Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Metabolomic profiling of brain from infants who died from Sudden Infant Death Syndrome reveals novel predictive biomarkers

Journal of Perinatology volume 37, pages 91–97 (2017)Cite this article

Subjects

Abstract

Objective:

Sudden Infant Death Syndrome (SIDS) is defined as the sudden death of an infant <1 year of age that cannot be explained following a thorough investigation. Currently, no reliable clinical biomarkers are available for the prediction of infants who will die of SIDS.

Study Design:

This study aimed to profile the medulla oblongata from postmortem human brain from SIDS victims (n=16) and compare their profiles with that of age-matched controls (n=7).

Results:

Using LC-Orbitrap-MS, we detected 12 710 features in electrospray ionization positive (ESI+) mode and 8243 in ESI− mode from polar extracts of brain. Five features acquired in ESI+ mode produced a predictive model for SIDS with an area under the receiver operating characteristic curve (AUC) of 1 (confidence interval (CI): 0.995–1) and a predictive power of 97.4%. Three biomarkers acquired in ESI− mode produced a predictive model with an AUC of 0.866 (CI: 0.767–0.942) and a predictive power of 77.6%. We confidently identified 5 of these features (l-(+)-ergothioneine, nicotinic acid, succinic acid, adenosine monophosphate and azelaic acid) and putatively identify another 4 out of the 15 in total.

Conclusions:

This study underscores the potential value of metabolomics for studying SIDS. Further characterization of the metabolome of postmortem SIDS brains could lead to the identification of potential antemortem biomarkers for novel prevention strategies for SIDS.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Centers for Disease Control and Prevention (CDC). Sudden infant death syndrome—United States, 1983-1994. MMWR Morb Mortal Wkly Rep 1996; 45 (40): 859–863.

    Google Scholar 

  2. Mathews TJ, Marian FM . Infant mortality statistics from the 2010 period linked birth/infant death data set. Natl Vital Stat Rep 2010; 58 (17): 1–31.

    CAS  PubMed  Google Scholar 

  3. Creery D, Mikrogianakis A . Sudden infant death syndrome. Clin Evid 2005; 13: 434–443.

    Google Scholar 

  4. Filiano JJ, Kinney HC . A perspective on neuropathologic findings in victims of the sudden infant death syndrome: the triple-risk model. Biol Neonate 1994; 65 (3-4): 194–197.

    Article  CAS  PubMed  Google Scholar 

  5. Willinger M, Hoffman HJ, Hartford RB . Infant sleep position and risk for sudden infant death syndrome: report of meeting held January 13 and 14, 1994, National Institutes of Health, Bethesda, MD. Pediatrics 1994; 93 (5): 814–819.

    CAS  PubMed  Google Scholar 

  6. Malloy MH, Freeman DH Jr . Sudden infant death syndrome among twins. Arch Pediatr Adolesc Med 1999; 153 (7): 736–740.

    Article  CAS  PubMed  Google Scholar 

  7. Wishart DS . Advances in metabolite identification. Bioanalysis 2011; 3 (15): 1769–1782.

    Article  CAS  PubMed  Google Scholar 

  8. Filipp FV . A gateway between omics data and systems biology. J Metabolomics Syst Biol 2013; 1 (1): 1.

    PubMed  PubMed Central  Google Scholar 

  9. Matsuda R, Bi C, Anguizola J, Sobansky M, Rodriguez E, Vargas Badilla J et al. Studies of metabolite-protein interactions: a review. J Chromatogr B Anal Technol Biomed Life Sci 2013; 966: 48–58.

    Article  Google Scholar 

  10. Graham S, Holscher C, Green B . Metabolic signatures of human Alzheimer’s disease (AD): 1H NMR analysis of the polar metabolome of post-mortem brain tissue. Metabolomics 2013; 1–10.

  11. Ramautar R, Berger R, van der Greef J, Hankemeier T . Human metabolomics: strategies to understand biology. Curr Opin Chem Biol 2013; 17 (5): 841–846.

    Article  CAS  PubMed  Google Scholar 

  12. Kinney HC . Neuropathology provides new insight in the pathogenesis of the sudden infant death syndrome. Acta Neuropathol 2009; 117 (3): 247–255.

    Article  PubMed  Google Scholar 

  13. Hunt CE, Brouillette RT . Sudden infant death syndrome: 1987 perspective. J Pediatr 1987; 110 (5): 669–678.

    Article  CAS  PubMed  Google Scholar 

  14. Sawaguchi T, Patricia F, Kadhim H, Groswasser J, Sottiaux M, Nishida H et al. Pathological data on apoptosis in the brainstem and physiological data on sleep apnea in SIDS victims. Early Hum Dev 2003; 75 (Suppl): S13–S20.

    Article  PubMed  Google Scholar 

  15. Machaalani R, Rodriguez M, Waters KA . Active caspase-3 in the sudden infant death syndrome (SIDS) brainstem. Acta Neuropathol 2007; 113 (5): 577–584.

    Article  CAS  PubMed  Google Scholar 

  16. Machaalani R, Waters KA . Neuronal cell death in the Sudden Infant Death Syndrome brainstem and associations with risk factors. Brain 2008; 131 (Pt 1): 218–228.

    PubMed  Google Scholar 

  17. Lavezzi AM, Ottaviani G, Matturri L . Identification of neurons responding to hypoxia in sudden infant death syndrome. Pathol Int 2003; 53 (11): 769–774.

    Article  PubMed  Google Scholar 

  18. Paine SM, Jacques TS, Sebire NJ . Neuropathological features of unexplained sudden unexpected death in infancy: current evidence and controversies. Neuropathol Appl Neurobiol 2013; 40 (4): 364–384.

    Article  Google Scholar 

  19. Kadhim H, Kahn A, Sebire G . Distinct cytokine profile in SIDS brain: a common denominator in a multifactorial syndrome? Neurology 2003; 61 (9): 1256–1259.

    Article  CAS  PubMed  Google Scholar 

  20. Matturri L, Biondo B, Mercurio P, Rossi L . Severe hypoplasia of medullary arcuate nucleus: quantitative analysis in sudden infant death syndrome. Acta Neuropathol 2000; 99 (4): 371–375.

    Article  CAS  PubMed  Google Scholar 

  21. Panigrahy A, Filiano J, Sleeper LA, Mandell F, Valdes-Dapena M, Krous HF et al. Decreased serotonergic receptor binding in rhombic lip-derived regions of the medulla oblongata in the sudden infant death syndrome. J Neuropathol Exp Neurol 2000; 59 (5): 377–384.

    Article  CAS  PubMed  Google Scholar 

  22. Kinney HC, Randall LL, Sleeper LA, Willinger M, Belliveau RA, Zec N et al. Serotonergic brainstem abnormalities in Northern Plains Indians with the sudden infant death syndrome. J Neuropathol Exp Neurol 2003; 62 (11): 1178–1191.

    Article  CAS  PubMed  Google Scholar 

  23. Paterson DS, Trachtenberg FL, Thompson EG, Belliveau RA, Beggs AH, Darnall R et al. Multiple serotonergic brainstem abnormalities in sudden infant death syndrome. JAMA 2006; 296 (17): 2124–2132.

    Article  CAS  PubMed  Google Scholar 

  24. Randall BB, Paterson DS, Haas EA, Broadbelt KG, Duncan JR, Mena OJ et al. Potential asphyxia and brainstem abnormalities in sudden and unexpected death in infants. Pediatrics 2013; 132 (6): e1616–e1625.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Graham SF, Chevallier OP, Roberts D, Holscher C, Elliott CT, Green BD . Investigation of the human brain metabolome to identify potential markers for early diagnosis and therapeutic targets of Alzheimer's disease. Anal Chem 2013; 85 (3): 1803–1811.

    Article  CAS  PubMed  Google Scholar 

  26. Graham SF, Chevallier OP, Kumar P, Türkoğlu O, Bahado-Singh RO . High resolution metabolomic analysis of ASD human brain uncovers novel biomarkers of disease. Metabolomics 2016; 12 (4): 1–10.

    Article  CAS  Google Scholar 

  27. Graham SF, Kumar P, Bahado-Singh RO, Robinson A, Mann D, Green BD . Novel metabolite biomarkers of Huntington’s disease (HD) as detected by high resolution mass spectrometry. J Proteome Res 2016; 15: 1592–1601.

    Article  CAS  PubMed  Google Scholar 

  28. Gowda H, Ivanisevic J, Johnson CH, Kurczy ME, Benton HP, Rinehart D et al. Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem 2014; 86 (14): 6931–6939.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y et al. HMDB 3.0—The Human Metabolome Database in. 2013 Nucleic Acids Res 2013; 41 (Database issue): D801–D807.

    CAS  PubMed  Google Scholar 

  30. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 2009; 37 (Database issue): D603–D610.

    Article  CAS  PubMed  Google Scholar 

  31. Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N et al. HMDB: the Human Metabolome Database. Nucleic Acids Res 2007; 35 (Database issue): D521–D526.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K et al. MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 2010; 45 (7): 703–714.

    Article  CAS  PubMed  Google Scholar 

  33. Eriksson L, Johansson E, Kettaneh-Wold N, Trygg J, Wikstrom C, Wold S . Multi- and Megavariate Data Analysis: Part I Basic principles and Applications. Umetrics: Umea, 2006.

  34. Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS . MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis. Nucleic Acids Res 2012; 40 (Web Server issue): W127–W133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Xia J, Psychogios N, Young N, Wishart DS . MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009; 37 (Web Server issue): W652–W660.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Xia J, Sinelnikov IV, Han B, Wishart DS . MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res 2015; 43 (W1): W251–W257.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 2007; 3 (3): 211–221.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Altman DG, Bland JM . Absence of evidence is not evidence of absence BMJ 1995; 311: 485 .

  39. Paul BD, Snyder SH . The unusual amino acid L-ergothioneine is a physiologic cytoprotectant. Cell Death Differ 2010; 17 (7): 1134–1140.

    Article  CAS  PubMed  Google Scholar 

  40. Aruoma OI, Spencer JPE, Mahmood N . Protection against oxidative damage and cell death by the natural antioxidant ergothioneine. Food Chem Toxicol 1999; 37 (11): 1043–1053.

    Article  CAS  PubMed  Google Scholar 

  41. Tamazian G, Chang JH, Knyazev S, Stepanov E, Kim KJ, Porozov Y . Modeling conformational redox-switch modulation of human succinic semialdehyde dehydrogenase. Proteins 2015; 83 (12): 2217–2229.

    Article  CAS  PubMed  Google Scholar 

  42. Pearl PL, Gibson KM, Acosta MT, Vezina LG, Theodore WH, Rogawski MA et al. Clinical spectrum of succinic semialdehyde dehydrogenase deficiency. Neurology 2003; 60 (9): 1413–1417.

    Article  CAS  PubMed  Google Scholar 

  43. Ting Wong CG, Bottiglieri T, Snead OC . GABA, γ-hydroxybutyric acid, and neurological disease. Ann Neurol 2003; 54 (S6): S3–S12.

    Article  Google Scholar 

  44. Gibson KM, Christensen E, Jakobs C, Fowler B, Clarke MA, Hammersen G et al. The clinical phenotype of succinic semialdehyde dehydrogenase deficiency (4-hydroxybutyric aciduria): case reports of 23 new patients. Pediatrics 1997; 99 (4): 567–574.

    Article  CAS  PubMed  Google Scholar 

  45. Broadbelt KG, Paterson DS, Belliveau RA, Trachtenberg FL, Haas EA, Stanley C et al. Decreased GABA(A) receptor binding in the medullary serotonergic system in the sudden infant death syndrome. J Neuropathol Exp Neurol 2011; 70 (9): 799–810.

    Article  CAS  PubMed  Google Scholar 

  46. Hess JR, Greenberg NA . The role of nucleotides in the immune and gastrointestinal systems: potential clinical applications. Nutr Clin Pract 2012; 27 (2): 281–294.

    Article  PubMed  Google Scholar 

  47. Pizzini RP, Kumar S, Kulkarni AD, Rudolph FB, Van Buren CT . Dietary nucleotides reverse malnutrition and starvation-induced immunosuppression. Arch Surg 1990; 125 (1): 86–89 discussion 90.

    Article  CAS  PubMed  Google Scholar 

  48. Li K, Anderson KJ, Peng Q, Noble A, Lu B, Kelly AP et al. Cyclic AMP plays a critical role in C3a-receptor–mediated regulation of dendritic cells in antigen uptake and T-cell stimulation. Blood 2008; 112 (13): 5084–5094.

    Article  CAS  PubMed  Google Scholar 

  49. Hoop B, Masjedi MR, Shih VE, Kazemi H . Brain glutamate metabolism during hypoxia and peripheral chemodenervation. J Appl Physiol 1990; 69 (1): 147–154.

    Article  CAS  PubMed  Google Scholar 

  50. Graupe K, Cunliffe WJ, Gollnick HP, Zaumseil RP . Efficacy and safety of topical azelaic acid (20 percent cream): an overview of results from European clinical trials and experimental reports. Cutis 1996; 57 (1 Suppl): 20–35.

    CAS  PubMed  Google Scholar 

  51. Blood-Siegfried J . The role of infection and inflammation in sudden infant death syndrome. Immunopharmacol Immunotoxicol 2009; 31 (4): 516–523.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Prandota J . Possible pathomechanisms of sudden infant death syndrome: key role of chronic hypoxia, infection/inflammation states, cytokine irregularities, and metabolic trauma in genetically predisposed infants. Am J Ther 2004; 11 (6): 517–546.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the University of Maryland Brain and Tissue Bank, which is a Brain and Tissue Repository of the NIH NeuroBioBank, for graciously providing the tissue samples for this study.

Author information

Authors and Affiliations

  1. Beaumont Health, Royal Oak, MI, USA

    S F Graham, P Kumar, O Türkoǧlu & R O Bahado-Singh

  2. Advanced ASSET Technology Centre, Institute for Global Food Security, Queen’s University Belfast, Belfast, UK

    O P Chevallier

Authors
  1. S F Graham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. O P Chevallier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. O Türkoǧlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. R O Bahado-Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S F Graham.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the Journal of Perinatology website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Graham, S., Chevallier, O., Kumar, P. et al. Metabolomic profiling of brain from infants who died from Sudden Infant Death Syndrome reveals novel predictive biomarkers. J Perinatol 37, 91–97 (2017). https://doi.org/10.1038/jp.2016.139

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/jp.2016.139

This article is cited by

Search

Advanced search

Quick links