Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites.
Humans
Metabolomics
/ methods
Diagnosis, Differential
Retinal Degeneration
/ diagnosis
Machine Learning
Male
Female
Retinitis Pigmentosa
/ diagnosis
Stargardt Disease
/ genetics
Adult
Middle Aged
Adolescent
Young Adult
Biomarkers
/ blood
Metabolome
Child
Cone-Rod Dystrophies
/ diagnosis
Mass Spectrometry
Macular Degeneration
/ blood
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
26 Apr 2024
26 Apr 2024
Historique:
received:
28
03
2023
accepted:
16
04
2024
medline:
27
4
2024
pubmed:
27
4
2024
entrez:
26
4
2024
Statut:
epublish
Résumé
The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti's crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in an external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study.
Identifiants
pubmed: 38670966
doi: 10.1038/s41467-024-47911-3
pii: 10.1038/s41467-024-47911-3
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3562Informations de copyright
© 2024. The Author(s).
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