In recent years, the complex connection between diet and human wellness has received considerable scientific focus. Conventional methods in nutritional research—primarily depending on dietary questionnaires and standard clinical biomarkers—frequently do not adequately reflect the intricate and evolving metabolic reactions to diet. The rise of metabolomics, an expansive and systematic examination of low-molecular-weight metabolites in biological systems [1,2], has revolutionized nutritional science. It provides a robust method for comprehending the biochemical effects of food and nutrient consumption on health and illness [3].

Metabolomics involves the thorough, high-throughput examination of small-molecule metabolites (<1500 Da) found in biological samples like plasma, urine, saliva, feces, and tissues [4,5]. As the end product of gene expression, protein function, and environmental influences, the metabolome provides the most direct functional representation of the phenotype, serving as an optimal perspective for examining the biochemical impacts of diet. Commonly known as the “final stage of the omics cascade,” metabolomics records the body’s dynamic responses to nutrient consumption and facilitates a comprehensive understanding of how the human body interacts with food [6,7].

Nutritional metabolomics, also known as nutrimetabolomics, combines metabolomic profiling with dietary evaluations to explore the molecular effects of nutrients, bioactive substances, dietary habits, and functional foods on human health [8,9]. This method facilitates (i) the identification of dietary biomarkers that provide objective measurements of food consumption, (ii) the uncovering of metabolic profiles linked to disease risk or health conditions, and (iii) the delineation of personal metabolic reactions to food (i.e., metabotypes), opening doors for personalized nutrition [10,11].

Additionally, nutritional metabolomics aids in comprehending the biological mechanisms that drive diet-related illnesses, such as obesity, diabetes, cardiovascular disease, and cancer [12]. It further aids in assessing nutritional health, adherence to diets, and the lasting impacts of eating patterns on aging, inflammation, and oxidative stress. Even with its vast potential, incorporating metabolomics into nutritional science comes with difficulties. These involve the necessity for uniform protocols, enhanced data analysis techniques, and extensive food composition databases. Nonetheless, the integration of systems biology, high-throughput omics methods, and sophisticated bioinformatics is swiftly transforming the field of nutrition research.

In conclusion, metabolomics signifies crucial progress in nutritional science, providing a comprehensive perspective on the impact of food on human metabolism and well-being. As the domain progresses, its impacts are expected to influence the future of precise nutrition, food recommendations, and health policies.

Metabolomics technologies like Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) provide complementary benefits regarding sensitivity, breadth, and reproducibility and have been effectively utilized in various nutritional scenarios [13]. These encompass research on the effects of dietary modifications (e.g., Mediterranean diet, high-fat, or plant-based diets) on metabolism, the influence of metabolites from gut microbiota, and the metabolic changes caused by particular nutrients like polyphenols, amino acids, fatty acids, or vitamins [14].

NMR offers several advantages, including minimal sample preparation, non-destructive analysis, and high reproducibility, making it ideal for quantitative studies and longitudinal cohort analyses [15,16]. However, its relatively low sensitivity (typically in the micromolar range) limits the detection of low-abundance metabolites. In contrast, MS—particularly when coupled with chromatographic separation techniques such as Gas Chromatography (GC)-MS or Liquid Chromatography (LC)-MS—provides much higher sensitivity, and broader metabolite coverage, detecting hundreds to thousands of compounds across diverse chemical classes [17]. Nevertheless, MS workflows often involve complex sample preparation, and are susceptible to ion suppression and matrix effects. They also require external calibration for quantitation. [18]. In complex matrices, NMR is less affected by matrix interference, but detects a narrower range of metabolites compared to LC-MS-based methods [19,20]. Integrative approaches that combine NMR and MS are increasingly recommended, as they combine the strengths of both techniques—NMR’s reproducibility and structural elucidation power, and MS’s superior sensitivity and coverage — to achieve a more comprehensive and reliable metabolomic characterization. [21].

Despite these limitations, in nutritional metabolomics, NMR spectroscopy is notable for its reliability, consistency, low sample preparation needs, and noninvasive characteristics compared to other analytical platforms [22] as mentioned above. NMR-based metabolomics allows scientists to capture a comprehensive and untargeted view of the metabolome from various biological samples, such as urine, plasma, saliva, feces, and tissues [23]. Untargeted NMR is different from targeted metabolomics because it doesn’t make assumptions about what it is looking for [24]. Instead, the aim is to detect as many metabolites as possible, which provides a comprehensive snapshot of the metabolic status. Key strengths include minimal sample preparation, high reproducibility and the ability to quantify multiple metabolite classes simultaneously (amino acids, organic acids, sugars, lipids). Applications of untargeted NMR include the discovery of disease biomarkers (e.g., for cancer, inflammatory bowel disease, and multiple sclerosis), understanding host-microbiota interactions (via fecal or urine metabolites), monitoring treatment responses, and exploring metabolic phenotypes in population cohorts [25,26,27,28]. As tools and spectral libraries (e.g., HMDB) improve, identification and quantitation become increasingly reliable [29], though challenges remain in resolving overlapping peaks, low-abundance metabolites and integrating NMR data with mass spectrometry or other omics platforms for deeper metabolic coverage.

Moreover, NMR spectroscopy allows for absolute quantification of metabolites when using internal standards like trimethylsilylpropane sulfonic acid (DSS) and 2,2,3,3-tetradeutero-3-trimethylsilylpropionic acid (TSP) [30], and it facilitates the structural determination of unknown compounds thanks to its capability to detect various nuclei (e.g., ¹H, ¹³C, ³¹P) [24]. These detailed profiles enable the observation of systemic effects triggered by particular nutrients, entire diets, or functional foods, as well as the discovery of new biomarkers related to dietary consumption, metabolic health, and nutrient-associated pathophysiological alterations. Furthermore, NMR data is often integrated with chemometrics and advanced multivariate statistical techniques, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), or Orthogonal PLS (OPLS), to uncover subtle metabolic variations linked to dietary interventions, patterns, or deficiencies [31]. This combination allows for achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science [32,33].

This entry examines the uses of NMR-based metabolomics in nutrition research, covering its foundational principles, methodological benefits, application in dietary evaluations and intervention studies, significance in personalized nutrition, and present challenges. NMR-based metabolomics provides a non-invasive and quantitative perspective for assessing metabolic responses to diet, playing an essential role in advancing the future of nutritional science.