Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile.
Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
Alzheimers Res Ther. 2023 Oct 14;15(1):176. doi: 10.1186/s13195-023-01304-8.
Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.
轻度认知障碍 (MCI) 通常被认为是痴呆的早期阶段，大约在初始诊断后 6 年左右，进展为痴呆的估计比例高达 80-90%。痴呆症认知障碍的诊断通常基于临床评估、神经心理学评估、脑脊液 (CSF) 生物标志物和神经影像学。诊断 MCI 的主要目的是确定其病因，特别是是否是由于阿尔茨海默病 (AD)。然而，只有有限比例的人能够进行病因确认，这导致外周液生物标志物作为一种诊断工具，包括由于 AD 引起的 MCI。生物流体分析的最新进展使我们能够使用复杂的统计模型和多模态机器学习 (ML) 算法，根据 CSF、外周血和唾液等液体生物标志物来诊断 MCI。这种方法在识别 MCI 的特定病因方面显示出了希望，包括 AD。经过 PRISMA 分析，29 篇文章表明，使用多模态算法的趋势越来越明显，这些算法纳入了其他生物标志物，如神经影像学、神经心理学测试和遗传信息。特别是，神经影像学通常与液体生物标志物一起用于横断面和纵向研究。我们的系统评价表明，具有成本效益的纵向多模态监测数据，代表了不同文化人群，并利用白盒 ML 算法，可能是为 MCI 导致的 AD 开发诊断模型的有价值的贡献。临床评估和生物标志物，以及 ML 技术，可能会证明对改善 AD 导致的 MCI 诊断工具至关重要。
