Tuena Cosimo, Pupillo Chiara, Stramba-Badiale Chiara, Stramba-Badiale Marco, Riva Giuseppe
Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy.
Department of Geriatrics and Cardiovascular Medicine, IRCCS Istituto Auxologico Italiano, Milan, Italy.
Front Hum Neurosci. 2024 Jan 29;17:1328713. doi: 10.3389/fnhum.2023.1328713. eCollection 2023.
Gait disorders and gait-related cognitive tests were recently linked to future Alzheimer's Disease (AD) dementia diagnosis in amnestic Mild Cognitive Impairment (aMCI). This study aimed to evaluate the predictive power of gait disorders and gait-related neuropsychological performances for future AD diagnosis in aMCI through machine learning (ML).
A sample of 253 aMCI (stable, converter) individuals were included. We explored the predictive accuracy of four predictors (gait profile plus MMSE, DSST, and TMT-B) previously identified as critical for the conversion from aMCI to AD within a 36-month follow-up. Supervised ML algorithms (Support Vector Machine [SVM], Logistic Regression, and k-Nearest Neighbors) were trained on 70% of the dataset, and feature importance was evaluated for the best algorithm.
The SVM algorithm achieved the best performance. The optimized training set performance achieved an accuracy of 0.67 (sensitivity = 0.72; specificity = 0.60), improving to 0.70 on the test set (sensitivity = 0.79; specificity = 0.52). Feature importance revealed MMSE as the most important predictor in both training and testing, while gait type was important in the testing phase.
We created a predictive ML model that is capable of identifying aMCI at high risk of AD dementia within 36 months. Our ML model could be used to quickly identify individuals at higher risk of AD, facilitating secondary prevention (e.g., cognitive and/or physical training), and serving as screening for more expansive and invasive tests. Lastly, our results point toward theoretically and practically sound evidence of mind and body interaction in AD.
步态障碍和与步态相关的认知测试最近被认为与遗忘型轻度认知障碍（aMCI）患者未来患阿尔茨海默病（AD）痴呆症的诊断有关。本研究旨在通过机器学习（ML）评估步态障碍和与步态相关的神经心理学表现对aMCI患者未来AD诊断的预测能力。
纳入了253名aMCI（稳定型、转化型）个体的样本。我们探讨了先前确定的四个预测指标（步态概况加上简易精神状态检查表[MMSE]、数字符号替换测验[DSST]和连线测验B部分[TMT-B]）在36个月随访期内对从aMCI转化为AD的预测准确性。监督式ML算法（支持向量机[SVM]、逻辑回归和k近邻算法）在70%的数据集上进行训练，并对最佳算法评估特征重要性。
SVM算法表现最佳。优化后的训练集性能准确率达到0.67（敏感性 = 0.72；特异性 = 0.60），在测试集上提高到0.70（敏感性 = 0.79；特异性 = 0.52）。特征重要性显示，MMSE在训练和测试中都是最重要的预测指标，而步态类型在测试阶段很重要。
我们创建了一个预测性ML模型，该模型能够识别出在36个月内有高风险患AD痴呆症的aMCI患者。我们的ML模型可用于快速识别AD高风险个体，促进二级预防（如认知和/或体育训练），并作为更广泛和侵入性测试的筛查手段。最后，我们的结果为AD中心身相互作用提供了理论和实践上合理的证据。
