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Nuclear magnetic resonance (NMR) is an advanced technology that allows the analysis of various biological matrices, from blood and urine to less conventional bodily fluids such as breast milk. This ability to explore different matrices with a single analytical approach makes it highly applicable for early disease detection and monitoring specific conditions, adapting to the unique nature of each sample.
Unlike other analytical techniques, NMR does not require extensive sample preparation processes, minimizing the risk of alterations to the original composition and enabling a precise view of the metabolic profile.
The versatlity of NMR is particularly relevant in the context of personalized medicine. It aims to tailor diagnosis and treatment to each patient’s unique characteristics. By analyzing specific fluids and tissues, this technique allows for studying changes in energy metabolism, nutrient balance, inflammatory responses, and lipid profiles. As a result, NMR not only facilitates more precise diagnosis but also enables the monitoring of disease progression and treatment effectiveness.
One example of the versatility of NMR in analyzing diverse biological matrices is a recent pilot study we conducted in collaboration with LactApp. A pioneering company in maternal and infant health, specializing in breastfeeding support and women’s health through a unique digital platform.
The recently published study used NMR to identify biomarkers of mastitis in breast milk.
Mastitis, a painful inflammation of the mammary gland, affects many breastfeeding mothers and contributes to early cessation of breastfeeding. To address this issue, the study included 14 women with acute mastitis (AM), 32 with subacute mastitis (SAM), and 19 without symptoms. Their breast milk samples were analyzed using NMR. This approach enabled the identification of 40 metabolites, including several with significant differences in the presence of mastitis.
Among the key findings, mastitis was found to alter the milk’s metabolic profile, with significant increases in acetate, total cholesterol, esterified cholesterl, and lysophosphatidylcholine in AM cases. SAM also showed increases in certain metabolites, but to a lesser extent.
The results highlight that total cholesterol, acetate, and sphingomyelin are key candidate biomarkers in AM. Meanwhile, cholesterol also showed potential as a marker in SAM. The logistic regression models and partial least squares discriminant analysis (PLS-DA) used in the study corroborated the precision of these biomarkers in differentiating between healthy and mastitis-affected breast milk.
This work not only highlights the versatility of NMR in detecting specific biomarkers in metabolic matrices across different sample types but also underscores its practical application in maternal and infant health, nutrition, and the management of chronic diseases.
Detecting mastitis biomarkers from breast milk can facilitate earlier diagnosis and enable prompt intervention, thereby promoting the continuation of breastfeeding. With these advancements, NMR continues to demonstrate its value in clinical research and paves the way for new applications in personalized medicine.
If you are a researcher and want to learn how NMR metabolomics can assist you in your studies, don’t hesitate to contact us! We would be delighted to explore new opportunities in biomarker analysis together and improve health through research.
Read the full article here.
