Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tio...Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tional,and gestational maternal diabetes,and their potential impact on the occurrence of congenital heart defects(CHD)during neonatal development.Methods:Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico,we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020.Our assessment encompassed a range of variables,including maternal age,gestational age,BMI,pregestational diabetes,gestational diabetes,hypertension,history of abortion,and presence of preeclampsia.Results:A cohort of 673 patients was included in our study.The average maternal age was 26 years,within a range of 22 to 32 years.The mean gestational age measured 39 weeks,with a median span of 38 to 39 weeks.Of the 673 patients,274(41%)mothers gave birth to neonates diagnosed with CHD.Within this group,22 cases were linked to pre-gestational diabetes,while 202 were not;20 instances were associated with gestational diabetes,compared to 200 without;and 148 cases exhibited an overweight or obese BMI,whereas 126 displayed a normal BMI.Conclusion:We identified a statistically significant correlation between pre-gestational diabetes mellitus and the occurrence of CHD.However,our analysis did not show a statistically significant association between maternal BMI and the likelihood of CHD.These results may aid in developing effective strategies to prevent and manage CHD in neonates.展开更多
CHDTEPDB(URL:http://chdtepdb.com/)is a manually integrated database for congenital heart disease(CHD)that stores the expression profiling data of CHD derived from published papers,aiming to provide rich resources for i...CHDTEPDB(URL:http://chdtepdb.com/)is a manually integrated database for congenital heart disease(CHD)that stores the expression profiling data of CHD derived from published papers,aiming to provide rich resources for investigating a deeper correlation between human CHD and aberrant transcriptome expression.The develop-ment of human diseases involves important regulatory roles of RNAs,and expression profiling data can reflect the underlying etiology of inherited diseases.Hence,collecting and compiling expression profiling data is of critical significance for a comprehensive understanding of the mechanisms and functions that underpin genetic diseases.CHDTEPDB stores the expression profiles of over 200 sets of 7 types of CHD and provides users with more convenient basic analytical functions.Due to the differences in clinical indicators such as disease type and unavoidable detection errors among various datasets,users are able to customize their selection of corresponding data for personalized analysis.Moreover,we provide a submission page for researchers to submit their own data so that increasing expression profiles as well as some other histological data could be supplemented to the database.CHDTEPDB is a user-friendly interface that allows users to quickly browse,retrieve,download,and analyze their target samples.CHDTEPDB will significantly improve the current knowledge of expression profiling data in CHD and has the potential to be exploited as an important tool for future research on the disease.展开更多
基金The San Juan Bautista School of Medicine’s Institutional Review Board approved the study(EMSJBIRB-7-2021).
文摘Background:Given the pervasive issues of obesity and diabetes both in Puerto Rico and the broader United States,there is a compelling need to investigate the intricate interplay among body mass index(BMI),pregesta-tional,and gestational maternal diabetes,and their potential impact on the occurrence of congenital heart defects(CHD)during neonatal development.Methods:Using the comprehensive System of Vigilance and Surveillance of Congenital Defects in Puerto Rico,we conducted a focused analysis on neonates diagnosed with CHD between 2016 and 2020.Our assessment encompassed a range of variables,including maternal age,gestational age,BMI,pregestational diabetes,gestational diabetes,hypertension,history of abortion,and presence of preeclampsia.Results:A cohort of 673 patients was included in our study.The average maternal age was 26 years,within a range of 22 to 32 years.The mean gestational age measured 39 weeks,with a median span of 38 to 39 weeks.Of the 673 patients,274(41%)mothers gave birth to neonates diagnosed with CHD.Within this group,22 cases were linked to pre-gestational diabetes,while 202 were not;20 instances were associated with gestational diabetes,compared to 200 without;and 148 cases exhibited an overweight or obese BMI,whereas 126 displayed a normal BMI.Conclusion:We identified a statistically significant correlation between pre-gestational diabetes mellitus and the occurrence of CHD.However,our analysis did not show a statistically significant association between maternal BMI and the likelihood of CHD.These results may aid in developing effective strategies to prevent and manage CHD in neonates.
文摘CHDTEPDB(URL:http://chdtepdb.com/)is a manually integrated database for congenital heart disease(CHD)that stores the expression profiling data of CHD derived from published papers,aiming to provide rich resources for investigating a deeper correlation between human CHD and aberrant transcriptome expression.The develop-ment of human diseases involves important regulatory roles of RNAs,and expression profiling data can reflect the underlying etiology of inherited diseases.Hence,collecting and compiling expression profiling data is of critical significance for a comprehensive understanding of the mechanisms and functions that underpin genetic diseases.CHDTEPDB stores the expression profiles of over 200 sets of 7 types of CHD and provides users with more convenient basic analytical functions.Due to the differences in clinical indicators such as disease type and unavoidable detection errors among various datasets,users are able to customize their selection of corresponding data for personalized analysis.Moreover,we provide a submission page for researchers to submit their own data so that increasing expression profiles as well as some other histological data could be supplemented to the database.CHDTEPDB is a user-friendly interface that allows users to quickly browse,retrieve,download,and analyze their target samples.CHDTEPDB will significantly improve the current knowledge of expression profiling data in CHD and has the potential to be exploited as an important tool for future research on the disease.
