Abstract

Background/Objectives: Discrimination between iron deficiency anemia (IDA) and anemia of inflammation (AI), the major causes of anemia, remains a challenge and novel biomarkers are needed. This prospective study characterizes metabolomic changes in patients with anemia, compares IDA and AI, and evaluates the ability of metabolomic profiling to categorize otherwise unclassifiable anemia (UA) cases. Methods: In the single-center cross-sectional study, patients with anemia were classified as IDA, AI, or their combination (IDA + AI) according to traditional iron biomarkers. Targeted metabolomic analysis was conducted with tandem mass spectrometry using the MxP® Quant 500 kit, with 617 individual metabolites and 227 calculated parameters in the final analysis. Results: The final cohort included 70 patients with anemia and 27 controls. The concentrations of six metabolites and six calculated parameters were significantly different in patients with anemia compared to controls. Anemia was associated with decreased concentrations of several polyunsaturated fatty acid (PUFA)-containing triglycerides (n = 3) and phosphatidylcholines (n = 2), as well as with increased asparagine/aspartate and docosahexaenoic acid/eicosapentaenoic acid ratio. Multivariate analysis using only metabolomic data differentiated between IDA and AI with a general classification accuracy of 88%. Unclassifiable anemia samples were reclassified mostly as IDA (n = 11) or IDA + AI (n = 6), with one sample reclassified as AI. Conclusions: Metabolomic markers of PUFAs differ between patients with anemia and controls, indicating possible alterations in lipid metabolism. Our preliminary findings suggest that metabolomic data may be capable of distinguishing between IDA and AI. However, owing to several limitations of the current study, the potential utility of metabolome-based approaches should be confirmed in independent validation studies.

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