ArticleHaematologica2021
Untargeted metabolic profiling in dried blood spots identifies disease fingerprint for pyruvate kinase deficiency.
Article in Haematologica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- Dried blood spot metabolomics reveals a metabolic fingerprint with diagnostic potential for Diamond Blackfan Anaemia.British journal of haematology · 2021Trial
- Metabolomic Profiling Has the Potential to Differentiate Between Iron Deficiency Anemia and Anemia of Inflammation.Metabolites · 2026Article
- Metabolic blood profile and response to treatment with the pyruvate kinase activator mitapivat in patients with sickle cell disease.HemaSphere · 2024Article
- Pyruvate kinase deficiency andHeliyon · 2024Article
- A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism.Translational pediatrics · 2022Review
- Untargeted metabolic analysis in dried blood spots reveals metabolic signature in 22q11.2 deletion syndrome.Translational psychiatry · 2022Article
- Metabolic Fingerprint in Hereditary Spherocytosis Correlates With Red Blood Cell Characteristics and Clinical Severity.HemaSphere · 2021Article
- Untargeted LC/MS-Based Metabolic Phenotyping of Hypopituitarism in Young Males.Frontiers in pharmacology · 2021Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnostic evaluation and clinical characterization of rare hereditary anemia (RHA) is to date still challenging. In particular, there is little knowledge on the broad metabolic impact of many of the molecular defects underlying RHA. In this study we explored the potential of untargeted metabolomics to diagnose a relatively common type of RHA: Pyruvate Kinase Deficiency (PKD). In total, 1903 unique metabolite features were identified in dried blood spot samples from 16 PKD patients and 32 healthy controls. A metabolic fingerprint was identified using a machine learning algorithm, and subsequently a binary classification model was designed. The model showed high performance characteristics (AUC 0.990, 95%CI 0.981-0.999) and an accurate class assignment was achieved for all newly added control (13) and patient samples (6), with the exception of one patient (accuracy 94%). Important metabolites in the metabolic fingerprint included glycolytic intermediates, polyamines and several acyl carnitines. In general, the application of untargeted metabolomics in dried blood spots is a novel functional tool that holds promise for diagnostic stratification and studies on disease pathophysiology in RHA.
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