Literature DB >> 24596594

Gene expression in thiazide diuretic or statin users in relation to incident type 2 diabetes.

Astrid Suchy-Dicey1, Susan R Heckbert2, Nicholas L Smith3, Barbara McKnight4, Jerome I Rotter5, Yd Ida Chen5, Bruce M Psaty6, Daniel A Enquobahrie1.   

Abstract

Thiazide diuretics and statins are used to improve cardiovascular outcomes, but may also cause type 2 diabetes (T2DM), although mechanisms are unknown. Gene expression studies may facilitate understanding of these associations. Participants from ongoing population-based studies were sampled for these longitudinal studies of peripheral blood microarray gene expression, and followed to incident diabetes. All sampled subjects were statin or thiazide users. Those who developed diabetes during follow-up comprised cases (44 thiazide users; 19 statin users), and were matched to drug-using controls who did not develop diabetes on several factors. Supervised normalization, surrogate variable analyses removed technical bias and confounding. Differentially-expressed genes were those with a false discovery rate Q-value<0.05. Among thiazide users, diabetes cases had significantly different expression of CCL14 (down-regulated 6%, Q-value=0.0257), compared with controls. Among statin users, diabetes cases had marginal but insignificantly different expression of ZNF532 (up-regulated 15%, Q-value=0.0584), CXORF21 (up-regulated 11%, Q-value=0.0584), and ZNHIT3 (up-regulated 19%, Q-value=0.0959), compared with controls. These genes comprise potential targets for future expression or mechanistic research on medication-related diabetes development.

Entities:  

Keywords:  Type 2 diabetes; chemokine ligand 14; gene expression; microarray; statins; supervised normalization; surrogate variable analysis; thiazide diuretics; whole blood; zinc finger proteins

Year:  2014        PMID: 24596594      PMCID: PMC3939004     

Source DB:  PubMed          Journal:  Int J Mol Epidemiol Genet        ISSN: 1948-1756


  36 in total

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3.  Incident diabetes in clinical trials of antihypertensive drugs.

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4.  Diversification of transcriptional modulation: large-scale identification and characterization of putative alternative promoters of human genes.

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Journal:  Genome Res       Date:  2005-12-12       Impact factor: 9.043

5.  Two classes of proteins dependent on either the presence or absence of thyroid hormone for interaction with the thyroid hormone receptor.

Authors:  J W Lee; H S Choi; J Gyuris; R Brent; D D Moore
Journal:  Mol Endocrinol       Date:  1995-02

6.  Statin use and risk of diabetes mellitus in postmenopausal women in the Women's Health Initiative.

Authors:  Annie L Culver; Ira S Ockene; Raji Balasubramanian; Barbara C Olendzki; Deidre M Sepavich; Jean Wactawski-Wende; Joann E Manson; Yongxia Qiao; Simin Liu; Philip A Merriam; Catherine Rahilly-Tierny; Fridtjof Thomas; Jeffrey S Berger; Judith K Ockene; J David Curb; Yunsheng Ma
Journal:  Arch Intern Med       Date:  2012-01-09

7.  Nuclear receptor-coregulator interaction profiling identifies TRIP3 as a novel peroxisome proliferator-activated receptor gamma cofactor.

Authors:  Arjen Koppen; Rene Houtman; Dirk Pijnenburg; Ellen H Jeninga; Rob Ruijtenbeek; Eric Kalkhoven
Journal:  Mol Cell Proteomics       Date:  2009-07-10       Impact factor: 5.911

8.  Supervised normalization of microarrays.

Authors:  Brigham H Mecham; Peter S Nelson; John D Storey
Journal:  Bioinformatics       Date:  2010-03-31       Impact factor: 6.937

9.  CCL genes in multiple sclerosis and systemic lupus erythematosus.

Authors:  Tamara Vyshkina; Andrew Sylvester; Saud Sadiq; Eduardo Bonilla; Andras Perl; Bernadette Kalman
Journal:  J Neuroimmunol       Date:  2008-07-03       Impact factor: 3.478

10.  Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma.

Authors:  Ilhem Diboun; Lorenz Wernisch; Christine Anne Orengo; Martin Koltzenburg
Journal:  BMC Genomics       Date:  2006-10-09       Impact factor: 3.969

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1.  A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes.

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Journal:  Genes (Basel)       Date:  2018-09-07       Impact factor: 4.096

  1 in total

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