Serum NMR metabolomics in distinct subtypes of hematologic malignancies

Hematologic malignancies encompass a diverse array of subtypes, contributing to substantial heterogeneity that poses challenges in predicting clinical outcomes. Leveraging the capabilities of nuclear magnetic resonance holds substantial promise in the detection of serum biomarkers and individual metabolic alterations in patients. This study involved the analysis of the sera from patients with acute myeloid leukemia, chronic lymphocytic leukemia, and non-Hodgkin lymphoma to investigate the affected metabolites and their associated pathways. The quantitative one-dimensional (1D) 1H nuclear magnetic resonance method was employed to identify alterations. Metabolite annotations were validated using two-dimensional (2D) analyses. Discriminating chemometric models and receiver operating characteristic curves were created using the MetaboAnalyst platform. The findings revealed significant alterations in the serum levels of amino acid catabolism products, citrate cycle intermediates, and phospholipids. The acute myeloid leukemia group showed differences in glucogenic amino acids related to the glycolysis pathway, whereas the chronic lymphocytic leukemia and non-Hodgkin lymphoma groups displayed variances in fumarate and acetate levels linked to the citrate cycle pathway. In the leukemia groups, higher levels of products from the protein degradation pathway were observed. The biomarker panels for each malignancy group exhibited outstanding discrimination from controls. Healthy individuals differed distinctly from patients, indicating commonly observed metabolic adaptation patterns among frequent hematologic malignancies. The small cohort study using nuclear magnetic resonance metabolomics in various hematologic malignancy subtypes revealed significant changes in serum amino acid and protein degradation end-product levels, suggesting prolonged leukocyte lifespan and increased energy demand. (c) 2025 International Society for Experimental Hematology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.