The approaches used to screen and diagnose gestational diabetes mellitus (GDM) vary widely. We generated a comparable estimate of the global and regional prevalence of GDM by International Association of Diabetes in Pregnancy Study Group (IADPSG)'s criteria.Methods: We searched PubMed and other databases and retrieved 57 studies to estimate the prevalence of GDM. Prevalence rate ratios of different diagnostic criteria, screening strategies and age groups, were used to standardize the prevalence of GDM in individual studies included in the analysis. Fixed effects meta-analysis was conducted to estimate standardized pooled prevalence of GDM by IDF regions and World Bank country income groups. Results:The pooled global standardized prevalence of GDM was 14.0% (95% confidence interval: 13.97-14.04%). The regional standardized prevalence of GDM were 7.1% (7.0-7.2%) in North America and Caribbean (NAC), 7.8% (7.2-8.4%) in Europe (EUR), 10.4%

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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.

Background and objectiveMost previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.MethodsThe ARIMA, STL+ARIMA, BP-ANN and LSTM network models were separately applied in simulations using malaria data and meteorological data in Yunnan Province from 2011 to 2017. We compared the predictive performance of each model through evaluation measures: RMSE, MASE, MAD. In addition, gradient-boosting regression trees (GBRTs) were used to combine the above four models. We also determined whether stacking structure improved the model prediction performance.ResultsThe root mean square errors (RMSEs) of the four sub-models were 13.176, 14.543, 9.571 and 7.208; the mean absolute scaled errors (MASEs) were 0.469, 0.472, 0.296 and 0.266 and the mean absolute deviation (MAD) were 6.403, 7.658, 5.871 and 5.691. After using the stacking architecture combined with the above four models, the RMSE, MASE and MAD values of the ensemble model decreased to 6.810, 0.224 and 4.625, respectively.ConclusionsA novel ensemble model based on the robustness of structured prediction and model combination through stacking was developed. The findings suggest that the predictive performance of the final model is superior to that of the other four sub-models, indicating that stacking architecture may have significant implications in infectious disease prediction.

Objective: This study aimed to assess whether metabolically healthy obesity (MHO) increases the risk of diabetes and to explore how the occurrence of metabolic disorders affects the risk of diabetes and which factors determine metabolic health. Methods: This study examined 49,702 older people without diabetes via the Binhai Health Screening Program in Tianjin. Results: Compared with individuals with metabolic health and normal weight, the risk of diabetes was increased in older adults with MHO (hazard ratio [HR]: 1.786, 95% CI: 1.407-2.279) but was not significantly increased when metabolic health was characterized by the absence of metabolic abnormalities. The older adults who were initially affected by MHO and then converted to having an unhealthy phenotype had a higher diabetes risk than older individuals with stable and healthy normal weight (HR: 3.727, 95% CI: 2.721-5.105). Waist circumference was an independent predictor of the transition from a metabolically healthy status to an unhealthy status in all BMI categories (odds ratio: 1.059, 95% CI: 1.026-1.032). Conclusions: The MHO phenotype was associated with an increased incidence of diabetes in older adults. The presence of metabolic disorders in the group with MHO was associated with an increased diabetes risk and was predicted by the waist circumference at baseline.