Clinical and environmental influences on metabolic biomarkers collected for newborn screening

Early Newborn Metabolic Patterning and Sudden Infant Death Syndrome

Importance

Sudden infant death syndrome (SIDS) is a major cause of infant death in the US. Previous research suggests that inborn errors of metabolism may contribute to SIDS, yet the relationship between SIDS and biomarkers of metabolism remains unclear.

Objective

To evaluate and model the association between routinely measured newborn metabolic markers and SIDS in combination with established risk factors for SIDS.

Design, Setting, and Participants

This was a case-control study nested within a retrospective cohort using data from the California Office of Statewide Health Planning and Development and the California Department of Public Health. The study population included infants born in California between 2005 and 2011 with full metabolic data collected as part of routine newborn screening (NBS). SIDS cases were matched to controls at a ratio of 1:4 by gestational age and birth weight z score. Matched data were split into training (2/3) and testing (1/3) subsets. Data were analyzed from January 2005 to December 2011.

Exposures

Metabolites measured by NBS and established risk factors for SIDS.

Main Outcomes and Measures

The primary outcome was SIDS. Logistic regression was used to evaluate the association between metabolic markers combined with known risk factors and SIDS.

Results

Of 2 276 578 eligible infants, 354 SIDS (0.016%) cases (mean [SD] gestational age, 38.3 [2.3] weeks; 220 male [62.1%]) and 1416 controls (mean [SD] gestational age, 38.3 [2.3] weeks; 723 male [51.1%]) were identified. In multivariable analysis, 14 NBS metabolites were significantly associated with SIDS in a univariate analysis: 17-hydroxyprogesterone, alanine, methionine, proline, tyrosine, valine, free carnitine, acetyl-L-carnitine, malonyl carnitine, glutarylcarnitine, lauroyl-L-carnitine, dodecenoylcarnitine, 3-hydroxytetradecanoylcarnitine, and linoleoylcarnitine. The area under the receiver operating characteristic curve for a 14-marker SIDS model, which included 8 metabolites, was 0.75 (95% CI, 0.72-0.79) in the training set and was 0.70 (95% CI, 0.65-0.76) in the test set. Of 32 infants in the test set with model-predicted probability greater than 0.5, a total of 20 (62.5%) had SIDS. These infants had 14.4 times the odds (95% CI, 6.0-34.5) of having SIDS compared with those with a model-predicted probability less than 0.1.

Conclusions and Relevance

Results from this case-control study showed an association between aberrant metabolic analytes at birth and SIDS. These findings suggest that we may be able to identify infants at increased risk for SIDS soon after birth, which could inform further mechanistic research and clinical efforts focused on monitoring and prevention.

Abnormal biochemical indicators of neonatal inherited metabolic disease in carriers

BACKGROUND

Traditional biochemical screening for neonatal inherited metabolic diseases has high false-positive rates and low positive predictive values, which are not conducive to early diagnosis and increase parents' anxiety. This study analysed the relationship between gene variant carriers and their biochemical indicators in traditional biochemical screening, aiming to find explanations for false positives in newborns.

RESULTS

This retrospective study included 962 newborns. Newborns underwent traditional biochemical screening at birth using blood staining and genomic sequencing of their stored blood staining using the NeoSeq Pro panel, which was able to detect 154 pathogenic genes and 86 diseases. A total of 632 newborns were carriers of gene variants. 56% of congenital hypothyroidism carriers had higher thyroid-stimulating hormone levels than normal newborns. Abnormal biochemical indices were detected in 71% of carriers of organic acid metabolic diseases, 69% of carriers of amino acid metabolic diseases, and 85% of carriers of fatty acid β oxidation disorders. In carriers associated with organic acid metabolic diseases, the propionylcarnitine (C3), C3/acetylcarnitine (C2), and methylmalonylcarnitine (C4DC) + 3-hydroxyisovalerylcarnitine (C5OH) levels were higher than those in non-carriers (C3: 4.12 vs. 1.66 µmol/L; C3/C2: 0.15 vs. 0.09; C4DC + C5OH: 0.22 vs. 0.19 µmol/L). In carriers associated with amino acid metabolic diseases, phenylalanine levels were higher than those in non-carriers (68.00 vs. 52.05 µmol/L). For carriers of fatty acid β oxidation disorders, butyrylcarnitine levels were higher than those in non-carriers (0.31 vs. 0.21 µmol/L), while the free carnitine levels were lower than those in non-carriers (14.65 vs. 21.87 µmol/L). There was a higher occurrence of carriers among newborns who received false-positive results for amino acid metabolic diseases compared to those who received negative results (15.52% vs. 6.71%). Similarly, there was a higher occurrence of carriers among newborns who received false-positive results for fatty acid β oxidation disorders compared to those who received negative results (28.30% vs. 7.29%).

CONCLUSIONS

This study showed that the carriers comprised a large number of newborns. Carriers had abnormal biochemical indicators compared with non-carriers, which could explain the false-positive rate for newborns using traditional newborn biochemical screening, especially in amino acid metabolic and fatty acid β oxidation disorders.

Association of Maternal Age and Blood Markers for Metabolic Disease in Newborns

Pregnancy at an advanced maternal age is considered a risk factor for adverse maternal, fetal, and neonatal outcomes. Here we investigated whether maternal age could be associated with differences in the blood levels of newborn screening (NBS) markers for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP). Population-level NBS data from screen-negative singleton infants were examined, which included blood metabolic markers and covariates such as age at blood collection, birth weight, gestational age, infant sex, parent-reported ethnicity, and maternal age at delivery. Marker levels were compared between maternal age groups (age range: 1544 years) using effect size analyses, which controlled for differences in group sizes and potential confounding from other covariates. We found that 13% of the markers had maternal age-related differences, including newborn metabolites with either increased (Tetradecanoylcarnitine [C14], Palmitoylcarnitine [C16], Stearoylcarnitine [C18], Oleoylcarnitine [C18:1], Malonylcarnitine [C3DC]) or decreased (3-Hydroxyisovalerylcarnitine [C5OH]) levels at an advanced maternal age (≥35 years, absolute Cohen's d > 0.2). The increased C3DC levels in this group correlated with a higher false-positive rate in newborn screening for malonic acidemia (p-value < 0.001), while no significant difference in screening performance was seen for the other markers. Maternal age is associated with inborn metabolic differences and should be considered together with other clinical variables in genetic disease screening.

Late-onset cblC defect: clinical, biochemical and molecular analysis

BACKGROUND

cblC defect is the most common type of methylmalonic acidemia in China. Patients with late-onset form (>1 year) are often misdiagnosed due to heterogeneous symptoms. This study aimed to describe clinical characteristics and evaluate long-term outcomes of Chinese patients with late-onset cblC defect.

METHODS

A total of 85 patients with late-onset cblC defect were enrolled. Clinical data, including manifestations, metabolites, molecular diagnosis, treatment and outcome, were summarized and analyzed.

RESULTS

The age of onset ranged from 2 to 32.8 years old (median age 8.6 years, mean age 9.4 years). The time between first symptoms and diagnosis ranged from a few days to 20 years (median time 2 months, mean time 20.7 months). Neuropsychiatric symptoms were presented as first symptoms in 68.2% of cases, which were observed frequently in schoolchildren or adolescents. Renal involvement and cardiovascular disease were observed in 20% and 8.2% of cases, respectively, which occurred with the highest prevalence in preschool children. Besides the initial symptoms, the disease progressed in most patients and cognitive decline became the most frequent symptom overall. The levels of propionylcarnitine, propionylcarnitine / acetylcarnitine ratio, methylmalonic acid, methylcitric acid and homocysteine, were decreased remarkably after treatment (P<0.001). Twenty-four different mutations of MMACHC were identified in 78 patients, two of which were novel. The c.482G>A variant was the most frequent mutated allele in this cohort (25%). Except for 16 patients who recovered completely, the remaining patients were still left with varying degrees of sequelae in a long-term follow-up. The available data from 76 cases were analyzed by univariate analysis and multivariate logistic regression analysis, and the results showed that the time from onset to diagnosis (OR = 1.025, P = 0. 024) was independent risk factors for poor outcomes.

CONCLUSIONS

The diagnosis of late-onset cblC defect is often delayed due to poor awareness of its various and nonspecific symptoms, thus having an adverse effect on the prognosis. It should be considered in patients with unexplained neuropsychiatric and other conditions such as renal involvement, cardiovascular diseases or even multiple organ damage. The c.482G>A variant shows the highest frequency in these patients. Prompt treatment appears to be beneficial.

Factors Influencing Creatine Kinase-MM Concentrations in Newborns and Implications for Newborn Screening for Duchenne Muscular Dystrophy

INTRODUCTION

Newborn screening for Duchenne muscular dystrophy can be performed via a first-tier creatine kinase-MM measurement followed by reflex testing to second-tier molecular analysis of the DMD gene. In order to establish appropriate cut-offs for the creatine kinase-MM screen, factors that influence creatine kinase-MM in newborns were investigated.

MATERIALS AND METHODS

Creatine kinase-MM data from a consented pilot study in New York State were collected over a two-year period and combined with de-identified validation data and analyzed. Univariate analysis and multiple linear regression analysis were performed.

RESULTS

The analysis indicated that age of newborn at specimen collection, gestational age and birth weight were significant influencers of CK-MM levels in newborns. In addition, to a lesser extent, sex, race/ethnicity and seasonal temperature also affect CK-MM levels in newborns.

CONCLUSIONS

To reduce false positive and false negative cases, newborn screening programs should be cognizant of factors that influence CK-MM when determining cut-offs for the assay. Variability based on age at specimen collection and birth weight are primarily observed within the first week of life. Therefore, particularly during this time period, multi-tiered cut-offs based on age of collection and lower cut-offs for premature and low birth weight babies are recommended. Other cut-off determinants may include sex, race/ethnicity and seasonal temperature.

Validation of a targeted metabolomics panel for improved second-tier newborn screening

Improved second-tier assays are needed to reduce the number of false positives in newborn screening (NBS) for inherited metabolic disorders including those on the Recommended Uniform Screening Panel (RUSP). We developed an expanded metabolite panel for second-tier testing of dried blood spot (DBS) samples from screen-positive cases reported by the California NBS program, consisting of true- and false-positives from four disorders: glutaric acidemia type I (GA1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). This panel was assembled from known disease markers and new features discovered by untargeted metabolomics and applied to second-tier analysis of single DBS punches using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a 3-min run. Additionally, we trained a Random Forest (RF) machine learning classifier to improve separation of true- and false positive cases. Targeted metabolomic analysis of 121 analytes from DBS extracts in combination with RF classification at a sensitivity of 100% reduced false positives for GA1 by 83%, MMA by 84%, OTCD by 100%, and VLCADD by 51%. This performance was driven by a combination of known disease markers (3-hydroxyglutaric acid, methylmalonic acid, citrulline, and C14:1), other amino acids and acylcarnitines, and novel metabolites identified to be isobaric to several long-chain acylcarnitine and hydroxy-acylcarnitine species. These findings establish the effectiveness of this second-tier test to improve screening for these four conditions and demonstrate the utility of supervised machine learning in reducing false-positives for conditions lacking clearly discriminating markers, with future studies aimed at optimizing and expanding the panel to additional disease targets.

Metabolomics of a neonatal cohort from the Alliance for Maternal and Newborn Health Improvement biorepository: Effect of preanalytical variables on reference intervals

BACKGROUND

The study was conducted to determine reference interval (RI) and evaluate the effect of preanalytical variables on Dried blood spot (DBS)-amino acids, acylcarnitines and succinylacetone of neonates.

METHODOLOGY

DBS samples were collected within 48-72 hours of life. Samples were analyzed for biochemical markers on tandem mass spectrometer at the University of Iowa. Comparison of RI across various categorical variables were performed.

RESULTS

A total of 610 reference samples were selected based on exclusion criteria; 53.2% being females. Mean gestational age (GA) of mothers at the time of delivery was 38.7±1.6 weeks; 24.5% neonates were of low birth weight and 14.3% were preterm. Out of the total 610 neonates, 23.1% were small for GA. Reference intervals were generated for eleven amino acids, thirty-two acylcarnitines and succinylacetone concentrations. Markers were evaluated with respect to the influence of gender, GA, weight and time of sampling and statistically significant minimal differences were observed for some biomarkers.

CONCLUSION

RI for amino acids, succinylacetone and acylcarnitine on DBS has been established for healthy neonates, which could be of use in the clinical practice. Clinically significant effect of GA, weight, gender and time of sampling on these markers were not identified.

Newborn screening for genetic disorders: Current status and prospects for the future

Newborn screening (NBS) is a public health service aimed at identifying infants with severe genetic disorders, thus providing effective treatment early enough to prevent or ameliorate the onset of symptoms. Current NBS uses biochemical analysis of dried blood spots, predominately with time-resolved fluorescence immunoassay and tandem mass spectrometry, which produces some false positives and false negatives. The application of enzymatic activity-based testing technology provides a reliable screening method for some disorders. Genetic testing is now commonly used for secondary or confirmatory testing after a positive result in some NBS programs. Recently, next-generation sequencing (NGS) has emerged as a robust tool that enables large panels of genes to be scanned together rapidly. Rapid advances in NGS emphasize the potential for genomic sequencing to improve NBS programs. However, some challenges still remain and require solution before this is applied for population screening.

Metabolic diversity in human populations and correlation with genetic and ancestral geographic distances

DNA polymorphic markers and self-defined ethnicity groupings are used to group individuals with shared ancient geographic ancestry. Here we studied whether ancestral relationships between individuals could be identified from metabolic screening data reported by the California newborn screening (NBS) program. NBS data includes 41 blood metabolites measured by tandem mass spectrometry from singleton babies in 17 parent-reported ethnicity groupings. Ethnicity-associated differences identified for 71% of NBS metabolites (29 of 41, Cohen's d &gt; 0.5) showed larger differences in blood levels of acylcarnitines than of amino acids (P &lt; 1e-4). A metabolic distance measure, developed to compare ethnic groupings based on metabolic differences, showed low positive correlation with genetic and ancient geographic distances between the groups' ancestral world populations. Several outlier group pairs were identified with larger genetic and smaller metabolic distances (Black versus White) or with smaller genetic and larger metabolic distances (Chinese versus Japanese) indicating the influence of genetic and of environmental factors on metabolism. Using machine learning, comparison of metabolic profiles between all pairs of ethnic groupings distinguished individuals with larger genetic distance (Black versus Chinese, AUC = 0.96), while genetically more similar individuals could not be separated metabolically (Hispanic versus Native American, AUC = 0.51). Additionally, we identified metabolites informative for inferring metabolic ancestry in individuals from genetically similar populations, which included biomarkers for inborn metabolic disorders (C10:1, C12:1, C3, C5OH, Leucine-Isoleucine). This work sheds new light on metabolic differences in healthy newborns in diverse populations, which could have implications for improving genetic disease screening.

dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening

The Recommended Uniform Screening Panel (RUSP) contains more than forty metabolic disorders recommended for inclusion in universal newborn screening (NBS). Tandem-mass-spectrometry-based screening of metabolic analytes in dried blood spot samples identifies most affected newborns, along with a number of false positive results. Due to their influence on blood metabolite levels, continuous and categorical covariates such as gestational age, birth weight, age at blood collection, sex, parent-reported ethnicity, and parenteral nutrition status have been shown to reduce the accuracy of screening. Here, we developed a database and web-based tools (dbRUSP) for the analysis of 41 NBS metabolites and six variables for a cohort of 500,539 screen-negative newborns reported by the California NBS program. The interactive database, built using the R shiny package, contains separate modules to study the influence of single variables and joint effects of multiple variables on metabolite levels. Users can input an individual's variables to obtain metabolite level reference ranges and utilize dbRUSP to select new candidate markers for the detection of metabolic conditions. The open-source format facilitates the development of data mining algorithms that incorporate the influence of covariates on metabolism to increase accuracy in genetic disease screening.

Targeted newborn metabolomics: prediction of gestational age from cord blood

OBJECTIVE

Our study sought to determine whether metabolites from a retrospective collection of banked cord blood specimens could accurately estimate gestational age and to validate these findings in cord blood samples from Busia, Uganda.

STUDY DESIGN

Forty-seven metabolites were measured by tandem mass spectrometry or enzymatic assays from 942 banked cord blood samples. Multiple linear regression was performed, and the best model was used to predict gestational age, in weeks, for 150 newborns from Busia, Uganda.

RESULTS

The model including metabolites and birthweight, predicted the gestational ages within 2 weeks for 76.7% of the Ugandan cohort. Importantly, this model estimated the prevalence of preterm birth <34 weeks closer to the actual prevalence (4.67% and 4.00%, respectively) than a model with only birthweight which overestimates the prevalence by 283%.

CONCLUSION

Models that include cord blood metabolites and birth weight appear to offer improvement in gestational age estimation over birth weight alone.

Real world external validation of metabolic gestational age assessment in Kenya

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.

Quantitative analysis of galactose using LDI-TOF MS based on a TiO2 nanowire chip

A novel method for quantifying galactose was developed to serve as a newborn screening test for galactosemia using laser desorption/ionization time-of-flight (LDI-TOF) mass spectrometry (MS) with a TiO2 nanowire chip. Herein, phosphate citrate buffer, serum, and dried blood spot (DBS) were employed for the quantitative analysis of galactose. To quantitatively analyze galactose, its reduction potential was used to oxidize o-phenylene diamine (OPD) into 2,3-diaminophenazine (DA), which were both detected using LDI-TOF MS with a TiO2 nanowire chip according to the concentration of galactose. The reproducibility and the interference of glucose were determined to demonstrate the applicability of this method. Moreover, mixtures of galactose, phenylalanine, and 17 α-OHP were analyzed to determine the interference induced by other biomarkers of metabolic disorders. The OPD oxidation of galactose was found to be selectively achieved under high-glucose conditions, similar to human blood, thereby showing good reproducibility. The intensities of the mass peaks of OPD and DA based on LDI-TOF MS with a TiO2 nanowire chip were linearly correlated in the galactose concentration range of 57.2–220.0 μg/mL (r2 = 0.999 and 0.950, respectively) for serum samples and 52.5–220.0 μg/mL (r2 = 0.993 and 0.985, respectively) for DBS after methanol precipitation/extraction. The enzyme immunoassay and LDI-TOF MS analysis results were statistically analyzed, and a mixture of phenylalanine, 17 α-OHP, and galactose was simultaneously investigated quantitatively at the cutoff level.

Metabolic biomarkers of small and large for gestational age newborns

BACKGROUND

Small for gestational age (SGA) and large for gestational age (LGA) newborns are at increased risk for developmental, metabolic and cardiovascular morbidities.

AIMS

To compare the metabolic biomarkers of SGA and LGA infants with those of appropriate for gestational age (AGA) newborns in order to shed more light on a possible pathogenesis of those morbidities.

STUDY DESIGN

An observational retrospective study.

SUBJECTS

70,809 term newborns divided into AGA, SGA, LGA, and severe subcategories (<3rd percentile or ≥97th percentile).

OUTCOME MEASURES

18 metabolites were measured by dried blood tandem mass spectrometry and compared in between groups in univariate and multivariate logistic regression.

RESULTS

SGA newborns had a significant likelihood for elevated methionine, proline, free carnitine, and reduced valine levels compared to AGA newborns (P < .0001). Severe SGA showed more apparent trends including elevated leucine. LGA newborns had a significant likelihood for low citrulline, glutamine, proline, tyrosine, and elevated leucine levels (P ≤ .0033). Severe LGA newborns showed the same trends, with the exception of citrulline and glutamine.

CONCLUSIONS

SGA and LGA newborns demonstrate distinct metabolic biomarkers in newborn screening. Most of the altered metabolites in the SGA group were elevated while those in the LGA group were decreased in comparison to AGA newborns. These trends were more apparent in the severe SGA subgroup while they mostly remained the same in the severe LGA subgroup. Whether these metabolic changes are involved with or can predict long-term outcome awaits further trials.

Genetic screening techniques and diseases for neonatal genetic diseases

Neonatal genetic disease is currently screened mainly based on metabolite biochemical technology. The false positive rate of biochemical screening technology is relatively high, and there are certain false negatives, and only few types of diseases can be screened. The genetic techniques have been gradually used for neonatal genetic disease screening in recent years. Gene detection technology includes quantitative PCR (qPCR) and high-throughput sequencing. High-throughput sequencing includes gene panel sequencing, whole-exome sequencing and whole-genome sequencing. At present, qPCR and gene panel sequencing are the main technologies to be used for newborn genetic disease screening. Genetic screening diseases range from single disease such as hearing loss, spinal muscular atrophy and severe combined immunodeficiency to multiple diseases. Besides standards and guidelines for the interpretation of sequence variants proposed by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology in 2015, the interpretation of genetic screening results should also consider biochemical results and other results. The development of newborn genetic screening needs to follow ethical principles, including the ethics of newborn genetic screening as a public health project, the privacy ethics of newborns and their family members, and the ethics of bioinformatics. The development of newborn genetic screening will enable more patients with inherited diseases to receive early diagnosis and treatment and improve their prognosis, which is a milestone in the field of neonatal screening.

Mode of delivery was associated with transient changes in the metabolomic profile of neonates

AIMS

To estimate potential differences in neonatal metabolomic profiles at birth and at the time of newborn screening by delivery mode.

METHODS

A prospective study at Women's Clinic at Landspitali-The National University Hospital of Iceland. Women having normal vaginal birth or elective caesarean section from November 2013 to April 2014 were offered participation. Blood samples from mothers before birth and umbilical cord at birth were collected and amino acids and acylcarnitines measured by tandem mass spectrometry. Results from the Newborn screening programme in Iceland were collected. Amino acids and acylcarnitines from different samples were compared by delivery mode.

RESULTS

Eighty three normal vaginal births and 32 elective caesarean sections were included. Mean differences at birth were higher for numerous amino acids, and some acylcarnitines in neonates born vaginally compared to elective caesarean section. Maternal blood samples and newborn screening results showed small differences that lost significance after correction for multiple testing. Many amino acids and some acylcarnitines were numerically higher in cord blood compared to maternal. Many amino acids and most acylcarnitines were numerically higher in newborn screening results compared to cord blood.

CONCLUSION

We observed transient yet distinct differences in metabolomic profiles between neonates by delivery mode.

External validation of machine learning models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants

Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.  

Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China. 

Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model.

Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility

Newborn metabolic vulnerability profile identifies preterm infants at risk for mortality and morbidity

BACKGROUND

Identifying preterm infants at risk for mortality or major morbidity traditionally relies on gestational age, birth weight, and other clinical characteristics that offer underwhelming utility. We sought to determine whether a newborn metabolic vulnerability profile at birth can be used to evaluate risk for neonatal mortality and major morbidity in preterm infants.

METHODS

This was a population-based retrospective cohort study of preterm infants born between 2005 and 2011 in California. We created a newborn metabolic vulnerability profile wherein maternal/infant characteristics along with routine newborn screening metabolites were evaluated for their association with neonatal mortality or major morbidity.

RESULTS

Nine thousand six hundred and thirty-nine (9.2%) preterm infants experienced mortality or at least one complication. Six characteristics and 19 metabolites were included in the final metabolic vulnerability model. The model demonstrated exceptional performance for the composite outcome of mortality or any major morbidity (AUC 0.923 (95% CI: 0.917-0.929). Performance was maintained across mortality and morbidity subgroups (AUCs 0.893-0.979).

CONCLUSIONS

Metabolites measured as part of routine newborn screening can be used to create a metabolic vulnerability profile. These findings lay the foundation for targeted clinical monitoring and further investigation of biological pathways that may increase the risk of neonatal death or major complications in infants born preterm.

IMPACT

We built a newborn metabolic vulnerability profile that could identify preterm infants at risk for major morbidity and mortality. Identifying high-risk infants by this method is novel to the field and outperforms models currently in use that rely primarily on infant characteristics. Utilizing the newborn metabolic vulnerability profile for precision clinical monitoring and targeted investigation of etiologic pathways could lead to reductions in the incidence and severity of major morbidities associated with preterm birth.

Does metabolomic profile differ with regard to birth weight?

BACKGROUND

Macrosomia and child obesity are growing health-care issues worldwide. The purpose of the study was to evaluate how extremely high or low birth weight affects metabolic markers evaluated in newborn screening.

METHODS

The study was register-based and included full-term singletons born in Iceland from 2009 to 2012 with newborn screening samples taken 72-96 h after birth. Three groups based on birth weight were compared: low birth weight (<2500 g), appropriate-for-gestational age, and extreme macrosomia (≥5000 g). The comparison was adjusted for possible confounding factors.

RESULTS

Compared to appropriate-for-gestational age neonates, both low birth weight and extreme macrosomia were associated with higher levels of glutamic acid. The amino acids alanine and threonine were increased in low birth weight neonates. Free carnitine and some medium- and long-chain acylcarnitines were higher in low birth weight infants. Hydroxybutyrylcarnitine was lower in low birth weight infants, but higher in extremely macrosomic neonates. Acetylcarnitine was higher in low birth weight and extremely macrosomic neonates. Succinylcarnitine was lower and hexadecenoylcarnitine higher in macrosomic newborns.

CONCLUSION

Low birth weight and extremely macrosomic neonates show distinctive differences in their metabolomic profile compared to appropriate-for-gestational age newborns. The differences are not explained by gestational age.

IMPACT

The key message of this article is that both low birth weight and extremely macrosomic newborns show dissimilar metabolomic profiles compared to appropriate-for-gestational age neonates. The article contributes to knowledge on what affects evaluation of results in newborn screening. The impact of this article is to provide information on metabolism at both ends of the birth weight range after accounting for confounding factors including gestational age.

Sex Affects Human Premature Neonates' Blood Metabolome According to Gestational Age, Parenteral Nutrition, and Caffeine Treatment

Prematurity is the leading cause of neonatal deaths and high economic costs; it depends on numerous biological and social factors, and is highly prevalent in males. Several factors can affect the metabolome of premature infants. Accordingly, the aim of the present study was to analyze the role played by gestational age (GA), parenteral nutrition (PN), and caffeine treatment in sex-related differences of blood metabolome of premature neonates through a MS/MS-based targeted metabolomic approach for the detection of amino acids and acylcarnitines in dried blood spots. GA affected the blood metabolome of premature neonates: male and female very premature infants (VPI) diverged in amino acids but not in acylcarnitines, whereas the opposite was observed in moderate or late preterm infants (MLPI). Moreover, an important reduction of metabolites was observed in female VPI fed with PN, suggesting that PN might not satisfy an infant's nutritional needs. Caffeine showed the highest significant impact on metabolite levels of male MLPI. This study proves the presence of a sex-dependent metabolome in premature infants, which is affected by GA and pharmacological treatment (e.g., caffeine). Furthermore, it describes an integrated relationship among several features of physiology and health.

The Impact of Seasonal Changes on Thyroxine and Thyroid-Stimulating Hormone in Newborns

Newborn screening for congenital hypothyroidism (CH) is performed by measuring the concentration of thyroxine (T4) and/or thyroid-stimulating hormone (TSH) in dried blood spots. Unfortunately, the levels of T4 and TSH vary due to multiple factors, and therefore the false-positive rate for the test is a challenge. We analyzed screening data from 2008 to 2017 to determine the effect of seasonal changes and manufacturer kit lot changes on T4 and TSH values and on numbers of infants referred. Over a 10-year period, we screened 2.4 million infants using commercially available fluoroimmunoassays to measure T4 and TSH concentrations in dried blood spots. During colder months, daily mean T4 and TSH values were higher and referral rates and false-positive rates were higher. However, there was no significant difference between the number of confirmed CH cases. Furthermore, in rare instances, we observed differences in T4 daily mean values during the 10-year period when manufacturer kit lot changes were made. Seasonal temperature variations influence measured T4 and TSH values and consequently lower the positive predictive value for CH testing in colder months. Newborn screening (NBS) programs should be aware that manufacturer kit lot changes may also influence T4 values.

Cystic fibrosis in Tuscany: evolution of newborn screening strategies over time to the present

BACKGROUND

Cystic fibrosis (CF) is a life-threatening disease affecting about 1:3000 newborns in Caucasian populations. The introduction of newborn screening for cystic fibrosis (CF NBS) has improved the clinical outcomes of individuals with CF through early diagnosis and early treatment. NBS strategies have been implemented over time. CF NBS was introduced extensively in 1984 in Tuscany, a region with 3.7 million people, characterized by a high allelic heterogeneity of CFTR gene.

AIM AND METHODS

The aim of the study is to present the results from 34 years (1984-2018) of CF NBS, retrospectively evaluating the sensitivity, specificity and predictive values of the tests. In particular, we studied the impact of the introduction of DNA molecular analysis in NBS in a region with high allelic heterogeneity, such as Tuscany.

RESULTS

Over these 34 years, 919,520 neonates were screened, using four different NBS strategies. From 1984 to 1991, CF NBS was performed by the determination of albumin on dried meconium (sensitivity 68.75%; specificity 99.82%). Subsequently, the analysis of immunoreactive trypsinogen on a blood spot was adopted as CF NBS protocol (sensitivity 83.33%; specificity 99.77%). From 1992 to 2010, this strategy was associated with lactase meconium dosage: IRT1/IRT2 + LACT protocol (sensitivity 87.50%; specificity 99.82%). From 2011, when the existing algorithm was integrated by analysis of CF causing variants of the CFTR gene (IRT1/IRT2 + LACT + IRT1/DNA protocol), a substantial improvement in sensitivity was seen (senisitivity 96.15%; specificity 99.75%). Other improved parameters with DNA analysis in the NBS programme, compared with the previous method, were the diagnosis time (52 days vs. 38 days) and the recall rate (0.58 to 0.38%).

CONCLUSION

The inclusion of DNA analysis in the NBS was a fundamental step in improving sensitivity, even in a region with high allelic variability.

Association Between Neonatal Thyroid Function and Anogenital Distance from Birth to 48 Months of Age

BACKGROUND

Evidence from animal studies has indicated that neonatal thyroid function is vital for the reproductive development. Anogenital distance (AGD), a sensitive biomarker of the fetal hormonal milieu, can be used to predict adult reproductive disorders. However, few human studies have examined the association between neonatal thyroid function and AGD. We aimed to explore their associations in a birth cohort study.

METHODS

Concentrations of thyroid stimulating hormone (TSH) and thyroid hormones (THs), including total triiodothyronine (TT₃), total thyroxine (TT₄), free triiodothyronine (FT₃), and free thyroxine (FT₄) were measured in cord plasma in the Shanghai-Minhang Birth Cohort. The offspring AGD (AGD_AP [anus-penis] and AGD_AS [anus-scrotum] for boys and AGD_AC [anus-clitoris] and AGD_AF [anus-fourchette] for girls), body weight and anogenital index (AGI = AGD/weight [mm/kg]) were obtained at each follow-up visit. In total, 344 children (194 boys and 150 girls) with cord plasma concentrations of THs and TSH and at least one AGD measurement at birth and at 6, 12, and 48 months of age were included. Multiple linear regression and generalized estimating equation (GEE) models were used to examine the associations of cord plasma concentrations of THs and TSH with AGI.

RESULTS

Multiple linear regression models showed inverse associations of TT₄, FT₃, and FT₄ with female AGI, although statistical significance was only reached at birth, 6 and 48 months of age. These associations were also found in GEE models: higher TT₄ and FT₄ concentrations were associated with lower AGI_AC (TT₄: β = -0.27, 95% CI: -0.50, -0.03 for middle vs. lowest tertile; FT₄: β = -0.38, 95% CI: -0.61, -0.16 for middle and β = -0.30, 95% CI: -0.55, -0.04 for highest vs. lowest tertile). Besides, girls with the highest tertile of FT₃ concentrations had lower AGI_AF than those with the lowest tertile (the highest vs. lowest tertile: β = -0.22, 95% CI: -0.36, -0.08). Positive associations between TSH and AGI at birth and at 12 months of age were observed in boys.

CONCLUSIONS

This study provides further evidence on the effects of neonatal thyroid function on reproductive development at an early life stage.

External validation of ELASTIC NET regression models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants

Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.   Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent group of infants, and externally validated in cohorts of infants from the Philippines and China.  Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within 5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within 6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility.

Ethnic variability in newborn metabolic screening markers associated with false-positive outcomes

Newborn screening (NBS) programmes utilise information on a variety of clinical variables such as gestational age, sex, and birth weight to reduce false-positive screens for inborn metabolic disorders. Here we study the influence of ethnicity on metabolic marker levels in a diverse newborn population. NBS data from screen-negative singleton babies (n = 100 000) were analysed, which included blood metabolic markers measured by tandem mass spectrometry and ethnicity status reported by the parents. Metabolic marker levels were compared between major ethnic groups (Asian, Black, Hispanic, White) using effect size analysis, which controlled for group size differences and influence from clinical variables. Marker level differences found between ethnic groups were correlated to NBS data from 2532 false-positive cases for four metabolic diseases: glutaric acidemia type 1 (GA-1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). In the result, 79% of the metabolic markers (34 of 43) had ethnicity-related differences. Compared to the other groups, Black infants had elevated GA-1 markers (C5DC, Cohen's d = .37, P < .001), Hispanics had elevated MMA markers (C3, Cohen's d = .13, P < .001, and C3/C2, Cohen's d = .27, P < .001); and Whites had elevated VLCADD markers (C14, Cohen's d = .28, P < .001, and C14:1, Cohen's d = .22, P < .001) and decreased OTCD markers (citrulline, Cohen's d = -.26, P < .001). These findings correlated with the higher false-positive rates in Black infants for GA-1, in Hispanics for MMA, and in Whites for OTCD and for VLCADD. Web-based tools are available to analyse ethnicity-related changes in newborn metabolism and to support developing methods to identify false-positives in metabolic screening.

Clinical and Genotypical Features of False-Negative Patients in 26 Years of Cystic Fibrosis Neonatal Screening in Tuscany, Italy

Cystic fibrosis (CF) is a life-threatening and common genetic disorder. Cystic fibrosis newborn screening (CF NBS) has been implemented in many countries over the last 30 years, becoming a widely accepted public health strategy in economically developed countries. False-negative (FN) cases can occur after CF NBS, with the number depending on the method. We evaluated the delayed diagnosis of CF, identifying the patients who had false-negative CF NBS results over 26 years (1992–2018) in Tuscany, Italy. The introduction of DNA analysis to the newborn screening protocol improved the sensitivity of the test and reduced the FNs. Our experience showed that, overall, at least 8.7% of cases of CF received FNs (18 cases) and were diagnosed later, with an average age of 6.6 years (range: 4 months to 22 years). Respiratory symptoms and salt-loss syndrome (metabolic hypochloremic alkalosis) are suggestive symptoms of CF and were commons events in FN patients. In Tuscany, a region with a high CFTR allelic heterogeneity, the salt-loss syndrome was a common event in FNs. Therefore, we provided evidence to support the claim that the FN patients had CFTR mutations rarer compared with the true-positive cases. We underline the importance of vigilance toward clinical manifestations suggestive of CF on the part of the primary care providers and hospital physicians in a region with an efficient newborn screening program.

A Detailed Analysis of the Factors Influencing Neonatal TSH: Results From a 6-Year Congenital Hypothyroidism Screening Program

Background: Neonatal thyrotropin (TSH) on dried blood spot (DBS), the most common screening strategy for primary congenital hypothyroidism (CH), is influenced by numerous factors that may hinder a true CH diagnosis. A second test can thus be performed to clarify the initial findings, although its application varies among screening programs. Objectives: The aim of this study was to evaluate the effect of maternal and neonatal factors on neonatal TSH levels and offer practical screening recommendations. Methods: We retrospectively analyzed screening data of 62,132 neonates born in Abruzzo, an Italian region considered mildly iodine deficient, between 2011 and 2016. We then performed a multiple linear regression to model the relationship between TSH (the dependent variable) and 13 independent variables extracted from blood collection cards. Results: Most neonates (53,551 of 62,132, 86%) had normal TSH and no clinical indications for a second screening. A minority (1,423, 2.3%) had elevated TSH in the initial DBS, which was confirmed in 97 cases (7%) on a second screen. The remaining neonates (6,594, 10.6%) had a normal initial TSH but underwent a second test in accordance with screening protocols, and were found to have delayed TSH elevation in 23 cases (0.4%). Those 120 newborns (97 + 23), considered highly suspicious for primary CH, were referred to a pediatrician for confirmatory testing and excluded from subsequent analysis of factors influencing TSH levels. Sex (β regression coefficient, β = 1.11 female to male, 95% CI 1.09, 1.12) and age at collection (β = 0.78 day 5 to days 2-3, 95% CI 0.74, 0.83) affected neonatal TSH, suggesting the utility of specific nomograms. In addition, prematurity (β = 0.85 term to preterm, 95% CI 0.80, 0.91), dopamine use (β = 0.71, 95% CI 0.62, 0.81), and birth weight (β = 1.40 normal vs. very low, 95% CI 1.05, 1.89) strongly influenced neonatal TSH. Conclusions: Neonatal TSH is influenced by several factors supporting the delineation of local sex- and age-adjusted TSH cutoffs, and the universal adoption of a second TSH test in neonates at risk of missed primary CH diagnosis.

Timing of Newborn Blood Collection Alters Metabolic Disease Screening Performance

Blood collection for newborn genetic disease screening is preferably performed within 24-48 h after birth. We used population-level newborn screening (NBS) data to study early postnatal metabolic changes and whether timing of blood collection could impact screening performance. Newborns were grouped based on their reported age at blood collection (AaBC) into early (12-23 h), standard (24-48 h), and late (49-168 h) collection groups. Metabolic marker levels were compared between the groups using effect size analysis, which controlled for group size differences and influence from the clinical variables of birth weight and gestational age. Metabolite level differences identified between groups were correlated to NBS data from false-positive cases for inborn metabolic disorders including carnitine transport defect (CTD), isovaleric acidemia (IVA), methylmalonic acidemia (MMA), and phenylketonuria (PKU). Our results showed that 56% of the metabolites had AaBC-related differences, which included metabolites with either decreasing or increasing levels after birth. Compared to the standard group, the early-collection group had elevated marker levels for PKU (phenylalanine, Cohen's d = 0.55), IVA (C5, Cohen's d = 0.24), MMA (C3, Cohen's d = 0.23), and CTD (C0, Cohen's d = 0.23). These findings correlated with higher false-positive rates for PKU (P < 0.05), IVA (P < 0.05), and MMA (P < 0.001), and lower false-positive rate for CTD (P < 0.001) in the early-collection group. Blood collection before 24 h could affect screening performance for some metabolic disorders. We have developed web-based tools integrating AaBC and other variables for interpretive analysis of screening data.

Advanced Maternal Age, Mode of Delivery, and Thyroid Hormone Levels in Chinese Newborns

Objective: Thyroid hormones are essential for fetal growth and neurodevelopment, however, data on cord blood thyroid hormones are sparse in China where maternal age at childbearing is increasing in recent decades. We aimed to assess cord blood levels of free triiodothyronine (FT3), free thyroxine (FT4), and thyroid stimulating hormone (TSH) in full-term Chinese newborns, and examine potential related perinatal factors. Methods: This study included 922 mother-newborn pairs from a prospective birth cohort enrolled in 2012-2013, Shanghai, China. Cord serum concentrations of FT3, FT4, TSH, and TPOAb were measured in newborns. Results: Newborns born via cesarean section had higher cord serum FT3 (mean ± SD: 1.90 ± 1.16 pmol/L) and lower cord serum TSH (5.15 ± 2.60 mIU/L) than those born via vaginal delivery (FT3: 1.62 ± 0.93 pmol/L; TSH: 9.27 ± 6.76 mIU/L). In cesarean section deliveries, the concentration of cord serum FT3 was 0.15 (95%CI: -0.03, 0.33; p = 0.10) pmol/L lower in infants of mothers aged 30-34 years, and 0.57 (95%CI: 0.22, 0.92; p = 0.002) pmol/L lower in infants of mothers ≥35 years compared to infants of mothers <30 years. Large-for-gestational-age (birth weight >90th percentile) was associated with higher TSH (p = 0.02). Similar results were also found in vaginal deliveries. Conclusions: In this Chinese term birth cohort, newborns born via cesarean section had higher cord serum FT3 and lower TSH than those born via vaginal delivery. Advanced maternal age was associated with lower fetal FT3. Further research is needed to understand whether this association may mediate the adverse impact of advanced maternal age on neurodevelopment in early life.

Newborn Metabolic Profile Associated with Hyperbilirubinemia With and Without Kernicterus

Our objective was to assess the relationship between hyperbilirubinemia with and without kernicterus and metabolic profile at newborn screening. Included were 1,693,658 infants divided into a training or testing subset in a ratio of 3:1. Forty‐two metabolites were analyzed using logistic regression (odds ratios (ORs), area under the receiver operating characteristic curve (AUC), 95% confidence intervals (CIs)). Several metabolite patterns remained consistent across gestational age groups for hyperbilirubinemia without kernicterus. Thyroid stimulating hormone (TSH) and C‐18:2 were decreased, whereas tyrosine and C‐3 were increased in infants across groupings. Increased C‐3 was also observed for kernicterus (OR: 3.17; 95% CI: 1.18–8.53). Thirty‐one metabolites were associated with hyperbilirubinemia without kernicterus in the training set. Phenylalanine (OR: 1.91; 95% CI: 1.85–1.97), ornithine (OR: 0.76; 95% 0.74–0.77), and isoleucine + leucine (OR: 0.63; 95% CI: 0.61–0.65) were the most strongly associated. This study showed that newborn metabolic function is associated with hyperbilirubinemia with and without kernicterus.

Initial Metabolic Profiles Are Associated with 7-Day Survival among Infants Born at 22-25 Weeks of Gestation

OBJECTIVE

To evaluate the association between early metabolic profiles combined with infant characteristics and survival past 7 days of age in infants born at 22-25 weeks of gestation.

STUDY DESIGN

This nested case-control consisted of 465 singleton live births in California from 2005 to 2011 at 22-25 weeks of gestation. All infants had newborn metabolic screening data available. Data included linked birth certificate and mother and infant hospital discharge records. Mortality was derived from linked death certificates and death discharge information. Each death within 7 days was matched to 4 surviving controls by gestational age and birth weight z score category, leaving 93 cases and 372 controls. The association between explanatory variables and 7-day survival was modeled via stepwise logistic regression. Infant characteristics, 42 metabolites, and 12 metabolite ratios were considered for model inclusion. Model performance was assessed via area under the curve.

RESULTS

The final model included 1 characteristic and 11 metabolites. The model demonstrated a strong association between metabolic patterns and infant survival (area under the curve [AUC] 0.885, 95% CI 0.851-0.920). Furthermore, a model with just the selected metabolites performed better (AUC 0.879, 95% CI 0.841-0.916) than a model with multiple clinical characteristics (AUC 0.685, 95% CI 0.627-0.742).

CONCLUSIONS

Use of metabolomics significantly strengthens the association with 7-day survival in infants born extremely premature. Physicians may be able to use metabolic profiles at birth to refine mortality risks and inform postnatal counseling for infants born at <26 weeks of gestation.

[Combined effect of gestational age and birth weight on metabolites related to inherited metabolic diseases in neonates]

OBJECTIVE

To study the combined effect of gestational age and birth weight on metabolites related to inherited metabolic diseases (IMD).

METHODS

A total of 3 381 samples ruled out of IMD by follow-up were randomly selected from 38 931 newborns who participated in the neonatal IMD screening during 2014-2016. The 3 381 neonates were categorized into seven groups according to their gestational age and birth weight: extremely preterm appropriate-for-gestational age (AGA) group (n=12), preterm small-for-gestational age (SGA) group (n=18), preterm AGA group (n=219), preterm large-for-gestational age (LGA) group (n=18), full-term SGA group (n=206), full-term AGA group (n=2 677), and full-term LGA group (n=231). Heel blood samples were collected from each group on postnatal days 3-7 after adequate breastfeeding. Levels of 17 key IMD-related metabolic indices in dried blood spots were measured using tandem mass spectrometry. Spearman′s correlation analysis was used to investigate the relationships between 17 IMD-related metabolic indices and their influencing factors, while covariance analysis was used to compare the metabolic indices between these groups.

RESULTS

After adjusting the influencing factors such as physiological and pathological status, compared with the full-term AGA group, the extremely preterm AGA, preterm SGA, and preterm AGA groups had significantly reduced levels of leucine\isoleucine\hydroxyproline and valine (P<0.05); the preterm AGA group had a significantly decreased ornithine level (P<0.05); the extremely preterm AGA and preterm AGA groups had a significantly reduced proline level (P<0.05). Besides, the phenylalanine level in the extremely preterm AGA and preterm AGA groups, the methionine level in the preterm SGA group, and the tyrosine level in the preterm AGA group all significantly increased (P<0.05). The increased levels of free carnitine, acetylcarnitine, and propionylcarnitine were found in the preterm SGA and preterm AGA groups. The oleylcarnitine level also significantly increased in the preterm SGA group (P<0.05). Most carnitine indices showed significant differences between the SGA group and the AGA/LGA group in both preterm and full-term infants (P<0.05).

CONCLUSIONS

Low gestational age and low birth weight may result in abnormal results in IMD screening. Therefore, gestational age and birth weight should be considered to comprehensively judge the abnormal results in IMD screening.

[Combined effect of gestational age and birth weight on metabolites related to inherited metabolic diseases in neonates]

OBJECTIVE

To study the combined effect of gestational age and birth weight on metabolites related to inherited metabolic diseases (IMD).

METHODS

A total of 3 381 samples ruled out of IMD by follow-up were randomly selected from 38 931 newborns who participated in the neonatal IMD screening during 2014-2016. The 3 381 neonates were categorized into seven groups according to their gestational age and birth weight: extremely preterm appropriate-for-gestational age (AGA) group (n=12), preterm small-for-gestational age (SGA) group (n=18), preterm AGA group (n=219), preterm large-for-gestational age (LGA) group (n=18), full-term SGA group (n=206), full-term AGA group (n=2 677), and full-term LGA group (n=231). Heel blood samples were collected from each group on postnatal days 3-7 after adequate breastfeeding. Levels of 17 key IMD-related metabolic indices in dried blood spots were measured using tandem mass spectrometry. Spearman&prime;s correlation analysis was used to investigate the relationships between 17 IMD-related metabolic indices and their influencing factors, while covariance analysis was used to compare the metabolic indices between these groups.

RESULTS

After adjusting the influencing factors such as physiological and pathological status, compared with the full-term AGA group, the extremely preterm AGA, preterm SGA, and preterm AGA groups had significantly reduced levels of leucine\isoleucine\hydroxyproline and valine (P&lt;0.05); the preterm AGA group had a significantly decreased ornithine level (P&lt;0.05); the extremely preterm AGA and preterm AGA groups had a significantly reduced proline level (P&lt;0.05). Besides, the phenylalanine level in the extremely preterm AGA and preterm AGA groups, the methionine level in the preterm SGA group, and the tyrosine level in the preterm AGA group all significantly increased (P&lt;0.05). The increased levels of free carnitine, acetylcarnitine, and propionylcarnitine were found in the preterm SGA and preterm AGA groups. The oleylcarnitine level also significantly increased in the preterm SGA group (P&lt;0.05). Most carnitine indices showed significant differences between the SGA group and the AGA/LGA group in both preterm and full-term infants (P&lt;0.05).

CONCLUSIONS

Low gestational age and low birth weight may result in abnormal results in IMD screening. Therefore, gestational age and birth weight should be considered to comprehensively judge the abnormal results in IMD screening.

A maternal high-fat diet during pregnancy and lactation, in addition to a postnatal high-fat diet, leads to metabolic syndrome with spatial learning and memory deficits: beneficial effects of resveratrol

We tested the hypothesis that high-fat diet consumption during pregnancy, lactation, and/or post weaning, altered the expression of molecular mediators involved in hippocampal synaptic efficacy and impaired spatial learning and memory in adulthood. The beneficial effect of resveratrol was assessed. Dams were fed a rat chow diet or a high-fat diet before mating, during pregnancy, and throughout lactation. Offspring were weaned onto either a rat chow or a high-fat diet. Four experimental groups were generated, namely CC, HC, CH, and HH (maternal chow diet or high-fat diet; postnatal chow diet or high-fat diet). A fifth group fed with HH plus resveratrol (HHR) was generated. Morris water maze test was used to evaluate spatial learning and memory. Blood pressure and IPGTT was measured to assess insulin resistance. Dorsal hippocampal expression of certain biochemical molecules, including sirtuin 1, ERK, PPARγ, adiponectin, and BDNF were measured. Rats in HH group showed impaired spatial memory, which was partly restored by the administration of resveratrol. Rats in HH group also showed impaired glucose tolerance and increased blood pressure, all of which was rescued by resveratrol administration. Additionally, SIRT1, phospho-ERK1/2, and phospho-PPARγ, adiponectin and BDNF were all dysregulated in rats placed in HH group; administration of resveratrol restored the expression and regulation of these molecules. Overall, our results suggest that maternal high-fat diet during pregnancy and/or lactation sensitizes the offspring to the adverse effects of a subsequent high-fat diet on hippocampal function; however, administration of resveratrol is demonstrated to be beneficial in rescuing these effects.

Medium chain acyl-CoA dehydrogenase deficiency in a premature infant

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is identified by newborn screening (NBS). The natural history of MCADD includes metabolic decompensation with hypoglycemia, hyperammonemia, seizures, coma, and death. NBS enables expectant management thus severe symptoms are rare in managed patients. We report premature birth of an MCADD affected infant and resultant management challenges. Nutritional support advanced from parenteral nutrition at 24 hours to enteral feeds. A NBS sample was collected day 2, positive results for MCADD was reported day six, and diagnostic tests were performed day seven. Lab results confirmed MCADD; however, representation of pathologic analytes was so extreme that ingestion of medium chain triglycerides was suspected and subsequently confirmed. Diet was adjusted and reflected in moderation of pathologic analytes. This case emphasizes the need for prompt review NBS results in premature infants. Implementing informatic intervention within electronic medical records, when a disorder requiring special nutritional intervention is identified, will protect premature infants in this vulnerable setting. Standard of care management provided premature infants may be contraindicated in the context of a comorbid inborn error of metabolism.

Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada

OBJECTIVES

Biological modelling of routinely collected newborn screening data has emerged as a novel method for deriving postnatal gestational age estimates. Validation of published models has previously been limited to cohorts largely consisting of infants of white Caucasian ethnicity. In this study, we sought to determine the validity of a published gestational age estimation algorithm among recent immigrants to Canada, where maternal landed immigrant status was used as a surrogate measure of infant ethnicity.

DESIGN

We conducted a retrospective validation study in infants born in Ontario between April 2009 and September 2011.

SETTING

Provincial data from Ontario, Canada were obtained from the Institute for Clinical Evaluative Sciences.

PARTICIPANTS

The dataset included 230 034 infants born to non-landed immigrants and 70 098 infants born to immigrant mothers. The five most common countries of maternal origin were India (n=10 038), China (n=7468), Pakistan (n=5824), The Philippines (n=5441) and Vietnam (n=1408). Maternal country of origin was obtained from Citizenship and Immigration Canada's Landed Immigrant Database.

PRIMARY AND SECONDARY OUTCOME MEASURES

Performance of a postnatal gestational age algorithm was evaluated across non-immigrant and immigrant populations.

RESULTS

Root mean squared error (RMSE) of 1.05 weeks was observed for infants born to non-immigrant mothers, whereas RMSE ranged from 0.98 to 1.15 weeks among infants born to immigrant mothers. Area under the receiver operating characteristic curve for distinguishing term versus preterm infants (≥37 vs <37 weeks gestational age or >34 vs ≤34 weeks gestational age) was 0.958 and 0.986, respectively, in the non-immigrant subgroup and ranged from 0.927 to 0.964 and 0.966 to 0.99 in the immigrant subgroups.

CONCLUSIONS

Algorithms for postnatal determination of gestational age may be further refined by development and validation of region or ethnicity-specific models. However, our results provide reassurance that an algorithm developed from Ontario-born infant cohorts performs well across a range of ethnicities and maternal countries of origin without modification.

Neonatal screening parameters in infants with congenital Cytomegalovirus infection

Congenital Cytomegalovirus infection (cCMV) is the most common cause of congenital infections worldwide that can cause long-term impairment (LTI). The metabolic alterations due to cCMV are largely unknown. This study aims to assess the metabolites included in the neonatal screening in relation to cCMV and cCMV outcome, allowing the identification of prognostic markers for clinical outcome. Essential amino acids, hormones, carnitines and enzymes from Dried Blood Spots (DBS) were analyzed of 102 children with cCMV and 179 children without cCMV, and they were related to symptoms at birth and LTI at 6years of age. In this cohort, the neonatal screening parameters did not change in relation to cCMV, nor to symptoms at birth or LTI. However, metabolic changes were observed in children born preterm, with lower concentrations of essential amino acids in premature infants with cCMV compared to premature controls. Finally, a higher concentration of palmytoilcarnitine (C16) in the group with higher viral load was observed. Though these data demonstrate limitations in the use of neonatal screening data as predictors for long-term cCMV outcome, the metabolism of preterm neonates with cCMV merits further evaluation.

The influence of seasonality and manufacturer kit lot changes on 17α-hydroxyprogesterone measurements and referral rates of congenital adrenal hyperplasia in newborns

Newborn screening for congenital adrenal hyperplasia (CAH) is performed by measuring the concentration of 17α-hydroxyprogesterone (17-OHP) in dried blood spots. Unfortunately, the level of 17-OHP varies due to multiple factors, and therefore, the false positive rate for the test is a challenge. We analyzed screening data from 2007 to 2015 to determine the effect of seasonal changes and manufacturer kit lot changes on 17-OHP values and on numbers of infants referred. Data from screening 2.2 million infants over a 9-year period indicates that in the NYS during the colder months, daily mean 17-OHP values are higher, more retests are performed, and more infants are referred even though fewer infants are born. The practice of using fixed cutoffs for referring infants for CAH leads to more false positive results in colder months. In addition, there was an overall 10% increase in the daily mean 17-OHP values from the 2 years before and after a manufacturer kit lot change that occurred in November 2013, suggestive of a functional change in the kit at that time.

CONCLUSION

Newborn screening programs should be cognizant of seasonal temperature variations and (un)anticipated manufacturer kit changes because they may affect 17-OHP values and CAH referral rates. What is Known: • Newborn screening for congenital adrenal hyperplasia is generally performed by measuring 17α-hydroxyprogesterone (17-OHP) levels in dried blood spots. • 17-OHP concentrations are affected by gestational age/weight of infant when specimen is collected, specimen collection time after birth, as well as race and sex of infant. What is New: • Seasonal temperature variations and unanticipated manufacturer kit changes affect 17-OHP levels and consequently referral rates in programs that use fixed cutoffs. • Daily mean 17-OHP is generally higher when the ambient temperature is lower.

Pregnancy-Related Changes of Amino Acid and Acylcarnitine Concentrations: The Impact of Obesity

Objective Our primary objective was to assess the difference in amino and fatty acid biomarkers throughout pregnancy in women with and without obesity. Interactions between biomarkers and obesity status for associations with maternal and fetal metabolic measures were secondarily analyzed. Methods Overall 39 women (15 cases, 24 controls) were enrolled in this study during their 15- to 20-weeks' visit at the University of Iowa Hospitals and Clinics. We analyzed 32 amino acid and acylcarnitine concentrations with tandem mass spectrometry for differences throughout pregnancy as well as among women with and without obesity (body mass index [BMI] ≥ 35, BMI < 25). Results There were substantial changes in amino acids and acylcarnitine metabolites between the second and third trimesters (nonfasting state) of pregnancy that were significant after correcting for multiple testing (p < 0.002). Examining differences by maternal obesity, C8:1 (second trimester) and C2, C4-OH, C18:1 (third trimester) were higher in women with obesity compared with women without obesity. Several metabolites were marginally (0.002 < p < 0.05) correlated with birth weight, maternal glucose, and maternal weight gain stratified by obesity status and trimester. Conclusions Understanding maternal metabolism throughout pregnancy and the influence of obesity is a critical step in identifying potential mechanisms that may contribute to adverse outcomes in pregnancies complicated by obesity.

Gestational dating by metabolic profile at birth: a California cohort study

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.

STUDY DESIGN

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.

CONCLUSION

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.

Accurate prediction of gestational age using newborn screening analyte data

BACKGROUND

Identification of preterm births and accurate estimates of gestational age for newborn infants is vital to guide care. Unfortunately, in developing countries, it can be challenging to obtain estimates of gestational age. Routinely collected newborn infant screening metabolic analytes vary by gestational age and may be useful to estimate gestational age.

OBJECTIVE

We sought to develop an algorithm that could estimate gestational age at birth that is based on the analytes that are obtained from newborn infant screening.

STUDY DESIGN

We conducted a population-based cross-sectional study of all live births in the province of Ontario that included 249,700 infants who were born between April 2007 and March 2009 and who underwent newborn infant screening. We used multivariable linear and logistic regression analyses to build a model to predict gestational age using newborn infant screening metabolite measurements and readily available physical characteristics data (birthweight and sex).

RESULTS

The final model of our metabolic gestational dating algorithm had an average deviation between observed and expected gestational age of approximately 1 week, which suggests excellent predictive ability (adjusted R-square of 0.65; root mean square error, 1.06 weeks). Two-thirds of the gestational ages that were predicted by our model were accurate within ±1 week of the actual gestational age. Our logistic regression model was able to discriminate extremely well between term and increasingly premature categories of infants (c-statistic, >0.99).

CONCLUSION

Metabolic gestational dating is accurate for the prediction of gestational age and could have value in low resource settings.

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.

Newborn screening for congenital adrenal hyperplasia in New York State

From 2007 to 2014 the New York State (NYS) Newborn Screening (NBS) program screened 2 million newborns for congenital adrenal hyperplasia (CAH). The data was analyzed to determine factors that affect 17α-hydroxyprogesterone levels and assist in developing algorithm changes that would improve the positive predictive value of the methodology being used. The concentration of 17-OHP in dried blood spots was measured using the AutoDELFIA Neonatal 17-OHP kit (Perkin Elmer, Turku, Finland). During the 8 year period of this study 2476 babies were referred, 105 babies were diagnosed with CAH (90 with the salt-wasting (SW), 8 with simple virilizing (SV), 5 with non-classical CAH, and 2 with another enzyme deficiency) and, 14 with possible CAH. Three false negative cases with SV-CAH were reported to the program. Of the total 108 known cases, 74 (69%) infants were detected by newborn screening in the absence of clinical information, or, known family history. The incidence of CAH in NYS is 1 in 18,170 with a ratio of SW to SV of 8.2:1. The incidence of CAH is lower in Black infants than in White, Hispanic and Asian infants. Despite a lower mean birth weight, female infants have a lower mean 17-OHP value than male infants and are under-represented in the referred category. As per other NBS programs the false positive rate is exacerbated by prematurity/low birth weight and by over-early specimen collection.

Circadian and Circannual Rhythms in Thyroid Hormones: Determining the TSH and Free T4 Reference Intervals Based Upon Time of Day, Age, and Sex

BACKGROUND

Establishing the reference interval for thyrotropin (TSH) and free thyroxine (T4) is clinically important because a number of disease states have been linked to alterations in TSH and free T4 concentrations that are within the 95% confidence interval for normal thyroid hormone values. Age, sex, time of day, and ethnicity are known to affect circulating levels of TSH and free T4 but have not been used to establish reference intervals. The purpose of this study was to define the reference interval for TSH and free T4 taking into account age, sex, ethnicity, and circadian and circannual variability.

METHODS

We performed a retrospective analysis of 465,593 TSH and 112,994 free T4 measurements from subjects ages 1-104 years with no thyroid disease using a single TSH and free T4 immunoassay method. Boundaries for the central 95% of patient values, taking into account hour of day, day of year, sex, and age were calculated.

RESULTS

Females had significantly higher TSH and free T4 levels than males; the magnitude of these differences did not exceed 0.1 mIU/L or 0.1 ng/dL respectively. Although the 2.5% TSH reference interval remains constant through the day, date, and age ranges, the upper limit (97.5%) of the TSH reference interval increases from 6.45 to 7.55 mIU/L with age, due primarily to a progressive increase in the amplitude of the nocturnal TSH surge. Additionally, significant ethnic differences in TSH circadian periodicity occur between African American, Pacific Island, and Caucasian populations.

CONCLUSIONS

The reference interval for TSH varies significantly by age, sex, hour of day, and ethnicity. Time of year does not affect the TSH reference interval, and age, sex, hour of day and time of year do not affect the free T4 reference interval.

Female and male human babies have distinct blood metabolomic patterns

A sex-gender approach in laboratory medicine is scarce; furthermore, the influence of sex on acylcarnitines and amino acid levels at birth has not been thoroughly investigated, even if sex impacts on newborn screening. We aimed to establish the influence of sex on amino acids and acylcarnitines levels in male and female newborns. Amino acids and acylcarnitines were analysed in dried blood spots using tandem mass spectrometry in male and female newborns. Data were analysed before and after body weight correction also using principal components analysis. This retrospective analytical study showed that females had small but significantly higher levels of amino acids and the correction for body weight amplified these differences. Acylcarnitines were overall higher in males before body weight correction with the exception of isovalerylcarnitine + methylbutyrylcarnitine (C5), which was significantly higher in females. Body weight correction decreased the sex differences in C5. Principal component analysis showed that both amino acids and acylcarnitines were necessary to describe the model for females, whereas only acylcarnitines were required for males. These metabolomics data underline the importance of including sex as a variable in future investigations of circulating metabolites; the existence of sex differences highlights the need for setting distinct reference values for female and male neonates in metabolite concentration.

Analysis and interpretation of acylcarnitine profiles in dried blood spot and plasma of preterm and full-term newborns

BACKGROUND

Acylcarnitines are biomarkers of fatty acid metabolism, and examining their patterns in preterm newborn may reveal metabolic changes associated with particular conditions related to prematurity. Isomeric acylcarnitines in dried blood spots (DBS) and plasma have never been assessed in preterm infants.

METHODS

We studied 157 newborn divided into four groups by weeks of gestational age (GA), as follows: 22-27 wk in group 1; 28-31 wk in group 2; 32-36 wk in group 3; and 37-42 wk in group 4. Samples were collected on the third day of life. Acylcarnitines were separated and quantified using ultra-performance liquid chromatography tandem mass spectrometry.

RESULTS

Acylcarnitine concentrations correlated significantly with GA and birth weight in both DBS and plasma samples. Concentrations were lower in preterm newborn, except for acylcarnitines derived from branched-chain amino acids, which were higher and correlated with enteral feeding. On day 3 of life, no correlations emerged with gender, respiratory distress syndrome, bronchopulmonary dysplasia, surfactant administration, or mechanical ventilation.

CONCLUSION

We established GA-based reference ranges for isomeric acylcarnitine concentrations in preterm newborn, which could be used to assess nutritional status and the putative neuroprotective role of acylcarnitines.

Maternal factors and complications of preterm birth associated with neonatal thyroid stimulating hormone

Thyroid hormones are important regulators of fetal neurodevelopment. Among preterm infants, thyroid stimulating hormone (TSH) is highly variable. Understanding this variability will further improvements in screening for thyroid disorders in preterm infants. We examined 61 maternal and infant clinical and demographic factors for associations with neonatal TSH levels in 698 preterm neonates. TSH was measured as part of routine State-mandated newborn screening in Iowa. Of the maternal characteristics, nulliparous women (p=8×10-4), women with preeclampsia (p=2×10-3), and those with induced labor (p=3×10-3) had infants with higher TSH levels. TSH levels at the time of newborn screening were associated with respiratory distress syndrome (RDS) (p<0.0001) and sepsis (p=3×10-3). We replicated findings between parity and preeclampsia previously observed in primarily term infants. We also observed strong relationships between neonatal TSH and complications of prematurity including RDS and sepsis, which have implications for future studies examining this relationship both prenatally and longitudinally after birth.