[Interaction between Abnormal Prepregnancy Body Mass Index and Hyperlipidemia during Pregnancy on the Risk of Gestational Diabetes Mellitus].

Objective To explore the interaction between abnormal prepregnancy body mass index(pBMI)and high blood lipid level during pregnancy on the risk of gestational diabetes mellitus(GDM). Methods A total of 235 patients with GDM and no blood lipid-related diseases before pregnancy were selected from Hangzhou Women's Hospital during March 2017 to July 2018 as the GDM group.At a ratio of 1∶3,a total of 705 individual age-matched pregnant women with normal glucose metabolism during prenatal examination from the same hospital were selected as the control group.The generalized multifactor dimension reduction(GMDR)method was employed to characterize the possible interaction between pBMI-blood lipid and GDM.The cross-validation consistency,equilibrium test accuracy,and P value were calculated to evaluate the interaction of each model. Results GMDR model analysis showed that the second-order model including pBMI and gestational blood lipid level had the best performance(P=0.001),with the cross-validation consistency of 10/10 and the equilibrium test accuracy of 64.48%,suggesting that there was a potential interaction between pBMI and gestational high blood lipid level.After adjustment of confounding factors,the model demonstrated that overweight/obesity patients with high triglyceride(TG) level had the highest risk of developing GDM(OR=14.349,95%CI=6.449-31.924,P<0.001).Stratified analysis showed that overweight/obesity patients under high TG level group had a higher risk of developing GDM than normal weight individuals(OR=2.243,95%CI=1.173-4.290,P=0.015). Conclusions Abnormal pBMI and high blood lipid level during pregnancy are the risk factors of GDM and have an interaction between each other.Overweight/obese pregnant women with high TG levels are more likely to develop GDM.

ALDH2, ADCY3 and BCMO1 polymorphisms and lifestyle-induced traits are jointly associated with CAD risk in Chinese Han people.

BACKGROUNDS: To investigate associations of genetic and environmental factors with coronary artery disease (CAD), we collected medical reports, lifestyle details, and blood samples of 2113 individuals, and then used the polymerase chain reaction (PCR)-ligase detection reaction (LDR) to genotype the targeted 102 SNPs. METHODS: We adopted elastic net algorithm to build an association model that considered simultaneously genetic and lifestyle/clinical factors associated with CAD in Chinese Han population. RESULTS: In this study, we developed an all covariates-based model to explain the risk of CAD, which incorporated 8 lifestyle/clinical factors and a gene-score variable calculated from 3 significant SNPs (rs671, rs6751537 and rs11641677), attaining an area under the curve (AUC) value of 0.71. It was found that, in terms of genetic variants, the AA genotype of rs671 in the additive (adjusted odds ratio (OR) = 2.51, p = 0.008) and recessive (adjusted OR = 2.12, p = 0.021) models, the GG genotype of rs6751537 in the additive (adjusted OR = 3.36, p = 0.001) and recessive (adjusted OR = 3.47, p = 0.001) models were associated with increased risk of CAD, while GG genotype of rs11641677 in additive model (adjusted OR = 0.39, p = 0.044) was associated with decreased risk of CAD. In terms of lifestyle/clinical factors, the history of hypertension (unadjusted OR = 2.37, p < 0.001) and dyslipidemia (unadjusted OR = 1.82, p = 0.007), age (unadjusted OR = 1.07, p < 0.001) and waist circumference (unadjusted OR = 1.02, p = 0.05) would significantly increase the risk of CAD, while height (unadjusted OR = 0.97, p = 0.006) and regular intake of chicken (unadjusted OR = 0.78, p = 0.008) reduced the risk of CAD. A significantinteraction was foundbetween rs671 and dyslipidemia (the relative excess risk due to interaction (RERI) = 3.36, p = 0.05). CONCLUSION: In this study, we constructed an association model and identified a set of SNPs and lifestyle/clinical risk factors of CAD in Chinese Han population. By considering both genetic and non-genetic risk factors, the built model may provide implications for CAD pathogenesis and clues for screening tool development in Chinese Han population.

NRN1 and CAT Gene Polymorphisms, Complex Noise, and Lifestyles interactively Affect the Risk of Noise-induced Hearing Loss.

OBJECTIVE: The effects of interactions between genetic and environmental factors on the noise-induced hearing loss (NIHL) are still unclear. This study aimed to assess interactions among gene polymorphisms, noise metrics, and lifestyles on the risk of NIHL. METHODS: A case-control study was conducted using 307 patients with NIHL and 307 matched healthy individuals from five manufacturing industries. General demographic data, lifestyle details, and noise exposure levels were recorded. The Kompetitive allele-specific polymerase chain reaction (KASP) was used to analyze the genotypes of 18 SNPs. RESULTS: GMDR model demonstrated a relevant interaction between NRN1 rs3805789 and CAT rs7943316 (P = 0.0107). Subjects with T allele of rs3805789 or T allele of rs7943316 had higher risks of NIHL than those with the SNP pair of rs3805789-CC and rs7943316-AA (P < 0.05). There was an interaction among rs3805789, rs7943316, and kurtosis (P = 0.0010). Subjects exposed to complex noise and carrying both rs3805789-CT and rs7943316-TT or rs3805789-CT/TT and rs7943316-AA had higher risks of NIHL than those exposed to steady noise and carrying both rs3805789-CC and rs7943316-AA (P < 0.05). The best six-locus model involving NRN1 rs3805789, CAT rs7943316, smoking, video volume, physical exercise, and working pressure for the risk of NIHL was found to be the interaction (P = 0.0010). An interaction was also found among smoking, video volume, physical exercise, working pressure, and kurtosis (P = 0.0107). CONCLUSION: Concurrence of NRN1 and CAT constitutes a genetic risk factor for NIHL. Complex noise exposure significantly increases the risk of NIHL in subjects with a high genetic risk score. Interactions between genes and lifestyles as well as noise metrics and lifestyles affect the risk of NIHL.