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 NMR chemical shifts of a substance in a complex mixture strongly depend on the composition of the mixture itself, as many weak interactions occur that are hardly predictable. Chemical shift variability is the major obstacle to automatically assigning, and subsequently quantitating, metabolite signals in body fluids, particularly urine. Here we demonstrate that the chemical shifts of signals in urine are actually predictable. This is achieved by constructing ca. 4000 artificial mixtures where the concentrations of 52 most abundant urine metabolites—including 11 inorganic ions—are varied, to sparsely but efficiently populate an N-dimensional concentration matrix. A strong relationship is established between the concentration matrix and the chemical shift matrix, so that chemical shifts of > 90 metabolite signals can be accurately predicted in real urine samples. The concentrations of the invisible inorganic ions are also accurately predicted, along with those of albumin and of several other abundant urine components.

Better understanding of pancreatic diseases, including pancreatic ductal adenocarcinoma (PDAC), is an urgent medical need, with little advances in preoperative differential diagnosis, preventing rational selection of therapeutic strategies. The clinical management of pancreatic cancer patients would benefit from the identification of variables distinctively associated with the multiplicity of pancreatic disorders. We investigated, by 1H nuclear magnetic resonance, the metabolomic fingerprint of pancreatic juice (the biofluid that collects pancreatic products) in 40 patients with different pancreatic diseases. Metabolic variables discriminated PDAC from other less aggressive pancreatic diseases and identified metabolic clusters of patients with distinct clinical behaviors. PDAC specimens were overtly glycolytic, with significant accumulation of lactate, which was probed as a disease-specific variable in pancreatic juice from a larger cohort of 106 patients. In human PDAC sections, high expression of the glucose transporter GLUT-1 correlated with tumor grade and a higher density of PD-1+ T cells, suggesting their accumulation in glycolytic tumors. In a preclinical model, PD-1+ CD8 tumor–infiltrating lymphocytes differentially infiltrated PDAC tumors obtained from cell lines with different metabolic consumption, and tumors metabolically rewired by knocking down the phosphofructokinase (Pfkm) gene displayed a decrease in PD-1+ cell infiltration. Collectively, we introduced pancreatic juice as a valuable source of metabolic variables that could contribute to differential diagnosis. The correlation of metabolic markers with immune infiltration suggests that upfront evaluation of the metabolic profile of PDAC patients could foster the introduction of immunotherapeutic approaches for pancreatic cancer.