- Email: [email protected]
Metabolomics in neonatal life
Early Human Development 89S1 (2013) S7–S10
Contents lists available at ScienceDirect
Early Human Development j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e a r l h u m d ev
Metabolomics in neonatal life Vassilios Fanos a, *, Nicoletta Iacovidou b , Melania Puddu a , Giovanni Ottonello a , Antonio Noto a , Luigi Atzori c a Neonatal b Medical
Intensive Care Unit, Puericulture Institute and Neonatal Section, University of Cagliari, Italy School, National and Kapodistrian University of Athens, Greece of Biomedical Sciences, University of Cagliari, Italy
c Department
article info
summary
Keywords: Metabolomics Newborn Review
Metabolomics (or metabonomics) is based on the systematic study of the complete set of metabolites in a biological sample and is considered the most innovative of the ‘omics’ sciences. The metabolome is currently regarded as the ‘new clinical biochemistry’; it is the most predictive phenotype, through consideration of epigenetic differences. Among more than 5000 papers listed in PubMed on this topic in the last three years, less than 60 refer to neonatal life. Aim of this review is to present the clinical applications of metabolomics in neonatology, including results of recent studies performed in experimental models and newborns. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
3.1. Intrauterine Growth Restriction (IUGR) [4]
Metabolomics is the process of describing the phenotype of a cell, tissue or organism through the full complement of metabolites present. Metabolomics measure global sets of low molecular weight metabolites (including amino acids, organic acids, sugars, fatty acids, lipids, steroids, small peptides, vitamins, etc.), thus providing a “snapshot” of the metabolic status of a cell, tissue or organism in relation to genetic variations or external stimuli [1]. The use of metabolomics appears to be a promising tool in neonatology. Urine (“a window on the organism”) is a biofluid particularly suitable for metabolomic analysis in neonatology because it may be collected by using simple, noninvasive techniques and because it may provide valuable diagnostics [1–5]. In this review, we report existing literature data on neonatal metabolomics, including our personal experience.
In a recent publication of our group – the first in the field, to the best of our knowledge – we reported metabolomics performed with proton nuclear magnetic resonance spectroscopy (1 H-NMR) on urine of 26 IUGR neonates. Urine was collected non-invasively within the first 24 hours of life (prior to feeding) up to 4 days postnatally. Differences in certain metabolites such as sarcosine, creatine and, particularly, creatinine and myo-inositol, discriminate the urine metabolic profiles of IUGRs and controls, in line with literature data. High plasma and urine values of myo-inositol are known to predict type 2 diabetes.
2. Data analysis in metabolomics This part has been extensively considered in a previous paper by us [1] and will not be discussed here. 3. Clinical metabolomics in prenatal life Our group has addressed this subject very recently [3]. In the present paper we focus our attention only on two topics. * Corresponding author. Prof. Vassilios Fanos, Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, University of Cagliari, Italy. Tel.: +39 0706093403; fax: +39 0706093440. E-mail address: [email protected] (V. Fanos). 0378-3782/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved.
3.2. Prenatal inflammation/infection Brain inflammation [1]: Recent data highlighted the plasma metabolome of fetal sheep brain after inflammatory-induced exposure (E. coli lipopolysaccharide [LPS]). Acute effects were observed within the first 3 days; LPS exposure caused hypoxia and a significant increase in the Krebs cycle intermediates and alanine and lactate, and a subsequent decrease in hexoses; oxysteroles (24-hydroxycholesterol [24OHC], 25-hydroxycholesterol [25OHC]), 12S-HETE and spermidine increased after LPS administration, peaking at 6 hours. Their proapoptotic pathways of white matter injury are highlighted by the authors. Late effects were also observed 6–days later. There was a delayed opposite effect of LPS on energy metabolites, hyperoxia and elevation of some metabolites: sphingomyelins, kynurenine, 3-hydroxykynurenine, putrescine and asymmetric dimethylarginine (the latter is important in the regulation of microvascular tone and the endothelial function).
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V. Fanos et al. / Early Human Development 89S1 (2013) S7–S10
Histological chorioamnionitis (HCA) [6]: Urinary metabolomic analysis seems to differentiate preterm infants born to mothers with HCA from those born to mothers without HCA, although a larger sample is necessary to achieve statistical significance. Individual metabolites discriminating were the following: mannitol, 4-hydroxyphenylacetate, p-cresol, myoinositol, trimethylamine-N-oxide and 1-methylnicotinamide. The next step could be the validation of biomarkers as early predictors of outcome in preterm babies exposed to intrauterine inflammation, in order to plan a more accurate and targeted follow-up. 4. Clinical metabolomics in neonatal life Studies reported in the literature on clinical metabolomics in neonatal life, including our personal data, are summarized in this section. 4.1. Mode of delivery, prenatal and postnatal maturation [6] The mode of delivery has an early term impact on the metabolic composition of the urine after delivery. Metabolomics, although in a very preliminary stage, seems to be able to distinguish between the spontaneous and cesarean populations. It has been possible to identify distinct metabolic profiles associated with different gestational ages (term, preterm, late preterm), suggesting that the metabolic state of the neonate at birth is closely dependent on its gestational age. Important metabolites in this process are alanine, citrate, creatinine, creatine and dimethylglycine. It is also possible to recognize a trend in postnatal maturation. 4.2. Twins [5,6] Metabolomics analysis in twins is currently under investigation. It appears to be possible to discriminate between monochorionic and dichorionic twins. Important metabolites appear to be galactitol, N-acetylcysteine, N-acetylglutamate, N-acetyltyrosine, methylguanidine, N-dimethylformamide and 5-hydroxyindol-3acetate. 4.3. Asphyxia, brain injury and hypothermia The studies performed with metabolomics in this field are mainly experimental [1,7]. Urine: The most important urinary metabolites (prior to the induction of hypoxia) discriminating between survivors and deaths in piglets treated with different oxygen concentrations were urea, creatinine, malonate, methylguanidine, and hydroxyisobutyric acid. Some of these, particularly malonate, urea, and creatinine, are known to play an important role in neurological and renal disorders, ATP metabolism, free-radical scavenging and cell death. Metabolomics could allow to develop a targeted treatment to reduce side effects of oxygen and to predict mortality before resuscitation. Blood: Other investigators confirmed blood lactate and creatinine as markers of asphyxia and reported new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers of asphyxia. Recent data suggest that metabolomic monitoring may have a potential role during therapeutic hypothermia for the optimal cooling temperatures, the duration of hypothermia, the rewarming regimens and mechanisms and the individualization of the therapy. Thus metabolomics could help to distinguish metabolite ensembles
associated with death (mg malondialdehyde/mg creatinine urinary ratio >3.5 [sensitivity and specificity about 90%]) or adverse neurological outcome (if high serum level: glutarate, malondialdehyde, 3-hydroxybutyrate, orotate) or less injury and better outcomes. Umbilical cord blood: Very recently, umbilical cord blood from newborns with hypoxic ischemic encephalopathy (HIE) has been investigated. A logistic regression model using 5 metabolites clearly delineates severity of asphyxia and classifies HIE infants with AUC = 0.92. The five metabolites are: decenoyl-L-carnitine, 3,5-tetradecadiencarnitine (acylcarnitines), PC ae C38:0 (glycerophospholipid), phenylalanine, prolin (aminoacids) [8]. 4.4. Maternal milk and formula evaluation [9] NMR spectroscopy and GC/MS were used to analyze the water-soluble and lipid fractions extracted from maternal milk samples. For comparison, preterm formula milk was studied as well. The multivariate statistical data analysis revealed biochemical variability both between human milk of mothers who delivered a preterm (HBM) and commercial milk and within the group of HBM samples. Metabolomics represents a promising tool for studying issues related to nutrition and health of preterm infants. 4.5. Respiratory diseases [10] Existing literature data support the possibility of making a prompt and sensitive diagnosis of pneumonia (including information about etiology and therapy monitoring), asthma and bronchiolitis. This technology is also potentially helpful in the management of sick premature infants with RDS, treated with surfactant. 4.6. Cardiovascular diseases [11] Metabolomics has been performed on cardiac tissue, plasma and urine, mainly post-asphyxia. Several metabolomic results may be able to provide a detailed picture of the clinical condition and anticipate survival/death, future complications such as cardiac hypertrophy, arrhythmias and diabetes. In a unique preliminary study with metabolomics in urine samples collected at birth from neonates, it was possible to anticipate persistent patent ductus arteriosus at day 4. 4.7. Renal diseases/injuries Metabolomic markers in urine seem to provide a robust approach for early identification and novel markers of acute kidney injury. Clinical studies: Newborns and infants with nephrouropathies (renal dysplasia, vesicoureteric reflux, urinary tract infections and acute kidney injury) were discriminated from healthy children by the 1 H-NMR-based metabolic profiling of urine. The differences were related to alterations of purine and pyridine and to alterations of the urea cycle [12]. Metabolomics was helpful in discriminating acute kidney injury after cardiopulmonary bypass surgery in a group of neonatal and pediatric patients, enabling a timely diagnosis of this complication after pediatric cardiac surgery [1,6]. Experimental studies: Two experimental studies in newborn rats demonstrated that gentamicin-induced nephrotoxicity produced a distinct pattern of 14 altered urinary metabolites.
V. Fanos et al. / Early Human Development 89S1 (2013) S7–S10
In particular, glucose, galactose, N-acetylglucosamine, myoinositol, butanoic acid, 3-hydroxybutyrate increased about threefold while citrulline and pseudouridine decreased. Other markers including creatine, nicotinic acid, prostaglandin E2 and cholic acid were identified as phenotypic biomarkers of gentamicin-induced toxicity [1,6].
S9
of chronic kidney damage (neutrophil gelatinase-associated lipolicalin [NGAL]) in the population of adults born preterm with extremely low weight. Differences in the metabolome between ex-term and ex-preterm young adults have also been recently observed. The most marked differences were found between ex-term young adults and ex-preterm young adults (elevated methylamines and acetylglycoproteins and lower hippurate).
4.8. Infectious diseases [1,6] At present only limited experimental studies are available. Sepsis: A predictive model for diagnosis of sepsis in rats using these metabolites classified cases with a sensitivity and specificity of 100%. Characteristic metabolites in septic rats were markedly different vs control rats: alanine, creatine, phosphoethanolamine and myo-inositol concentrations increased in lung tissue; creatine increased and myo-inositol decreased in BAL fluid; alanine, creatine, phosphoethanolamine and acetoacetate increased, whereas formate decreased, in serum. Sepsis in burned patients: Other plasma biomarkers, involved mainly in oxidative stress and tissue damage, may provide evidence for distinguishing burn septic patients from nonseptic ones: hypoxanthine, indoxyl sufate, glucuronic acid, gluconic acid, proline, uracil, nitrotyrosine, uric acid, and trihydroxycholanoic acid. Sepsis-induced acute lung injury: Finally a metabolomic approach to sepsis-induced acute lung injury has been suggested. NMR metabolomics analysis is a potentially useful technique for diagnosing sepsis. 4.9. Metabolic diseases [1,6,13] Metabolomics allows to study not just a single metabolic defect but consequences and alterations in an integrated way body-wide. Some investigators applied untargeted mass spectrometry-based metabolomics to methylmalonic acidemia (MMA) and propionic acidemia (PA). Plasma propionyl carnitine was easily identified as the best biomarker of disease. Moreover, two acylcarnitine metabolites and several unidentified species differentiated MMA and PA. 4.10. Drug assessment (pharma-metabolomics) [14] The major applications of metabolomics in pharmaceutical research and development are preclinical safety evaluation of drug candidates, drug safety evaluation in clinical trials, assessment of the beneficial effects of pharmaceuticals; pharmacometabolomics (or pharma-metabolomics), predicting the metabolism and toxicity of a drug based on the analysis of a pre-dose metabolic profile, identification of drug-related alterations in metabolic pathways and finally discovering the mechanisms underlying sporadic idiosyncratic toxicity.
5.2. Chronic lung disease Preliminary unpublished data of our group suggest that urinary metabolomics will be able to discriminate children with Chronic Lung Disease from controls at 2.5 years of age. 5.3. Epilepsy in ex ELBW [2] A preliminary study was performed to explore the metabolic differences between a group of children born ELBW who developed epilepsy and a group of children born ELBW without epilepsy which served as control. It was possible to discriminate between the group of epileptic children and the group of non-epileptic children. The metabolites discriminating the two groups were citric acid, alanine and taurine (increased in epileptics compared to controls), glycine, malonic acid, creatinine, sugars (decreased in epileptics compared to controls). 5.4. Outcome of congenital cytomegalovirus (CMV) infection [16] In a study on 40 children the metabolic differences between groups of children born with CMV infection, asymptomatic, symptomatic at birth with or without sequelae and a group of children born without CMV infection as control were explored. Metabolomics in urine collected at birth was able to characterize the group of symptomatic children who had serious complications in childhood from those who had an infection without complications. 6. Conclusions In conclusion, together with the other “omics”, metabolomics appears to be a promising tool in neonatology or a real revolution [17–19]: the monitoring of postnatal metabolic maturation, the identification of biomarkers as early predictors of outcome, the diagnosis and monitoring of various diseases and the “tailored” management of neonatal disorders are the most promising applications for this technology [20]. We believe that metabolomics will represent a very frequent approach to neonatal diseases in the near future, but our belief needs to be substantiated by more data. Conflict of interest statement
5. Long-term diseases (perinatal programming and longterm metabolic consequences in very low and extremely low birth weight infants) 5.1. Young adults [15] Urinary metabolomics detected differences between healthy adults born preterm with extremely low birth weight (below 1000 g), and healthy adults born at term, who served as controls. Alterations in the metabolism of arginine, proline, purine, pyrimidine, histidine and beta-alanine, as well as in the urea cycle were observed. It was possible to associate this condition of apparent health with an increase in markers
The authors have no conflict of interest to declare. References 1. Fanos V, Antonucci R, Barberini L, Noto A, Atzori L. Clinical application of metabolomics in neonatology. J Matern Fetal Neonatal Med 2012;25(Suppl 1): 104–9. 2. Fanos V, Van den Anker J, Noto A, Mussap M, Atzori L. Metabolomics in neonatology: Fact or fiction? Semin Fetal Neonatal Med 2013;18:3–12. 3. Fanos V, Mussap M, Noto A, Atzori L. Metabolomics in perinatology: where are we now? Acta Med Port 2012;25(S2):117–120. 4. Dessì A, Ottonello G, Fanos V. Physiopathology of intrauterine growth retardation: from classic data to metabolomics. J Matern Fetal Neonatal Med 2012;25(Suppl 5):13–8.
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5. Paladini P, Paladini A, Noto A, Atzori L, Barberini L, Puxeddu E, et al. Urinary metabolomics in twins at birth. J Pediatr Neonat Individual Med 2012;1:113 (abs 3). 6. Syggelou A, Iacovidou N, Atzori L, Xanthos T, Fanos V. Metabolomics in the developing human being. Pediatr Clin North Am 2012;59(5):1039–58. 7. Fanos V, Atzori L, Dessì A, D’Aloja E, Finco G, Faa G. The kidney in postasphyctic syndrome: state of the art. In: Fanos V, Chevalier RL, Faa G, Cataldi L. Developmental Nephrology: from Embryology to Metabolomics. Hygeia Press, 2011. 8. Walsh BH, Broadhurst DI, Mandal R, Wishart DS, Boylan GB, Kenny LC, et al. The metabolomic profile of umbilical cord blood in neonatal hypoxic ischaemic encephalopathy. PLoS One 2012;7(12):e50520. 9. Cesare Marincola F, Noto A, Caboni P, A Reali, L. Barberini, M. Lussu, et al. A metabolomic study of preterm human and formula milk by high resolution NMR and GC/MS analysis: preliminary results. J Matern Fetal Neonatal Med 2012 Oct;25(Suppl 5):62–7. 10. Atzei A, Atzori L, Moretti C, Barberini L, Noto A, Ottonello G, et al. Metabolomics in paediatric respiratory diseases and bronchiolitis. J Matern Fetal Neonatal Med 2011;24(Suppl 2):59–62. 11. Bassareo PP, Fanos V, Deidda M, Barberini L, Mercuro G. Metabolomic approach to foetal and neonatal heart. J Matern Fetal Neonatal Med 2012;25(Suppl 5): 19–21. 12. Atzori L, Antonucci R, Barberini L, Locci E, Cesare Marincola F, Scano P, et al. 1 H NMR-based metabolic profiling of urine from children with nephrouropathies. Front Biosci (Elite Ed) 2010;2:725–32.
13. Fanos V, Barberini L, Antonucci R, Atzori L. Metabolomics in neonatology and pediatrics. Clin Biochem 2011;44:452–4. 14. Fanos V, Barberini L, Antonucci R, Atzori L. Pharma-metabolomics in neonatology: is it a dream or a fact? Curr Pharm Des 2012;18(21):2996–3006. 15. Atzori L, Mussap M, Noto A, Barberini L, Puddu M, Coni E, et al. Clinical metabolomics and urinary NGAL for the early prediction of chronic kidney disease in healthy adults born ELBW. J Matern Fetal Neonatal Med 2011;24(Suppl 2):40–3. 16. Locci E, Lazzarotto T, Atzori L, Barberini L, Murgia F, Lussu M, et al. Metabolomic analysis of newborn’s urine with cytomegalovirus congenital infection. From the descriptive to the predictive ability: preliminary results. J Pediatr Neonatal Individualized Med 2012;1:147–8 (abstract 50). 17. Fanos V, Fanni C, Ottonello G, Noto A, Dessì A, Mussap M. Metabolomics in adult and pediatric nephrology. Molecules 2013;18(5):4844–57. 18. Fanos V, Antonucci R, Zaffanello M, Mussap M. Neonatal drug induced nephrotoxicity: old and next generation biomarkers for early detection and management of neonatal drug-induced nephrotoxicity, with special emphasis on uNGAL and on metabolomics. Curr Med Chem 2012;19(27):4595–605. 19. Fanos V, Antonucci R, Barberini L, Atzori L. Urinary metabolomics in the newborn and infants. Adv Clin Chem 2012;58:193–223. 20. Fanos V. Pediatric and neonatal individualized medicine. J Pediatr Neonatal Individualized Med 2012;1(1):7–10.
Contents lists available at ScienceDirect
Early Human Development j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e a r l h u m d ev
Metabolomics in neonatal life Vassilios Fanos a, *, Nicoletta Iacovidou b , Melania Puddu a , Giovanni Ottonello a , Antonio Noto a , Luigi Atzori c a Neonatal b Medical
Intensive Care Unit, Puericulture Institute and Neonatal Section, University of Cagliari, Italy School, National and Kapodistrian University of Athens, Greece of Biomedical Sciences, University of Cagliari, Italy
c Department
article info
summary
Keywords: Metabolomics Newborn Review
Metabolomics (or metabonomics) is based on the systematic study of the complete set of metabolites in a biological sample and is considered the most innovative of the ‘omics’ sciences. The metabolome is currently regarded as the ‘new clinical biochemistry’; it is the most predictive phenotype, through consideration of epigenetic differences. Among more than 5000 papers listed in PubMed on this topic in the last three years, less than 60 refer to neonatal life. Aim of this review is to present the clinical applications of metabolomics in neonatology, including results of recent studies performed in experimental models and newborns. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
3.1. Intrauterine Growth Restriction (IUGR) [4]
Metabolomics is the process of describing the phenotype of a cell, tissue or organism through the full complement of metabolites present. Metabolomics measure global sets of low molecular weight metabolites (including amino acids, organic acids, sugars, fatty acids, lipids, steroids, small peptides, vitamins, etc.), thus providing a “snapshot” of the metabolic status of a cell, tissue or organism in relation to genetic variations or external stimuli [1]. The use of metabolomics appears to be a promising tool in neonatology. Urine (“a window on the organism”) is a biofluid particularly suitable for metabolomic analysis in neonatology because it may be collected by using simple, noninvasive techniques and because it may provide valuable diagnostics [1–5]. In this review, we report existing literature data on neonatal metabolomics, including our personal experience.
In a recent publication of our group – the first in the field, to the best of our knowledge – we reported metabolomics performed with proton nuclear magnetic resonance spectroscopy (1 H-NMR) on urine of 26 IUGR neonates. Urine was collected non-invasively within the first 24 hours of life (prior to feeding) up to 4 days postnatally. Differences in certain metabolites such as sarcosine, creatine and, particularly, creatinine and myo-inositol, discriminate the urine metabolic profiles of IUGRs and controls, in line with literature data. High plasma and urine values of myo-inositol are known to predict type 2 diabetes.
2. Data analysis in metabolomics This part has been extensively considered in a previous paper by us [1] and will not be discussed here. 3. Clinical metabolomics in prenatal life Our group has addressed this subject very recently [3]. In the present paper we focus our attention only on two topics. * Corresponding author. Prof. Vassilios Fanos, Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, University of Cagliari, Italy. Tel.: +39 0706093403; fax: +39 0706093440. E-mail address: [email protected] (V. Fanos). 0378-3782/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved.
3.2. Prenatal inflammation/infection Brain inflammation [1]: Recent data highlighted the plasma metabolome of fetal sheep brain after inflammatory-induced exposure (E. coli lipopolysaccharide [LPS]). Acute effects were observed within the first 3 days; LPS exposure caused hypoxia and a significant increase in the Krebs cycle intermediates and alanine and lactate, and a subsequent decrease in hexoses; oxysteroles (24-hydroxycholesterol [24OHC], 25-hydroxycholesterol [25OHC]), 12S-HETE and spermidine increased after LPS administration, peaking at 6 hours. Their proapoptotic pathways of white matter injury are highlighted by the authors. Late effects were also observed 6–days later. There was a delayed opposite effect of LPS on energy metabolites, hyperoxia and elevation of some metabolites: sphingomyelins, kynurenine, 3-hydroxykynurenine, putrescine and asymmetric dimethylarginine (the latter is important in the regulation of microvascular tone and the endothelial function).
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V. Fanos et al. / Early Human Development 89S1 (2013) S7–S10
Histological chorioamnionitis (HCA) [6]: Urinary metabolomic analysis seems to differentiate preterm infants born to mothers with HCA from those born to mothers without HCA, although a larger sample is necessary to achieve statistical significance. Individual metabolites discriminating were the following: mannitol, 4-hydroxyphenylacetate, p-cresol, myoinositol, trimethylamine-N-oxide and 1-methylnicotinamide. The next step could be the validation of biomarkers as early predictors of outcome in preterm babies exposed to intrauterine inflammation, in order to plan a more accurate and targeted follow-up. 4. Clinical metabolomics in neonatal life Studies reported in the literature on clinical metabolomics in neonatal life, including our personal data, are summarized in this section. 4.1. Mode of delivery, prenatal and postnatal maturation [6] The mode of delivery has an early term impact on the metabolic composition of the urine after delivery. Metabolomics, although in a very preliminary stage, seems to be able to distinguish between the spontaneous and cesarean populations. It has been possible to identify distinct metabolic profiles associated with different gestational ages (term, preterm, late preterm), suggesting that the metabolic state of the neonate at birth is closely dependent on its gestational age. Important metabolites in this process are alanine, citrate, creatinine, creatine and dimethylglycine. It is also possible to recognize a trend in postnatal maturation. 4.2. Twins [5,6] Metabolomics analysis in twins is currently under investigation. It appears to be possible to discriminate between monochorionic and dichorionic twins. Important metabolites appear to be galactitol, N-acetylcysteine, N-acetylglutamate, N-acetyltyrosine, methylguanidine, N-dimethylformamide and 5-hydroxyindol-3acetate. 4.3. Asphyxia, brain injury and hypothermia The studies performed with metabolomics in this field are mainly experimental [1,7]. Urine: The most important urinary metabolites (prior to the induction of hypoxia) discriminating between survivors and deaths in piglets treated with different oxygen concentrations were urea, creatinine, malonate, methylguanidine, and hydroxyisobutyric acid. Some of these, particularly malonate, urea, and creatinine, are known to play an important role in neurological and renal disorders, ATP metabolism, free-radical scavenging and cell death. Metabolomics could allow to develop a targeted treatment to reduce side effects of oxygen and to predict mortality before resuscitation. Blood: Other investigators confirmed blood lactate and creatinine as markers of asphyxia and reported new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers of asphyxia. Recent data suggest that metabolomic monitoring may have a potential role during therapeutic hypothermia for the optimal cooling temperatures, the duration of hypothermia, the rewarming regimens and mechanisms and the individualization of the therapy. Thus metabolomics could help to distinguish metabolite ensembles
associated with death (mg malondialdehyde/mg creatinine urinary ratio >3.5 [sensitivity and specificity about 90%]) or adverse neurological outcome (if high serum level: glutarate, malondialdehyde, 3-hydroxybutyrate, orotate) or less injury and better outcomes. Umbilical cord blood: Very recently, umbilical cord blood from newborns with hypoxic ischemic encephalopathy (HIE) has been investigated. A logistic regression model using 5 metabolites clearly delineates severity of asphyxia and classifies HIE infants with AUC = 0.92. The five metabolites are: decenoyl-L-carnitine, 3,5-tetradecadiencarnitine (acylcarnitines), PC ae C38:0 (glycerophospholipid), phenylalanine, prolin (aminoacids) [8]. 4.4. Maternal milk and formula evaluation [9] NMR spectroscopy and GC/MS were used to analyze the water-soluble and lipid fractions extracted from maternal milk samples. For comparison, preterm formula milk was studied as well. The multivariate statistical data analysis revealed biochemical variability both between human milk of mothers who delivered a preterm (HBM) and commercial milk and within the group of HBM samples. Metabolomics represents a promising tool for studying issues related to nutrition and health of preterm infants. 4.5. Respiratory diseases [10] Existing literature data support the possibility of making a prompt and sensitive diagnosis of pneumonia (including information about etiology and therapy monitoring), asthma and bronchiolitis. This technology is also potentially helpful in the management of sick premature infants with RDS, treated with surfactant. 4.6. Cardiovascular diseases [11] Metabolomics has been performed on cardiac tissue, plasma and urine, mainly post-asphyxia. Several metabolomic results may be able to provide a detailed picture of the clinical condition and anticipate survival/death, future complications such as cardiac hypertrophy, arrhythmias and diabetes. In a unique preliminary study with metabolomics in urine samples collected at birth from neonates, it was possible to anticipate persistent patent ductus arteriosus at day 4. 4.7. Renal diseases/injuries Metabolomic markers in urine seem to provide a robust approach for early identification and novel markers of acute kidney injury. Clinical studies: Newborns and infants with nephrouropathies (renal dysplasia, vesicoureteric reflux, urinary tract infections and acute kidney injury) were discriminated from healthy children by the 1 H-NMR-based metabolic profiling of urine. The differences were related to alterations of purine and pyridine and to alterations of the urea cycle [12]. Metabolomics was helpful in discriminating acute kidney injury after cardiopulmonary bypass surgery in a group of neonatal and pediatric patients, enabling a timely diagnosis of this complication after pediatric cardiac surgery [1,6]. Experimental studies: Two experimental studies in newborn rats demonstrated that gentamicin-induced nephrotoxicity produced a distinct pattern of 14 altered urinary metabolites.
V. Fanos et al. / Early Human Development 89S1 (2013) S7–S10
In particular, glucose, galactose, N-acetylglucosamine, myoinositol, butanoic acid, 3-hydroxybutyrate increased about threefold while citrulline and pseudouridine decreased. Other markers including creatine, nicotinic acid, prostaglandin E2 and cholic acid were identified as phenotypic biomarkers of gentamicin-induced toxicity [1,6].
S9
of chronic kidney damage (neutrophil gelatinase-associated lipolicalin [NGAL]) in the population of adults born preterm with extremely low weight. Differences in the metabolome between ex-term and ex-preterm young adults have also been recently observed. The most marked differences were found between ex-term young adults and ex-preterm young adults (elevated methylamines and acetylglycoproteins and lower hippurate).
4.8. Infectious diseases [1,6] At present only limited experimental studies are available. Sepsis: A predictive model for diagnosis of sepsis in rats using these metabolites classified cases with a sensitivity and specificity of 100%. Characteristic metabolites in septic rats were markedly different vs control rats: alanine, creatine, phosphoethanolamine and myo-inositol concentrations increased in lung tissue; creatine increased and myo-inositol decreased in BAL fluid; alanine, creatine, phosphoethanolamine and acetoacetate increased, whereas formate decreased, in serum. Sepsis in burned patients: Other plasma biomarkers, involved mainly in oxidative stress and tissue damage, may provide evidence for distinguishing burn septic patients from nonseptic ones: hypoxanthine, indoxyl sufate, glucuronic acid, gluconic acid, proline, uracil, nitrotyrosine, uric acid, and trihydroxycholanoic acid. Sepsis-induced acute lung injury: Finally a metabolomic approach to sepsis-induced acute lung injury has been suggested. NMR metabolomics analysis is a potentially useful technique for diagnosing sepsis. 4.9. Metabolic diseases [1,6,13] Metabolomics allows to study not just a single metabolic defect but consequences and alterations in an integrated way body-wide. Some investigators applied untargeted mass spectrometry-based metabolomics to methylmalonic acidemia (MMA) and propionic acidemia (PA). Plasma propionyl carnitine was easily identified as the best biomarker of disease. Moreover, two acylcarnitine metabolites and several unidentified species differentiated MMA and PA. 4.10. Drug assessment (pharma-metabolomics) [14] The major applications of metabolomics in pharmaceutical research and development are preclinical safety evaluation of drug candidates, drug safety evaluation in clinical trials, assessment of the beneficial effects of pharmaceuticals; pharmacometabolomics (or pharma-metabolomics), predicting the metabolism and toxicity of a drug based on the analysis of a pre-dose metabolic profile, identification of drug-related alterations in metabolic pathways and finally discovering the mechanisms underlying sporadic idiosyncratic toxicity.
5.2. Chronic lung disease Preliminary unpublished data of our group suggest that urinary metabolomics will be able to discriminate children with Chronic Lung Disease from controls at 2.5 years of age. 5.3. Epilepsy in ex ELBW [2] A preliminary study was performed to explore the metabolic differences between a group of children born ELBW who developed epilepsy and a group of children born ELBW without epilepsy which served as control. It was possible to discriminate between the group of epileptic children and the group of non-epileptic children. The metabolites discriminating the two groups were citric acid, alanine and taurine (increased in epileptics compared to controls), glycine, malonic acid, creatinine, sugars (decreased in epileptics compared to controls). 5.4. Outcome of congenital cytomegalovirus (CMV) infection [16] In a study on 40 children the metabolic differences between groups of children born with CMV infection, asymptomatic, symptomatic at birth with or without sequelae and a group of children born without CMV infection as control were explored. Metabolomics in urine collected at birth was able to characterize the group of symptomatic children who had serious complications in childhood from those who had an infection without complications. 6. Conclusions In conclusion, together with the other “omics”, metabolomics appears to be a promising tool in neonatology or a real revolution [17–19]: the monitoring of postnatal metabolic maturation, the identification of biomarkers as early predictors of outcome, the diagnosis and monitoring of various diseases and the “tailored” management of neonatal disorders are the most promising applications for this technology [20]. We believe that metabolomics will represent a very frequent approach to neonatal diseases in the near future, but our belief needs to be substantiated by more data. Conflict of interest statement
5. Long-term diseases (perinatal programming and longterm metabolic consequences in very low and extremely low birth weight infants) 5.1. Young adults [15] Urinary metabolomics detected differences between healthy adults born preterm with extremely low birth weight (below 1000 g), and healthy adults born at term, who served as controls. Alterations in the metabolism of arginine, proline, purine, pyrimidine, histidine and beta-alanine, as well as in the urea cycle were observed. It was possible to associate this condition of apparent health with an increase in markers
The authors have no conflict of interest to declare. References 1. Fanos V, Antonucci R, Barberini L, Noto A, Atzori L. Clinical application of metabolomics in neonatology. J Matern Fetal Neonatal Med 2012;25(Suppl 1): 104–9. 2. Fanos V, Van den Anker J, Noto A, Mussap M, Atzori L. Metabolomics in neonatology: Fact or fiction? Semin Fetal Neonatal Med 2013;18:3–12. 3. Fanos V, Mussap M, Noto A, Atzori L. Metabolomics in perinatology: where are we now? Acta Med Port 2012;25(S2):117–120. 4. Dessì A, Ottonello G, Fanos V. Physiopathology of intrauterine growth retardation: from classic data to metabolomics. J Matern Fetal Neonatal Med 2012;25(Suppl 5):13–8.
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5. Paladini P, Paladini A, Noto A, Atzori L, Barberini L, Puxeddu E, et al. Urinary metabolomics in twins at birth. J Pediatr Neonat Individual Med 2012;1:113 (abs 3). 6. Syggelou A, Iacovidou N, Atzori L, Xanthos T, Fanos V. Metabolomics in the developing human being. Pediatr Clin North Am 2012;59(5):1039–58. 7. Fanos V, Atzori L, Dessì A, D’Aloja E, Finco G, Faa G. The kidney in postasphyctic syndrome: state of the art. In: Fanos V, Chevalier RL, Faa G, Cataldi L. Developmental Nephrology: from Embryology to Metabolomics. Hygeia Press, 2011. 8. Walsh BH, Broadhurst DI, Mandal R, Wishart DS, Boylan GB, Kenny LC, et al. The metabolomic profile of umbilical cord blood in neonatal hypoxic ischaemic encephalopathy. PLoS One 2012;7(12):e50520. 9. Cesare Marincola F, Noto A, Caboni P, A Reali, L. Barberini, M. Lussu, et al. A metabolomic study of preterm human and formula milk by high resolution NMR and GC/MS analysis: preliminary results. J Matern Fetal Neonatal Med 2012 Oct;25(Suppl 5):62–7. 10. Atzei A, Atzori L, Moretti C, Barberini L, Noto A, Ottonello G, et al. Metabolomics in paediatric respiratory diseases and bronchiolitis. J Matern Fetal Neonatal Med 2011;24(Suppl 2):59–62. 11. Bassareo PP, Fanos V, Deidda M, Barberini L, Mercuro G. Metabolomic approach to foetal and neonatal heart. J Matern Fetal Neonatal Med 2012;25(Suppl 5): 19–21. 12. Atzori L, Antonucci R, Barberini L, Locci E, Cesare Marincola F, Scano P, et al. 1 H NMR-based metabolic profiling of urine from children with nephrouropathies. Front Biosci (Elite Ed) 2010;2:725–32.
13. Fanos V, Barberini L, Antonucci R, Atzori L. Metabolomics in neonatology and pediatrics. Clin Biochem 2011;44:452–4. 14. Fanos V, Barberini L, Antonucci R, Atzori L. Pharma-metabolomics in neonatology: is it a dream or a fact? Curr Pharm Des 2012;18(21):2996–3006. 15. Atzori L, Mussap M, Noto A, Barberini L, Puddu M, Coni E, et al. Clinical metabolomics and urinary NGAL for the early prediction of chronic kidney disease in healthy adults born ELBW. J Matern Fetal Neonatal Med 2011;24(Suppl 2):40–3. 16. Locci E, Lazzarotto T, Atzori L, Barberini L, Murgia F, Lussu M, et al. Metabolomic analysis of newborn’s urine with cytomegalovirus congenital infection. From the descriptive to the predictive ability: preliminary results. J Pediatr Neonatal Individualized Med 2012;1:147–8 (abstract 50). 17. Fanos V, Fanni C, Ottonello G, Noto A, Dessì A, Mussap M. Metabolomics in adult and pediatric nephrology. Molecules 2013;18(5):4844–57. 18. Fanos V, Antonucci R, Zaffanello M, Mussap M. Neonatal drug induced nephrotoxicity: old and next generation biomarkers for early detection and management of neonatal drug-induced nephrotoxicity, with special emphasis on uNGAL and on metabolomics. Curr Med Chem 2012;19(27):4595–605. 19. Fanos V, Antonucci R, Barberini L, Atzori L. Urinary metabolomics in the newborn and infants. Adv Clin Chem 2012;58:193–223. 20. Fanos V. Pediatric and neonatal individualized medicine. J Pediatr Neonatal Individualized Med 2012;1(1):7–10.