Zhen Weizhe, Wang Yu, Zhen Hongjun, Zhang Weihe, Shao Wen, Sun Yu, Qiao Yanan, Jia Shuhong, Zhou Zhi, Wang Yuye, Chen Leian, Zhang Jiali, Peng Dantao
Graduate School, Beijing University of Chinese Medicine, Beijing, China.
Department of Neurology, China-Japan Friendship Hospital, Beijing, China.
Front Aging Neurosci. 2024 Dec 19;16:1513930. doi: 10.3389/fnagi.2024.1513930. eCollection 2024.
Alzheimer's disease (AD) has a major negative impact on people's quality of life, life, and health. More research is needed to determine the relationship between age and the pathologic products associated with AD. Meanwhile, the construction of an early diagnostic model of AD, which is mainly characterized by pathological products, is very important for the diagnosis and treatment of AD.
We collected clinical study data from September 2005 to August 2024 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Using correlation analysis method like cor function, we analyzed the pathology products (t-Tau, p-Tau, and Aβ proteins), age, gender, and Minimum Mental State Examination (MMSE) scores in the ADNI data. Next, we investigated the relationship between pathologic products and age in the AD and non-AD groups using linear regression. Ultimately, we used these features to build a diagnostic model for AD.
A total of 1,255 individuals were included in the study (mean [SD] age, 73.27 [7.26] years; 691male [55.1%]; 564 female [44.9%]). The results of the correlation analysis showed that the correlations between pathologic products and age were, in descending order, Tau (Corr=0.75), p-Tau (Corr=0.71), and Aβ (Corr=0.54). In the AD group, t-Tau protein showed a tendency to decrease with age, but it was not statistically significant. p-Tau protein levels similarly decreased with age and its decrease was statistically significant. In contrast to Tau protein, in the AD group, Aβ levels increased progressively with age. In the non-AD group, the trend of pathologic product levels with age was consistently opposite to that of the AD group. We finally screened the optimal AD diagnostic model (AUC=0.959) based on the results of correlation analysis and by using the Xgboost algorithm and SVM algorithm.
In a novel finding, we observed that Tau protein and Aβ had opposite trends with age in both the AD and non-AD groups. The linear regression curves of the AD and non-AD groups had completely opposite trends. Through a machine learning approach, we constructed an AD diagnostic model with excellent performance based on the selected features.
阿尔茨海默病（AD）对人们的生活质量、生活及健康产生重大负面影响。需要更多研究来确定年龄与AD相关病理产物之间的关系。同时，构建以病理产物为主要特征的AD早期诊断模型对AD的诊断和治疗非常重要。
我们从阿尔茨海默病神经影像学倡议（ADNI）数据库收集了2005年9月至2024年8月的临床研究数据。使用cor函数等相关分析方法，我们分析了ADNI数据中的病理产物（总tau蛋白、磷酸化tau蛋白和淀粉样β蛋白）、年龄、性别和简易精神状态检查表（MMSE）评分。接下来，我们使用线性回归研究AD组和非AD组中病理产物与年龄之间的关系。最终，我们利用这些特征构建了AD诊断模型。
共有1255人纳入研究（平均[标准差]年龄，73.27[7.26]岁；男性691人[55.1%]；女性564人[44.9%]）。相关分析结果显示，病理产物与年龄的相关性由高到低依次为tau蛋白（相关系数=0.75）、磷酸化tau蛋白（相关系数=0.71）和淀粉样β蛋白（相关系数=0.54）。在AD组中，总tau蛋白随年龄增长呈下降趋势，但无统计学意义。磷酸化tau蛋白水平同样随年龄下降且具有统计学意义。与tau蛋白相反，在AD组中，淀粉样β蛋白水平随年龄逐渐升高。在非AD组中，病理产物水平随年龄变化的趋势与AD组始终相反。我们最终基于相关分析结果并使用Xgboost算法和支持向量机算法筛选出了最佳AD诊断模型（曲线下面积=0.959）。
我们有一项新发现，即tau蛋白和淀粉样β蛋白在AD组和非AD组中随年龄变化趋势相反。AD组和非AD组的线性回归曲线趋势完全相反。通过机器学习方法，我们基于所选特征构建了一个性能优异的AD诊断模型。
