Center for Advanced Studies and Technology - CAST, University G. d'Annunzio of Chieti-Pescara, Italy.
Department of Neuroscience, Imaging, and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Italy.
J Alzheimers Dis. 2022;85(4):1639-1655. doi: 10.3233/JAD-210573.
Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.
To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.
We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.
The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.
Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.
阿尔茨海默病（AD）是一种由多种病因驱动的神经退行性疾病。轻度认知障碍（MCI）是健康衰老与痴呆之间的过渡状态。目前尚无可靠的生物标志物可用于预测从 MCI 向 AD 的转化。
评估机器学习（ML）在阿尔茨海默病神经影像学倡议（ADNI）和阿尔茨海默病代谢组学联盟（ADMC）数据库提供的大量数据中的应用，以预测 MCI 向 AD 的转化。
我们实施了基于机器学习的随机森林（RF）算法，以预测 MCI 向 AD 的转化。通过 RF 分析与研究人群（587 例 MCI 患者）相关的数据，分别或联合使用这些数据作为特征，并评估其分类能力。考虑了四类变量：神经心理学测试评分、AD 相关的脑脊液（CSF）生物标志物、外周生物标志物和结构磁共振成像（MRI）变量。
基于 ML 的算法在预测 MCI 向 AD 转化方面的准确率为 86%。在评估最有助于预测的特征时，神经心理学测试评分、MRI 数据和 CSF 生物标志物在 MCI 向 AD 的预测中最为相关。外周参数与神经心理学测试评分联合使用时效果较好。年龄和性别差异调节了预测的准确性。在女性和年轻患者中，AD 转化的预测更为有效。
我们的研究结果支持这样一种观点，即 AD 相关的神经退行性过程是由多种病理机制和因素的协同作用引起的，这些因素在大脑内外起作用，并受到年龄和性别的动态影响。
