scholarly journals Identification of Diagnostic CpG Signatures in Patients with Gestational Diabetes Mellitus via Epigenome-Wide Association Study Integrated with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yan Liu ◽  
Hui Geng ◽  
Bide Duan ◽  
Xiuzhi Yang ◽  
Airong Ma ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Association Study ◽  
Cpg Methylation ◽  
Ppi Network ◽  
Cpg Sites ◽  
Model Based ◽  
Svm Model

Background. Gestational diabetes mellitus (GDM) is the most prevalent metabolic disease during pregnancy, but the diagnosis is controversial and lagging partly due to the lack of useful biomarkers. CpG methylation is involved in the development of GDM. However, the specific CpG methylation sites serving as diagnostic biomarkers of GDM remain unclear. Here, we aimed to explore CpG signatures and establish the predicting model for the GDM diagnosis. Methods. DNA methylation data of GSE88929 and GSE102177 were obtained from the GEO database, followed by the epigenome-wide association study (EWAS). GO and KEGG pathway analyses were performed by using the clusterProfiler package of R. The PPI network was constructed in the STRING database and Cytoscape software. The SVM model was established, in which the β-values of selected CpG sites were the predictor variable and the occurrence of GDM was the outcome variable. Results. We identified 62 significant CpG methylation sites in the GDM samples compared with the control samples. GO and KEGG analyses based on the 62 CpG sites demonstrated that several essential cellular processes and signaling pathways were enriched in the system. A total of 12 hub genes related to the identified CpG sites were found in the PPI network. The SVM model based on the selected CpGs within the promoter region, including cg00922748, cg05216211, cg05376185, cg06617468, cg17097119, and cg22385669, was established, and the AUC values of the training set and testing set in the model were 0.8138 and 0.7576. The AUC value of the independent validation set of GSE102177 was 0.6667. Conclusion. We identified potential diagnostic CpG signatures by EWAS integrated with the SVM model. The SVM model based on the identified 6 CpG sites reliably predicted the GDM occurrence, contributing to the diagnosis of GDM. Our finding provides new insights into the cross-application of EWAS and machine learning in GDM investigation.

scholarly journals Epigenetic Changes in Neonates Born to Mothers With Gestational Diabetes Mellitus May Be Associated With Neonatal Hypoglycaemia

2021 ◽  
Vol 12 ◽  
Author(s):  
Yoshifumi Kasuga ◽  
Tomoko Kawai ◽  
Kei Miyakoshi ◽  
Yoshifumi Saisho ◽  
Masumi Tamagawa ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Dna Methylation ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Start Site ◽  
Epigenetic Changes ◽  
Neonatal Hypoglycaemia ◽  
Transcription Start ◽  
Cpg Sites ◽  
Alternative Transcription

The detection of epigenetic changes associated with neonatal hypoglycaemia may reveal the pathophysiology and predict the onset of future diseases in offspring. We hypothesized that neonatal hypoglycaemia reflects the in utero environment associated with maternal gestational diabetes mellitus. The aim of this study was to identify epigenetic changes associated with neonatal hypoglycaemia. The association between DNA methylation using Infinium HumanMethylation EPIC BeadChip and neonatal plasma glucose (PG) level at 1 h after birth in 128 offspring born at term to mothers with well-controlled gestational diabetes mellitus was investigated by robust linear regression analysis. Cord blood DNA methylation at 12 CpG sites was significantly associated with PG at 1 h after birth after adding infant sex, delivery method, gestational day, and blood cell compositions as covariates to the regression model. DNA methylation at two CpG sites near an alternative transcription start site of ZNF696 was significantly associated with the PG level at 1 h following birth (false discovery rate-adjusted P < 0.05). Methylation levels at these sites increased as neonatal PG levels at 1 h after birth decreased. In conclusion, gestational diabetes mellitus is associated with DNA methylation changes at the alternative transcription start site of ZNF696 in cord blood cells. This is the first report of DNA methylation changes associated with neonatal PG at 1 h after birth.

scholarly journals A Genome-Wide Association Study of Gestational Diabetes Mellitus in Korean Women

Diabetes ◽  
2012 ◽  
Vol 61 (2) ◽  
pp. 531-541 ◽  
Author(s):  
S. H. Kwak ◽  
S.-H. Kim ◽  
Y. M. Cho ◽  
M. J. Go ◽  
Y. S. Cho ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Korean Women ◽  
Genome Wide ◽  
A Genome

scholarly journals Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning

2020 ◽  
Author(s):  
Yan-Ting Wu ◽  
Chen-Jie Zhang ◽  
Ben Willem Mol ◽  
Andrew Kawai ◽  
Cheng Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Chinese Population ◽  
Feature Selection Method ◽  
Area Under The Curve ◽  
First Trimester ◽  
Record System ◽  
Increased Risk

Abstract Context Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking. Objectives Establishing effective models to predict early GDM. Setting Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Main measures Based on a machine learning (ML) driven feature selection method, 17 variables were selected for early GDM prediction. In order to facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable dataset and the 73-variable dataset to build models predicting early GDM for different situations respectively. Results 16,819 and 14,992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low BMI (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = 0.0935). TT3 and TT4 were superior to FT3 and FT4 in predicting GDM. Lipoprotein (a) was demonstrated a promising predictive value (AUC = 0.66). Conclusions We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.

scholarly journals Machine Learning Approaches for Early Prediction of Gestational Diabetes Mellitus Based on Prospective Cohort Study

2021 ◽  
Author(s):  
Jingyuan Wang ◽  
Xiujuan Chen ◽  
Yueshuai Pan ◽  
Kai Chen ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Cohort Study ◽  
Gestational Diabetes ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Early Prediction ◽  
Test Dataset ◽  
Svm Model

Abstract Purpose: To develop and verify an early prediction model of gestational diabetes mellitus (GDM) using machine learning algorithm.Methods: The dataset collected from a pregnant cohort study in eastern China, from 2017 to 2019. It was randomly divided into 75% as the training dataset and 25% as the test dataset using the train_test_split function. Based on Python, four classic machine learning algorithm and a New-Stacking algorithm were first trained by the training dataset, and then verified by the test dataset. The four models were Logical Regression (LR), Random Forest (RT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The sensitivity, specificity, accuracy, and area under the Receiver Operating Characteristic Curve (AUC) were used to analyse the performance of models.Results: Valid information from a total of 2811 pregnant women were obtained. The accuracies of the models ranged from 80.09% to 86.91% (RF), sensitivities ranged from 63.30% to 81.65% (SVM), specificities ranged from 79.38% to 97.53% (RF), and AUCs ranged from 0.80 to 0.82 (New-Stacking).Conclusion: This paper successfully constructed a New-Stacking model theoretically, for its better performance in specificity, accuracy and AUC. But the SVM model got the highest sensitivity, the SVM model was recommends as the prediction model for clinical.

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