Dietary patterns in relation to gestational depression and sleep disturbance in Chinese pregnant women

Examination of sleep in relation to dietary and lifestyle behaviors during Ramadan: A multi-national study using structural equation modeling among 24,500 adults amid COVID-19

Background

Of around 2 billion Muslims worldwide, approximately 1.5 billion observe Ramadan fasting (RF) month. Those that observe RF have diverse cultural, ethnic, social, and economic backgrounds and are distributed over a wide geographical area. Sleep is known to be significantly altered during the month of Ramadan, which has a profound impact on human health. Moreover, sleep is closely connected to dietary and lifestyle behaviors.

Methods

This cross-sectional study collected data using a structured, self-administered electronic questionnaire that was translated into 13 languages and disseminated to Muslim populations across 27 countries. The questionnaire assessed dietary and lifestyle factors as independent variables, and three sleep parameters (quality, duration, and disturbance) as dependent variables. We performed structural equation modeling (SEM) to examine how dietary and lifestyle factors affected these sleep parameters.

Results

In total, 24,541 adults were enrolled in this study. SEM analysis revealed that during RF, optimum sleep duration (7-9 h) was significantly associated with sufficient physical activity (PA) and consuming plant-based proteins. In addition, smoking was significantly associated with greater sleep disturbance and lower sleep quality. Participants that consumed vegetables, fruits, dates, and plant-based proteins reported better sleep quality. Infrequent consumption of delivered food and infrequent screen time were also associated with better sleep quality. Conflicting results were found regarding the impact of dining at home versus dining out on the three sleep parameters.

Conclusion

Increasing the intake of fruits, vegetables, and plant-based proteins are important factors that could help improve healthy sleep for those observing RF. In addition, regular PA and avoiding smoking may contribute to improving sleep during RF.

Intake of ultra-processed foods and sleep-related outcomes: A systematic review and meta-analysis

The aim of this review article was to evaluate the association between the intake of ultra-processed foods and sleep-related outcomes through a systematic review and meta-analysis. Pubmed, LILACS, Scielo, Scopus, Embase, and Web of Science were searched on December 31, 2021, for studies that evaluated the association between ultra-processed foods and sleep-related outcomes (self-reported sleep duration and quality). Pooled odds ratios were assessed through a random-effects model; heterogeneity was evaluated using the I² statistic. Fifteen cross-sectional studies were included; 14 showed that the high intake of ultra-processed foods was statistically significantly associated with sleep-related outcomes (sleep duration and quality). In the crude analysis, compared with low intake, high intake of ultra-processed foods increased the odds of sleep-related outcomes, with increased odds among children and/or adolescents, and null results among adults. When adjusted for cofounders, we found statistically significant results for all ages. The high intake of ultra-processed foods was associated with sleep-related outcomes, with moderate credibility of the evidence. Longitudinal studies and clinical trials confirming these findings are necessary.

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.

Factors influencing health-related quality of life in adolescent girls: a path analysis using a multi-mediation model

BACKGROUND

Recent studies have reported gender differences in adolescents' health-related quality of life (HRQOL), with females scoring significantly lower than males. Researchers have identified the female puberty process as one of the causes of the differences in HRQOL between male and female adolescents. This study examines mechanisms of how social support, dietary habits, sleep quality, and depression contribute to predicting HRQOL in relation to menstrual health among adolescent girls.

METHODS

A cross-sectional study was conducted with 295 students recruited from middle and high schools in Korea using a self-report questionnaire. A multi-mediation model was constructed based on previous literature and tested using path analysis with AMOS, version 21.0.

RESULTS

The study results showed that menstrual health, social support, sleep quality, dietary habits, and depression had significant effects on HRQOL. Both sleep quality and depression had significant direct effects on menstrual health. Dietary habits, social support, sleep quality, and depression had significant indirect effects on HRQOL, mediated through menstrual health. According to serial mediation analysis, the path from social support to HRQOL via dietary habits → sleep quality → depression → menstrual health → HRQOL was significant. However, mediation models including the path of dietary habits → depression were not supported. The study variables explained 57% of the total variance for HRQOL.

CONCLUSIONS

The findings suggest menstrual health is an important factor that mediates the effects of eating, sleeping, psychological health, and social support on HRQOL. Early complaints about sleep disorders and depressive symptoms with poor dietary habits could be an ominous sign for adolescent girls at high risk of menstrual problems and lower HRQOL. Empirical evidence from this study suggests the need to develop and test interventions addressing multiple modifiable behavioral and psychosocial factors to improve HRQOL in adolescent girls. Interventions or supportive systems that aim to improve eating habits and sleep quality thereby achieving a healthier lifestyle need to be developed and incorporated into school health services.

Associated factors of depression in pregnant women in Korea based on the 2019 Korean Community Health Survey: a cross-sectional study

Purpose

Various individual and social factors influence depression in pregnant women. The purpose of this study was to identify the influence of socioeconomic status, health behaviors, and health status on depression of pregnant women in Korea.

Methods

This study analyzed data from the 2019 Korean Community Health Survey conducted from August to October 2019. A structural questionnaire with Patient Health Quetsionnaire-9 (PHQ-9), health behavior, health status, and psychological characteristics was used. The data of 1,096 pregnant women between the ages of 19 and 55 years were analyzed using descriptive statistics, independent-test and chi-square tests, and multiple regression.

Results

The mean score of prenatal depression as measured by the PHQ-9 during pregnancy was 2.35 points out of 0 to 27 points. Low income (B=0.69, p<.001), low-education level (B=0.70, p<.001), skipping breakfast (B=0.34, p=.001), less than 8 hours of sleeping (B=0.26, p=.009), binge drinking during pregnancy (B=0.46, p=.001), and stress (B=1.89, p<.001) were significantly associated with increased depression scores. In contrast, depression scores significantly decreased as subjective health status (B=–0.59, p<.001) and subjective oral health status (B=–.17, p=.003) increased.

Conclusion

Findings support the need for healthcare policies and clinical screening to alleviate prenatal depression, especially for pregnant women with low socioeconomic status, poor health behavior, poor health status, and high stress.

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

PURPOSE

A multicentre prospective cohort study, known as the Chinese Pregnant Women Cohort Study (CPWCS), was established in 2017 to collect exposure data during pregnancy (except environmental exposure) and analyse the relationship between lifestyle during pregnancy and obstetric outcomes. Data about mothers and their children's life and health as well as children's laboratory testing will be collected during the offspring follow-up of CPWCS, which will enable us to further investigate the longitudinal relationship between exposure in different periods (during pregnancy and childhood) and children's development.

PARTICIPANTS

9193 pregnant women in 24 hospitals in China who were in their first trimester (5-13 weeks gestational age) from 25 July 2017 to 26 November 2018 were included in CPWCS by convenience sampling. Five hospitals in China which participated in CPWCS with good cooperation will be selected as the sample source for the Chinese Pregnant Women Cohort Study (Offspring Follow-up) (CPWCS-OF).

FINDINGS TO DATE

Some factors affecting pregnancy outcomes and health problems during pregnancy have been discovered through data analysis. The details are discussed in the 'Findings to date' section.

FUTURE PLANS

Infants and children and their mothers who meet the criteria will be enrolled in the study and will be followed up every 2 years. The longitudinal relationship between exposure (questionnaire data, physical examination and biospecimens, medical records, and objective environmental data collected through geographical information system and remote sensing technology) in different periods (during pregnancy and childhood) and children's health (such as sleeping problem, oral health, bowel health and allergy-related health problems) will be analysed.

TRAIL REGISTRATION NUMBER

CPWCS was registered with ClinicalTrials.gov on 18 January 2018: NCT03403543. CPWCS-OF was registered with ClinicalTrials.gov on 24 June 2020: NCT04444791.