Xiaoxuan Zou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xiaoxuan Zou has a total of 16 co-authors. Here are the top co-authors by number of shared publications:
- Chi Chiu Wang
Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
1 shared publication Dongmei Su
Department of Reproductive Health Research, National Research Institute for Family Planning, Beijing, China (J. Lei, H.W., R.C., D.S., J.J., J. Li, X.M., Y.Y.).
1 shared publicationHaishan Yang
Department of Laboratory Medicine (H.Y.), Haidian District Maternal and Child Health Care Hospital, Beijing, China
1 shared publicationHaiyan Liu
Department of Gynaecology and Obstetrics (H.L.), Haidian District Maternal and Child Health Care Hospital, Beijing, China
1 shared publication- Hanbin Wu
Affiliations Department of Obstetrics and Gynaecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China, National Research Institute for Family Planning, Beijing, China, National Human Genetic Resource Center, Beijing, China
1 shared publication Jiajing Jia
Department of Reproductive Health Research, National Research Institute for Family Planning, Beijing, China (J. Lei, H.W., R.C., D.S., J.J., J. Li, X.M., Y.Y.).
1 shared publication
Latest Publications
- 1 April 2026
- Published by Wolters Kluwer Health in Hypertension
- Vol. 83 (4)
DNA Methylation Markers for Pregnancy Hypertension via Machine Learning Methods
BACKGROUND: This study aims to develop a prediction model to identify individuals at risk of hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, by integrating epigenetic biomarkers and clinical factors in the first trimester of pregnancy. METHODS: A 2-stage nested case-control study, matched by age and body mass index, was conducted with 618 pregnant women in China, with peripheral blood samples collected in the first trimester to evaluate the average methylation levels of differentially methylated regions (DMRs) between controls and HDP cases. In stage 1 (discovery set), 24 controls and 27 cases were used to identify the differential DMRs. In stage 2, 294 controls and 273 cases were used to validate the previously identified DMRs. DMRs selected from the intersectional results of lasso regression, XGBoost, random forest, and Shapley Additive Explanations models were further combined with women’s clinical risk factors to construct prediction models using logistic regression. RESULTS: In stage 1, 52 differential DMRs were identified with a false-positive rate CTSA , HMGB1 , and miR1908/FADS2 genes were selected to construct a prediction model for HDPs. After combining the selected DMRs with clinical factors, the model achieved an area under the curve of 0.863 (95% CI, 0.826–0.901) in the training set and 0.757 (95% CI, 0.686–0.828) in the test set. CONCLUSION: Findings of this study offer potential opportunities to identify high-risk patients with HDP in early pregnancy through DMRs identified in peripheral blood and provide new insights into the epigenetic cause of HDP.
- 1 April 2026
- Published by Wolters Kluwer Health in Hypertension
- Vol. 83 (4)
DNA Methylation Markers for Pregnancy Hypertension via Machine Learning Methods
BACKGROUND: This study aims to develop a prediction model to identify individuals at risk of hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, by integrating epigenetic biomarkers and clinical factors in the first trimester of pregnancy. METHODS: A 2-stage nested case-control study, matched by age and body mass index, was conducted with 618 pregnant women in China, with peripheral blood samples collected in the first trimester to evaluate the average methylation levels of differentially methylated regions (DMRs) between controls and HDP cases. In stage 1 (discovery set), 24 controls and 27 cases were used to identify the differential DMRs. In stage 2, 294 controls and 273 cases were used to validate the previously identified DMRs. DMRs selected from the intersectional results of lasso regression, XGBoost, random forest, and Shapley Additive Explanations models were further combined with women’s clinical risk factors to construct prediction models using logistic regression. RESULTS: In stage 1, 52 differential DMRs were identified with a false-positive rate CTSA , HMGB1 , and miR1908/FADS2 genes were selected to construct a prediction model for HDPs. After combining the selected DMRs with clinical factors, the model achieved an area under the curve of 0.863 (95% CI, 0.826–0.901) in the training set and 0.757 (95% CI, 0.686–0.828) in the test set. CONCLUSION: Findings of this study offer potential opportunities to identify high-risk patients with HDP in early pregnancy through DMRs identified in peripheral blood and provide new insights into the epigenetic cause of HDP.