Prevalence, characteristics and risk factors of birth defects in central China livebirths, 2015-2022.

Objective: This study analyzed the prevalence, epidemiological characteristics and risk factors of birth defects among livebirths in central China, aiming to provide evidences for the prevention of birth defects and government Decision-makings. Methods: Birth data from China's Hubei Province between 2015 and 2022 were collected, including basic information of the livebirths, the mothers and the fathers, as well as information about delivery and each prenatal examination. The livebirths prevalence of birth defects was calculated and the trends were mapped. The basic characteristics of birth defects were evaluated by the difference analysis between case and health groups. Univariate and multivariate Poisson regression was performed to examine the independent risk factors for birth defects. Results: Among 43,568 livebirths, 166 livebirths were born with birth defects, resulted in a total prevalence rate of 3.81 per 1,000 livebirths, showing a remarkable uptrend from 0.41per 1,000 livebirths in 2015 to 9.23 per 1,000 livebirths in 2022. The peak of the prevalence was in January and February. Congenital malformation of the musculoskeletal system was the main type of birth defect in central China livebirths, followed by cleft lip and cleft palate. Overall, newborns with birth defect had significantly earlier delivery gestational age, poorer health and higher proportion of infants with low birth weight than healthy births. The gender of livebirths, excess weight at delivery (≥80 kg) of mothers, more than 2 times of gravidity or parity of mothers, and advanced paternal age (≥40 years) were independent risk factors for birth defects (or specific birth defects). Conclusion: The livebirths prevalence of birth defects shows increasing trend in central China, which deserves the attention of the government and would-be parents. Elevated paternal age, excess maternal weight, gravidity and parity should be considered when planning their families.

Metabolic Characteristics of Gestational Diabetes Mellitus and the Effects on Pregnancy Outcomes.

Objective: To describe the metabolic characteristics of gestational diabetes mellitus (GDM) and assess their effects on perinatal outcomes. Methods: A two-center nested case‒control study was designed, including 192 pregnant women with GDM and 191 pregnant women with normal glucose tolerance (NGT). Serum glucose and insulin concentrations based upon the 75 g oral glucose tolerance test (OGTT) were measured. Several indices were calculated to describe the metabolic characteristics of the subjects. The relationship between glucose metabolism parameters and pregnancy outcomes was evaluated using stepwise linear regression and binary logistic regression. Results: Compared with the NGT group, the GDM group showed significantly higher fasting and postprandial glucose parameters but significantly lower fasting and postprandial insulin responses. Meanwhile, the GDM group had significantly lower HOMA-ß, DI and ISIMatsuda but comparable HOMA-IR. The IFG subgroup showed significantly lower FINS/FPG only, while the IGT and IFSG subgroups showed deficiency in both fasting and postprandial insulin response. The IFSG subgroup had the highest glucose parameters and the lowest insulin parameters, as well as significantly lower ISIMatsuda and HOMA-ß than the NGT group. FPG had a significant effect on infants' birth weight, and 1hPG and FINS/FPG had a significant effect on delivery gestational age. AUC-INS, IGI60 and DI were related to premature delivery risk after adjusting for confounders. The IFG subgroup of GDM was 2.319 times more likely to be subject to cesarean section than the NGT group. FPG, FINS/FPG, AUC-GLU, AUC-INS/AUC-GLU and HOMA-ß were related to macrosomia risk. Conclusion: Beta cell dysfunction rather than insulin resistance determines the occurrence of GDM in the central Chinese population. Women with predominant insulin secretion defects had a similar risk of adverse perinatal outcomes to women with NGT. Our study provided a basis for the selection of glucose metabolism monitoring indicators useful for the prevention of adverse perinatal outcomes.

Genetic variants of the GLP-1R gene affect the susceptibility and glucose metabolism of gestational diabetes mellitus: a two-center nested case‒control study.

BACKGROUND: Gestational diabetes mellitus (GDM) is the most common complication during pregnancy, occurring under the combined action of environmental and genetic factors. Genetic variants of glucagon-like peptide-1 receptor (GLP-1R) have been reported to affect insulin secretion and susceptibility to type 2 diabetes. This study aimed to explore the role of GLP-1R polymorphisms in GDM and glucose metabolism. METHODS: A two-center nested case‒control study was designed, including 200 pregnant women with GDM and 200 pregnant women without GDM genotyped for five tag SNPs of GLP-1R using Sanger sequencing. Logistic regression was used to evaluate the relationship between GLP-1R polymorphisms and GDM risk. Glucose and insulin concentrations were measured based upon the 75 g oral glucose tolerance test (OGTT). Beta cell function of different genotypes was estimated with the 60 min insulinogenic index (IGI60) and OGTT-derived disposition index (DI). RESULTS: Mutant genotype AG + GG of tag SNP rs6458093 nominally increased GDM risk (p = 0.049), especially among subjects younger than 35 years (p = 0.024) and with BMI no less than 24 (p = 0.041), after adjusting for confounders. Meanwhile, compared with subjects with wild genotype AA, subjects with genotype AG + GG of rs6458093 also showed nominally significantly lower IGI60 (p = 0.032) and DI (p = 0.029), as well as significantly higher 75 g OGTT-based 1 h glucose load plasma glucose levels (p = 0.045). Moreover, the mutant heterozygous genotype GA of tag SNP rs3765467 nominally decreased GDM risk among subjects older than 35 years (p = 0.037) but showed no association with insulin secretion and glucose homeostasis. CONCLUSIONS: Tag SNP rs6458093 of GLP-1R was nominally associated with increased GDM risk and affected beta cell function and postprandial glucose metabolism, while tag SNP rs3765467 of GLP-1R was nominally associated with decreased GDM risk, providing evidence for molecular markers and etiological study of GDM.

Framework for fusing traffic information from social and physical transportation data.

Tremendous volumes of messages on social media platforms provide supplementary traffic information and encapsulate crowd wisdom for solving transportation problems. However, social media messages manifested in human languages are usually characterized with redundant, fuzzy and subjective features. Here, we develop a data fusion framework to identify social media messages reporting non-recurring traffic events by connecting the traffic events with traffic states inferred from taxi global positioning system (GPS) data. Temporal-spatial information of traffic anomalies caused by the traffic events are then retrieved from anomalous traffic states. The proposed framework successfully identified accidental traffic events with various scales and exhibited strong performance in event descriptions. Even though social media messages are generally posted after the occurrence of anomalous traffic states, resourceful event descriptions in the messages are helpful in explaining traffic anomalies and for deploying suitable countermeasures.