Associations between circulating IgG antibodies to Apolipoprotein B100-derived peptide antigens and acute coronary syndrome in a Chinese Han population

Second-trimester maternal lipid profiles rather than glucose levels predict the occurrence of neonatal macrosomia regardless of glucose tolerance status: A matched cohort study in Beijing

AIMS

The mechanism underlying fetal overgrowth during pregnancy remains elusive. We aimed to establish a predictive model to identify the high-risk individuals with macrosomia in the second trimester of pregnancy.

DESIGN

A total of 2577 pregnant women with a routine 75-g oral glucose tolerance test during 24-28 gestational weeks were screened in a prospective cohort. Gestational diabetes mellitus (GDM) cases were 1:1 matching with age (±2 years) in normal glucose tolerance (NGT) ones from the same region. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were performed to determine the index and its inflection point for predicting macrosomia occurrence.

RESULTS

The data of perinatal outcomes of 565 GDM and 549 NGT who had given birth to single live babies at term were analyzed. Notably, we found serum apolipoprotein B (ApoB) level higher than 4.04 g/L combined with triglycerides (TG)/high-density lipoprotein cholesterol (HDLC) ratio above 1.36 formed the predictive model in both groups. The area under the ROC curve of this predictive model included ApoB and TG/HDL-C reached 0.807 (95% CI: 0.771-0.873) with a sensitivity of 71.9% and a specificity of 78.6%. Mediation analysis revealed that ApoB and TG/HDL-C ratio mediated the harmful effect of FBG on the risk of macrosomia.

CONCLUSION

Maternal ApoB levels and TG/HDL-C ratio could predict macrosomia occurrence in pregnancy, which might be a new target for early intervention to prevent excess fetal growth.

LncRNA MALAT1 gene polymorphisms in coronary artery disease: a case-control study in a Chinese population

Background: Coronary artery disease (CAD) is one of the main fatal diseases all over the world. CAD is a complex disease, which has multiple risk factors mechanisms. In recent years, genome-wide association study (GWAS) had revealed single nucleotide polymorphism genes (SNPs) which were closely related with CAD risks. The relationship between long non-coding RNA (lncRNA) MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) and CAD risk is largely unknown. To our knowledge, this is the first study which demonstrated the interaction effects of SNP-SNP and SNP-environment with CAD risk. In general, our case-control study is to detect the association between MALAT1 (rs619586, rs4102217) SNPs and CAD risk. Methods: Three hundred and sixty-five CAD patients and three hundred and eighty-four matched control participants blood samples were collected in Liaoning province, China. Two polymorphisms (rs619586, rs4102217) in lncRNA MALAT1 were genotyped by KASP platform. Results: In a stratified analysis, we found that non-drinkers with GC genotype and the recessive model of rs4102217 had higher CAD risk (P=0.010, odds ratio (OR): 1.96, 95% confidence interval (CI) = 1.17-3.28; P=0.026, OR: 1.73, 95% CI = 1.07-2.79) and diabetes mellitus (DM) history group (P=0.010, OR: 4.07, 95% CI = 1.41-11.81; P=0.019, OR: 3.29, 95% CI = 1.22-8.88). In SNP-SNP interactions analysis between MALAT1 and CAD risk, we found rs4102217 had an increase in smokers (GG: OR: 2.04, 95% CI = 1.42-2.92; CC+GC: OR: 2.64, 95% CI = 1.64-4.26) and a decrease in drinkers (CC+GC: OR: 0.33, 95% CI = 0.20-0.55). Smokers with MALAT1 rs619586 AA genotype (OR: 2.20, 95% CI = 1.57-3.07) and GG+AG genotype (OR: 2.11, 95% CI = 1.17-3.81) had a higher risk of CAD. Moreover, drinkers with AA genotype (OR: 0.22, 95% CI = 0.10-0.48) and GG+AG genotype (OR: 0.38, 95% CI = 0.22-0.65) had a lower risk of CAD. According to the MDR software, MALAT1 rs4102217 polymorphism-smoking-drinking was the best interaction model, which has higher risk of CAD (Testing Bal.ACC. = 0.6979). Conclusion: Our study demonstrated that the GC genotype and the recessive model of rs4102217 potentially increased CAD risk in some specific group.