Stanton L. Berberich

Immune, stress, and mood markers related to recurrent oral herpes outbreaks

This was a prospective and longitudinal study designed to compare daily mood states and weekly changes in plasma levels of immune and neuroendocrine markers with recurrent herpes labialis lesion recurrences during a 3-month period among 9 subjects. Results from a paired t test showed that there was a significant decrease in plasma levels of natural killer cells and serum levels of epinephrine from the week before recurrent herpes labialis lesion occurrence (T1,9 = 2.70; p < 0.05) to the week of recrudescence (T1,9 = 2.41; p < 0.05). On the other hand, in the week before recrudescence the number of natural killer cells was 58 units higher than the overall group mean for natural killer cell level (227 units). In the week before outbreak, elevated natural killer cell numbers were associated with a mood of discontentment (r = 0.64; p = 0.05). Elevated levels of epinephrine averaged across the 12 weekly blood draws were significantly correlated with higher scores on affect intensity (r = 0.72; p < 0.05). This study provides new data on the pattern of changes in stress, mood states, and immune and neuroendocrine markers associated with the recurrence of perioral herpes lesions. Putative mechanisms linking neuroendocrine and immune function are discussed.

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 heritability of metabolic profiles in newborn twins

Identifying genetic and metabolic biomarkers in neonates has the potential to improve diagnosis and treatment of common complex neonatal diseases, and potentially lead to risk assessment and preventative measures for common adulthood illnesses such as diabetes and cardiovascular disease. There is a wealth of information on using fatty acid, amino acid and organic acid metabolite profiles to identify rare inherited congenital diseases through newborn screening, but little is known about these metabolic profiles in the context of the ‘healthy’ newborn. Recent studies have implicated many of the amino acid and fatty acid metabolites utilized in newborn screening in common complex adult diseases such as cardiovascular disease, insulin resistance and obesity. To determine the heritability of metabolic profiles in newborns, we examined 381 twin pairs obtained from the Iowa Neonatal Metabolic Screening Program. Heritability was estimated using multilevel mixed-effects linear regression adjusting for gestational age, gender, weight and age at time of sample collection. The highest heritability was for short-chain acylcarnitines, specifically C4 (h2=0.66, P=2 × 10−16), C4-DC (h2=0.83, P<10−16) and C5 (h2=0.61, P=1 × 10−9). Thyroid stimulating hormone (h2=0.58, P=2 × 10−5) and immunoreactive trypsinogen (h2=0.52, P=3 × 10−9) also have a strong genetic component. This is direct evidence for a strong genetic contribution to the metabolic profile at birth and that newborn screening data can be utilized for studying the genetic regulation of many clinically relevant metabolites.

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.

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 (R2) 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.

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.

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

Abstract 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=2×10–3) 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.

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

Abstract 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.