Metabolomics is one of the most attractive whilst still developing field of omics sciences, studying metabolites and the alterations of their levels. As an approach, metabolomics has the potential to identify using high‐throughput analytical precision statistically significant alterations, covering a broad spectrum of metabolic processes. What metabolomics can offer are qualitative and quantitative information, incorporating the consideration of more variables in analytical procedures. Measuring of metabolites and metabolic profiling provide an instant ‘snapshot’ which permits the follow up of the dynamics of complex biological systems and their mechanisms under physiological or abnormal conditions. Nuclear magnetic resonance (NMR) provides an excellent technique for profiling the biological fluids and is especially adept at characterising complex solutions. Advances in biochemical data obtained from NMR spectra allow us to observe the metabolome in a very accurate manner and thus estimate the complex index of biochemical processes, determining the health status of an organism. To date, metabolomics profiling is a powerful approach for examining disease‐related metabolic changes and is highly effective in the identification of new biomarkers. Key Concepts NMR is a novel, noninvasive, fast, accurate and reproducible bioanalytical method for metabolites identification and quantification in biological samples. Simple 1 H 1D NMR spectra are exploited for the definition of the physiological healthy status of organism's metabolism, based on a specific spectral pattern. NMR experiments selection depends on the biological fluid of interest. Metabolites identification and quantification and their role in a metabolomic experiment. The advantages of metabolic profiling and its utility in a potential diagnostic model. Monitoring of an individual's metabolic responses through the examination of its spectral fingerprint along specific time points. Significant alterations in metabolites concentrations are meaningful to exact interactions of pathways, perturbations and metabolite–metabolite correlations. Computational tools and global repositories in service of metabolomic data. Multivariate analysis techniques and their implementation in the field of NMR metabolomics. NMR metabolomics in clinical research, the intervention of the field and the impact in accessing unknown biological phenomena.

In this study, non‐targeted 1H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS‐DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS‐DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants’ metabolite fingerprinting by NMR spectroscopy and their geographical origin. Practical Application This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to “farm to table” mission.

Urine metabolomics is gaining traction as a means of identifying metabolic signatures associated with health and disease states. Thirty-one (31) late preterm (LP) neonates admitted to the neonatal intensive care unit (NICU) and 23 age-matched healthy LPs admitted to the maternity ward of a tertiary hospital were included in the study. Proton nuclear magnetic resonance (1H NMR) spectroscopy was employed for urine metabolomic analysis on the 1st and 3rd days of life of the neonates. The data were analyzed using univariate and multivariate statistical analysis. A unique metabolic pattern of enhanced metabolites was identified in the NICU-admitted LPs from the 1st day of life. Metabolic profiles were distinct in LPs presenting with respiratory distress syndrome (RDS). The discrepancies likely reflect differences in the gut microbiota, either due to variations in nutrient intake or as a result of medical interventions, such as the administration of antibiotics and other medications. Altered metabolites could potentially serve as biomarkers for identifying critically ill LP neonates or those at high risk for adverse outcomes later in life, including metabolic risks. The discovery of novel biomarkers may uncover potential targets for drug discovery and optimal periods for effective intervention, offering a personalized approach.

Introduction Premature adrenarche (PA) for long time was considered a benign condition but later has been connected to various diseases in childhood and adulthood which remains controversial. Objective To investigate the effect of premature adrenarche on the metabolic phenotype, and correlate the clinical and biochemical data with the metabolic profile of children with PA. Methods Nuclear magnetic resonance (NMR)-based untargeted and targeted metabolomic approach in combination with multivariate and univariate statistical analysis applied to study the metabolic profiles of children with PA. Plasma, serum, and urine samples were collected from fifty-two children with Idiopathic PA and forty-eight age-matched controls from the division of Pediatric Endocrinology of the University Hospital of Patras were enrolled. Results Metabolomic results showed that plasma and serum glucose, myo-inositol, amino acids, a population of unsaturated lipids, and esterified cholesterol were higher and significantly different in PA children. In the metabolic profiles of children with PA and age-matched control group a gradual increase of glucose and myo-inositol levels was observed in serum and plasma, which was positively correlated their body mass index standard deviation score (BMI SDS) values respectively. Urine 1H NMR metabolic fingerprint of PA children showed positive correlation and a clustering-dependent relationship with their BMI and bone age (BA) respectively. Conclusion This study provides evidence that PA driven metabolic changes begin during the childhood and PA may has an inductive role in a BMI–driven increase of specific metabolites. Finally, urine may be considered as the best biofluid for identification of the PA metabolism as it reflects more clearly the PA metabolic fingerprint.