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Ward VC, Lee AC, Hawken S, Otieno NA, Mujuru HA, Chimhini G, Wilson K, Darmstadt GL. Overview of the Global and US Burden of Preterm Birth. Clin Perinatol 2024; 51:301-311. [PMID: 38705642 DOI: 10.1016/j.clp.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Preterm birth (PTB) is the leading cause of morbidity and mortality in children globally, yet its prevalence has been difficult to accurately estimate due to unreliable methods of gestational age dating, heterogeneity in counting, and insufficient data. The estimated global PTB rate in 2020 was 9.9% (95% confidence interval: 9.1, 11.2), which reflects no significant change from 2010, and 81% of prematurity-related deaths occurred in Africa and Asia. PTB prevalence in the United States in 2021 was 10.5%, yet with concerning racial disparities. Few effective solutions for prematurity prevention have been identified, highlighting the importance of further research.
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Affiliation(s)
- Victoria C Ward
- Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA.
| | - Anne Cc Lee
- Department of Pediatrics, Global Advancement of Infants and Mothers, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Center for Practice Changing Research, 501 Smyth Road, Box 201-B, Ottawa, Ontario K1H 8L6, Canada
| | - Nancy A Otieno
- Kenya Medical Research Institute (KEMRI), Centre for Global Health Research, Division of Global Health Protection, Box 1578 Kisumu 40100, Kenya
| | - Hilda A Mujuru
- Department of Child Adolescent and Women's Health, Faculty of Medicine and Health Sciences, University of Zimbabwe, MP 167, Mount Pleasant, Harare, Zimbabwe
| | - Gwendoline Chimhini
- Department of Child Adolescent and Women's Health, Faculty of Medicine and Health Sciences, University of Zimbabwe, MP 167, Mount Pleasant, Harare, Zimbabwe
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Center for Practice Changing Research, 501 Smyth Road, Box 201-B, Ottawa, Ontario K1H 8L6, Canada; Department of Medicine, University of Ottawa, 501 Smyth Road, Ottawa, ON K1H 8L6, Canada; Bruyère Research Institute, 43 Bruyère Street, Ottawa, ON K1N 5C8, Canada
| | - Gary L Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA
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Ward VC, Hawken S, Chakraborty P, Darmstadt GL, Wilson K. Estimating Gestational Age and Prediction of Preterm Birth Using Metabolomics Biomarkers. Clin Perinatol 2024; 51:411-424. [PMID: 38705649 DOI: 10.1016/j.clp.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Preterm birth (PTB) is a leading cause of morbidity and mortality in children aged under 5 years globally, especially in low-resource settings. It remains a challenge in many low-income and middle-income countries to accurately measure the true burden of PTB due to limited availability of accurate measures of gestational age (GA), first trimester ultrasound dating being the gold standard. Metabolomics biomarkers are a promising area of research that could provide tools for both early identification of high-risk pregnancies and for the estimation of GA and preterm status of newborns postnatally.
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Affiliation(s)
- Victoria C Ward
- Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive Li Ka Shing Building, Stanford, CA 94305, USA
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Centre for Practice Changing Research, 501 Smyth Road, Box 201-B, Ottawa, Ontario, Canada K1H 8L6; School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, Canada K1G 5Z3.
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, 415 Smyth Road, Ottawa, Ontario K1H 8M8, Canada; Department of Pediatrics, University of Ottawa, Roger Guindon Hall, 451 Smyth Rd, Ottawa Ontario, Canada K1H 8M5
| | - Gary L Darmstadt
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Centre for Practice Changing Research, 501 Smyth Road, Box 201-B, Ottawa, Ontario, Canada K1H 8L6; Department of Medicine, University of Ottawa, Roger Guindon Hall, 451 Smyth Road, Ottawa, Ontario, Canada K1H 8M5; Bruyère Research Institute, 85 Primrose Avenue, Ottawa, Ontario, Canada K2A2E5
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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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Steurer MA, Ryckman KK, Baer RJ, Costello J, Oltman SP, McCulloch CE, Jelliffe-Pawlowski LL, Rogers EE. Developing a resiliency model for survival without major morbidity in preterm infants. J Perinatol 2023; 43:452-457. [PMID: 36220984 PMCID: PMC10079534 DOI: 10.1038/s41372-022-01521-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 09/08/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Develop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates <32 weeks of gestation using machine learning. STUDY DESIGN Models using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center. RESULTS Discrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882-0.908) for survival and 0.867 (95% CI 0.857-0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the external validation dataset (c-statistic 0.817, CI 0.741-0.893 and 0.804, CI 0.770-0.837, respectively). CONCLUSION Our successfully predicts survival and survival without major morbidity in preterm babies born at <32 weeks. This score can be used to adjust for multiple variables across administrative datasets.
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Affiliation(s)
- Martina A Steurer
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
| | - Kelli K Ryckman
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Rebecca J Baer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Jean Costello
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Scott P Oltman
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Charles E McCulloch
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Laura L Jelliffe-Pawlowski
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Elizabeth E Rogers
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
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Hawken S, Ducharme R, Murphy MSQ, Olibris B, Bota AB, Wilson LA, Cheng W, Little J, Potter BK, Denize KM, Lamoureux M, Henderson M, Rittenhouse KJ, Price JT, Mwape H, Vwalika B, Musonda P, Pervin J, Chowdhury AKA, Rahman A, Chakraborty P, Stringer JSA, Wilson K. Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers. PLoS One 2023; 18:e0281074. [PMID: 36877673 PMCID: PMC9987787 DOI: 10.1371/journal.pone.0281074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/14/2023] [Indexed: 03/07/2023] Open
Abstract
BACKGROUND Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data. METHODS We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. RESULTS Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh). CONCLUSIONS Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.
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Affiliation(s)
- Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- * E-mail:
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Malia S. Q. Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Brieanne Olibris
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - A. Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Lindsay A. Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Wei Cheng
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Julian Little
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Beth K. Potter
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Kathryn M. Denize
- Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Monica Lamoureux
- Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Matthew Henderson
- Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Katelyn J. Rittenhouse
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Joan T. Price
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | | | - Bellington Vwalika
- Department of Obstetrics and Gynaecology, University of Zambia School of Medicine, Lusaka, Zambia
| | - Patrick Musonda
- Department of Medical Statistics, University of Zambia College of Public Health, Lusaka, Zambia
| | - Jesmin Pervin
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | | | - Anisur Rahman
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Jeffrey S. A. Stringer
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
- Faculty of Medicine, Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
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Newborn screen metabolic panels reflect the impact of common disorders of pregnancy. Pediatr Res 2022; 92:490-497. [PMID: 34671094 PMCID: PMC10265936 DOI: 10.1038/s41390-021-01753-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 09/08/2021] [Accepted: 09/14/2021] [Indexed: 11/08/2022]
Abstract
BACKGROUND Hypertensive disorders of pregnancy and maternal diabetes profoundly affect fetal and newborn growth, yet disturbances in intermediate metabolism and relevant mediators of fetal growth alterations remain poorly defined. We sought to determine whether there are distinct newborn screen metabolic patterns among newborns affected by maternal hypertensive disorders or diabetes in utero. METHODS A retrospective observational study investigating distinct newborn screen metabolites in conjunction with data linked to birth and hospitalization records in the state of California between 2005 and 2010. RESULTS A total of 41,333 maternal-infant dyads were included. Infants of diabetic mothers demonstrated associations with short-chain acylcarnitines and free carnitine. Infants born to mothers with preeclampsia with severe features and chronic hypertension with superimposed preeclampsia had alterations in acetylcarnitine, free carnitine, and ornithine levels. These results were further accentuated by size for gestational age designations. CONCLUSIONS Infants of diabetic mothers demonstrate metabolic signs of incomplete beta oxidation and altered lipid metabolism. Infants of mothers with hypertensive disorders of pregnancy carry analyte signals that may reflect oxidative stress via altered nitric oxide signaling. The newborn screen analyte composition is influenced by the presence of these maternal conditions and is further associated with the newborn size designation at birth. IMPACT Substantial differences in newborn screen analyte profiles were present based on the presence or absence of maternal diabetes or hypertensive disorder of pregnancy and this finding was further influenced by the newborn size designation at birth. The metabolic health of the newborn can be examined using the newborn screen and is heavily impacted by the condition of the mother during pregnancy. Utilizing the newborn screen to identify newborns affected by common conditions of pregnancy may help relate an infant's underlying biological disposition with their clinical phenotype allowing for greater risk stratification and intervention.
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Sazawal S, Das S, Ryckman KK, Khanam R, Nisar I, Deb S, Jasper EA, Rahman S, Mehmood U, Dutta A, Chowdhury NH, Barkat A, Mittal H, Ahmed S, Khalid F, Ali SM, Raqib R, Ilyas M, Nizar A, Manu A, Russell D, Yoshida S, Baqui AH, Jehan F, Dhingra U, Bahl R. Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa. J Glob Health 2022; 12:04021. [PMID: 35493781 PMCID: PMC9022771 DOI: 10.7189/jogh.12.04021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature. Methods A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes. Results With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature. Conclusions In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs.
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Affiliation(s)
- Sunil Sazawal
- Center for Public Health Kinetics, New Delhi, India,Public Health Laboratory-IDC, Chake Chake, Tanzania
| | - Sayan Das
- Center for Public Health Kinetics, New Delhi, India
| | | | - Rasheda Khanam
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Saikat Deb
- Center for Public Health Kinetics, New Delhi, India,Public Health Laboratory-IDC, Chake Chake, Tanzania
| | | | | | | | - Arup Dutta
- Center for Public Health Kinetics, New Delhi, India
| | | | | | | | | | | | | | - Rubhana Raqib
- International Center for Diarrheal Disease Research, Dhaka, Bangladesh
| | | | | | - Alexander Manu
- Department of Maternal, Newborn, Child and Adolescent Health, and Ageing, Geneva, Switzerland
| | | | - Sachiyo Yoshida
- Department of Maternal, Newborn, Child and Adolescent Health, and Ageing, Geneva, Switzerland
| | - Abdullah H Baqui
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Usha Dhingra
- Center for Public Health Kinetics, New Delhi, India
| | - Rajiv Bahl
- Department of Maternal, Newborn, Child and Adolescent Health, and Ageing, Geneva, Switzerland
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Jasper EA, Oltman SP, Rogers EE, Dagle JM, Murray JC, Kamya M, Kakuru A, Kajubi R, Ochieng T, Adrama H, Okitwi M, Olwoch P, Jagannathan P, Clark TD, Dorsey G, Ruel T, Jelliffe-Pawlowski LL, Ryckman KK. Targeted newborn metabolomics: prediction of gestational age from cord blood. J Perinatol 2022; 42:181-186. [PMID: 35067676 PMCID: PMC8830770 DOI: 10.1038/s41372-021-01253-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 10/11/2021] [Accepted: 10/14/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Our study sought to determine whether metabolites from a retrospective collection of banked cord blood specimens could accurately estimate gestational age and to validate these findings in cord blood samples from Busia, Uganda. STUDY DESIGN Forty-seven metabolites were measured by tandem mass spectrometry or enzymatic assays from 942 banked cord blood samples. Multiple linear regression was performed, and the best model was used to predict gestational age, in weeks, for 150 newborns from Busia, Uganda. RESULTS The model including metabolites and birthweight, predicted the gestational ages within 2 weeks for 76.7% of the Ugandan cohort. Importantly, this model estimated the prevalence of preterm birth <34 weeks closer to the actual prevalence (4.67% and 4.00%, respectively) than a model with only birthweight which overestimates the prevalence by 283%. CONCLUSION Models that include cord blood metabolites and birth weight appear to offer improvement in gestational age estimation over birth weight alone.
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Affiliation(s)
| | - Scott P Oltman
- University of California, San Francisco, Department of Epidemiology & Biostatistics, Kampala, Uganda.,UCSF California Preterm Birth Initiative, Kampala, Uganda
| | - Elizabeth E Rogers
- University of California San Francisco, Department of Pediatrics, Kampala, Uganda
| | - John M Dagle
- University of Iowa, Department of Pediatrics, Kampala, Uganda
| | | | - Moses Kamya
- Infectious Diseases Research Collaboration, Kampala, Uganda.,Department of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Abel Kakuru
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Richard Kajubi
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Teddy Ochieng
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Harriet Adrama
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Martin Okitwi
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Peter Olwoch
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | | | - Tamara D. Clark
- Department of Medicine, University of California, San Francisco School of Medicine, San Francisco, CA
| | - Grant Dorsey
- Department of Medicine, University of California, San Francisco School of Medicine, San Francisco, CA
| | - Theodore Ruel
- Department of Pediatrics, University of California, San Francisco School of Medicine, San Francisco, CA
| | - Laura L Jelliffe-Pawlowski
- University of California, San Francisco, Department of Epidemiology & Biostatistics, Kampala, Uganda.,UCSF California Preterm Birth Initiative, Kampala, Uganda
| | - Kelli K Ryckman
- Department of Epidemiology, University of Iowa, Iowa, IA, USA.
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Wehby GL. Gestational Age, Newborn Metabolic Markers and Academic Achievement. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031549. [PMID: 35162571 PMCID: PMC8834716 DOI: 10.3390/ijerph19031549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/19/2022] [Accepted: 01/26/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Gestational age is associated with greater school achievement and variation in newborn metabolic markers. Whether metabolic markers are related to gestational age differences in achievement is unknown. This study examines whether newborn metabolic markers are associated with gestational age differences in performance on standardized school tests. METHODS This retrospective cohort study linked birth certificates of children born in Iowa between 2002 and 2010 to newborn screening records and school tests between 2009 and 2018. The analytical sample includes up to 229,679 children and 973,247 child-grade observations. Regression models estimate the associations between gestational age and 37 newborn metabolic markers with national percentile ranking (NPR) scores on math, reading comprehension, and science tests. RESULTS An additional gestational week is associated with 0.6 (95% CI: 0.6, 0.7), 0.5 (95% CI: 0.4, 0.5), and 0.4 (95% CI: 0.4, 0.5) higher NPRs on math, reading, and science, respectively. Compared to full term children (37-44 weeks), preterm children (32-36 weeks) have 2.2 (95% CI: -2.6, -1.8), 1.5 (95% CI: -1.9, -1.1), and 1.0 (95% CI: -1.4, -0.7) lower NPRs on math, reading comprehension, and science. Very preterm children (20-31 weeks) have 8.3 (95% CI: -9.4, -7.2), 5.2 (95% CI: -6.2, -4.0), and 4.7 (95% CI: -5.6, -3.8) lower NPRs than full term children on math, reading, and science. Metabolic markers are associated with 27%, 36%, and 45% of gestational age differences in math, reading, and science scores, respectively, and over half of the difference in test scores between preterm or very preterm and full term children. CONCLUSIONS Newborn metabolic markers are strongly related to gestational age differences in school test scores, suggesting that early metabolic differences are important markers of long-term child development.
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Affiliation(s)
- George L. Wehby
- Department of Health Management and Policy, University of Iowa, Iowa City, IA 52242, USA;
- Department of Economics, University of Iowa, Iowa City, IA 52242, USA
- Department of Preventive & Community Dentistry, University of Iowa, Iowa City, IA 52242, USA
- Public Policy Center, University of Iowa, Iowa City, IA 52242, USA
- National Bureau of Economic Research, Cambridge, MA 02138, USA
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Ryckman KK, Ramesh A, Cho H, Oltman SP, Rogers EE, Dagle JM, Jelliffe-Pawlowski LL. Evaluation of heparinized syringes for measuring newborn metabolites in neonates with a central arterial line. Clin Biochem 2022; 99:78-81. [PMID: 34688611 PMCID: PMC8671267 DOI: 10.1016/j.clinbiochem.2021.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/24/2021] [Accepted: 10/19/2021] [Indexed: 01/03/2023]
Abstract
Newborn metabolic screening is emerging as a novel method for predicting neonatal morbidity and mortality in neonates born very preterm (<32 weeks gestation). The purpose of our study was to determine if blood collected by an electrolyte-balanced dry lithium heparin syringe, as is routine for blood gas measurements, affects targeted metabolite and biomarker levels. Two blood samples (one collected with a heparinized syringe and the other with a non-heparinized syringe) were obtained at the same time from 20 infants with a central arterial line and tested for 49 metabolites and biomarkers using standard procedures for newborn screening. Overall, the median metabolite levels did not significantly differ by syringe type. However, there was wide variability, particularly for amino acids and immunoreactive trypsinogen, for individual paired samples and therefore, consideration should be given to sample collection when using these metabolites in prediction models of neonatal morbidity and mortality.
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Affiliation(s)
| | | | | | - Scott P Oltman
- University of California, San Francisco, Department of Epidemiology & Biostatistics,UCSF California Preterm Birth Initiative
| | - Elizabeth E Rogers
- UCSF California Preterm Birth Initiative,University of California San Francisco, Department of Pediatrics
| | | | - Laura L Jelliffe-Pawlowski
- University of California, San Francisco, Department of Epidemiology & Biostatistics,UCSF California Preterm Birth Initiative
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11
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Hawken S, Ward V, Bota AB, Lamoureux M, Ducharme R, Wilson LA, Otieno N, Munga S, Nyawanda BO, Atito R, Stevenson DK, Chakraborty P, Darmstadt GL, Wilson K. Real world external validation of metabolic gestational age assessment in Kenya. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000652. [PMID: 36962760 PMCID: PMC10021775 DOI: 10.1371/journal.pgph.0000652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/20/2022] [Indexed: 11/29/2022]
Abstract
Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.
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Affiliation(s)
- Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Victoria Ward
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - A Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Monica Lamoureux
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Nancy Otieno
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Stephen Munga
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Bryan O Nyawanda
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Raphael Atito
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - David K Stevenson
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada
- Departments of Pediatrics, and of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Canada
| | - Gary L Darmstadt
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Ottawa, Canada
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12
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Immunoreactive Trypsinogen and Free Carnitine Changes on Newborn Screening after Birth in Patients Who Develop Type 1 Diabetes. Nutrients 2021; 13:nu13103669. [PMID: 34684667 PMCID: PMC8538382 DOI: 10.3390/nu13103669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 11/16/2022] Open
Abstract
Are free carnitine concentrations on newborn screening (NBS) 48–72 h after birth lower in patients who develop type 1 diabetes than in controls? A retrospective case-control study of patients with type 1 diabetes was conducted. NBS results of patients from a Sydney hospital were compared against matched controls from the same hospital (1:5). Multiple imputation was performed for estimating missing data (gestational age) using gender and birthweight. Conditional logistic regression was used to control for confounding and to generate parameter estimates (α = 0.05). The Hommel approach was used for post-hoc analyses. Results are reported as medians and interquartile ranges. A total of 159 patients were eligible (80 females). Antibodies were detectable in 86. Median age at diagnosis was 8 years. Free carnitine concentrations were lower in patients than controls (25.50 µmol/L;18.98–33.61 vs. 27.26; 21.22–34.86 respectively) (p = 0.018). Immunoreactive trypsinogen was higher in this group (20.24 µg/L;16.15–29–52 vs. 18.71; 13.96–26.92) (p = 0.045), which did not persist in the post-hoc analysis. Carnitine levels are lower and immunoreactive trypsinogen might be higher, within 2–3 days of birth and years before development of type 1 diabetes as compared to controls, although the differences were well within reference ranges and provide insight into the pathogenesis into neonatal onset of type 1 diabetes development rather than use as a diagnostic tool. Given trypsinogen’s use for evaluation of new-onset type 1 diabetes, larger studies are warranted.
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Sazawal S, Ryckman KK, Das S, Khanam R, Nisar I, Jasper E, Dutta A, Rahman S, Mehmood U, Bedell B, Deb S, Chowdhury NH, Barkat A, Mittal H, Ahmed S, Khalid F, Raqib R, Manu A, Yoshida S, Ilyas M, Nizar A, Ali SM, Baqui AH, Jehan F, Dhingra U, Bahl R. Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa. BMC Pregnancy Childbirth 2021; 21:609. [PMID: 34493237 PMCID: PMC8424940 DOI: 10.1186/s12884-021-04067-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 08/14/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings. METHODS This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed. RESULTS Overall model estimated GA had MAE of 5.2 days (95% CI 4.6-6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6-6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31-94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0-99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5-23.7; p = 0.002). CONCLUSIONS Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.
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Affiliation(s)
- Sunil Sazawal
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India.
| | - Kelli K Ryckman
- College of Public Health, Department of Epidemiology, University of Iowa, 145 N. Riverside Dr. , S435, Iowa City, IA, 52242, USA
| | - Sayan Das
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India
| | - Rasheda Khanam
- Department of International Health, Johns Hopkins Bloomberg School for Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA
| | - Imran Nisar
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Elizabeth Jasper
- College of Public Health, Department of Epidemiology, University of Iowa, 145 N. Riverside Dr. , S435, Iowa City, IA, 52242, USA
| | - Arup Dutta
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India
| | - Sayedur Rahman
- Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh
| | - Usma Mehmood
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Bruce Bedell
- College of Public Health, Department of Epidemiology, University of Iowa, 145 N. Riverside Dr. , S435, Iowa City, IA, 52242, USA
| | - Saikat Deb
- Public Health Laboratory-IDC, Chake Chake, Pemba, Tanzania
| | - Nabidul Haque Chowdhury
- Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh
| | - Amina Barkat
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Harshita Mittal
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India
| | - Salahuddin Ahmed
- Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh
| | - Farah Khalid
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Rubhana Raqib
- International Centre for Diarrhoeal Disease Research, Mohakhali, Dhaka, 1212, Bangladesh
| | - Alexander Manu
- Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland
| | - Sachiyo Yoshida
- Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland
| | - Muhammad Ilyas
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Ambreen Nizar
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | | | - Abdullah H Baqui
- Department of International Health, Johns Hopkins Bloomberg School for Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA
| | - Fyezah Jehan
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Usha Dhingra
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India
| | - Rajiv Bahl
- Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland.
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14
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Wilson K, Ward V, Chakraborty P, Darmstadt GL. A novel way of determining gestational age upon the birth of a child. J Glob Health 2021; 11:03078. [PMID: 34552714 PMCID: PMC8442512 DOI: 10.7189/jogh.11.03078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Affiliation(s)
- Kumanan Wilson
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Bruyère and Hospital Research Institutes, Ottawa Ontario, Canada
| | - Victoria Ward
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Pranesh Chakraborty
- Department of Pediatrics, Children’s Hospital of Eastern Ontario and University of Ottawa, Ottawa, Ontario, Canada
- Newborn Screening Ontario, Ottawa, Ontario, Canada
| | - Gary L Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
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15
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Sazawal S, Ryckman KK, Mittal H, Khanam R, Nisar I, Jasper E, Rahman S, Mehmood U, Das S, Bedell B, Chowdhury NH, Barkat A, Dutta A, Deb S, Ahmed S, Khalid F, Raqib R, Ilyas M, Nizar A, Ali SM, Manu A, Yoshida S, Baqui AH, Jehan F, Dhingra U, Bahl R. Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation. J Glob Health 2021; 11:04044. [PMID: 34326994 PMCID: PMC8285766 DOI: 10.7189/jogh.11.04044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Globally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision. METHODS Dried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard). RESULTS Overall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P < 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%. CONCLUSION Metabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately.
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Affiliation(s)
- Sunil Sazawal
- Center for Public Health Kinetics, Global Division, New Delhi, India
- Public Health Laboratory-IDC, Chake Chake, Pemba,Tanzania
| | - Kelli K Ryckman
- University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USA
| | - Harshita Mittal
- Center for Public Health Kinetics, Global Division, New Delhi, India
| | - Rasheda Khanam
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Imran Nisar
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Elizabeth Jasper
- University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USA
| | | | - Usma Mehmood
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Sayan Das
- Center for Public Health Kinetics, Global Division, New Delhi, India
| | - Bruce Bedell
- University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USA
| | | | - Amina Barkat
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Arup Dutta
- Center for Public Health Kinetics, Global Division, New Delhi, India
| | - Saikat Deb
- Center for Public Health Kinetics, Global Division, New Delhi, India
- Public Health Laboratory-IDC, Chake Chake, Pemba,Tanzania
| | | | - Farah Khalid
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Rubhana Raqib
- International Center for Diarrheal Disease Research, Bangladesh, Mohakhali, Dhaka, Bangladesh
| | - Muhammad Ilyas
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Ambreen Nizar
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | | | - Alexander Manu
- World Health Organization (MCA/MRD), Geneva, Switzerland
| | | | - Abdullah H Baqui
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Fyezah Jehan
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Usha Dhingra
- Center for Public Health Kinetics, Global Division, New Delhi, India
| | - Rajiv Bahl
- World Health Organization (MCA/MRD), Geneva, Switzerland
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16
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Hawken S, Murphy MSQ, Ducharme R, Bota AB, Wilson LA, Cheng W, Tumulak MAJ, Alcausin MML, Reyes ME, Qiu W, Potter BK, Little J, Walker M, Zhang L, Padilla C, Chakraborty P, Wilson K. External validation of machine learning models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants. Gates Open Res 2021; 4:164. [PMID: 34104876 PMCID: PMC8160452 DOI: 10.12688/gatesopenres.13131.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/07/2021] [Indexed: 11/30/2022] Open
Abstract
Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted. Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China. Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China. For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings. Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility
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Affiliation(s)
- Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Malia S Q Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - A Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Wei Cheng
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Ma-Am Joy Tumulak
- Newborn Screening Reference Centre, University of the Philippines Manila, Manila, Philippines
| | | | - Ma Elouisa Reyes
- Newborn Screening Reference Centre, University of the Philippines Manila, Manila, Philippines
| | - Wenjuan Qiu
- Pediatric Endocrinology and Genetic Metabolism, XinHua Hospital, Shanghai, Shanghai, China
| | - Beth K Potter
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Julian Little
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Mark Walker
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.,Better Outcomes Registry & Network, Ottawa, Canada
| | - Lin Zhang
- Department of Gynecology and Obsetrics, XinHua Hospital, Shanghai, Shanghai, China.,MOE-Shanghai Key Lab of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Carmencita Padilla
- Department of Pediatrics, University of the Philippines Manila, Manilla, Philippines.,Institute of Human Genetics, National Institutes of Health, University of Philippines Manila, Manila, Philippines
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada.,Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.,Department of Medicine, University of Ottowa, Ottowa, ON, Canada.,Bruyère Research Institute, Ottowa, ON, Canada
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17
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Metabolites from midtrimester plasma of pregnant patients at high risk for preterm birth. Am J Obstet Gynecol MFM 2021; 3:100393. [PMID: 33991707 DOI: 10.1016/j.ajogmf.2021.100393] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 04/26/2021] [Accepted: 05/03/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND There is an increased awareness regarding the association between exposure to environmental contaminants and adverse pregnancy outcomes including preterm birth. Whether an individual's metabolic profile can be utilized during pregnancy to differentiate the subset of patients who are ultimately destined to delivered preterm remains uncertain but could have MEANINGFUL clinical implications. OBJECTIVE We sought to objectively quantify metabolomic profiles of patients at high risk of preterm birth by evaluating midtrimester maternal plasma and to measure whether endogenous metabolites and exogenous environmental substances differ among those who ultimately deliver preterm compared with those who deliver at term. STUDY DESIGN This was a case-control analysis from a prospective cohort of patients carrying a singleton, nonanomalous gestation who were at high risk of spontaneous preterm birth. Subjects with a plasma blood sample drawn at <28 weeks' gestation and no evidence of preterm labor at the time of enrollment were included. Metabolites were extracted from frozen samples, and metabolomic analysis was performed using liquid chromatography/mass spectrometry. The primary outcome was preterm birth at 16.0 to 36.9 weeks' gestation. RESULTS A total of 42 patients met the inclusion criteria. Of these, 25 (59.5%) delivered preterm at <37 weeks' gestation, at a median of 30.14 weeks' gestation (interquartile range, 28.14-34.14). A total of 812 molecular features differed between preterm birth cases and term controls with a minimum fold change of 1.2 and P<.05. Of these, 570 of 812 (70.1%) were found in higher abundances in preterm birth cases; the other 242 of 812 (29.9%) were in higher abundance in term birth controls. The identity of the small molecule/compound represented by the molecular features differing statistically between preterm birth cases and term controls was identified as ranging from those involved with endogenous metabolic pathways (including lipid catabolism, steroids, and steroid-related molecules) to exogenous exposures (including avocadyne, diosgenin, polycyclic aromatic hydrocarbons, acetaminophen metabolites, aspartame, and caffeine). Random forest analyses evaluating the relative contribution of each of the top 30 compounds in differentiating preterm birth and term controls accurately classified 21 of 25 preterm birth cases (84%). CONCLUSION Both endogenous metabolites and exogenous exposures differ in maternal plasma in the midtrimester among patients who ultimately delivered preterm compared with those who deliver at term.
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18
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Gestational age-dependent development of the neonatal metabolome. Pediatr Res 2021; 89:1396-1404. [PMID: 32942288 DOI: 10.1038/s41390-020-01149-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/08/2020] [Accepted: 08/20/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Prematurity is a severe pathophysiological condition, however, little is known about the gestational age-dependent development of the neonatal metabolome. METHODS Using an untargeted liquid chromatography-tandem mass spectrometry metabolomics protocol, we measured over 9000 metabolites in 298 neonatal residual heel prick dried blood spots retrieved from the Danish Neonatal Screening Biobank. By combining multiple state-of-the-art metabolome mining tools, we retrieved chemical structural information at a broad level for over 5000 (60%) metabolites and assessed their relation to gestational age. RESULTS A total of 1459 (~16%) metabolites were significantly correlated with gestational age (false discovery rate-adjusted P < 0.05), whereas 83 metabolites explained on average 48% of the variance in gestational age. Using a custom algorithm based on hypergeometric testing, we identified compound classes (617 metabolites) overrepresented with metabolites correlating with gestational age (P < 0.05). Metabolites significantly related to gestational age included bile acids, carnitines, polyamines, amino acid-derived compounds, nucleotides, phosphatidylcholines and dipeptides, as well as treatment-related metabolites, such as antibiotics and caffeine. CONCLUSIONS Our findings elucidate the gestational age-dependent development of the neonatal blood metabolome and suggest that the application of metabolomics tools has great potential to reveal novel biochemical underpinnings of disease and improve our understanding of complex pathophysiological mechanisms underlying prematurity-associated disorders. IMPACT A large variation in the neonatal dried blood spot metabolome from residual heel pricks stored at the Danish Neonatal Screening Biobank can be explained by gestational age. While previous studies have assessed the relation of selected metabolic markers to gestational age, this study assesses metabolome-wide changes related to prematurity. Using a combination of recently developed metabolome mining tools, we assess the relation of over 9000 metabolic features to gestational age. The ability to assess metabolome-wide changes related to prematurity in neonates could pave the way to finding novel biochemical underpinnings of health complications related to preterm birth.
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Oltman SP, Rogers EE, Baer RJ, Jasper EA, Anderson JG, Steurer MA, Pantell MS, Petersen MA, Partridge JC, Karasek D, Ross KM, Feuer SK, Franck LS, Rand L, Dagle JM, Ryckman KK, Jelliffe-Pawlowski LL. Newborn metabolic vulnerability profile identifies preterm infants at risk for mortality and morbidity. Pediatr Res 2021; 89:1405-1413. [PMID: 33003189 PMCID: PMC8061535 DOI: 10.1038/s41390-020-01148-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 01/09/2023]
Abstract
BACKGROUND Identifying preterm infants at risk for mortality or major morbidity traditionally relies on gestational age, birth weight, and other clinical characteristics that offer underwhelming utility. We sought to determine whether a newborn metabolic vulnerability profile at birth can be used to evaluate risk for neonatal mortality and major morbidity in preterm infants. METHODS This was a population-based retrospective cohort study of preterm infants born between 2005 and 2011 in California. We created a newborn metabolic vulnerability profile wherein maternal/infant characteristics along with routine newborn screening metabolites were evaluated for their association with neonatal mortality or major morbidity. RESULTS Nine thousand six hundred and thirty-nine (9.2%) preterm infants experienced mortality or at least one complication. Six characteristics and 19 metabolites were included in the final metabolic vulnerability model. The model demonstrated exceptional performance for the composite outcome of mortality or any major morbidity (AUC 0.923 (95% CI: 0.917-0.929). Performance was maintained across mortality and morbidity subgroups (AUCs 0.893-0.979). CONCLUSIONS Metabolites measured as part of routine newborn screening can be used to create a metabolic vulnerability profile. These findings lay the foundation for targeted clinical monitoring and further investigation of biological pathways that may increase the risk of neonatal death or major complications in infants born preterm. IMPACT We built a newborn metabolic vulnerability profile that could identify preterm infants at risk for major morbidity and mortality. Identifying high-risk infants by this method is novel to the field and outperforms models currently in use that rely primarily on infant characteristics. Utilizing the newborn metabolic vulnerability profile for precision clinical monitoring and targeted investigation of etiologic pathways could lead to reductions in the incidence and severity of major morbidities associated with preterm birth.
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Affiliation(s)
- Scott P. Oltman
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California,Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, California
| | - Elizabeth E. Rogers
- Department of Pediatrics, University of California San Francisco, San Francisco, California
| | - Rebecca J. Baer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California,Department of Pediatrics, University of California San Diego, La Jolla, CA
| | | | - James G. Anderson
- Department of Pediatrics, University of California San Francisco, San Francisco, California
| | - Martina A. Steurer
- Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, California,Department of Pediatrics, University of California San Francisco, San Francisco, California
| | - Matthew S. Pantell
- Department of Pediatrics, University of California San Francisco, San Francisco, California
| | - Mark A. Petersen
- Department of Pediatrics, University of California San Francisco, San Francisco, California
| | - J. Colin Partridge
- Department of Pediatrics, University of California San Francisco, San Francisco, California
| | - Deborah Karasek
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California,Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California San Francisco, San Francisco, California
| | - Kharah M. Ross
- Owerko Centre, Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta
| | - Sky K. Feuer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California,Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California San Francisco, San Francisco, California
| | - Linda S. Franck
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California,School of Nursing, University of California San Francisco, San Francisco California
| | - Larry Rand
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California,Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California San Francisco, San Francisco, California
| | - John M. Dagle
- Department of Pediatric, University of Iowa, Iowa City, IA
| | - Kelli K. Ryckman
- Department of Epidemiology, University of Iowa, Iowa City, IA,Department of Pediatric, University of Iowa, Iowa City, IA
| | - Laura L. Jelliffe-Pawlowski
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California,Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, California
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McCarthy ME, Oltman SP, Rogers EE, Ryckman K, Jelliffe-Pawlowski LL, Danilack VA. The independent and combined influences of small for gestational age and socioeconomic status on newborn metabolite levels. J Matern Fetal Neonatal Med 2021; 35:6192-6198. [PMID: 33882790 DOI: 10.1080/14767058.2021.1909562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
OBJECTIVES To determine whether socioeconomic status (SES) and small birthweight for gestational age (SGA) exhibit independent or joint effects on infant levels of 42 metabolites. STUDY DESIGN Population-based retrospective cohort of metabolic newborn screening information linked to hospital discharge data. SGA infants defined by birthweight <10th percentile for gestational age by sex. SES was determined by a combined metric including education level, participation in the WIC nutritional assistance program, and receiving California MediCal insurance. We performed linear regression to determine the effects of SES independently, SGA independently, and the interaction of SGA and SES on 42 newborn metabolite levels. RESULTS 736,435 California infants born in 2005-2011 were included in the analysis. SGA was significantly associated with 36 metabolites. SES was significantly associated with 41 of 42 metabolites. Thirty-eight metabolites exhibited a dose-response relationship between SGA and metabolite levels as SES worsened. Fourteen metabolites showed significant interaction between SES and SGA. Eight metabolites showed significant individual and joint effects of SES and SGA: alanine, glycine, free carnitine, C-3DC, C-5DC, C-16:1, C-18:1, and C-18:2. CONCLUSIONS SES and SGA exhibited independent effects on a majority of metabolites and joint effects on select metabolites. A better understanding of how SES and SGA status are related to infant metabolites may help identify maternal and newborn interventions that can lead to better outcomes for infants born SGA.
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Affiliation(s)
- Molly E McCarthy
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, CA, USA.,UCSF California Preterm Birth Initiative, University of California San Francisco School of Medicine, San Francisco, CA, USA.,Brown University School of Public Health, Providence, RI, USA
| | - Scott P Oltman
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, CA, USA.,UCSF California Preterm Birth Initiative, University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Elizabeth E Rogers
- UCSF California Preterm Birth Initiative, University of California San Francisco School of Medicine, San Francisco, CA, USA.,Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Kelli Ryckman
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, USA
| | - Laura L Jelliffe-Pawlowski
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, CA, USA.,UCSF California Preterm Birth Initiative, University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Valery A Danilack
- Brown University School of Public Health, Providence, RI, USA.,Department of Obstetrics and Gynecology, Women & Infants Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA
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21
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Oltman SP, Jasper EA, Kajubi R, Ochieng T, Kakuru A, Adrama H, Okitwi M, Olwoch P, Kamya M, Bedell B, McCarthy M, Dagle J, Jagannathan P, Clark TD, Dorsey G, Rand L, Ruel T, Rogers EE, Ryckman KK, Jelliffe-Pawlowski LL. Gestational age dating using newborn metabolic screening: A validation study in Busia, Uganda. J Glob Health 2021; 11:04012. [PMID: 33692896 PMCID: PMC7916447 DOI: 10.7189/jogh.11.04012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Scott P Oltman
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, California, USA.,Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
| | - Elizabeth A Jasper
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, Iowa, USA
| | - Richard Kajubi
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Teddy Ochieng
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Abel Kakuru
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Harriet Adrama
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Martin Okitwi
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Peter Olwoch
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Moses Kamya
- Infectious Diseases Research Collaboration, Kampala, Uganda.,Department of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Bruce Bedell
- Department of Pediatrics, University of Iowa, Iowa City, Iowa, USA
| | - Molly McCarthy
- Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
| | - John Dagle
- Department of Pediatrics, University of Iowa, Iowa City, Iowa, USA
| | - Prasanna Jagannathan
- Department of Medicine, Stanford University Medical Center, Stanford, California, USA
| | - Tamara D Clark
- Department of Medicine, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Grant Dorsey
- Department of Medicine, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Larry Rand
- Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA.,Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California San Francisco, San Francisco, California, USA
| | - Theodore Ruel
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Elizabeth E Rogers
- Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Kelli K Ryckman
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, Iowa, USA
| | - Laura L Jelliffe-Pawlowski
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, California, USA.,Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
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Wang B, Zhang Q, Wang Q, Ma J, Cao X, Chen Y, Pan Y, Li H, Xiang J, Wang T. Investigating the Metabolic Model in Preterm Neonates by Tandem Mass Spectrometry: A Cohort Study. Horm Metab Res 2021; 53:112-123. [PMID: 33246344 DOI: 10.1055/a-1300-2294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The changes of metabolite profiles in preterm birth have been demonstrated using newborn screening data. However, little is known about the holistic metabolic model in preterm neonates. The aim was to investigate the holistic metabolic model in preterm neonates. All metabolite values were obtained from a cohort data of routine newborn screening. A total of 261 758 newborns were recruited and randomly divided into a training subset and a testing subset. Using the training subset, 949 variates were considered to establish a logistic regression model for identifying preterm birth (<37 weeks) from term birth (≥37 weeks). Sventy-two variates (age at collection, TSH, 17α-OHP, proline, tyrosine, C16:1-OH, C18:2, and 65 ratios) entered into the final metabolic model for identifying preterm birth from term birth. Among the variates entering into the final model of PTB [Leucine+Isoleucine+Proline-OH)/Valine (OR=38.36], (C3DC+C4-OH)/C12 (OR=15.58), Valine/C5 (OR=6.32), [Leucine+isoleucine+Proline-OH)/Ornithine (OR=2.509)], and Proline/C18:1 (OR=2.465) have the top five OR values, and [Leucine+Isoleucine+Proline-OH)/C5 (OR=0.05)], [Leucine+Isoleucine+Proline-OH)/Phenylalanine (OR=0.214)], proline/valine (OR=0.230), C16/C18 (OR=0.259), and Alanine/free carnitine (OR=0.279) have the five lowest OR values. The final metabolic model had a capacity of identifying preterm infants with >80% accuracy in both the training and testing subsets. When identifying neonates ≤32 weeks from those >32 weeks, it had a robust performance with nearly 95% accuracy in both subsets. In summary, we have established an excellent metabolic model in preterm neonates. These findings could provide new insights for more efficient nutrient supplements and etiology of preterm birth.
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Affiliation(s)
- Benjing Wang
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Qin Zhang
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Qi Wang
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Jun Ma
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Xiaoju Cao
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Yaping Chen
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Yuhong Pan
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Hong Li
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Jingjing Xiang
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Ting Wang
- Center for Reproduction and Genetic, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
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23
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Bota AB, Ward V, Hawken S, Wilson LA, Lamoureux M, Ducharme R, Murphy MSQ, Denize KM, Henderson M, Saha SK, Akther S, Otieno NA, Munga S, Atito RO, Stringer JSA, Mwape H, Price JT, Mujuru HA, Chimhini G, Magwali T, Mudawarima L, Chakraborty P, Darmstadt GL, Wilson K. Metabolic gestational age assessment in low resource settings: a validation protocol. Gates Open Res 2021; 4:150. [PMID: 33501414 PMCID: PMC7801859 DOI: 10.12688/gatesopenres.13155.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/22/2021] [Indexed: 11/20/2022] Open
Abstract
Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.
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Affiliation(s)
- A. Brianne Bota
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Victoria Ward
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen Hawken
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Lindsay A. Wilson
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Monica Lamoureux
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Malia S. Q. Murphy
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Kathryn M. Denize
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Matthew Henderson
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Samir K. Saha
- Child Health Research Foundation, Mizapur, Bangladesh
| | - Salma Akther
- Child Health Research Foundation, Mizapur, Bangladesh
| | - Nancy A. Otieno
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Stephen Munga
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Raphael O. Atito
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | | | | | - Joan T. Price
- Department of Obstetrics and Gynecology, UNC School of Medicine, Chapel Hill, NC, USA
| | - Hilda Angela Mujuru
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Gwendoline Chimhini
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Thulani Magwali
- Department of Obstetrics and Gynaecology, University of Zimbabwe, Avondale, Zimbabwe
| | - Louisa Mudawarima
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Gary L. Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Otttawa, Canada
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24
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Sylvester KG, Hao S, You J, Zheng L, Tian L, Yao X, Mo L, Ladella S, Wong RJ, Shaw GM, Stevenson DK, Cohen HJ, Whitin JC, McElhinney DB, Ling XB. Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US. BMJ Open 2020; 10:e040647. [PMID: 33268420 PMCID: PMC7713207 DOI: 10.1136/bmjopen-2020-040647] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVES The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision. STUDY DESIGN A retrospective cohort study. SETTING Two medical centres from the USA. PARTICIPANTS Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms. OUTCOME MEASURES Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry. RESULTS A model to determine gestational age was developed (R2=0.98) and validated (R2=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R2=-0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively. CONCLUSIONS In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.
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Affiliation(s)
- Karl G Sylvester
- Department of Surgery, Stanford University School of Medicine, Stanford, California, USA
| | - Shiying Hao
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California, USA
- Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, California, USA
| | - Jin You
- Department of Surgery, Stanford University School of Medicine, Stanford, California, USA
| | - Le Zheng
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California, USA
- Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, California, USA
| | - Lu Tian
- Department of Health Research and Policy, Stanford University, Stanford, California, USA
| | - Xiaoming Yao
- Translational Medicine Laboratory, West China Hospital, Chengdu, China
| | - Lihong Mo
- Department of Obstetrics and Gynecology, University of California San Francisco-Fresno, Fresno, California, USA
| | - Subhashini Ladella
- Department of Obstetrics and Gynecology, University of California San Francisco-Fresno, Fresno, California, USA
| | - Ronald J Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Gary M Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - David K Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Harvey J Cohen
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - John C Whitin
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Doff B McElhinney
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California, USA
- Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, California, USA
| | - Xuefeng B Ling
- Department of Surgery, Stanford University School of Medicine, Stanford, California, USA
- Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Palo Alto, California, USA
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25
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Coyle K, Quan AML, Wilson LA, Hawken S, Bota AB, Coyle D, Murray JC, Wilson K. Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification. Am J Obstet Gynecol MFM 2020; 3:100279. [PMID: 33451597 PMCID: PMC7805344 DOI: 10.1016/j.ajogmf.2020.100279] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 10/22/2020] [Accepted: 11/16/2020] [Indexed: 11/04/2022]
Abstract
Background Preterm birth complications are the leading cause of death among children under 5 years of age, and this imposes a heavy burden on healthcare and social systems, particularly in low- and middle-income countries where reliable estimates of gestational age may be difficult to obtain. Metabolic analyte data can aid in accurately estimating gestational age. However, important costs are associated with this approach, which are related to the collection and analysis of newborn samples, and its cost-effectiveness has yet to be determined. Objective This study aimed to evaluate the cost-effectiveness of an internationally validated gestational age estimation algorithm based on neonatal blood spot metabolite data in combination with clinical and demographic variables (birthweight, sex, and multiple birth status) compared with a basic algorithm that uses only clinical and demographic variables in classifying infants as preterm or term (using a 37-week dichotomous preterm or term classification) and determining gestational age. Study Design The cost per correctly classified preterm infant and per correctly classified small-for-gestational-age infant for the metabolic algorithm vs the basic algorithm were estimated with data from an implementation study in Bangladesh. Results Over 1 year, the metabolic algorithm correctly classified an average of 8.7 (95% confidence interval, 1.3–14.7) additional preterm infants and 145.3 (95% confidence interval, 128.0–164.7) additional small-for-gestational-age infants per 1323 infants screened compared with the basic algorithm using only clinical and demographic variables. The incremental annual cost of adopting the metabolic algorithm was $100,031 (95% confidence interval, $86,354–$115,725). If setup costs were included, the cost was $120,496 (95% confidence interval, $106,322–$136,656). Compared with the basic algorithm, the incremental cost per preterm infant correctly classified by the metabolic algorithm is $11,542 ($13,903 with setup), and the incremental cost per small-for-gestational-age infant is $688 ($829 with setup). Conclusion This research quantifies the cost per detection of preterm or small-for-gestational-age infant in the implementation of a newborn screening program to aid in improved classification of preterm and, in particular, small-for-gestational-age infants in low- and middle-income countries.
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Affiliation(s)
- Kathryn Coyle
- Department of Health Sciences, Institute of Environment, Health and Societies, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom
| | - Amanda My Linh Quan
- Dalla School of Public Health, University of Toronto, Toronto, Ontario Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - A Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Doug Coyle
- Department of Health Sciences, Institute of Environment, Health and Societies, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom; Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Kumanan Wilson
- Department of Medicine, University of Ottawa, Ottowa, Ontario, Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Bruyère Research Institute, Ottawa, Ontario, Canada.
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Hawken S, Murphy MSQ, Ducharme R, Bota AB, Wilson LA, Cheng W, Tumulak MAJ, Alcausin MML, Reyes ME, Qiu W, Potter BK, Little J, Walker M, Zhang L, Padilla C, Chakraborty P, Wilson K. External validation of ELASTIC NET regression models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants. Gates Open Res 2020; 4:164. [DOI: 10.12688/gatesopenres.13131.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/14/2020] [Indexed: 11/20/2022] Open
Abstract
Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted. Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent group of infants, and externally validated in cohorts of infants from the Philippines and China. Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China. For the full model including sex, birth weight and metabolomic markers, GA estimates were within 5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within 6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings. Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility.
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Newborn Screening Samples for Diabetes Research: An Underused Resource. Cells 2020; 9:cells9102299. [PMID: 33076340 PMCID: PMC7602529 DOI: 10.3390/cells9102299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 02/08/2023] Open
Abstract
Inborn errors of metabolism and diabetes share common derangements in analytes of metabolic networks that are tested for in newborn screening, usually performed 48-72 h after birth. There is limited research examining the metabolic imprint of diabetes on newborn screening results. This paper aims to demonstrate the links between diabetes, biochemical genetics and newborn screening in investigating disease pathophysiology in diabetes, provide possible reasons for the lack of research in diabetes in newborn screening and offer recommendations on potential research areas. We performed a systematic search of the available literature from 1 April 1998 to 31 December 2018 involving newborn screening and diabetes using OVID, MEDLINE, Cochrane and the PROSPERO register, utilizing a modified extraction tool adapted from Cochrane. Eight studies were included after screening 1312 records. Five studies reanalyzed dried blood spots (DBS) on filter paper cards, and three studies utilized pre-existing results. The results of these studies and how they relate to cord blood studies, the use of cord blood versus newborn screening dried blood spots as a sample and considerations on newborn screening and diabetes research is further discussed. The timing of sampling of newborn screening allows insight into neonatal physiology in a catabolic state with minimal maternal and placental influence. This, combined with the wide coverage of newborn screening worldwide, may aid in our understanding of the origins of diabetes.
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Bota AB, Ward V, Hawken S, Wilson LA, Lamoureux M, Ducharme R, Murphy MSQ, Denize KM, Henderson M, Saha SK, Akther S, Otieno NA, Munga S, Atito RO, Stringer JSA, Mwape H, Price JT, Mujuru HA, Chimhini G, Magwali T, Mudawarima L, Chakraborty P, Darmstadt GL, Wilson K. Metabolic gestational age assessment in low resource settings: a validation protocol. Gates Open Res 2020. [DOI: 10.12688/gatesopenres.13155.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children’s Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario’s newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.
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Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform 2019; 20:1957-1971. [PMID: 29304189 PMCID: PMC6954408 DOI: 10.1093/bib/bbx170] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/29/2017] [Indexed: 12/14/2022] Open
Abstract
Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.
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Affiliation(s)
| | - Vladimir Shulaev
- Corresponding author: Vladimir Shulaev, Department of Biological Sciences, BioDiscovery Institute, University of North Texas, Denton, TX 76210, USA. Tel.: 940-369-5368; Fax: 940-565-3821; E-mail:
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30
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Fineman DC, Baer RJ, Chambers CD, Rajagopal S, Maltepe E, Rinaudo PF, Fineman JR, Jelliffe-Pawlowski LL, Steurer MA. Outcomes of pulmonary vascular disease in infants conceived with non-IVF fertility treatment and assisted reproductive technologies at 1 year of age. Pediatr Pulmonol 2019; 54:1844-1852. [PMID: 31328432 DOI: 10.1002/ppul.24457] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 07/02/2019] [Indexed: 11/08/2022]
Abstract
BACKGROUND Assisted reproductive technologies (ARTs) have been associated with the development of endothelial dysfunction. OBJECTIVE To determine potential differences in outcomes associated with pulmonary vascular disease in infants born to mothers receiving any infertility treatment including ART and non-IVF fertility treatments (NIFTs). DESIGN/METHODS The sample was derived from an administrative database containing detailed information on infant and maternal characteristics for live-born infants in California (2007-2012) with gestational age (GA) 22 to 44 weeks. Cases were defined as infants with ICD-9 code for pulmonary vascular disease (PVD) and records for ART/NIFT. Controls were randomly selected at a 1:4 ratio. The primary outcome was 1-year mortality. Crude and adjusted odds ratio (OR) with 95% confidence interval (CI) were calculated. RESULTS We identified 159 cases and 636 controls. Mothers that utilized ART/NIFT were older, to be of the Caucasian race, to have pre-eclampsia, private insurance, and education >12 years (P < .001). Cases compared to controls were more premature, had lower birth weights, and were more often the product of a multiple gestation pregnancy (P < .001). Cases had a higher 1-year mortality (18.2% vs 9.1%; OR: 2.2; 95% CI: 1.4, 3.6), more severe PVD (86.2% vs 72.3%; OR: 2.4; 95% CI: 1.5, 3.9), and a longer hospital stay (66.7 ± 73.0 vs 32.5 ± 47.2 days; P < .001) than controls. However, when adjusting for GA these differences become statistically insignificant. CONCLUSION Children born following ART/NIFT with PVD had increased mortality compared to infants with PVD but without ART/NIFT. The primary driver of this relationship is prematurity.
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Affiliation(s)
| | - Rebecca J Baer
- Department of Pediatrics, University of California, San Diego, La Jolla, California.,California Preterm Birth Initiative, University of California San Francisco, San Francisco, California.,Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, California
| | - Christina D Chambers
- Department of Pediatrics, University of California, San Diego, La Jolla, California
| | - Satish Rajagopal
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, California
| | - Emin Maltepe
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, California
| | - Paolo F Rinaudo
- Department of Pediatrics, University of California San Francisco, San Francisco, California
| | - Jeffrey R Fineman
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, California
| | - Laura L Jelliffe-Pawlowski
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
| | - Martina A Steurer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California.,Department of Pediatrics, University of California San Francisco, San Francisco, California.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
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Steurer MA, Baer RJ, Oltman S, Ryckman KK, Feuer SK, Rogers E, Keller RL, Jelliffe-Pawlowski LL. Morbidity of Persistent Pulmonary Hypertension of the Newborn in the First Year of Life. J Pediatr 2019; 213:58-65.e4. [PMID: 31399244 DOI: 10.1016/j.jpeds.2019.06.053] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 06/04/2019] [Accepted: 06/21/2019] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To assess postdischarge mortality and morbidity in infants diagnosed with different etiologies and severities of persistent pulmonary hypertension of the newborn (PPHN), and to identify risk factors for these adverse clinical outcomes. STUDY DESIGN This was a population-based study using an administrative dataset linking birth and death certificates, hospital discharge and readmissions records from 2005 to 2012 in California. Cases were infants ≥34 weeks' gestational age with International Classification of Diseases,9th edition, codes consistent with PPHN. The primary outcome was defined as postdischarge mortality or hospital readmission during the first year of life. Crude and adjusted risk ratio (aRR) with 95% CIs were calculated to quantify the risk for the primary outcome and to identify risk factors. RESULTS Infants with PPHN (n = 7847) had an aRR of 3.5 (95% CI, 3.3-3.7) for the primary outcome compared with infants without PPHN (n = 3 974 536), and infants with only mild PPHN (n = 2477) had an aRR of 2.2 (95% CI, 2.0-2.5). Infants with congenital diaphragmatic hernia as the etiology for PPHN had an aRR of 8.2 (95% CI, 6.7-10.2) and infants with meconium aspiration syndrome had an aRR of 4.2 (95% CI, 3.7-4.6) compared with infants without PPHN. Hispanic ethnicity, small for gestational age, severe PPHN, and etiology of PPHN were risk factors for the primary outcome. CONCLUSIONS The postdischarge morbidity burden of infants with PPHN is large. These findings extend to infants with mild PPHN and etiologies with pulmonary vascular changes that are thought to be short term and recoverable. These data could inform counseling of parents.
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Affiliation(s)
- Martina A Steurer
- Department of Pediatrics, University of California San Francisco, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA; California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA.
| | - Rebecca J Baer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA; Department of Pediatrics, University of California San Diego, La Jolla, CA
| | - Scott Oltman
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA; California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA
| | - Kelli K Ryckman
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA
| | - Sky K Feuer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA
| | - Elizabeth Rogers
- Department of Pediatrics, University of California San Francisco, San Francisco, CA; California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA
| | - Roberta L Keller
- Department of Pediatrics, University of California San Francisco, San Francisco, CA
| | - Laura L Jelliffe-Pawlowski
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA; California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA
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32
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Wilson LA, Murphy MS, Ducharme R, Denize K, Jadavji NM, Potter B, Little J, Chakraborty P, Hawken S, Wilson K. Postnatal gestational age estimation via newborn screening analysis: application and potential. Expert Rev Proteomics 2019; 16:727-731. [PMID: 31422714 PMCID: PMC6816481 DOI: 10.1080/14789450.2019.1654863] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Introduction: Preterm birth is a major global health concern, contributing to 35% of all neonatal deaths in 2016. Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in the absence of prenatal ultrasound are needed. Previous work has demonstrated the potential for metabolomics to estimate GA by analyzing data captured through routine newborn screening. Areas covered: Circulating analytes found in newborn blood samples vary by GA. Leveraging newborn screening and demographic data, our group developed an algorithm capable of estimating GA postnatally to within approximately 1 week of ultrasound-validated GA. Since then, we have built on the model by including additional analytes and validating the model's performance through internal and external validation studies, and through implementation of the model internationally. Expert opinion: Currently, using metabolomics to estimate GA postnatally holds considerable promise but is limited by issues of cost-effectiveness and resource access in low-income settings. Future work will focus on enhancing the precision of this approach while prioritizing point-of-care testing that is both accessible and acceptable to individuals in low-resource settings.
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Affiliation(s)
- Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Malia Sq Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Kathryn Denize
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario , Ottawa , Canada
| | - Nafisa M Jadavji
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Beth Potter
- Department of Epidemiology and Community Health, University of Ottawa , Ottawa , Canada
| | - Julian Little
- Department of Epidemiology and Community Health, University of Ottawa , Ottawa , Canada
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario , Ottawa , Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
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33
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[Serum metabolomics of preterm and full-term infants based on gas chromatography-mass spectrometry]. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2019; 21. [PMID: 30907351 PMCID: PMC7389367 DOI: 10.7499/j.issn.1008-8830.2019.03.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
OBJECTIVE To study the features of serum metabolites in preterm infants based on gas chromatography-mass spectrometry (GC-MS), and to find differentially expressed metabolites in the serum of preterm infants. METHODS Serum samples were collected from 19 preterm infants and 20 full-term infants before feeding. GC-MS was used to measure metabolic profiles, and the metabolic features of 397 serum metabolites in preterm infants were analyzed. RESULTS There was a significant difference in serum metabolic features between the preterm and full-term infants before feeding. There were significant differences between the full-term and preterm infants in the levels of metabolites such as O-phosphonothreonine, digicitrin, tannic acid, and fructose-1,6-diphosphate (P<0.01), suggesting that the above differentially expressed metabolites were highly differentiated between the preterm and full-term infants. Most differentially expressed metabolites were involved in the metabolic pathways such as ABC transporters, β-alanine and pyrimidines and were correlated with some clinical parameters (albumin and total bilirubin) (P<0.05). CONCLUSIONS There is a significant difference in serum metabolites between preterm and full-term infants before feeding. Metabolomics plays an important role in improving metabolic disorders and exploring metabolism-related diseases in preterm infants.
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34
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Murphy MSQ, Hawken S, Cheng W, Wilson LA, Lamoureux M, Henderson M, Pervin J, Chowdhury A, Gravett C, Lackritz E, Potter BK, Walker M, Little J, Rahman A, Chakraborty P, Wilson K. External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh. eLife 2019; 8:e42627. [PMID: 30887951 PMCID: PMC6424558 DOI: 10.7554/elife.42627] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 02/08/2019] [Indexed: 11/13/2022] Open
Abstract
This study sought to evaluate the performance of metabolic gestational age estimation models developed in Ontario, Canada in infants born in Bangladesh. Cord and heel prick blood spots were collected in Bangladesh and analyzed at a newborn screening facility in Ottawa, Canada. Algorithm-derived estimates of gestational age and preterm birth were compared to ultrasound-validated estimates. 1036 cord blood and 487 heel prick samples were collected from 1069 unique newborns. The majority of samples (93.2% of heel prick and 89.9% of cord blood) were collected from term infants. When applied to heel prick data, algorithms correctly estimated gestational age to within an average deviation of 1 week overall (root mean square error = 1.07 weeks). Metabolic gestational age estimation provides accurate population-level estimates of gestational age in this data set. Models were effective on data obtained from both heel prick and cord blood, the latter being a more feasible option in low-resource settings.
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Affiliation(s)
- Malia SQ Murphy
- Clinical Epidemiology ProgramOttawa Hospital Research InstituteOttawaCanada
| | - Steven Hawken
- Clinical Epidemiology ProgramOttawa Hospital Research InstituteOttawaCanada
- Department of Epidemiology and Community HealthUniversity of OttawaOttawaCanada
| | - Wei Cheng
- Clinical Epidemiology ProgramOttawa Hospital Research InstituteOttawaCanada
| | - Lindsay A Wilson
- Clinical Epidemiology ProgramOttawa Hospital Research InstituteOttawaCanada
| | - Monica Lamoureux
- Newborn Screening OntarioChildren’s Hospital of Eastern OntarioOttawaCanada
| | - Matthew Henderson
- Newborn Screening OntarioChildren’s Hospital of Eastern OntarioOttawaCanada
| | - Jesmin Pervin
- International Centre for Diarrhoeal Disease ResearchDhakaBangladesh
| | | | - Courtney Gravett
- Global Alliance to Prevent Prematurity and StillbirthLynnwoodUnited Stares
| | - Eve Lackritz
- Global Alliance to Prevent Prematurity and StillbirthLynnwoodUnited Stares
| | - Beth K Potter
- Department of Epidemiology and Community HealthUniversity of OttawaOttawaCanada
| | - Mark Walker
- Clinical Epidemiology ProgramOttawa Hospital Research InstituteOttawaCanada
| | - Julian Little
- Department of Epidemiology and Community HealthUniversity of OttawaOttawaCanada
| | - Anisur Rahman
- International Centre for Diarrhoeal Disease ResearchDhakaBangladesh
| | | | - Kumanan Wilson
- Clinical Epidemiology ProgramOttawa Hospital Research InstituteOttawaCanada
- Department of Epidemiology and Community HealthUniversity of OttawaOttawaCanada
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McCarthy ME, Oltman SP, Baer RJ, Ryckman KK, Rogers EE, Steurer-Muller MA, Witte JS, Jelliffe-Pawlowski LL. Newborn Metabolic Profile Associated with Hyperbilirubinemia With and Without Kernicterus. Clin Transl Sci 2018; 12:28-38. [PMID: 30369069 PMCID: PMC6342241 DOI: 10.1111/cts.12590] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 09/14/2018] [Indexed: 11/29/2022] Open
Abstract
Our objective was to assess the relationship between hyperbilirubinemia with and without kernicterus and metabolic profile at newborn screening. Included were 1,693,658 infants divided into a training or testing subset in a ratio of 3:1. Forty‐two metabolites were analyzed using logistic regression (odds ratios (ORs), area under the receiver operating characteristic curve (AUC), 95% confidence intervals (CIs)). Several metabolite patterns remained consistent across gestational age groups for hyperbilirubinemia without kernicterus. Thyroid stimulating hormone (TSH) and C‐18:2 were decreased, whereas tyrosine and C‐3 were increased in infants across groupings. Increased C‐3 was also observed for kernicterus (OR: 3.17; 95% CI: 1.18–8.53). Thirty‐one metabolites were associated with hyperbilirubinemia without kernicterus in the training set. Phenylalanine (OR: 1.91; 95% CI: 1.85–1.97), ornithine (OR: 0.76; 95% 0.74–0.77), and isoleucine + leucine (OR: 0.63; 95% CI: 0.61–0.65) were the most strongly associated. This study showed that newborn metabolic function is associated with hyperbilirubinemia with and without kernicterus.
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Affiliation(s)
- Molly E McCarthy
- Department of Epidemiology and Biostatistics, Global Health Sciences and the Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA.,Department of Public Health, Brown University, Providence, Rhode Island, USA
| | - Scott P Oltman
- Department of Epidemiology and Biostatistics and the California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
| | - Rebecca J Baer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA.,Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Kelli K Ryckman
- Departments of Epidemiology and Pediatrics, University of Iowa, Iowa City, Iowa, USA
| | - Elizabeth E Rogers
- Department of Pediatrics and the California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
| | - Martina A Steurer-Muller
- Department of Epidemiology and Biostatistics, Pediatrics and the California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
| | - John S Witte
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, USA
| | - Laura L Jelliffe-Pawlowski
- Department of Epidemiology and Biostatistics and the California Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
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36
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Peng G, Shen P, Gandotra N, Le A, Fung E, Jelliffe-Pawlowski L, Davis RW, Enns GM, Zhao H, Cowan TM, Scharfe C. Combining newborn metabolic and DNA analysis for second-tier testing of methylmalonic acidemia. Genet Med 2018; 21:896-903. [PMID: 30209273 PMCID: PMC6416784 DOI: 10.1038/s41436-018-0272-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 08/03/2018] [Indexed: 11/27/2022] Open
Abstract
Purpose Improved second-tier tools are needed to reduce false-positive outcomes in newborn screening (NBS) for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP). Methods We designed an assay for multiplex sequencing of 72 metabolic genes (RUSPseq) from newborn dried blood spots. Analytical and clinical performance was evaluated in 60 screen-positive newborns for methylmalonic acidemia (MMA) reported by the California Department of Public Health NBS program. Additionally, we trained a Random Forest machine learning classifier on NBS data to improve prediction of true and false-positive MMA cases. Results Of 28 MMA patients sequenced, we found two pathogenic or likely pathogenic (P/LP) variants in a MMA-related gene in 24 patients, and one pathogenic variant and a variant of unknown significance (VUS) in 1 patient. No such variant combinations were detected in MMA false positives and healthy controls. Random Forest–based analysis of the entire NBS metabolic profile correctly identified the MMA patients and reduced MMA false-positive cases by 51%. MMA screen-positive newborns were more likely of Hispanic ethnicity. Conclusion Our two-pronged approach reduced false positives by half and provided a reportable molecular finding for 89% of MMA patients. Challenges remain in newborn metabolic screening and DNA variant interpretation in diverse multiethnic populations.
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Affiliation(s)
- Gang Peng
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.,Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Peidong Shen
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
| | - Neeru Gandotra
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Anthony Le
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Eula Fung
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Laura Jelliffe-Pawlowski
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, CA, USA
| | - Ronald W Davis
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
| | - Gregory M Enns
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Hongyu Zhao
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.,Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Tina M Cowan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Curt Scharfe
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
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37
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Oltman SP, Rogers EE, Baer RJ, Anderson JG, Steurer MA, Pantell MS, Partridge JC, Rand L, Ryckman KK, Jelliffe-Pawlowski LL. Initial Metabolic Profiles Are Associated with 7-Day Survival among Infants Born at 22-25 Weeks of Gestation. J Pediatr 2018; 198:194-200.e3. [PMID: 29661562 PMCID: PMC6016556 DOI: 10.1016/j.jpeds.2018.03.032] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 02/02/2018] [Accepted: 03/14/2018] [Indexed: 12/30/2022]
Abstract
OBJECTIVE To evaluate the association between early metabolic profiles combined with infant characteristics and survival past 7 days of age in infants born at 22-25 weeks of gestation. STUDY DESIGN This nested case-control consisted of 465 singleton live births in California from 2005 to 2011 at 22-25 weeks of gestation. All infants had newborn metabolic screening data available. Data included linked birth certificate and mother and infant hospital discharge records. Mortality was derived from linked death certificates and death discharge information. Each death within 7 days was matched to 4 surviving controls by gestational age and birth weight z score category, leaving 93 cases and 372 controls. The association between explanatory variables and 7-day survival was modeled via stepwise logistic regression. Infant characteristics, 42 metabolites, and 12 metabolite ratios were considered for model inclusion. Model performance was assessed via area under the curve. RESULTS The final model included 1 characteristic and 11 metabolites. The model demonstrated a strong association between metabolic patterns and infant survival (area under the curve [AUC] 0.885, 95% CI 0.851-0.920). Furthermore, a model with just the selected metabolites performed better (AUC 0.879, 95% CI 0.841-0.916) than a model with multiple clinical characteristics (AUC 0.685, 95% CI 0.627-0.742). CONCLUSIONS Use of metabolomics significantly strengthens the association with 7-day survival in infants born extremely premature. Physicians may be able to use metabolic profiles at birth to refine mortality risks and inform postnatal counseling for infants born at <26 weeks of gestation.
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Affiliation(s)
- Scott P Oltman
- Department of Epidemiology and Biostatistics and the Preterm Birth Initiative, University of California San Francisco, San Francisco, CA.
| | - Elizabeth E Rogers
- Department of Pediatrics, University of California San Francisco, San Francisco, CA
| | - Rebecca J Baer
- Preterm Birth Initiative, University of California San Francisco, San Francisco, CA; Department of Pediatrics, University of California San Diego, La Jolla, CA
| | - James G Anderson
- Department of Pediatrics, University of California San Francisco, San Francisco, CA
| | - Martina A Steurer
- Department of Epidemiology and Biostatistics and Pediatrics, University of California San Francisco, San Francisco, CA
| | - Matthew S Pantell
- Department of Pediatrics, University of California San Francisco, San Francisco, CA
| | - J Colin Partridge
- Department of Pediatrics, University of California San Francisco, San Francisco, CA
| | - Larry Rand
- Preterm Birth Initiative, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco, San Francisco, CA
| | - Kelli K Ryckman
- Department of Epidemiology and Pediatrics, University of Iowa, Iowa City, IA
| | - Laura L Jelliffe-Pawlowski
- Department of Epidemiology and Biostatistics and the Preterm Birth Initiative, University of California San Francisco, San Francisco, CA
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38
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Murphy MSQ, Hawken S, Cheng W, Wilson LA, Lamoureux M, Henderson M, Potter B, Little J, Chakraborty P, Wilson K. Metabolic profiles derived from residual blood spot samples: A longitudinal analysis. Gates Open Res 2018; 2:28. [PMID: 30234195 PMCID: PMC6139383 DOI: 10.12688/gatesopenres.12822.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/23/2018] [Indexed: 11/20/2022] Open
Abstract
Background: Secondary use of newborn screening dried blood spot samples include use for biomedical or epidemiological research. However, the effects of storage conditions on archival samples requires further examination. The objective of this study was to determine the utility of residual newborn samples for deriving reliable metabolic gestational age estimates. Methods: Residual newborn dried blood spot samples that had been stored for 2-, 4-, 6-, or 12-months in temperature controlled (21°C) conditions were re-analyzed for the full panel of newborn screening analytes offered by a provincial newborn screening lab in Ottawa, Canada. Data from re-analyzed samples were compared to corresponding baseline newborn screening values for absolute agreement, and Pearson and intraclass correlation. Performance of a gestational age estimation algorithm originally developed from baseline newborn screening values was then validated on data derived from stored samples. Results: A total of 307 samples were used for this study. 17-hydroxyprogesterone and newborn hemoglobin profiles measured by immunoassay and high-performance liquid chromatography, respectively, were among the most stable markers across all time points of analysis. Acylcarnitines exhibited the greatest degree of variation in stability upon repeat measurement. The largest shifts in newborn analyte profiles and the poorest performance of metabolic gestational age algorithms were observed when samples were analyzed 12-months after sample collection. Conclusions: Duration of sample storage, independent of temperature and humidity, affects newborn screening profiles and gestational age estimates derived from metabolic gestational dating algorithms. When considering use of dried blood spot samples either for clinical or research purposes, care should be taken when interpreting data stemming from secondary use.
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Affiliation(s)
- Malia S Q Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, K1Y 4E9, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, K1Y 4E9, Canada
| | - Wei Cheng
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, K1Y 4E9, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, K1Y 4E9, Canada
| | - Monica Lamoureux
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, K1H 5B2, Canada
| | - Matthew Henderson
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, K1H 5B2, Canada
| | - Beth Potter
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, K1G 5Z3, Canada
| | - Julian Little
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, K1G 5Z3, Canada
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, K1H 5B2, Canada
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, K1Y 4E9, Canada
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Abstract
This article presents an account of the research carried out so far in the use of metabolomics to find biomarkers of preterm birth (PTB) in fetal, maternal, and newborn biofluids. Metabolomic studies have employed mainly nuclear magnetic resonance spectroscopy or mass spectrometry-based methodologies to analyze, on one hand, prenatal biofluids (amniotic fluid, maternal urine/maternal blood, cervicovaginal fluid) to identify predictive biomarkers of PTB, and on the other hand, biofluids collected at or after birth (amniotic fluid, umbilical cord blood, newborn urine, and newborn blood, maternal blood, or breast milk) to assess and follow up the health status of PTB babies. Besides advancing on the biochemical knowledge of PTB metabolism mainly during the in utero period and at birth, the work carried out has also helped to identify important requirements related to experimental design and analytical protocol that need to be addressed, if translation of these biomarkers to the clinic is to be envisaged. An outlook of possible future developments for the translation of laboratory results to the clinic is presented.
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Affiliation(s)
- Ana M Gil
- 1 Department of Chemistry, CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
| | - Daniela Duarte
- 1 Department of Chemistry, CICECO-Aveiro Institute of Materials, University of Aveiro, Aveiro, Portugal
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40
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Steurer MA, Oltman S, Baer RJ, Feuer S, Liang L, Paynter RA, Rand L, Ryckman KK, Keller RL, Pawlowski LLJ. Altered metabolites in newborns with persistent pulmonary hypertension. Pediatr Res 2018; 84:272-278. [PMID: 29895840 PMCID: PMC7691760 DOI: 10.1038/s41390-018-0023-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 03/06/2018] [Accepted: 04/03/2018] [Indexed: 11/09/2022]
Abstract
BACKGROUND There is an emerging evidence that pulmonary hypertension is associated with amino acid, carnitine, and thyroid hormone aberrations. We aimed to characterize metabolic profiles measured by the newborn screen (NBS) in infants with persistent pulmonary hypertension of the newborn (PPHN) METHODS: Nested case-control study from population-based database. Cases were infants with ICD-9 code for PPHN receiving mechanical ventilation. Controls receiving mechanical ventilation were matched 2:1 for gestational age, sex, birth weight, parenteral nutrition administration, and age at NBS collection. Infants were divided into derivation and validation datasets. A multivariable logistic regression model was derived from candidate metabolites, and the area under the receiver operator characteristic curve (AUROC) was generated from the validation dataset. RESULTS We identified 1076 cases and 2152 controls. Four metabolites remained in the final model. Ornithine (OR 0.32, CI 0.26-0.41), tyrosine (OR 0.48, CI 0.40-0.58), and TSH 0.50 (0.45-0.55) were associated with decreased odds of PPHN; phenylalanine was associated with increased odds of PPHN (OR 4.74, CI 3.25-6.90). The AUROC was 0.772 (CI 0.737-0.807). CONCLUSIONS In a large, population-based dataset, infants with PPHN have distinct, early metabolic profiles. These data provide insight into the pathophysiology of PPHN, identifying potential therapeutic targets and novel biomarkers to assess the response.
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Affiliation(s)
- Martina A. Steurer
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA,California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Scott Oltman
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Rebecca J. Baer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA,Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Sky Feuer
- California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Liang Liang
- Department of Genetics, Stanford University, Palo Alto, CA, USA
| | - Randi A. Paynter
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA,California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Larry Rand
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA and
| | - Kelli K. Ryckman
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA, USA
| | - Roberta L. Keller
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Laura L. Jelliffe Pawlowski
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA,California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, USA
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41
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Anderson JG, Rogers EE, Baer RJ, Oltman SP, Paynter R, Partridge JC, Rand L, Jelliffe-Pawlowski LL, Steurer MA. Racial and Ethnic Disparities in Preterm Infant Mortality and Severe Morbidity: A Population-Based Study. Neonatology 2018; 113:44-54. [PMID: 29073624 DOI: 10.1159/000480536] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 08/22/2017] [Indexed: 11/19/2022]
Abstract
BACKGROUND Disparities exist in the rates of preterm birth and infant mortality across different racial/ethnic groups. However, only a few studies have examined the impact of race/ethnicity on the outcomes of premature infants. OBJECTIVE To report the rates of mortality and severe neonatal morbidity among multiple gestational age (GA) groups stratified by race/ethnicity. METHODS A retrospective cohort study utilizing linked birth certificate, hospital discharge, readmission, and death records up to 1 year of life. Live-born infants ≤36 weeks born in the period 2007-2012 were included. Maternal self-identified race/ethnicity, as recorded on the birth certificate, was used. ICD-9 diagnostic and procedure codes captured neonatal morbidities (intraventricular hemorrhage, retinopathy of prematurity, periventricular leukomalacia, bronchopulmonary dysplasia, and necrotizing enterocolitis). Multiple logistic regression was performed to evaluate the impact of race/ethnicity on mortality and morbidity, adjusting for GA, birth weight, sex, and multiple gestation. RESULTS Our cohort totaled 245,242 preterm infants; 26% were white, 46% Hispanic, 8% black, and 12% Asian. At 22-25 weeks, black infants were less likely to die than white infants (odds ratio [OR] 0.76; 95% confidence interval [CI] 0.62-0.94). However, black infants born at 32-34 weeks (OR 1.64; 95% CI 1.15-2.32) or 35-36 weeks (OR 1.57; 95% CI 1.00-2.24) were more likely to die. Hispanic infants born at 35-36 weeks were less likely to die than white infants (OR 0.66; 95% CI 0.50-0.87). Racial disparities at different GAs were also detected for severe morbidities. CONCLUSIONS The impact of race/ethnicity on mortality and severe morbidity varied across GA categories in preterm infants. Disparities persisted even after adjusting for important potential confounders.
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Affiliation(s)
- James G Anderson
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
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Fell DB, Hawken S, Wong CA, Wilson LA, Murphy MSQ, Chakraborty P, Lacaze-Masmonteil T, Potter BK, Wilson K. Using newborn screening analytes to identify cases of neonatal sepsis. Sci Rep 2017; 7:18020. [PMID: 29269842 PMCID: PMC5740154 DOI: 10.1038/s41598-017-18371-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 12/11/2017] [Indexed: 12/20/2022] Open
Abstract
Neonatal sepsis is associated with high mortality and morbidity, yet challenges with available diagnostic approaches can lead to delays in therapy. Our study assessed whether newborn screening analytes could be utilized to identify associations with neonatal sepsis. We linked a newborn screening registry with health databases to identify cases of sepsis among infants born in Ontario from 2010-2015. Correlations between sepsis and screening analytes were examined within three gestational age groups (early preterm: <34 weeks; late preterm: 34-36 weeks; term: ≥37 weeks), using multivariable logistic regression models. We started with a model containing only clinical factors, then added groups of screening analytes. Among 793,128 infants, 4,794 were diagnosed with sepsis during the neonatal period. Clinical variables alone or in combination with hemoglobin values were not strongly predictive of neonatal sepsis among infants born at term or late preterm. However, model fit improved considerably after adding markers of thyroid and adrenal function, acyl-carnitines, and amino acids. Among infants born at early preterm gestation, neither clinical variables alone nor models incorporating screening analytes adequately predicted neonatal sepsis. The combination of clinical variables and newborn screening analytes may have utility in identifying term or late preterm infants at risk for neonatal sepsis.
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Affiliation(s)
- Deshayne B Fell
- School of Epidemiology and Public Health, University of Ottawa, Ottawa Ontario, Canada.,Children's Hospital of Eastern Ontario Research Institute, Ottawa Ontario, Canada.,Institute for Clinical Evaluative Sciences (ICES), University of Ottawa, Ottawa Ontario, Canada
| | - Steven Hawken
- School of Epidemiology and Public Health, University of Ottawa, Ottawa Ontario, Canada.,Institute for Clinical Evaluative Sciences (ICES), University of Ottawa, Ottawa Ontario, Canada.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa Ontario, Canada
| | - Coralie A Wong
- Institute for Clinical Evaluative Sciences (ICES), University of Ottawa, Ottawa Ontario, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa Ontario, Canada
| | - Malia S Q Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa Ontario, Canada
| | - Pranesh Chakraborty
- Department of Pediatrics, University of Ottawa, Ottawa Ontario, Canada.,Newborn Screening Ontario (NSO), Children's Hospital of Eastern Ontario, Ottawa Ontario, Canada
| | | | - Beth K Potter
- School of Epidemiology and Public Health, University of Ottawa, Ottawa Ontario, Canada.,Institute for Clinical Evaluative Sciences (ICES), University of Ottawa, Ottawa Ontario, Canada
| | - Kumanan Wilson
- Institute for Clinical Evaluative Sciences (ICES), University of Ottawa, Ottawa Ontario, Canada. .,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa Ontario, Canada. .,Department of Medicine, University of Ottawa, Ottawa Ontario, Canada.
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43
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Steurer MA, Baer RJ, Keller RL, Oltman S, Chambers CD, Norton ME, Peyvandi S, Rand L, Rajagopal S, Ryckman KK, Moon-Grady AJ, Jelliffe-Pawlowski LL. Gestational Age and Outcomes in Critical Congenital Heart Disease. Pediatrics 2017; 140:peds.2017-0999. [PMID: 28885171 DOI: 10.1542/peds.2017-0999] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/12/2017] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND OBJECTIVES It is unknown how gestational age (GA) impacts neonatal morbidities in infants with critical congenital heart disease (CCHD). We aim to quantify GA-specific mortality and neonatal morbidity in infants with CCHD. METHODS Cohort study using a database linking birth certificate, infant hospital discharge, readmission, and death records, including infants 22 to 42 weeks' GA without chromosomal anomalies (2005-2012, 2 988 925 live births). The International Classification of Diseases, Ninth Revision diagnostic and procedure codes were used to define CCHD and neonatal morbidities (intraventricular hemorrhage, retinopathy, periventricular leukomalacia, chronic lung disease, necrotizing enterocolitis). Adjusted absolute risk differences (ARDs) with 95% confidence intervals (CIs) were calculated. RESULTS We identified 6903 out of 2 968 566 (0.23%) infants with CCHD. The incidence of CCHD was highest at 29 to 31 weeks' GA (0.9%) and lowest at 39 to 42 weeks (0.2%). Combined neonatal morbidity or mortality in infants with and without CCHD was 82.8% and 57.9% at <29 weeks and declined to 10.9% and 0.1% at 39 to 42 weeks' GA. In infants with CCHD, being born at 34 to 36 weeks was associated with a higher risk of death or morbidity than being born at 37 to 38 weeks (adjusted ARD 9.1%, 95% CI 5.5% to 12.7%), and being born at 37 to 38 weeks was associated with a higher risk of death or morbidity than 39 to 42 weeks (adjusted ARD 3.2%, 95% CI 1.6% to 4.9%). CONCLUSIONS Infants born with CCHD are at high risk of neonatal morbidity. Morbidity remains increased across all GA groups in comparison with infants born at 39 to 42 weeks. This substantial risk of neonatal morbidity is important to consider when caring for this patient population.
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Affiliation(s)
| | - Rebecca J Baer
- Department of Pediatrics, University of California, San Diego, La Jolla, California; and
| | | | | | - Christina D Chambers
- Department of Pediatrics, University of California, San Diego, La Jolla, California; and
| | - Mary E Norton
- Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California
| | | | - Larry Rand
- Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California
| | | | - Kelli K Ryckman
- Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa
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Hawken S, Ducharme R, Murphy MSQ, Atkinson KM, Potter BK, Chakraborty P, Wilson K. Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada. BMJ Open 2017; 7:e015615. [PMID: 28871012 PMCID: PMC5589017 DOI: 10.1136/bmjopen-2016-015615] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES Biological modelling of routinely collected newborn screening data has emerged as a novel method for deriving postnatal gestational age estimates. Validation of published models has previously been limited to cohorts largely consisting of infants of white Caucasian ethnicity. In this study, we sought to determine the validity of a published gestational age estimation algorithm among recent immigrants to Canada, where maternal landed immigrant status was used as a surrogate measure of infant ethnicity. DESIGN We conducted a retrospective validation study in infants born in Ontario between April 2009 and September 2011. SETTING Provincial data from Ontario, Canada were obtained from the Institute for Clinical Evaluative Sciences. PARTICIPANTS The dataset included 230 034 infants born to non-landed immigrants and 70 098 infants born to immigrant mothers. The five most common countries of maternal origin were India (n=10 038), China (n=7468), Pakistan (n=5824), The Philippines (n=5441) and Vietnam (n=1408). Maternal country of origin was obtained from Citizenship and Immigration Canada's Landed Immigrant Database. PRIMARY AND SECONDARY OUTCOME MEASURES Performance of a postnatal gestational age algorithm was evaluated across non-immigrant and immigrant populations. RESULTS Root mean squared error (RMSE) of 1.05 weeks was observed for infants born to non-immigrant mothers, whereas RMSE ranged from 0.98 to 1.15 weeks among infants born to immigrant mothers. Area under the receiver operating characteristic curve for distinguishing term versus preterm infants (≥37 vs <37 weeks gestational age or >34 vs ≤34 weeks gestational age) was 0.958 and 0.986, respectively, in the non-immigrant subgroup and ranged from 0.927 to 0.964 and 0.966 to 0.99 in the immigrant subgroups. CONCLUSIONS Algorithms for postnatal determination of gestational age may be further refined by development and validation of region or ethnicity-specific models. However, our results provide reassurance that an algorithm developed from Ontario-born infant cohorts performs well across a range of ethnicities and maternal countries of origin without modification.
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Affiliation(s)
- Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology, Public Health and Preventative Medicine, University of Ottawa, Ottawa, Ontario, Canada
- uOttawa, Institute for Clinical Evaluative Sciences, Ottawa, Ontario, Canada
| | - Robin Ducharme
- uOttawa, Institute for Clinical Evaluative Sciences, Ottawa, Ontario, Canada
| | - Malia S Q Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Katherine M Atkinson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden
| | - Beth K Potter
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology, Public Health and Preventative Medicine, University of Ottawa, Ottawa, Ontario, Canada
- uOttawa, Institute for Clinical Evaluative Sciences, Ottawa, Ontario, Canada
| | - Pranesh Chakraborty
- Department of Paediatrics, University of Ottawa, Ottawa, Ontario, Canada
- Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology, Public Health and Preventative Medicine, University of Ottawa, Ottawa, Ontario, Canada
- uOttawa, Institute for Clinical Evaluative Sciences, Ottawa, Ontario, Canada
- Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
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Steurer MA, Peyvandi S, Baer RJ, MacKenzie T, Li BC, Norton ME, Jelliffe-Pawlowski LL, Moon-Grady AJ. Epidemiology of Live Born Infants with Nonimmune Hydrops Fetalis-Insights from a Population-Based Dataset. J Pediatr 2017; 187:182-188.e3. [PMID: 28533037 DOI: 10.1016/j.jpeds.2017.04.025] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 02/24/2017] [Accepted: 04/11/2017] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To evaluate the incidence, etiology, and 1-year mortality of nonimmune hydrops fetalis (NIHF) and to identify risk factors for mortality in a contemporary population-based dataset. STUDY DESIGN The California Office of Statewide Health Planning and Development maintains a database linking maternal and infant hospital discharge, readmissions, and birth and death certificate date from 1 year before to 1 year after birth. We searched the database (2005-2012) for infants with NIHF (identified by the International Classification of Diseases, 9th Revision, Clinical Modification code). Hazard models were used to identify risk factors for mortality in infants with NIHF; results are presented as hazard ratios (HRs, 95% CI). RESULTS The incidence of NIHF was 2.5 out of 10 000 among live born infants. Neonatal mortality was 35.1% (364 out of 1037) and overall mortality was 43.2% (448 out of 1037) at 1 year of age. Gestational age (GA) was predictive of mortality with a HR of 2.4 (95% CI 1.9-3.2) for preterm compared with term infants. The GA-adjusted HR for mortality was 1.3 (95% CI 1.1-1.6) for polyhydramnios and 1.5 (95% CI 1.2-2.0) for large for gestational age infants compared with appropriate for GA infants. Aneuploid infants with critical congenital heart disease had an adjusted HR of 2.3 (95% CI 1.5-3.6) compared with euploid infants without a structural birth defect. CONCLUSIONS In this large, population-based study, prematurity, polyhydramnios, and large for gestational age were predictors of increased mortality. Mortality is highly variable among euploid and aneuploid infants with and without structural birth defects and critical congenital heart disease.
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Affiliation(s)
- Martina A Steurer
- Department of Pediatrics, University of California, San Francisco, CA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
| | - Shabnam Peyvandi
- Department of Pediatrics, University of California, San Francisco, CA
| | - Rebecca J Baer
- Department of Pediatrics, University of California, San Diego, CA
| | - Tippi MacKenzie
- Department of Pediatric Surgery, University of California San Francisco, San Francisco, CA
| | - Ben C Li
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco, San Francisco, CA
| | - Mary E Norton
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco, San Francisco, CA
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Murphy MSQ, Hawken S, Atkinson KM, Milburn J, Pervin J, Gravett C, Stringer JSA, Rahman A, Lackritz E, Chakraborty P, Wilson K. Postnatal gestational age estimation using newborn screening blood spots: a proposed validation protocol. BMJ Glob Health 2017; 2:e000365. [PMID: 29104765 PMCID: PMC5659179 DOI: 10.1136/bmjgh-2017-000365] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Revised: 06/06/2017] [Accepted: 06/07/2017] [Indexed: 11/30/2022] Open
Abstract
Background Knowledge of gestational age (GA) is critical for guiding neonatal care and quantifying regional burdens of preterm birth. In settings where access to ultrasound dating is limited, postnatal estimates are frequently used despite the issues of accuracy associated with postnatal approaches. Newborn metabolic profiles are known to vary by severity of preterm birth. Recent work by our group and others has highlighted the accuracy of postnatal GA estimation algorithms derived from routinely collected newborn screening profiles. This protocol outlines the validation of a GA model originally developed in a North American cohort among international newborn cohorts. Methods Our primary objective is to use blood spot samples collected from infants born in Zambia and Bangladesh to evaluate our algorithm’s capacity to correctly classify GA within 1, 2, 3 and 4 weeks. Secondary objectives are to 1) determine the algorithm's accuracy in small-for-gestational-age and large-for-gestational-age infants, 2) determine its ability to correctly discriminate GA of newborns across dichotomous thresholds of preterm birth (≤34 weeks, <37 weeks GA) and 3) compare the relative performance of algorithms derived from newborn screening panels including all available analytes and those restricted to analyte subsets. The study population will consist of infants born to mothers already enrolled in one of two preterm birth cohorts in Lusaka, Zambia, and Matlab, Bangladesh. Dried blood spot samples will be collected and sent for analysis in Ontario, Canada, for model validation. Discussion This study will determine the validity of a GA estimation algorithm across ethnically diverse infant populations and assess population specific variations in newborn metabolic profiles.
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Affiliation(s)
- Malia S Q Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Katherine M Atkinson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Jennifer Milburn
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Jesmin Pervin
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Courtney Gravett
- Global Alliance to Prevent Prematurity and Stillbirth, Seattle, USA
| | - Jeffrey S A Stringer
- Department of Obstetrics and Gynecology, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Anisur Rahman
- Matlab Health Research Centre, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Eve Lackritz
- Global Alliance to Prevent Prematurity and Stillbirth, Seattle, USA
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
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Romero R, Erez O, Maymon E, Chaemsaithong P, Xu Z, Pacora P, Chaiworapongsa T, Done B, Hassan SS, Tarca AL. The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study. Am J Obstet Gynecol 2017; 217:67.e1-67.e21. [PMID: 28263753 PMCID: PMC5813489 DOI: 10.1016/j.ajog.2017.02.037] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 02/10/2017] [Accepted: 02/23/2017] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy. STUDY DESIGN A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation. RESULTS The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%). CONCLUSION The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.
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Affiliation(s)
- Roberto Romero
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI; Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI.
| | - Offer Erez
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
| | - Eli Maymon
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
| | - Piya Chaemsaithong
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
| | - Zhonghui Xu
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI
| | - Percy Pacora
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
| | - Tinnakorn Chaiworapongsa
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
| | - Bogdan Done
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI
| | - Sonia S Hassan
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
| | - Adi L Tarca
- Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI.
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Leviton A, Allred EN, Joseph RM, O'Shea TM, Kuban KCK. Newborn blood gas derangements of children born extremely preterm and neurocognitive dysfunctions at age 10 years. Respir Physiol Neurobiol 2017; 242:66-72. [PMID: 28396202 DOI: 10.1016/j.resp.2017.04.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 03/30/2017] [Accepted: 04/01/2017] [Indexed: 01/03/2023]
Abstract
Among 740 children born extremely preterm, we evaluated the relationship between the highest and lowest quartiles of the distributions of PaO2 and PaCO2, as well as the lowest quartile of pH on one day, and separately on two days, and the risk of neurocognitive, language, and behavioral dysfunctions at age 10 years. Children who had hypoxemia, hyperoxemia, hypocapnia, hypercapnia, and acidemia, sometimes on only one day, and sometimes on two or more days, were more likely than others to have a high illness severity score (within the first 12 postnatal hours), and 10 years later to have multiple dysfunctions. The tendency of blood gas derangements to be associated with high illness severity scores and with multiple dysfunctions 10 years later is compatible with the possibility that blood gas derangements are indicators of physiologic instability/vulnerability/immaturity rather than contributors to brain damage.
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Affiliation(s)
- Alan Leviton
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | | | | | - T Michael O'Shea
- University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Karl C K Kuban
- Boston University School of Medicine, Boston, MA, USA; Boston Medical Center, Boston, MA, USA
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Jing Y, Wu X, Gao P, Fang Z, Wu J, Wang Q, Li C, Zhu Z, Cao Y. Rapid differentiating colorectal cancer and colorectal polyp using dried blood spot mass spectrometry metabolomic approach. IUBMB Life 2017; 69:347-354. [PMID: 28322027 DOI: 10.1002/iub.1617] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Accepted: 02/19/2017] [Indexed: 12/16/2022]
Abstract
Colorectal cancer (CRC) is the third leading causes of cancer mortality, and the early-stage detection could significantly enhance survival rates. Cancer influences the important metabolic pathways and the changes in metabolite levels had been used in many studies as the potential biomarkers. This study is aimed at screening metabolite biomarkers with CRC diagnosis potentials. The direct infusion mass spectrometry (MS) metabolomic analysis based on dried blood spot was used to distinguish CRC from polyp. The target metabolites were composed of 23 amino acids and 26 acylcarnitines. The 21 metabolites in blood were selected via multivariate analysis. A regression model was established based on parameters C16, Arg, C4/C8, C5/C3, Val, Phe/Tyr, Ala, C4/C3. Tenfold cross validation (CV) method was used to test this model and showed sensitivity of 81.18% and specificity of 83.95%. The metabolomic analysis is a practicable method for CRC detection. The use of direct MS analysis in metabolite screening could be finished in several minutes and served as a higher-throughput method to distinguish CRC and polyps. © 2017 IUBMB Life, 69(5):347-354, 2017.
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Affiliation(s)
- Yu Jing
- Jinzhou Medical University, Jinzhou, Liaoning, China.,The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.,Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China
| | - Xue Wu
- The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.,Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China
| | - Peng Gao
- Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China.,Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian.,Clinical Laboratory, Dalian Sixth People's Hospital, Dalian, People's Republic of China
| | - Zhongze Fang
- Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China.,Department of Toxicology, School of Public Health, Tianjin Medical University, Heping District, Tianjin, China
| | - Jingjing Wu
- Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China.,Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian
| | - Qingjun Wang
- The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.,Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China
| | - Chen Li
- The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.,Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China
| | - Zhitu Zhu
- The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.,Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China
| | - Yunfeng Cao
- Key Laborotary of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China.,Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian.,Key Laboratory of Contraceptives and Devices Research (NPFPC), Shanghai Engineer and Technology Research Center of Reproductive Health Drug and Devices, Shanghai Institute of Planned Parenthood Research, Shanghai
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50
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Acylcarnitine Profiles Reflect Metabolic Vulnerability for Necrotizing Enterocolitis in Newborns Born Premature. J Pediatr 2017; 181:80-85.e1. [PMID: 27836286 PMCID: PMC5538349 DOI: 10.1016/j.jpeds.2016.10.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 07/29/2016] [Accepted: 10/05/2016] [Indexed: 12/24/2022]
Abstract
OBJECTIVE To evaluate the association between newborn acylcarnitine profiles and the subsequent development of necrotizing enterocolitis (NEC) with the use of routinely collected newborn screening data in infants born preterm. STUDY DESIGN A retrospective cohort study was conducted with the use of discharge records for infants born preterm admitted to neonatal intensive care units in California from 2005 to 2009 who had linked state newborn screening results. A model-development cohort of 94 110 preterm births from 2005 to 2008 was used to develop a risk-stratification model that was then applied to a validation cohort of 22 992 births from 2009. RESULTS Fourteen acylcarnitine levels and acylcarnitine ratios were associated with increased risk of developing NEC. Each log unit increase in C5 and free carnitine /(C16 + 18:1) was associated with a 78% and a 76% increased risk for developing NEC, respectively (OR 1.78, 95% CI 1.53-2.02, and OR 1.76, 95% CI 1.51-2.06). Six acylcarnitine levels, along with birth weight and total parenteral nutrition, identified 89.8% of newborns with NEC in the model-development cohort (area under the curve 0.898, 95% CI 0.889-0.907) and 90.8% of the newborns with NEC in the validation cohort (area under the curve 0.908, 95% CI 0.901-0.930). CONCLUSIONS Abnormal fatty acid metabolism was associated with prematurity and the development of NEC. Metabolic profiling through newborn screening may serve as an objective biologic surrogate of risk for the development of disease and thus facilitate disease-prevention strategies.
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