Metabolomics application in maternal-fetal medicine

Metabolomics in maternal-fetal medicine is still an "embryonic" science. However, there is already an increasing interest in metabolome of normal and complicated pregnancies, and neonatal outcomes. Tissues used for metabolomics interrogations of pregnant women, fetuses and newborns are amniotic fluid, blood, plasma, cord blood, placenta, urine, and vaginal secretions. All published papers highlight the strong correlation between biomarkers found in these tissues and fetal malformations, preterm delivery, premature rupture of membranes, gestational diabetes mellitus, preeclampsia, neonatal asphyxia, and hypoxic-ischemic encephalopathy. The aim of this review is to summarize and comment on original data available in relevant published works in order to emphasize the clinical potential of metabolomics in obstetrics in the immediate future

Growth abnormalities of fetuses and infants

The objective of this special issue is to address recent research trends and developments about the advancements of image processing and vision in healthcare. A substantial number of papers were submitted, and after a thorough peer review process, some of these were selected to be included in this special issue. Growth abnormalities (either growth restriction or large for gestational age) during perinatal and postnatal life are a hot topic issue, since they are often linked to alteration of uterine environment caused by placental insufficiency, maternal metabolic syndrome, and in general under- or overnutrition of the fetus. These fetal abnormalities account for the leading causes of perinatal morbidity and mortality. Moreover, under the hypothesis of developmental origin of adult diseases, they bear consequences in later life, programming the infant physiology for a higher risk of noncommunicable diseases, cardiovascular adult diseases, and neurodevelopment delay. Low birth weight, caused either by preterm birth and/or by intrauterine growth restriction, is recently known to be associated with increased rates of cardiovascular disease and noninsulin dependent diabetes in adult life. The “developmental origins of adult disease” hypothesis, often called “the Barker hypothesis,” proposes that these diseases originate through adaptations of the fetus when it is undernourished. These adaptations may be cardiovascular, metabolic, or endocrine and they may permanently change the structure and function of the body, increasing coronary heart disease risk factors, such as hypertension, type 2 diabetes mellitus, insulin resistance, and hyperlipidaemia. This hypothesis originally involved from observation by Barker and colleagues that the regions in England with the highest rates of infant mortality in the early 20th century also had the highest rates of mortality from coronary heart disease decades later. As the most commonly registered cause of infant death at the start of 20th century was low birth weight, these observations led to the hypothesis that low birth weight babies who survived infancy and childhood might be at increased risk of coronary heart disease in later life. There is an increased evidence of the link between intrauterine and perinatal alterations and adult diseases. Although the main focus so far has been the timing of delivery and follow-up, the study of the pathophysiology and of possible recovery is of paramount importance and needs the contributions of physicians from several fields, biologists, bioinformaticians, and engineers

Perinatal nutrient restriction reduces nephron endowment increasing renal morbidity in adulthood: A review

Perinatal malnutrition has been included among the causes of renal disease in adulthood. Here, we consider the relationships between early supply of specific nutrients (such as protein, fat, vitamins and electrolytes) and renal endowment. Prenatal and postnatal nutrition mismatch is also discussed. In addition, this article presents the role of nutrition of both mothers and pre-term infants on nephron endowment, with final practical considerations. (C) 2010 Elsevier Ireland Ltd. All rights reserved

Human Breast Milk: Exploring the Linking Ring Among Emerging Components

Maternal breast milk (BM) is a complex and unique fluid that evolution adapted to satisfy neonatal needs; in addition to classical nutrients, it contains several bioactive components. BM characteristically shows inter-individual variability, modifying its composition during different phases of lactation. BM composition, determining important consequences on neonatal gut colonization, influences both short and long-term development. Maternal milk can also shape neonatal microbiota, through its glycobiome rich in Lactobacilli spp. and Bifidobacteria spp. Therefore, neonatal nourishment during the first months of life seems the most important determinant of individual's outcomes. Our manuscript aims to provide new evidence in the characterization of BM metabolome and microbiome, and its comparison to formula milk, allowing the evaluation of each nutrient's influence on neonatal metabolism. This result very interesting since potentially offers an innovative approach to investigate the complex relationship between BM components and infant's health, also providing the chance to intervene in a sartorial way on diet composition, according to the nutritional requests. Future research, integrating metabolomics, microbiomics and stem cells knowledge, could make significant steps forward in understanding BM extraordinary properties and functions

What to Expect from COVID-19 and from COVID-19 Vaccine for Expecting or Lactating Women

Recent studies identified pregnancy as a high-risk condition for the development of maternal-fetal complications in the case of the SARS-CoV-2 infection. Therefore, the scientific community is now considering pregnant women a "fragile" category that should be vaccinated with high priority. The number of pregnant women undergoing hospitalization since summer 2021, including Intensive Care Unit admission, is growing, as well as the risk of preterm birth. Evidence from both animals and humans suggest that, similarly to other vaccines routinely administered in pregnancy, COVID-19 vaccines are not crossing the placenta, do not increase the risk of miscarriage, preterm birth, stillbirth, the birth of small gestational age neonates, as well as the risk of congenital abnormalities. To date, the World Health Organization and scientific literature are promoting and encouraging the vaccination of all pregnant and lactating women. The aim of our narrative review is to present the available literature regarding this issue with the aim to provide appropriate answers to the most frequent requests, doubts, and fears that have led many expecting and lactating women not to become vaccinated during this pandemic period

Exploring perinatal asphyxia by metabolomics

Brain damage related to perinatal asphyxia is the second cause of neuro-disability worldwide. Its incidence was estimated in 2010 as 8.5 cases per 1000 live births worldwide, with no further recent improvement even in more industrialized countries. If so, hypoxic-ischemic encephalopathy is still an issue of global health concern. It is thought that a consistent number of cases may be avoided, and its sequelae may be preventable by a prompt and efficient physical and therapeutic treatment. The lack of early, reliable, and specific biomarkers has up to now hampered a more effective use of hypothermia, which represents the only validated therapy for this condition. The urge to unravel the biological modifications underlying perinatal asphyxia and hypoxic-ischemic encephalopathy needs new diagnostic and therapeutic tools. Metabolomics for its own features is a powerful approach that may help for the identification of specific metabolic profiles related to the pathological mechanism and foreseeable outcome. The metabolomic profiles of animal and human infants exposed to perinatal asphyxia or developing hypoxic-ischemic encephalopathy have so far been investigated by means of 1H nuclear magnetic resonance spectroscopy and mass spectrometry coupled with gas or liquid chromatography, leading to the identification of promising metabolomic signatures. In this work, an extensive review of the relevant literature was performed

Emerging biomarkers and metabolomics for assessing toxic nephropathy and acute kidney injury (AKI) in neonatology

Identification of novel drug-induced toxic nephropathy and acute kidney injury (AKI) biomarkers has been designated as a top priority by the American Society of Nephrology. Increasing knowledge in the science of biology and medicine is leading to the discovery of still more new biomarkers and of their roles in molecular pathways triggered by physiological and pathological conditions. Concomitantly, the development of the so-called “omics” allows the progressive clinical utilization of a multitude of information, from those related to the human genome (genomics) and proteome (proteomics), including the emerging epigenomics, to those related to metabolites (metabolomics). In preterm newborns, one of the most important factors causing the pathogenesis and the progression of AKI is the interaction between the individual genetic code, the environment, the gestational age, and the disease. By analyzing a small urine sample, metabolomics allows to identify instantly any change in phenotype, including changes due to genetic modifications. The role of liquid chromatography-mass spectrometry (LC-MS), proton nuclear magnetic resonance (1H NMR), and other emerging technologies is strategic, contributing basically to the sudden development of new biochemical and molecular tests. Urine neutrophil gelatinase-associated lipocalin (uNGAL) and kidney injury molecule-1 (KIM-1) are closely correlated with the severity of kidney injury, representing noninvasive sensitive surrogate biomarkers for diagnosing, monitoring, and quantifying kidney damage. To become routine tests, uNGAL and KIM-1 should be carefully tested in multicenter clinical trials and should be measured in biological fluids by robust, standardized analytical methods

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