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Zhang Y, Sylvester KG, Wong RJ, Blumenfeld YJ, Hwa KY, Chou CJ, Thyparambil S, Liao W, Han Z, Schilling J, Jin B, Marić I, Aghaeepour N, Angst MS, Gaudilliere B, Winn VD, Shaw GM, Tian L, Luo RY, Darmstadt GL, Cohen HJ, Stevenson DK, McElhinney DB, Ling XB. Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling. BMC Pregnancy Childbirth 2024; 24:783. [PMID: 39587571 PMCID: PMC11587579 DOI: 10.1186/s12884-024-06974-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 11/11/2024] [Indexed: 11/27/2024] Open
Abstract
BACKGROUND Preterm birth (PTB) is a serious health problem. PTB complications is the main cause of death in infants under five years of age worldwide. The ability to accurately predict risk for PTB during early pregnancy would allow early monitoring and interventions to provide personalized care, and hence improve outcomes for the mother and infant. OBJECTIVE This study aims to predict the risks of early preterm (< 35 weeks of gestation) or very early preterm (≤ 26 weeks of gestation) deliveries by using high-resolution maternal urinary metabolomic profiling in early pregnancy. DESIGN A retrospective cohort study was conducted by two independent preterm and term cohorts using high-density weekly urine sampling. Maternal urine was collected serially at gestational weeks 8 to 24. Global metabolomics approaches were used to profile urine samples with high-resolution mass spectrometry. The significant features associated with preterm outcomes were selected by Gini Importance. Metabolite biomarker identification was performed by liquid chromatography tandem mass spectrometry (LCMS-MS). XGBoost models were developed to predict early or very early preterm delivery risk. SETTING AND PARTICIPANTS The urine samples included 329 samples from 30 subjects at Stanford University, CA for model development, and 156 samples from 24 subjects at the University of Alabama, Birmingham, AL for validation. RESULTS 12 metabolites associated with PTB were selected and identified for modelling among 7,913 metabolic features in serial-collected urine samples of pregnant women. The model to predict early PTB was developed using a set of 12 metabolites that resulted in the area under the receiver operating characteristic (AUROCs) of 0.995 (95% CI: [0.992, 0.995]) and 0.964 (95% CI: [0.937, 0.964]), and sensitivities of 100% and 97.4% during development and validation testing, respectively. Using the same metabolites, the very early PTB prediction model achieved AUROCs of 0.950 (95% CI: [0.878, 0.950]) and 0.830 (95% CI: [0.687, 0.826]), and sensitivities of 95.0% and 60.0% during development and validation, respectively. CONCLUSION Models for predicting risk of early or very early preterm deliveries were developed and tested using metabolic profiling during the 1st and 2nd trimesters of pregnancy. With patient validation studies, risk prediction models may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights of preterm birth.
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Affiliation(s)
- Yaqi Zhang
- College of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Karl G Sylvester
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Ronald J Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Yair J Blumenfeld
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Kuo Yuan Hwa
- Center for Biomedical Industry, National Taipei University of Technology, Taipei, 10608, Taiwan
| | - C James Chou
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | | | - Zhi Han
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Bo Jin
- mProbe Inc., Palo Alto, CA, 94303, USA
| | - Ivana Marić
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Nima Aghaeepour
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94303, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94303, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94303, USA
| | - Virginia D Winn
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Gary M Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Lu Tian
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Ruben Y Luo
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Gary L Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Harvey J Cohen
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - David K Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Doff B McElhinney
- Departments of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Xuefeng B Ling
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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Zhang Y, Sylvester KG, Jin B, Wong RJ, Schilling J, Chou CJ, Han Z, Luo RY, Tian L, Ladella S, Mo L, Marić I, Blumenfeld YJ, Darmstadt GL, Shaw GM, Stevenson DK, Whitin JC, Cohen HJ, McElhinney DB, Ling XB. Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy. Metabolites 2023; 13:715. [PMID: 37367874 PMCID: PMC10301596 DOI: 10.3390/metabo13060715] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/21/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.
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Affiliation(s)
- Yaqi Zhang
- College of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China;
- Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (K.G.S.); (C.J.C.); (Z.H.)
| | - Karl G. Sylvester
- Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (K.G.S.); (C.J.C.); (Z.H.)
| | - Bo Jin
- mProbe Inc., Palo Alto, CA 94303, USA; (B.J.); (J.S.)
| | - Ronald J. Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; (R.J.W.); (I.M.); (G.L.D.); (G.M.S.); (D.K.S.); (J.C.W.); (H.J.C.)
| | | | - C. James Chou
- Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (K.G.S.); (C.J.C.); (Z.H.)
| | - Zhi Han
- Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (K.G.S.); (C.J.C.); (Z.H.)
| | - Ruben Y. Luo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Lu Tian
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | | | - Lihong Mo
- UC Davis Health, Sacramento, CA 95817, USA
| | - Ivana Marić
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; (R.J.W.); (I.M.); (G.L.D.); (G.M.S.); (D.K.S.); (J.C.W.); (H.J.C.)
| | - Yair J. Blumenfeld
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Gary L. Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; (R.J.W.); (I.M.); (G.L.D.); (G.M.S.); (D.K.S.); (J.C.W.); (H.J.C.)
| | - Gary M. Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; (R.J.W.); (I.M.); (G.L.D.); (G.M.S.); (D.K.S.); (J.C.W.); (H.J.C.)
| | - David K. Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; (R.J.W.); (I.M.); (G.L.D.); (G.M.S.); (D.K.S.); (J.C.W.); (H.J.C.)
| | - John C. Whitin
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; (R.J.W.); (I.M.); (G.L.D.); (G.M.S.); (D.K.S.); (J.C.W.); (H.J.C.)
| | - Harvey J. Cohen
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; (R.J.W.); (I.M.); (G.L.D.); (G.M.S.); (D.K.S.); (J.C.W.); (H.J.C.)
| | - Doff B. McElhinney
- Departments of Cardiothoracic Surgery and Pediatrics (Cardiology), Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Xuefeng B. Ling
- Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; (K.G.S.); (C.J.C.); (Z.H.)
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