ArticleNature communications2024
Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 14 citations in OpenAlex.
- Exploration of Metabolic Alterations in Methylmalonic Acidemia across Distinct Genetic Backgrounds.ACS omega · 2026Article
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
- Lipid-Retinoid Metabolic Network Links Genetic Variation to Molecular Heterogeneity in Inherited Retinal Degeneration.International journal of molecular sciences · 2026Article
- Chronic changes in acute zonal occult outer retinopathy mimicking non-progressive retinitis pigmentosa: clinical and laboratory characteristics.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Ionomic profiling of vitreous humor and serum in rhegmatogenous retinal detachment.Scientific reports · 2026Article
- Candidate Obesity Biomarkers Identified Through Multi-Omics Analysis, Mendelian Randomization, and Mediation Analysis.Food science & nutrition · 2026Article
- Genetic Analysis of Choroideremia-Related Rab Escort Proteins.International journal of molecular sciences · 2025Article
- Untargeted Swab Touch Spray-Mass Spectrometry Analysis with Machine Learning for On-Site Breast Surgical Margin Assessment.Analytical chemistry · 2025Article
- Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods.Biomedicines · 2024Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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