PDF(Pubmed)
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.
摘要：
遗传性视网膜变性（IRD）的诊断由于其表型和基因型的复杂性而具有挑战性。在进行基因诊断之前，临床信息很重要。代谢组学研究生物制品的全貌，这是通过遗传密码和生物反应确定的。我们证明了IRD的常见诊断，包括视网膜色素变性（RP），锥杆营养不良（CRD），Stargardt病（STGD），和Bietti的晶体营养不良（BCD），可以根据它们的代谢物热图进行区分。在除BCD外的每个IRD中，与对照组相比，在火山地块中鉴定出数百种代谢物，被认为是潜在的诊断标志物。CRD和STGD的表型重叠，但可以通过其代谢组学特征在机器学习模型的帮助下以100％的准确性进行区分。此外，眼睛-,USH2A相关，和其他RP，在临床发现中具有相当相似的特征，也可以使用机器学习模型进行诊断，准确率为85.7%。需要进一步的研究来验证外部数据集中的结果。通过结合质谱和机器学习，我们的研究提出了一种基于代谢组学的诊断工作流程,用于IRD的临床和分子诊断.
