Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder.

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review.

INTRODUCTION: Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC. OBJECTIVES: To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine. METHODS: A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine). RESULTS: Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented. CONCLUSION: The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior.

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Metabolomics can provide new insights into perinatal nutrition.

Perinatal nutrition is a key factor related to the Developmental Origin of Health and Disease hypothesis, which states that each and every event that happens during the periconceptional period and pregnancy can affect the health status of an individual. Metabolomics can be a very useful tool for gathering information about the effect of perinatal nutrition on both mothers and newborn infants. This non-systematic review focuses on the main metabolites detected by this technique, with regard to gestational diabetes, intrauterine growth restriction and breast milk. Conclusion. Nutrition, metabolome and microbiome interactions are gaining interest in the scientific community.

Sardinian Infants of Diabetic Mothers: A Metabolomics Observational Study.

Gestational diabetes mellitus (GDM) is a condition characterized by glucose intolerance, with hyperglycemia of varying severity with onset during pregnancy. An uncontrolled GDM can lead to an increased risk of morbidity in the fetus and newborn, and an increased risk of obesity or developing type 2 diabetes, hypertension or neurocognitive developmental impairment in adulthood. In this study, we used nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry (GS-MS) to analyze the urinary metabolomic profile of newborns of diabetic mothers (NDMs) with the aim of identifying biomarkers useful for the monitoring of NDMs and for early diagnosis of predisposition to develop related chronic diseases. A total of 26 newborns were recruited: 21 children of diabetic mothers, comprising 13 in diet therapy (NDM-diet) and 8 in insulin therapy (NDM-insulin), and 5 control children of non-diabetic mothers (CTR). Urine samples were collected at five time points: at birth (T1), on the third day of life (T2), one week (T3), one month (T4) and six months postpartum (T5). At T1, variations were observed in the levels of seven potential biomarkers (acetate, lactate, glycylproline/proline, isocitrate, N,N-dimethylglycine, N-acetylglucosamine and N-carbamoyl-aspartate) in NMD-insulin infants compared to NDM-diet and CTR infants. In particular, the altered metabolites were found to be involved in several metabolic pathways such as citrate metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, amino sugar and nucleotide sugar metabolism, and pyruvate metabolism. In contrast, these changes were not visible at subsequent sampling times. The impact of early nutrition (maternal and formula milk) on the metabolomic profile was considered as a potential contributing factor to this finding.

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.

Differential diagnosis between syndrome of inappropriate antidiuretic hormone secretion and cerebral/renal salt wasting syndrome in children over 1 year: proposal for a simple algorithm.

Hyponatremia, especially if acute and severe, can be a life-threatening condition. Several conditions can trigger hyponatremia. In this review, we will discuss two conditions that can determine euvolemic hyponatremia: the cerebral/renal salt wasting (CRSW) syndrome and the syndrome of inappropriate secretion of antidiuretic hormone (SIADH), including the two subtypes: reset osmostat (RO) and nephrogenic syndrome of inappropriate antidiuresis (NSIAD) and their differential diagnoses. Despite the passage of over 70 years since its first description, to date, the true etiopathogenesis of CRSW syndrome, a rare cause of hypovolemic/euvolemic hyponatremia, is almost unknown. SIADH, including RO and NSIAD, is sometimes difficult to differentiate from CRSW syndrome; in its differential diagnosis, the clinical approach based on the evaluation of the extracellular volume (ECV) was proven insufficient. We therefore suggest a simple diagnostic algorithm based on the assessment of the degree of hyponatremia, urinary osmolality, and the assessment of the fraction of urate excretion (FEUa) in conditions of hyponatremia and after serum sodium correction, to be applied in children over 1 year of life.

Twelve Months with COVID-19: What Gastroenterologists Need to Know.

Corona virus disease-19 (COVID-19) is the latest global pandemic. COVID-19 is mainly transmitted through respiratory droplets and, apart from respiratory symptoms, patients often present with gastrointestinal symptoms and liver involvement. Given the high percentage of COVID-19 patients that present with gastrointestinal symptoms (GIS), in this review, we report a practical up-to-date reference for the physician in their clinical practice with patients affected by chronic gastrointestinal (GI) diseases (inflammatory bowel disease, coeliac disease, chronic liver disease) at the time of COVID-19. First, we summarised data on the origin and pathogenetic mechanism of SARS-CoV-2. Then, we performed a literature search up to December 2020 examining clinical manifestations of GI involvement. Next, we illustrated and summarised the most recent guidelines on how to adhere to GI procedures (endoscopy, liver biopsy, faecal transplantation), maintaining social distance and how to deal with immunosuppressive treatment. Finally, we focussed on some special conditions such as faecal-oral transmission and gut microbiota. The rapid accumulation of information relating to this condition makes it particularly essential to revise the literature to take account of the most recent publications for medical consultation and patient care.

Psychosocial Impact of 8 Weeks COVID-19 Quarantine on Italian Parents and their Children.

OBJECTIVES: Italy was affected greatly by Coronavirus disease 2019 (COVID-19), emerging mainly in the Italian province of Lombardy. This outbreak led to profound governmental interventions along with a strict quarantine. This quarantine may have psychosocial impact on children and parents in particular. The aim of this study was to evaluate the impact of 8 weeks COVID-19 quarantine on psychosocial functioning of Italian parents and their children. METHODS: In this cross-sectional survey, we included parents and children resided in Italy during the 8 weeks COVID-19 quarantine. We evaluated social and emotional functioning, clinical symptoms possibly related to emotional distress, and change in perspectives using a questionnaire. RESULTS: The majority of 2315 parents (98% mothers) frequently experienced fear of getting ill (92%) and fluctuating moods (84%), the latter showing correlation to experiencing stress due to being in continuous close vicinity to their children (77%, r = 0.33). Parents reported a positive effect on the relationship with their partner (79%) and their children (89%). Irritability in children was frequent (74%) and correlated to parental fluctuating moods (r = 0.40). The vast majority of the participants (91%) reported that their perspectives for the future had changed. CONCLUSIONS FOR PRACTICE: Our findings suggest a profound impact of the COVID-19 quarantine on emotional functioning of parents and their children in Italy. Despite the protective measure of quarantine against national viral spread and subsequent infection, health care professionals should be aware of this emotional impact, in order to develop protective or therapeutic interventions.

Overview of CAPICE-Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe-an EU Marie Sklodowska-Curie International Training Network.

The Roadmap for Mental Health and Wellbeing Research in Europe (ROAMER) identified child and adolescent mental illness as a priority area for research. CAPICE (Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe) is a European Union (EU) funded training network aimed at investigating the causes of individual differences in common childhood and adolescent psychopathology, especially depression, anxiety, and attention deficit hyperactivity disorder. CAPICE brings together eight birth and childhood cohorts as well as other cohorts from the EArly Genetics and Life course Epidemiology (EAGLE) consortium, including twin cohorts, with unique longitudinal data on environmental exposures and mental health problems, and genetic data on participants. Here we describe the objectives, summarize the methodological approaches and initial results, and present the dissemination strategy of the CAPICE network. Besides identifying genetic and epigenetic variants associated with these phenotypes, analyses have been performed to shed light on the role of genetic factors and the interplay with the environment in influencing the persistence of symptoms across the lifespan. Data harmonization and building an advanced data catalogue are also part of the work plan. Findings will be disseminated to non-academic parties, in close collaboration with the Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN-Europe).

The clinical impact of maternal weight on offspring health: lights and shadows in breast milk metabolome.

INTRODUCTION: Pre-pregnancy overweight and obesity, depending on maternal nutrition and metabolic state, can influence fetal, neonatal, and long-term offspring health, regarding cardio-metabolic, respiratory, immunological, and cognitive outcomes. Thus, maternal weight can act, through mechanisms not fully understood, on the physiology and metabolism of some fetal organs and tissues, to adapt themselves to the intrauterine environment and nutritional reserves. These effects can occur by modulating gene expression, neonatal microbiome, and through breastfeeding. AREAS COVERED: In this paper, we investigated the potential effects of metabolites found altered in breast milk (BM) of overweight/obese mothers, through an extensive review of metabolomics studies, and the potential short and long-term clinical effects in the offspring, especially overweight, glucose homeostasis, insulin resistance, oxidative stress, infections, immune processes, neurodevelopment. EXPERT OPINION: Metabolomics seems the ideal tool to investigate BM variation depending on maternal or fetal/neonatal factors. In particular, BM metabolome alterations according to maternal conditions were recently pointed out in cases of gestational diabetes, preeclampsia, intrauterine growth restriction and maternal overweight/obesity. In our opinion, even if BM is the food of choice in neonatal nutrition, the deepest comprehension of its composition in overweight/obese mothers could allow targeted supplementation, to improve offspring health and metabolic homeostasis.

Could metabolomics drive the fate of COVID-19 pandemic? A narrative review on lights and shadows.

Human Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection activates a complex interaction host/virus, leading to the reprogramming of the host metabolism aimed at the energy supply for viral replication. Alterations of the host metabolic homeostasis strongly influence the immune response to SARS-CoV-2, forming the basis of a wide range of outcomes, from the asymptomatic infection to the onset of COVID-19 and up to life-threatening acute respiratory distress syndrome, vascular dysfunction, multiple organ failure, and death. Deciphering the molecular mechanisms associated with the individual susceptibility to SARS-CoV-2 infection calls for a system biology approach; this strategy can address multiple goals, including which patients will respond effectively to the therapeutic treatment. The power of metabolomics lies in the ability to recognize endogenous and exogenous metabolites within a biological sample, measuring their concentration, and identifying perturbations of biochemical pathways associated with qualitative and quantitative metabolic changes. Over the last year, a limited number of metabolomics- and lipidomics-based clinical studies in COVID-19 patients have been published and are discussed in this review. Remarkable alterations in the lipid and amino acid metabolism depict the molecular phenotype of subjects infected by SARS-CoV-2; notably, structural and functional data on the lipids-virus interaction may open new perspectives on targeted therapeutic interventions. Several limitations affect most metabolomics-based studies, slowing the routine application of metabolomics. However, moving metabolomics from bench to bedside cannot imply the mere determination of a given metabolite panel; rather, slotting metabolomics into clinical practice requires the conversion of metabolic patient-specific data into actionable clinical applications.

Urinary Metabolomics Study of Patients with Bicuspid Aortic Valve Disease.

Bicuspid aortic valve (BAV) is the most common congenital heart defect responsible for valvular and aortic complications in affected patients. Causes and mechanisms of this pathology are still elusive and thus the lack of early detection biomarkers leads to challenges in its diagnosis and prevention of associated cardiovascular anomalies. The aim of this study was to explore the potential use of urine Nuclear Magnetic Resonance (NMR) metabolomics to evaluate a molecular fingerprint of BAV. Both multivariate and univariate statistical analyses were performed to compare the urinary metabolome of 20 patients with BAV with that of 24 matched controls. Orthogonal partial least squared discriminant analysis (OPLS-DA) showed statistically significant discrimination between cases and controls, suggesting seven metabolites (3-hydroxybutyrate, alanine, betaine, creatine, glycine, hippurate, and taurine) as potential biomarkers. Among these, glycine, hippurate and taurine individually displayed medium sensitivity and specificity by receiver operating characteristic (ROC) analysis. Pathway analysis indicated two metabolic pathways likely perturbed in BAV subjects. Possible contributions of gut microbiota activity and energy imbalance are also discussed. These results constitute encouraging preliminary findings in favor of the use of urine-based metabolomics for early diagnosis of BAV.

Urinary metabolome of infants with colic treated with Lactobacillus reuteri DSM 17938: a pilot randomized trial.

BACKGROUND: Lactobacillus reuteri DSM 17938 is the only probiotic recommended for treatment of colicky infants, but its mechanism of action is not clear. The study aim was to examine urinary metabolomic fingerprint of colicky breastfed infants before and after 1 month of orally administered Lactobacillus reuteri DSM 17938 or placebo. METHODS: This randomized, blinded, placebo-controlled clinical trial was carried out with a well-documented probiotic. Thirty-two infants were enrolled, 16 in the probiotic group and 16 in the placebo group. Urine samples were collected from each subject before starting supplementation and at the end of the study period. Metabolomic profiles were obtained using a gas chromatography/mass spectrometry instrument. Subsequently, to compare groups before and after probiotic supplementation, univariate and multivariate statistical analysis were performed. RESULTS: In the L. reuteri treated group all metabolites for all class of nutrients (sugars, amino acids, carboxylic acids) resulted more abundant after the study period. The comparison with a control group (placebo treated), confirmed this effect on urines. CONCLUSIONS: The metabolomic analysis of urine samples from infants treated with L. reuteri DSM 17938 allowed to detect some interesting features related to the effect of this treatment on urinary metabolome. To validate the results, a test on a larger cohort is required.

Human microbiome and allergy.

Human microbiome contributes to critical functions that impact health and disease. It influences the development of the immune system, and the pathogenesis of immunological disorders included allergy. While it is easy to understand how airway microbiome, influencing local inflammation and immune activity, could contribute to shaping asthma phenotype, it is not so obvious to understand the influence by the gut microbiome, but there is growing evidence about it. The increase of allergic disorders in western countries led to investigate the role environment is playing and how it may change our microbiome and immune system, with the hope of finding new preventive approaches for allergy.

Children's heart and COVID-19: Up-to-date evidence in the form of a systematic review.

The new coronavirus disease outbreak in 2019 (COVID-19) represents a dramatic challenge for healthcare systems worldwide. As to viral tropism, lungs are not the only COVID-19 target but also the heart may be involved in a not negligible percentage of the infected patients. Myocarditis-related cardiac dysfunction and potentially life-threatening arrhythmias are the main aftermaths. A few studies showed that myocardial injury in adult patients is often linked with a fatal outcome. Conversely, scientific evidence in children is sparse, although several reports were published with the description of a cardiac involvement in COVID-19 paediatric patients. In these young subjects, a background of surgically treated congenital heart disease seems to be a predisposing factor.Conclusion: This systematic review is aimed at summarizing all COVID-19 cases with a cardiac involvement published in paediatric age and trying to explain the underlying mechanisms responsible for COVID-19-related myocardial damage.What is Known:• Coronaviruses proved to be able to jump from animals to humans.• The outbreak of COVID-19 started from China (Dec 2019) and became pandemic.What is New:• Even in childhood, COVID-19 is not without the risk of cardiac involvement.• Myocarditis, heart failure, and arrhythmias are among the possible manifestations.

Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment.

Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach.

The choice of amniotic fluid in metabolomics for the monitoring of fetus health - update.

Introduction: In recent years, several studies have highlighted the promising role of metabolomics in the analysis of amniotic fluid (AF), to describe and characterize the interactions occurring between the mother and the fetus during prenatal development. Among the available biological fluids, AF represents an ideal substrate to provide dynamic information regarding fetal organogenesis and metabolism through pregnancy, since it originates from both maternal and fetal tissues and contains substances derived from placenta, fetal skin, lungs, gastric fluid, and fetal urine. Areas covered: In this paper, we provide an update reporting the most recent results on AF metabolomics in the assessment of feto-maternal health, regarding physiological pregnancies but even fields such as prematurity, bronchopulmonary dysplasia, fetal malformations, chromosomopathies, maternal diseases, placental inflammation or infections, maternal diet or exposure to exogenous substances, according to the literature found on MEDLINE since 2015. Expert opinion: Metabolomics shows a promising role in describing both physiology and disease; the goal would be the identification of biomarkers able to precociously and efficaciously detect pathological conditions, allowing the identification of complicated pregnancy and improving their management. However, this field is under development and its reliability still needs to be clarified, especially through more numerous and accurate studies.

Clinical insights gained through metabolomic analysis of human breast milk.

Introduction: Among the OMICS technologies, that have emerged in recent years, metabolomics has allowed relevant step forwards in clinical research. Several improvements in disease diagnosis and clinical management have been permitted, even in neonatology. Among potentially evaluable biofluids, breast milk (BM) results are highly interesting, representing a fluid of conjunction between mothers newborns, describing their interaction.Areas covered: in this review, updating a previous review article, we discuss research articles and reviews on BM metabolomics and found in MEDLINE using metabolomics, breast milk, neonatal nutrition, breastfeeding, human milk composition, and preterm neonates as keywords.Expert opinion: Our research group has a profound interest in metabolomics research. In 2012, we published the first metabolomic analysis on BM samples, reporting interesting data on its composition and relevant differences with formula milk (FM), useful to improve FM composition. As confirmed by successive studies, such technology can detect the specific BM composition and its dependence on several variables, including lactation stage, gestational age, maternal or environmental conditions. Moreover, since BM contaminants or drug levels can be detected, metabolomics also results useful to determine BM safety. These are only a few practical applications of BM analysis, which will be reviewed in this paper.

Focus on neonatal and infantile onset of nephrogenic syndrome of inappropriate antidiuresis: 12 years later.

Nephrogenic syndrome of inappropriate antidiuresis (NSIAD), first described in 2005, is a rare genetic X-linked disease, presenting with hyponatremia, hyposmolarity, euvolemia, inappropriately concentrated urine, increased natriuresis, and undetectable or very low arginine-vasopressine (AVP) circulating levels. It can occur in neonates, infants, or later in life. NSIAD must be early recognized and treated to prevent severe hyponatremia, which can show a dangerous impact on neonatal outcome. In fact, it potentially leads to death or, in case of survival, neurologic sequelae. This review is an update of NSIAD 12 years after the first description, focusing on reported cases of neonatal and infantile onset. The different molecular patterns affecting the AVP receptor 2 (V2R) and determining its gain of function are reported in detail; moreover, we also provide a comparison between the different triggers involved in the development of hyponatremia, the evolution of the symptoms, and modality and efficacy of the different treatments available.