1.Association between adiponectin copy number variation region and gestational diabetes mellitus
Ziheng LI ; Haiyan LIU ; Yao DONG ; Kailin WANG ; Jin LIU ; Huilu CUI ; Qing LI ; Anqun HU ; Zongguang LI ; Bin WANG ; Yingjie ZHENG
Chinese Journal of Epidemiology 2025;46(5):867-873
Objective:To investigate the association between adiponectin-related copy number variation (CNV) region (CNVR) and gestational diabetes mellitus (GDM).Methods:Pregnant women who had prenatal screening in Anqing Municipal Hospital, Anhui Province, from February 2018 to December 2020 were surveyed for baseline information collection, and blood samples were collected. The outcome information was obtained by post pregnancy follow-up. Latex-enhanced immunoturbidimetry and ASA-CHIA chip were used to detect serum adiponectin levels and CNV of pregnant women, respectively. After genotyping, CNV data were processed with software PennCNV 1.0.5 following standard quality control procedure. CNVR were identified and integrated by using software R 4.3.3. Then the associations between CNVR and adiponectin was evaluated, and gene annotation and over-representation analysis were conducted. The log-binomial regression model was used to adjust relevant covariates and analyze the association between adiponectin-related CNVR and GDM.Results:The detection rate of GDM was 9.54% (176/1 845) in the pregnant women. The genotyping information of 1 840 people (99.73%) passed quality evaluation. A total of 33 878 CNVs were identified, and 1 449 CNVRs were obtained after integration. After the false discovery rate method correction, CNVR_132 (CHR2: 47611743-47635062), CNVR_254 (CHR3: 10182703-10183872), CNVR_691 (CHR7: 150637053-150834539) and CNVR_1101 (CHR14: 104248431-104830620) were correlated with adiponectin levels (all P<0.05). Over- representation analysis showed that the molecular function of ribonucleotide binding [Gene Ontology (GO): 0032553] was significantly enriched based on the GO database. The log-binomial regression model, adjusting age, pre-pregnancy BMI, history of miscarriage, smoking history, and family history of diabetes, indicated that CNVR_132 (CHR2: 47611743-47635062) and CNVR_1101 (CHR14: 104248431-104830620) were not statistically associated with the risk for GDM (both P>0.05). However, CNVR_254 (CHR3: 10182703-10183872, a RR=1.83, 95% CI: 1.15-2.91) and CNVR_691 (CHR7: 150637053-150834539, a RR=1.73, 95% CI: 1.23-2.43) might be associated with an increased risk for GDM (all P<0.05). Conclusion:Adiponectin-related CNVR_254 (CHR3: 10182703-10183872) and CNVR_691 (CHR7: 150637053-150834539) might be risk factors for the incidence of GDM.
2.Association between adiponectin copy number variation region and gestational diabetes mellitus
Ziheng LI ; Haiyan LIU ; Yao DONG ; Kailin WANG ; Jin LIU ; Huilu CUI ; Qing LI ; Anqun HU ; Zongguang LI ; Bin WANG ; Yingjie ZHENG
Chinese Journal of Epidemiology 2025;46(5):867-873
Objective:To investigate the association between adiponectin-related copy number variation (CNV) region (CNVR) and gestational diabetes mellitus (GDM).Methods:Pregnant women who had prenatal screening in Anqing Municipal Hospital, Anhui Province, from February 2018 to December 2020 were surveyed for baseline information collection, and blood samples were collected. The outcome information was obtained by post pregnancy follow-up. Latex-enhanced immunoturbidimetry and ASA-CHIA chip were used to detect serum adiponectin levels and CNV of pregnant women, respectively. After genotyping, CNV data were processed with software PennCNV 1.0.5 following standard quality control procedure. CNVR were identified and integrated by using software R 4.3.3. Then the associations between CNVR and adiponectin was evaluated, and gene annotation and over-representation analysis were conducted. The log-binomial regression model was used to adjust relevant covariates and analyze the association between adiponectin-related CNVR and GDM.Results:The detection rate of GDM was 9.54% (176/1 845) in the pregnant women. The genotyping information of 1 840 people (99.73%) passed quality evaluation. A total of 33 878 CNVs were identified, and 1 449 CNVRs were obtained after integration. After the false discovery rate method correction, CNVR_132 (CHR2: 47611743-47635062), CNVR_254 (CHR3: 10182703-10183872), CNVR_691 (CHR7: 150637053-150834539) and CNVR_1101 (CHR14: 104248431-104830620) were correlated with adiponectin levels (all P<0.05). Over- representation analysis showed that the molecular function of ribonucleotide binding [Gene Ontology (GO): 0032553] was significantly enriched based on the GO database. The log-binomial regression model, adjusting age, pre-pregnancy BMI, history of miscarriage, smoking history, and family history of diabetes, indicated that CNVR_132 (CHR2: 47611743-47635062) and CNVR_1101 (CHR14: 104248431-104830620) were not statistically associated with the risk for GDM (both P>0.05). However, CNVR_254 (CHR3: 10182703-10183872, a RR=1.83, 95% CI: 1.15-2.91) and CNVR_691 (CHR7: 150637053-150834539, a RR=1.73, 95% CI: 1.23-2.43) might be associated with an increased risk for GDM (all P<0.05). Conclusion:Adiponectin-related CNVR_254 (CHR3: 10182703-10183872) and CNVR_691 (CHR7: 150637053-150834539) might be risk factors for the incidence of GDM.
3.Comparison of vaginal flora between normal and abnormal pregnant women throughout pregnancy
Yaxin LI ; Haiyan LIU ; Zongguang LI ; Ziqiang QIAN ; Yanmin CAO ; Yao DONG ; Kailin WANG ; Ziheng LI ; Huilu CUI ; Anqun HU ; Qing LI ; Yingjie ZHENG
Chinese Journal of Microbiology and Immunology 2024;44(6):525-535
Objective:To evaluate the characteristics of vaginal flora between normal and abnormal pregnant women throughout pregnancy.Methods:Vaginal swab specimens were collected from pregnant women in the first (<14 gestation weeks, GW), second (14~28 GW) and third trimester (>28 GW) in Anqing, Anhui Province from February 2018 to February 2020. Pregnant women were divided into normal and abnormal groups according to all clinical diagnosis. The sequences of 16S rRNA gene (V3-V4) from vaginal swabs were analyzed using QIIME2 platform. The differences in the dominance of Lactobacillus, community state type (CST) transition, Alpha diversity and Beta diversity were analyzed. Diversity data after log transition were used in the analysis of linear mixed model. Results:A total of 34 pregnant women (10 normal and 24 abnormal) with 102 samples were included for analysis. The composition of vaginal flora between two groups: the relative abundance of Lactobacillus was the highest at the genus level and Lactobacillus crispatus and Lactobacillus iners was the top two species with high relative abundance. The dominance of Lactobacillus, Alpha diversity and transition of CST were also similar. Both groups had a gradually decreased trend of Alpha diversity with GW, and the Chao1, Observed species and Faith′s PD indexes′ were different in different GW ( P<0.05). All Beta diversity metrics in normal group had descending trend, with lower value of the index of first distance which implied a higher microbiota stability, while Bray-Curtis, Weighted UniFrac distance had ascending trend in abnormal group, indicating lower stability. Jaccard distance′s first distance was statistically differed among GW and Unweighted UniFrac distance′s differed between normal and abnormal groups. Conclusions:The first distance of Unweighte UniFrac distance in abnormal pregnant women is higher than that of normal pregnant women and the vaginal flora in abnormal group has lower stability.

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