Background: Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical tool in metabolomics, but it is often hindered by the high cost and technical complexity of the machines, limiting its clinical and point-of-care applications. Recent advances in benchtop NMR technology have sought to overcome these barriers by providing more compact, affordable, and user-friendly instruments. This systematic review aims to assess the potential of benchtop NMR in clinical metabolomics, highlighting its practical advantages, current applications, and technological challenges relative to high-field systems. Methods: For this systematic review we searched Web of Science and PubMed databases to identify studies employing benchtop NMR spectroscopy in clinical and biomedical applications. The review focuses on works that evaluated metabolic profiling in human and animal disease contexts, compared benchtop and high-field performance, and utilized advanced data analysis methods, including multivariate and machine learning approaches. Results: Among the 74 records identified, 15 research articles were eligible, including 11 studies involving human biospecimens and 4 studies concerning animal samples. The selected works were published between 2018 and 2025. These studies demonstrated the potential clinical utility of low-field NMR in differentiating disease states such as tuberculosis, type 2 diabetes, neonatal sepsis, and chronic kidney disease, achieving diagnostic accuracies comparable to high-field instruments. Conclusions: Although limited by lower sensitivity and spectral resolution, benchtop NMR represents a significant step toward the democratization of NMR-based metabolomics. Continued hardware development, improved pulse sequences, and the integration of artificial intelligence for spectral processing and modeling are expected to enhance its analytical power and accelerate its clinical adoption.
Benchtop NMR in Biomedicine: An Updated Literature Overview / Fantato, Linda; Salobehaj, Maria; Patrussi, Jacopo; Meoni, Gaia; Vignoli, Alessia; Tenori, Leonardo. - In: METABOLITES. - ISSN 2218-1989. - ELETTRONICO. - 16:(2025), pp. 3.0-3.0. [10.3390/metabo16010003]
Benchtop NMR in Biomedicine: An Updated Literature Overview
Fantato, Linda;Salobehaj, Maria;Patrussi, Jacopo;Meoni, Gaia;Vignoli, Alessia
;Tenori, Leonardo
2025
Abstract
Background: Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical tool in metabolomics, but it is often hindered by the high cost and technical complexity of the machines, limiting its clinical and point-of-care applications. Recent advances in benchtop NMR technology have sought to overcome these barriers by providing more compact, affordable, and user-friendly instruments. This systematic review aims to assess the potential of benchtop NMR in clinical metabolomics, highlighting its practical advantages, current applications, and technological challenges relative to high-field systems. Methods: For this systematic review we searched Web of Science and PubMed databases to identify studies employing benchtop NMR spectroscopy in clinical and biomedical applications. The review focuses on works that evaluated metabolic profiling in human and animal disease contexts, compared benchtop and high-field performance, and utilized advanced data analysis methods, including multivariate and machine learning approaches. Results: Among the 74 records identified, 15 research articles were eligible, including 11 studies involving human biospecimens and 4 studies concerning animal samples. The selected works were published between 2018 and 2025. These studies demonstrated the potential clinical utility of low-field NMR in differentiating disease states such as tuberculosis, type 2 diabetes, neonatal sepsis, and chronic kidney disease, achieving diagnostic accuracies comparable to high-field instruments. Conclusions: Although limited by lower sensitivity and spectral resolution, benchtop NMR represents a significant step toward the democratization of NMR-based metabolomics. Continued hardware development, improved pulse sequences, and the integration of artificial intelligence for spectral processing and modeling are expected to enhance its analytical power and accelerate its clinical adoption.| File | Dimensione | Formato | |
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