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 |
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| Hlavní autori: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Birgit orcidid: 0000-0002-8889-3362 surname: Dooijeweert fullname: Dooijeweert, Birgit email: b.vandooijeweert@umcutrecht.nl organization: Utrecht University – sequence: 2 givenname: Melissa H. surname: Broeks fullname: Broeks, Melissa H. organization: Utrecht University – sequence: 3 givenname: Eduard J. orcidid: 0000-0002-3934-7189 surname: Beers fullname: Beers, Eduard J. organization: University Medical Center Utrecht – sequence: 4 givenname: Nanda M. surname: Verhoeven‐Duif fullname: Verhoeven‐Duif, Nanda M. organization: Utrecht University – sequence: 5 givenname: Wouter W. surname: Solinge fullname: Solinge, Wouter W. organization: Utrecht University – sequence: 6 givenname: Edward E. S. surname: Nieuwenhuis fullname: Nieuwenhuis, Edward E. S. organization: Utrecht University – sequence: 7 givenname: Judith J. surname: Jans fullname: Jans, Judith J. organization: Utrecht University – sequence: 8 givenname: Richard surname: Wijk fullname: Wijk, Richard organization: Utrecht University – sequence: 9 givenname: Marije surname: Bartels fullname: Bartels, Marije organization: University Medical Center Utrecht |
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| Cites_doi | 10.1056/NEJMoa1902678 10.3324/haematol.11498 10.3109/1354750X.2014.1002807 10.3390/metabo9010012 10.1038/nrm3314 10.1002/pbc.25534 10.1182/blood-2009-10-178129 10.1016/j.freeradbiomed.2013.03.022 10.3324/haematol.2020.266957 10.1111/j.1365-2141.2008.07269.x 10.1016/S2352-3026(18)30020-6 10.1016/j.ajhg.2018.10.027 10.1182/blood-2010-07-299636 10.1056/NEJM198312153092404 10.1016/j.aca.2017.04.038 10.3324/haematol.2013.086637 10.1186/s12864-015-2354-y 10.1111/bjh.15701 10.1002/cpbi.86 10.1002/pbc.28748 10.1111/ejh.12995 |
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| Keywords | machine-learning algorithm untargeted metabolomics Diamond Blackfan Anaemia dried blood spots disease fingerprint |
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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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| 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 |
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