For this genomic medicine pilot demonstration project, the team implemented and evaluated a Personalized Diabetes Medicine Program (PDMP) in four diverse health care settings to enhance the identification of individuals and family members affected by monogenic diabetes.
Toni I. Pollin, Ph.D.
University of Maryland School of Medicine
Associate Professor of Medicine and Epidemiology Public Health
Director, Program for Personalized and Genomic Medicine
Diabetes mellitus, a term describing a group of disorders characterized by high blood sugar, affects nearly 30 million individuals in the United States and is a leading cause of death and disability. At least 1-2 percent of diabetes (approximately 300,000 to 600,000 individuals or more) results from a mutation in a single gene and is known as monogenic diabetes. For a number of reasons, including clinical similarity, historically high testing cost, and lack of awareness, approximately 95% of cases, of monogenic diabetes are misdiagnosed as the much more common and genetically more complex type 1 (T1DM) or type 2 diabetes (T2DM).
Having a correct diagnosis of monogenic diabetes can enable more personalized and often less invasive treatment, resulting in better glucose control, better prediction of disease course, and better prediction of diabetes risk in family members. For example, about half of those diagnosed with the rare condition of permanent diabetes in infancy have a specific genetic cause of their diabetes that enables effective treatment with oral medication alone rather than insulin injections, and 50 percent of their children are at risk for inheriting the same form of diabetes.
Personalized Diabetes Medicine Program (PDMP)
The PDMP was based at the University of Maryland Center for Diabetes and Endocrinology and was disseminated to three partner centers: the Baltimore Veterans Administration Medical Center (BVAMC, with opportunities to disseminate nationally), Geisinger Medical Center (an integrated health system) and Bay West Endocrinology Associates (a community-based private practice group). In addition, the team engaged the community at large through media advertisements and health fairs.
The PDMP consisted of a screening procedure using a simple patient questionnaire, chart/electronic health record (EHR) review, routine lab testing, and detailed family history review to identify patients most likely to have monogenic diabetes; sequencing relevant genes of eligible patients using next generation sequencing technology followed by confirmation of diabetes-causal mutations in their CLIA-approved Translational Genomics Laboratory; incorporating mutations and decision support in the EHR; genetic counseling; implementing a mutation-based treatment strategy and family screening; and establishing a PDMP registry of diabetes-causal variants and variants of unknown clinical significance to inform ongoing clinical and research efforts.
They tracked implementation metrics of the PDMP and conducted an impact evaluation, including evaluation of clinical outcomes as measured by changes in glycemic control in patients diagnosed with a monogenic form of diabetes. Finally, they engaged a Payer Advisory Panel in the development of the impact evaluation process to enhance our ability to collect meaningful evidence to inform clinical practice recommendations and guide insurance coverage decisions as a first step to enabling implementation of an evidence-based PDMP to diagnose inherited forms of diabetes across the United States.