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Aim: To develop a visual prediction model for gestational diabetes (GD) in pregnant women and to establish an effective and practical tool for clinical application. Methods: To establish a prediction model, the modelling set included 1756 women enrolled in the Zunyi birth cohort, the internal validation set included 1234 enrolled women, and pregnant women in the Wuhan cohort were included in the external validation set. We established a demographic–lifestyle factor model (DLFM) and a demographic–lifestyle–environmental pollution factor model (DLEFM) based on whether the women were exposed to environmental pollutants. The least absolute shrinkage and selection lasso–logistic regression analyses were used to identify the independent predictors of GD and construct a nomogram for predicting its occurrence. Results: The DLEFM regression analysis showed that a family history of diabetes (odd ratio [OR] 2.28; 95% confidence interval [CI] 1.05‐4.71), a history of GD in pregnant women (OR 4.22; 95% CI 1.89‐9.41), being overweight or obese before pregnancy (OR 1.71; 95% CI 1.27‐2.29), a history of hypertension (OR 2.61; 95% CI 1.41‐4.72), sedentary time (h/day) (OR 1.16; 95% CI 1.08‐1.24), monobenzyl phthalate (OR 1.95; 95% CI 1.45‐2.67) and Q4 mono‐ethyl phthalate concentration (OR 1.85; 95% CI 1.26‐2.73) were independent predictors. The area under the receiver operating curves for the internal validation of the DLEFM and the DLFM constructed using these seven factors was 0.827 and 0.783, respectively. The calibration curve of the DLEFM was close to the diagonal line. The DLEFM was thus the more optimal model, and the one which we chose. Conclusions: A nomogram based on preconception factors was constructed to predict the occurrence of GD in the second and third trimesters. It provided an effective tool for the early prediction and timely management of GD. [ABSTRACT FROM AUTHOR]

Objective: Our aim was to elucidate the polycyclic aromatic hydrocarbon (PAH) metabolites exposure levels of pregnant women in the underdeveloped region of Zunyi, southwest China.Methods: Sociodemographic information was collected via questionnaires, and urine samples were collected at the same time. A total of 3047 pregnant women participated in the study. Gas chromatography/mass spectrometry was used to detect the urine concentrations of 10 PAH metabolites. A generalised linear model (GLM) was used to identify predictive factors of PAH metabolites.Results: All PAH metabolites had a detection rate greater than 60% (67.21%-90.57%) except for 4-OH-PHE at 55.54%. The median concentrations were 0.02-0.11 µg/g Cre except for 1-OH-NAP, 2-OH-NAP, 2-OH-FLU and 9-OH-FLU (0.36-0.50 µg/g Cre). The cluster analysis identified the phenanthrene and fluorene metabolite clusters (containing no other metabolites), while naphthalene metabolites (1-OH-NAP, 2-OH-NAP) could not be clustered without other metabolites. GLM analysis identified that pregnant women with the following characteristics have high urinary concentration of PAH metabolites: overweight, in the last trimester of pregnancy, distance between their house and main traffic lines as <5 m, use fuel for cooking, passive smoking, renovated their residence for less than 3 years, middle family income and office workers.Conclusion: The results clarified pregnant women from the economically underdeveloped area could be the victims of PAHs. In addition, PAHs present a demographic and seasonal differential distribution, which will aid in the development of targeted interventions and reduce exposure to PAHs during pregnancy. [ABSTRACT FROM AUTHOR]