Literature DB >> 33602293

GCKR common functional polymorphisms are associated with metabolic syndrome and its components: a 10-year retrospective cohort study in Iranian adults.

Asiyeh Sadat Zahedi1, Mahdi Akbarzadeh1, Bahareh Sedaghati-Khayat1, Atefeh Seyedhamzehzadeh1, Maryam S Daneshpour2.   

Abstract

BACKGROUND: Previous studies reported that common functional variants (rs780093, rs780094, and rs1260326) in the glucokinase regulator gene (GCKR) were associated with metabolic syndrome despite the simultaneous association with the favorable and unfavorable metabolic syndrome components. We decided to evaluate these findings in a cohort study with a large sample size of Iranian adult subjects, to our knowledge for the first time. We investigated the association of the GCKR variants with incident MetS in mean follow-up times for nearly 10 years.
METHODS: Analysis of this retrospective cohort study was performed among 5666 participants of the Tehran Cardiometabolic Genetics Study (TCGS) at 19-88 years at baseline. Linear and logistic regression analyses were used to investigate the metabolic syndrome (JIS criteria) association and its components with rs780093, rs780094, and rs1260326 in an additive genetic model. Cox regression was carried out to peruse variants' association with the incidence of metabolic syndrome in the TCGS cohort study.
RESULTS: In the current study, we have consistently replicated the association of the GCKR SNPs with higher triglyceride and lower fasting blood sugar levels (p < 0.05) in Iranian adults. The CT genotype of the variants was associated with lower HDL-C levels. The proportional Cox adjusted model regression resulted that TT carriers of rs780094, rs780093, and rs1260326 were associated with 20%, 23%, and 21% excess risk metabolic syndrome incidence, respectively (p < 0.05).
CONCLUSIONS: Elevated triglyceride levels had the strongest association with GCKR selected variants among the metabolic syndrome components. Despite the association of these variants with decreased fasting blood sugar levels, T alleles of the variants were associated with metabolic syndrome incidence; so whether individuals are T allele carriers of the common functional variants, they have a risk factor for the future incidence of metabolic syndrome.

Entities:  

Keywords:  GCKR; Metabolic syndrome; Single nucleotide polymorphisms; Triglyceride

Year:  2021        PMID: 33602293      PMCID: PMC7890822          DOI: 10.1186/s13098-021-00637-4

Source DB:  PubMed          Journal:  Diabetol Metab Syndr        ISSN: 1758-5996            Impact factor:   3.320


  46 in total

1.  Appropriate waist circumference cut-off points among Iranian adults: the first report of the Iranian National Committee of Obesity.

Authors:  Fereidoun Azizi; Davood Khalili; Hassan Aghajani; Alireza Esteghamati; Farhad Hosseinpanah; Alireza Delavari; Bagher Larijani; Parvin Mirmiran; Yadollah Mehrabi; Roya Kelishadi; Farzad Hadaegh
Journal:  Arch Iran Med       Date:  2010-05       Impact factor: 1.354

2.  A protein from rat liver confers to glucokinase the property of being antagonistically regulated by fructose 6-phosphate and fructose 1-phosphate.

Authors:  E Van Schaftingen
Journal:  Eur J Biochem       Date:  1989-01-15

3.  Predictive power of the components of metabolic syndrome in its development: a 6.5-year follow-up in the Tehran Lipid and Glucose Study (TLGS).

Authors:  Z Heidari; F Hosseinpanah; Y Mehrabi; M Safarkhani; F Azizi
Journal:  Eur J Clin Nutr       Date:  2010-06-30       Impact factor: 4.016

4.  Association of polymorphisms in GCKR and TRIB1 with nonalcoholic fatty liver disease and metabolic syndrome traits.

Authors:  Aya Kitamoto; Takuya Kitamoto; Takahiro Nakamura; Yuji Ogawa; Masato Yoneda; Hideyuki Hyogo; Hidenori Ochi; Seiho Mizusawa; Takato Ueno; Kazuwa Nakao; Akihiro Sekine; Kazuaki Chayama; Atsushi Nakajima; Kikuko Hotta
Journal:  Endocr J       Date:  2014-05-01       Impact factor: 2.349

5.  Metabolic impact of glucokinase overexpression in liver: lowering of blood glucose in fed rats is accompanied by hyperlipidemia.

Authors:  R M O'Doherty; D L Lehman; S Télémaque-Potts; C B Newgard
Journal:  Diabetes       Date:  1999-10       Impact factor: 9.461

6.  Association of rs780094 in GCKR with metabolic traits and incident diabetes and cardiovascular disease: the ARIC Study.

Authors:  Mark Bi; Wen Hong Linda Kao; Eric Boerwinkle; Ron C Hoogeveen; Laura J Rasmussen-Torvik; Brad C Astor; Kari E North; Josef Coresh; Anna Köttgen
Journal:  PLoS One       Date:  2010-07-22       Impact factor: 3.240

Review 7.  Banting Lecture 1995. A lesson in metabolic regulation inspired by the glucokinase glucose sensor paradigm.

Authors:  F M Matschinsky
Journal:  Diabetes       Date:  1996-02       Impact factor: 9.461

8.  A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium.

Authors:  Aldi T Kraja; Dhananjay Vaidya; James S Pankow; Mark O Goodarzi; Themistocles L Assimes; Iftikhar J Kullo; Ulla Sovio; Rasika A Mathias; Yan V Sun; Nora Franceschini; Devin Absher; Guo Li; Qunyuan Zhang; Mary F Feitosa; Nicole L Glazer; Talin Haritunians; Anna-Liisa Hartikainen; Joshua W Knowles; Kari E North; Carlos Iribarren; Brian Kral; Lisa Yanek; Paul F O'Reilly; Mark I McCarthy; Cashell Jaquish; David J Couper; Aravinda Chakravarti; Bruce M Psaty; Lewis C Becker; Michael A Province; Eric Boerwinkle; Thomas Quertermous; Leena Palotie; Marjo-Riitta Jarvelin; Diane M Becker; Sharon L R Kardia; Jerome I Rotter; Yii-Der Ida Chen; Ingrid B Borecki
Journal:  Diabetes       Date:  2011-03-08       Impact factor: 9.461

9.  Rationale and Design of a Genetic Study on Cardiometabolic Risk Factors: Protocol for the Tehran Cardiometabolic Genetic Study (TCGS).

Authors:  Maryam S Daneshpour; Mohammad-Sadegh Fallah; Bahareh Sedaghati-Khayat; Kamran Guity; Davood Khalili; Mehdi Hedayati; Ahmad Ebrahimi; Farhad Hajsheikholeslami; Parvin Mirmiran; Fahimeh Ramezani Tehrani; Amir-Abbas Momenan; Arash Ghanbarian; Atieh Amouzegar; Parisa Amiri; Fereidoun Azizi
Journal:  JMIR Res Protoc       Date:  2017-02-23

10.  Survival response to increased ceramide involves metabolic adaptation through novel regulators of glycolysis and lipolysis.

Authors:  Niraj K Nirala; Motiur Rahman; Stanley M Walls; Alka Singh; Lihua Julie Zhu; Takeshi Bamba; Eiichiro Fukusaki; Sargur M Srideshikan; Greg L Harris; Y Tony Ip; Rolf Bodmer; Usha R Acharya
Journal:  PLoS Genet       Date:  2013-06-20       Impact factor: 5.917

View more
  2 in total

1.  Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score.

Authors:  Ximei Huang; Youngmin Han; Kyunghye Jang; Minjoo Kim
Journal:  Antioxidants (Basel)       Date:  2022-06-17

2.  Association between glucokinase regulator gene polymorphisms and serum uric acid levels in Taiwanese adolescents.

Authors:  Jhih-Syuan Liu; Chang-Hsun Hsieh; Li-Ju Ho; Chieh-Hua Lu; Ruei-Yu Su; Fu-Huang Lin; Sheng-Chiang Su; Feng-Chih Kuo; Nain-Feng Chu; Yi-Jen Hung
Journal:  Sci Rep       Date:  2022-04-01       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.