Shvetcov Artur, Thomson Shannon, Spathos Jessica, Cho Ann-Na, Wilkins Heather M, Andrews Shea J, Delerue Fabien, Couttas Timothy A, Issar Jasmeen Kaur, Isik Finula, Kaur Simranpreet, Drummond Eleanor, Dobson-Stone Carol, Duffy Shantel L, Rogers Natasha M, Catchpoole Daniel, Gold Wendy A, Swerdlow Russell H, Brown David A, Finney Caitlin A
Department of Psychological Medicine, Sydney Children's Hospitals Network, Sydney, NSW 2031, Australia.
Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia.
Int J Mol Sci. 2023 Oct 9;24(19):15011. doi: 10.3390/ijms241915011.
Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.
阿尔茨海默病（AD）是一个日益严重的全球健康危机，影响着数百万人，并产生了巨大的经济成本。然而，临床诊断仍然具有挑战性，误诊和漏诊很普遍。人们越来越关注可能对AD的诊断和早期检测有用的假定血液生物标志物。在本研究中，我们使用了机器学习和功能网络分析的无偏组合来识别AD中的血液基因生物标志物候选物。通过监督机器学习，我们还确定了这些候选物是否确实是AD特有的，或者它们是否指示其他神经退行性疾病，如帕金森病（PD）和肌萎缩侧索硬化症（ALS）。我们的分析表明，参与剪接体组装、RNA结合、转录、蛋白质合成、线粒体核糖体和NADH脱氢酶的基因是相对于认知健康对照识别AD患者的表现最佳的基因。然而，这种转录组特征并非AD所特有，随后的机器学习表明，这种特征也可以预测相对于没有神经退行性疾病的对照的PD和ALS。综合来看，我们的结果表明全血mRNA确实可用于筛查神经退行性疾病患者，但在诊断特定神经退行性疾病方面可能效果较差。
