Gutierrez-Tordera Laia, Papandreou Christopher, Novau-Ferré Nil, García-González Pablo, Rojas Melina, Marquié Marta, Chapado Luis A, Papagiannopoulos Christos, Fernàndez-Castillo Noèlia, Valero Sergi, Folch Jaume, Ettcheto Miren, Camins Antoni, Boada Mercè, Ruiz Agustín, Bulló Mònica
Nutrition and Metabolic Health Research Group, Department of Biochemistry and Biotechnology, Rovira i Virgili University (URV), 43201, Reus, Spain.
Institute of Health Pere Virgili (IISPV), 43204, Reus, Spain.
Cell Biosci. 2024 Jan 16;14(1):8. doi: 10.1186/s13578-023-01190-5.
Alzheimer's disease (AD) diagnosis relies on clinical symptoms complemented with biological biomarkers, the Amyloid Tau Neurodegeneration (ATN) framework. Small non-coding RNA (sncRNA) in the blood have emerged as potential predictors of AD. We identified sncRNA signatures specific to ATN and AD, and evaluated both their contribution to improving AD conversion prediction beyond ATN alone.
This nested case-control study was conducted within the ACE cohort and included MCI patients matched by sex. Patients free of type 2 diabetes underwent cerebrospinal fluid (CSF) and plasma collection and were followed-up for a median of 2.45-years. Plasma sncRNAs were profiled using small RNA-sequencing. Conditional logistic and Cox regression analyses with elastic net penalties were performed to identify sncRNA signatures for A+(T|N)+ and AD. Weighted scores were computed using cross-validation, and the association of these scores with AD risk was assessed using multivariable Cox regression models. Gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) enrichment analysis of the identified signatures were performed.
The study sample consisted of 192 patients, including 96 A+(T|N)+ and 96 A-T-N- patients. We constructed a classification model based on a 6-miRNAs signature for ATN. The model could classify MCI patients into A-T-N- and A+(T|N)+ groups with an area under the curve of 0.7335 (95% CI, 0.7327 to 0.7342). However, the addition of the model to conventional risk factors did not improve the prediction of AD beyond the conventional model plus ATN status (C-statistic: 0.805 [95% CI, 0.758 to 0.852] compared to 0.829 [95% CI, 0.786, 0.872]). The AD-related 15-sncRNAs signature exhibited better predictive performance than the conventional model plus ATN status (C-statistic: 0.849 [95% CI, 0.808 to 0.890]). When ATN was included in this model, the prediction further improved to 0.875 (95% CI, 0.840 to 0.910). The miRNA-target interaction network and functional analysis, including GO and KEGG pathway enrichment analysis, suggested that the miRNAs in both signatures are involved in neuronal pathways associated with AD.
The AD-related sncRNA signature holds promise in predicting AD conversion, providing insights into early AD development and potential targets for prevention.
阿尔茨海默病（AD）的诊断依赖于临床症状，并辅以生物标志物，即淀粉样蛋白- tau -神经退行性变（ATN）框架。血液中的小非编码RNA（sncRNA）已成为AD的潜在预测指标。我们确定了ATN和AD特有的sncRNA特征，并评估了它们在单独的ATN之外对改善AD转化预测的贡献。
这项巢式病例对照研究在ACE队列中进行，纳入了按性别匹配的轻度认知障碍（MCI）患者。无2型糖尿病的患者接受脑脊液（CSF）和血浆采集，并进行了中位时间为2.45年的随访。使用小RNA测序对血浆sncRNA进行分析。进行了带有弹性网络惩罚的条件逻辑回归和Cox回归分析，以确定A+(T|N)+和AD的sncRNA特征。使用交叉验证计算加权分数，并使用多变量Cox回归模型评估这些分数与AD风险的关联。对所确定的特征进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析。
研究样本包括192名患者，其中96名A+(T|N)+患者和96名A-T-N-患者。我们构建了一个基于6种miRNA特征的ATN分类模型。该模型能够将MCI患者分为A-T-N-组和A+(T|N)+组，曲线下面积为0.7335（95%CI，0.7327至0.7342）。然而，将该模型添加到传统风险因素中，并没有比传统模型加ATN状态更好地改善AD预测（C统计量：0.805 [95%CI，0.758至0.852]，而传统模型加ATN状态为0.829 [95%CI，0.786，0.872]）。与AD相关的15种sncRNA特征表现出比传统模型加ATN状态更好的预测性能（C统计量：0.849 [95%CI，0.808至0.890]）。当将ATN纳入该模型时，预测进一步提高到0.875（95%CI，0.840至0.910）。miRNA-靶标相互作用网络和功能分析，包括GO和KEGG通路富集分析，表明两个特征中的miRNA都参与了与AD相关的神经元通路。
与AD相关的sncRNA特征在预测AD转化方面具有前景，为AD的早期发展提供了见解，并为预防提供了潜在靶点。
