Association of Variants in PPARgamma2, IGF2BP2, and KCNQ1 with a Susceptibility to Gestational Diabetes Mellitus in a Korean Population.
10.3349/ymj.2013.54.2.352
- Author:
Seung Joo CHON
1
;
Suk Young KIM
;
Nu Ree CHO
;
Dle Lae MIN
;
Yu Jin HWANG
;
Mizuko MAMURA
Author Information
1. Department of Obstetrics & Gynecology, Gil Hospital, Graduate School of Medicine, Gachon University of Medicine and Science, Incheon, Korea. ksyob@gilhospital.com
- Publication Type:Original Article
- Keywords:
Gestational diabetes mellitus;
type 2 diabetes mellitus;
gene;
single nucleotide polymorphism
- MeSH:
Diabetes, Gestational/*genetics;
Female;
Genetic Association Studies;
Genetic Predisposition to Disease;
Genotype;
Humans;
KCNQ1 Potassium Channel/*genetics;
Logistic Models;
PPAR gamma/*genetics;
Polymorphism, Single Nucleotide;
Pregnancy;
RNA-Binding Proteins/*genetics;
Republic of Korea
- From:Yonsei Medical Journal
2013;54(2):352-357
- CountryRepublic of Korea
- Language:English
-
Abstract:
PURPOSE: Patients with gestational diabetes mellitus (GDM) have been reported to exhibit the same genetic susceptibility as that observed in those with type 2 diabetes mellitus (T2DM). Recent polymorphism studies have shown that several genes are related to T2DM and GDM. The aim of this study was to examine whether certain candidate genes, previously shown to be associated with T2DM, also offer a specific genetic predisposition to GDM. MATERIALS AND METHODS: The current study was conducted in 136 Korean pregnant women, who gave birth at Gil Hospital, from October 2008 to May 2011. These study subjects included 95 subjects with GDM and 41 non-diabetic controls. We selected the specific genes of PPARgamma2, IGF2BP2, and KCNQ1 for study and amplified them using the polymerase chain reaction. This was followed by genotyping for single nucleotide polymorphisms. We then compared the genotype frequencies between patients with GDM and non-diabetic controls using the chi2 test. We obtained and analyzed clinical information using Student's t-test, and statistical analyses were conducted using logistic regression with SPSS Statistics software, version 19.0. RESULTS: Significant differences were observed in maternal age, body mass index, weight gain and weight at time of delivery between the groups compared. Among pregnant women, polymorphisms in PPARgamma2 and IGF2BP2 were shown to be highly correlated with GDM occurrence, whereas no correlation was found for KCNQ1 polymorphisms. CONCLUSION: Our results indicated that genetic polymorphisms could also be of value in predicting the occurrence and diagnosis of GDM.
