Hwangbo Nathan, Zhang Xinyu, Raftery Daniel, Gu Haiwei, Hu Shu-Ching, Montine Thomas J, Quinn Joseph F, Chung Kathryn A, Hiller Amie L, Wang Dongfang, Fei Qiang, Bettcher Lisa, Zabetian Cyrus P, Peskind Elaine R, Li Ge, Promislow Daniel E L, Davis Marie Y, Franks Alexander
Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106, USA.
Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA.
Metabolites. 2022 Mar 22;12(4):277. doi: 10.3390/metabo12040277.
In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.
近年来，代谢组学已成为一种强大的工具，用于更好地理解神经退行性疾病的生理学，并识别疾病进展的潜在生物标志物。我们使用来自人类脑脊液代谢组的靶向和非靶向水性及脂质组学谱，构建多变量预测模型，以区分阿尔茨海默病（AD）、帕金森病（PD）患者和年龄匹配的健康对照。我们强调了代谢组学研究中与测量代谢物数量远远超过样本量相关的几个统计挑战。我们发现，PD与对照组之间以及PD与AD之间的代谢组存在明显分离，而AD与对照组之间的分离较弱。与现有文献一致，我们发现丙氨酸、犬尿氨酸、色氨酸和丝氨酸与PD相对于对照组的分类相关，而丙氨酸、肌酸和长链神经酰胺与AD相对于对照组的分类相关。我们对非靶向和靶向代谢物谱进行了单变量通路分析，发现维生素E和尿素循环代谢通路与PD相关，而天冬氨酸/天冬酰胺和C21 - 甾体激素生物合成通路与AD相关。我们还发现代谢物缺失量因表型而异，突出了在未来代谢组学研究中检查缺失数据的重要性。
