Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA.
Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA.
J Alzheimers Dis. 2020;78(4):1381-1392. doi: 10.3233/JAD-200305.
Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification.
Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD.
Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls.
Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72-0.76, with the sensitivity and specificity values ranging from 0.75-0.85 and 0.69-0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD.
A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.
目前，尚无客观、临床可用的工具可用于准确诊断阿尔茨海默病（AD）。因此，迫切需要一种新的、微创、具有成本效益且易于获得的工具来诊断 AD、评估疾病严重程度并预测病程。代谢组学是发现用于 AD 检测和分类的新的、生物学和临床上相关的生物标志物的有前途的工具。
利用人工智能和机器学习，我们旨在评估在血浆中检测到的代谢物是否可作为诊断轻度认知障碍（MCI）和 AD 的客观且可行的临床工具。
使用来自美国各地不同地点的基于社区的样本队列，我们采用了一种结合质子磁共振波谱（1H NMR）、液相色谱与质谱联用（LC-MS）和各种机器学习统计方法的方法，以确定能够识别 AD 和 MCI 患者与健康对照者的生物标志物。
在 212 种测量的代谢物中，有 5 种被确定为区分对照组、MCI 或 AD 患者的最佳代谢物。我们的模型具有 0.72-0.76 的 AUC 值，灵敏度和特异性值分别为 0.75-0.85 和 0.69-0.81。单变量和途径分析确定脂质代谢是 MCI 和 AD 中最受干扰的生化途径。
综合获取代谢组学数据的方法，结合机器学习技术，确定了一组强大的诊断生物标志物，能够识别 MCI 和 AD 患者。此外，我们的数据证实了其他研究小组的报告，即脂质代谢在患有痴呆症的个体中受到显著干扰。这项工作可能为 AD 的发病机制提供更多的见解，并鼓励对 AD 脂质组学进行更深入的分析。
