The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion

Authors

Rinki Murphy, Faculty of Medical and Health Sciences
Rinki Murphy, Faculty of Medical and Health Sciences
Kevin Colclough, University of Exeter
Toni I. Pollin, University of Maryland School of Medicine
Jennifer M. Ikle, Stanford University School of Medicine
Pernille Svalastoga, Haukeland Universitetssjukehus
Kristin A. Maloney, University of Maryland School of Medicine
Kristin A. Maloney, University of Maryland School of Medicine
Cécile Saint-Martin, Hôpital Universitaire Pitié Salpêtrière
Janne Molnes, Universitetet i Bergen
Paul W. Franks, Harvard T.H. Chan School of Public Health
Stephen S. Rich, University of Virginia School of Medicine
Robert Wagner, Deutsches Diabetes-Zentrum
Tina Vilsbøll, Steno Diabetes Center Copenhagen
Kimberly K. Vesco, Kaiser Permanente Center for Health Research
Miriam S. Udler, Massachusetts General Hospital
Tiinamaija Tuomi, Helsinki University Hospital
Arianne Sweeting, Faculty of Medicine and Health
Emily K. Sims, Indiana University School of Medicine
Jennifer L. Sherr, Yale School of Medicine
Robert K. Semple, Edinburgh Medical School
Rebecca M. Reynolds, Edinburgh Medical School
Maria J. Redondo, Baylor College of Medicine
Leanne M. Redman, Pennington Biomedical Research Center
Richard E. Pratley, AdventHealth Translational Research Institute
Rodica Pop-Busui, University of Michigan Medical School
Wei Perng, University of Colorado Anschutz Medical Campus
Ewan R. Pearson, University of Dundee School of Medicine
Susan E. Ozanne, University of Cambridge
Katharine R. Owen, University of Oxford Medical Sciences Division
Richard Oram, University of Exeter Medical School
Viswanathan Mohan, Madras Diabetes Research Foundation
Shivani Misra, Imperial College London
James B. Meigs, Harvard Medical School

Document Type

Article

Publication Date

12-1-2023

Abstract

Background: Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field. Methods: Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice. Results: There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted. Conclusions: We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.

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