Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients
Abstract Preeclampsia (PE) is a serious pregnancy complication that contributes to maternal and perinatal morbidity and mortality. Understanding its pathogenesis and revealing predictive biomarkers are essential for guiding treatment decisions. In order to explore the global changes of serum metabol...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87905-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862536306425856 |
|---|---|
| author | Yan Cao Lanlan Meng Yifei Wang Shenglong Zhao Yuanyuan Zheng Rui Ran Jie Du Hongqiang Wu Jiaqi Han Zhengwen Xu Yifan Lu Lin Liu Lu Chen Jing Wang Youran Li Yanhong Zhai Zhi Sun Zheng Cao |
| author_facet | Yan Cao Lanlan Meng Yifei Wang Shenglong Zhao Yuanyuan Zheng Rui Ran Jie Du Hongqiang Wu Jiaqi Han Zhengwen Xu Yifan Lu Lin Liu Lu Chen Jing Wang Youran Li Yanhong Zhai Zhi Sun Zheng Cao |
| author_sort | Yan Cao |
| collection | DOAJ |
| description | Abstract Preeclampsia (PE) is a serious pregnancy complication that contributes to maternal and perinatal morbidity and mortality. Understanding its pathogenesis and revealing predictive biomarkers are essential for guiding treatment decisions. In order to explore the global changes of serum metabolites in PE patients and identify potential predictive biomarkers for suspected PE patients (pregnant women who had already shown PE-related symptoms in the middle to late stages of pregnancy, but were not yet confirmatively diagnosed as PE.), a large-scale serum metabolomic analysis was conducted in this study with a prospective cohort of 328 suspected PE patients in the middle or late pregnancy stages, as well as a retrospective cohort of 30 healthy pregnant women and 30 PE patients. Using liquid chromatography mass spectrometry (LC − MS), serum metabolomic profiling revealed that the development of PE was closely associated with disturbed amino acid metabolism. Moreover, a panel of seven predictive biomarkers including 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, gamma-glutamyl-leucine, 2-hydroxyvaleric acid, LysoPC(16:1(9Z)/0:0), PC(DiMe(13,5)/MonoMe(13,5)), ADP-D-glycero-beta-D-manno-heptose and phenylalanyl-tryptophan were identified for PE development by performing multiple statistical analysis and LASSO regression analysis. The combination of these biomarkers showed promise in the prediction of PE development for suspected PE patients, with an AUC of 0.753 and 0.885 for the discovery and validation cohorts, respectively. These findings highlight the potential of large-scale prospective metabolomic studies combined with machine learning algorithms in identifying key biomarkers for predicting PE development, while retrospective metabolomics studies provide insights into the pathogenesis of PE. |
| format | Article |
| id | doaj-art-fdcda2335dde4162bac3c5a8097247a1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fdcda2335dde4162bac3c5a8097247a12025-02-09T12:30:51ZengNature PortfolioScientific Reports2045-23222025-02-0115111110.1038/s41598-025-87905-9Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patientsYan Cao0Lanlan Meng1Yifei Wang2Shenglong Zhao3Yuanyuan Zheng4Rui Ran5Jie Du6Hongqiang Wu7Jiaqi Han8Zhengwen Xu9Yifan Lu10Lin Liu11Lu Chen12Jing Wang13Youran Li14Yanhong Zhai15Zhi Sun16Zheng Cao17Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Obstetrics and Gynecology, Shanghai Tenth People’s Hospital, Tongji UniversityDepartment of Obstetrics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Obstetrics, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalBiotree Metabolomics Technology Research CenterBiotree Metabolomics Technology Research CenterBiotree Metabolomics Technology Research CenterDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalDepartment of Pharmacy, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care HospitalAbstract Preeclampsia (PE) is a serious pregnancy complication that contributes to maternal and perinatal morbidity and mortality. Understanding its pathogenesis and revealing predictive biomarkers are essential for guiding treatment decisions. In order to explore the global changes of serum metabolites in PE patients and identify potential predictive biomarkers for suspected PE patients (pregnant women who had already shown PE-related symptoms in the middle to late stages of pregnancy, but were not yet confirmatively diagnosed as PE.), a large-scale serum metabolomic analysis was conducted in this study with a prospective cohort of 328 suspected PE patients in the middle or late pregnancy stages, as well as a retrospective cohort of 30 healthy pregnant women and 30 PE patients. Using liquid chromatography mass spectrometry (LC − MS), serum metabolomic profiling revealed that the development of PE was closely associated with disturbed amino acid metabolism. Moreover, a panel of seven predictive biomarkers including 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, gamma-glutamyl-leucine, 2-hydroxyvaleric acid, LysoPC(16:1(9Z)/0:0), PC(DiMe(13,5)/MonoMe(13,5)), ADP-D-glycero-beta-D-manno-heptose and phenylalanyl-tryptophan were identified for PE development by performing multiple statistical analysis and LASSO regression analysis. The combination of these biomarkers showed promise in the prediction of PE development for suspected PE patients, with an AUC of 0.753 and 0.885 for the discovery and validation cohorts, respectively. These findings highlight the potential of large-scale prospective metabolomic studies combined with machine learning algorithms in identifying key biomarkers for predicting PE development, while retrospective metabolomics studies provide insights into the pathogenesis of PE.https://doi.org/10.1038/s41598-025-87905-9PreeclampsiaSerum metabolomic studyPredictive biomarkersAltered metabolic pathwaysMachine learning algorithms |
| spellingShingle | Yan Cao Lanlan Meng Yifei Wang Shenglong Zhao Yuanyuan Zheng Rui Ran Jie Du Hongqiang Wu Jiaqi Han Zhengwen Xu Yifan Lu Lin Liu Lu Chen Jing Wang Youran Li Yanhong Zhai Zhi Sun Zheng Cao Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients Scientific Reports Preeclampsia Serum metabolomic study Predictive biomarkers Altered metabolic pathways Machine learning algorithms |
| title | Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients |
| title_full | Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients |
| title_fullStr | Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients |
| title_full_unstemmed | Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients |
| title_short | Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients |
| title_sort | large scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients |
| topic | Preeclampsia Serum metabolomic study Predictive biomarkers Altered metabolic pathways Machine learning algorithms |
| url | https://doi.org/10.1038/s41598-025-87905-9 |
| work_keys_str_mv | AT yancao largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT lanlanmeng largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT yifeiwang largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT shenglongzhao largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT yuanyuanzheng largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT ruiran largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT jiedu largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT hongqiangwu largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT jiaqihan largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT zhengwenxu largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT yifanlu largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT linliu largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT luchen largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT jingwang largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT youranli largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT yanhongzhai largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT zhisun largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients AT zhengcao largescaleprospectiveserummetabolomicprofilingrevealscandidatepredictivebiomarkersforsuspectedpreeclampsiapatients |