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

ABSTRACT: 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.

SUBMITTER: van Dooijeweert B

PROVIDER: S-EPMC8251760 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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

van Dooijeweert Birgit B Broeks Melissa H MH van Beers Eduard J EJ Verhoeven-Duif Nanda M NM van Solinge Wouter W WW Nieuwenhuis Edward E S EES Jans Judith J JJ van Wijk Richard R Bartels Marije M

British journal of haematology 20210517 6

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 incorpo ...[more]

PMID: 33997957

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Project description:IntroductionUntargeted metabolomics holds significant promise for biomarker detection and development. In resource-limited settings, a dried blood spot (DBS)-based platform would offer significant advantages over plasma-based approaches that require a cold supply chain.ObjectivesThe primary goal of this study was to compare the ability of DBS- and plasma-based assays to characterize maternal metabolites. Utility of the two assays was also assessed in the context of a case-control predictive model in pregnant women living with HIV.MethodsUntargeted metabolomics was performed on archived paired maternal plasma and DBS from n = 79 women enrolled in a large clinical trial.ResultsA total of 984 named biochemicals were detected across both plasma and DBS samples, of which 627 (63.7%), 260 (26.4%), and 97 (9.9%) were detected in both plasma and DBS, plasma alone, and DBS alone, respectively. Variation attributable to study individual (R2 = 0.54, p < 0.001) exceeded that of the sample type (R2 = 0.21, p < 0.001), suggesting that both plasma and DBS were capable of differentiating individual metabolomic profiles. Log-transformed metabolite abundances were strongly correlated (mean Spearman rho = 0.51) but showed low agreement (mean intraclass correlation of 0.15). However, following standardization, DBS and plasma metabolite profiles were strongly concordant (mean intraclass correlation of 0.52). Random forests classification models for cases versus controls identified distinct feature sets with comparable performance in plasma and DBS (86.5% versus 91.2% mean accuracy, respectively).ConclusionMaternal plasma and DBS samples yield distinct metabolite profiles highly predictive of the individual subject. In our case study, classification models showed similar performance albeit with distinct feature sets. Appropriate normalization and standardization methods are critical to leverage data from both sample types. Ultimately, the choice of sample type will likely depend on the compounds of interest as well as logistical demands.

Dataset Information

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

Publications

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

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