Literature DB >> 19247372

Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association.

Kazuaki Miyake1, Woosung Yang, Kazuo Hara, Kazuki Yasuda, Yukio Horikawa, Haruhiko Osawa, Hiroto Furuta, Maggie C Y Ng, Yushi Hirota, Hiroyuki Mori, Keisuke Ido, Kazuya Yamagata, Yoshinori Hinokio, Yoshitomo Oka, Naoko Iwasaki, Yasuhiko Iwamoto, Yuichiro Yamada, Yutaka Seino, Hiroshi Maegawa, Atsunori Kashiwagi, He-Yao Wang, Toshihito Tanahashi, Naoto Nakamura, Jun Takeda, Eiichi Maeda, Ken Yamamoto, Katsushi Tokunaga, Ronald C W Ma, Wing-Yee So, Juliana C N Chan, Naoyuki Kamatani, Hideichi Makino, Kishio Nanjo, Takashi Kadowaki, Masato Kasuga.   

Abstract

Prediction of the disease status is one of the most important objectives of genetic studies. To select the genes with strong evidence of the association with type 2 diabetes mellitus, we validated the associations of the seven candidate loci extracted in our earlier study by genotyping the samples in two independent sample panels. However, except for KCNQ1, the association of none of the remaining seven loci was replicated. We then selected 11 genes, KCNQ1, TCF7L2, CDKAL1, CDKN2A/B, IGF2BP2, SLC30A8, HHEX, GCKR, HNF1B, KCNJ11 and PPARG, whose associations with diabetes have already been reported and replicated either in the literature or in this study in the Japanese population. As no evidence of the gene-gene interaction for any pair of the 11 loci was shown, we constructed a prediction model for the disease using the logistic regression analysis by incorporating the number of the risk alleles for the 11 genes, as well as age, sex and body mass index as independent variables. Cumulative risk assessment showed that the addition of one risk allele resulted in an average increase in the odds for the disease of 1.29 (95% CI=1.25-1.33, P=5.4 x 10(-53)). The area under the receiver operating characteristic curve, an estimate of the power of the prediction model, was 0.72, thereby indicating that our prediction model for type 2 diabetes may not be so useful but has some value. Incorporation of data from additional risk loci is most likely to increase the predictive power.

Entities:  

Mesh:

Year:  2009        PMID: 19247372     DOI: 10.1038/jhg.2009.17

Source DB:  PubMed          Journal:  J Hum Genet        ISSN: 1434-5161            Impact factor:   3.172


  34 in total

Review 1.  Annotating individual human genomes.

Authors:  Ali Torkamani; Ashley A Scott-Van Zeeland; Eric J Topol; Nicholas J Schork
Journal:  Genomics       Date:  2011-08-02       Impact factor: 5.736

Review 2.  The potential of novel biomarkers to improve risk prediction of type 2 diabetes.

Authors:  Christian Herder; Bernd Kowall; Adam G Tabak; Wolfgang Rathmann
Journal:  Diabetologia       Date:  2014-01       Impact factor: 10.122

Review 3.  Type 2 diabetes and obesity: genomics and the clinic.

Authors:  Mary E Travers; Mark I McCarthy
Journal:  Hum Genet       Date:  2011-06-07       Impact factor: 4.132

4.  Association between KCNJ11 gene polymorphisms and risk of type 2 diabetes mellitus in East Asian populations: a meta-analysis in 42,573 individuals.

Authors:  Lijuan Yang; Xianghai Zhou; Yingying Luo; Xiuqin Sun; Yong Tang; Wulan Guo; Xueyao Han; Linong Ji
Journal:  Mol Biol Rep       Date:  2011-05-15       Impact factor: 2.316

5.  Estimation of the risk of a qualitative phenotype: dependence on population risk.

Authors:  Naoyuki Kamatani; Shigeo Kamitsuji; Yasuaki Akazawa; Takashi Kido; Masanori Akita
Journal:  J Hum Genet       Date:  2016-08-25       Impact factor: 3.172

6.  Three-dimensional structure of beta-cell-specific zinc transporter, ZnT-8, predicted from the type 2 diabetes-associated gene variant SLC30A8 R325W.

Authors:  Rob Nm Weijers
Journal:  Diabetol Metab Syndr       Date:  2010-06-05       Impact factor: 3.320

7.  Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population.

Authors:  Rosalind J Neuman; Jon Wasson; Gil Atzmon; Julio Wainstein; Yair Yerushalmi; Joseph Cohen; Nir Barzilai; Ilana Blech; Benjamin Glaser; M Alan Permutt
Journal:  PLoS One       Date:  2010-03-26       Impact factor: 3.240

8.  Prediction model for knee osteoarthritis based on genetic and clinical information.

Authors:  Hiroshi Takahashi; Masahiro Nakajima; Kouichi Ozaki; Toshihiro Tanaka; Naoyuki Kamatani; Shiro Ikegawa
Journal:  Arthritis Res Ther       Date:  2010-10-12       Impact factor: 5.156

9.  Evaluation of the association between the AC3 genetic polymorphisms and obesity in a Chinese Han population.

Authors:  Hairu Wang; Ming Wu; Weiguang Zhu; Jin Shen; Xiaoming Shi; Jie Yang; Qihui Zhao; Chuan Ni; Yaochu Xu; Hongbing Shen; Chong Shen; Harvest F Gu
Journal:  PLoS One       Date:  2010-11-04       Impact factor: 3.240

10.  Disparities in the prevalence of diabetes: is it race/ethnicity or socioeconomic status? Results from the Boston Area Community Health (BACH) survey.

Authors:  Carol L Link; John B McKinlay
Journal:  Ethn Dis       Date:  2009       Impact factor: 1.847

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.