Variations in the Prevalence of Anemia of Varying Severity Among Urban Non-Pregnant Women - China, 2021.

What is already known about this topic?: Anemia is a significant public health issue affecting women globally. Prior studies in China predominantly concentrated on anemia in pregnant or reproductive-age women, leaving a gap in available data concerning anemia in non-pregnant women of all age groups in China. What is added by this report?: In 2021, the prevalence of anemia and moderate to severe anemia among women aged 18 years and older in urban China was 14.8% and 5.7%, respectively. Anemia prevalence exhibited significant variations based on factors such as age, body mass index (BMI), geographic location, and socioeconomic status. What are the implications for public health practice?: The strategy for addressing anemia should account for non-pregnant women aged 30-49 years and those aged 70 years and older, taking into consideration differences related to socioeconomic development and geography.

An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study.

BACKGROUND: Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors. METHODS: The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020. RESULTS: The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy. CONCLUSIONS: We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Development and Comparative Analysis of an Early Prediction Model for Acute Kidney Injury within 72-Hours Post-ICU Admission Using Evidence from the MIMIC-III Database.

BACKGROUND: Prompt recognition of patients predisposed to acute kidney injury (AKI) within 72 hours of intensive care unit (ICU) admission holds significant clinical importance as it can considerably lower mortality rates. However, existing AKI prediction models often require complex data collection yet yield only moderate performance. This study aims to develop a straightforward and efficient AKI prediction model, providing ICU physicians with a powerful tool to expedite the detection of AKI patients. METHODS: This study proposed a novel generative adversarial imputation networks-least absolute shrinkage and selection operator-extreme gradient boosting (Gain-Lasso-XGBoost) framework and developed an AKI prediction model on the basis of the medical information mart for intensive care (MIMIC-III) database. All the steps, including data preprocessing, feature selection, development, and optimization of prediction models, are organically integrated into the framework which has strong scalability. To compare the performance of our model with current models, we conducted a systematic review to collect all studies on the basis of the MIMIC-III database with similar objectives. RESULTS: From 15 demographic and clinical variables, 8 features and 5 features were identified as the optimal group of features and processed into the model development. The model optimization further improved the performance of our proposed framework, and the area under curve (AUC) results with 8 and 5 feature vectors achieved 0.849 and 0.830, respectively. Compared with other studies, our method extracted only 8 or 5 feature vectors and obtained superior performance, with an average AUC 1.9% higher than the state-of-the-art approaches in the same type. CONCLUSIONS: Our study suggested that the onset of AKI be effectively and quickly predicted using simplified features, and not just for more specific patient groups. It may help clinicians accurately identify patients at risk of AKI after ICU admission and provide timely monitoring and treatment.

The Association of Pregnancy with Disease Progression in Patients Previously Treated for Differentiated Thyroid Cancer: A Propensity Score-Matched Retrospective Cohort Study.

Background: Differentiated thyroid cancer (DTC) is increasingly common in women of reproductive age. However, whether pregnancy increases the risk of DTC progression/recurrence after treatment remains controversial. The study aimed to assess the association of pregnancy with risk of progression in patients previously treated for DTC. Methods: This was a retrospective cohort study following 123 pregnant women and 1376 nonpregnant women at Peking University Third Hospital after initial treatment for DTC between January 2012 and December 2022. To control the effect of confounding, we carefully matched pregnancy (n = 107) and nonpregnancy groups (n = 298) in terms of baseline characteristics by using propensity score matching (PSM). Results: At baseline, the pregnancy and nonpregnancy groups were balanced in all matched variables. At follow-up, the percentage of DTC progression in the two groups was 12 (11.8%) and 47 (15.8%), respectively. Regression models showed no evidence of association of pregnancy with the risk of progression (odds ratio: 0.74 and 95% confidence interval: 0.37-1.50; p = 0.404), and remained consistent across long/short follow-up and other subgroup variables. We found that the shorter the time interval between treatment and pregnancy, the higher the risk of DTC progression (ptrend = 0.019). Conclusions: The risk of DTC progression in pregnant women was not higher than that in the well-matched, nonpregnant women. For young women previously treated for DTC, disease progression might not be a concern for their future pregnancy plan, but it seems safer to wait at least 1 year before pregnancy compared with immediate pregnancy.

Prevalence of Liver Steatosis and Fibrosis in the General Population and Various High-Risk Populations: A Nationwide Study With 5.7 Million Adults in China.

BACKGROUND & AIMS: This study aimed to estimate the prevalence of liver steatosis and fibrosis in the general population and populations with potential risk factors in China, so as to inform policies for the screening and management of fatty liver disease and liver fibrosis in general and high-risk populations. METHODS: This cross-sectional, population-based, nationwide study was based on the database of the largest health check-up chain in China. Adults from 30 provinces who underwent a check-up between 2017 and 2022 were included. Steatosis and fibrosis were assessed and graded by transient elastography. Overall and stratified prevalence was estimated among the general population and various subpopulations with demographic, cardiovascular, and chronic liver disease risk factors. A mixed effect regression model was used to examine predictors independently associated with steatosis and fibrosis. RESULTS: In 5,757,335 participants, the prevalence of steatosis, severe steatosis, advanced fibrosis, and cirrhosis was 44.39%, 10.57%, 2.85%, and 0.87%, respectively. Participants who were male, with obesity, diabetes, hypertension, dyslipidemia, metabolic syndrome, or elevated alanine aminotransferase or aspartate aminotransferase had a significantly higher prevalence of all grades of steatosis and fibrosis, and those with fatty liver, decreased albumin or platelet count, and hepatitis B virus infection also had a significantly higher prevalence of fibrosis than their healthy counterparts. Most cardiovascular and chronic liver disease risk factors were independent predictors for steatosis and fibrosis, except for dyslipidemia for fibrosis. CONCLUSIONS: A substantial burden of liver steatosis and fibrosis was found in China. Our study provides evidence for shaping future pathways for screening and risk stratification of liver steatosis and fibrosis in the general population. The findings of this study highlight that fatty liver and liver fibrosis should be included in disease management programs as targets for screening and regular monitoring in high-risk populations, especially in those with diabetes.

Cost-effectiveness of artificial intelligence-assisted liquid-based cytology testing for cervical cancer screening in China.

Background: The 2021 World Health Organization (WHO) guidelines for cervical cancer screening recommend human papillomavirus (HPV) DNA or mRNA testing. Artificial intelligence (AI)-assisted liquid-based cytology (LBC) systems also have the potential to facilitate rapid scale-up of cervical cancer screening. We aimed to evaluate the cost-effectiveness of AI-assisted LBC testing, compared with the manual LBC and HPV-DNA testing, for primary cervical cancer screening in China. Methods: We developed a Markov model for a cohort of 100,000 women aged 30 years over a lifetime to simulate the natural history of cervical cancer progression. We evaluated the incremental cost-effectiveness ratios (ICER) of 18 screening strategies (a combination of the three screening methods with six screening frequencies) from a healthcare provider's perspective. The willingness-to-pay threshold (US$30,828) was chosen as three times the Chinese per-capita gross domestic product in 2019. Univariate and probabilistic sensitivity analyses were performed to examine the robustness of the results. Findings: Compared with no screening, all 18 screening strategies were cost-effective, with an ICER of $622-24,482 per quality-adjusted life-year (QALY) gained. If HPV testing after scaling up to population level screening costs $10.80 or more, screening once every 5 years using AI-assisted LBC would be the most cost-effective strategy with an ICER of $8790/QALY gained compared with the lower-cost non-dominated strategy on the cost-effectiveness frontier. Its probability of being cost-effective was higher (55.4%) than other strategies. Sensitivity analyses showed that the most cost-effective strategy would become AI-assisted LBC testing once every 3 years if the sensitivity (74.1%) and specificity (95.6%) of this method were both reduced by ≥10%. The most cost-effective strategy would become HPV-DNA testing once every 5 years if the cost of AI-assisted LBC was more expensive than manual LBC or if the HPV-DNA test cost is slightly reduced (from $10.8 to <$9.4). Interpretation: AI-assisted LBC screening once every 5 years could be more cost-effective than manually-read LBC. Using AI-assisted LBC could have comparable cost-effectiveness to HPV DNA screening, but the relative pricing of HPV DNA testing is critical in this result. Funding: National Natural Science Foundation of China, National Key R&D Program of China.

Effect of levothyroxine treatment on fetal growth among women with mild subclinical hypothyroidism and thyroid peroxidase antibody negative: a cohort study.

BACKGROUND: Some clinicians used levothyroxine (LT4) treatment for mild subclinical hypothyroidism (SCH) pregnant women (2.5 < thyroid-stimulating hormone (TSH) ≤ the pregnancy-specific reference range with normal free thyroxine (FT4) level) with thyroid peroxidase antibody negative (TPOAb-), although the recent clinical guideline did not recommend it. It is unknown whether LT4 treatment for pregnant women with mild SCH and TPOAb- have impact on fetal growth. Therefore, the aim of the study was to investigate the effect of LT4 treatment on fetal growth and birth weight among mild SCH pregnant women with TPOAb-. METHODS: This was a birth cohort study including 14,609 pregnant women between 2016 and 2019 in Tongzhou Maternal and Child Health Hospital of Beijing, China. Pregnant women were divided into 3 groups as follows: Euthyroid (n = 14,285, 0.03 ≤ TSH ≤ 2.5mIU/L, normal FT4), TPOAb-; Untreated mild SCH with TPOAb- (n = 248, 2.5 < TSH ≤ 2.9mIU/L, normal FT4, without LT4 treatment); Treated mild SCH with TPOAb- (n = 76, 2.5 < TSH ≤ 2.9mIU/L, normal FT4, with LT4 treatment). The main outcome measures were Z-scores of fetal growth indicators (abdominal circumference (AC), biparietal diameter (BPD), femur length (FL), head circumference (HC), estimated fetal weight (EFW)), fetal growth restriction (FGR) and birth weight. RESULTS: There was no difference in fetal growth indicators and birth weight between the untreated mild SCH women with TPOAb- and the euthyroid pregnant women. But the HC Z-score was lower in the LT4 treated mild SCH women with TPOAb-, compared with the euthyroid pregnant women (ß = -0.223, 95%CI: -0.422, -0.023). The LT4 treated mild SCH women with TPOAb- had lower fetal HC Z-score (ß = -0.236, 95%CI: -0.457, -0.015), compared with the untreated mild SCH women with TPOAb-. CONCLUSIONS: We observed that LT4 treatment for mild SCH with TPOAb- was associated with decreased fetal HC, which was not observed for untreated mild SCH women with TPOAb-. The adverse effect of LT4 treatment for mild SCH with TPOAb- provided new evidence for the recent clinical guideline.

Depressive Symptoms and Sleep Duration as Risk Factors for Breast Cancer - China, 2020.

What is already known about this topic?: Psychological and lifestyle factors are known to potentially play a significant role in the development of breast cancer. However, current evidence-based studies present controversial findings on the associations between depression, sleep duration, and breast cancer risk. What is added by this report?: This study investigated the potential risk factors of depressive symptoms and short sleep duration for breast cancer within the Breast Cancer Cohort Study in Chinese Women. The findings revealed that women experiencing depressive symptoms and short sleep duration exhibited a heightened risk of developing breast cancer, particularly among the older population. What are the implications for public health practice?: Public policy ought to prioritize early health education interventions targeting psychological factors in order to facilitate the prevention of breast cancer.

Breast Cancer Awareness and Association with Frequency of Screening Among Women - China, 2020.

What is already known about this topic?: Breast cancer awareness plays a crucial role in promoting screening attendance, enabling early detection, and improving survival rates associated with breast cancer. Nevertheless, a persistent issue is the low public awareness of breast cancer warning signs and risk factors. What is added by this report?: Breast cancer awareness rate was 10.2%, with particularly low rates among never-screened and inadequately screened women. Factors associated with low awareness levels included low income, agricultural occupation, limited educational attainment, smoking, and the absence of professional recommendations. What are the implications for public health practice?: Consideration should be given to effective health education and delivery strategies aimed at women who have never been screened or have received inadequate screening.

Breast Cancer Screening Coverage - China, 2018-2019.

What is already known about this topic?: In 2015, only 18.9% of adult women underwent breast cancer screening in China. What is added by this report?: Breast cancer screening coverage for women aged 20 years and above in China reached 22.3% during 2018-2019. Women with lower socioeconomic status had lower screening coverage. There were significant variations across the provincial-level administrative divisions. What are the implications for public health practice?: The promotion of breast cancer screening requires the maintenance of national and local policies, as well as financial support for screening services. In addition, there is a need for the strengthening of health education and the improvement of accessibility to health services.

Individual and joint effect of indoor air pollution index and ambient particulate matter on fetal growth: a prospective cohort study.

BACKGROUND: Limited studies have examined the effect of prenatal exposure to particulate matter with diameter of <2.5 µm (PM2.5) and <1 µm (PM1) on fetal growth using ultrasound measurements with inconsistent results. No study has evaluated the joint effect of the indoor air pollution index and ambient particulate matter on fetal growth. METHODS: We conducted a prospective birth cohort study in Beijing, China in 2018, including 4319 pregnant women. We estimated prenatal PM2.5 and PM1 exposure using a machine-learning method and calculated the indoor air pollution index based on individual interviews. Gender- and gestational age-adjusted Z-score of the abdominal circumference (AC), head circumference (HC), femur length (FL) and estimated fetal weight (EFW) was calculated and then undergrowth was defined. A generalized estimating equation was used to evaluate the individual and joint effect of indoor air pollution index, PM2.5 and PM1 on fetal Z-score and undergrowth parameters. RESULTS: One unit increase in the indoor air pollution index was associated with -0.044 (95% CI: -0.087, -0.001) and -0.050 (95% CI: -0.094, -0.006) decrease in the AC and HC Z-scores, respectively. PM1 and PM2.5 were associated with decreased AC, HC, FL and EFW Z-scores, and higher risk of undergrowth. Compared with exposure to lower PM1 (≤ median) and no indoor air pollution, those exposed to higher PM1 (> median) and indoor air pollution had decreased EFW Z-scores (ß = -0.152, 95% CI: -0.230, -0.073) and higher risk of EFW undergrowth (RR = 1.651, 95% CI: 1.106, 2.464). Indoor air pollution and ambient PM2.5 exposure had a similar joint effect on the Z-scores and undergrowth parameters of fetal growth. CONCLUSIONS: This study suggested that indoor air pollution and ambient PM exposure had individual and joint negative effects on fetal growth.

Risk of De Novo Hypertensive Disorders of Pregnancy After Exposure to PM1 and PM2.5 During the Period From Preconception to Delivery: Birth Cohort Study.

BACKGROUND: Particulate matter (PM) is detrimental to the respiratory and circulatory systems. However, no study has evaluated the lag effects of weekly exposure to fine PM during the period from preconception to delivery on the risk of hypertensive disorders of pregnancy (HDPs). OBJECTIVE: We set out to investigate the lag effect windows of PM on the risk of HDPs on a weekly scale. METHODS: Data from women with de novo HDPs and normotensive pregnant women who were part of the Peking University Retrospective Birth Cohort, based on the hospital information system of Tongzhou district, were obtained for this study. Meteorological data and data on exposure to fine PM were predicted by satellite remote sensing data based on maternal residential address. The de novo HDP group consisted of pregnant women who were diagnosed with gestational hypertension or preeclampsia. Fine PM was defined as PM2.5 and PM1. The gestational stage of participants was from preconception (starting 12 weeks before gestation) to delivery (before the 42nd gestational week). A distributed-lag nonlinear model (DLNM) was nested in a Cox regression model to evaluate the lag effects of weekly PM exposure on de novo HDP hazard by controlling the nonlinear relationship of exposure-reaction. Stratified analyses by employment status (employed or unemployed), education level (higher or lower), and parity (primiparity or multiparity) were performed. RESULTS: A total of 22,570 pregnant women (mean age 29.1 years) for whom data were available between 2013 and 2017 were included in this study. The prevalence of de novo HDPs was 6.7% (1520/22,570). Our findings showed that PM1 and PM2.5 were significantly associated with an elevated hazard of HDPs. Exposure to PM1 during the 5th week before gestation to the 6th gestational week increased the hazard of HDPs. A significant lag effect of PM2.5 was observed from the 1st week before gestation to the 6th gestational week. The strongest lag effects of PM1 and PM2.5 on de novo HDPs were observed at week 2 and week 6 (hazard ratio [HR] 1.024, 95% CI 1.007-1.042; HR 1.007, 95% CI 1.000-1.015, respectively, per 10 µg/m3 increase). The stratified analyses indicated that pregnant women who were employed, had low education, and were primiparous were more vulnerable to PM exposure for de novo HDPs. CONCLUSIONS: Exposure to PM1 and PM2.5 was associated with the risk of de novo HDPs. There were significant lag windows between the preconception period and the first trimester. Women who were employed, had low education, and were primiparous were more vulnerable to the effects of PM exposure; more attention should be paid to these groups for early prevention of de novo HDPs.

HPV-negative high-grade cervical precancerous lesions or invasive cancer in China: A post hoc analysis of a multicentric clinical study.

OBJECTIVE: To evaluate HPV-negative cervical high-grade precancerous lesions or cancer in China. METHODS: Histologically confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+) were searched in a multicentric clinical study. All patients underwent cobas HPV testing, liquid-based cytology, DNA ploidy analysis, and colposcopy-guided biopsy. According to clinical practice, patients underwent p16ink4a staining and cone biopsy. Comparisons were made between HPV-negative and -positive patients for clinical characteristics. RESULTS: The study found 61 cases of cobas HPV-negative CIN2+ among 797 cases of histologically confirmed CIN2+, including 38 CIN2, 20 CIN3, and 3 invasive cancers. The prevalence of HPV-negative CIN2+ and CIN3+ was 7.7% (95% confidence interval [CI] 5.8-9.5) and 5.7% (95% CI 3.5-8.0), respectively. Among 24 cases with p16 staining, 20 showed p16 positivity. The proportions of normal or minor abnormalities in terms of colposcopy, cytology, and DNA ploidy were higher in HPV-negative cases than in HPV-positive cases. When adding cytology to the screening of symptomatic or previously HPV-positive women, the prevalence of HPV-negative CIN2+ or CIN3 would decrease by approximately 50%. CONCLUSION: Less than one-tenth of CIN2+ are missed by HPV-only screening, and they have smaller lesions than HPV-positive cases. Colposcopy should be considered for symptomatic or previously HPV-positive women with HPV-negative results.

Effects of fine ambient particulate matters on de novo hypertensive disorders of pregnancy and blood pressure before 20 weeks.

The effects of fine particulate matter (PM) on de novo hypertensive disorders of pregnancy (HDP) were inconsistent during the first and second trimesters. This study aimed to assess the trimester-specific effects of PM2.5 and PM1 prior to diagnosis of de novo HDP. The exposure of fine PM was predicted by satellite remote sensing data according to maternal residential addresses. De novo HDP was defined as gestational hypertension and preeclampsia during the current pregnancy. A logistic regression model was performed to assess the association of PM2.5 and PM1 with HDP during the first and early second trimesters (0-13 weeks and 14-20 weeks). The generalized estimating equation model was conducted to assess the effect of PM2.5 and PM1 on blood pressure. The present study included 22,821 pregnant women (mean age, 29.1 years) from 2013 to 2017. PM2.5 and PM1 were significantly associated with an increased risk of de novo HDP during the first trimester (OR = 1.070, 95% CI: 1.013-1.130; OR = 1.264, 95% CI: 1.058-1.511 for per 10 µg/m3) and early second trimester (OR = 1.045, 95% CI: 1.003-1.088; OR = 1.170, 95% CI: 1.002-1.366 for per 10 µg/m3). Significant trends of increased de novo HDP risk was also observed with the increment of PM (all P for trend <0.05). The stratified analyses demonstrated that the associations between exposure to fine PM and the risk of HDP were more pronounced among the pregnant women with maternal age above 35 and low maternal education level (all OR >1.047). Each 10 µg/m3 increase of PM1 and PM2.5 before diagnosis of de novo HDP elevated 0.204 (95% CI: 0.098-0.310) and 0.058 (95%CI: 0.033-0.083) mmHg of systolic blood pressure. Exposure to PM2.5 and PM1 during the first and early second trimester were positively associated with the risk of de novo HDP. The fine PM before diagnosis of de novo HDP elevated the systolic blood pressure.

Prediction of Adverse Outcomes in De Novo Hypertensive Disorders of Pregnancy: Development and Validation of Maternal and Neonatal Prognostic Models.

Effectively identifying high-risk patients with de novo hypertensive disorder of pregnancy (HDP) is required to enable timely intervention and to reduce adverse maternal and perinatal outcomes. Electronic medical record of pregnant women with de novo HDP were extracted from a birth cohort in Beijing, China. The adverse outcomes included maternal and fetal morbidities, mortality, or any other adverse complications. A multitude of machine learning statistical methods were employed to develop two prediction models, one for maternal complications and the other for perinatal deteriorations. The maternal model using the random forest algorithm produced an AUC of 0.984 (95% CI (0.978, 0.991)). The strongest predictors variables selected by the model were platelet count, fetal head/abdominal circumference ratio, and gestational age at the diagnosis of de novo HDP; The perinatal model using the boosted tree algorithm yielded an AUC of 0.925 (95% CI (0.907, 0.945]). The strongest predictor variables chosen were gestational age at the diagnosis of de novo HDP, fetal femur length, and fetal head/abdominal circumference ratio. These prediction models can help identify de novo HDP patients at increased risk of complications who might need intense maternal or perinatal care.

Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study.

Background: Predicting birth weight and identifying its risk factors are clinically important. This study aims to use interpretable machine learning to predict birth weight and identity important predictors. Methods: This prospective cohort study was conducted in Tongzhou Maternal and Child Health Care Hospital of Beijing, China, recruiting pregnant women between June 2018 and February 2019. We used 24 features to predict infant birth weight, including gestational age, mother's age, parity, history of macrosomia delivery, pre-pregnancy body mass index (BMI), height, father's BMI, lifestyle (diet, physical activity, smoking), and biomarker (fasting glucose and lipids) features. Study outcome was birth weight of infant. We used 8 supervised learning models including 4 individual [linear regression, ridge regression, lasso regression, support vector machines regression (SVR)], and 4 ensemble estimators (random forest, AdaBoost, gradient boosted trees, and voting ensemble for regression) to predict birth weight. Model accuracy was measured by root mean squared error (RMSE) of 10-fold cross validation on the training set and RMSE of prediction on the test set. We used permutation importance algorithm to understand the prediction from the models and what affected them. Result: This study included 4,754 mother-child dyads. RMSEs were lower in voting ensemble for regression, linear regression, and SVR than random forest, AdaBoost, and gradient boosted tree. The 5 most important predictors for infant birth weight were gestational age, fetal sex, preterm birth, mother's height, and pre-pregnancy BMI. After adding ultrasound-measured indicators of fetal growth into predictors, mother's height and pre-pregnancy BMI remained the most important predictors in predicting the outcome. Conclusion: Mother's height and pre-pregnancy BMI were identified as important predictors for infant birth weight. Interpretable machine learning is a promising tool in the prediction of birth weight.

Associations between Dynamic Vitamin D Level and Thyroid Function during Pregnancy.

Optimal Vitamin D (VitD) status and thyroid function are essential for pregnant women. This study aimed to explore associations between dynamic VitD status and thyroid function parameters in each trimester and throughout the pregnancy period. Information on all 8828 eligible participants was extracted from the Peking University Retrospective Birth Cohort in Tongzhou. Dynamic VitD status was represented as a combination of deficiency/sufficiency in the first and second trimesters. Thyroid function was assessed in three trimesters. The associations between VitD and thyroid function were assessed by multiple linear regression and generalized estimating equation models in each trimester and throughout the pregnancy period, respectively. The results indicated that both free thyroxine (fT4; ß = 0.004; 95%CI: 0.003, 0.006; p < 0.001) and free triiodothyronine (fT3; ß = 0.009; 95%CI: 0.004, 0.015; p = 0.001) had positive associations with VitD status in the first trimester. A VitD status that was sufficient in the first trimester and deficient in the second trimester had a lower TSH (ß = −0.370; 95%CI: −0.710, −0.031; p = 0.033) compared with the group with sufficient VitD for both first and second trimesters. In conclusion, the associations between VitD and thyroid parameters existed throughout the pregnancy. Maintaining an adequate concentration of VitD is critical to support optimal thyroid function during pregnancy.

The First-Trimester Gestational Weight Gain Associated With de novo Hypertensive Disorders During Pregnancy: Mediated by Mean Arterial Pressure.

The relationship between first-trimester GWG ( T1GWG) and risk of hypertensive disorders of pregnancy (HDP) remained uncertain. This study aimed to investigate the association between T1GWG and risk of de novo HDP. Meanwhile, we explored the mediated effect and constructed an early GWG category to evaluate the predictive capacity for HDP. T1GWG was defined as the weight difference between 13 ± 1 gestational weeks and pre-conception. HDP group was defined as having diagnosis of de novo HDP, including gestational hypertension or de novo pre-eclampsia (PE) during the current pregnancy. Early GWG category was constructed according to the risk of HDP within each pre-pregnancy body mass index (BMI) group. Cox regression model was utilized to check the association between the T1GWG and HDP. Serial mediation model was adopted to evaluate the potential mediators including mean arterial pressure (MAP) at 13th and 20th week. The logistic regression model with bootstrap was performed to assess the predictive capacity of Early GWG category and MAP for the risk of HDP. A total of 17,901 pregnant women (mean age, 29.0 years) were recruited from 2013 to 2017 at the Tongzhou Maternal and Child Health Hospital in Beijing, China. Compared to women in Class 1 of early GWG category, women in the Class 2, 3, 4 have increased risks of HDP by 1.42, 4.27, and 4.62 times, respectively (hazard ratio [HR] = 2.42, 95% CI: 2.11-2.77; HR = 5.27, 95% CI: 4.05-6.86; HR = 5.62, 95% CI: 4.05-7.79). The MAP measured at 13th and 20th week totally mediated 33.1 and 26.7% of association between T1GWG GWG and HDP in total participants and overweight/obesity pregnancies, respectively. The area under receiver operator characteristic curve for predictive model utilizing early GWG category and MAP measured at 13th and 20th week for the risk of HDP is 0.760 (95% CI: 0.739-0.777). The T1GWG was associated with de novo HDP, which was partially mediated by MAP measured at 13th and 20th week. Early GWG category showed a better predictive capacity for the risk of HDP compared to the National Academy of Medicine criteria for T1GWG.

The Influence of Maternal Vitamin E Concentrations in Different Trimesters on Gestational Diabetes and Large-for-Gestational-Age: A Retrospective Study in China.

Vitamin E can protect pregnant women from oxidative stress and further affect pregnancy outcomes. This study aimed to investigate maternal vitamin E concentration in each trimester and its associations with gestational diabetes (GDM) and large-for-gestational-age (LGA). The data were derived from Peking University Retrospective Birth Cohort in Tongzhou, collected from 2015 to 2018 (n = 19,647). Maternal serum vitamin E were measured from blood samples collected in each trimester. Logistic regressions were performed to analyze the association between maternal vitamin E levels and outcomes. The median levels of maternal vitamin E increased from the first (10.00 mg/L) to the third (16.00 mg/L) trimester. Among mothers who had inadequate vitamin E levels, most of them had excessive amounts. Excessive vitamin E level in the second trimester was a risk factor for GDM (aOR = 1.640, 95% CI: 1.316-2.044) and LGA (aOR = 1.334, 95% CI: 1.022-1.742). Maternal vitamin E concentrations in the first and second trimesters were positively associated with GDM (first: aOR = 1.056, 95% CI: 1.038-1.073; second: aOR = 1.062, 95% CI: 1.043-1.082) and LGA (first: aOR = 1.030, 95% CI: 1.009-1.051; second: aOR = 1.040, 95% CI: 1.017-1.064). Avoiding an excess of vitamin E during pregnancy might be an effective measure to reduce GDM and LGA. Studies to explore the potential mechanisms are warranted.