Altuna Miren, García-Sebastián Maite, Cipriani Raffaela, Capetillo-Zarate Estibaliz, Alberdi Elena, Estanga Ainara, Ecay-Torres Mirian, Iriondo Ane, Saldias Jon, Cañada Marta, López Carolina, Arriba Maria, Tainta Mikel, Martínez-Lage Pablo
Center for Research and Memory Clinic, CITA-Alzheimer Foundation, Donostia-San Sebastián, 20009, Spain.
Debabarrena Integrated Health Organization, Osakidetza Basque Health Service, Mendaro, 20690, Spain.
Sci Rep. 2025 Aug 27;15(1):31526. doi: 10.1038/s41598-025-17394-3.
Early identification of Alzheimer's disease (AD) pathology is essential for timely intervention, particularly in primary care. We evaluated the diagnostic performance of a scalable, multimodal framework in a real-world, population-based cohort. A total of 277 community-dwelling individuals aged ≥ 60 years from the STOP-ALZHEIMER DEBA study (Basque Country, Spain) underwent brief cognitive screening (MMSE, M@T, Fototest, AD8) with optimized cut-offs, along with clinical risk assessment. Among them, 181 participants also completed structural MRI, plasma biomarker profiling (p-tau181, Aβ42/40, GFAP, NfL), and cerebrospinal fluid (CSF) analysis. We assessed performance for detecting cognitive impairment, CSF amyloid positivity (A+), and combined amyloid-tau positivity (A + T+). Optimized cognitive tests showed moderate accuracy (AUC 0.66-0.77), with the Fototest performing best. For biological outcomes, GFAP and p-tau181 had the highest predictive value (AUCs: 0.813 and 0.755 for A+; 0.852 and 0.710 for A + T+), and their combination further improved accuracy (AUC = 0.842). Fully adjusted models incorporating optimized cognitive scores, plasma biomarkers, APOE genotype, MRI, and demographics achieved high diagnostic performance (AUC = 0.886 for A+; 0.893 for A + T+). Results were consistent across sex and age strata. These findings support a stepwise diagnostic strategy combining brief, minimally invasive tools to enhance early AD detection in community settings.
早期识别阿尔茨海默病（AD）病理对于及时干预至关重要，尤其是在初级保健中。我们在一个基于人群的真实世界队列中评估了一个可扩展的多模态框架的诊断性能。来自西班牙巴斯克地区的“STOP-ALZHEIMER DEBA”研究中，共有277名年龄≥60岁的社区居民接受了优化临界值的简短认知筛查（简易精神状态检查表、M@T、Fototest、AD8）以及临床风险评估。其中，181名参与者还完成了结构磁共振成像、血浆生物标志物分析（磷酸化tau181、淀粉样蛋白β42/40、胶质纤维酸性蛋白、神经丝轻链）和脑脊液分析。我们评估了检测认知障碍、脑脊液淀粉样蛋白阳性（A+）以及联合淀粉样蛋白- tau阳性（A+T+）的性能。优化后的认知测试显示出中等准确性（曲线下面积为0.66 - 0.77），其中Fototest表现最佳。对于生物学结果，胶质纤维酸性蛋白和磷酸化tau181具有最高的预测价值（A+的曲线下面积分别为0.813和0.755；A+T+的曲线下面积分别为0.852和0.710），它们的组合进一步提高了准确性（曲线下面积 = 0.842）。纳入优化认知评分、血浆生物标志物、载脂蛋白E基因型、磁共振成像和人口统计学特征的完全调整模型具有较高的诊断性能（A+的曲线下面积 = 0.886；A+T+的曲线下面积 = 0.893）。结果在性别和年龄层中保持一致。这些发现支持了一种逐步诊断策略，即结合简短、微创工具以加强社区环境中AD的早期检测。
