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Aims: To examine the independent and interactive effects of maternal gestational diabetes mellitus (GDM) and high pre‐pregnancy body mass index (BMI) on the risk of offspring adverse growth patterns. Materials and Methods: One thousand six hundred and eighty one mother‐child pairs were followed for 8 years in Tianjin, China. Group‐based trajectory modelling was used to identify offspring growth patterns. Logistic regression was performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs) of GDM and high pre‐pregnancy BMI for offspring adverse growth patterns. Restricted cubic spline was used to identify cut‐off points. Additive interactions and multiplicative interactions were used to test interactive effects between GDM and high pre‐pregnancy BMI for adverse growth patterns. Results: Four distinct growth patterns were identified in offspring, including normal growth pattern, persistent lean growth pattern, late obesity growth pattern (LOGP), and persistent obesity growth pattern (POGP). Maternal high pre‐pregnancy BMI was associated with LOGP and POGP (adjusted OR, 95% CI: 2.38, 1.74–3.25 & 4.92, 2.26–10.73). GDM greatly enhanced the adjusted OR of high pre‐pregnancy BMI for LOGP up to 3.48 (95% CI: 2.25–5.38). Additive interactions and multiplicative interactions between both risk factors were significant for LOGP but not for POGP. Conclusions: Maternal high pre‐pregnancy BMI was associated with increased risk of LOGP and POGP, whereas GDM greatly enhanced the risk of high pre‐pregnancy BMI for LOGP. [ABSTRACT FROM AUTHOR]

To evaluate the independent association of seasonal variation with GDM incidence in Tianjin, China, and to test whether there is an additive interaction between seasonal variation and pre-pregnancy body mass index (BMI) on GDM incidence. A population-based observational cohort study was conducted using the healthcare records data from Tianjin, China. Logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction between pre-pregnancy BMI groups and seasons was estimated by using relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (S). Among the 112,639 pregnant women, 20.8% developed GDM at 24–28 weeks of gestation. The multivariable adjusted ORs and 95% CIs were 1.00, 1.00 (0.96–1.05), 1.15 (1.09–1.20) and 1.22 (1.16–1.29) respectively based on seasons (spring, summer, autumn and winter). Compared with the spring/summer and pre-pregnant BMI < 24 kg/m2 group, co-presence of autumn/winter and pre-pregnancy BMI ≥ 24 kg/m2 increased the OR from 1.00 to 2.70 (95% CI 2.28–3.20), with a significant additive interaction: RERI (0.32, 95% CI 0.19–0.45), S (1.21, 95% CI 1.12–1.31) and AP (0.11, 95% CI 0.07–0.16). Autumn/winter is an independent risk factor for GDM incidence, and can significantly amplify the obesity-associated risk for GDM incidence. The underlying mechanism warrants further investigations. We suggest that seasonality is an additional factor when interpreting OGTT results for the diagnosis of GDM. [ABSTRACT FROM AUTHOR]

Objective: To identify the optimal weight gain at the end of the second trimester. Design: This was a population-based cohort study from the antenatal care system in Tianjin, China. We calculated gestational weight gain (GWG) based on the weight measured in the first trimester and the end of the second trimester. Restricted cubic spline analysis was performed to model the possible non-linear relationships between GWG and adverse outcomes. The optimal GWG was defined as the value of the lowest risk. Non-inferiority margins and the shape of the spline curves identified the recommended ranges in Chinese-specific BMI categories. Setting: Tianjin Maternal and Child Health Cohort. Participants: Singleton pregnant women aged 18–45 years. Results: In total, 69 859 pregnant women were included. Adverse outcome (including stillbirth, preterm birth, hypertensive disorders of pregnancy, gestational diabetes mellitus, small and large for gestational age) was significantly associated with GWG at the end of the second trimester. The risk score was non-linearly correlated with GWG in the underweight, normal weight and overweight groups. GWG at the end of the second trimester should not be < 7 kg in underweight group. For most normal-weight women, a GWG of about 8 kg is optimal. Pregnant women who are overweight should not have a GWG of more than 9 kg. We advised women with overweight and obesity to keep positive growth of GWG (> 0 kg) in the first and second trimesters. Conclusions: According to the comprehensive adverse maternal and infant outcomes, we recommend the optimal GWG at the end of the second trimester. This study may provide a considerable reference for weight management. [ABSTRACT FROM AUTHOR]

aim to investigate the influence of weight gain during pregnancy on the risk of offspring adiposity before five years old. Methods: We retrospectively collected health information from the Tianjin mother-child cohort. Offspring outcome was BMI Z-score and prevalence of childhood adiposity from 0.5– 5 years old. Gestational weight gain was analyzed using continuous and categorical variables evaluated by the IOM guidelines. Multivariate analysis adjusted maternal age, prepregnancy BMI, maternal height, smoking, cesarean section, gestational age at birth, birth weight, birth length, and mode of infant feeding during 0– 6 months. Results: Gestational weight gain contributed to offspring's BMI Z-score from 1– 5 years old, and the effect was most obvious in the first half of pregnancy (multivariate analysis, at 1, 2, 2.5, 3, 4, and 5 years of age: β 0.011, 95% CI 0.008– 0.014; β 0.017, 95% CI 0.015– 0.020; β 0.005, 95%CI 0.002-0.008; β 0.018, 95% CI 0.015– 0.021; β 0.014, 95% CI 0.009– 0.020; β 0.013, 95% CI 0.005– 0.021). Excessive weight gain was associated with a higher prevalence of offspring adiposity before five years, even if prepregnancy BMI is normal. Multivariate regression analysis further confirmed that excessive weight gain during the first half of pregnancy significantly increased the risk of childhood obesity at aged one and three (AOR 1.083, 95% CI 1.003– 1.169; AOR 1.158, 95% CI 1.036– 1.293). Conclusion: Offspring have a higher risk of preschool adiposity when gestational weight gain was excessive during the first half of pregnancy. [ABSTRACT FROM AUTHOR]

Aims: The study aimed to explore additive interactions of CDKAL1 rs7747752 and GUDCA/DCA for GDM risk and whether the interactive effects on the risk of GDM was mediated via increasing lysophosphatidylcholines (LPC) 18:0 and/or saturated fatty acid (SFA) 16:0. Methods: A 1:1 age-matched study nested in a prospective cohort of pregnant women (207 pairs) was organized in Tianjin, China. Additive interactions were used to test interaction effects while mediation analyses and Sobel tests were used to test mediation effects of LPC18:0 and SFA16:0 between copresence of rs7747752 and low GUDCA/DCA, and GDM risk. Results: The CDKAL1 rs7747752 was associated with GDM (P<0.05). The rs7747752 C polymorphism markedly enhanced ORs of low GUDCA from 4.04 (0.72-22.8) to 9.02 (1.63-49.7) and low DCA from 1.67 (0.68-4.11) to 4.24 (1.84-9.76), both with significant additive interactions. Further adjustment for LPC18:0 attenuated the interactive effects of rs7747752 and low DCA, with a significant mediation effect (P=0.003). High SFA16:0 did not mediate the interactive effects of rs7747752 and low DCA/GUDCA on GDM risk. Conclusions: The CDKAL1 rs7747752 C carrier status and low GUDCA/DCA had significant additive interactions on the risk of GDM with the effect from interaction with DCA being partially mediated via increasing LPC18:0. [ABSTRACT FROM AUTHOR]

Aim: The rise of diabetes mellitus (DM) and impaired fasting glucose (IFG) in children has become a worldwide challenge, but population-based survey targeted on pre-school children is lacking. The aim of the study was to determine the prevalence of DM and IFG in pre-school children and their association with obesity and susceptible behaviours.Methods: A population-based survey was conducted and 21,571 Chinese children aged 5-6 years were recruited from 66 kindergartens in Tianjin, China during the year of 2016-2018. Fasting glucose was measured for all participants. Children with fasting glucose over 7.0 mmol/L was defined as DM, and 5.6-6.9 mmol/L was defined as IFG.Results: The prevalence of DM and IFG in the current study among Chinese children aged 5-6 years old was 0.05% and 1.18%, respectively, with significant gender and regional disparities (p < 0.001). The risk of abnormal fasting glucose increased with obesity (OR 1.44, 95% CI 1.00-2.06, p < 0.05), especially with moderate/severe obesity (OR 1.84, 95% CI 1.10-3.10, p < 0.05). Further analyses found that as compared to children who had at least 2 h/day physical activity, physical activity <1 h/day increased the risk of abnormal fasting glucose independently of BMI z-score (OR 1.79, 95% CI 1.10-2.91, p < 0.05).Conclusions: The study was the first population-based large-scale survey of DM and IFG which targeted on pre-school children, and demonstrated that moderate/severe obesity and inadequate physical activity raised the risk of DM and IFG early from pre-school. The findings highlighted the necessity of early detection for abnormal glucose and control of obesity and susceptible behaviours. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to develop a machine learning–based prediction model for gestational diabetes mellitus (GDM) in early pregnancy in Chinese women. Materials and methods: We used an established population‐based prospective cohort of 19,331 pregnant women registered as pregnant before the 15th gestational week in Tianjin, China, from October 2010 to August 2012. The dataset was randomly divided into a training set (70%) and a test set (30%). Risk factors collected at registration were examined and used to construct the prediction model in the training dataset. Machine learning, that is, the extreme gradient boosting (XGBoost) method, was employed to develop the model, while a traditional logistic model was also developed for comparison purposes. In the test dataset, the performance of the developed prediction model was assessed by calibration plots for calibration and area under the receiver operating characteristic curve (AUR) for discrimination. Results: In total, 1484 (7.6%) women developed GDM. Pre‐pregnancy body mass index, maternal age, fasting plasma glucose at registration, and alanine aminotransferase were selected as risk factors. The machine learning XGBoost model‐predicted probability of GDM was similar to the observed probability in the test data set, while the logistic model tended to overestimate the risk at the highest risk level (Hosmer–Lemeshow test p value: 0.243 vs. 0.099). The XGBoost model achieved a higher AUR than the logistic model (0.742 vs. 0.663, p < 0.001). This XGBoost model was deployed through a free, publicly available software interface (https://liuhongwei.shinyapps.io/gdm%5frisk%5fcalculator/). Conclusion: The XGBoost model achieved better performance than the logistic model. [ABSTRACT FROM AUTHOR]

Aims: To investigate the risk of postpartum metabolic syndrome in women with GDM compared with those without GDM in a Chinese population.Methods: Tianjin GDM observational study included 1263 women with a history of GDM and 705 women without GDM. Multivariate logistic regression was used to assess risks of postpartum metabolic syndrome between women with and without GDM. Postpartum metabolic syndrome was diagnosed by two commonly used criteria.Results: During a mean 3.53 years of follow up, 256 cases of metabolic syndrome were identified by using the NCEP ATPIII criteria and 244 cases by using the IDF criteria. Multivariable-adjusted odds ratios of metabolic syndrome in women with GDM compared with those without GDM were 3.66 (95% confidence interval [CI] 2.02-6.63) for NCEP ATPIII criteria and 3.90 (95% CI 2.13-7.14) for IDF criteria. Women with GDM had higher multivariable-adjusted odds ratios of central obesity, hypertriglyceridemia, and high blood pressure than women without GDM. The multivariable-adjusted odds ratios of low HDL cholesterol and hyperglycemia were not significant between women with and without GDM, however, the multivariable-adjusted odds ratio of hyperglycemia became significant when we used the modified criteria.Conclusions: The present study indicated that women with prior GDM had significantly higher risks for postpartum metabolic syndrome, as well as its individual components. [ABSTRACT FROM AUTHOR]

Objective: Gestational weight gain (GWG) is associated with infant birth weight and childhood obesity; however, the patterns of GWG on infant birth weight are poorly understood.Methods: This analysis in 16,218 mother-child dyads from Tianjin, China, determined the risk of infant size at birth according to GWG occurring throughout the first and second trimester (early GWG) or during the third trimester (late GWG), according to maternal prepregnancy BMI and the 2009 Institute of Medicine recommendations.Results: Excessive GWG in early and late pregnancy had an increased risk for large-for-gestational-age (LGA) infants (odds ratio [OR]: 2.4; 95% confidence interval [CI]: 1.5-4.0, P < 0.001). Regardless of prepregnancy BMI, excessive GWG early in pregnancy (< 24 weeks) was associated with an increased risk of LGA infants (OR: 2.5; 95% CI: 2.1-3.1, P < 0.001), and inadequate early GWG was associated with a higher risk of small-for-gestational-age (SGA) infants (OR: 1.4; 95% CI: 1.2-1.7, P < 0.001).Conclusions: The pattern of GWG early in pregnancy, regardless of GWG later in pregnancy, had the greatest impact on infant size at birth. Interventions initiated early in pregnancy may facilitate better adherence to the GWG guidelines and minimize the risk of LGA and SGA infants, a potential precursor for childhood obesity. [ABSTRACT FROM AUTHOR]

• High HYAL1 in early pregnancy was associated with elevated risk of GDM. • There was a significant interaction between HYAL1 and ceramide 18:0 for GDM risk. • There was a significant interaction between HYAL1 and ceramide 18:1 for GDM risk. • There was a significant interaction between HYAL1 and ceramide 24:0 for GDM risk. To explore association of serum hyaluronidase 1 (HYAL1) level in early pregnancy with gestational diabetes mellitus (GDM), and to examine interactive effects of HYAL1 with ceramides species on GDM risk. We conducted a 1:1 matched case-control study (n = 414) of pregnant women from 2010 to 2012 in Tianjin, China. Blood samples were collected at the first antenatal care visit (at a median of 10th gestational weeks). Binary conditional logistic regression and restricted cubic spline (RCS) analysis were used to examine full-range risk association between HYAL1 and GDM. Additive interactions and multiplicative interactions were employed to test interactive effects of HYAL1 with ceramides species on GDM risk. Ln HYAL1 was linearly associated with GDM risk and the adjusted OR of HYAL1 ≥ vs. < its median for GDM was significant (1.65, 95%CI: 1.08–2.52). High HYAL1 markedly enhanced the ORs of high ceramide 18:0 for GDM from 2.31 (1.06–5.01) to 6.74 (2.85–16.0), and low ceramide 24:0 from 3.08 (1.33–7.11) to 8.15 (3.03–21.9), with significant additive interactions. High HYAL1 in early pregnancy may increase the risk of GDM in Chinese women, possibly via enhancing the effects of high ceramide 18:0 and low ceramide 24:0 on GDM risk. [ABSTRACT FROM AUTHOR]