat, subcutaneous fat, internal mammary artery, skeletal muscle, and blood. The researchers found that while some circuits might regulate the level of gene expression in just one tissue, that’s often not the case. In fact, the researchers’ computational models show that such genetic circuitry can be organized into super networks that work together to influence how multiple tissues carry out fundamental life processes, such as metabolizing glucose or regulating lipid levels. When these networks are perturbed, perhaps by things like inherited variants that affect gene expression, or environmental influences such as a high-carb diet, sedentary lifestyle, the aging process, or infectious disease, the researchers’ modeling work suggests that multiple tissues can be affected, resulting in chronic, systemic disorders including cardiometabolic disease.

The work, published in the journal Science and partially supported by NIH, was initiated by Johan L.M. Björkegren, a scientist from the Karolinska Institute, Stockholm, who has teamed up with Big Data innovator Eric Schadt at the Icahn School of Medicine at Mount Sinai, New York. In 2007, Björkegren contacted heart surgeon Arno Ruusalepp at the University of Tartu in Estonia to launch a study called STARNET, in which they collected tissue samples from 600 human volunteers with cardiovascular disease undergoing coronary artery bypass surgery.  The researchers analyzed the RNA in each of the tissue samples, which enabled them to create a composite view of gene activity in each of the various tissue types. In addition, they analyzed the participants’ genomes, looking for DNA variations associated with risk of cardiometabolic disease.

The researchers then went on to use sophisticated algorithms to infer gene interactions near and far, developing models that provide an unprecedented view of the complex molecular networks that may be in play in cardiometabolic disease across the seven tissues studied. Such modeling has uncovered many intriguing, and in some cases unexpected, leads for future study.

 

by Dr. Francis Collins

 

 

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National Institues of Health

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