Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer’s disease

Abstract

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.

Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images

“…Efforts have been made to discover diagnostic biomarkers that were previously unidentified through traditional means using machine learning (ML) technology [9,10]. ML techniques for analysing brain imaging data have become prevalent in medical data analysis [11].…”

Diabetes accelerates Alzheimer's disease progression in the first year post mild cognitive impairment diagnosis

“… 1 , 2 The rate of conversion from MCI to dementia ranges from 8% to 15%, escalating to more than 80% after 6 years. 3 Identified predictors of progressing to dementia in individuals with MCI include older age, female sex, diabetes, apolipoprotein E ( APOE ) ε4 carriership, hippocampal and entorhinal atrophy, lower scores on the Mini‐Mental State Examination or Montreal Cognitive Assessment (MoCA), and higher Alzheimer's Disease Assessment Scale‐Cognition scores. 4 , 5 The high conversion rate from MCI to Alzheimer's disease (AD) not only jeopardizes individual health but also imposes a substantial financial burden on families and societies, making it a pressing global health issue.…”

Dynamic Individual Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer’s Disease using Joint Modeling

“…Models have been developed demonstrating and predicting the high risk of developing dementia for this group (7)(8)(9). However, such models, now overwhelmingly relying on machine learning, while providing an estimate of the group-wise risk of progression, tend to fare less well at individual prognosis and prediction of individual outcomes.…”