Cardiometabolic Disease: Big Data Tackles a Big Health Problem

Cardiometabolic risk lociMore 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.

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International “Big Data” Study Offers Fresh Insights into T2D

World map

Caption: This international “Big Data” study involved hundreds of researchers in 22 countries (red).

It’s estimated that about 10 percent of the world’s population either has type 2 diabetes (T2D) or will develop the disease during their lives [1]. Type 2 diabetes (formerly called “adult-onset”) happens when the body doesn’t produce or use insulin properly, causing glucose levels to rise. While diet and exercise are critical contributory factors to this potentially devastating disease, genetic factors are also important. In fact, over the last decade alone, studies have turned up more than 80 genetic regions that contribute to T2D risk, with much more still to be discovered.

Now, a major international effort, which includes work from my own NIH intramural research laboratory, has published new data that accelerate understanding of how a person’s genetic background contributes to T2D risk. The new study, reported in Nature and unprecedented in its investigative scale and scope, pulled together the largest-ever inventory of DNA sequence changes involved in T2D, and compared their distribution in people from around the world [2]. This “Big Data” strategy has already yielded important new insights into the biology underlying the disease, some of which may yield novel approaches to diabetes treatment and prevention.

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Flipping a Genetic Switch on Obesity?

Illustration of a DNA switchWhen weight loss is the goal, the equation seems simple enough: consume fewer calories and burn more of them exercising. But for some people, losing and keeping off the weight is much more difficult for reasons that can include a genetic component. While there are rare genetic causes of extreme obesity, the strongest common genetic contributor discovered so far is a variant found in an intron of the FTO gene. Variations in this untranslated region of the gene have been tied to differences in body mass and a risk of obesity [1]. For the one in six people of European descent born with two copies of the risk variant, the consequence is carrying around an average of an extra 7 pounds [2].

Now, NIH-funded researchers reporting in The New England Journal of Medicine [3] have figured out how this gene influences body weight. The answer is not, as many had suspected, in regions of the brain that control appetite, but in the progenitor cells that produce white and beige fat. The researchers found that the risk variant is part of a larger genetic circuit that determines whether our bodies burn or store fat. This discovery may yield new approaches to intervene in obesity with treatments designed to change the way fat cells handle calories.

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Mining the Big Data Mountain

Cartoon of three men mining mountains of data

Credit: Chris Jones, NIH

Biomedical researchers and clinicians are generating an enormous, ever-expanding trove of digital data through DNA sequencing, biomedical imaging, and by replacing a patient’s medical chart with a lifelong electronic medical record. What can be done with all of this “Big Data”?

Besides being handy for patients and doctors, Big Data may provide priceless raw material for the next era of biomedical research. Today, I want to share an example of research that is leveraging the power of Big Data.

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Exploring the Complex Genetics of Schizophrenia

Illustration of a human head showing a brain and DNA

Credit: Jonathan Bailey, National Human Genome Research Institute, NIH

Schizophrenia is one of the most prevalent, tragic, and frustrating of all human illnesses, affecting about 1% of the human population, or 2.4 million Americans [1]. Decades of research have failed to provide a clear cause in most cases, but family clustering has suggested that inheritance must play some role. Over the last five years, multiple research projects known as genome-wide association studies (GWAS) have identified dozens of common variations in the human genome associated with increased risk of schizophrenia [2]. However, the individual effects of these variants are weak, and it’s often not been clear which genes were actually affected by the variations. Now, advances in DNA sequencing technology have made it possible to move beyond these association studies to study the actual DNA sequence of the protein-coding region of the entire genome for thousands of individuals with schizophrenia. Reports just published have revealed a complex constellation of rare mutations that point to specific genes—at least in certain cases.

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