Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan.
Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 10675, Taiwan.
Int J Environ Res Public Health. 2022 Apr 15;19(8):4839. doi: 10.3390/ijerph19084839.
Alzheimer's disease (AD) is a neurodegenerative disorder with an insidious onset and irreversible condition. Patients with mild cognitive impairment (MCI) are at high risk of converting to AD. Early diagnosis of unstable MCI patients is therefore vital for slowing the progression to AD. However, current diagnostic methods are either highly invasive or expensive, preventing their wide applications. Developing low-invasive and cost-efficient screening methods is desirable as the first-tier approach for identifying unstable MCI patients or excluding stable MCI patients. This study developed feature selection and machine learning algorithms to identify blood-sample gene biomarkers for predicting stable MCI patients. Two datasets obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were utilized to conclude 29 genes biomarkers (31 probes) for predicting stable MCI patients. A random forest-based classifier performed well with area under the receiver operating characteristic curve (AUC) values of 0.841 and 0.775 for cross-validation and test datasets, respectively. For patients with a prediction score greater than 0.9, an excellent concordance of 97% was obtained, showing the usefulness of the proposed method for identifying stable MCI patients. In the context of precision medicine, the proposed prediction model is expected to be useful for identifying stable MCI patients and providing medical doctors and patients with new first-tier diagnosis options.
阿尔茨海默病（AD）是一种神经退行性疾病，发病隐匿，且不可逆转。轻度认知障碍（MCI）患者有很高的转化为 AD 的风险。因此，早期诊断不稳定 MCI 患者对于减缓向 AD 的进展至关重要。然而，目前的诊断方法要么高度侵入性，要么昂贵，限制了它们的广泛应用。开发低侵入性和具有成本效益的筛选方法是可取的，作为识别不稳定 MCI 患者或排除稳定 MCI 患者的一线方法。本研究开发了特征选择和机器学习算法，以确定用于预测稳定 MCI 患者的血液样本基因生物标志物。使用来自阿尔茨海默病神经影像学倡议（ADNI）数据库的两个数据集，得出了 29 个用于预测稳定 MCI 患者的基因生物标志物（31 个探针）。基于随机森林的分类器表现良好，交叉验证和测试数据集的接收者操作特征曲线（AUC）值分别为 0.841 和 0.775。对于预测评分大于 0.9 的患者，获得了 97%的极好一致性，表明该方法在识别稳定 MCI 患者方面的有用性。在精准医学的背景下，该预测模型有望用于识别稳定 MCI 患者，并为医生和患者提供新的一线诊断选择。
