Department of Food Science and Technology, CEBAS-CSIC, Campus de Espinardo. P.O. Box 164, 30100 Espinardo (Murcia), Spain.
Curr Med Chem. 2014 Mar;21(7):823-48. doi: 10.2174/0929867320666131119124056.
This review was designed as a handbook of metabolomic markers of high significance for a wide range of human diseases. This is the first report to collate results from recent studies in a format that allows ready identification of key metabolites by cross-comparisons of results from one disease to another. All the data presented in this work were obtained by previous research carried out exclusively during clinical trials in humans. Also, discussion of the pathophysiological pathways linked to the markers described is provided. The clinical assays focused on non-targeted or targeted metabolomics and metabolite profiling (focused assays which only refer to a limited array of known biomarkers, applying discriminatory and bioinformatic tools to them) as well as predictive modelling based on clinical trials. The data also highlight pathways and biological compounds that are disrupted at early stages of the diseases, in order to help elucidate target compounds and the pathophysiology of the considered diseases for early prognosis and diagnosis using noninvasive samples (saliva, sputum, serum, plasma, blood, urine, tissue, faecal water or faeces). In the tables, the candidate metabolites for biomarkers of diagnosis, or the biomarkers themselves, are detailed, indicating the type of sample in which they were detected and their up- or down-regulation (if calculated). The metabolites derived from each study have been filtered carefully, according to the analytical platform, and biostatistical discriminant analyses developed. Among the pool of data provided, those reaching a level of significance of p=0.05-0.0001, according to the Bonferroni correction, Steel-Dwass t- or Wilcoxon matched pair tests, are shown.
本综述旨在提供一份高影响力代谢标志物手册,涵盖广泛的人类疾病。这是第一份将来自不同疾病研究结果进行交叉比较,从而能够方便地识别关键代谢物的报告。本工作中呈现的所有数据均来自以前在人类临床试验中进行的研究。此外,还提供了与所描述标志物相关的病理生理途径的讨论。临床检测方法侧重于非靶向或靶向代谢组学和代谢物图谱分析(仅针对有限的已知生物标志物阵列进行的靶向检测,对其应用判别和生物信息学工具)以及基于临床试验的预测建模。这些数据还突出了在疾病早期受到干扰的途径和生物化合物,以便帮助阐明考虑疾病的靶化合物和病理生理学,从而使用非侵入性样本(唾液、痰液、血清、血浆、血液、尿液、组织、粪便水或粪便)进行早期预后和诊断。在表格中,详细列出了候选代谢物作为诊断标志物,或作为标志物本身,同时指出了在其中检测到它们的样本类型及其上调或下调情况(如果有计算)。根据分析平台,仔细筛选了从每项研究中得出的代谢物,并进行了生物统计学判别分析。在所提供的数据中,显示了根据 Bonferroni 校正、Steel-Dwass t 检验或 Wilcoxon 匹配对检验达到 p=0.05-0.0001 水平的那些数据。