Comparative epidemiology of gestational diabetes in ethnic Chinese from Shanghai birth cohort and growing up in Singapore towards healthy outcomes cohort

The Association Between Eating Quickly and Excessive Gestational Weight Gain

Objectives

Maintaining an appropriate gestational weight gain (GWG) is essential for a safe pregnancy and delivery. This study aimed to determine the association between the habit of eating quickly and the risk of excessive GWG.

Methods

We administered a questionnaire on eating habits to 1246 pregnant Japanese women in their second to third trimesters. We categorized the participants into three groups according to their answers to the question "Do you eat quickly?" Group 1, "always" or "usually"; Group 2, "sometimes"; and Group 3, "rarely" or "never." We assessed GWG according to the "The Optimal Weight Gain Chart" (Ministry of Health, Labor and Welfare, Japan), and those who exceeded the criteria were considered "excessive." Logistic regression analysis was performed with the risk of excess GWG as the dependent variable and quick food intake as the independent variable, to obtain relevant odds ratios (ORs) and 95% confidence intervals (CIs). Model 1 was unadjusted, and Model 2 was adjusted for age, prepregnancy body mass index, energy intake, mother's educational attainment, household income, exercise habits, and childbearing experience.

Results

The OR (95% CI) for Groups 2 and 3 in Model 1, compared with Group 1, was 0.80 (0.62-1.05) and 0.61 (0.43-0.88), respectively (p for trend = 0.047). In Model 2, the OR (95% CI) for Groups 2 and 3 were 0.73 (0.55-0.96) and 0.59 (0.40-0.86), respectively (p for trend = 0.003).

Conclusion

These results suggest that quick food ingestion increases the risk of excessive GWG.

Association between Maternal Blood Glucose Levels during Pregnancy and Birth Outcomes: A Birth Cohort Study

OBJECTIVE

This study aimed to investigate the relationship between maternal blood glucose levels during pregnancy and neonatal birth outcomes in Northwest China.

METHODS

This population-based cohort study included 10,010 first-trimester pregnant women who joined the birth cohort of the Northwest Women's and Children's Hospital from July 2018 to July 2020. Basic demographic characteristics, lifestyle and behavior patterns were collected. Oral glucose tolerance test (OGTT) results during the second trimester and pregnancy outcomes after childbirth were also collected. A generalized linear model was constructed to analyze the effects of blood glucose levels on neonatal birth outcomes.

RESULTS

We found that every 1 mmol/L increase in fasting plasma glucose (FPG) was associated with an increase in birth weight (β = 100.22 g, 95% confidence interval (95%CI): 81.91, 118.52), birth weight Z score (β = 0.23, 95%CI: 0.19, 0.27) and birth weight Z centile (β = 6.72%, 95%CI: 5.51, 7.94). Moreover, the risk of macrosomia, premature birth and being born large for gestational age (LGA) increased by 2.01 (95%CI: 1.67, 2.43), 1.35 (95%CI: 1.09, 1.66) and 1.80 (95%CI: 1.57, 2.07) times, respectively. Additionally, for every 1 mmol/L increase in FPG associated with a decrease in gestational age (β = -0.12 weeks, 95%CI: -0.19, -0.06), the risk of SGA decreased by 0.70 (OR = 0.70, 95%CI: 0.55, 0.89) times. Every 1 mmol/L increase in 1/2-h PG had similar outcomes as FPG, besides premature birth and SGA.

CONCLUSIONS

Higher blood glucose in pregnant women may increase neonatal birth weight, decrease gestational age and lead to a higher risk of macrosomia, premature birth and LGA. Mothers should actively prevent and control hyperglycemia to promote maternal and infant health.

Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in Chinese Pregnant Women: A Prospective Cohort Study

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.

Early life programming of health and disease: the long-term consequences of obesity in pregnancy: a narrative review

The prevalence of overweight and obesity is rising in all parts of the world and among young women it presents a very clear danger during pregnancy. Women who are overweight or who gain excessive weight during pregnancy are at greater risk of complications in pregnancy and labour, and are more likely to lose their child to stillbirth, or themselves die during pregnancy. This narrative review considers the evidence that in addition to increasing risk of poor pregnancy outcomes, obesity has the capacity to programme fetuses to be at greater risk of cardiometabolic disorders later in life. An extensive body of evidence from prospective and retrospective cohorts, and record linkage studies demonstrates associations of maternal obesity and/or gestational diabetes with cardiovascular disease, type-1 and type-2 diabetes. Studies in animals suggest that these associations are underpinned by adaptations that occur in fetal life, which remodel the structures of major organs including the brain, kidney and pancreas. This article is protected by copyright. All rights reserved.