Researchers are leveraging “omics” and genome-wide association studies (GWAS) to identify new therapeutic pathways to treat diabetes, according to presenters at Friday’s symposium Can “Omic” and Genome-Wide Association Studies (GWAS) Reveal Therapeutic Links between Type 1 and Type 2 Diabetes? The session’s presenters discussed the latest, state-of-the-art research and provided attendees with insights into ways that “omics” and GWAS are shaping the future of health care.
Stephen S. Rich, PhD, FAHA, discussed researchers’ efforts to use whole genome sequencing to identify patients’ risk of developing beta-cell failure and progression to diabetes.
“Genetics is the first and critical step in identifying those in which the earliest intervention can be targeted to intercept disease progression,” said Dr. Rich, Professor of Public Health Sciences and Director of the Center for Public Health Genomics at the University of Virginia School of Medicine. “Other ‘omics’ technologies will add to our ability to design therapeutics and ways to lower disease risk.”
Dr. Rich reviewed studies that highlight the contribution of genetic variation in autoantibody development in diabetes from a cross-sectional perspective at a point in time. He also described a pilot project utilizing whole genome sequence data that followed at-risk children with rapid/slow development of autoantibodies.
“It’s critically important to identify those children at high genetic risk for development of beta-cell failure in order to monitor for development of autoantibodies,” he said. “The risk of beta-cell failure may be partly genetic, although how much is not known. The phenotyping of subjects is as important as the genetics.”
Dr. Rich noted that the genes/variants that predict initiation of the autoimmune process may differ, or overlap with, the genes/variants contributing to progression of beta-cell failure and ultimate conversion to clinical disease.
In the future, GWAS will be replaced by whole genome sequencing, he added. “GWAS provides the map of the towns and roadways of the genome. Whole genome sequencing will detail the streets and houses,” he explained.
Dr. Rich also noted that large, collaborative sample sizes across diverse populations will be required to further the field. “The genetics will be conceptually easy; the analysis will be difficult. We need more bright, young minds,” he said.
Flemming Pociot, MD, DMSc, agreed that a better understanding of the disease-causing mechanisms is essential for better prediction, diagnosis, and treatment of diabetes. Dr. Pociot reviewed high-throughput omics technologies that have contributed detailed knowledge about the pathogenesis of both type 1 and type 2 diabetes.
“In the last few years, metabolomics technologies have provided insights into the pathogenic pathways and the understanding of diabetes and its preclinical stages,” said Dr. Pociot, Professor in the Department of Pediatrics at Herlev and Gentofte Hospital and Steno Diabetes Center in Gentofte, Denmark.
Metabolomics refers to characterizing the repertoire of small molecules in biological samples, he explained. Metabolomics is relevant because of its proximity to molecular mechanisms explaining the phenotype.
“The combination of meta-bolic profiles with genomic, transcriptomic, proteomic, and clinical data has generated comprehensive data sets that can be leveraged to identify underlying biological networks that drive disease susceptibility,” Dr. Pociot said. “At the same time, it holds the potential of directly identifying new targets for intervention and/or treatment.”
Despite the lack of a direct genetic link between type 1 and type 2 diabetes, the common metabolomics network in the islets of Langerhans may perturb beta-cell function in both forms of the disease, Dr. Pociot said. “Studies of pancreatic islet-metabolomic links between type 1 and type 2 diabetes are likely to inform us on mutual mechanisms and shape treatment modalities,” he said. Targeted metabolomics, or analyzing a limited number of metabolites, is now finding its way into clinical work to better subgroup patients for optimal care and monitoring, he noted. “This tendency will only expand and improve as the metabolomics technologies become more affordable,” he added.