Maternal Environmental Exposure, Infant GSTP1 Polymorphism, and Risk of Isolated Congenital Heart Disease

Cresci, Monica; Foffa, Ilenia; Ait-Alì, Lamia; Pulignani, Silvia; Kemeny, Alessandra; Gianicolo, Emilio Antonio Luca; Andreassi, Maria Grazia

doi:10.1007/s00246-012-0436-z

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…In our study, the BPNN predictors seem reasonable as most of them are reportedly associated with CHD. The risk factors of CHD reported in previous epidemiological studies include maternal low education level, [ 24 , 25 ] family history of CHD, [ 26 – 28 ] maternal diseases (e.g., upper respiratory tract infection, hyperhomocysteinaemia, phenylketonuria, diabetes mellitus, hypertension, thyroid disorders, obesity), [ 25 , 29 – 31 ] maternal and paternal exposures to occupational/environmental risks, [ 32 – 34 ] and maternal mental stress. [ 25 ] Most predictors selected by logistic regression in our study are consistent with previous reports.…”

Section: Discussionmentioning

confidence: 99%

An artificial neural network prediction model of congenital heart disease based on risk factors

Luo

Zheng

et al. 2017

Medicine

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An artificial neural network (ANN) model was developed to predict the risks of congenital heart disease (CHD) in pregnant women.This hospital-based case-control study involved 119 CHD cases and 239 controls all recruited from birth defect surveillance hospitals in Hunan Province between July 2013 and June 2014. All subjects were interviewed face-to-face to fill in a questionnaire that covered 36 CHD-related variables. The 358 subjects were randomly divided into a training set and a testing set at the ratio of 85:15. The training set was used to identify the significant predictors of CHD by univariate logistic regression analyses and develop a standard feed-forward back-propagation neural network (BPNN) model for the prediction of CHD. The testing set was used to test and evaluate the performance of the ANN model. Univariate logistic regression analyses were performed on SPSS 18.0. The ANN models were developed on Matlab 7.1.The univariate logistic regression identified 15 predictors that were significantly associated with CHD, including education level (odds ratio = 0.55), gravidity (1.95), parity (2.01), history of abnormal reproduction (2.49), family history of CHD (5.23), maternal chronic disease (4.19), maternal upper respiratory tract infection (2.08), environmental pollution around maternal dwelling place (3.63), maternal exposure to occupational hazards (3.53), maternal mental stress (2.48), paternal chronic disease (4.87), paternal exposure to occupational hazards (2.51), intake of vegetable/fruit (0.45), intake of fish/shrimp/meat/egg (0.59), and intake of milk/soymilk (0.55). After many trials, we selected a 3-layer BPNN model with 15, 12, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The prediction model has accuracies of 0.91 and 0.86 on the training and testing sets, respectively. The sensitivity, specificity, and Yuden Index on the testing set (training set) are 0.78 (0.83), 0.90 (0.95), and 0.68 (0.78), respectively. The areas under the receiver operating curve on the testing and training sets are 0.87 and 0.97, respectively.This study suggests that the BPNN model could be used to predict the risk of CHD in individuals. This model should be further improved by large-sample-size research.

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Section: Discussionmentioning

confidence: 99%

An artificial neural network prediction model of congenital heart disease based on risk factors

Luo

Zheng

et al. 2017

Medicine

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“…Hazardous substances from environmental or occupational exposure, and organic solvent exposure have all been shown to be potential risk factors for CHDs (Gorini et al 2014 ; Kalisch-Smith et al 2020 ). Maternal exposure to environmental toxins during pregnancy can increase the risk of CHDs in offspring (Cresci et al 2013 ). The living environment during pregnancy is also very important.…”

Section: Introductionmentioning

confidence: 99%

Maternal environmental, occupational, and urinary metabolite levels of benzene compounds and their association with congenital heart diseases in offspring: a case‒control study in China

Author)

Kang

et al. 2023

Environ Sci Pollut Res

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The conclusions about the association of maternal pregnancy environment, occupation, and benzene compounds with fetal CHD are not entirely consistent. Eight hundred seven CHD cases and 1008 controls were included in this study. All occupations were classified and coded against the Occupational Classification Dictionary of the People’s Republic of China (2015 version). Logistic regressions were used to explore the correlation among environmental factors, occupation types, and CHDs in offspring. We found that living near public facilities and having exposure to chemical reagents and hazardous substances were significant risk factors for CHDs in offspring. We found that offspring of mothers who worked in agriculture and similar work during pregnancy suffered from CHD. The risk of all CHDs in the offspring of pregnant women working in production manufacturing and related work was significantly higher than that in unemployed pregnant women, the risk was also observed in 4 subtypes of CHDs. We compared the concentrations of the five metabolite (MA, mHA, HA, PGA, and SPMA) levels of benzene compounds in the urine of mothers in case and control groups and found no significant differences. Our study suggests that maternal exposure during pregnancy and certain environmental and occupational conditions are risk factors for CHD in offspring, but did not support an association between concentrations of metabolites of benzene compounds in the urine of pregnant women and CHDs in their offspring.

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“…Whilst genetics play a vital role in the development of CHD, only 20–30% of individuals with CHD can be identified based on a single genetic factor [8] . Large-scale studies have suggested that environmental factors such as parental drug profiles, maternal health status can cause or interact with genetic variations to contribute to CHD [9] , [10] , [11] , [12] .…”

Section: Introductionmentioning

confidence: 99%

An update on the CHDGKB for the systematic understanding of risk factors associated with non-syndromic congenital heart disease

Yang

Liu

Chen

et al. 2021

Computational and Structural Biotechnology Journal

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The Congenital Heart Disease Genetic Knowledge Base (CHDGKB) was established in 2020 to provide comprehensive knowledge about the genetics and pathogenesis of non-syndromic CHD (NS-CHD). In addition to the genetic causes of NS-CHD, environmental factors such as maternal drug use and gene-environment interactions can also lead to CHD. There is a need to integrate this information into a platform for clinicians and researchers to better understand the overall risk factors associated with NS-CHD. The updated CHDGKB contains the genetic and non-genetic risk factors from over 4200 records from PubMed that was manually curated to include the information associated with NS-CHD. The current version of CHDGKB, named CHD-RF-KB (KnowledgeBase for non-syndromic Congenital Heart Disease-associated Risk Factors), is an important tool that allows users to evaluate the recurrence risk and prognosis of NS-CHD, to guide treatment and highlight the precautions of NS-CHD. In this update, we performed extensive functional analyses of the genetic and non-genetic risk information in CHD-RF-KB. These data can be used to systematically understand the heterogeneous relationship between risk factors and NS-CHD phenotypes.

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Maternal Environmental Exposure, Infant GSTP1 Polymorphism, and Risk of Isolated Congenital Heart Disease

Cited by 15 publications

References 28 publications

An artificial neural network prediction model of congenital heart disease based on risk factors

An artificial neural network prediction model of congenital heart disease based on risk factors

Maternal environmental, occupational, and urinary metabolite levels of benzene compounds and their association with congenital heart diseases in offspring: a case‒control study in China

An update on the CHDGKB for the systematic understanding of risk factors associated with non-syndromic congenital heart disease

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