Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites

Wei-Chieh Wang, Chu-Hsuan Huang, Hsin-Hsiang Chung, Pei-Lung Chen, Fung-Rong Hu, Chang-Hao Yang, Chung-May Yang, Chao-Wen Lin, Cheng-Chih Hsu () and Ta-Ching Chen ()
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Wei-Chieh Wang: National Taiwan University
Chu-Hsuan Huang: Cathay General Hospital
Hsin-Hsiang Chung: National Taiwan University
Pei-Lung Chen: National Taiwan University
Fung-Rong Hu: National Taiwan University Hospital
Chang-Hao Yang: National Taiwan University Hospital
Chung-May Yang: National Taiwan University Hospital
Chao-Wen Lin: National Taiwan University Hospital
Cheng-Chih Hsu: National Taiwan University
Ta-Ching Chen: National Taiwan University Hospital

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-47911-3

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