Prediction of gestational diabetes mellitus in the Born in Guangzhou Cohort Study, China

Schaefer, Kimberly K.; Xiao, Wan-Qing; Chen, Qiaozhu; He, Jianxing; Lu, Jinhua; Chan, Fanfan; Chen, Niannian; Yuan, Mingyang; Xia, Huimin; Lam, Hubert; Hirst, Jane E; Qiu, Xiu

doi:10.1002/ijgo.12627

Cited by 18 publications

(15 citation statements)

References 23 publications

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“…According to a cross-sectional study of 16 hospitals from five selected provinces in mainland China, women aged 36–45 years had a nearly 4-fold risk of having GDM compared to those with 18–25 years old (OR = 3.98, 95% CI: 1.41–11.28) [ 35 ]. Another prospective cohort study of the Born in Guangzhou Cohort Study in China showed similar results that women older than 35 years had a 3.95-fold increased risk of GDM (95% CI: 2.80–5.58) compared to women aged 16–25 years [ 49 ].…”

Section: Risk Factors For Gdm In Chinamentioning

confidence: 85%

Prevalence, Prevention, and Lifestyle Intervention of Gestational Diabetes Mellitus in China

Juan

Yang

2020

IJERPH

178

128

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Gestational diabetes mellitus (GDM) has become an epidemic and has caused a tremendous healthy and economic burden in China, especially after the “two-child policy” put into effect on October 2015. The prevalence of GDM has continued to increase during the past few decades and is likely to see a further rise in the future. The public health impact of GDM is becoming more apparent in China and it might lead to the development of chronic non-communicable diseases in the long-term for both mothers and their children. Early identification of high-risk individuals could help to take preventive and intervention measures to reduce the risk of GDM and adverse perinatal outcomes. Therefore, a focus on prevention and intervention of GDM in China is of great importance. Lifestyle interventions, including dietary and physical exercise intervention, are effective and first-line preventive strategies for GDM prevention and intervention. The GDM One-day Care Clinic established in 2011, which educates GDM patients on the basic knowledge of GDM, dietary intervention, physical exercise, weight management, and blood glucose self-monitoring methods, sets a good model for group management of GDM and has been implemented throughout the hospitals as well as maternal and child health centers in China. The current review focus on the prevalence, risk factors, as well as prevention and lifestyle intervention of GDM in China for better understanding of the latest epidemiology of GDM in China and help to improve maternal and neonatal pregnancy outcomes and promote long-term health for women with GDM.

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Section: Risk Factors For Gdm In Chinamentioning

confidence: 85%

Prevalence, Prevention, and Lifestyle Intervention of Gestational Diabetes Mellitus in China

Juan

Yang

2020

IJERPH

178

128

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“…Various first-trimester prediction models for GDM have been proposed [21][22][23]. However, these models are not commonly used in routine clinical care.…”

Section: Discussionmentioning

confidence: 99%

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Guo

Zhang

et al. 2020

BMC Pregnancy Childbirth

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Background: This study sought to develop and validate a nomogram for prediction of gestational diabetes mellitus (GDM) in an urban, Chinese, antenatal population. Methods: Age, pre-pregnancy body mass index (BMI), fasting plasma glucose (FPG) in the first trimester and diabetes in first degree relatives were incorporated as validated risk factors. A prediction model (nomogram) for GDM was developed using multiple logistic regression analysis, from a retrospective study conducted on 3956 women who underwent their first antenatal visit during 2015 in Shanghai. Performance of the nomogram was assessed through discrimination and calibration. We refined the predicting model with t-distributed stochastic neighbor embedding (t-SNE) to distinguish GDM from non-GDM. The results were validated using bootstrap resampling and a prospective cohort of 6572 women during 2016 at the same institution. Results: Advanced age, pre-pregnancy BMI, high first-trimester, fasting, plasma glucose, and, a family history of diabetes were positively correlated with the development of GDM. This model had an area under the receiver operating characteristic (ROC) curve of 0.69 [95% CI:0.67-0.72, p < 0.0001]. The calibration curve for probability of GDM showed good consistency between nomogram prediction and actual observation. In the validation cohort, the ROC curve was 0.70 [95% CI: 0.68-0.72, p < 0.0001] and the calibration plot was well calibrated. In exploratory and validation cohorts, the distinct regions of GDM and non-GDM were distinctly separated in the t-SNE, generating transitional boundaries in the image by color difference. Decision curve analysis showed that the model had a positive net benefit at threshold between 0.05 and 0.78. Conclusions: This study demonstrates the ability of our model to predict the development of GDM in women, during early stage of pregnancy.

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“…Most GDM risk assessment models use traditional regression models (e.g., logistic regression or Cox regression), which make an implicit assumption that each risk factor is linearly related to GDM outcomes 9 . As a result, such models may ignore the complex relationships of a large number of risk factors with non‐linear interactions, and the predictive performance is always suboptimal 10–12 . There is a need to explore novel methods that take multiple risk factors into consideration and to discover more subtle relationships between risk factors and outcomes for the development of highly accurate prediction tools for GDM.…”

Section: Introductionmentioning

confidence: 99%

Machine learning risk score for prediction of gestational diabetes in early pregnancy in Tianjin, China

Liu

Leng

et al. 2020

Diabetes Metabolism Res

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Aims This study aimed to develop a machine learning–based prediction model for gestational diabetes mellitus (GDM) in early pregnancy in Chinese women. Materials and methods We used an established population‐based prospective cohort of 19,331 pregnant women registered as pregnant before the 15th gestational week in Tianjin, China, from October 2010 to August 2012. The dataset was randomly divided into a training set (70%) and a test set (30%). Risk factors collected at registration were examined and used to construct the prediction model in the training dataset. Machine learning, that is, the extreme gradient boosting (XGBoost) method, was employed to develop the model, while a traditional logistic model was also developed for comparison purposes. In the test dataset, the performance of the developed prediction model was assessed by calibration plots for calibration and area under the receiver operating characteristic curve (AUR) for discrimination. Results In total, 1484 (7.6%) women developed GDM. Pre‐pregnancy body mass index, maternal age, fasting plasma glucose at registration, and alanine aminotransferase were selected as risk factors. The machine learning XGBoost model‐predicted probability of GDM was similar to the observed probability in the test data set, while the logistic model tended to overestimate the risk at the highest risk level (Hosmer–Lemeshow test p value: 0.243 vs. 0.099). The XGBoost model achieved a higher AUR than the logistic model (0.742 vs. 0.663, p < 0.001). This XGBoost model was deployed through a free, publicly available software interface (https://liuhongwei.shinyapps.io/gdm_risk_calculator/). Conclusion The XGBoost model achieved better performance than the logistic model.

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Prediction of gestational diabetes mellitus in the Born in Guangzhou Cohort Study, China

Abstract: The assessment of risk factors for GDM could provide a foundation for improving risk-based screening strategies in this and similar populations.

Cited by 18 publications

References 23 publications

Prevalence, Prevention, and Lifestyle Intervention of Gestational Diabetes Mellitus in China

Prevalence, Prevention, and Lifestyle Intervention of Gestational Diabetes Mellitus in China

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Machine learning risk score for prediction of gestational diabetes in early pregnancy in Tianjin, China

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