Literature DB >> 27207547

Type 1 Diabetes Genetic Risk Score: A Novel Tool to Discriminate Monogenic and Type 1 Diabetes.

M N Weedon1, A T Hattersley1,2, K A Patel1,2, R A Oram1,2, S E Flanagan1, E De Franco1, K Colclough3, M Shepherd1,2, S Ellard1,3.   

Abstract

Distinguishing patients with monogenic diabetes from those with type 1 diabetes (T1D) is important for correct diagnosis, treatment, and selection of patients for gene discovery studies. We assessed whether a T1D genetic risk score (T1D-GRS) generated from T1D-associated common genetic variants provides a novel way to discriminate monogenic diabetes from T1D. The T1D-GRS was highly discriminative of proven maturity-onset diabetes of young (MODY) (n = 805) and T1D (n = 1,963) (receiver operating characteristic area under the curve 0.87). A T1D-GRS of >0.280 (>50th T1D centile) was indicative of T1D (94% specificity, 50% sensitivity). We then analyzed the T1D-GRS of 242 white European patients with neonatal diabetes (NDM) who had been tested for all known NDM genes. Monogenic NDM was confirmed in 90, 59, and 8% of patients with GRS <5th T1D centile, 50-75th T1D centile, and >75th T1D centile, respectively. Applying a GRS 50th T1D centile cutoff in 48 NDM patients with no known genetic cause identified those most likely to have a novel monogenic etiology by highlighting patients with probable early-onset T1D (GRS >50th T1D centile) who were diagnosed later and had less syndromic presentation but additional autoimmune features compared with those with proven monogenic NDM. The T1D-GRS is a novel tool to improve the use of biomarkers in the discrimination of monogenic diabetes from T1D.
© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

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Year:  2016        PMID: 27207547      PMCID: PMC4920219          DOI: 10.2337/db15-1690

Source DB:  PubMed          Journal:  Diabetes        ISSN: 0012-1797            Impact factor:   9.461


  25 in total

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Authors:  Jeffrey P Krischer; Kristian F Lynch; Desmond A Schatz; Jorma Ilonen; Åke Lernmark; William A Hagopian; Marian J Rewers; Jin-Xiong She; Olli G Simell; Jorma Toppari; Anette-G Ziegler; Beena Akolkar; Ezio Bonifacio
Journal:  Diabetologia       Date:  2015-02-10       Impact factor: 10.122

2.  Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations.

Authors:  Ewan R Pearson; Isabelle Flechtner; Pål R Njølstad; Maciej T Malecki; Sarah E Flanagan; Brian Larkin; Frances M Ashcroft; Iwar Klimes; Ethel Codner; Violeta Iotova; Annabelle S Slingerland; Julian Shield; Jean-Jacques Robert; Jens J Holst; Penny M Clark; Sian Ellard; Oddmund Søvik; Michel Polak; Andrew T Hattersley
Journal:  N Engl J Med       Date:  2006-08-03       Impact factor: 91.245

3.  Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes.

Authors:  Christiane Winkler; Jan Krumsiek; Florian Buettner; Christof Angermüller; Eleni Z Giannopoulou; Fabian J Theis; Anette-Gabriele Ziegler; Ezio Bonifacio
Journal:  Diabetologia       Date:  2014-09-04       Impact factor: 10.122

4.  Genetic cause of hyperglycaemia and response to treatment in diabetes.

Authors:  Ewan R Pearson; Bryan J Starkey; Roy J Powell; Fiona M Gribble; Penny M Clark; Andrew T Hattersley
Journal:  Lancet       Date:  2003-10-18       Impact factor: 79.321

5.  Islet autoantibodies can discriminate maturity-onset diabetes of the young (MODY) from Type 1 diabetes.

Authors:  T J McDonald; K Colclough; R Brown; B Shields; M Shepherd; P Bingley; A Williams; A T Hattersley; Sian Ellard
Journal:  Diabet Med       Date:  2011-09       Impact factor: 4.359

6.  The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes.

Authors:  B M Shields; T J McDonald; S Ellard; M J Campbell; C Hyde; A T Hattersley
Journal:  Diabetologia       Date:  2012-01-05       Impact factor: 10.122

7.  The effect of early, comprehensive genomic testing on clinical care in neonatal diabetes: an international cohort study.

Authors:  Elisa De Franco; Sarah E Flanagan; Jayne A L Houghton; Hana Lango Allen; Deborah J G Mackay; I Karen Temple; Sian Ellard; Andrew T Hattersley
Journal:  Lancet       Date:  2015-07-28       Impact factor: 79.321

8.  Role of Type 1 Diabetes-Associated SNPs on Risk of Autoantibody Positivity in the TEDDY Study.

Authors:  Carina Törn; David Hadley; Hye-Seung Lee; William Hagopian; Åke Lernmark; Olli Simell; Marian Rewers; Anette Ziegler; Desmond Schatz; Beena Akolkar; Suna Onengut-Gumuscu; Wei-Min Chen; Jorma Toppari; Juha Mykkänen; Jorma Ilonen; Stephen S Rich; Jin-Xiong She; Andrea K Steck; Jeffrey Krischer
Journal:  Diabetes       Date:  2014-11-24       Impact factor: 9.337

9.  Improved genetic testing for monogenic diabetes using targeted next-generation sequencing.

Authors:  S Ellard; H Lango Allen; E De Franco; S E Flanagan; G Hysenaj; K Colclough; J A L Houghton; M Shepherd; A T Hattersley; M N Weedon; R Caswell
Journal:  Diabetologia       Date:  2013-06-15       Impact factor: 10.122

10.  Activating germline mutations in STAT3 cause early-onset multi-organ autoimmune disease.

Authors:  Sarah E Flanagan; Emma Haapaniemi; Mark A Russell; Richard Caswell; Hana Lango Allen; Elisa De Franco; Timothy J McDonald; Hanna Rajala; Anita Ramelius; John Barton; Kaarina Heiskanen; Tarja Heiskanen-Kosma; Merja Kajosaari; Nuala P Murphy; Tatjana Milenkovic; Mikko Seppänen; Åke Lernmark; Satu Mustjoki; Timo Otonkoski; Juha Kere; Noel G Morgan; Sian Ellard; Andrew T Hattersley
Journal:  Nat Genet       Date:  2014-07-20       Impact factor: 38.330

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  56 in total

Review 1.  Genetic Risk Scores for Type 1 Diabetes Prediction and Diagnosis.

Authors:  Maria J Redondo; Richard A Oram; Andrea K Steck
Journal:  Curr Diab Rep       Date:  2017-10-28       Impact factor: 4.810

Review 2.  Neonatal Diabetes Mellitus: An Update on Diagnosis and Management.

Authors:  Michelle Blanco Lemelman; Lisa Letourneau; Siri Atma W Greeley
Journal:  Clin Perinatol       Date:  2017-12-16       Impact factor: 3.430

Review 3.  Immune Mechanisms and Pathways Targeted in Type 1 Diabetes.

Authors:  Laura M Jacobsen; Brittney N Newby; Daniel J Perry; Amanda L Posgai; Michael J Haller; Todd M Brusko
Journal:  Curr Diab Rep       Date:  2018-08-30       Impact factor: 4.810

4.  Diabetes: Quantifying genetic susceptibility in T1DM - implications for diagnosis after age 30.

Authors:  Everett Meyer; David M Maahs
Journal:  Nat Rev Endocrinol       Date:  2018-02-09       Impact factor: 43.330

Review 5.  Paediatric genomics: diagnosing rare disease in children.

Authors:  Caroline F Wright; David R FitzPatrick; Helen V Firth
Journal:  Nat Rev Genet       Date:  2018-02-05       Impact factor: 53.242

Review 6.  Monogenic diabetes: the impact of making the right diagnosis.

Authors:  Anastasia G Harris; Lisa R Letourneau; Siri Atma W Greeley
Journal:  Curr Opin Pediatr       Date:  2018-08       Impact factor: 2.856

7.  Approach to the Patient with MODY-Monogenic Diabetes.

Authors:  David T Broome; Kevin M Pantalone; Sangeeta R Kashyap; Louis H Philipson
Journal:  J Clin Endocrinol Metab       Date:  2021-01-01       Impact factor: 5.958

8.  Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes.

Authors:  A L J Carr; D J Perry; A L Lynam; S Chamala; C S Flaxman; S A Sharp; L A Ferrat; A G Jones; M L Beery; L M Jacobsen; C H Wasserfall; M L Campbell-Thompson; I Kusmartseva; A Posgai; D A Schatz; M A Atkinson; T M Brusko; S J Richardson; B M Shields; R A Oram
Journal:  Diabet Med       Date:  2020-07-23       Impact factor: 4.359

Review 9.  Precision medicine in diabetes: an opportunity for clinical translation.

Authors:  Jordi Merino; Jose C Florez
Journal:  Ann N Y Acad Sci       Date:  2018-01       Impact factor: 5.691

Review 10.  Congenital forms of diabetes: the beta-cell and beyond.

Authors:  Lisa R Letourneau; Siri Atma W Greeley
Journal:  Curr Opin Genet Dev       Date:  2018-02-16       Impact factor: 5.578

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