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Parity and the risk of diabetes mellitus among Chinese women: a cross-sectional evidence from the Tongji-Dongfeng cohort study.

ABSTRACT: Little is known about the long-term health impact of pregnancy on women. The objective of this study was to examine the association between parity and the risk of diabetes among a population of Chinese women.A total of 14,196 women (aged ≥ 45 years) from the Dongfeng-Tongji Cohort study who had experienced at least one live birth completed baseline questionnaires, medical examinations, and provided baseline blood samples. Participants were categorized into four groups according to parity (one, two, three, or four or more live births). Logistic regression models were used to investigate the association between parity and the risk of diabetes after controlling potential confounders.The prevalence of diabetes in the study population was 18.0% (2,552/14,196). Fasting plasma glucose levels increased with the increasing number of live births (P<0.001) and parity had a positive graded association with diabetes without adjustment for any covariates (P for trend <0.001). After adjusting for potential confounders, women who had had two, three, and four or more live births had 1.35 times (95% CI, 1.20-1.52), 1.59 times (95% CI, 1.39-1.82) and 1.44 times (95% CI, 1.21-1.71), respectively, higher risk of diabetes compared with women who had had one live birth.Multiparity was associated with increasing risk of diabetes in this population of Chinese women. These findings suggested that multiparity may be a risk factor for the development of diabetes among Chinese women. Future studies are needed to examine the physiological changes during pregnancy for risk of diabetes in later life.

SUBMITTER: Tian Y

PROVIDER: S-EPMC4126778 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Project description:BackgroundGrowing evidence suggests that dietary acid load plays an important role in the development of type 2 diabetes. However, prospective studies on the relationship between dietary acid load and gestational diabetes mellitus (GDM) are limited in the pregnant population. This study aimed to investigate the effect of dietary acid load during early pregnancy on the risk of GDM in Chinese pregnant women.MethodsA total of 1,327 pregnant women were enrolled from an ongoing prospective study of the Tongji Birth cohort (TJBC) in Wuhan, China. Dietary intake was assessed before 20 weeks using a 74-item semiquantitative food frequency questionnaire (FFQ). The dietary acid load was estimated using potential renal acid load (PRAL), net endogenous acid production (NEAP), and animal protein to potassium ratio (A:P ratio). A 75g 2-h oral glucose tolerance test (OGTT) was performed at 24-28 gestational weeks to diagnose GDM.ResultsThe mean (standard deviation) values for PRAL score, NEAP score, and A:P ratio were 0.8 ± 11.3 mEq/day, 45.3 ± 16.5 mEq/day, and 9.8 ± 6.0, respectively. There was a significant positive correlation of dietary acid load with the intake of red meat, poultry, fish, and eggs, and a negative correlation with the intake of vegetables, fruits, nuts, and legumes (all P < 0.05). Compared to the lowest tertile, the highest tertile of dietary acid load, including PRAL score (odds ratio [OR]: 2.26, 95% confidence interval [CI] = 1.38-3.71, P-trend = 0.002), NEAP score (OR: 2.02, 95% CI = 1.25-3.27, P-trend = 0.009), and A:P ratio (2.08, 95% CI = 1.30-3.31, P-trend = 0.005), significantly increased the risk of GDM. In addition, the dietary acid load was also significantly associated with an increase in 1-h and 2-h post-load blood glucose concentrations (all P-trend < 0.05).ConclusionWe found a significant positive association between dietary acid load during early pregnancy and the risk of GDM in a Chinese population, suggesting that the reduction of food sources of dietary acid load may be an effective strategy for preventing the risk of GDM.

Project description:Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner's inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628-0.648)) and specificity (0.683 (0.669-0.698)) and lowest error rate (0.362 (0.352-0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663-0.670)) and specificity (0.725 (0.721-0.729)) and decreased the error rate (0.333 (0.330-0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.

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Parity and the risk of diabetes mellitus among Chinese women: a cross-sectional evidence from the Tongji-Dongfeng cohort study.

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