Verma Nishant, Beretvas S Natasha, Pascual Belen, Masdeu Joseph C, Markey Mia K
Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton Street Stop C0800, Austin, TX 78712, United States.
NeuroTexas Institute Research Foundation, St. David's HealthCare, 1015 E. 32nd Street Suite 404, Austin, TX 78705, United States.
Curr Alzheimer Res. 2018 Mar 14;15(5):429-442. doi: 10.2174/1567205014666171106150309.
Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease.
Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers.
Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials.
The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer's disease.
在临床试验中，将优化的认知指标（阿尔茨海默病评估量表-认知分量表，ADAS-Cog）与阿尔茨海默病的萎缩标志物相结合来追踪疾病进展，可能比目前使用的方法具有更高的敏感性，而目前的方法在最近的多项试验中均得出了阴性结果。此外，明确认知和影像学检测所产生的子成分之间的关系，对于解决阿尔茨海默病的症状和解剖学变异性至关重要。
我们使用潜在变量分析，深入研究了通过ADAS-Cog评估的认知障碍与脑萎缩之间的关系。开发了一种用于阿尔茨海默病临床试验的生物标志物，该标志物结合了认知和萎缩标志物。
发现特定脑区的萎缩与记忆、语言和实践等认知领域的障碍密切相关。在模拟试验和实际问题中，所提出的生物标志物在追踪认知障碍进展方面显示出比ADAS-Cog显著更高的敏感性。该生物标志物还改善了对将发展为阿尔茨海默病的轻度认知障碍（MCI）患者的筛选（在80%敏感性时特异性为78.8±4.9%），用于临床试验。
所提出的生物标志物通过显著提高检测治疗效果的敏感性以及改善对将发展为阿尔茨海默病的MCI患者的筛选，提升了聚焦于轻度认知障碍（MCI）阶段的临床试验的疗效。
