An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China.
An early prediction model for gestational diabetes mellitus based on genetic variants and clinical characteristics in China.
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基于中国遗传变异和临床特征的妊娠期糖尿病早期预测模型
DOI：
10.1186/s13098-022-00788-y
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发表时间：
2022-01-24
影响因子：
4.8
通讯作者：
Chen D
中科院分区：
文献类型：
--
作者：
Wu Q;Chen Y;Zhou M;Liu M;Zhang L;Liang Z;Chen D
To evaluate the influence of genetic variants and clinical characteristics on the risk of gestational diabetes mellitus (GDM) and to construct and verify a prediction model of GDM in early pregnancy. Four hundred seventy five women with GDM and 487 women without, as a control, were included to construct the prediction model of GDM in early pregnancy. Both groups had complete genotyping results and clinical data. They were randomly divided into a trial cohort (70%) and a test cohort (30%). Then, the model validation cohort, including 985 pregnant women, was used for the external validation of the GDM early pregnancy prediction model. We found maternal age, gravidity, parity, BMI and family history of diabetes were significantly associated with GDM (OR > 1; P < 0.001), and assisted reproduction was a critical risk factor for GDM (OR = 1.553, P = 0.055). MTNR1B rs10830963, C2CD4A/B rs1436953 and rs7172432, CMIP rs16955379 were significantly correlated with the incidence of GDM (AOR > 1, P < 0.05). Therefore, these four genetic susceptible single nucleotide polymorphisms (SNPs) and six clinical characteristics were included in the construction of the GDM early pregnancy prediction model. In the trial cohort, a predictive model of GDM in early pregnancy was constructed, in which genetic risk score was independently associated with GDM (AOR = 2.061, P < 0.001) and was the most effective predictor with the exception of family history of diabetes. The ROC-AUC of the prediction model was 0.727 (95% CI 0.690–0.765), and the sensitivity and specificity were 69.9% and 64.0%, respectively. The predictive power was also verified in the test cohort and the validation cohort. Based on the genetic variants and clinical characteristics, this study developed and verified the early pregnancy prediction model of GDM. This model can help screen out the population at high-risk for GDM in early pregnancy, and lifestyle interventions can be performed for them in a timely manner in early pregnancy. The online version contains supplementary material available at 10.1186/s13098-022-00788-y.
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影响因子：
5.9
作者：
Fukushima Y;Kurose S;Shinno H;Thi Thu HC;Takao N;Tsutsumi H;Hasegawa T;Nakajima T;Kimura Y
通讯作者：
Kimura Y
全基因组关联研究的荟萃分析确定了东亚人 2 型糖尿病的 8 个新位点
影响因子：
30.8
作者：
Cho, Yoon Shin;Chen, Chien-Hsiun;Hu, Cheng;Long, Jirong;Ong, Rick Twee Hee;Sim, Xueling;Takeuchi, Fumihiko;Wu, Ying;Go, Min Jin;Yamauchi, Toshimasa;Chang, Yi-Cheng;Kwak, Soo Heon;Ma, Ronald C. W.;Yamamoto, Ken;Adair, Linda S.;Aung, Tin;Cai, Qiuyin;Chang, Li-Ching;Chen, Yuan-Tsong;Gao, Yutang;Hu, Frank B.;Kim, Hyung-Lae;Kim, Sangsoo;Kim, Young Jin;Lee, Jeannette Jen-Mai;Lee, Nanette R.;Li, Yun;Liu, Jian Jun;Lu, Wei;Nakamura, Jiro;Nakashima, Eitaro;Ng, Daniel Peng-Keat;Tay, Wan Ting;Tsai, Fuu-Jen;Wong, Tien Yin;Yokota, Mitsuhiro;Zheng, Wei;Zhang, Rong;Wang, Congrong;So, Wing Yee;Ohnaka, Keizo;Ikegami, Hiroshi;Hara, Kazuo;Cho, Young Min;Cho, Nam H.;Chang, Tien-Jyun;Bao, Yuqian;Hedman, Asa K.;Morris, Andrew P.;McCarthy, Mark I.;Takayanagi, Ryoichi;Park, Kyong Soo;Jia, Weiping;Chuang, Lee-Ming;Chan, Juliana C. N.;Maeda, Shiro;Kadowaki, Takashi;Lee, Jong-Young;Wu, Jer-Yuarn;Teo, Yik Ying;Tai, E. Shyong;Shu, Xiao Ou;Mohlke, Karen L.;Kato, Norihiro;Han, Bok-Ghee;Seielstad, Mark
通讯作者：
Seielstad, Mark
影响因子：
7.7
作者：
Kwak SH;Kim SH;Cho YM;Go MJ;Cho YS;Choi SH;Moon MK;Jung HS;Shin HD;Kang HM;Cho NH;Lee IK;Kim SY;Han BG;Jang HC;Park KS
通讯作者：
Park KS
影响因子：
16.2
作者：
American Diabetes Association
通讯作者：
American Diabetes Association
影响因子：
5.6
作者：
Cao Y;Wang T;Wu Y;Juan J;Qin X;Tang X;Wu T;Hu Y
通讯作者：
Hu Y