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Bradburn E, Conde-Agudelo A, Roberts NW, Villar J, Papageorghiou AT. Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis. EClinicalMedicine 2024; 70:102498. [PMID: 38495518 PMCID: PMC10940947 DOI: 10.1016/j.eclinm.2024.102498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 01/21/2024] [Accepted: 02/02/2024] [Indexed: 03/19/2024] Open
Abstract
Background Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA). Methods Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed. Findings Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively. Interpretation We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation. Funding The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.
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Affiliation(s)
- Elizabeth Bradburn
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
| | - Agustin Conde-Agudelo
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Nia W. Roberts
- Bodleian Health Care Libraries, University of Oxford, Oxford, UK
| | - Jose Villar
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Aris T. Papageorghiou
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
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Al Ghadban Y, Du Y, Charnock-Jones DS, Garmire LX, Smith GCS, Sovio U. Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study. BJOG 2023. [PMID: 37984426 DOI: 10.1111/1471-0528.17723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 09/11/2023] [Accepted: 11/02/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVES To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites. DESIGN Case-cohort design within a prospective cohort study. SETTING Cambridge, UK. POPULATION OR SAMPLE A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB. METHODS An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB. MAIN OUTCOME MEASURES sPTB and sETB. RESULTS We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860). CONCLUSIONS We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.
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Affiliation(s)
- Yasmina Al Ghadban
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - Yuheng Du
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - D Stephen Charnock-Jones
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Gordon C S Smith
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Ulla Sovio
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Centre for Trophoblast Research (CTR), Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
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Chen L, Tang Q, Zhang K, Huang Q, Ding Y, Jin B, Liu S, Hwa K, Chou CJ, Zhang Y, Thyparambil S, Liao W, Han Z, Mortensen R, Schilling J, Li Z, Heaton R, Tian L, Cohen HJ, Sylvester KG, Arent RC, Zhao X, McElhinney DB, Wu Y, Bai W, Ling XB. Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications. BMC Cancer 2023; 23:844. [PMID: 37684587 PMCID: PMC10492322 DOI: 10.1186/s12885-023-11192-8] [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: 11/23/2022] [Accepted: 07/19/2023] [Indexed: 09/10/2023] Open
Abstract
MOTIVATION Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer. METHODS A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13). RESULTS Eleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%. CONCLUSION Our comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.
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Affiliation(s)
- Linfeng Chen
- Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Qiming Tang
- Shanghai Yunxiang Medical Technology Co., Ltd., Shanghai, China
- Binhai Industrial Technology Research Institute, Zhejiang University, Tianjin, China
| | - Keying Zhang
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China
| | | | | | - Bo Jin
- Tianjin Yunjian Medical Laboratory Institute Co., Ltd, Tianjin, China
| | - Szumam Liu
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - C James Chou
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Yani Zhang
- Tianjin Yunjian Medical Laboratory Institute Co., Ltd, Tianjin, China
| | | | | | - Zhi Han
- School of Medicine, Stanford University, Stanford, CA, USA
| | | | | | - Zhen Li
- Shanghai Yunxiang Medical Technology Co., Ltd., Shanghai, China
- Binhai Industrial Technology Research Institute, Zhejiang University, Tianjin, China
| | | | - Lu Tian
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Harvey J Cohen
- School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Rebecca C Arent
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Xinyang Zhao
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Yumei Wu
- Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, China.
| | - Wenpei Bai
- Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
| | - Xuefeng B Ling
- School of Medicine, Stanford University, Stanford, CA, USA.
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Tam CCF, Chan YH, Wong YK, Li Z, Zhu X, Su KJ, Ganguly A, Hwa K, Ling XB, Tse HF. Multi-Omics Signatures Link to Ticagrelor Effects on Vascular Function in Patients With Acute Coronary Syndrome. Arterioscler Thromb Vasc Biol 2022; 42:789-798. [PMID: 35387483 DOI: 10.1161/atvbaha.121.317513] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Long-term antiplatelet agents including the potent P2Y12 antagonist ticagrelor are indicated in patients with a previous history of acute coronary syndrome. We sought to compare the effect of ticagrelor with that of aspirin monotherapy on vascular endothelial function in patients with prior acute coronary syndrome. METHODS This was a prospective, single center, parallel group, investigator-blinded randomized controlled trial. We randomized 200 patients on long-term aspirin monotherapy with prior acute coronary syndrome in a 1:1 fashion to receive ticagrelor 60 mg BD (n=100) or aspirin 100 mg OD (n=100). The primary end point was change from baseline in brachial artery flow-mediated dilation at 12 weeks. Secondary end points were changes to platelet activation marker (CD41_62p) and endothelial progenitor cell (CD34/133) count measured by flow cytometry, plasma level of adenosine, IL-6 (interleukin-6) and EGF (epidermal growth factor), and multi-omics profiling at 12 weeks. RESULTS After 12 weeks, brachial flow-mediated dilation was significantly increased in the ticagrelor group compared with the aspirin group (ticagrelor: 3.48±3.48% versus aspirin: -1.26±2.85%, treatment effect 4.73 [95% CI, 3.85-5.62], P<0.001). Nevertheless ticagrelor treatment for 12 weeks had no significant effect on platelet activation markers, circulating endothelial progenitor cell count or plasma level of adenosine, IL-6, and EGF (all P>0.05). Multi-omics pathway assessment revealed that changes in the metabolism and biosynthesis of amino acids (cysteine and methionine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis) and phospholipids (glycerophosphoethanolamines and glycerophosphoserines) were associated with improved brachial artery flow-mediated dilation in the ticagrelor group. CONCLUSIONS In patients with prior acute coronary syndrome, ticagrelor 60 mg BD monotherapy significantly improved brachial flow-mediated dilation compared with aspirin monotherapy and was associated with significant changes in metabolomic and lipidomic signatures. REGISTRATION URL: https://www. CLINICALTRIALS gov; Unique identifier: NCT03881943.
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Affiliation(s)
- Chor-Cheung Frankie Tam
- Division of Cardiology, Queen Mary Hospital, The University of Hong Kong, China (C.-C.F.T., Y.-H.C., Y.-K.W., H.-F.T.)
| | - Yap-Hang Chan
- Division of Cardiology, Queen Mary Hospital, The University of Hong Kong, China (C.-C.F.T., Y.-H.C., Y.-K.W., H.-F.T.)
| | - Yuen-Kwun Wong
- Division of Cardiology, Queen Mary Hospital, The University of Hong Kong, China (C.-C.F.T., Y.-H.C., Y.-K.W., H.-F.T.)
| | - Zhen Li
- mProbe Inc, Mountain View, CA (Z.L., X.Z.)
| | - Xiurui Zhu
- mProbe Inc, Mountain View, CA (Z.L., X.Z.)
| | | | - Anindita Ganguly
- Center for Biomedical Industry, Department of Molecular Science and Engineering National Taipei University of Technology, Taiwan (A.G., K.H.)
| | - Kuoyuan Hwa
- Center for Biomedical Industry, Department of Molecular Science and Engineering National Taipei University of Technology, Taiwan (A.G., K.H.)
| | | | - Hung-Fat Tse
- Division of Cardiology, Queen Mary Hospital, The University of Hong Kong, China (C.-C.F.T., Y.-H.C., Y.-K.W., H.-F.T.).,Cardiac and Vascular Center, Hong Kong University Shenzhen Hospital, China (H.-F.T.).,Hong Kong-Guangdong Joint Laboratory on Stem Cell and Regenerative Medicine, the University of Hong Kong, China (H.-F.T.).,Center for Translational Stem Cell Biology, Hong Kong SAR, China (H.-F.T.)
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Reiss JD, Peterson LS, Nesamoney SN, Chang AL, Pasca AM, Marić I, Shaw GM, Gaudilliere B, Wong RJ, Sylvester KG, Bonifacio SL, Aghaeepour N, Gibbs RS, Stevenson DK. Perinatal infection, inflammation, preterm birth, and brain injury: A review with proposals for future investigations. Exp Neurol 2022; 351:113988. [DOI: 10.1016/j.expneurol.2022.113988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 01/06/2022] [Accepted: 01/13/2022] [Indexed: 11/26/2022]
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