MetabOCT: a clinical trial looking for a metabolomic signature predicting the onset of Leber's hereditary optic neuropathy in healthy MtDNA mutations carriers.

IF 3.3 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Metabolomics Pub Date : 2025-09-09 DOI:10.1007/s11306-025-02328-x

Christophe Orssaud, Pascal Reynier

引用次数: 0

Abstract

Introduction: The definition of Leber's hereditary optic neuropathy (LHON) does not take into account a preclinical phase during which the thickness of retinal nerve fiber layer (RNFL) is increased, prior to optic nerve atrophy, reducing the chances of visual recovery.

Objectives: Search for a metabolomic signature characterizing this preclinical phase and identify biomarkers predicting the risk of LHON onset.

Methods and results: The blood and tear metabolomic profiles of 90 asymptomatic LHON mutation carriers followed for one year will be explored as a function of RNFL thickness and compared to those of a healthy control.

Conclusion: Identifying pre-clinical biomarkers would open a window for clinical trials.

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MetabOCT：一项临床试验，寻找代谢组学特征，预测健康MtDNA突变携带者Leber遗传性视神经病变的发病。

Leber's遗传性视神经病变（LHON）的定义没有考虑到视神经萎缩之前视网膜神经纤维层（RNFL）厚度增加的临床前阶段，从而降低了视力恢复的机会。目的：寻找表征这一临床前阶段的代谢组学特征，并确定预测LHON发病风险的生物标志物。方法和结果：对90名无症状LHON突变携带者进行为期一年的随访，研究其血液和眼泪代谢组学特征与RNFL厚度的关系，并与健康对照进行比较。结论：确定临床前生物标志物将为临床试验打开一扇窗。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Metabolomics 医学-内分泌学与代谢

CiteScore

6.60

自引率

2.80%

发文量

审稿时长

2 months

期刊介绍： Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to: metabolomic applications within man, including pre-clinical and clinical pharmacometabolomics for precision medicine metabolic profiling and fingerprinting metabolite target analysis metabolomic applications within animals, plants and microbes transcriptomics and proteomics in systems biology Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.