Madeleine Ernst1,2, Simon Rogers3, Ulrik Lausten-Thomsen4, Anders Björkbom5,6, Susan Svane Laursen5,6, Julie Courraud5,6, Anders Børglum6,7,8,9, Merete Nordentoft6,10,11, Thomas Werge6,11,12,13, Preben Bo Mortensen6,14,15, David M Hougaard5,6, Arieh S Cohen5,6. 1. Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark. maet@ssi.dk. 2. iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark. maet@ssi.dk. 3. School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK. 4. Department of Neonatology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark. 5. Section for Clinical Mass Spectrometry, Department of Congenital Disorders, Danish Center for Neonatal Screening, Statens Serum Institut, Copenhagen, Denmark. 6. iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark. 7. Center for Genomics and Personalized Medicine, Aarhus, Denmark. 8. Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark. 9. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark. 10. Danish Research Institute for Suicide Prevention, Copenhagen Research Center for Mental Health (CORE), Mental Health Center Copenhagen, Copenhagen, Denmark. 11. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 12. Center for GeoGenetics, Globe Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 13. Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Capital Region of Denmark, Copenhagen, Denmark. 14. NCRR - National Centre for Register-based Research, Aarhus University,, Aarhus, Denmark. 15. CIRRAU-Centre for Integrated Register-based Research at Aarhus University, Aarhus, Denmark.
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.
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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