title: Gestational dating by metabolic profile at birth: a California cohort study.
abstract: BACKGROUND
  Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications.
  
  
  OBJECTIVE
  We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group.
  
  
  METHODS
  We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11-20 weeks' gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions.
  
  
  RESULTS
  Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and term births. Using a linear discriminate analyses-derived linear function, we were able to sort PTBs and term births accurately with sensitivities and specificities of ≥95% in both the training and testing subsets. Assignment of a specific week of gestation in those identified as PTBs resulted in the correct assignment of week ±2 weeks in 89.8% of all newborns in the training and 91.7% of those in the testing subset. When PTB rates were modeled using the metabolic dating algorithm compared to fetal ultrasound, PTB rates were 7.15% vs 6.11% in the training subset and 7.31% vs 6.25% in the testing subset.
  
  
  CONCLUSIONS
  When considered in combination with birthweight and hours of age at test, metabolic profile evaluated within 8 days of birth appears to be a useful measure of PTB and, among those born preterm, of specific week of gestation ±2 weeks. Dating by metabolic profile may be useful in instances where there is no fetal ultrasound due to lack of availability or late entry into care.
authors.affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco School of Medicine, San Francisco, CA. Electronic address: Laura.Jelliffe@ucsf.edu.
authors.forename: Laura L
authors.initials: LL
authors.lastname: Jelliffe-Pawlowski
authors.affiliation: Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco School of Medicine, San Francisco, CA.
authors.forename: Mary E
authors.initials: ME
authors.lastname: Norton
authors.affiliation: Department of Pediatrics, University of California, San Diego School of Medicine, La Jolla, CA.
authors.forename: Rebecca J
authors.initials: RJ
authors.lastname: Baer
authors.affiliation: Global Health Sciences, University of California, San Francisco, San Francisco, CA.
authors.forename: Nicole
authors.initials: N
authors.lastname: Santos
authors.affiliation: Department of Epidemiology and Biostatistics, University of California, San Francisco School of Medicine, San Francisco, CA; Global Health Sciences, University of California, San Francisco, San Francisco, CA.
authors.forename: George W
authors.initials: GW
authors.lastname: Rutherford
mesh_terms: D019326:17-alpha-Hydroxyprogesterone / Q000097:blood; D000465:Algorithms; D000596:Amino Acids / Q000097:blood; D015415:Biomarkers / Q000097:blood; D002140:California; D002331:Carnitine / Q000031:analogs & derivatives / Q000097:blood; D015331:Cohort Studies; D016002:Discriminant Analysis; D005260:Female; D005865:Gestational Age; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D055432:Metabolomics; D015999:Multivariate Analysis; D011247:Pregnancy; D013972:Thyrotropin / Q000097:blood; D005694:UTP-Hexose-1-Phosphate Uridylyltransferase / Q000097:blood; D016216:Ultrasonography, Prenatal
chemical_list: D000596:Amino Acids; D015415:Biomarkers; C116917:acylcarnitine; D019326:17-alpha-Hydroxyprogesterone; D013972:Thyrotropin; D005694:UTP-Hexose-1-Phosphate Uridylyltransferase; D002331:Carnitine
keywords: 17-hydroxyprogesterone; acylcarnitines; amino acids; galactose-1-phosphate-uridyl-transferase; gestational dating; metabolic; metabolomics; preterm birth; thyroid-stimulating hormone