Cohort profile: the Chinese Pregnant Women Cohort Study and Offspring Follow-up (CPWCSaOF)

Longitudinal study of multidimensional factors influencing maternal and offspring health outcomes: a study protocol

BACKGROUND

Reducing preventable adverse maternal and offspring outcomes is a global priority. The causes of adverse maternal and fetal outcomes are complex with multidimensional influencing factors. In addition, the Covid-19 epidemic has had a significant psychological and physical impact on people. China is now stepping into the post-epidemic era. We are curious about the psychological and physical situation of maternity in China at this stage. Therefore, we plan to initiate a prospective longitudinal study to investigate the multidimensional influences and mechanisms that affect maternal and offspring health.

METHOD

We will recruit eligible pregnant women at Renmin Hospital of Hubei Province, China. The expected sample size is 1490. We will assess socio-demographics, Covid-19 related information, social capital, sleep, mental health and medical records, including clinical examination and biochemical tests. Eligible pregnant women will be enrolled in the study with less than 14 weeks of gestation. Participants will receive a total of nine follow-up visits between mid-pregnancy and one year postpartum. The offspring will be followed up at birth, 6 weeks, 3 months, 6 months and one year. In addition, a qualitative study will be conducted to understand the underlying causes that affect maternal and offspring health outcomes.

DISCUSSION

This is the first longitudinal study of maternity in Wuhan, Hubei Province which integrates physical, psychological and social capital dimensions. Wuhan is the first city to be affected by Covid-19 in China. As China moves into the post-epidemic era, this study will provide us with a better understanding of the long-term impact of the epidemic on maternal and offspring health outcomes. We will implement a range of rigorous measures to enhance participants' retention rate and ensure the quality of data. The study will provide empirical results for maternal health in the post-epidemic era.

Optimal Gestational Weight Gain for Women with Gestational Diabetes Mellitus — China, 2011–2021

What is already known about this topic?

Joint effects of gestational weight gain (GWG) and hyperglycemia on adverse pregnancy outcomes suggest that lower optimal GWG is optimal for women with gestational diabetes mellitus (GDM). However, there is still a lack of guidelines.

What is added by this report?

Optimal weekly GWG range after diagnosis of GDM for underweight, normal-weight, overweight, and obese women was 0.37-0.56 kg/week, 0.26-0.48 kg/week, 0.19-0.32 kg/week, and 0.12-0.23 kg/week, respectively.

What are the implications for public health practice?

The findings may be used to inform prenatal counseling regarding optimal gestational weight gain for women with gestational diabetes mellitus, and suggest the need for weight gain management.

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.

Longitudinal association of maternal dietary patterns with antenatal depression: Evidence from the Chinese Pregnant Women Cohort Study

BACKGROUND

Limited evidence to show the longitudinal associations between maternal dietary patterns and antenatal depression (AD) from cohort studies across the entire gestation period.

METHODS

Data came from the Chinese Pregnant Women Cohort Study. The qualitative food frequency questionnaire (Q-FFQ) and Edinburgh Postnatal Depression Scale (EPDS) were used to collect diet and depression data. Dietary patterns were derived by using factor analysis. Generalized estimating equation models were used to analyze the association between diet and AD.

RESULTS

A total of 4139 participants finishing 3-wave of follow-up were finally included. Four constant diets were identified, namely plant-based, animal-protein, vitamin-rich and oily-fatty patterns. The prevalence of depression was 23.89%, 21.12% and 22.42% for the first, second and third trimesters. There were reverse associations of plant-based pattern (OR:0.85, 95%CI:0.75-0.97), animal-protein pattern (OR:0.85, 95%CI:0.74-0.99) and vitamin-rich pattern (OR:0.58, 95%CI:0.50-0.67) with AD, while a positive association between oily-fatty pattern and AD (OR:1.47, 95%CI:1.29-1.68). Except for the plant-based pattern, other patterns had linear trend relationships with AD (P_trend < 0.05). Moreover, a 1-SD increase in vitamin-rich pattern scores was associated with a 20% lower AD risk (OR:0.80, 95%CI:0.76-0.84), while a 1-SD increase in oily-fatty pattern scores was associated with a 19% higher risk (OR:1.19, 95%CI:1.13-1.24). Interactions between dietary patterns and lifestyle habits were observed.

LIMITATIONS

The self-reported Q-FFQ and EPDS may cause recall bias.

CONCLUSIONS

There are longitudinal associations between maternal dietary patterns and antenatal depression. Our findings are expected to provide evidence for a dietary therapy strategy to improve or prevent depression during pregnancy.