Predicting gestational age using neonatal metabolic markers.

BACKGROUND: 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. OBJECTIVE: We hypothesized that metabolic and endocrine markers captured by routine newborn screening could improve gestational age estimation in the absence of prenatal ultrasound technology. STUDY DESIGN: 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. RESULTS: 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. CONCLUSIONS: 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.

Metabolic heritability at birth: implications for chronic disease research.

Recent genome-wide association studies of the adult human metabolome have identified genetic variants associated with relative levels of several acylcarnitines, which are important clinical correlates for chronic conditions such as type 2 diabetes and obesity. We have previously shown that these same metabolite levels are highly heritable at birth; however, no studies to our knowledge have examined genetic associations with these metabolites measured at birth. Here, we examine, in 743 newborns, 58 single nucleotide polymorphisms (SNPs) in 11 candidate genes previously associated with differing relative levels of short-chain acylcarnitines in adults. Six SNPs (rs2066938, rs3916, rs3794215, rs555404, rs558314, rs1799958) in the short-chain acyl-CoA dehydrogenase gene (ACADS) were associated with neonatal C4 levels. Most significant was the G allele of rs2066938, which was associated with significantly higher levels of C4 (P = 1.5 × 10(-29)). This SNP explains 25 % of the variation in neonatal C4 levels, which is similar to the variation previously reported in adult C4 levels. There were also significant (P < 1 × 10(-4)) associations between neonatal levels of C5-OH and SNPs in the solute carrier family 22 genes (SLC22A4 and SLC22A5) and the 3-methylcrotonyl-CoA carboxylase 1 gene (MCCC1). We have replicated, in newborns, SNP associations between metabolic traits and the ACADS and SLC22A4 genes observed in adults. This research has important implications not only for the identification of rare inborn errors of metabolism but also for personalized medicine and early detection of later life risks for chronic conditions.

Genetic associations with neonatal thyroid-stimulating hormone levels.

BACKGROUND: Elevations or deficits in thyroid hormone levels are responsible for a wide range of neonatal and adult phenotypes. Several genome-wide, candidate gene, and meta-analysis studies have examined thyroid hormones in adults; however, to our knowledge, no genetic association studies have been performed with neonatal thyroid levels. METHODS: A population of Iowa neonates, term (n = 827) and preterm (n = 815), were genotyped for 45 single-nucleotide polymorphisms (SNPs). Thyroid-stimulating hormone (TSH) values were obtained from the Iowa Neonatal Metabolic Screening Program. ANOVA was performed to identify genetic associations with TSH concentrations. RESULTS: The strongest association was rs4704397 in the PDE8B gene (P = 1.3 × 10(-4)), followed by rs965513 (P = 6.4 × 10(-4)) on chromosome 9 upstream of the FOXE1 gene. Both of these SNPs met statistical significance after correction for multiple testing. Six other SNPs were marginally significant (P < 0.05). CONCLUSION: We demonstrated for the first time two genetic associations with neonatal TSH levels that replicate findings with adult TSH levels. These SNPs should be considered early predictors of risk for adult diseases and conditions associated with thyroid hormone levels. Furthermore, this study provides a better understanding of the thyroid profile and potential risk for thyroid disorders in newborns.

Association of amino acids with common complications of prematurity.

BACKGROUND: Tandem mass spectrometry has been proposed as a method of diagnosing or predicting the development of common complex neonatal diseases. Our objective was to identify metabolites associated with common complications of prematurity. METHODS: We performed a retrospective analysis of medical data and metabolite measurements from routine neonatal screening on 689 preterm (<37 wk of gestational age) neonates. RESULTS: We observed higher levels of phenylalanine (PHE) in infants with respiratory distress syndrome (RDS; P = 1.7 × 10(-5)), the only association that was significant after correction for multiple testing. We found suggestive significance (P < 0.001) of higher essential amino acids in infants with patent ductus arteriosus (PDA). Functionality of these findings was explored in the ductus arteriosus (DA) isolated from term and preterm mouse pups. None of the amino acids had a direct vasodilatory effect on the isolated DA. CONCLUSION: We found that newborns with RDS had higher levels of PHE that may be a result of impaired PHE hydroxylase activity. We also detected marginally higher levels of all measured essential amino acids in infants with PDA. We did not find dilation of the mouse ductus for these metabolites, indicating that instead of potentially causing PDA, they are probably serving as markers of catabolism.

Replication of clinical associations with 17-hydroxyprogesterone in preterm newborns.

Nationally, newborn screening programs use 17-hydroxyprogesterone (17-OHP) as the biomarker to detect the rare but potentially fatal inherited disease, congenital adrenal hyperplasia. However, this biomarker is highly variable, with a high false-positive rate of detection, particularly in neonates born preterm. Several studies have examined various clinical and genetic factors to explain the variability of 17-OHP in preterm infants. The purpose of this study was to replicate previous clinical and genetic associations with 17-OHP in a well-characterized cohort of 762 preterm infants. We replicated previous findings that respiratory distress syndrome (p = 2 x 10(-3)) is associated with higher 17-OHP. Higher 17-OHP and false positives were significantly associated with lower gestational age and birth weight, as previously reported. Incorporating gestational age and birth weight together decreases the false-positive rate.

Clinical validation of cutoff target ranges in newborn screening of metabolic disorders by tandem mass spectrometry: a worldwide collaborative project.

PURPOSE: To achieve clinical validation of cutoff values for newborn screening by tandem mass spectrometry through a worldwide collaborative effort. METHODS: Cumulative percentiles of amino acids and acylcarnitines in dried blood spots of approximately 25­30 million normal newborns and 10,742 deidentified true positive cases are compared to assign clinical significance, which is achieved when the median of a disorder range is, and usually markedly outside, either the 99th or the 1st percentile of the normal population. The cutoff target ranges of analytes and ratios are then defined as the interval between selected percentiles of the two populations. When overlaps occur, adjustments are made to maximize sensitivity and specificity taking all available factors into consideration. RESULTS: As of December 1, 2010, 130 sites in 45 countries have uploaded a total of 25,114 percentile data points, 565,232 analyte results of true positive cases with 64 conditions, and 5,341 cutoff values. The average rate of submission of true positive cases between December 1, 2008, and December 1, 2010, was 5.1 cases/day. This cumulative evidence generated 91 high and 23 low cutoff target ranges. The overall proportion of cutoff values within the respective target range was 42% (2,269/5,341). CONCLUSION: An unprecedented level of cooperation and collaboration has allowed the objective definition of cutoff target ranges for 114 markers to be applied to newborn screening of rare metabolic disorders.

Clinical and environmental influences on metabolic biomarkers collected for newborn screening.

OBJECTIVES: Identifying common clinical and environmental factors that influence newborn metabolic biomarkers will improve the utilization of metabolite panels for clinical diagnostic medicine. DESIGN AND METHODS: Environmental effects including gender, season of birth, gestational age, birth weight, feeding method and age at time of collection were evaluated for over 50 metabolites collected by the Iowa Neonatal Metabolic Screening Program on 221,788 newborns over a six year period. RESULTS: We replicated well known observations that low birth weight and preterm infants have higher essential amino acids and lower medium and long chain acylcarnitine levels than their term counterparts. Smaller, but still significant, differences were observed for gender and timing of sample collection, specifically the season in which the infant was born. Most intriguing were our findings of higher thyroid stimulating hormone in the winter months (P<1×10(-40)) which correlated with an increased false positive rate of congenital hypothyroidism in the winter (0.9%) compared to summer (0.6%). Previous studies, conducted globally, have identified an increased prevalence of suspected and confirmed cases of congenital hypothyroidism in the winter months. We found that the percentage of unresolved suspected cases were slightly higher in the winter (0.3% vs. 0.2%). CONCLUSIONS: We identified differences in metabolites by gestational age, birth weight, gender and season. Some are widely reported such as gestational age and birth weight, while others such as the effect of seasonality are not as well studied.

The influence of maternal disease on metabolites measured as part of newborn screening.

OBJECTIVE: Measurements of neonatal metabolites are commonly used in newborn screening (NBS) programs to detect inborn errors of metabolism. Variation in these metabolites, particularly in infants born preterm (<37 weeks gestation), can result from multiple etiologies. We sought to evaluate the impact of maternal complications of pregnancy and environmental stressors on NBS metabolites. METHODS: We examined 49 metabolic biomarkers obtained from routine NBS in 452 infants born preterm for association with maternal environmental stressors and complications of pregnancy. RESULTS: Neonatal free carnitine (C0, p = 1.4 × 10(-7)), acetylcarnitine (C2, p = 2.7 × 10(-7)), octenoylcarnitine (C8:1, p = 5.2 × 10(-11)) and linoleoylcarnitine (C18:2, p = 9.1 × 10(-7)) were elevated in infants born to preeclamptic mothers. Similar elevations were observed in small for gestational age infants and in infants where labor was not initiated prior to delivery. When accounting for all three factors, associations remained strongest between acylcarnitines and preeclampsia. CONCLUSION: We observed that maternal conditions, particularly preeclampsia, influence NBS biomarkers. This is important for identifying maternal conditions that influence metabolites measured during routine NBS that are also markers of fetal growth and overall health.