Wang Xiwu, Ye Teng, Jiang Deguo, Zhou Wenjun, Zhang Jie
Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China.
Department of Ultrasound, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Front Aging Neurosci. 2024 Aug 12;16:1410544. doi: 10.3389/fnagi.2024.1410544. eCollection 2024.
Alzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown.
Participants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer's Disease Neuroimaging Initiative database ( = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes.
We found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 - 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 - 1057.5) to 400.9 (95% CI: 306.9 - 516.8).
Our findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.
阿尔茨海默病（AD）具有高度异质性，临床进展和神经生物学存在显著个体差异。淀粉样蛋白沉积被认为是导致认知衰退的原因，因此是AD认知衰退差异的主要因素。然而，早期有症状的AD患者（因AD导致的轻度认知障碍或轻度痴呆）脑内已有淀粉样蛋白异常证据，其临床异质性仍不清楚。
从阿尔茨海默病神经影像倡议数据库中选取基线诊断为轻度认知障碍或轻度痴呆、淀粉样蛋白PET扫描呈阳性且在5年随访期内接受过一次以上阿尔茨海默病评估量表认知分量表-13（ADAS-Cog-13）评估的参与者（n = 421；年龄 = 73±7；受教育年限 = 16±3；女性比例 = 43%；APOE4携带者分布 = 68%）。在ADAS-Cog-13数据背景下进行非参数k均值纵向聚类分析以确定认知亚型。
我们发现早期AD患者的认知衰退情况差异很大，并确定了4个以不同认知进展速度为特征的聚类。在这些组中，其他认知和功能结局的变化率幅度、从轻度认知障碍到痴呆的临床进展以及假定反映神经退行性变和神经元损伤的标志物变化存在显著差异。基于简化逻辑回归模型的列线图预测陡峭认知轨迹的曲线下面积为0.912（95%CI：0.88 - 0.94）。临床试验模拟表明，将列线图纳入富集策略将使所需样本量从926.8（95%CI：822.6 - 1057.5）减少到400.9（95%CI：306.9 - 516.8）。
我们的研究结果表明其在早期AD患者分层中有用，因此可能增加未来AD临床试验找到治疗方法的机会。
