The Application of NMR-Based Metabolomics in the Field of Nutritional Studies

Definition

Nuclear Magnetic Resonance (NMR)-based metabolomics has emerged as a powerful analytical technique in nutritional science, enabling comprehensive profiling of metabolites in biological samples. This entry explores the integration of NMR metabolomics in nutrition research, highlighting its principles, methodological considerations, and applications in dietary assessment, nutritional interventions, and biomarker discovery. The entry also addresses the advantages and limitations of NMR compared to other metabolomic techniques and discusses its future potential in personalized nutrition and health monitoring.

1. Introduction

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.

2. Biomarkers of Food Intake

Accurate and objective measurement of dietary intake is one of the most critical challenges in nutritional science. Traditional self-reported tools such as food frequency questionnaires (FFQs), 24-h recalls, and dietary diaries [34,35] are subject to several well-documented limitations, including recall bias, underreporting, overreporting, and socio-cultural influences [36]. These methodological shortcomings often result in misclassification of dietary exposures, thereby reducing the reliability and interpretability of associations between diet and health outcomes [37]. To overcome these limitations, the identification and validation of biomarkers of food intake (BFIs) has become a major focus in the field of nutritional metabolomics [38]. These biomarkers provide objective, quantifiable measures of the consumption of specific foods or dietary patterns and serve as essential tools for evaluating dietary adherence in intervention trials, epidemiological studies, and public health strategies [37]. In recent years, metabolomics has developed as a key technology for the identification of new dietary biomarkers [39] and through its use, a number of food intake biomarkers have emerged in the literature [37,40].

2.1. NMR in the Identification of BFIs

The measurement of BFIs in biofluids such as urine, plasma, serum, or saliva represents an objective tool for dietary assessment since provide a direct biochemical reflection of food consumption [41]. By identifying and quantifying metabolites or specific compounds derived from foods, BFIs enable researchers to establish a more reliable link between dietary exposure and health outcomes. Biofluids are particularly useful because they capture both acute and chronic dietary intake. For instance, short-lived metabolites of fruits, vegetables, coffee, or whole grains may be detected in urine within hours, while more stable biomarkers, such as specific fatty acids or carotenoids, may persist in plasma for days or weeks. Furthermore, BFIs can be integrated with multivariate statistical approaches and metabolomics workflows to uncover dietary patterns and validate adherence to nutritional interventions [42]. This objective assessment tool is particularly valuable in large epidemiological studies and in the context of personalized nutrition, where accurate dietary exposure data are essential to identify associations with metabolic health, chronic disease risk, and individual dietary responses. Overall, the use of BFIs in biofluids represents a paradigm shift in nutritional science, moving from subjective dietary assessment toward an evidence-based, molecularly informed understanding of diet–health interactions.

A growing number of studies have successfully employed NMR metabolomics to identify robust and food-specific biomarkers. McNamara and Brennan [37] in this work clearly describe the reason why this technique is suitable for the BFIs: it captures quantitative metabolite data in a robust fashion, in a non-destructive method, relatively fast and requires little sample preparation. NMR has been used to identify biomarkers of coffee, citrus fruit, wine, fish, and cruciferous vegetable consumption. For example, hippurate, trigonelline, and citrate have been consistently linked to coffee intake [43], while proline betaine reflects citrus consumption [44]; some notable examples are listed in Table 1. These biomarkers validate self-reported data and enable objective monitoring of food-specific intake within dietary patterns. Moreover, molecular biomarkers could detect biochemical changes associated with disease processes [45]. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics has proved to be exquisitely valuable in human disease diagnosis, such as the metabolic syndrome, obesity, type 2 diabetes or insulin resistance [46].

2.2. NMR in Dietary Pattern Recognition

Diet plays a central role in shaping human health, influencing metabolic function, disease risk, and overall well-being. Traditionally, nutrition research has focused on single nutrients (e.g., protein, carbohydrates, vitamins) or individual food items (e.g., dairy, fruits, fish). However, such reductionist approaches often fail to capture the complex interactions between foods, nutrients, and human metabolism. For this reason, the concept of dietary patterns has emerged as a more holistic framework for studying nutrition [47].

Table 1. Applications of NMR in the Identification of BFIs.

Food	Application	Reference
Fruits and Vegetables
Citrus fruit	Proline and betaine detected in urine, and serum.	Mitry [48]; Heinzmann, et al. [44].
Tomatoes	Trans-lycopene, cis-lycopene in plasma and serum.	Chiva-Blanch, et al. [49].
Cruciferous vegetables	S-methyl cysteine sulfoxide, sulforaphane metabolites in urine.	Edmands, et al. [50].
Carrots	α- and β-carotene in plasma, and serum.	Arathi, et al. [51].
Whole Grains, Cereals and Legumes
Whole-grain wheat and rye	Alkylresorcinols in plasma, urine, and red blood cells.	Landberg, et al. [52]; Chelladurai, et al. [53].
Oats and barley	Avenanthramides, β-glucan metabolites in plasma, and urine.	Wang, et al. [54].
Lentils, Chickpeas, and Beans	2-hydroxybutyric acid, lysine, trigonelline in serum, and urine.	Madrid-Gambin, et al. [55].
	Animal Products
Fish and seafood	Trimethylamine N-oxide (TMAO), ω-3 fatty acids (EPA, DHA), in plasma, and urine.	Lemos [56]; Hanhineva [57]; Lombardo, et al. [58]; Burton, et al. [59]; Xyda, et al. [60].
Red meat	Carnitine, creatine, creatinine, anserine in urine, and plasma.	Carrizo, et al. [61]; Pan, et al. [62];
Dairy products	Odd-chain fatty acids (C15:0, C17:0), trans-palmitoleic acid in serum, and urine.	Trimigno, et al. [40]; Münger, et al. [63]; Correia, et al. [64].
	Beverages
Coffee	Trigonelline, caffeine, paraxanthine, 3-hydroxyhippuric acid in urine, and plasma.	Rådjursöga, et al. [65]; Rothwell, et al. [66].
Tea	Catechins, theaflavins, 4-O-methylgallic acid in plasma, and urine.	Rothwell, et al. [66]; Law, et al. [67]; Madrid-Gambin, et al. [68]; Daykin, et al. [69].
Wine	Tartaric acid, ethyl glucuronide, resveratrol metabolites in urine, and plasma.	van Dorsten, et al. [70]; Vázquez-Fresno, et al. [71]; Hong [72].

Table 1. Applications of NMR in the Identification of BFIs.

Food	Application	Reference
Fruits and Vegetables
Citrus fruit	Proline and betaine detected in urine, and serum.	Mitry [48]; Heinzmann, et al. [44].
Tomatoes	Trans-lycopene, cis-lycopene in plasma and serum.	Chiva-Blanch, et al. [49].
Cruciferous vegetables	S-methyl cysteine sulfoxide, sulforaphane metabolites in urine.	Edmands, et al. [50].
Carrots	α- and β-carotene in plasma, and serum.	Arathi, et al. [51].
Whole Grains, Cereals and Legumes
Whole-grain wheat and rye	Alkylresorcinols in plasma, urine, and red blood cells.	Landberg, et al. [52]; Chelladurai, et al. [53].
Oats and barley	Avenanthramides, β-glucan metabolites in plasma, and urine.	Wang, et al. [54].
Lentils, Chickpeas, and Beans	2-hydroxybutyric acid, lysine, trigonelline in serum, and urine.	Madrid-Gambin, et al. [55].
	Animal Products
Fish and seafood	Trimethylamine N-oxide (TMAO), ω-3 fatty acids (EPA, DHA), in plasma, and urine.	Lemos [56]; Hanhineva [57]; Lombardo, et al. [58]; Burton, et al. [59]; Xyda, et al. [60].
Red meat	Carnitine, creatine, creatinine, anserine in urine, and plasma.	Carrizo, et al. [61]; Pan, et al. [62];
Dairy products	Odd-chain fatty acids (C15:0, C17:0), trans-palmitoleic acid in serum, and urine.	Trimigno, et al. [40]; Münger, et al. [63]; Correia, et al. [64].
	Beverages
Coffee	Trigonelline, caffeine, paraxanthine, 3-hydroxyhippuric acid in urine, and plasma.	Rådjursöga, et al. [65]; Rothwell, et al. [66].
Tea	Catechins, theaflavins, 4-O-methylgallic acid in plasma, and urine.	Rothwell, et al. [66]; Law, et al. [67]; Madrid-Gambin, et al. [68]; Daykin, et al. [69].
Wine	Tartaric acid, ethyl glucuronide, resveratrol metabolites in urine, and plasma.	van Dorsten, et al. [70]; Vázquez-Fresno, et al. [71]; Hong [72].

Dietary patterns describe the combination, frequency, and proportions of foods and beverages consumed in a habitual diet, reflecting overall lifestyle and cultural practices. Thus, beyond single food items, NMR-based metabolomics has shown potential in identifying metabolic signatures associated with dietary patterns. These signatures often include combinations of amino acids, organic acids, and microbial metabolites, reflecting the overall composition and quality of the diet. Several major dietary patterns have been extensively studied.

2.2.1. Mediterranean Diet

Mediterranean diet (MD) is characterized by high intake of fruits, vegetables, legumes, whole grains, olive oil, and moderate wine and limited red meat consumption. It is associated with improved cardiovascular health, reduced inflammation, and lower risk of chronic diseases. The NMR studies of biofluids such as urine and serum have identified several metabolites reflecting a high intake of foods such as fruits, vegetables, and legumes. In the first case, changes in urinary levels of 3-hydroxybutyrate, citric acid, and cis-aconitate, oleic acid, suberic acid, various amino acids and some microbial metabolites have been detected in individuals adhering to a MD [73,74]. Plasma metabolomics also shows improved lipid metabolism and reduced markers of oxidative stress. Moreover, it pointed out that citric acid emerged as the most significant metabolite differentiating “low” versus “high” adherence groups to the MD, with additional discriminatory metabolites including pyruvic acid, betaine, mannose, acetic acid, and myo-inositol [73]. Among these, citric acid showed strong correlations with fruit, fruit juice, and vegetable consumption, while being negatively associated with sweet foods and carbonated drinks. Its performance as a biomarker was further enhanced when evaluated as a ratio with pyruvic acid [73].

2.2.2. Vegetarian and Vegan Diet

Plant-based diets, including vegetarian and vegan dietary (VD) patterns, have gained significant attention in recent years due to their potential health benefits and sustainability implications. These diets are predominantly composed of fruits, vegetables, legumes, whole grains, nuts, and seeds, while varying in their exclusion of animal-derived products. Vegetarian diets typically exclude meat, poultry, and fish but may include dairy products and eggs, depending on the sub-type (lacto-ovo-vegetarian, lacto-vegetarian, ovo-vegetarian). Vegan diets are more restrictive, excluding all animal-derived products, including dairy, eggs, honey, and often processed foods containing animal additives [75]. Numerous studies have shown that plant-based diets are associated with a reduced risk of chronic diseases such as cardiovascular disease, type 2 diabetes, obesity, and certain types of cancer. This is primarily attributed to their high content of dietary fiber, antioxidants, polyphenols, vitamins, and unsaturated fatty acids, alongside a lower intake of saturated fats and cholesterol [76,77].

NMR-based profiling has been instrumental in uncovering metabolic signatures of these VD patterns. Taken together, NMR metabolomics identifies in the urinary profile a consistent metabolic signature, which includes (i) lower branched-chain amino acid (BCAAs), methionine, taurine, creatine/creatinine, TMAO, and (ii) higher hippurate, glycine, citrate, Short chain fatty acids (SCFAs), betaine, myo-inositol (

Table 2. NMR-detected metabolites in the urinary profile distinguishing vegetarian and vegan diets from omnivorous diets.

Metabolite	Trend in Vegetarians/Vegans 1	Biological/Dietary Source
BCAAs: leucine, isoleucine, valine	↓ Decreased	Mainly from animal protein (meat, dairy)
Methionine	↓ Decreased	Animal protein (meat, eggs, dairy)
Taurine	↓ Decreased	Animal tissue (meat, seafood)
Creatine/Creatinine	↓ Decreased	Animal muscle, absent in plants
TMAO	↓ Decreased	Produced from choline/carnitine in animal foods
Glycine	↑ Increased	Plant proteins, conjugation of polyphenols
Citrate, Succinate, Malate	↑ Increased	Organic acids from fruits/vegetables, TCA intermediates
Pyruvate, Lactate	↑Variable	Altered glycolytic flux, fiber fermentation
SCFAs: acetate, propionate, butyrate	↑ Increased	Microbial fermentation of dietary fiber
Formate	↑ Increased	Microbial fermentation by gut microbiota
Hippurate	↑ Increased	Polyphenol metabolism (fruits, vegetables, tea)
Phenylacetylglutamine	↑ Increased	Microbial metabolism of aromatic polyphenols
Cinnamoylglycine	↑ Increased	Polyphenol-rich foods
Betaine	↑ Increased	Whole grains, beets, spinach
Myo-inositol	↑ Increased	Legumes, cereals, nuts

¹ Key Insights: ↓ Animal-derived metabolites (BCAAs, taurine, creatine, TMAO) are consistent markers of reduced animal product intake. ↑ Plant and microbial-derived metabolites (hippurate, SCFAs, betaine, myo-inositol) reflect higher intake of fiber, legumes, fruits, vegetables, and polyphenols. These markers can be used as objective biomarkers of dietary adherence in nutritional epidemiology study.

) [78]. These biomarkers not only provide objective measures of adherence to plant-based diets but also reflect the metabolic and gut microbial adaptations that distinguish them from omnivorous dietary patterns [78].

2.2.3. Western Diet

The Western diet (WD) is typically characterized by a high consumption of red and processed meats, refined grains, saturated fats, added sugars, and low intake of dietary fiber, fruits, and vegetables [79]. It has been strongly associated with metabolic disorders such as obesity, type 2 diabetes, cardiovascular disease, and non-alcoholic fatty liver disease (NAFLD) [79,80]. Understanding the biochemical imprints of this dietary pattern is crucial for developing objective markers of adherence and linking dietary exposure with disease risk.

NMR-based metabolomics applied in serum and urine samples has consistently revealed distinctive features of the WD [81]. One of the most recurrent signatures is the elevation of BCAAs, including leucine, isoleucine, and valine. These metabolites are strongly associated with a high intake of animal protein and fat and have been repeatedly linked to the development of insulin resistance and metabolic syndrome [82]. Parallel to this, increased levels of aromatic amino acids such as tyrosine and phenylalanine have been observed, reflecting the excessive consumption of meat and processed foods typical of WD habits [83]. The WD also induces a marked increase in lipid-associated metabolites, including signals from low-density lipoproteins (LDL), saturated fatty acid derivatives, and TMAO, a metabolite strongly linked to red meat intake and cardiovascular risk [84,85]. Conversely, metabolites typically associated with plant-based diets and gut microbial activity, such as hippurate and phenylacetylglutamine, are consistently reduced, reflecting the lower consumption of dietary fiber and reduced microbial diversity in the gut of WD consumers [86,87]. Furthermore, the concentration SCFAs, including acetate, butyrate, and propionate, is often decreased, consistent with the reduced fermentation of fiber in the colon. Since SCFAs are key mediators of host–microbiota interactions, their depletion further underscores the detrimental impact of the WD on metabolic health and gut homeostasis [88,89].

Altogether, these findings demonstrate how NMR metabolomics not only enables the identification of objective biomarkers of MD, and VD adherence but also provides mechanistic insights into the metabolic pathways altered by poor dietary habits, such as WD. This reinforces the value of metabolomics as a tool to monitor dietary patterns in epidemiological studies and to support preventive nutrition strategies, as demonstrated by the study from Prendiville, et al. [90], whosuccessfully developed a metabolomic-based multivariate model which was capable of classifying participants into one of four dietary patterns.

3. NMR Metabolomics and Gut Microbiota–Diet Interactions

The gut microbiota plays a central role in human health by modulating nutrient metabolism, producing bioactive compounds, and interacting with host metabolic pathways [91,92,93]. Among the many factors influencing the composition and functionality of gut microbial communities, diet is one of the strongest modulators.

The integration of metabolomics and microbiota sequencing data provides a comprehensive view of the functional interplay between microbial communities and their metabolic outputs. While microbiota sequencing (e.g., 16S rRNA or shotgun metagenomics) reveals the taxonomic composition and functional potential of microbial ecosystems, metabolomics captures the small molecules produced or modified by these microbes and the host. Combining these datasets enables researchers to link microbial taxa or genes to specific metabolites, uncover biomarkers, and elucidate mechanistic pathways underlying host-microbe interactions. Thus, the biochemical interplay between dietary components and microbial metabolites can be comprehensively studied through NMR-based metabolomics. This methodological strength makes NMR particularly suitable for studying microbiota–diet relationships, as it allows longitudinal monitoring of systemic and gut-derived metabolic changes in response to dietary interventions [94,95]. One of the clearest examples of this interaction is the relationship between dietary fiber intake and the production of microbial-derived SCFAs. Dietary fibers, being non-digestible carbohydrates, reach the colon intact, where they undergo fermentation by gut microorganisms, yielding SCFAs such as acetate, propionate, and butyrate [96,97]. These metabolites act as signaling molecules with broad physiological roles, influencing gut barrier integrity, immune modulation, and host energy metabolism. NMR-based metabolomics offers unique advantages for the direct detection and quantification of SCFAs, as this spectroscopy technique can identify acetate, propionate, butyrate, and other fermentation products such as lactate and succinate in fecal water extracts [98,99,100] (Figure 1). Beyond detection, the integration of metabolomic data with multivariate statistical approaches enables the identification of distinct SCFA profiles associated with high-fiber diets, for example those rich in whole grains, legumes, and fruits, compared to low-fiber dietary patterns [101,102].

Another area in which NMR metabolomics has provided valuable insights is the study of probiotic supplementation and its effects on both the fecal and plasma metabolome. Probiotics are defined by the Food and Agriculture Organization of the United Nation/World Health Organization (FAO/WHO) as “live microorganisms, which when administered in adequate amounts, confer a health benefit on the host” [103]. They can modulate the gut microbiota in ways that are reflected in metabolic outputs detectable by NMR [104,105]. For example, supplementation with strains such as Lactobacillus acidophilus, and Bifidobacterium lactis has been associated with altered levels of organic acids, amino acids, and microbial fermentation products in feces [106]. Feces-derived metabolites such as caproate, valerate, butyrate, propionate, lactate, acetate, succinate, methanol, threonine and methionine significantly increased, and they are related to SCFA metabolism and TCA cycle metabolism. On the contrary potentially harmful metabolites such as p-cresol and ammonia, which are linked to proteolytic fermentation, decreased [107].

Systemic effects of probiotics can also be detected in plasma metabolomes, where NMR profiling has revealed modifications in lipid metabolism, branched-chain amino acids, and anti-inflammatory metabolites [105,108]. The integration of metabolomics with microbiota sequencing allows researchers to uncover microbial–metabolite interaction networks that respond to interventions like probiotics. These networks help explain how probiotics exert beneficial effects, moving beyond correlation to mechanistic interpretation. Recent clinical trials have demonstrated that multi-strain probiotics not only enrich SCFA-producing bacteria but also modulate host metabolic markers relevant to glucose homeostasis, lipid profiles, and systemic inflammation, underscoring their potential in personalized nutrition strategies [109,110,111].

Altogether, these findings demonstrate how NMR-based metabolomics represents a powerful approach to unravel the biochemical consequences of dietary modulation of the gut microbiota, providing biomarkers of both dietary exposure and microbial functionality.

4. Conclusions and Future Perspectives

NMR metabolomics represents a robust and reproducible platform for monitoring metabolic responses to dietary interventions. It provides objective biomarkers of dietary adherence, characterizes key pathway alterations, and enables stratification of individuals based on metabolic responsiveness. For Mediterranean diet, NMR highlights have identified several metabolites reflecting a high intake of foods such as fruits, vegetables, and legumes. In the first case, changes in urinary levels of 3-hydroxybutyrate, citric acid, and cis-aconitate, oleic acid, suberic acid, various amino acids and some microbial metabolites; for Vegan or Vegetarian diets, it documents shifts in amino acids, microbiome-derived metabolites, and favorable lipoprotein patterns, and for Western diet an elevation of BCAAs, including leucine, isoleucine, and valine, strongly associated with a high intake of animal protein and fat.

Looking ahead, integrating NMR metabolomics with complementary omics technologies such as microbiome sequencing, lipidomics, and transcriptomics will facilitate a systems-level understanding of diet–microbiome–host interactions and uncover causal mechanisms underlying metabolic phenotypes. Advances in computational approaches, including machine learning and network-based modeling, will further enhance the interpretation of complex multi-omics datasets and enable predictive modeling of dietary responses. Standardization of protocols for sample collection, data acquisition, and analysis remains essential to ensure reproducibility and comparability across studies. Ultimately, these developments will accelerate the translation of metabolomics into clinical and nutritional practice, supporting precision nutrition strategies and personalized dietary recommendations.

In this context, NMR spectroscopy holds great promise for advancing personalized nutrition and health monitoring. Its inherent reproducibility, quantitative accuracy, and non-destructive nature make it particularly suitable for longitudinal metabolic profiling, where consistent measurements over time are crucial for capturing dynamic physiological responses. By providing stable and comparable metabolic fingerprints, NMR enables the monitoring of individual metabolic trajectories in response to dietary interventions, lifestyle modifications, or disease progression. Furthermore, when integrated with other omics technologies—such as genomics, transcriptomics, proteomics, and microbiome analyses—NMR contributes to a comprehensive understanding of metabolic individuality. This integrative, multi-omics framework will be essential for developing tailored nutritional strategies and precision health applications, supporting the ongoing shift toward predictive, preventive, and personalized medicine.