Maternal Child Metabolomics Research  
Preterm birth is a major worldwide public health concern, however identifying preterm infants in low- and middle-income settings poses a challenge where availability of prenatal ultrasounds is limited. Dr. Wilson has received 3 million dollars in funding from the Bill & Melinda Gates Foundation to develop alternative methods of estimating newborn gestational age and early child health outcomes through the analysis of newborn heel prick and cord blood samples. This work will aid in developing population level estimates of preterm birth and guide the care of newborns in low resource settings.   

PHASE I: Developing a Gestational Age Algorithm   
Using Ontario newborn screens data, Dr. Wilson and his research team determined that the newborns of different stages of prematurity are metabolically distinct. This discovery led to the development of an algorithm based on newborn screening data capable of estimating gestational age to within one week of ultrasound-validated GA. The model was further validated in an ethnic subpopulation in Ontario whose mothers were recent landed immigrants. The model was further refined to facilitate its implementation in low resource settings by incorporating non-mass spectrometry derived analytes including  fetal-to-adult Hb ratio, thyroid stimulating hormone and 17-hydroxypogesterone.

PHASE II: International Validation of a Gestational Age Algorithm
We have tested the effectiveness of our algorithm in low resource settings in a prospective cohort study. Algorithms correctly estimated gestational age to within 1 week in heel and cord blood, with cord blood being a more feasible option in low-resource settings.

PHASE III: Implementation of a Gestational Age Algorithm. 
Dr Wilson his team in collaboration with researchers at Stanford University are currently conducting a pilot study at four international sites to implement the process of gestational age estimation. All sites are low resource settings with high rates of preterm birth and low access to ultrasound. Into the data screening process we have incorporated a protocol to identify select screen positive results and notify collection sites in real time in order to initiate treatment.

Research Collaborators

Steven Hawken, PhD

Pranesh Chakroborty, MD

Deschayne Fell, PhD

Mark Walker, MD


Publications
Wilson, Lindsay A, Fell, D. B., et al. (2019) ‘Association between newborn screening analytes and hypoxic ischemic encephalopathy.’, Scientific reports, 9(1), p. 15704. doi: 10.1038/s41598-019-51919-x.   

Wilson, LA, Murphy, M.SQ, Ducharme, R, Denize, K, Jadavji NM, Potter, B, Little J, Chakraborty P, Hawken S, Wilson K. (2019) ‘Postnatal gestational age estimation via newborn screening analysis: application and potential.’, Expert review of proteomics, 16(9), pp. 727–731. doi: 10.1080/14789450.2019.1654863. 

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. (2019) ‘External validation of postnatal gestational age estimation using newborn metabolic profiles in matlab, Bangladesh’, eLife. eLife Sciences Publications Ltd, 8. doi: 10.7554/eLife.42627. 

Fell DB, Wilson LA, Hawken S, Spruin S, Murphy MSQ, Potter BK, Little J, Chakraborty P, Lacaze-Masmonteil T, Wilson K.  Association between newborn screening analyte profiles and infant mortality.  Journal of Maternal-Fetal and Neonatal Medicine. 2019 May 21:1-4. doi: 10.1080/14767058.2019.1615048.

 Murphy M, Chakraborty P, Pervin J, Rahman A, Wilson L, Lamoureux M, Denize K, Henderson M, Hawken S, Potter B, Little J, Wilson K.  Incidental screen positive findings in a prospective cohort study in Matlab, Bangladesh: insights into expanded newborn screening for low-resource settings.   Orphanet Journal of Rare Diseases. 2019 Jan 30;14(1):25. doi: 10.1186/s13023-018-0993-1

Murphy, Malia S.Q. et al. (2018) ‘Metabolic profiles derived from residual blood spot samples: A longitudinal analysis’, Gates Open Research. F1000 Research, Ltd., 2, p. 28. doi: 10.12688/gatesopenres.12822.1.

Sood MM, Murphy MSQ, Hawken S, Wong CA, Potter BK, Burns KD, TSampalieros A, Atkinson KM, Chakraborty P, Wilson K.  Association between newborn metabolic profiles and pediatric kidney disease.  Kidney International Reports.  May 2018;3(3):691-700.  doi: 10.1016/j.ekir.2018.02.001 

Wilson K, Duque DR, Murphy MS, Hawken S, Pham-Huy A, Kwong J, Deeks SL, Potter BK, Crowcroft NS, Bulman DE, Chakraborty P, Little J. T-cell receptor excision circle levels and safety of paediatric immunization: A population-based self-controlled case series analysis. Hum Vaccin Immunother.  2018 Jun 3;14(6):1378-1391. doi: 10.1080/21645515.2018.1433971.

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. Nature Scientific Reports. 2017; 7. doi:10.1038/s41598-017-18371-1

Hawken S, Ducharme R, Murphy MSQ, Atkinson KM, Potter BK, Chakraborty P, Wilson K. Validation of a gestational age prediction algorithm in ethnic subgroups using routinely collected newborn screening metabolic profiles. BMJ Open. 2017; 7:e015615. doi: 10.1136/bmjopen-2016-015615 

 Wilson K, Hawken S, Murphy M, Atkinson KM, Potter B, Sprague A, Walker M, Chakraborty P, Little J. Postnatal prediction of gestational age using newborn fetal hemoglobin levels. EBioMedicine. 2017; 15:203–209. doi: 10.1016/j.ebiom.2016.11.032

Wilson K, Hawken S, Potter B, Chakraborty P, Walker M, Ducharme R, Little J. Accurate prediction of gestational age using newborn screening analyte data. American Journal of Obstetrics and Gynecology.  2016 Apr; 214(4):513.e1-9

Wilson K, Hawken S, Ducharme R, Potter BK, Little J, Thebaud B, Chakroborty P. (2014) ‘Metabolomics of prematurity: Analysis of patterns of amino acids, enzymes, and endocrine markers by categories of gestational age’, Pediatric Research, 75(2), pp. 367–373. doi: 10.1038/pr.2013.212.