Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the ‐omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population‐wide health screening technique.

The current pandemic emergence of novel coronavirus disease (COVID-19) poses a relevant threat to global health. SARS-CoV-2 infection is characterized by a wide range of clinical manifestations, ranging from absence of symptoms to severe forms that need intensive care treatment. Here, plasma-EDTA samples of 30 patients compared with age- and sex-matched controls were analyzed via untargeted nuclear magnetic resonance (NMR)-based metabolomics and lipidomics. With the same approach, the effect of tocilizumab administration was evaluated in a subset of patients. Despite the heterogeneity of the clinical symptoms, COVID-19 patients are characterized by common plasma metabolomic and lipidomic signatures (91.7% and 87.5% accuracy, respectively, when compared to controls). Tocilizumab treatment resulted in at least partial reversion of the metabolic alterations due to SARS-CoV-2 infection. In conclusion, NMR-based metabolomic and lipidomic profiling provides novel insights into the pathophysiological mechanism of human response to SARS-CoV-2 infection and to monitor treatment outcomes.

Blood derivatives are the biofluids of choice for metabolomic clinical studies since blood can be collected with low invasiveness and is rich in biological information. However, the choice of the blood collection tubes has an undeniable impact on the plasma and serum metabolic content. Here, we compared the metabolomic and lipoprotein profiles of blood samples collected at the same time and place from six healthy volunteers but using different collection tubes (each enrolled volunteer provided multiple blood samples at a distance of a few weeks/months): citrate plasma, EDTA plasma, and serum tubes. All samples were analyzed via nuclear magnetic resonance spectroscopy. Several metabolites showed statistically significant alterations among the three blood matrices, and also metabolites’ correlations were shown to be affected. The effects of blood collection tubes on the lipoproteins’ profiles are relevant too, but less marked. Overcoming the issue associated with different blood collection tubes is pivotal to scale metabolomics and lipoprotein analysis at the level of epidemiological studies based on samples from multicenter cohorts. We propose a statistical solution, based on regression, that is shown to be efficient in reducing the alterations induced by the different collection tubes for both the metabolomic and lipoprotein profiles.

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

Aim: To characterize the impact of periodontitis and of Steps I–II of periodontal therapy on microbiome composition, function, and metabolic output across the oral and gut environments. Methods: A multi‐omics analysis was performed on saliva and stool samples collected from 50 systemically healthy individuals with and without Stage III–IV periodontitis. For participants with periodontitis, samples were analyzed both at baseline and 3 months after Steps I–II of periodontal therapy. High‐throughput whole metagenome sequencing was used to profile microbial taxa and functional genes, NMR‐based metabolomics profiled host–microbial metabolites. Single‐omic differential abundance analysis between healthy samples and periodontitis samples was performed with MaAsLin2, while analysis between pre‐ and post‐treatment was conducted with timeOmics. Variable selection and subsequent supervised multivariate analysis to determine group‐separating markers utilized multi‐level sparse Partial Least Squares Discriminant Analysis (sPLS‐DA) through mixOmics. KEGG pathway enrichment was analyzed using clusterProfiler, whereas multi‐omic data integration was performed with multi‐block Partial Least Squares regression analysis. Results: Periodontitis was associated with significant compositional and functional changes in both saliva and stool, with increased abundance of pathobionts and loss of health‐associated taxa in both niches. A subset of species was shared across oral and gut habitats, with detectable differences across clinical groups. As functional potential, periodontitis enriched microbial pro‐inflammatory pathways (lipopolysaccharide biosynthesis, bacterial motility) and depleted beneficial short‐chain fatty acid (SCFA)‐ and vitamin‐producing functions. Metabolomic profiles revealed reduced SCFAs and amino acids in periodontitis, with elevated pro‐inflammatory metabolites (succinate, trimethylamine) in both saliva and stool. Following therapy, microbial communities and their metabolic output partially reverted toward health‐associated profiles, particularly in saliva. Stool samples showed subtler but consistent shifts, including a decrease in some typically oral species and decreased succinate and methylamine and restoration of amino acid and SCFA‐related metabolites. Conclusions: Periodontitis is associated with coordinated microbial and metabolic signatures across the oral and gut environments. Non‐surgical periodontal therapy promotes partial ecological restoration in both niches, supporting the view of oral health as a modifiable target for influencing systemic microbial homeostasis. Trial Registration: ClinicalTrials.gov identification number: NCT04826926