How could metabolomics change pediatric health?

Metabolomics in Children Cow's Milk Protein Allergy: Possible Contribution from a System Biology Approach?

One of the most frequent triggers of food anaphylaxis in pediatric age but also among the most common, early, and complex causes of childhood food allergy is cow's milk protein allergy (CMPA). The diagnostic course and management of this allergy is defined in a complex clinical picture due to several factors. First of all, the epidemiological data are not uniform, mainly as a consequence of the diagnostic methodology used in the various studies and the different age ranges covered. In addition, there is the complexity of terminology, since although CMPA traditionally refers to immune-mediated reactions to cow's milk, it is a term encompassing numerous clinical features with different symptoms and the requirement for specific treatments. Moreover, the differential diagnosis with other very frequent diseases, especially in the first year of life, such as gastro-esophageal reflux disease or colic, is still complex. This can result in misdiagnosis and incorrect treatment, with harmful health consequences and significant economic repercussions. In this context, the combination of several omics sciences together, which have already proved useful in clarifying the allergenicity of cow's milk proteins with greater precision, could improve the diagnostic tests currently in use through the identification of new, more specific, and precise biomarkers that make it possible to improve diagnostic accuracy and predict the patient's response to the various available treatments for the recovery of tolerance.

Metabolomics in NEC: An Updated Review

Necrotizing enterocolitis (NEC) represents the most common and lethal acute gastrointestinal emergency of newborns, mainly affecting those born prematurely. It can lead to severe long-term sequelae and the mortality rate is approximately 25%. Furthermore, the diagnosis is difficult, especially in the early stages, due to multifactorial pathogenesis and complex clinical pictures with mild and non-specific symptoms. In addition, the existing tests have poor diagnostic value. Thus, the scientific community has been focusing its attention on the identification of non-invasive biomarkers capable of prediction, early diagnosis and discriminating NEC from other intestinal diseases in order to intervene early and block the progression of the pathology. In this regard, the use of "omics" technologies, especially metabolomics and microbiomics, could be a fundamental synergistic strategy to study the pathophysiology of NEC. In addition, a deeper knowledge of the microbiota-host cross-talk can clarify the metabolic pathways potentially involved in the pathology, allowing for the identification of specific biomarkers. In this article, the authors analyze the state-of-the-art concerning the application of metabolomics and microbiota analysis to investigate this pathology and discuss the future possibility of the metabolomic fingerprint of patients for diagnostic purposes.

Metabolomics: Perspectives on Clinical Employment in Autism Spectrum Disorder

Precision medicine is imminent, and metabolomics is one of the main actors on stage. We summarize and discuss the current literature on the clinical application of metabolomic techniques as a possible tool to improve early diagnosis of autism spectrum disorder (ASD), to define clinical phenotypes and to identify co-occurring medical conditions. A review of the current literature was carried out after PubMed, Medline and Google Scholar were consulted. A total of 37 articles published in the period 2010-2022 was included. Selected studies involve as a whole 2079 individuals diagnosed with ASD (1625 males, 394 females; mean age of 10, 9 years), 51 with other psychiatric comorbidities (developmental delays), 182 at-risk individuals (siblings, those with genetic conditions) and 1530 healthy controls (TD). Metabolomics, reflecting the interplay between genetics and environment, represents an innovative and promising technique to approach ASD. The metabotype may mirror the clinical heterogeneity of an autistic condition; several metabolites can be expressions of dysregulated metabolic pathways thus liable of leading to clinical profiles. However, the employment of metabolomic analyses in clinical practice is far from being introduced, which means there is a need for further studies for the full transition of metabolomics from clinical research to clinical diagnostic routine.

Design, methodology, and baseline of eastern China student health and wellbeing cohort study

Purpose

To describe the study design, methodology, and cohort profile of the Eastern China Student Health and Wellbeing Cohort Study. The cohort baseline includes (1) targeted disease (myopia, obesity, elevated blood pressure, and mental health) and (2) exposures (individual behaviors, environment, metabolomics, and gene and epigenetics).

Participants

Annual physical examination, questionnaire-based survey, and bio-sampling have been carried out in the study population. In the first stage (2019-2021), a total of 6,506 students in primary schools are enrolled in the cohort study.

Findings to date

Of all the cohort participants, the ratio of male to female is 1.16 among a total of 6,506 student participants, of which 2,728 (41.9%) students are from developed regions and 3,778 (58.1%) students are from developing regions. The initial age of observation is 6-10 years, and they will be observed until they graduate from high school (>18 years of age). (1) Targeted diseases: The growth rates of myopia, obesity, and high blood pressure vary by regions, and for developed regions, the prevalence of myopia, obesity, and elevated blood pressure is 29.2%, 17.4%, and 12.6% in the first year, respectively. For developing regions, the prevalence of myopia, obesity, and elevated blood pressure is 22.3%, 20.7%, and 17.1% in the first year, respectively. The average score of CES-D is 12.9 ± 9.8 in developing regions/11.6 ± 9.0 in developed regions. (2) Exposures: ① The first aspect of individual behaviors: the questionnaire topics include diet, physical exercise, bullying, and family. ② The second aspect of environment and metabolomics: the average desk illumination is 430.78 (355.84-611.56) LX, and the average blackboard illumination is 365.33 (286.83-516.84) LX. Metabolomics like bisphenol A in the urine is 0.734 ng/ml. ③ The third aspect of gene and epigenetics: SNPs (rs524952, rs524952, rs2969180, rs2908972, rs10880855, rs1939008, rs9928731, rs72621438, rs9939609, rs8050136 and so on) are detected.

Future plans

Eastern China Student Health and Wellbeing Cohort Study is aiming to focus on the development of student-targeted diseases. For children with student common diseases, this study will focus on targeted disease-related indicators. For children without targeted disease, this study aims to explore the longitudinal relationship between exposure factors and outcomes, excluding baseline confounding factors. Exposure factors include three aspects: (1) individual behaviors, (2) environment and metabolomics, and (3) gene and epigenetics. The cohort study will continue until 2035.

Utilizing Amniotic Fluid Metabolomics to Monitor Fetal Well-Being: A Narrative Review of the Literature

Fetal and perinatal periods are critical phases for long-term development. Early diagnosis of maternal complications is challenging due to the great complexity of these conditions. In recent years, amniotic fluid has risen in a prominent position in the latest efforts to describe and characterize prenatal development. Amniotic fluid may provide real-time information on fetal development and metabolism throughout pregnancy as substances from the placenta, fetal skin, lungs, gastric fluid, and urine are transferred between the mother and the fetus. Applying metabolomics to monitor fetal well-being, in such a context, could help in the understanding, diagnosis, and treatment of these conditions and is a promising area of research. This review shines a spotlight on recent amniotic fluid metabolomics studies and their methods as an interesting tool for the assessment of many conditions and the identification of biomarkers. Platforms in use, such as proton nuclear magnetic resonance (¹H NMR) and ultra-high-performance liquid chromatography (UHPLC), have different merits, and a combinatorial approach could be valuable. Metabolomics may also be used in the quest for habitual diet-induced metabolic signals in amniotic fluid. Finally, analysis of amniotic fluid can provide information on exposure to exogenous substances by detecting the exact levels of metabolites carried to the fetus and associated metabolic effects.

Metabolomic Studies in Inborn Errors of Metabolism: Last Years and Future Perspectives

The inborn errors of metabolism (IEMs or Inherited Metabolic Disorders) are a heterogeneous group of diseases caused by a deficit of some specific metabolic pathways. IEMs may present with multiple overlapping symptoms, sometimes difficult delayed diagnosis and postponed therapies. Additionally, many IEMs are not covered in newborn screening and the diagnostic profiling in the metabolic laboratory is indispensable to reach a correct diagnosis. In recent years, Metabolomics helped to obtain a better understanding of pathogenesis and pathophysiology of IEMs, by validating diagnostic biomarkers, discovering new specific metabolic patterns and new IEMs itself. The expansion of Metabolomics in clinical biochemistry and laboratory medicine has brought these approaches in clinical practice as part of newborn screenings, as an exam for differential diagnosis between IEMs, and evaluation of metabolites in follow up as markers of severity or therapies efficacy. Lastly, several research groups are trying to profile metabolomics data in platforms to have a holistic vision of the metabolic, proteomic and genomic pathways of every single patient. In 2018 this team has made a review of literature to understand the value of Metabolomics in IEMs. Our review offers an update on use and perspectives of metabolomics in IEMs, with an overview of the studies available from 2018 to 2022.

Childhood Obesity and the Cryptic Language of the Microbiota: Metabolomics’ Upgrading

The growing obesity epidemic in childhood is increasingly concerning for the related physical and psychological consequences, with a significant impact on health care costs in both the short and the long term. Nonetheless, the scientific community has not yet completely clarified the complex metabolic mechanisms underlying body weight alterations. In only a small percentage of cases, obesity is the result of endocrine, monogenic, or syndromic causes, while in much more cases, lifestyle plays a crucial role in obesity development. In this context, the pediatric age appears to be of considerable importance as prevention strategies together with early intervention can represent important therapeutic tools not only to counteract the comorbidities that increasingly affect children but also to hinder the persistence of obesity in adulthood. Although evidence in the literature supporting the alteration of the microbiota as a critical factor in the etiology of obesity is abundant, it is not yet fully defined and understood. However, increasingly clear evidence is emerging regarding the existence of differentiated metabolic profiles in obese children, with characteristic metabolites. The identification of specific pathology-related biomarkers and the elucidation of the altered metabolic pathways would therefore be desirable in order to clarify aspects that are still poorly understood, such as the consequences of the interaction between the host, the diet, and the microbiota. In fact, metabolomics can characterize the biological behavior of a specific individual in response to external stimuli, offering not only an eventual effective screening and prevention strategy but also the possibility of evaluating adherence and response to dietary intervention.

Data harnessing to nurture the human mind for a tailored approach to the child

Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.

Metabolomic profiling of intrauterine growth-restricted preterm infants: a matched case-control study

BACKGROUND

The biochemical variations occurring in intrauterine growth restriction (IUGR), when a fetus is unable to achieve its genetically determined potential, are not fully understood. The aim of this study is to compare the urinary metabolomic profile between IUGR and non-IUGR very preterm infants to investigate the biochemical adaptations of neonates affected by early-onset-restricted intrauterine growth.

METHODS

Neonates born <32 weeks of gestation admitted to neonatal intensive care unit (NICU) were enrolled in this prospective matched case-control study. IUGR was diagnosed by an obstetric ultra-sonographer and all relevant clinical data during NICU stay were captured. For each subject, a urine sample was collected within 48 h of life and underwent untargeted metabolomic analysis using mass spectrometry ultra-performance liquid chromatography. Data were analyzed using multivariate and univariate statistical analyses.

RESULTS

Among 83 enrolled infants, 15 IUGR neonates were matched with 19 non-IUGR controls. Untargeted metabolomic revealed evident clustering of IUGR neonates versus controls showing derangements of pathways related to tryptophan and histidine metabolism and aminoacyl-tRNA and steroid hormones biosynthesis.

CONCLUSIONS

Neonates with IUGR showed a distinctive urinary metabolic profile at birth. Although results are preliminary, metabolomics is proving to be a promising tool to explore biochemical pathways involved in this disease.

IMPACT

Very preterm infants with intrauterine growth restriction (IUGR) have a distinctive urinary metabolic profile at birth. Metabolism of glucocorticoids, sexual hormones biosynthesis, tryptophan-kynurenine, and methionine-cysteine pathways seem to operate differently in this sub-group of neonates. This is the first metabolomic study investigating adaptations exclusively in extremely and very preterm infants affected by early-onset IUGR. New knowledge on metabolic derangements in IUGR may pave the ways to further, more tailored research from a perspective of personalized medicine.

Ancestral SARS-CoV-2, but not Omicron, replicates less efficiently in primary pediatric nasal epithelial cells

Children typically experience more mild symptoms of Coronavirus Disease 2019 (COVID-19) when compared to adults. There is a strong body of evidence that children are also less susceptible to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with the ancestral viral isolate. However, the emergence of SARS-CoV-2 variants of concern (VOCs) has been associated with an increased number of pediatric infections. Whether this is the result of widespread adult vaccination or fundamental changes in the biology of SARS-CoV-2 remain to be determined. Here, we use primary nasal epithelial cells (NECs) from children and adults, differentiated at an air–liquid interface to show that the ancestral SARS-CoV-2 replicates to significantly lower titers in the NECs of children compared to those of adults. This was associated with a heightened antiviral response to SARS-CoV-2 in the NECs of children. Importantly, the Delta variant also replicated to significantly lower titers in the NECs of children. This trend was markedly less pronounced in the case of Omicron. It is also striking to note that, at least in terms of viral RNA, Omicron replicated better in pediatric NECs compared to both Delta and the ancestral virus. Taken together, these data show that the nasal epithelium of children supports lower infection and replication of ancestral SARS-CoV-2, although this may be changing as the virus evolves.

Children typically experience more mild symptoms of COVID-19 when compared to adults; why is this? This study uses nasal epithelial cells from children and adults to show that the ancestral SARS-CoV-2 and Delta, but not the Omicron variant, replicate less efficiently in pediatric nasal epithelial cells.

Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology

During the last decade, genetic testing has emerged as an important etiological diagnostic tool for Mendelian diseases, including pediatric neurological conditions. A genetic diagnosis has a considerable impact on disease management and treatment; however, many cases remain undiagnosed after applying standard diagnostic sequencing techniques. This review discusses various methods to improve the molecular diagnostic rates in these genomic cold cases. We discuss extended analysis methods to consider, non-Mendelian inheritance models, mosaicism, dual/multiple diagnoses, periodic re-analysis, artificial intelligence tools, and deep phenotyping, in addition to integrating various omics methods to improve variant prioritization. Last, novel genomic technologies, including long-read sequencing, artificial long-read sequencing, and optical genome mapping are discussed. In conclusion, a more comprehensive molecular analysis and a timely re-analysis of unsolved cases are imperative to improve diagnostic rates. In addition, our current understanding of the human genome is still limited due to restrictions in technologies. Novel technologies are now available that improve upon some of these limitations and can capture all human genomic variation more accurately. Last, we recommend a more routine implementation of high molecular weight DNA extraction methods that is coherent with the ability to use and/or optimally benefit from these novel genomic methods.

Metabolomic profiles and microbiota of GDM offspring: The key for future perspective?

Gestational diabetes mellitus (GDM), or any degree of glucose intolerance recognized for the first time during pregnancy, is one of the diseases that most frequently aggravates the course of gestation. Missed or late diagnosis and inadequate treatment are associated with high maternal and fetal morbidity, with possible short- and long-term repercussions. Estimates on the prevalence of GDM are alarming and increasing by about 30% in the last 10-20 years. In addition, there is the negative influence of the SARS-CoV-2 emergency on the glycemic control of pregnant women, making the matter increasingly topical. To date, knowledge on the metabolic maturation of newborns is still incomplete. However, in light of the considerable progress of the theory of "developmental origins of health and disease," the relevant role of the intrauterine environment cannot be overlooked. In fact, due to the high plasticity of the early stages of development, some detrimental metabolic alterations during fetal growth, including maternal hyperglycemia, are associated with a higher incidence of chronic diseases in adult life. In this context, metabolomic analysis which allows to obtain a detailed phenotypic portrait through the dynamic detection of all metabolites in cells, tissues and different biological fluids could be very useful for the early diagnosis and prevention of complications. Indeed, if the diagnostic timing is optimized through the identification of specific metabolites, the detailed understanding of the altered metabolic pathway could also allow better management and more careful monitoring, also from a nutritional profile, of the more fragile children. In this context, a further contribution derives from the analysis of the intestinal microbiota, the main responsible for the fecal metabolome, given its alteration in pregnancies complicated by GDM and the possibility of transmission to offspring. The purpose of this review is to analyze the available data regarding the alterations in the metabolomic profile and microbiota of the offspring of mothers with GDM in order to highlight future prospects for reducing GDM-related complications in children of mothers affected by this disorder.

Metabolomics, Microbiomics, Machine learning during the COVID-19 pandemic

COVID-19 pandemic has a significant impact worldwide, from the point of view of public health, social, and economic aspects. The correct strategies of diagnosis and global management are still under debate. In the next future, we firmly believe that combining the so-called 3 M's (metabolomics, microbiomics, and machine learning [artificial intelligence]) will be the optimal, accurate tool for the early diagnosis of COVID-19 subjects, risk assessment and stratification, patient management, and decision-making. If the currently available preliminary data obtain further confirms, through future studies on larger samples, simple biomarkers will provide predictive models for data analysis and interpretation, allowing a step toward personalized holistic medicine.

Developments in pediatrics in 2020: choices in allergy, autoinflammatory disorders, critical care, endocrinology, genetics, infectious diseases, microbiota, neonatology, neurology, nutrition, ortopedics, respiratory tract illnesses and rheumatology

In this article, we describe the advances in the field of pediatrics that have been published in the Italian Journal of Pediatrics in 2020. We report progresses in understanding allergy, autoinflammatory disorders, critical care, endocrinology, genetics, infectious diseases, microbiota, neonatology, neurology, nutrition, orthopedics, respiratory tract illnesses, rheumatology in childhood.

Epigenetics and Modulations of Early Flavor Experiences: Can Metabolomics Contribute to Prevention during Weaning?

The significant increase in chronic non-communicable diseases has changed the global epidemiological landscape. Among these, obesity is the most relevant in the pediatric field. This has pushed the world of research towards a new paradigm: preventive and predictive medicine. Therefore, the window of extreme plasticity that characterizes the first stage of development cannot be underestimated. In this context, nutrition certainly plays a primary role, being one of the most important epigenetic modulators known to date. Weaning, therefore, has a crucial role that must be analyzed far beyond the simple achievement of nutritional needs. Furthermore, the taste experience and the family context are fundamental for future food choices and can no longer be underestimated. The use of metabolomics allows, through the recognition of early disease markers and food-specific metabolites, the planning of an individualized and precise diet. In addition, the possibility of identifying particular groups of subjects at risk and the careful monitoring of adherence to dietary therapy may represent the basis for this change.

Novel molecular biomarkers and diagnosis of acute appendicitis in children

Reliable and efficient diagnosis of pediatric appendicitis is essential for the establishment of a clinical management plan and improvement of patient outcomes. Current strategies used to diagnose a child presenting with a suspected appendicitis include laboratory studies, clinical scores and diagnostic imaging. Although these modalities work in conjunction with each other, one optimal diagnostic strategy has yet to be agreed upon. The recent introduction of precision medicine techniques such as genomics, transcriptomics, proteomics and metabolomics has increased both the diagnostic sensitivity and specificity of appendicitis. Using these novel strategies, the integration of precision medicine into clinical practice via point-of-care technologies is a plausible future. These technologies would assist in the screening, diagnosis and prognosis of pediatric appendicitis.

Influence of innate immune activation on endocrine and metabolic pathways in infancy

Prematurity is the leading cause of neonatal morbidity and mortality worldwide. Premature infants often require extended hospital stays, with increased risk of developing infection compared with term infants. A picture is emerging of wide-ranging deleterious consequences resulting from innate immune system activation in the newborn infant. Those who survive infection have been exposed to a stimulus that can impose long-lasting alterations into later life. In this review, we discuss sepsis-driven alterations in integrated neuroendocrine and metabolic pathways and highlight current knowledge gaps in respect of neonatal sepsis. We review established biomarkers for sepsis and extend the discussion to examine emerging findings from human and animal models of neonatal sepsis that propose novel biomarkers for early identification of sepsis. Future research in this area is required to establish a greater understanding of the distinct neonatal signature of early and late-stage infection, to improve diagnosis, curtail inappropriate antibiotic use, and promote precision medicine through a biomarker-guided empirical and adjunctive treatment approach for neonatal sepsis. There is an unmet clinical need to decrease sepsis-induced morbidity in neonates, to limit and prevent adverse consequences in later life and decrease mortality.

Breast Milk and COVID-19: From Conventional Data to "Omics" Technologies to Investigate Changes Occurring in SARS-CoV-2 Positive Mothers

In this context of COVID-19 pandemic, great interest has been aroused by the potential maternal transmission of SARS-CoV-2 by transplacental route, during delivery, and, subsequently, through breastfeeding. Some open questions still remain, especially regarding the possibility of finding viable SARS-CoV-2 in breast milk (BM), although this is not considered a worrying route of transmission. However, in BM, it was pointed out the presence of antibodies against SARS-CoV-2 and other bioactive components that could protect the infant from infection. The aim of our narrative review is to report and discuss the available literature on the detection of anti-SARS-CoV-2 antibodies in BM of COVID-19 positive mothers, and we discussed the unique existing study investigating BM of SARS-CoV-2 positive mothers through metabolomics, and the evidence regarding microbiomics BM variation in COVID-19. Moreover, we tried to correlate metabolomics and microbiomics findings in BM of positive mothers with potential effects on breastfed infants metabolism and health. To our knowledge, this is the first review summarizing the current knowledge on SARS-CoV-2 effects on BM, resuming both "conventional data" (antibodies) and "omics technologies" (metabolomics and microbiomics).

Use of omic technologies in early life gastrointestinal health and disease: from bench to bedside

Introduction: At birth, the gastrointestinal (GI) tract is colonized by a complex community of microorganisms, forming the basis of the gut microbiome. The gut microbiome plays a fundamental role in host health, disorders of which can lead to an array of GI diseases, both short and long term. Pediatric GI diseases are responsible for significant morbidity and mortality, but many remain poorly understood. Recent advancements in high-throughput technologies have enabled deeper profiling of GI morbidities. Technologies, such as metagenomics, transcriptomics, proteomics and metabolomics, have already been used to identify associations with specific pathologies, and highlight an exciting area of research. However, since these diseases are often complex and multifactorial by nature, reliance on a single experimental approach may not capture the true biological complexity. Therefore, multi-omics aims to integrate singular omic data to further enhance our understanding of disease.Areas covered: This review will discuss and provide an overview of the main omic technologies that are used to study complex GI pathologies in early life.Expert opinion: Multi-omic technologies can help to unravel the complexities of several diseases during early life, aiding in biomarker discovery and enabling the development of novel therapeutics and augment predictive models.

A Metabolomic Approach in Search of Neurobiomarkers of Perinatal Asphyxia: A Review of the Current Literature

Perinatal asphyxia and the possible sequelae of hypoxic-ischemic encephalopathy (HIE), are associated with high morbidity and mortality rates. The use of therapeutic hypothermia (TH) commencing within the first 6 h of life-currently the only treatment validated for the management of HIE-has been proven to reduce the mortality rate and disability seen at follow up at 18 months. Although there have been attempts to identify neurobiomarkers assessing the severity levels in HIE; none have been validated in clinical use to date, and the lack thereof limits the optimal treatment for these vulnerable infants. Metabolomics is a promising field of the "omics technologies" that may: identify neurobiomarkers, help improve diagnosis, identify patients prone to developing HIE, and potentially improve targeted neuroprotection interventions. This review focuses on the current evidence of metabolomics, a novel tool which may prove to be a useful in the diagnosis, management and treatment options for this multifactorial complex disease. Some of the most promising metabolites analyzed are the group of acylcarnitines: Hydroxybutyrylcarnitine (Malonylcarnitine) [C3-DC (C4-OH)], Tetradecanoylcarnitine [C14], L-Palmitoylcarnitine [C16], Hexadecenoylcarnitine [C16:1], Stearoylcarnitine [C18], and Oleoylcarnitine [C18:1]. A metabolomic "fingerprint" or "index," made up of 4 metabolites (succinate × glycerol/(β-hydroxybutyrate × O-phosphocholine)), seems promising in identifying neonates at risk of developing severe HIE.

Stratified Management for Bacterial Infections in Late Preterm and Term Neonates: Current Strategies and Future Opportunities Toward Precision Medicine

Bacterial infections remain a major cause of morbidity and mortality in the neonatal period. Therefore, many neonates, including late preterm and term neonates, are exposed to antibiotics in the first weeks of life. Data on the importance of inter-individual differences and disease signatures are accumulating. Differences that may potentially influence treatment requirement and success rate. However, currently, many neonates are treated following a "one size fits all" approach, based on general protocols and standard antibiotic treatment regimens. Precision medicine has emerged in the last years and is perceived as a new, holistic, way of stratifying patients based on large-scale data including patient characteristics and disease specific features. Specific to sepsis, differences in disease susceptibility, disease severity, immune response and pharmacokinetics and -dynamics can be used for the development of treatment algorithms helping clinicians decide when and how to treat a specific patient or a specific subpopulation. In this review, we highlight the current and future developments that could allow transition to a more precise manner of antibiotic treatment in late preterm and term neonates, and propose a research agenda toward precision medicine for neonatal bacterial infections.

Breathomics in Asthmatic Children Treated with Inhaled Corticosteroids

BACKGROUND

"breathomics" enables indirect analysis of metabolic patterns underlying a respiratory disease. In this study, we analyze exhaled breath condensate (EBC) in asthmatic children before (T0) and after (T1) a three-week course of inhaled beclomethasone dipropionate (BDP).

METHODS

we recruited steroid-naive asthmatic children for whom inhaled steroids were indicated and healthy children, evaluating asthma control, spirometry and EBC (in asthmatics at T0 and T1). A liquid-chromatography-mass-spectrometry untargeted analysis was applied to EBC and a mass spectrometry-based target analysis to urine samples.

RESULTS

metabolomic analysis discriminated asthmatic (n = 26) from healthy children (n = 16) at T0 and T1, discovering 108 and 65 features relevant for the discrimination, respectively. Searching metabolomics databases, seven putative biomarkers with a plausible role in asthma biochemical-metabolic processes were found. After BDP treatment, asthmatic children, in the face of an improved asthma control (p < 0.001) and lung function (p = 0.01), showed neither changes in EBC metabolomic profile nor in urinary endogenous steroid profile.

CONCLUSIONS

"breathomics" can discriminate asthmatic from healthy children, with prostaglandin, fatty acid and glycerophospholipid as putative markers. The three-week course of BDP-in spite of a significant clinical improvement-was not associated with changes in EBC metabolic arrangement and urinary steroid profile.

Do Neonatal Infections Require a Positive Blood Culture?

Neonatal sepsis is a major cause of worldwide morbidity and mortality. Blood cultures are considered the gold standard for diagnosis, but results are often delayed for 24 to 48 hours, and sensitivity, although improved by modern techniques, such as automated blood cultures, is variable and affected by the bacterial load. For these reasons, empiric antibiotics are frequently administered to avoid potential devastating consequences of untreated sepsis. Unnecessary antibiotic treatment has been associated with increased mortality and other adverse outcomes; therefore, antibiotics should be discontinued as soon as sepsis has been ruled out. Negative cultures pose a challenge to clinicians, who must distinguish between real sepsis and sepsis-like conditions (noninfectious or viral) which do not require antibiotics. Focal infections with negative blood cultures do require antibiotic treatment. Ultra-low bacteremia, primary or secondary to recent antibiotic exposure, is often associated with negative cultures, and some consider a short course of empiric antibiotics sufficient for clearing of bacteremia. Biomarkers and molecular methods based on polymerase chain reaction are important add-ons to clinical signs or symptoms for establishing the diagnosis of sepsis. Other promising future potential adjuvants are metabolomics. Antibiotic stewardship should be implemented to avoid or discontinue unnecessary treatment. Prevention of infection still remains the most important step for dealing with neonatal sepsis. KEY POINTS: · Blood cultures are the gold standard diagnosis of neonatal sepsis but sometimes may be negative.. · Other bacterial, viral, and noninfectious conditions may mimic sepsis, prompting initiation of empiric antibiotic treatments.. · Since a definition of neonatal sepsis is lacking, recognizing real septic episodes may be challenging..

Plasma metabolites in treatment-requiring retinopathy of prematurity: Potential biomarkers identified by metabolomics

Retinopathy of prematurity (ROP) is a potentially blinding condition caused by disruption of retinal vascularization and metabolism. This study aims to identify altered metabolites from plasma in patients with treatment-requiring ROP (TR-ROP) compared with controls. An untargeted metabolomics analysis was performed to reveal the metabolomic profiles of the plasma between TR-ROP patients (n = 38) and age-matched infants (n = 23). The Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the potential signaling pathways of the changed metabolites. Under positive ion mode, a total of 29 metabolites were significantly altered in plasma between TR-ROP patients and controls, and 23 altered metabolites were identified under negative ion mode. KEGG analyses indicated that "protein digestion and absorption" and "aminoacyl-tRNA biosynthesis" were the most enriched pathways of the altered metabolites. These results demonstrated that metabolomic profiles changed in plasma of TR-ROP, and the altered metabolites could be served as potential biomarkers for the diagnosis and prognosis of TR-ROP patients. Besides, the metabolomic profiles might provide clues to discover novel therapeutic strategies in ROP treatment.

Early Urinary Metabolomics in Patent Ductus Arteriosus Anticipates the Fate: Preliminary Data

Introduction: In premature neonates, the persistence of hemodynamically significant ductus arteriosus (hsPDA) can be associated with short- and long-term consequences, impairing their outcome. The correct strategy of management for such condition is under debate, especially regarding contraindications and/or side effects. In recent years, metabolomics was applied to several perinatal, pediatric, and adult conditions to investigate potential biomarkers of disease, which have become useful for early diagnosis and/or therapeutic management. Aim of the Study: The main purpose of our exploratory study was to asses, through ¹H-NMR metabolomics analysis of urinary samples at birth, possible metabolic pathways differentiating, with a significant predictive power, those preterm neonates who will subsequently develop hsPDA and neonates of comparable gestational age (GA) who will undergo spontaneous ductal closure or the persistence of an irrelevant PDA (no-hsPDA). Moreover, we investigated potential prenatal or perinatal clinical factors potentially influencing the development of hsPDA. Materials and Methods: We enrolled n = 35 preterm neonates with GA between 24 and 32 weeks; urinary samples were collected within the first 12 h of life. Patients were closely monitored regarding intensive care, respiratory support, fluid balance and administered drugs; an echocardiogram was performed at 48-72 h. Results: Our results reported a significant correlation between lower GA at birth and the development of hsPDA. Moreover, neonates with GA ≤ 30w developing hsPDA were characterized by lower Apgar scores at 1' and 5', higher rates of perinatal asphyxia, higher need of delivery room resuscitation and subsequent surfactant administration. Interestingly, metabolomics analysis at birth detected a clear separation between the ¹H-NMR urinary spectra of subjects GA ≤ 30w not developing hsPDA (n = 19) and those of subjects born at GA ≤ 30w in which hsPDA was confirmed at 48-72 h of life (n = 5). Conclusions: This is the first study applying metabolomics to investigate the PDA condition. Although preliminary and conducted on a limited sample, our results reveal that metabolomics could be a promising tool in the early identification of hsPDA, potentially superior to the clinical or laboratory predictive tools explored to date and even to the clinical observations and correlations in our sample, through the detection of specific urinary metabolites.