Objective To investigate whether plasma lipid profiles are independently associated with pregnancy complications including gestational diabetes mellitus (GDM), hypertensive disorder complicating pregnancy (HDCP), and intrahepatic cholestasis of pregnancy (ICP). Study Design A prospective study was conducted among 1,704 pregnant women at three medical institutions in Chengdu, China. The concentrations of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured at gestational weeks 12 ± 1, 24 ± 1, and 34 ± 1. Logistic regression models were used to estimate the association between lipid profiles and pregnancy complications. Receiver operating characteristic analysis was performed to determine the value of lipid profiles to predict GDM and HDCP. Results After adjusting for potential confounders, TG, TC, and LDL-C in the first trimester were independently associated with GDM (TG: odds ratio [OR] =2.00, 95% confidence interval [CI]: 1.57–2.56; TC: OR = 1.38, 95% CI: 1.16–1.64; LDL-C: OR = 1.43, 95% CI: 1.14–1.79) and HDCP (TG: OR = 2.42, 95% CI: 1.56–3.78, TC: OR = 1.64, 95% CI: 1.04–2.57; LDL-C: OR = 1.87, 95% CI: 1.07–3.25). The TC concentration during the whole pregnancy (first trimester: OR = 1.53, 95% CI: 1.13–2.08; second trimester: OR = 1.31, 95% CI: 1.06–1.61; third trimester: OR = 1.39, 95% CI: 1.17–2.04) and LDL-C in the last two trimesters (second trimester: OR = 1.62, 95% CI: 1.30–2.04; third trimester: OR = 1.56, 95% CI: 1.29–1.88) were positively associated with ICP. HDL-C in the third trimester was negatively associated with the risk of ICP (OR = 0.46, 95% CI: 0.22–0.98). Combining lipid profiles in the first trimester with the other common predictors to predict GDM or HDCP owned stronger predictive power with the largest area under the curve (GDM: 0.643 [95% CI: 0.613–0.673], HDCP: 0.707 [95% CI: 0.610–0.804]) than either indicator alone. Conclusion Maternal lipid profiles during the whole pregnancy are significantly associated with GDM, HDCP, and ICP. Combining lipid profiles in the first trimester with the other common predictors could effectively improve the power of predicting GDM and HDCP.

Objective:To evaluate the effects of gestational weight gain (GWG) in the first trimester (GWG-F) and the rate of gestational weight gain in the second trimester (RGWG-S) on gestational diabetes mellitus (GDM), exploring the optimal GWG ranges for the avoidance of GDM in Chinese women.Design:A population-based prospective study was conducted. Gestational weight was measured regularly in every antenatal visit and assessed by the Institute of Medicine (IOM) criteria (2009). GDM was assessed with the 75-g, 2-h oral glucose tolerance test at 24–28 weeks of gestation. Multivariable logistic regression was performed to assess the effects of GWG-F and RGWG-S on GDM, stratified by pre-pregnancy BMI. In each BMI category, the GWG values corresponding to the lowest prevalence of GDM were defined as the optimal GWG range.Setting:Southwest China.Participants:Pregnant women (n 1910) in 2017.Results:After adjusting for confounders, GWG-F above IOM recommendations increased the risk of GDM (OR; 95 % CI) among underweight (2·500; 1·106, 5·655), normal-weight (1·396; 1·023, 1·906) and overweight/obese women (3·017; 1·118, 8·138) compared with women within IOM recommendations. No significant difference was observed between RGWG-S and GDM (P > 0·05) after adjusting for GWG-F based on the previous model. The optimal GWG-F ranges for the avoidance of GDM were 0·8–1·2, 0·8–1·2 and 0·35–0·70 kg for underweight, normal-weight and overweight/obese women, respectively.Conclusions:Excessive GWG in the first trimester, rather than the second trimester, is associated with increased risk of GDM regardless of pre-pregnancy BMI. Obstetricians should provide more pre-emptive guidance in achieving adequate GWG-F.

Background: More than one-fifth of the world's population consumes Chinese cuisines regularly, but no evidence-based healthy diets fitting the Chinese food culture are available for implementation. Methods: A multicenter, patient- and outcome assessor–blind, randomized feeding trial was conducted among 265 participants with 130 to 159 mmHg baseline systolic blood pressure (SBP) for 4 major Chinese cuisines (Shangdong, Huaiyang, Cantonese, Szechuan). After a 7-day run-in period on a control diet matching the usual local diets, participants were randomized to continue with the control diet or the cuisine-based Chinese heart-healthy diet for another 28 days. The primary outcome was SBP, and secondary outcomes included diastolic blood pressure and food preference score. Linear regression models were used to estimate the intervention effects and adjustments for the center. The incremental cost per 1 mmHg reduction in SBP was also calculated. Results: A total of 265 participants were randomized (135 on the Chinese heart-healthy diet and 130 on the control diet), with 52% women, mean age of 56.5±9.8 years, and mean SBP and diastolic blood pressure of 139.4±8.3 and 88.1±8.0 mmHg, respectively, at baseline. The change in SBP and diastolic blood pressure from baseline to the end of the study in the control group was –5.0 (95% CI, –6.5 to –3.5) mmHg and –2.8 (95% CI, –3.7 to –1.9) mmHg, respectively. The net difference of change between the 2 groups in SBP and diastolic blood pressure were –10.0 (95% CI, –12.1 to –7.9) mmHg and –3.8 (95% CI, –5.0 to –2.5) mmHg, respectively. The effect size did not differ among cuisines ( P for interaction=0.173). The mean food preference score was 9.5 (with 10 the best preferred) at baseline, and the net change during intervention was 0.1 (95% CI, –0.1 to 0.2; P =0.558). The incremental cost-effectiveness ratio per 1 mmHg SBP reduction was CNY 0.4 (USD 0.06) per day. No difference in the number of adverse events was found between the 2 groups ( P =0.259), and none of the adverse events was associated with the intervention. Conclusions: The Chinese heart-healthy diet is effective, palatable, and cost-effective in reducing blood pressure in Chinese adults with high blood pressure, with a clinically significant effect applicable across major Chinese cuisine cultures.

Objectives: Fruit intake may influence gestational diabetes mellitus (GDM) risk. However, prospective evidence remains controversial and limited. The current study aimed to investigate whether total fruit and specific fruit intake influence GDM risk. Design: A prospective cohort study was conducted. Dietary information was collected by a 3-d 24-h dietary recall. All participants underwent a standard 75-g oral glucose tolerance test at 24–28 gestational weeks. Log-binomial models were used to estimate the association between fruit intake and GDM risk, and the results are presented as relative risks (RR) and 95 % CI. Setting: Southwest China. Participants: Totally, 1453 healthy pregnant women in 2017. Results: Total fruit intake was not associated with lower GDM risk (RR of 1·03 (95 % CI 0·83, 1·27) (Ptrend = 0·789)). The RR of GDM risk was 0·73 for the highest anthocyanin-rich fruit intake quartile compared with the lowest quartile (95 % CI 0·56, 0·93; Ptrend = 0·015). A higher grape intake had a linear inverse association with GDM risk (Q4 v. Q1: RR = 0·65; 95 % CI 0·43, 0·98; Ptrend = 0·044), and after further adjustment for anthocyanin intake, the inverse association tended to be non-linear (Q4 v. Q1: RR = 0·65; 95 % CI 0·44, 0·98; Ptrend = 0·079). However, we did not find an association between glycaemic index-grouped fruit, glycaemic load-grouped fruit or other fruit subtype intake and GDM risk. Conclusions: In conclusion, specific fruit intake (particularly anthocyanin-rich fruit and grapes) but not total fruit intake was inversely associated with GDM risk.

Objective: To explore the associations of duration and quality of sleep during pregnancy with preterm birth and small for gestational age (SGA).Methods: A prospective study was carried out on 1082 healthy women with singleton pregnancies from Chengdu, China. Self-report questionnaires, including duration and quality of sleep and other information, were administered at 8-12, 24-28, and 32-36 weeks of pregnancy. Data on gestational age and weight and length of the neonates were recorded after delivery. After controlling the potential confounders, a multivariable logistic regression model was performed to evaluate whether duration and quality of sleep were associated with preterm birth and SGA.Results: Participants with short duration of sleep during the third trimester were more likely to report preterm birth (odds ratio [OR] 2.16, 95% confidence interval [CI] 1.26-4.81) and SGA (OR 2.67, 95% CI 1.18-6.54). Participants with poor quality of sleep during the third trimester were at high risk for preterm birth (OR 2.26,) and SGA (OR 2.08,. Conclusion:Short duration and poor quality of sleep during pregnancy are associated with an increased risk of preterm birth and SGA. Sleep characteristics should be assessed during prenatal evaluations to decrease adverse maternal and fetal outcomes.