Metabolomics of prematurity: Analysis of patterns of amino acids, enzymes, and endocrine markers by categories of gestational age

1] Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada [2] Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, Ontario, Canada [3] Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Pediatric Research (Impact Factor: 2.31). 11/2013; 75(2). DOI: 10.1038/pr.2013.212
Source: PubMed


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.

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.

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 hormone values consistently decreased with increasing prematurity. None of the three enzyme markers we examined showed a trend in levels across categories of prematurity.

This study demonstrates that children at different stages of prematurity are metabolically distinct. Future research should focus on the mechanism by which specific analytes are influenced by prematurity.

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    ABSTRACT: Background: Identification of preterm births and accurate estimates of gestational age (GA) for newborns is vital to guide care for the newborn. Unfortunately, in developing countries it can be challenging to obtain estimates of GA. Routinely collected newborn screening metabolic analytes vary by GA and may be useful to estimate GA. Objective(s): We sought to develop an algorithm that could estimate GA at birth based on the analytes obtained from newborn screening. Study design: We conducted a population based cross-sectional study of all live births in the province of Ontario including 249,700 infants born between April 2007 and March 2009 who underwent newborn screening. We used multivariable linear and logistic regression analyses to build a model to predict gestational age using newborn screening metabolite measurements and readily available physical characteristics data (birth weight and sex). Results: The final model of our metabolic gestational dating algorithm had an average deviation between observed and expected GA of about 1 week suggesting excellent predictive ability (adjusted R-square of 0.65, and a root mean square error of 1.06 weeks). Two thirds of GAs predicted by our model were accurate within +/- 1 week of the actual GA. Our logistic regression model was able to discriminate extremely well between term and increasingly premature categories of infants (c-statistic>0.99). Conclusion(s): Metabolic gestational dating is accurate for predicting gestational age and could have value in low resource settings.
    Full-text · Article · Oct 2015 · American journal of obstetrics and gynecology