Literature DB >> 21219419

Dorothy Hodgkin Lecture 2010. From hype to hope? A journey through the genetics of Type 2 diabetes.

M I McCarthy1.   

Abstract

Recent advances in genetic analysis have enabled researchers to perform genome-wide surveys for common DNA sequence variants associated with risk of Type 2 diabetes and related traits. Over the past 4 years, these endeavours have extended the number of proven Type 2 diabetes-susceptibility loci from a handful to the current total of over 40. Each of these loci provides an opportunity to uncover insights into the biology of glucose regulation and the pathogenesis of Type 2 diabetes, insights which should support clinical translation to identify novel ways of treating and preventing disease. Here, I describe (i) progress in identification of diabetes-susceptibility loci; (ii) biological insights that have been gained in the relatively short period since these loci were discovered; and (iii) the challenges that need to be addressed if we are to maximize the translational benefits of this research.
© 2011 The Author. Diabetic Medicine © 2011 Diabetes UK.

Entities:  

Mesh:

Year:  2011        PMID: 21219419     DOI: 10.1111/j.1464-5491.2010.03194.x

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  6 in total

1.  Type 2 diabetes: unravelling the interaction between genetic predisposition and lifestyle.

Authors:  W Rathmann; B Kowall; G Giani
Journal:  Diabetologia       Date:  2011-06-28       Impact factor: 10.122

2.  Patient characteristics and participation in a genetic study: a type 2 diabetes cohort.

Authors:  Loabat Amiri; Andrea E Cassidy-Bushrow; Heather Dakki; Jia Li; Karen Wells; Susan A Oliveria; Marianne Ulcickas Yood; Abraham Thomas; David E Lanfear
Journal:  J Investig Med       Date:  2014-01       Impact factor: 2.895

3.  Common variants in the type 2 diabetes KCNQ1 gene are associated with impairments in insulin secretion during hyperglycaemic glucose clamp.

Authors:  Jana V van Vliet-Ostaptchouk; Timon W van Haeften; Gijs W D Landman; Erwin Reiling; Nanne Kleefstra; Henk J G Bilo; Olaf H Klungel; Anthonius de Boer; Cleo C van Diemen; Cisca Wijmenga; H Marike Boezen; Jacqueline M Dekker; Esther van 't Riet; Giel Nijpels; Laura M C Welschen; Hata Zavrelova; Elinda J Bruin; Clara C Elbers; Florianne Bauer; N Charlotte Onland-Moret; Yvonne T van der Schouw; Diederick E Grobbee; Annemieke M W Spijkerman; Daphne L van der A; Annemarie M Simonis-Bik; Elisabeth M W Eekhoff; Michaela Diamant; Mark H H Kramer; Dorret I Boomsma; Eco J de Geus; Gonneke Willemsen; P Eline Slagboom; Marten H Hofker; Leen M 't Hart
Journal:  PLoS One       Date:  2012-03-05       Impact factor: 3.240

Review 4.  A methodological perspective on genetic risk prediction studies in type 2 diabetes: recommendations for future research.

Authors:  Sara M Willems; Raluca Mihaescu; Eric J G Sijbrands; Cornelia M van Duijn; A Cecile J W Janssens
Journal:  Curr Diab Rep       Date:  2011-12       Impact factor: 4.810

5.  The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort.

Authors:  Daichi Shigemizu; Testuo Abe; Takashi Morizono; Todd A Johnson; Keith A Boroevich; Yoichiro Hirakawa; Toshiharu Ninomiya; Yutaka Kiyohara; Michiaki Kubo; Yusuke Nakamura; Shiro Maeda; Tatsuhiko Tsunoda
Journal:  PLoS One       Date:  2014-03-20       Impact factor: 3.240

6.  metabolicMine: an integrated genomics, genetics and proteomics data warehouse for common metabolic disease research.

Authors:  Mike Lyne; Richard N Smith; Rachel Lyne; Jelena Aleksic; Fengyuan Hu; Alex Kalderimis; Radek Stepan; Gos Micklem
Journal:  Database (Oxford)       Date:  2013-08-09       Impact factor: 3.451

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.