Dried blood spot metabolomics reveals a metabolic fingerprint with diagnostic potential for Diamond Blackfan Anaemia

Summary The diagnostic evaluation of Diamond Blackfan Anaemia (DBA), an inherited bone marrow failure syndrome characterised by erythroid hypoplasia, is challenging because of a broad phenotypic variability and the lack of functional screening tests. In this study, we explored the potential of untar...

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Vydáno v:British journal of haematology Ročník 193; číslo 6; s. 1185 - 1193
Hlavní autoři: Dooijeweert, Birgit, Broeks, Melissa H., Beers, Eduard J., Verhoeven‐Duif, Nanda M., Solinge, Wouter W., Nieuwenhuis, Edward E. S., Jans, Judith J., Wijk, Richard, Bartels, Marije
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Blackwell Publishing Ltd 01.06.2021
John Wiley and Sons Inc
Témata:
Anemia
Blood
Bone marrow
Diamond Blackfan Anaemia
disease fingerprint
dried blood spots
Genetic variability
Hematology
Hypoplasia
Learning algorithms
Machine learning
machine‐learning algorithm
Metabolism
Metabolites
Metabolomics
Paediatrics
Research Paper
untargeted metabolomics
machine-learning algorithm
untargeted metabolomics
Diamond Blackfan Anaemia
dried blood spots
disease fingerprint
ISSN:0007-1048, 1365-2141, 1365-2141
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Shrnutí:Summary The diagnostic evaluation of Diamond Blackfan Anaemia (DBA), an inherited bone marrow failure syndrome characterised by erythroid hypoplasia, is challenging because of a broad phenotypic variability and the lack of functional screening tests. In this study, we explored the potential of untargeted metabolomics to diagnose DBA. In dried blood spot samples from 18 DBA patients and 40 healthy controls, a total of 1752 unique metabolite features were identified. This metabolic fingerprint was incorporated into a machine‐learning algorithm, and a binary classification model was constructed using a training set. The model showed high performance characteristics (average accuracy 91·9%), and correct prediction of class was observed for all controls (n = 12) and all but one patient (n = 4/5) from the validation or ‘test’ set (accuracy 94%). Importantly, in patients with congenital dyserythropoietic anaemia (CDA) – an erythroid disorder with overlapping features – we observed a distinct metabolic profile, indicating the disease specificity of the DBA fingerprint and underlining its diagnostic potential. Furthermore, when exploring phenotypic heterogeneity, DBA treatment subgroups yielded discrete differences in metabolic profiles, which could hold future potential in understanding therapy responses. Our data demonstrate that untargeted metabolomics in dried blood spots is a promising new diagnostic tool for DBA.
Bibliografie:Authors contributed equally.
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ISSN:0007-1048
1365-2141
1365-2141
DOI:10.1111/bjh.17524