Haichen Zhang1,2, Jeffrey W Kleinberger2, Kristin A Maloney2, Yue Guan3, Trevor J Mathias2, Katharine Bisordi2, Elizabeth A Streeten2, Kristina Blessing4, Mallory N Snyder4, Lee A Bromberger5, Jessica Goehringer4, Amy Kimball6, Coleen M Damcott2, Casey O Taylor7,8, Michaela Nicholson2, Devon Nwaba2, Kathleen Palmer2, Danielle Sewell9, Nicholas Ambulos9, Linda J B Jeng10, Alan R Shuldiner2, Philip Levin11, David J Carey4, Toni I Pollin2. 1. Department of Endocrinology, Peking Union Medical College Hospital, Beijing, China. 2. Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD. 3. Rollins School of Public Health, Emory University, Atlanta, GA. 4. Geisinger Health System, Danville, PA. 5. Metabolism, Osteoporosis/Obesity, Diabetes, Endocrinology and Lipids (MODEL) Clinical Research, Research Division of Bay Endocrinology Associates, Baltimore, MD. 6. Harvey Institute for Human Genetics, Greater Baltimore Medical Center, Baltimore, MD. 7. Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD. 8. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD. 9. University of Maryland Marlene and Stewart Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, MD. 10. Division of Rare Diseases and Medical Genetics, US Food and Drug Administration, Silver Spring, MD. 11. Bay West Endocrinology Associates, Baltimore, MD.
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.
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.
Authors: Marc Gregory Yu; Hillary A Keenan; Hetal S Shah; Scott G Frodsham; David Pober; Zhiheng He; Emily A Wolfson; Stephanie D'Eon; Liane J Tinsley; Susan Bonner-Weir; Marcus G Pezzolesi; George Liang King Journal: J Clin Invest Date: 2019-07-02 Impact factor: 14.808
Authors: Gaya Thanabalasingham; Aparna Pal; Mary P Selwood; Christina Dudley; Karen Fisher; Polly J Bingley; Sian Ellard; Andrew J Farmer; Mark I McCarthy; Katharine R Owen Journal: Diabetes Care Date: 2012-03-19 Impact factor: 19.112
Authors: Kristin Wiisanen Weitzel; Madeline Alexander; Barbara A Bernhardt; Neil Calman; David J Carey; Larisa H Cavallari; Julie R Field; Diane Hauser; Heather A Junkins; Phillip A Levin; Kenneth Levy; Ebony B Madden; Teri A Manolio; Jacqueline Odgis; Lori A Orlando; Reed Pyeritz; R Ryanne Wu; Alan R Shuldiner; Erwin P Bottinger; Joshua C Denny; Paul R Dexter; David A Flockhart; Carol R Horowitz; Julie A Johnson; Stephen E Kimmel; Mia A Levy; Toni I Pollin; Geoffrey S Ginsburg Journal: BMC Med Genomics Date: 2016-01-05 Impact factor: 3.063
Authors: Pamela Bowman; Åsta Sulen; Fabrizio Barbetti; Jacques Beltrand; Pernille Svalastoga; Ethel Codner; Ellen H Tessmann; Petur B Juliusson; Torild Skrivarhaug; Ewan R Pearson; Sarah E Flanagan; Tarig Babiker; Nicholas J Thomas; Maggie H Shepherd; Sian Ellard; Iwar Klimes; Magdalena Szopa; Michel Polak; Dario Iafusco; Andrew T Hattersley; Pål R Njølstad Journal: Lancet Diabetes Endocrinol Date: 2018-06-04 Impact factor: 32.069
Authors: Jennifer N Todd; Jeffrey W Kleinberger; Haichen Zhang; Shylaja Srinivasan; Sherida E Tollefsen; Lynne L Levitsky; Lorraine E Levitt Katz; Jeanie B Tryggestad; Fida Bacha; Giuseppina Imperatore; Jean M Lawrence; Catherine Pihoker; Jasmin Divers; Jason Flannick; Dana Dabelea; Jose C Florez; Toni I Pollin Journal: Diabetes Care Date: 2021-08-06 Impact factor: 17.152