Commentary on a combined approach to the problem of developing biomarkers for the prediction of spontaneous preterm labor that leads to preterm birth
Globally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of cli...
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| Vydáno v: | Placenta (Eastbourne) Ročník 98; s. 13 - 23 |
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| Médium: | Journal Article |
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Elsevier Ltd
01.09.2020
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| ISSN: | 0143-4004, 1532-3102, 1532-3102 |
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| Abstract | Globally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of clinical, biophysical and biochemical markers at different gestational ages. Since the aetiology of spontaneous preterm birth is also multifactorial, it is unlikely that a single biomarker test, at a specific gestational age will emerge as the definitive predictive test.
The Biomarkers Group of PREBIC, comprising clinicians, basic scientists and other experts in the field, with a particular interest in preterm birth have produced this commentary with short, medium and long-term aims: i) to alert clinicians to the advances that are being made in the prediction of spontaneous preterm birth; ii) to encourage clinicians and scientists to continue their efforts in this field, and not to be disheartened or nihilistic because of a perceived lack of progress and iii) to enable development of novel interventions that can reduce the mortality and morbidity associated with preterm birth.
Using language that we hope is clear to practising clinicians, we have identified 11 Sections in which there exists the potential, feasibility and capability of technologies for candidate biomarkers in the prediction of spontaneous preterm birth and how current limitations to this research might be circumvented.
The combination of biophysical, biochemical, immunological, microbiological, fetal cell, exosomal, or cell free RNA at different gestational ages, integrated as part of a multivariable predictor model may be necessary to advance our attempts to predict sPTL and PTB. This will require systems biological data using “omics” data and artificial intelligence/machine learning to manage the data appropriately. The ultimate goal is to reduce the mortality and morbidity associated with preterm birth. |
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| AbstractList | Globally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of clinical, biophysical and biochemical markers at different gestational ages. Since the aetiology of spontaneous preterm birth is also multifactorial, it is unlikely that a single biomarker test, at a specific gestational age will emerge as the definitive predictive test.INTRODUCTIONGlobally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of clinical, biophysical and biochemical markers at different gestational ages. Since the aetiology of spontaneous preterm birth is also multifactorial, it is unlikely that a single biomarker test, at a specific gestational age will emerge as the definitive predictive test.The Biomarkers Group of PREBIC, comprising clinicians, basic scientists and other experts in the field, with a particular interest in preterm birth have produced this commentary with short, medium and long-term aims: i) to alert clinicians to the advances that are being made in the prediction of spontaneous preterm birth; ii) to encourage clinicians and scientists to continue their efforts in this field, and not to be disheartened or nihilistic because of a perceived lack of progress and iii) to enable development of novel interventions that can reduce the mortality and morbidity associated with preterm birth.METHODSThe Biomarkers Group of PREBIC, comprising clinicians, basic scientists and other experts in the field, with a particular interest in preterm birth have produced this commentary with short, medium and long-term aims: i) to alert clinicians to the advances that are being made in the prediction of spontaneous preterm birth; ii) to encourage clinicians and scientists to continue their efforts in this field, and not to be disheartened or nihilistic because of a perceived lack of progress and iii) to enable development of novel interventions that can reduce the mortality and morbidity associated with preterm birth.Using language that we hope is clear to practising clinicians, we have identified 11 Sections in which there exists the potential, feasibility and capability of technologies for candidate biomarkers in the prediction of spontaneous preterm birth and how current limitations to this research might be circumvented.RESULTSUsing language that we hope is clear to practising clinicians, we have identified 11 Sections in which there exists the potential, feasibility and capability of technologies for candidate biomarkers in the prediction of spontaneous preterm birth and how current limitations to this research might be circumvented.The combination of biophysical, biochemical, immunological, microbiological, fetal cell, exosomal, or cell free RNA at different gestational ages, integrated as part of a multivariable predictor model may be necessary to advance our attempts to predict sPTL and PTB. This will require systems biological data using "omics" data and artificial intelligence/machine learning to manage the data appropriately. The ultimate goal is to reduce the mortality and morbidity associated with preterm birth.DISCUSSIONThe combination of biophysical, biochemical, immunological, microbiological, fetal cell, exosomal, or cell free RNA at different gestational ages, integrated as part of a multivariable predictor model may be necessary to advance our attempts to predict sPTL and PTB. This will require systems biological data using "omics" data and artificial intelligence/machine learning to manage the data appropriately. The ultimate goal is to reduce the mortality and morbidity associated with preterm birth. Globally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of clinical, biophysical and biochemical markers at different gestational ages. Since the aetiology of spontaneous preterm birth is also multifactorial, it is unlikely that a single biomarker test, at a specific gestational age will emerge as the definitive predictive test. The Biomarkers Group of PREBIC, comprising clinicians, basic scientists and other experts in the field, with a particular interest in preterm birth have produced this commentary with short, medium and long-term aims: i) to alert clinicians to the advances that are being made in the prediction of spontaneous preterm birth; ii) to encourage clinicians and scientists to continue their efforts in this field, and not to be disheartened or nihilistic because of a perceived lack of progress and iii) to enable development of novel interventions that can reduce the mortality and morbidity associated with preterm birth. Using language that we hope is clear to practising clinicians, we have identified 11 Sections in which there exists the potential, feasibility and capability of technologies for candidate biomarkers in the prediction of spontaneous preterm birth and how current limitations to this research might be circumvented. The combination of biophysical, biochemical, immunological, microbiological, fetal cell, exosomal, or cell free RNA at different gestational ages, integrated as part of a multivariable predictor model may be necessary to advance our attempts to predict sPTL and PTB. This will require systems biological data using "omics" data and artificial intelligence/machine learning to manage the data appropriately. The ultimate goal is to reduce the mortality and morbidity associated with preterm birth. AbstractIntroductionGlobally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of clinical, biophysical and biochemical markers at different gestational ages. Since the aetiology of spontaneous preterm birth is also multifactorial, it is unlikely that a single biomarker test, at a specific gestational age will emerge as the definitive predictive test. MethodsThe Biomarkers Group of PREBIC, comprising clinicians, basic scientists and other experts in the field, with a particular interest in preterm birth have produced this commentary with short, medium and long-term aims: i) to alert clinicians to the advances that are being made in the prediction of spontaneous preterm birth; ii) to encourage clinicians and scientists to continue their efforts in this field, and not to be disheartened or nihilistic because of a perceived lack of progress and iii) to enable development of novel interventions that can reduce the mortality and morbidity associated with preterm birth. ResultsUsing language that we hope is clear to practising clinicians, we have identified 11 Sections in which there exists the potential, feasibility and capability of technologies for candidate biomarkers in the prediction of spontaneous preterm birth and how current limitations to this research might be circumvented. DiscussionThe combination of biophysical, biochemical, immunological, microbiological, fetal cell, exosomal, or cell free RNA at different gestational ages, integrated as part of a multivariable predictor model may be necessary to advance our attempts to predict sPTL and PTB. This will require systems biological data using “omics” data and artificial intelligence/machine learning to manage the data appropriately. The ultimate goal is to reduce the mortality and morbidity associated with preterm birth. |
| Author | Jørgensen, J.S. Singh, R. Menon, R. Forslund, S.K. McElrath, T.F. Helmer, H. Tribe, R.M. Cobo, T. Gaba, A. Exner, M.M. Vinter, C.A. Boniface, J.J. Khan, R.N. Rasmussen, M. Petro, K. Vink, J.S. Richardson, L.S. Lamont, R.F. Christensen, I.B. Zhong, N. |
| Author_xml | – sequence: 1 givenname: R.F. surname: Lamont fullname: Lamont, R.F. email: rflamont@icloud.com organization: Research Unit of Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark – sequence: 2 givenname: L.S. surname: Richardson fullname: Richardson, L.S. organization: Dept of Obstetrics and Gynecology, The University of Texas Medical Branch at Galveston, Dept. Electrical and Computer Engineering Texas A&M University, College Station, TX, USA – sequence: 3 givenname: J.J. surname: Boniface fullname: Boniface, J.J. organization: Sera Prognostics, Inc., 2749 East Parleys Way, Suite 200, Salt Lake City, UT, 84109, USA – sequence: 4 givenname: T. surname: Cobo fullname: Cobo, T. organization: BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), Institut Clínic de Ginecología, Obstetrícia I Neonatología, Fetal i+D Fetal Medicine Research Center, Institut d’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), University of Barcelona. Barcelona. Spain, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain – sequence: 5 givenname: M.M. surname: Exner fullname: Exner, M.M. organization: Hologic, Inc., 10210 Genetic Center Dr, San Diego, CA, 92121, USA – sequence: 6 givenname: I.B. surname: Christensen fullname: Christensen, I.B. organization: ARCEDI Biotech ApS, Aarhus, Denmark – sequence: 7 givenname: S.K. surname: Forslund fullname: Forslund, S.K. organization: Experimental and Clinical Research Center, A Cooperation of Charité-Universitätsmedizin and the Max-Delbrück Center, Berlin, Germany – sequence: 8 givenname: A. surname: Gaba fullname: Gaba, A. organization: Department of Obstetrics and Maternal-fetal Medicine, Vienna Medical University, Austria – sequence: 9 givenname: H. surname: Helmer fullname: Helmer, H. organization: Department of Obstetrics and Maternal-fetal Medicine, Vienna Medical University, Austria – sequence: 10 givenname: J.S. surname: Jørgensen fullname: Jørgensen, J.S. organization: Research Unit of Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark – sequence: 11 givenname: R.N. surname: Khan fullname: Khan, R.N. organization: Division of Medical Science and Graduate Entry Medicine, School of Medicine, University of Nottingham, Room 4115, Medical School, Royal Derby Hospital Centre, Derby, DE22 3DT, UK – sequence: 12 givenname: T.F. surname: McElrath fullname: McElrath, T.F. organization: Brigham & Women's Hospital, Boston, USA – sequence: 13 givenname: K. surname: Petro fullname: Petro, K. organization: Hologic, Inc., 10210 Genetic Center Dr, San Diego, CA, 92121, USA – sequence: 14 givenname: M. surname: Rasmussen fullname: Rasmussen, M. organization: MIRVIE Inc., 820 Dubuque Ave., South San Francisco, CA, 94080, USA – sequence: 15 givenname: R. surname: Singh fullname: Singh, R. organization: ARCEDI Biotech ApS, Aarhus, Denmark – sequence: 16 givenname: R.M. surname: Tribe fullname: Tribe, R.M. organization: Dept. of Women and Children's Health, School of Life Course Sciences, King's College London, St Thomas' Hospital Campus, London, SE1 7EH, UK – sequence: 17 givenname: J.S. surname: Vink fullname: Vink, J.S. organization: Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY, USA – sequence: 18 givenname: C.A. surname: Vinter fullname: Vinter, C.A. organization: Research Unit of Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark – sequence: 19 givenname: N. surname: Zhong fullname: Zhong, N. organization: New York State Institute for Basic Research in Developmental Disabilities, 105 Forest Hill Road, Staten Island, NY, 10314, USA – sequence: 20 givenname: R. surname: Menon fullname: Menon, R. email: ram.menon@utmb.edu organization: Dept of Obstetrics and Gynecology, The University of Texas Medical Branch at Galveston, Dept. Electrical and Computer Engineering Texas A&M University, College Station, TX, USA |
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| Keywords | Transvaginal ultrasound of cervical length Biobanking Immunology Preterm birth Proteomics Biomarkers Vaginal microbiome Bioinformatics Computational statistics |
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| SubjectTerms | Biobanking Bioinformatics Biomarkers Biomarkers - blood Computational statistics Female Humans Immunology Internal Medicine Obstetric Labor, Premature - blood Obstetrics and Gynecology Pregnancy Preterm birth Proteomics Transvaginal ultrasound of cervical length Vaginal microbiome |
| Title | Commentary on a combined approach to the problem of developing biomarkers for the prediction of spontaneous preterm labor that leads to preterm birth |
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