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Metabolites
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NMR-Based Metabolomics in Biomedicine and Food Science
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NMR-Based Metabolomics in Biomedicine and Food Science

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Metabolomic Profiling Technology".

Deadline for manuscript submissions: 13 February 2026 | Viewed by 1029

Special Issue Editors

Dr. Gaia Meoni

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Guest Editor
Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
Interests: nuclear magnetic resonance (NMR) spectroscopy for food and biomedical analysis; investigating the use of NMR spectroscopy to characterize food matrices and identify molecular markers for geographic origin and quality assessments; exploring the use of NMR spectroscopy to study the molecular mechanisms of aging, Parkinson's disease, cancer, viral infections, and periodontal disease; developing NMR-based methodologies for the analysis of complex biological systems and food products
Special Issues, Collections and Topics in MDPI journals
Dr. Alessia Vignoli

E-Mail Website
Guest Editor
Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
Interests: characterization of the chemical composition of complex matrices such as biofluids by means of nuclear magnetic resonance (NMR) spectroscopy; applications of NMR-based metabolomics and lipoproteomics in medicine for diagnostic and prognostic purposes; metabolomics and lipoproteomics in cancer and age-related disorders, particularly Alzheimer’s disease and cerebrovascular diseases; development of computational tools for assessing metabolomic and lipoproteomic data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The dynamic field of metabolomics is reshaping our understanding of biological systems, delivering profound insights into cellular biochemistry, physiological states, and metabolic adaptations. Within this context, nuclear magnetic resonance (NMR) spectroscopy has emerged as an essential, non-destructive analytical tool for metabolomics. NMR’s unique ability to analyze complex biological samples with high reproducibility makes it ideally suited for both fingerprinting and profiling, allowing us to obtain an in-depth characterization of endogenous and exogenous metabolites. This Special Issue aims to illuminate the role of NMR-based metabolomics in advancing research across biomedicine and food science, with a focus on innovative applications and methodologies.

Metabolites act as biochemical messengers between gene expression and external environmental influences, offering a powerful snapshot of the physiological states simultaneously influenced by genetic, dietary, and lifestyle factors. This metabolomic profiling is pivotal in biomedicine for identifying disease biomarkers, elucidating metabolic pathways, and capturing holistic phenotypes in health and disease. In food science, NMR-based metabolomics provides critical insights into food quality, safety, and nutritional value, making it a valuable tool that can contribute to optimizing the composition and quality of food, enhancing consumer health, and guiding agricultural practices.

We invite contributions that leverage the capabilities of NMR in metabolomic research, including original research articles, systematic reviews, and meta-analyses. Topics of interest include the use of NMR in biomedicine, food sciences, and novel NMR methodologies in metabolomics. Studies focused on food science may include the metabolic profiling of food products, assessments of food authenticity and traceability, and investigations into the impacts of the bioactive compounds in food.

This Special Issue strives to highlight groundbreaking research and foster discussions that advance the use of NMR-based metabolomics, making it an indispensable tool in both biomedicine and food science and setting new standards for understanding complex biochemical landscapes.

Dr. Gaia Meoni
Dr. Alessia Vignoli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • NMR
  • biomedicine
  • food
  • fingerprinting
  • profiling
  • AI
  • ML

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Published Papers (2 papers)

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Research

24 pages, 2191 KB  
Article
NMR-Based Metabolomic Profiling Highlights Functional Nutritional Gaps Between Human Milk, Infant Formulas, and Animal Milks
by Gaia Meoni, Leonardo Tenori, Giovanni Niero, Massimo De Marchi and Claudio Luchinat
Metabolites 2025, 15(9), 620; https://doi.org/10.3390/metabo15090620 - 18 Sep 2025
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Abstract
Background/Objectives: Human milk represents the gold standard for infant nutrition, providing a complex and bioactive metabolite profile essential for early development. Despite efforts to enrich infant and toddler formulas with functional ingredients, significant biochemical differences persist. This study aimed to characterize and compare [...] Read more.
Background/Objectives: Human milk represents the gold standard for infant nutrition, providing a complex and bioactive metabolite profile essential for early development. Despite efforts to enrich infant and toddler formulas with functional ingredients, significant biochemical differences persist. This study aimed to characterize and compare the metabolomic composition of human milk, cow’s milk (both conventional and lactose-free), goat’s milk, and a variety of commercial formulas, including both infant (0–12 months) and toddler (1–3 years) formulations, using 1H NMR-based metabolomics. Methods: A total of 90 milk samples were analyzed, including eight formula milk brands, four human milk samples, four goat milk brands, and seven cow milk products. 1H NMR spectra were acquired and processed to identify 54 metabolites. Multivariate and univariate statistical analyses were used to assess compositional similarities and differences among milk types. Results: Human milk displayed a unique metabolic signature, enriched in fucosylated oligosaccharides (2′-FL, 3′-FL), N-acetylated carbohydrates, and amino acids such as glutamine and glutamate. Goat milk was characterized by higher levels of creatine, carnitine, and succinate, whereas cow milk showed elevated orotate, butyrate, and mannose. Formulations exhibited a relatively homogeneous composition across brands but differed from human and animal milk, particularly in choline, formate, and added micronutrients. Toddler formulas contained more energy-related compounds (e.g., creatine, maltose) compared to infant formulas. Conclusions: While modern formulas provide nutritional adequacy, they remain metabolically distinct from human milk. NMR-based metabolomics offers a powerful tool for guiding future improvements in formula composition toward more biomimetic profiles. Full article
(This article belongs to the Special Issue NMR-Based Metabolomics in Biomedicine and Food Science)
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19 pages, 3707 KB  
Article
An NMR Metabolomics Analysis Pipeline for Human Neutrophil Samples with Limited Source Material
by Grace Filbertine, Genna A. Abdullah, Lucy Gill, Rudi Grosman, Marie M. Phelan, Direkrit Chiewchengchol, Nattiya Hirankarn and Helen L. Wright
Metabolites 2025, 15(9), 612; https://doi.org/10.3390/metabo15090612 - 15 Sep 2025
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Abstract
Background/Objectives: Untargeted 1H NMR metabolomics is a robust and reproducible approach used to study the metabolism in biological samples, providing unprecedented insight into altered cellular processes associated with human diseases. Metabolomics is increasingly used alongside other techniques to detect an instantaneous altered [...] Read more.
Background/Objectives: Untargeted 1H NMR metabolomics is a robust and reproducible approach used to study the metabolism in biological samples, providing unprecedented insight into altered cellular processes associated with human diseases. Metabolomics is increasingly used alongside other techniques to detect an instantaneous altered cellular function, for example, the role of neutrophils in the inflammatory response. However, in some clinical settings, blood samples may be limited, restricting the amount of cellular material available for a metabolomic analysis. In this study, we wanted to establish an optimal 1D 1H NMR metabolomic pipeline for use with human neutrophil samples with low amounts of input material. Methods: We compared the effect of different neutrophil isolation protocols on metabolite profiles. We also compared the effect of the absolute cell counts (100,000 to 5,000,000) on the identities of metabolites that were detected with an increasing number of scans (NS) from 256 to 2048. Results/Conclusions: The variance in the neutrophil profile was equivalent between the isolation methods, and the choice of isolation method did not significantly alter the metabolite profile. The minimum number of cells required for the detection of neutrophil metabolites was 400,000 at an NS of 256 for the spectra acquired with a cryoprobe (700 MHz). Increasing the NS to 2048 increased metabolite detection at the very lowest cell counts (<400,000 neutrophils); however, this was associated with a significant increase in the analysis time, which would be rate-limiting for large studies. The application of a correlation-reliability-score-filtering method to the spectral bins preserved the essential discriminatory features of the PLS-DA models whilst improving the dataset robustness and analytical precision. Full article
(This article belongs to the Special Issue NMR-Based Metabolomics in Biomedicine and Food Science)
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