Model for Integration of Monogenic Diabetes Diagnosis Into Routine Care: The Personalized Diabetes Medicine Program

• Type of Material: Article
• Authors: Haichen Zhang, Peking Union Medical College Hospital; Jeffrey W. Kleinberger, University of Maryland; Kristin A. Maloney, University of Maryland; Yue Guan, Emory University; Trevor J. Mathias, University of Maryland; Katherine Bisordi, University of Maryland; Elizabeth A. Streeten, University of Maryland; Kristina Blessing, Geisinger Health System; Mallory N. Snyder, Geisinger Health System; Lee A. Bromberger, Bay Endocrinology Associates; Jessica Goehringer, Geisinger Health System; Amy Kimball, Greater Baltimore Med Ctr; Coleen M. Damcott, University of Maryland; Casey O. Taylor, Johns Hopkins University; Michaela Nicholson, University of Maryland; Devon Nwaba, University of Maryland; Kathleen Palmer, University of Maryland; Danielle Sewell, University of Maryland; Nicholas Ambulos, University of Maryland; Linda J. B. Jeng, US Food and Drug Administration; Alan R. Shuldiner, University of Maryland; Philip Levin, Bay West Endocrinology Associates; David J. Carey, Geisinger Health System; Toni I. Pollin, University of Maryland
• Language: English
• Date: 2022-08-01
• Publisher: AMER DIABETES ASSOC
• Publication Version: Final Published Version
• Copyright Statement: © 2022 by the American Diabetes Association
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.2337/dc21-1975
• Title of Journal or Parent Work: DIABETES CARE
• Volume: 45
• Issue: 8
• Start Page: 1799
• End Page: 1806
• Grant/Funding Information: This study was supported by National Human Genome Research Institute grant U01 HG007775 (T.I.P.) as part of the National Institutes of Health IGNITE network; Eunice Kennedy Shriver National Institute of Child Health and Human Development Grant U24 grant U24HD093486; and the University of Maryland School of Medicine Center for Innovative Biomedical Resources, University of Maryland School of Medicine Biorepository, Baltimore, Maryland.
• Keywords: Science & Technology; GUIDELINES; POPULATION; STANDARDS; MUTATIONS; Endocrinology & Metabolism; HNF1A; Life Sciences & Biomedicine; CLINICAL-DIAGNOSIS; INSULIN; YOUNG
• Research Categories: Health Sciences, Public Health; Health Sciences, Nutrition

Abstract

OBJECTIVE To implement, disseminate, and evaluate a sustainable method for identifying, diagnosing, and promoting individualized therapy for monogenic diabetes. RESEARCH DESIGN AND METHODS Patients were recruited into the implementation study through a screening questionnaire completed in the waiting room or through the patient portal, physician recognition, or self-referral. Patients suspected of having monogenic diabetes based on the processing of their questionnaire and other data through an algorithm underwent next-generation sequencing for 40 genes implicated in monogenic diabetes and related conditions. RESULTS Three hundred thirteen probands with suspected monogenic diabetes (but most diagnosed with type 2 diabetes) were enrolled from October 2014 to January 2019. Sequencing identified 38 individuals with monogenic diabetes, with most variants found in GCK or HNF1A. Positivity rates for ascertainment methods were 3.1% for clinic screening, 5.3% for electronic health record portal screening, 16.5% for physician recognition, and 32.4% for self-referral. The algorithmic criterion of non–type 1 diabetes before age 30 years had an overall positivity rate of 15.0%. CONCLUSIONS We successfully modeled the efficient incorporation of monogenic diabetes diagnosis into the diabetes care setting, using multiple strategies to screen and identify a subpopulation with a 12.1% prevalence of monogenic diabetes by molecular testing. Self-referral was particularly efficient (32% prevalence), suggesting that educating the lay public in addition to clinicians may be the most effective way to increase the diagnosis rate in monogenic diabetes. Scaling up this model will assure access to diagnosis and customized treatment among those with monogenic diabetes and, more broadly, access to personalized medicine across disease areas.