背景:阿尔茨海默病(AD)的最大挑战之一是确定容易获得的疾病预测途径和标志物,用于预防和治疗。在这里,我们分析了来自患有和不患有脑淀粉样蛋白负荷的老年无症状个体的Alzheimer预言者(INSIGHT-preAD)队列的血液样本。
方法:我们进行了血液RNAseq,和血浆代谢组学和脂质组学使用液相色谱-质谱法对48例淀粉样蛋白阳性和48例淀粉样蛋白阴性(SUVr截止为0·7918)。使用基于负二项分布的差异基因表达分别分析了三个数据集,非参数(Wilcoxon)和参数(相关调整的学生t)检验。使用稀疏偏最小二乘判别和主成分分析进行数据整合。使用接收器工作特性曲线测试了来自三个数据集的Bootstrap选择的前十名特征的判别能力。对22名受试者的子集进行纵向代谢组学分析。
结果:单变量分析确定了三种中链脂肪酸,在淀粉样蛋白阳性和阴性受试者中差异定量的4-硝基苯酚和一组64种富含炎症和脂肪酸代谢的转录物。重要的是,在22名受试者的子组中,三种中链脂肪酸的含量随时间相关(p<0·05)。多组学整合分析表明,代谢物根据淀粉样蛋白状态有效区分受试者,而脂质则没有,转录本显示出趋势。最后,前十位代谢物和转录本代表了最具判别性的组学特征,预测淀粉样蛋白阳性的概率为99·4%.
结论:这项研究提示了一个潜在的血液组学特征,用于预测无症状高危受试者的淀粉样蛋白阳性,允许侵入性较小,更方便,与PET研究或腰椎穿刺相比,AD的风险评估费用更低。基金:医院大学研究所和塞韦乌和莫埃勒埃普尼埃研究所(IHU-A-ICM),法国研究部,阿尔茨海默氏症基金会,辉瑞,和狂热。
BACKGROUND: One of the biggest challenge in Alzheimer\'s disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer\'s predicTors (INSIGHT-preAD) cohort of elderly asymptomatic individuals with and without brain amyloid load.
METHODS: We performed blood RNAseq, and plasma metabolomics and lipidomics using liquid chromatography-mass spectrometry on 48 individuals amyloid positive and 48 amyloid negative (SUVr cut-off of 0·7918). The three data sets were analysed separately using differential gene expression based on negative binomial distribution, non-parametric (Wilcoxon) and parametric (correlation-adjusted Student\'t) tests. Data integration was conducted using sparse partial least squares-discriminant and principal component analyses. Bootstrap-selected top-ten features from the three data sets were tested for their discriminant power using Receiver Operating Characteristic curve. Longitudinal metabolomic analysis was carried out on a subset of 22 subjects.
RESULTS: Univariate analyses identified three medium chain fatty acids, 4-nitrophenol and a set of 64 transcripts enriched for inflammation and fatty acid metabolism differentially quantified in amyloid positive and negative subjects. Importantly, the amounts of the three medium chain fatty acids were correlated over time in a subset of 22 subjects (p < 0·05). Multi-omics integrative analyses showed that metabolites efficiently discriminated between subjects according to their amyloid status while lipids did not and transcripts showed trends. Finally, the ten top metabolites and transcripts represented the most discriminant omics features with 99·4% chance prediction for amyloid positivity.
CONCLUSIONS: This
study suggests a potential blood omics signature for prediction of amyloid positivity in asymptomatic at-risk subjects, allowing for a less invasive, more accessible, and less expensive risk assessment of AD as compared to PET studies or lumbar puncture. FUND: Institut Hospitalo-Universitaire and Institut du Cerveau et de la Moelle Epiniere (IHU-A-ICM), French Ministry of Research, Fondation Alzheimer, Pfizer, and Avid.