Dataset Information

Predicting gestational age using neonatal metabolic markers.

ABSTRACT: Accurate gestational age estimation is extremely important for clinical care decisions of the newborn as well as for perinatal health research. Although prenatal ultrasound dating is one of the most accurate methods for estimating gestational age, it is not feasible in all settings. Identifying novel and accurate methods for gestational age estimation at birth is important, particularly for surveillance of preterm birth rates in areas without routine ultrasound dating.We hypothesized that metabolic and endocrine markers captured by routine newborn screening could improve gestational age estimation in the absence of prenatal ultrasound technology.This is a retrospective analysis of 230,013 newborn metabolic screening records collected by the Iowa Newborn Screening Program between 2004 and 2009. The data were randomly split into a model-building dataset (n = 153,342) and a model-testing dataset (n = 76,671). We performed multiple linear regression modeling with gestational age, in weeks, as the outcome measure. We examined 44 metabolites, including biomarkers of amino acid and fatty acid metabolism, thyroid-stimulating hormone, and 17-hydroxyprogesterone. The coefficient of determination (R(2)) and the root-mean-square error were used to evaluate models in the model-building dataset that were then tested in the model-testing dataset.The newborn metabolic regression model consisted of 88 parameters, including the intercept, 37 metabolite measures, 29 squared metabolite measures, and 21 cubed metabolite measures. This model explained 52.8% of the variation in gestational age in the model-testing dataset. Gestational age was predicted within 1 week for 78% of the individuals and within 2 weeks of gestation for 95% of the individuals. This model yielded an area under the curve of 0.899 (95% confidence interval 0.895-0.903) in differentiating those born preterm (<37 weeks) from those born term (?37 weeks). In the subset of infants born small-for-gestational age, the average difference between gestational ages predicted by the newborn metabolic model and the recorded gestational age was 1.5 weeks. In contrast, the average difference between gestational ages predicted by the model including only newborn weight and the recorded gestational age was 1.9 weeks. The estimated prevalence of preterm birth <37 weeks' gestation in the subset of infants that were small for gestational age was 18.79% when the model including only newborn weight was used, over twice that of the actual prevalence of 9.20%. The newborn metabolic model underestimated the preterm birth prevalence at 6.94% but was closer to the prevalence based on the recorded gestational age than the model including only newborn weight.The newborn metabolic profile, as derived from routine newborn screening markers, is an accurate method for estimating gestational age. In small-for-gestational age neonates, the newborn metabolic model predicts gestational age to a better degree than newborn weight alone. Newborn metabolic screening is a potentially effective method for population surveillance of preterm birth in the absence of prenatal ultrasound measurements or newborn weight.

SUBMITTER: Ryckman KK

PROVIDER: S-EPMC4808601 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Predicting gestational age using neonatal metabolic markers.

Ryckman Kelli K KK Berberich Stanton L SL Dagle John M JM

American journal of obstetrics and gynecology 20151202 4

<h4>Background</h4>Accurate gestational age estimation is extremely important for clinical care decisions of the newborn as well as for perinatal health research. Although prenatal ultrasound dating is one of the most accurate methods for estimating gestational age, it is not feasible in all settings. Identifying novel and accurate methods for gestational age estimation at birth is important, particularly for surveillance of preterm birth rates in areas without routine ultrasound dating.<h4>Obje ...[more]

PMID: 26645954

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Project description:Background: Intrauterine growth restriction is a common cause of small for gestational age (SGA) infants worldwide. SGA infants are deficient in digestive enzymes required for fat digestion and absorption compared to appropriate for gestational age (AGA) infants, putting them at risk for impaired neurocognitive development. Objective: The objective was to determine if a hydrolyzed fat (HF) infant formula containing soy free fatty acids, 2-monoacylglycerolpalmitate, cholesterol, and soy lecithin could increase brain tissue incorporation of essential fatty acids or white matter to enhance brain development in SGA and AGA neonatal piglet models. Methods: Sex-matched, littermate pairs of SGA (0.5-0.9 kg) and AGA (1.2-1.8 kg) 2 days old piglets (N = 60) were randomly assigned to control (CON) or HF formula diets in a 2 × 2 factorial design. On day 14, 24 piglets were used for hippocampal RNA-sequencing; the rest began a spatial learning task. On days 26-29, brain structure was assessed by magnetic resonance imaging (MRI). Cerebellum and hippocampus were analyzed for fatty acid content. Results: SGA piglets grew more slowly than AGA piglets, with no effect of diet on daily weight gain or weight at MRI. HF diet did not affect brain weight. HF diet increased relative volumes of 7 brain regions and white matter (WM) volume in both SGA and AGA piglets. However, HF did not ameliorate SGA total WM integrity deficits. RNA sequencing revealed SGA piglets had increased gene expression of synapse and cell signaling pathways and decreased expression of ribosome pathways in the hippocampus compared to AGA. HF decreased expression of immune response related genes in the hippocampus of AGA and SGA piglets, but did not correct gene expression patterns in SGA piglets. Piglets learned the T-maze task at the same rate, but SGA HF, SGA CON, and AGA HF piglets had more accurate performance than AGA CON piglets on reversal day 2. HF increased arachidonic acid (ARA) percentage in the cerebellum and total ARA in the hippocampus. Conclusions: HF enhanced brain development in the neonatal piglet measured by brain volume and WM volume in specific brain regions; however, more studies are needed to assess long-term outcomes.

Project description:Intrauterine growth restriction is a common cause of small for gestational age (SGA) infants worldwide. SGA infants are deficient in digestive enzymes required for fat digestion and absorption compared to appropriate for gestational age (AGA), putting them at risk for impaired neurocognitive development. The objective was to determine if a hydrolyzed fat (HF) infant formula containing soy free fatty acids, 2-monoacylglycerolpalmitate, cholesterol, and soy lecithin could increase brain tissue incorporation of essential fatty acids or white matter to enhance brain development in SGA and AGA neonatal piglet models. Sex-matched, littermate pairs of SGA (0.5-0.9kg) and AGA (1.2-1.8kg) 2 day old piglets (N=60) were randomly assigned to control (CON) or HF formula diets in a 2 x 2 factorial design. On day 14, 24 piglets were used for hippocampal RNA-sequencing; the rest began a spatial learning task. On days 26-29, brain structure was assessed by magnetic resonance imaging (MRI). Cerebellum and hippocampus were analyzed for fatty acid content. SGA piglets grew more slowly than AGA piglets, with no effect of diet on daily weight gain or weight at MRI. HF diet did not affect brain weight. HF diet increased relative volumes of 7 brain regions and white matter (WM) volume in both SGA and AGA piglets. However, HF did not ameliorate SGA total WM integrity deficits. RNA sequencing revealed SGA piglets had increased gene expression of synapse and cell signaling pathways and decreased expression of ribosome pathways in the hippocampus compared to AGA. HF decreased expression of immune response related genes in the hippocampus of AGA and SGA piglets, but did not correct gene expression patterns in SGA piglets. Piglets learned the T-maze task at the same rate, but SGA HF, SGA CON, and AGA HF piglets had more accurate performance than AGA CON piglets on reversal day 2. HF increased arachidonic acid (ARA) percentage in the cerebellum and total ARA in the hippocampus. HF enhanced brain development in the neonatal piglet measured by brain volume and WM volume in specific brain regions; however, more studies are needed to assess long-term outcomes.

Project description:ObjectiveNo validated biomarker at birth exists to predict which newborns will develop severe hyperbilirubinemia. This study's primary aim was to build and validate a prediction model for severe hyperbilirubinemia using umbilical cord blood bilirubins (CBB) and risk factors at birth in neonates at risk for maternal-fetal blood group incompatibility. This study's secondary aim was to compare the accuracy of CBB to the direct antigen titer.MethodsInclusion criteria for this prospective cohort study included: ?35 weeks gestational age, mother with blood type O and/or Rh negative or positive antibody screen, and <24 hours of age. The primary outcome was severe hyperbilirubinemia, defined as phototherapy during the initial hospital stay. Secondary outcomes were a total serum bilirubin concentration >95th and >75th percentile during the initial hospital stay. The predictive performance and accuracy of the two tests (CBB and direct antigen titer) for each outcome was assessed using area under a receiver-operating characteristic curve (AUC), sensitivity, and specificity.ResultsWhen compared to neonates who did not receive phototherapy (n = 463), neonates who received phototherapy (n = 36) had a greater mean CBB ± standard deviation (2.5 ± 0.7 vs. 1.6 ± 0.4 mg/dL, p<0.001). For every 0.3 mg/dL increase in CBB, a neonate was 3.20 (95% confidence interval, 2.31-4.45), 2.10 (1.63-2.70), and 3.12 (2.44-3.99) times more likely to receive phototherapy or have a total serum bilirubin concentration >95th and >75th percentile, respectively. The AUC ± standard error (95% confidence interval) for CBB for phototherapy and a total serum bilirubin concentration >95th and >75th percentile was 0.89 ± 0.03 (0.82-0.95), 0.81 ± 0.04 (0.73-0.90), and 0.84 ± 0.02 (0.80-0.89), respectively. However, the AUC for gestational age and maternal Asian race for these outcomes was only 0.55 ± 0.05 (0.45-0.66), 0.66 ± 0.05 (0.56-0.76), and 0.57 ± 0.04 (0.05-0.64), respectively. When the CBB was combined with gestational age and maternal Asian race, the AUC for a total serum bilirubin concentration >95th percentile improved to 0.87 ± 0.03 (0.81-0.92) (p = 0.034 vs. the model with CBB only and p<0.001 vs. the model with clinical risk factors only). In a sub-group of subjects (n = 189), the AUC for the direct antigen titer for phototherapy was 0.64 ± 0.06 (0.52-0.77) with a 52% sensitivity and 77% specificity. In contrast, a CBB cut-point of 1.85 mg/dL was 92% sensitive and 70% specific for phototherapy with an AUC of 0.87 ± 0.04 (0.80-0.95).ConclusionCBB, in combination with gestational age and maternal race, may be a useful, non-invasive test to predict shortly after birth which neonates will develop severe hyperbilirubinemia.

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Predicting gestational age using neonatal metabolic markers.

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Predicting gestational age using neonatal metabolic markers.

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