Metabolomics of prematurity: analysis of patterns of amino acids, enzymes, and endocrine markers by categories of gestational age

Metabolomic analysis of the human placenta reveals perturbations in amino acids, purine metabolites, and small organic acids in spontaneous preterm birth

Spontaneous preterm delivery presents one of the most complex challenges in obstetrics and is a leading cause of perinatal morbidity and mortality. Although it is a common endpoint for multiple pathological processes, the mechanisms governing the etiological complexity of spontaneous preterm birth and the placental responses are poorly understood. This study examined placental tissues collected between May 2019 and May 2022 from a well-defined cohort of women who experienced spontaneous preterm birth (n = 72) and healthy full-term deliveries (n = 30). Placental metabolomic profiling of polar metabolites was performed using Ultra-High Performance Liquid Chromatography/Mass Spectrometry (UHPLC/MS) analysis. The resulting data were analyzed using multi- and univariate statistical methods followed by unsupervised clustering. A comprehensive metabolomic evaluation of the placenta revealed that spontaneous preterm birth was associated with significant changes in the levels of 34 polar metabolites involved in intracellular energy metabolism and biochemical activity, including amino acids, purine metabolites, and small organic acids. We found that neither the preterm delivery phenotype nor the inflammatory response explain the reported differential placental metabolome. However, unsupervised clustering revealed two molecular subtypes of placentas from spontaneous preterm pregnancies exhibiting differential enrichment of clinical parameters. We also identified differences between early and late preterm samples, suggesting distinct placental functions in early spontaneous preterm delivery. Altogether, we present evidence that spontaneous preterm birth is associated with significant changes in the level of placental polar metabolites. Dysregulation of the placental metabolome may underpin important (patho)physiological mechanisms involved in preterm birth etiology and long-term neonatal outcomes.

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.

Metabolic Pathways Associated With Psychoneurological Symptoms in Children With Cancer Receiving Chemotherapy

CONTEXT

Children with cancer undergoing chemotherapy experience a cluster of psychoneurological symptoms (PNS), including pain, fatigue, anxiety, and depressive symptoms. Metabolomics is promising to differentiate metabolic pathways associated with the PNS cluster.

OBJECTIVES

Identify metabolic pathways associated with the PNS cluster in children with cancer before and after chemotherapy.

METHODS

Pain, fatigue, anxiety, and depressive symptoms were assessed using the Pediatric PROMIS scales. T-scores were computed and divided dichotomously by a cutoff point of 50; the PNS cluster was a sum of the four symptoms ranging from 0 (all T-scores <50) to 4 (all T-scores ≥50). Serum metabolites were processed using liquid chromatography mass-spectrometry untargeted metabolomics approach. Linear regression models examined metabolites associated with the PNS cluster. Metabolic pathway enrichment analysis was performed.

RESULTS

Participant demographics (n = 40) were 55% female, 60% white, 62.5% aged 13-19 years, and 62.5% diagnoses of Hodgkin's lymphoma and B-cell acute lymphocytic leukemia. Among 9276 unique metabolic features, 454 were associated with pain, 281 with fatigue, 596 with anxiety, 551 with depressive symptoms, and 300 with the PNS cluster across one chemotherapy cycle. Fatty acids pathways were associated with pain: de novo fatty acid biosynthesis (p < .001), fatty acid metabolism (p = .001), fatty acid activation (p = .004), and omega-3 fatty acid metabolism (p = .009). Tryptophan amino acid pathway was associated with fatigue (p < .001), anxiety (p = .015), and the PNS cluster (p = .037). Carnitine shuttle was associated with the PNS cluster (p = .015).

CONCLUSION

Fatty acids and amino acids pathways were associated with PNS in children undergoing chemotherapy. These findings require further investigation in a larger sample.

A Longitudinal 1H NMR-Based Metabolic Profile Analysis of Urine from Hospitalized Premature Newborns Receiving Enteral and Parenteral Nutrition

Preterm newborns are extremely vulnerable to morbidities, complications, and death. Preterm birth is a global public health problem due to its socioeconomic burden. Nurturing preterm newborns is a critical medical issue because they have limited nutrient stores and it is difficult to establish enteral feeding, which leads to inadequate growth frequently associated with poor neurodevelopmental outcomes. Parenteral nutrition (PN) provides nutrients to preterm newborns, but its biochemical effects are not completely known. To study the effect of PN treatment on preterm newborns, an untargeted metabolomic ¹H nuclear magnetic resonance (NMR) assay was performed on 107 urine samples from 34 hospitalized patients. Multivariate data (Principal Component Analysis, PCA, Orthogonal partial least squares discriminant analysis OPLS-DA, parallel factor analysis PARAFAC-2) and univariate analyses were used to identify the association of specific spectral data with different nutritional types (NTs) and gestational ages. Our results revealed changes in the metabolic profile related to the NT, with the tricarboxylic acid cycle and galactose metabolic pathways being the most impacted pathways. Low citrate and succinate levels, despite higher glucose relative urinary concentrations, seem to constitute the metabolic profile found in the studied critically ill preterm newborns who received PN, indicating an energetic dysfunction that must be taken into account for better nutritional management.

Longitudinal Serum Metabolomics in Extremely Premature Infants: Relationships With Gestational Age, Nutrition, and Morbidities

An increasing number of extremely premature infants survive the neonatal period and beyond. Little is known about the maturation of the preterm infant’s metabolome and its relation to the development of morbidities. Using 1H-NMR, we investigated the serum metabolic profile of 87 infants born at a gestational age (GA) <28 weeks [mean GA (SD) 25.4 (1.4) weeks] in samples longitudinally collected from birth to term equivalent age. The infant metabolome was analyzed in relation to GA, postnatal age, nutrition, and preterm morbidities. At postnatal day 1, low GA correlated with high levels of 3-hydroxyisobutyrate, acetate, acetoacetate, acetone, formate, glucose, and valine. Nearly all quantified metabolites displayed postnatal concentration changes. For example, the two phospholipid-related metabolites myo-inositol and ethanolamine displayed a similar decline from birth over the first weeks of life, irrespectively of GA. The proportion of enteral/parenteral energy intake in the first 28 days significantly correlated with mean levels of 52% of the analyzed metabolites. Low enteral energy intake was associated with high serum levels of 3-hydroxyisobutyrate, creatinine, glucose, glycerol, histidine, lactate, leucine, lysine, methionine, ornithine, phenylalanine, proline, threonine, and uridine. There were also significant correlations between high enteral intake and high serum levels of isoleucine and tyrosine. Retinopathy of prematurity (ROP) and bronchopulmonary dysplasia (BPD) outcomes were not significantly associated with metabolite levels in the neonatal period after correcting for multiple testing. In conclusion, the serum metabolome of extremely premature infants changes substantially in the neonatal period, largely driven by the gradual transfer from total parenteral nutrition to full enteral feeding. Further studies are needed to disentangle the intricate relationships between the metabolome, nutritional management, GA, and the development of preterm morbidities.

Associations of maternal and infant metabolite profiles with foetal growth and the odds of adverse birth outcomes

BACKGROUND

Adaptations in maternal and foetal metabolic pathways may predispose to altered foetal growth and adverse birth outcomes.

OBJECTIVE

To assess the associations of maternal early-pregnancy metabolite profiles and infant metabolite profiles at birth with foetal growth from first trimester onwards and the odds of adverse birth outcomes.

METHODS

In a prospective population-based cohort among 976 Dutch pregnant women and their children, serum concentrations of amino acids, non-esterified fatty acids (NEFA), phospholipids (PL) and carnitines in maternal early-pregnancy blood and in cord blood were obtained by liquid-chromatography tandem mass spectrometry. Information on foetal growth was available from first trimester onwards.

RESULTS

After false discovery rate correction for multiple testing, higher infant total and individual NEFA concentrations were associated with a lower weight, length, and head circumference at birth. Higher infant total and individual acyl-lysophosphatidylcholine (lyso.PC.a) and alkyl-lysophosphatidylcholine concentrations were associated with higher weight and head circumference (lyso.PC.a only) at birth, higher odds of LGA and lower odds of SGA. Few individual maternal metabolites were associated with foetal growth measures in third trimester and at birth, but not with the odds of adverse birth outcomes.

CONCLUSIONS

Our results suggest that infant metabolite profiles, particularly total and individual lyso.PC.a and NEFA concentrations, were strongly related to growth measures at birth and the odds of adverse birth outcomes. Few individual maternal early-pregnancy metabolites, but not total metabolite concentrations, are associated with foetal growth measures in third trimester and at birth.

Second-tier Testing for 21-Hydroxylase Deficiency in the Netherlands: A Newborn Screening Pilot Study

CONTEXT

Newborn screening (NBS) for classic congenital adrenal hyperplasia (CAH) consists of 17-hydroxyprogesterone (17-OHP) measurement with gestational age-adjusted cutoffs. A second heel puncture (HP) is performed in newborns with inconclusive results to reduce false positives.

OBJECTIVE

We assessed the accuracy and turnaround time of the current CAH NBS algorithm in comparison with alternative algorithms by performing a second-tier 21-deoxycortisol (21-DF) pilot study.

METHODS

Dried blood spots (DBS) of newborns with inconclusive and positive 17-OHP (immunoassay) first HP results were sent from regional NBS laboratories to the Amsterdam UMC Endocrine Laboratory. In 2017-2019, 21-DF concentrations were analyzed by LC-MS/MS in parallel with routine NBS. Diagnoses were confirmed by mutation analysis.

RESULTS

A total of 328 DBS were analyzed; 37 newborns had confirmed classic CAH, 33 were false-positive and 258 were categorized as negative in the second HP following the current algorithm. With second-tier testing, all 37 confirmed CAH had elevated 21-DF, while all 33 false positives and 253/258 second-HP negatives had undetectable 21-DF. The elevated 21-DF of the other 5 newborns may be NBS false negatives or second-tier false positives. Adding the second-tier results to inconclusive first HPs reduced the number of false positives to 11 and prevented all 286 second HPs. Adding the second tier to both positive and inconclusive first HPs eliminated all false positives but delayed referral for 31 CAH patients (1-4 days).

CONCLUSION

Application of the second-tier 21-DF measurement to inconclusive first HPs improved our CAH NBS by reducing false positives, abolishing the second HP, and thereby shortening referral time.

Metabolomic Profile of Young Adults Born Preterm

Prematurity is a risk factor for the development of chronic adult diseases. Metabolomics can correlate the biochemical changes to a determined phenotype, obtaining real information about the state of health of a subject at that precise moment. Significative differences in the metabolomic profile of preterm newborns compared to those born at term have been already identified at birth. An observational case–control study was performed at the University Hospital of Siena. The aim was to evaluate and compare the metabolomic profiles of young adults born preterm to those born at term. Urinary samples were collected from 67 young adults (18–23 years old) born preterm (mean gestational age of 30 weeks, n = 49), and at term of pregnancy (mean gestational age of 38 weeks, n = 18). The urinary spectra of young adults born preterm was different from those born at term and resembled what was previously described at birth. The Random Forest algorithm gave the best classification (accuracy 82%) and indicated the following metabolites as responsible for the classification: citrate, CH2 creatinine, fumarate and hippurate. Urine spectra are promising tools for the early identification of neonates at risk of disease in adulthood and may provide insight into the pathogenesis and effects of fetal programming and infants’ outcomes.

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.

Gestational age-dependent development of the neonatal metabolome

BACKGROUND

Prematurity is a severe pathophysiological condition, however, little is known about the gestational age-dependent development of the neonatal metabolome.

METHODS

Using an untargeted liquid chromatography-tandem mass spectrometry metabolomics protocol, we measured over 9000 metabolites in 298 neonatal residual heel prick dried blood spots retrieved from the Danish Neonatal Screening Biobank. By combining multiple state-of-the-art metabolome mining tools, we retrieved chemical structural information at a broad level for over 5000 (60%) metabolites and assessed their relation to gestational age.

RESULTS

A total of 1459 (~16%) metabolites were significantly correlated with gestational age (false discovery rate-adjusted P < 0.05), whereas 83 metabolites explained on average 48% of the variance in gestational age. Using a custom algorithm based on hypergeometric testing, we identified compound classes (617 metabolites) overrepresented with metabolites correlating with gestational age (P < 0.05). Metabolites significantly related to gestational age included bile acids, carnitines, polyamines, amino acid-derived compounds, nucleotides, phosphatidylcholines and dipeptides, as well as treatment-related metabolites, such as antibiotics and caffeine.

CONCLUSIONS

Our findings elucidate the gestational age-dependent development of the neonatal blood metabolome and suggest that the application of metabolomics tools has great potential to reveal novel biochemical underpinnings of disease and improve our understanding of complex pathophysiological mechanisms underlying prematurity-associated disorders.

IMPACT

A large variation in the neonatal dried blood spot metabolome from residual heel pricks stored at the Danish Neonatal Screening Biobank can be explained by gestational age. While previous studies have assessed the relation of selected metabolic markers to gestational age, this study assesses metabolome-wide changes related to prematurity. Using a combination of recently developed metabolome mining tools, we assess the relation of over 9000 metabolic features to gestational age. The ability to assess metabolome-wide changes related to prematurity in neonates could pave the way to finding novel biochemical underpinnings of health complications related to preterm birth.

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.

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.

Metabolic gestational age assessment in low resource settings: a validation protocol

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.

Serum Newborn Screening Blood Metabolites Are not Associated With Childhood-onset Inflammatory Bowel Disease: A Population-based Matched Case-control Study

BACKGROUND

Originally used for screening of inborn errors of metabolism, routine metabolite profiles of newborns have also been associated with prematurity and some childhood diseases. We sought to determine whether metabolites measured during routine newborn screening could identify infants who develop inflammatory bowel disease (IBD) in childhood.

METHODS

We conducted a population-based matched case-control study using health administrative data from Ontario, Canada. Children born 2006 to 2015 with IBD were identified using a validated algorithm and matched to 5 controls based on birth date, sex, rural/urban household, and mean neighborhood income quintile at birth. Cases and controls were linked deterministically to metabolic profiles from Newborn Screening Ontario. We fit a lasso penalized logistic regression model and used 10-fold cross-validation to obtain internally valid performance measures. Models included metabolites, amino acids, and endocrine markers. Models also included ratios of metabolites, gestational age, birth weight, mode of delivery, age at serum collection, maternal age at delivery, maternal history of IBD, and parity.

RESULTS

Three hundred eight cases of IBD, diagnosed at 5.5 ± 2.8 years, were matched to 1540 controls. No individual metabolites were associated with IBD. The c-statistic was 0.50 for the training data. After 10-fold cross-validation the C statistic was 0.50, indicating no significant association between metabolites and IBD diagnosis.

CONCLUSIONS

Newborn screening serum metabolites could not identify children who will develop IBD in this population-based cohort. Future studies with an expanded panel of metabolites may provide improved prediction of IBD.

Metabolic gestational age assessment in low resource settings: a validation protocol

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children’s Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario’s newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.

L-Arginine Modulates Neonatal Leukocyte Recruitment in a Gestational Age-Dependent Manner

(1) Background: L-arginine is a complex modulator of immune functions, and its levels are known to decrease under septic conditions. L-arginine may suppress leukocyte recruitment in vivo; however, little is known about the gestational age-specific effects of L-arginine on leukocyte recruitment in preterm infants. We now asked whether L-arginine alters leukocyte recruitment in preterm and term neonates. (2) Methods: Leukocytes were isolated from preterm (28 + 0 to 32 + 6 weeks of gestation) and term (>37 weeks of gestation) newborns as well as from healthy adults. After incubation with 10 µg/mL L-arginine, we assessed leukocyte rolling and adhesion in dynamic microflow chamber experiments and leukocyte transmigration in fluorescence assays. In addition, we measured the expression of inducible nitric oxide synthase (iNOS) and Arginase 1 (Arg-1) in neutrophils by flow cytometry. (3) Results: Leukocyte rolling, adhesion, and transmigration increased with gestational age. Leukocyte rolling, adhesion, and transmigration were decreased by L-arginine in term-born infants and adults. Preterm leukocytes showed no change in recruitment upon L-arginine exposure. Leukocyte adhesion after L-arginine exposure reached similar levels among all groups. In line, the expression of iNOS and Arg-1 was similar in all three age groups. (4) Conclusion: L-arginine dampens the ex vivo recruitment capacity of leukocytes from term-born infants, whereas no effect was seen in premature infants. As levels of iNOS and Arg-1 in neutrophils remain ontogenetically unchanged, the anti-inflammatory effect of L-arginine on the leukocyte recruitment cascade needs further investigation. These results add to the controversial debate of L-arginine supplementation in premature infants in sepsis.

Fecal Volatile Organic Compound Profiles are Not Influenced by Gestational Age and Mode of Delivery: A Longitudinal Multicenter Cohort Study

Fecal volatile organic compounds (VOC) reflect human and gut microbiota metabolic pathways and their interaction. VOC behold potential as non-invasive preclinical diagnostic biomarkers in various diseases, e.g., necrotizing enterocolitis and late onset sepsis. There is a need for standardization and assessment of the influence of clinical and environmental factors on the VOC outcome before this technique can be applied in clinical practice. The aim of this study was to investigate the influence of gestational age (GA) and mode of delivery on the fecal VOC pattern in preterm infants born below 30 weeks of gestation. Longitudinal fecal samples, collected on days 7, 14, and 21 postnatally, were analyzed by an electronic nose device (Cyranose 320^®). In total, 58 preterm infants were included (29 infants born at GA 24–26 weeks vs. 29 at 27–29 completed weeks, 24 vaginally born vs. 34 via C-section). No differences were identified at any predefined time point in terms of GA and delivery mode (p > 0.05). We, therefore, concluded that correction for these factors in this population is not warranted when performing fecal VOC analysis in the first three weeks of life.

Congenital Adrenal Hyperplasia: Time to Replace 17OHP with 21-Deoxycortisol

Congenital adrenal hyperplasia (CAH) due to steroid 21-hydroxylase deficiency (21OHD) has a worldwide incidence of 1 in 15-20,000. Affected individuals have adrenal insufficiency and androgen excess; the androgen excess begins during fetal life, typically resulting in 46,XX disordered sexual development. In 21OHD, 17-hydroxyprogesterone (17OHP), the steroid proximal to 21-hydroxylase, accumulates. Most industrialized countries have newborn screening programs that measure 17OHP; such screening has permitted rapid detection of newborns with 21OHD, saving lives previously lost to mineralocorticoid deficiency and salt wasting. However, newborn screening is plagued by false positives. 17OHP is above most "cutoff values" in the first 24 h of life, is high in otherwise normal premature infants, and in many term infants with physiologic stress from unrelated diseases. In addition, newborn 17OHP may be elevated in other forms of CAH, including 11-hydroxylase deficiency, 3β-hydroxysteroid dehydrogenase deficiency, and P450 oxidoreductase deficiency. In 21OHD, some of the accumulated intra-adrenal 17OHP is converted to 21-deoxycortisol (21-deoxy) by 11β-hydroxylase (CYP11B1); 21-deoxy is not elevated in premature infants or in other forms of CAH, and hence is a more specific marker for 21OHD. However, 21-deoxy assays have not been generally available until recently, hence experience is limited. We urge clinical investigators, commercial reference laboratories, and newborn screening programs to investigate replacing 17OHP with 21-deoxy as the analyte of choice for studies of 21OHD.

Postnatal gestational age estimation via newborn screening analysis: application and potential

Introduction: Preterm birth is a major global health concern, contributing to 35% of all neonatal deaths in 2016. Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in the absence of prenatal ultrasound are needed. Previous work has demonstrated the potential for metabolomics to estimate GA by analyzing data captured through routine newborn screening. Areas covered: Circulating analytes found in newborn blood samples vary by GA. Leveraging newborn screening and demographic data, our group developed an algorithm capable of estimating GA postnatally to within approximately 1 week of ultrasound-validated GA. Since then, we have built on the model by including additional analytes and validating the model's performance through internal and external validation studies, and through implementation of the model internationally. Expert opinion: Currently, using metabolomics to estimate GA postnatally holds considerable promise but is limited by issues of cost-effectiveness and resource access in low-income settings. Future work will focus on enhancing the precision of this approach while prioritizing point-of-care testing that is both accessible and acceptable to individuals in low-resource settings.

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.

Determinants of the urinary and serum metabolome in children from six European populations

BACKGROUND

Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment-health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project ( http://www.projecthelix.eu ).

METHODS

Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6-11) recruited from birth cohorts in six European countries were measured using high-throughput ¹H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates AbsoluteIDQ p180 kit).

RESULTS

We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythreonic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated (r > 0.18) between urine and serum.

CONCLUSIONS

We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children.

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&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.

Metabolomic signatures of low birthweight: Pathways to insulin resistance and oxidative stress

Several studies suggest that low birthweight resulting from restricted intrauterine growth can leave a metabolic footprint which may persist into adulthood. To investigate this, we performed metabolomic profiling on 5036 female twins, aged 18-80, with weight at birth information available from the TwinsUK cohort and performed independent replication in two additional cohorts. Out of 422 compounds tested, 25 metabolites associated with birthweight in these twins, replicated in 1951 men and women from the Hertfordshire Cohort Study (HCS, aged 66) and in 2391 men and women from the North Finland Birth 1986 cohort (NFBC, aged 16). We found distinct heterogeneity between sexes and, after adjusting for multiple tests and heterogeneity, two metabolites were reproducible overall (propionylcarnitine and 3-4-hydroxyphenyllactate). Testing women only, we found other metabolites associated with lower birthweight from the meta-analysis of the three cohorts (2-hydroxy-butyric acid and γ-glutamylleucine). Higher levels of all these metabolites can be linked to insulin resistance, oxidative stress or a dysfunction of energy metabolism, suggesting that low birthweight in both twins and singletons are having an impact on these pathways in adulthood.

Biofluid Metabolomics in Preterm Birth Research

This article presents an account of the research carried out so far in the use of metabolomics to find biomarkers of preterm birth (PTB) in fetal, maternal, and newborn biofluids. Metabolomic studies have employed mainly nuclear magnetic resonance spectroscopy or mass spectrometry-based methodologies to analyze, on one hand, prenatal biofluids (amniotic fluid, maternal urine/maternal blood, cervicovaginal fluid) to identify predictive biomarkers of PTB, and on the other hand, biofluids collected at or after birth (amniotic fluid, umbilical cord blood, newborn urine, and newborn blood, maternal blood, or breast milk) to assess and follow up the health status of PTB babies. Besides advancing on the biochemical knowledge of PTB metabolism mainly during the in utero period and at birth, the work carried out has also helped to identify important requirements related to experimental design and analytical protocol that need to be addressed, if translation of these biomarkers to the clinic is to be envisaged. An outlook of possible future developments for the translation of laboratory results to the clinic is presented.

Using newborn screening analytes to identify cases of neonatal sepsis

Neonatal sepsis is associated with high mortality and morbidity, yet challenges with available diagnostic approaches can lead to delays in therapy. Our study assessed whether newborn screening analytes could be utilized to identify associations with neonatal sepsis. We linked a newborn screening registry with health databases to identify cases of sepsis among infants born in Ontario from 2010-2015. Correlations between sepsis and screening analytes were examined within three gestational age groups (early preterm: <34 weeks; late preterm: 34-36 weeks; term: ≥37 weeks), using multivariable logistic regression models. We started with a model containing only clinical factors, then added groups of screening analytes. Among 793,128 infants, 4,794 were diagnosed with sepsis during the neonatal period. Clinical variables alone or in combination with hemoglobin values were not strongly predictive of neonatal sepsis among infants born at term or late preterm. However, model fit improved considerably after adding markers of thyroid and adrenal function, acyl-carnitines, and amino acids. Among infants born at early preterm gestation, neither clinical variables alone nor models incorporating screening analytes adequately predicted neonatal sepsis. The combination of clinical variables and newborn screening analytes may have utility in identifying term or late preterm infants at risk for neonatal sepsis.

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.

Postnatal gestational age estimation using newborn screening blood spots: a proposed validation protocol

Background

Knowledge of gestational age (GA) is critical for guiding neonatal care and quantifying regional burdens of preterm birth. In settings where access to ultrasound dating is limited, postnatal estimates are frequently used despite the issues of accuracy associated with postnatal approaches. Newborn metabolic profiles are known to vary by severity of preterm birth. Recent work by our group and others has highlighted the accuracy of postnatal GA estimation algorithms derived from routinely collected newborn screening profiles. This protocol outlines the validation of a GA model originally developed in a North American cohort among international newborn cohorts.

Methods

Our primary objective is to use blood spot samples collected from infants born in Zambia and Bangladesh to evaluate our algorithm’s capacity to correctly classify GA within 1, 2, 3 and 4 weeks. Secondary objectives are to 1) determine the algorithm's accuracy in small-for-gestational-age and large-for-gestational-age infants, 2) determine its ability to correctly discriminate GA of newborns across dichotomous thresholds of preterm birth (≤34 weeks, <37 weeks GA) and 3) compare the relative performance of algorithms derived from newborn screening panels including all available analytes and those restricted to analyte subsets. The study population will consist of infants born to mothers already enrolled in one of two preterm birth cohorts in Lusaka, Zambia, and Matlab, Bangladesh. Dried blood spot samples will be collected and sent for analysis in Ontario, Canada, for model validation.

Discussion

This study will determine the validity of a GA estimation algorithm across ethnically diverse infant populations and assess population specific variations in newborn metabolic profiles.

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.

Renal injury in neonates: use of "omics" for developing precision medicine in neonatology

Preterm birth is associated with increased risks of morbidity and mortality along with increased healthcare costs. Advances in medicine have enhanced survival for preterm infants but the overall incidence of major morbidities has changed very little. Abnormal renal development is an important consequence of premature birth. Acute kidney injury (AKI) in the neonatal period is multifactorial and may increase lifetime risk of chronic kidney disease.Traditional biomarkers in newborns suffer from considerable confounders, limiting their use for early identification of AKI. There is a need to develop novel biomarkers that can identify, in real time, the evolution of renal dysfunction in an early diagnostic, monitoring and prognostic fashion. Use of "omics", particularly metabolomics, may provide valuable information regarding functional pathways underlying AKI and prediction of clinical outcomes.The emerging knowledge generated by the application of "omics" (genomics, proteomics, metabolomics) in neonatology provides new insights that can help to identify markers of early diagnosis, disease progression, and identify new therapeutic targets. Additionally, omics will have major implications in the field of personalized healthcare in the future. Here, we will review the current knowledge of different omics technologies in neonatal-perinatal medicine including biomarker discovery, defining as yet unrecognized biologic therapeutic targets, and linking of omics to relevant standard indices and long-term outcomes.

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.

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.