Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling
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...
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2024-11-01
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| Online Access: | https://doi.org/10.1186/s12884-024-06974-2 |
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| author | Yaqi Zhang Karl G. Sylvester Ronald J. Wong Yair J. Blumenfeld Kuo Yuan Hwa C. James Chou Sheeno Thyparambil Weili Liao Zhi Han James Schilling Bo Jin Ivana Marić Nima Aghaeepour Martin S. Angst Brice Gaudilliere Virginia D. Winn Gary M. Shaw Lu Tian Ruben Y. Luo Gary L. Darmstadt Harvey J. Cohen David K. Stevenson Doff B. McElhinney Xuefeng B. Ling |
| author_facet | Yaqi Zhang Karl G. Sylvester Ronald J. Wong Yair J. Blumenfeld Kuo Yuan Hwa C. James Chou Sheeno Thyparambil Weili Liao Zhi Han James Schilling Bo Jin Ivana Marić Nima Aghaeepour Martin S. Angst Brice Gaudilliere Virginia D. Winn Gary M. Shaw Lu Tian Ruben Y. Luo Gary L. Darmstadt Harvey J. Cohen David K. Stevenson Doff B. McElhinney Xuefeng B. Ling |
| author_sort | Yaqi Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4bb1d4cbbaad42d7a0c124ebe708cf79 |
| institution | DOAJ |
| issn | 1471-2393 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
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| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-4bb1d4cbbaad42d7a0c124ebe708cf792025-08-20T02:49:09ZengBMCBMC Pregnancy and Childbirth1471-23932024-11-0124111110.1186/s12884-024-06974-2Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profilingYaqi Zhang0Karl G. Sylvester1Ronald J. Wong2Yair J. Blumenfeld3Kuo Yuan Hwa4C. James Chou5Sheeno Thyparambil6Weili Liao7Zhi Han8James Schilling9Bo Jin10Ivana Marić11Nima Aghaeepour12Martin S. Angst13Brice Gaudilliere14Virginia D. Winn15Gary M. Shaw16Lu Tian17Ruben Y. Luo18Gary L. Darmstadt19Harvey J. Cohen20David K. Stevenson21Doff B. McElhinney22Xuefeng B. Ling23College of Automation, Guangdong Polytechnic Normal UniversityDepartment of Surgery, Stanford University School of MedicineDepartment of Pediatrics, Stanford University School of MedicineDepartment of Obstetrics and Gynecology, Stanford University School of MedicineCenter for Biomedical Industry, National Taipei University of TechnologyDepartment of Surgery, Stanford University School of MedicinemProbe Inc.mProbe Inc.Department of Surgery, Stanford University School of MedicinemProbe Inc.mProbe Inc.Department of Pediatrics, Stanford University School of MedicineDepartment of Pediatrics, Stanford University School of MedicineDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of MedicineDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of MedicineDepartment of Obstetrics and Gynecology, Stanford University School of MedicineDepartment of Pediatrics, Stanford University School of MedicineDepartment of Surgery, Stanford University School of MedicineDepartment of Surgery, Stanford University School of MedicineDepartment of Pediatrics, Stanford University School of MedicineDepartment of Pediatrics, Stanford University School of MedicineDepartment of Pediatrics, Stanford University School of MedicineDepartments of Cardiothoracic Surgery, Stanford University School of MedicineDepartment of Surgery, Stanford University School of MedicineAbstract 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.https://doi.org/10.1186/s12884-024-06974-2Early pregnancyPreterm risk predictionSpontaneous preterm birthBiomarkerUrinary metaboliteLC-MS/MS |
| spellingShingle | Yaqi Zhang Karl G. Sylvester Ronald J. Wong Yair J. Blumenfeld Kuo Yuan Hwa C. James Chou Sheeno Thyparambil Weili Liao Zhi Han James Schilling Bo Jin Ivana Marić Nima Aghaeepour Martin S. Angst Brice Gaudilliere Virginia D. Winn Gary M. Shaw Lu Tian Ruben Y. Luo Gary L. Darmstadt Harvey J. Cohen David K. Stevenson Doff B. McElhinney Xuefeng B. Ling Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling BMC Pregnancy and Childbirth Early pregnancy Preterm risk prediction Spontaneous preterm birth Biomarker Urinary metabolite LC-MS/MS |
| title | Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling |
| title_full | Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling |
| title_fullStr | Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling |
| title_full_unstemmed | Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling |
| title_short | Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling |
| title_sort | prediction of risk for early or very early preterm births using high resolution urinary metabolomic profiling |
| topic | Early pregnancy Preterm risk prediction Spontaneous preterm birth Biomarker Urinary metabolite LC-MS/MS |
| url | https://doi.org/10.1186/s12884-024-06974-2 |
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