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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Vydané v:British journal of haematology Ročník 193; číslo 6; s. 1185 - 1193
Hlavní autori: 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:English
Vydavateľské údaje: England Blackwell Publishing Ltd 01.06.2021
John Wiley and Sons Inc
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ISSN:0007-1048, 1365-2141, 1365-2141
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Abstract 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.
AbstractList 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.
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.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.
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.
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.
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.
Author Wijk, Richard
Dooijeweert, Birgit
Bartels, Marije
Nieuwenhuis, Edward E. S.
Beers, Eduard J.
Solinge, Wouter W.
Verhoeven‐Duif, Nanda M.
Broeks, Melissa H.
Jans, Judith J.
AuthorAffiliation 4 Van Creveldkliniek University Medical Center Utrecht Utrecht the Netherlands
1 Central Diagnostic Laboratory‐Research University Medical Center Utrecht Utrecht University Utrecht the Netherlands
3 Section Metabolic Diagnostics Department of Genetics University Medical Center Utrecht Utrecht University Utrecht the Netherlands
2 Department of Paediatric Haematology University Medical Center Utrecht Utrecht University Utrecht the Netherlands
AuthorAffiliation_xml – name: 4 Van Creveldkliniek University Medical Center Utrecht Utrecht the Netherlands
– name: 3 Section Metabolic Diagnostics Department of Genetics University Medical Center Utrecht Utrecht University Utrecht the Netherlands
– name: 2 Department of Paediatric Haematology University Medical Center Utrecht Utrecht University Utrecht the Netherlands
– name: 1 Central Diagnostic Laboratory‐Research University Medical Center Utrecht Utrecht University Utrecht the Netherlands
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CitedBy_id crossref_primary_10_1097_MOP_0000000000001196
crossref_primary_10_1002_hem3_109
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crossref_primary_10_3389_fphys_2021_735543
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Issue 6
Keywords machine-learning algorithm
untargeted metabolomics
Diamond Blackfan Anaemia
dried blood spots
disease fingerprint
Language English
License Attribution-NonCommercial
2021 The Authors. British Journal of Haematology published by British Society for Haematology and John Wiley & Sons Ltd.
This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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Snippet Summary The diagnostic evaluation of Diamond Blackfan Anaemia (DBA), an inherited bone marrow failure syndrome characterised by erythroid hypoplasia, is...
The diagnostic evaluation of Diamond Blackfan Anaemia (DBA), an inherited bone marrow failure syndrome characterised by erythroid hypoplasia, is challenging...
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StartPage 1185
SubjectTerms 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
Title Dried blood spot metabolomics reveals a metabolic fingerprint with diagnostic potential for Diamond Blackfan Anaemia
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fbjh.17524
https://www.ncbi.nlm.nih.gov/pubmed/33997957
https://www.proquest.com/docview/2541353628
https://www.proquest.com/docview/2528436364
https://pubmed.ncbi.nlm.nih.gov/PMC8251760
Volume 193
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