Analyses of big data may help identify the molecular pathology of cardiometabolic disease and improve the development of new medications, according to a blog post from Francis Collins, MD, the National Institutes of Health (NIH) director.

Collins cited an NIH-supported study that was recently published in Science. The researchers involved in the study included Johan L.M. Björkegren, MD, PhD and Eric Schadt, PhD of the Icahn School of Medicine in New York and heart surgeon Arno Ruusalepp, MD, of the University of Tartu in Estonia.

“By analyzing gene-expression data from multiple tissues in hundreds of patients with coronary artery disease, we were able to identify disease-causing genes that either were specific to single tissues or acted across multiple tissues in networks to cause cardiometabolic diseases,” Björkegren said in a news release.

During the study, known as STARNET, the researchers collected tissue samples from 600 volunteers who had cardiovascular disease and underwent CABG. They analyzed the RNA in each of the tissue samples and the participants’ genomes and identified 8 million regions of DNA that affect gene activity and might influence progression of cardiometabolic disease.

Afterward, they searched the NIH’s National Human Genome Research Institute GWAS catalog, which includes more than 150 markers of risk for coronary artery disease. They then developed models based on algorithms to examine molecular networks and seven types of vascular and metabolic tissues 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.

“Such modeling has uncovered many intriguing, and in some cases unexpected, leads for future study,” Collins wrote. “For example, the analyses point to a super network of hundreds of genes that may interact across tissues to regulate the risk of coronary artery disease. The modeling also indicates that blood lipid levels share the most regulatory genes among cardiometabolic conditions, suggesting that lipids may be a central factor and perhaps a key one to focus on in efforts to find new ways to treat and prevent this chronic disease.”

Björkegren and his colleagues also worked with AstraZeneca and researchers from the Science for Life Laboratory in Sweden to try to improve drug target development. They made a breakthrough when examining the proprotein convertase subtilisin/kexin type 9 (PCSK9) gene, according to Collins. Last year, the FDA approved alirocumab (Praluent) and evolocumab (Repatha), which are PCSK9 inhibitors to treat high cholesterol.

Whereas researchers previously though the PCSK9 gene was expressed only in the liver, Collins noted that the STARNET study found that PCSK9 activity in visceral abdominal fat was associated with the risk of early MIs. During a follow-up analysis, the researchers found that participants with more belly fat had higher circulating PCSK9 and low-density lipoprotein cholesterol levels.

“Björkegren and colleagues note their network models remain works in progress that will need further validation and refinement to bolster their reliability,” Collins wrote. “But with more Big Data studies being published all of the time and the Precision Medicine Initiative Cohort Program nearing its launch, this view will only increase in resolution in the years ahead, helping to pave the way for a new generation of strategies for improving human health.”