Novel plasma protein biomarkers: A time-dependent predictive model for Alzheimer's disease

• Published in: Archives of gerontology and geriatrics 2025-02, Vol.129, p.105650, Article 105650
• Main Authors: Zhuang, Tianchi, Yang, Yingqi, Ren, Haili, Zhang, Haoxiang, Gao, Chang, Chen, Shen, Shen, Jiemiao, Ji, Minghui, Cui, Yan
• Format: Article
• Language: English
• Subjects: Aged; Alzheimer Disease - blood; Alzheimer Disease - diagnosis; Alzheimer's disease; Apolipoprotein E4 - genetics; Biomarkers; Biomarkers - blood; Blood; Blood Proteins - analysis; Cox proportional hazards model; Female; Humans; Incidence; Longitudinal Studies; Male; Mendelian Randomization Analysis; Middle Aged; Predictive Value of Tests; Proportional Hazards Models; Proteomics; Risk Assessment; ROC Curve
• ISSN: 0167-4943; 1872-6976; 1872-6976
• DOI: 10.1016/j.archger.2024.105650

•CSF collection via lumbar puncture is invasive, difficult to implement widely.•Blood tests are minimally invasive, cost-effective and accessible, suitable for large-scale use in primary care.•Prior studies viewed Alzheimer's disease as binary, ignoring its protracted course from pathological changes to onset, focusing on protein signatures between two groups.•Our study is the first to predict Alzheimer's disease risk dynamically using novel plasma protein biomarkers. The accurate prediction of Alzheimer's disease (AD) is crucial for the efficient management of its progression. The objective of this research was to construct a new risk predictive model utilizing novel plasma protein biomarkers for predicting AD incidence in the future and analyze their potential biological correlation with AD incidence. A cohort of 440 participants aged 60 years and older from the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal cohort was utilized. The baseline plasma proteomics data was employed to conduct Cox regression, LASSO regression, and cross-validation to identify plasma protein signatures predictive of AD risk. Subsequently, a multivariable Cox proportional hazards model based on these signatures was constructed. The performance of the risk prediction model was evaluated using time-dependent receiver operating characteristic (t-ROC) curves and Kaplan-Meier curves. Additionally, we analyzed the correlations between protein signature expression in plasma and predicted AD risk, the time of AD onset, the expression of protein signatures in cerebrospinal fluid (CSF), the expression of CSF and plasma biomarkers, and APOE ε4 genotypes. Colocalization and Mendelian randomization analyses was conducted to investigate the association between protein features and AD risk. GEO database was utilized to analyze the differential expression of protein features in the blood and brain of AD patients. We identified seven protein signatures (APOE, CGA, CRP, CCL26, CCL20, NRCAM, and PYY) that independently predicted AD incidence in the future. The risk prediction model demonstrated area under the ROC curve (AUC) values of 0.77, 0.76, and 0.77 for predicting AD incidence at 4, 6, and 8 years, respectively. Furthermore, the model remained stable in the range of the 3rd to the 12th year (ROC ≥ 0.74). The low-risk group, as defined by the model, exhibited a significantly later AD onset compared to the high-risk group (P < 0.0001). Moreover, all protein signatures exhibite