期刊
LIFE-BASEL
卷 11, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/life11101052
关键词
PPAR gamma; SNPs; machine learning; models; preeclampsia
资金
- Foundation for Medical Research (FRM)
- National Agency for Research (ANR)
- National Institute for Research in Public Health (IRESP: TGIR cohorte sante 2008 program)
- French Ministry of Health (DGS)
- French Ministry of Research
- INSERM Bone and Joint Diseases (PRO-A)
- Human Nutrition National Research Programs
- Paris-Sud University
- Nestle
- French National Institute for Population Health Surveillance (InVS)
- French National Institute for Health Education (INPES)
- European Union
- Diabetes National Research Program (French Association of Diabetic Patients (AFD))
- ANSES
- Mutuelle Generale de l'Education Nationale (a complementary health insurance
- MGEN)
- French National Agency for Food Security
- French-Speaking Association for the Study of Diabetes and Metabolism (ALFEDIAM)
- China Scholarship Council (CSC, Beijing, China)
- Inserm
- Universite de Paris
- Campus France
The study revealed a significant association between the C1431T variant of PPARγ and preeclampsia, while seven features including SNP variants, obesity, and smoking were identified as potential predictors of preeclampsia. Machine-learning algorithms, particularly the boost tree-based model, showed excellent predictive performance for preeclampsia, with high accuracy and AUC values in both training and testing sets.
Peroxisome proliferator-activated receptor gamma (PPAR gamma) is essential for placental development, whose SNPs have shown increased susceptibility to pregnancy-related diseases, such as preeclampsia. Our aim was to investigate the association between preeclampsia and three PPAR gamma SNPs (Pro12Ala, C1431T, and C681G), which together with nine clinical factors were used to build a pragmatic model for preeclampsia prediction. Data were collected from 1648 women from the EDEN cohort, of which 35 women had preeclamptic pregnancies, and the remaining 1613 women had normal pregnancies. Univariate analysis comparing preeclamptic patients to the control resulted in the SNP C1431T being the only factor significantly associated with preeclampsia (p < 0.05), with a confidence interval of 95% and odds ratio ranging from 4.90 to 8.75. On the other hand, three methods of multivariate feature selection highlighted seven features that could be potential predictors of preeclampsia: maternal C1431T and C681G variants, obesity, body mass index, number of pregnancies, primiparity, cigarette use, and education. These seven features were further used as input into eight different machine-learning algorithms to create predictive models, whose performances were evaluated based on metrics of accuracy and the area under the receiver operating characteristic curve (AUC). The boost tree-based model performed the best, with respective accuracy and AUC values of 0.971 +/- 0.002 and 0.991 +/- 0.001 in the training set and 0.951 and 0.701 in the testing set. A flowchart based on the boost tree model was constructed to depict the procedure for preeclampsia prediction. This final decision tree showed that the C1431T variant of PPAR gamma is significantly associated with susceptibility to preeclampsia. We believe that this final decision tree could be applied in the clinical prediction of preeclampsia in the very early stages of pregnancy.
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