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Background: Prematurity may influence the levels of
amino acids, enzymes, and endocrine markers obtained
through newborn screening. Identifying which analytes are
the most affected by degree of prematurity could provide
insight into how prematurity impacts metabolism.
Methods: Analytes from blood spots assayed by Newborn
Screening Ontario between March 2006 and April 2009 were
used in this analysis. We examined the associations between
the degree of prematurity and the levels of amino acids,
enzymes, and endocrine markers in all newborns with and
without adjustment for birth weight, feeding status, sample
timing, transfusion, and sex.
results: Our analysis included the following cohorts: 373,819
children born at term (>36 wk gestation), 26,483 near-term
children (33–36 wk gestation), 4,354 very premature children
(28–32 wk gestation), and 1,146 extremely premature children
(<28 wk gestation). Of the amino acids showing consistent
trends across categories of prematurity, the levels of three
amino acids (arginine, leucine, and valine) were at least 50%
different between the cohorts of extremely premature and
term children. The levels of 17-hydroxyprogesterone increased
with increasing prematurity, while thyrotropin-stimulating hor-
mone values consistently decreased with increasing prematu-
rity. None of the three enzyme markers we examined showed
a trend in levels across categories of prematurity.
conclusion: This study demonstrates that children at dif-
ferent stages of prematurity are metabolically distinct. Future
research should focus on the mechanism by which specific
analytes are influenced by prematurity.
which levels of various analytes are measured (1,2). In Ontario
and several other jurisdictions, these analytes include markers
of amino acid metabolism, fatty acid oxidation, and endocrine
function (3–7). Analyte results are used to calculate risk for
rare inborn errors of metabolism and endocrine disorders.
n population-wide newborn screening, dried blood spot
samples are obtained usually within 24–72 h of birth from
Children having positive screening results are sent for confir-
It is generally recognized that, aside from the presence of a rare
metabolic or endocrine disease, several other factors can affect
the analytes measured for newborn screening. One of these is ges-
tational age (GA) at birth (8–11). Prematurity may influence ana-
lyte levels through a variety of mechanisms, including increased
catabolic stress, lessened maturation of metabolic pathways and/
or organ systems, and differential distribution of other confound-
ing variables such as feeding status (9,12). Identifying which
analytes are the most affected by extent of prematurity could pro-
vide insight into how prematurity influences metabolism.
In Ontario, Canada, virtually every child undergoes newborn
screening (13). Results for all analytes measured are stored along
with key demographic variables. We examined the associations
between different degrees of prematurity and subclinical lev-
els of specific analytes, after removing screen positives/extreme
values. In particular, we examined the associations between the
degree of prematurity and the levels of amino acids, enzymes,
and endocrine markers in all newborns with and without
adjustment for sex, birth weight, supplemental feeding, timing
of sample collection, and receipt of blood or blood product.
Characteristics of Population
Newborn screening analyte data for a total of 412,494 chil-
dren born in Ontario between April 2006 and March 2009
were available at the Institute for Clinical Evaluative Sciences
(Ottawa, Ontario, Canada). Of these, 405,802 also had data on
GA and were therefore included in our analysis. Our analy-
sis included 373,819 children born at term, 26,483 near-term
children, 4,354 very premature children, and 1,146 extremely
premature children. Table 1 presents the sex proportions,
mean birth weights, median time of sample collection, percent
receiving transfusion, and mode of infant feeding by category
of prematurity. In our overall sample, 51.3% of infants were
male and 48.7% were female with similar proportions in all
Received 1 March 2013; accepted 24 June 2013; advance online publication 22 January 2014. doi:10.1038/pr.2013.212
1Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada; 2Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, Ontario, Canada; 3Clinical
Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; 4Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa,
Ontario, Canada; 5Department of Pediatrics, University of Ottawa, Ottawa, Ontario, Canada; 6Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa,
Ontario, Canada; 7Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada; 8Newborn Screening Ontario, Children’s Hospital of Eastern Ontario,
Ottawa, Ontario, Canada. Correspondence: Kumanan Wilson (email@example.com)
Metabolomics of prematurity: analysis of patterns of amino
acids, enzymes, and endocrine markers by categories of
Kumanan Wilson1–3, Steven Hawken2–4, Robin Ducharme2–4, Beth K. Potter2–4, Julian Little4, Bernard Thébaud5–7
and Pranesh Chakraborty5,8
Copyright © 2014 International Pediatric Research Foundation, Inc.
Volume 75 | Number 2 | February 2014 Pediatric ReSeARCh 367
Metabolomics of prematurity Articles
the majority of the analytes studied, values were available for chil-
dren born in all 3 y of study. Analysis of biotinidase, galactose 1 phos-
phate uridyl transferase, and 17-OHP by Newborn Screening Ontario
began in 2007, and immunoreactive trypsinogen analysis began in
2008. In this study, we did not examine acylcarninitine levels, which
will be analyzed in a separate study. Since the aim of our study was to
look for trends in subclinical analyte levels, extremely low and high
outliers for each analyte were removed. This was achieved by remov-
ing identified screen positives in addition to excluding observations
below the 0.01th percentile and above the 99.99th percentile, effec-
tively removing all screen positives from the analysis.
Using encrypted individual Ontario Health Insurance Plan num-
bers, screening data were linked to patient records in Ontario’s health
administrative data housed at the Institute for Clinical Evaluative
Sciences, a repository of linked health information databases. We
conducted our analysis on screened children for whom data were
available on GA.
Based on GA at birth, we classified infants into four categories of
prematurity: term (>36 wk gestation), near term (33–36 wk gesta-
tion), very premature (28–32 wk gestation), and extremely prema-
ture (<28 wk gestation). All study data were housed at the Institute
for Clinical Evaluative Sciences and were individually linked to
the Ontario Registered Persons Database. The Registered Persons
Database includes all Ontario residents who have ever been issued an
Ontario Health Insurance Program number (i.e., nearly all residents
of the province).
We examined differences in levels of amino acids, enzymes, and
endocrine markers by the extent of prematurity. We computed means
for each analyte within each category of GA and compared the means
between categories using: (i) standardized differences between term
and premature children (differences were expressed in units of SD
using the SD of the term category as the benchmark) and (ii) percent
differences (differences were expressed as a percentage of the mean
analyte level in term children).
To investigate the potential impact of covariates, we used linear mod-
els to test the robustness of relationships between prematurity catego-
ries and mean analyte levels after controlling for sex, birth weight, mode
of feeding, timing of sample collection, and receipt of blood or blood
product transfusion. For feeding and transfusion status, we used data as
recorded on the newborn screening record (i.e., feeding status proximal
to the blood spot collection). Possible values included breast, formula,
total parenteral nutrition, nil per os, and combinations thereof.
We also conducted a restricted “base case” analysis in which the
category or range of a number of important covariates were fixed at
a level consistent with those of healthy infants. In this analysis, we
compared children across the levels of prematurity who: (i) were not
below the 10th percentile for birth weight given their GA (SGA10),
(ii) were exclusively breastfed at the time of sample collection, (iii) did
not receive any blood transfusion or other blood products, and (iv)
had their blood spot sample collected between 72 h and 1 wk of age.
Our choice of window for sample collection took the distribution
of observed values into consideration by excluding extreme values
without eliminating the majority of extremely premature infants. We
conducted this restricted analysis because trends in covariates with
increasing prematurity may also be associated with increased cata-
bolic stress, making it difficult to separate potentially causal effects.
Furthermore, some of these important covariates may be effect modi-
fiers and may significantly interact with the relationship between
analyte levels and prematurity. P values were calculated for two-tailed
tests of differences between least-squares means for each prematurity
group vs. term children.
To examine the interrelated nature of the analytes as markers of com-
mon metabolic pathways that may be influenced by GA, we generated
Pearson correlation heat maps among pairs of analytes in all infants,
term infants and premature infants. In addition, we generated a differ-
ence heat map to highlight the pairs of analytes in which the Pearson
correlations were different between term and preterm children.
Supplementary material is linked to the online version of the paper at http://
STATEMENT OF FINANCIAL SUPPORT
This study was supported by the Institute for Clinical Evaluative Sciences,
which is funded by an annual grant from the Ontario Ministry of Health and
Long-Term Care. J.L. is supported by the Canada Research Chair program.
K.W. is supported by the Chair in Public Health Policy at The Ottawa Hospital,
the University of Ottawa’s Department of Medicine, and the Ottawa Hospital
Disclaimer: The opinions, results, and conclusions reported in this article
are those of the authors and are independent from the funding sources. No
endorsement by the Institute for Clinical Evaluative Sciences or the Ontario
MOHLTC is intended or should be inferred.
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