Povala Guilherme, Bellaver Bruna, De Bastiani Marco Antônio, Brum Wagner S, Ferreira Pamela C L, Bieger Andrei, Pascoal Tharick A, Benedet Andrea L, Souza Diogo O, Araujo Ricardo M, Zatt Bruno, Rosa-Neto Pedro, Zimmer Eduardo R
Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
Graduate Program in Computing, Universidade Federal de Pelotas (UFPEL), Pelotas, Brazil.
Cell Biosci. 2021 Dec 11;11(1):204. doi: 10.1186/s13578-021-00712-3.
Changes in soluble amyloid-beta (Aβ) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer's disease (AD). However, whether Aβ levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aβ isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity.
We used CSF measurements of three soluble Aβ peptides (Aβ, Aβ and Aβ) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals.
Our findings indicate that Aβ isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML model. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed.
Our results demonstrate that, by applying a refined ML analysis, a combination of Aβ isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction.
在阿尔茨海默病（AD）临床前期的早期阶段，脑脊液（CSF）中可溶性淀粉样β蛋白（Aβ）水平的变化是可检测到的。然而，Aβ水平能否预测认知未受损（CU）个体下游的AD病理特征仍不清楚。考虑到这一点，我们旨在研究可溶性Aβ亚型的组合是否能够预测tau病理（T+）和神经变性（N+）阳性。
我们将CU个体（n = 318）中三种可溶性Aβ肽（Aβ、Aβ和Aβ）的CSF测量值作为机器学习（ML）模型的输入特征，旨在预测T+和N+。输入数据用于通过嵌套交叉验证技术构建2046个经过调整的预测ML模型。此外，蛋白质组学数据被用于研究在T+和N+个体中改变的生物过程的功能富集情况。
我们的研究结果表明，Aβ亚型能够分别以0.929和0.936的曲线下面积（AUC）预测T+和N+。此外，蛋白质组学分析在被我们的ML模型错误分类的个体中鉴定出17种差异表达蛋白（DEP）。更具体地说，基因本体生物过程的富集分析显示，被错误预测为T+的CU个体中髓鞘形成和葡萄糖代谢相关过程上调。在包括氨基酸生物合成、糖酵解/糖异生、碳代谢、细胞粘附分子和朊病毒病在内的途径中也观察到DEP的显著富集。
我们的结果表明，通过应用精细的ML分析，Aβ亚型的组合能够以较高的AUC预测T+和N+。CSF蛋白质组学分析突出了一组有前景的蛋白质，可进一步探索以改善T+和N+的预测。
