Junhong Leng

Junhong Leng


Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 October 2014 in Journal of Translational Medicine
Verified
Journal Article
Open access
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XYXilin Yang
Xilin Yang
HTHuiguang Tian
Huiguang Tian
FZFuxia Zhang
Fuxia Zhang
CZCuiping Zhang
Cuiping Zhang
YLYi Li
Yi Li
JCJuliana CN CN Chan
Juliana CN CN Chan
There are no randomised controlled trials to demonstrate whether lifestyle modifications can improve pregnancy outcomes of gestational diabetes mellitus (GDM) diagnosed by the International Association of Diabetes and Pregnancy Study Group’s (IADPSG) criteria. We tested the effectiveness of lifestyle modifications implemented in a 3-tier’s shared care (SC) on pregnancy outcomes of GDM. Between December 2010 and October 2012, we randomly assigned 700 women with IADPSG-defined GDM but without diabetes at 26.3 (interquartile range: 25.4-27.3) gestational weeks in Tianjin, China, to receive SC or usual care (UC). The SC group received individual consultations and group sessions and performed regular self-monitoring of blood glucose compared to one hospital-based education session in the UC group. The outcomes were macrosomia defined as birth weight ≥ 4.0 kg and the pregnancy-induced hypertension (PIH). Women in the SC (n = 339) and UC (n = 361) groups delivered their infants at similar gestational weeks. Birth weight of infants in the SC group was lower than that in the UC group (3469 vs. 3371 grams, P = 0.021). The rate of macrosomia was 11.2% (38/339) in the SC group compared to 17.5% (63/361) in the UC group with relative risk (RR) of 0.64 (95% CI: 0.44-0.93). The rate of PIH was 8.0% (27/339) in the SC compared to 4.4% (16/361) in the UC with RR of 1.80 (0.99-3.28). Apgar score at 1 min <7 was lower but preeclampsia was higher in the SC than in the UC. Lifestyle modifications using a SC system improved pregnancy outcomes in Chinese women with GDM. Clinicaltrials.gov; NCT01565564 .

26 August 2020 in Diabetes/Metabolism Research and Reviews
Verified
Journal Article
Closed access
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HLHongwei Liu
Hongwei Liu
JLJing Li
Jing Li
JLJunhong Leng
Junhong Leng
HWHui Wang
Hui Wang
JLJinnan Liu
Jinnan Liu
XYXilin Yang
Xilin Yang

19 April 2022 in BMC Pregnancy and Childbirth
Verified
Journal Article
Open access
PDF file
SZShuang Zhang
Shuang Zhang
HLHuikun Liu
Huikun Liu
NLNan Li
Nan Li
WDWei Dong
Wei Dong
WLWeiqin Li
Weiqin Li
JLJunhong Leng
Junhong Leng
Background: Gestational diabetes mellitus (GDM) is associated with adverse health consequences for women and their offspring. It is associated with maternal body mass index (BMI) and may be associated with gestational weight gain (GWG). But due to the heterogeneity of diagnosis and treatment and the potential effect of GDM treatment on GWG, the association between the two has not been thoroughly clarified. Compared to body weight, BMI has the advantage that it considers height during the whole course of pregnancy. Understanding BMI changes during pregnancy may provide new evidence for the prevention of GDM. Methods: This study investigated the BMI change of pregnant women based on a retrospective study covering all communities in Tianjin, China. According to the results of GDM screening at 24–28 weeks of gestation, pregnancies were divided into the GDM group and the non-GDM group. We compared gestational BMI change and GWG in the two groups from early pregnancy to GDM screening. GWG was evaluated according to the IOM guidelines. Logistic regression was applied to determine the significance of variables with GDM. Results: A total of 41,845 pregnant women were included in the final analysis (GDM group, n = 4257 vs. non-GDM group, n = 37,588). BMI gain has no significant differences between the GDM and non-GDM groups at any early pregnancy BMI categories (each of 2 kg/m2), as well as weight gain (P > 0.05). Early pregnancy BMI was a risk factor for GDM (OR 1.131, 95% CI 1.122–1.139). And BMI gain was associated with a decreased risk of GDM in unadjusted univariate analysis (OR 0.895, 95% CI 0.869–0.922). After adjusting on early pregnancy BMI and other confounding factors, the effect of BMI gain was no longer significant (AOR 1.029, 95% CI 0.999–1.061), as well as weight gain (AOR 1.006, 95% CI 0.995–1.018) and GWG categories (insufficient: AOR 1.016, 95% CI 0.911–1.133; excessive: AOR 1.044, 95% CI 0.957–1.138). Conclusions: BMI in early pregnancy was a risk factor for GDM, while BMI gain before GDM screening was not associated with the risk of GDM. Therefore, the optimal BMI in early pregnancy is the key to preventing GDM.

22 April 2020 in BMJ Open Diabetes Research & Care
Verified
Journal Article
Open access
PDF file
SGSi Gao
Si Gao
JLJunhong Leng
Junhong Leng
HLHongyan Liu
Hongyan Liu
SWShuo Wang
Shuo Wang
WLWeiqin Li
Weiqin Li
XYXilin Yang
Xilin Yang
Objective To develop and validate a set of risk scores for the prediction of gestational diabetes mellitus (GDM) before the 15th gestational week using an established population-based prospective cohort. Methods From October 2010 to August 2012, 19 331 eligible pregnant women were registered in the three-tiered antenatal care network in Tianjin, China, to receive their antenatal care and a two-step GDM screening. The whole dataset was randomly divided into a training dataset (for development of the risk score) and a test dataset (for validation of performance of the risk score). Logistic regression was performed to obtain coefficients of selected predictors for GDM in the training dataset. Calibration was estimated using Hosmer-Lemeshow test, while discrimination was checked using area under the receiver operating characteristic curve (AUC) in the test dataset. Results In the training dataset (total=12 887, GDM=979 or 7.6%), two risk scores were developed, one only including predictors collected at the first antenatal care visit for early prediction of GDM, like maternal age, body mass index, height, family history of diabetes, systolic blood pressure, and alanine aminotransferase; and the other also including predictors collected during pregnancy, that is, at the time of GDM screening, like physical activity, sitting time at home, passive smoking, and weight gain, for maximum performance. In the test dataset (total=6444, GDM=506 or 7.9%), the calibrations of both risk scores were acceptable (both p for Hosmer-Lemeshow test >0.25). The AUCs of the first and second risk scores were 0.710 (95% CI: 0.680 to 0.741) and 0.712 (95% CI: 0.682 to 0.743), respectively (p for difference: 0.9273). Conclusion Both developed risk scores had adequate performance for the prediction of GDM in Chinese pregnant women in Tianjin, China. Further validations are needed to evaluate their performance in other populations and using different methods to identify GDM cases.

25 August 2017 in Scientific Reports
Verified
Journal Article
Open access
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NLNan Li
Nan Li
SZShuang Zhang
Shuang Zhang
WLWeiqin Li
Weiqin Li
LWLeishen Wang
Leishen Wang
HLHuikun Liu
Huikun Liu
JLJunhong Leng
Junhong Leng
To estimate the prevalence of hyperuricemia and its major risk factors among Chinese preschool children. A cross-sectional survey was carried out in six central urban districts of Tianjin from March to June 2015. A cluster sampling was employed to obtain a random sample of preschool children. A total of 4073 children aged from 3 to 6 years participated in this survey. Logistic regression was used to obtain odds ratios and 95% confidence intervals. The overall mean serum uric acid concentrations was 243.0 ± 53.2 μmol/L, corresponding to a mean concentrations of 247.3 ± 53.7 μmol/L among boys and 238.3 ± 52.4 μmol/L among girls. The overall prevalence of serum uric acid ≥ 310 μmol/L among children was 10.1%. Boys, obesity, diastolic blood pressure, and serum triglyceride concentrations were associated with the increased risk of hyperuricemia in univariable models, although the statistically significant association between diastolic blood pressure, serum triglyceride concentrations and hyperuricemia disappeared in multivariable models. The prevalence of hyperuricemia among children aged from 3 to 6 years was relatively high. Several metabolic syndrome components were associated with the risk of hyperuricemia.

10 March 2022 in Nutrients
Verified
Journal Article
Open access
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SLShuying Li
Shuying Li
XTXiubiao Tian
Xiubiao Tian
YWYiyun Wang
Yiyun Wang
XZXumei Zhang
Xumei Zhang
LZLiwen Zhang
Liwen Zhang
QZQiang Zhang
Qiang Zhang
Circumstantial evidence links one-carbon metabolism (OCM) related nutrients, such as folate and vitamin B12, with gestational diabetes mellitus (GDM). However, few studies have evaluated the combined effects of these nutrients with OCM related gene polymorphisms on GDM. This study investigated whether OCM related genetic variants modified the associations of folate and B12 with GDM. Logistic regression was used to estimate odds ratios (ORs) for OCM related nutrients and single nucleotide polymorphisms (SNPs) in genes encoding main OCM related enzymes (MTHFR, MTR, and MTRR) on GDM. Higher folate concentrations were associated with increased GDM risk (OR: 1.59; 95% CI: 1.22, 2.13). However, higher B12 concentrations were associated with reduced GDM risk (OR: 0.76; 95% CI: 0.65, 0.92). Pregnancies with MTHFR rs1801131 G alleles had a significantly lower risk of GDM than pregnancies with T alleles (OR: 0.65; 95% CI: 0.47, 0.91) under the dominant model. The genotype-stratified analysis revealed the association between folate and GDM (OR: 1.66, 95% CI: 1.20, 2.30) or B12 and GDM (OR: 0.80, 95% CI: 0.65, 0.98) was more evident in pregnancies with TT genotype. Higher folate and lower B12 are associated with GDM. Pregnancies with MTHFR rs1801131 TT genotype are more susceptible to OCM nutrient-related GDM.