European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.
Department of Metabolism, Digestion and Reproduction, National Phenome Centre, Imperial College London, Hammersmith Campus, IRDB Building, London, UK.
Sci Rep. 2020 Dec 10;10(1):21745. doi: 10.1038/s41598-020-78031-9.
Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer's Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer's Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer's Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer's Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer's pathology in previous studies.
利用非侵入性且广泛可用的方法发现早期疾病标志物对于开发成功的阿尔茨海默病治疗方法至关重要。迄今为止，很少有研究检查尿液，这是最容易获得的生物流体。在这里，我们报告了迄今为止最大的研究，该研究使用综合代谢表型平台（NMR 光谱和 UHPLC-MS）深入探测阿尔茨海默病和轻度认知障碍患者的尿液代谢组。使用代谢组学定量性状基因座进行特征减少，从而确定与遗传变异相关的代谢物列表。这种方法有助于提高疾病状态识别的准确性，并为与病理过程的合理机制联系提供途径。使用这些 mQTL，我们构建了一个随机森林模型，该模型不仅可以正确区分阿尔茨海默病患者和年龄匹配的对照组，还可以区分后来被诊断为阿尔茨海默病的轻度认知障碍患者和未被诊断为阿尔茨海默病的患者。经过训练的模型提名的排名靠前的代谢特征的进一步注释表明，胆固醇衍生代谢物和小分子的参与与之前的研究中的阿尔茨海默病病理学有关。
