Exploring the Predicting Biomarkers From Mild Cognitive Impairment to Dementia (EBMID)

Overview

Mild cognitive impairment (MCI) represents a transitional stage between healthy aging and dementia, and affects more than 15% of the population over the age of 60 in China. About 15% patients with MCI could progress into dementia after two years and about one-third develop into dementia within five years, which will lead to suffering, as well as staggering economic and care burden. So, exploring the predicting biomarkers from MCI to dementia to identify and delay progression to dementia at an early stage is of great social and clinical significance. Some reports based on a single neural biomarker suggest that risk models can predict the conversion of MCI to dementia, but no widely recognized prediction models basing on multiple complex markers have been used in clinical practice. The objectives of this study are to outline the spectrum of MCI transforming into dementia through a 5-year prospective longitudinal cohort study; Secondly, screening biomarkers for MCI transmit to dementia are based on clinical symptoms, neuropsychology, neuroimaging, neuroelectrophysiology, and humoral markers tests data.

The 900 patients with MCI will be enrolled in this study, and data will be collected in the baseline including demographics, clinical symptoms, assessment of neuropsychology, neuroimaging, neuroelectrophysiology, blood samples, cerebrospinal fluid, etc. The changes of these data were dynamically observed through an annual follow-up for 5 years. According to the neuropsychological evaluation results of follow-up, the subjects were divided into MCI progression (MCI-P) and MCI stabilization (MCI-S). Difference in clinical phenotype, neuropsychology, electrophysiology, neuroimaging, and body fluid multi-omics indicators between the two subtypes were compared and analyzed. The neuropsychological testes in patients with MCI included some neuropsychological scales such as, Clinical Dementia Rating (CDR), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), etc. Multi-model neuroimaging evaluation screen the candidate neuroimaging markers, including structure and functional brain magnetic resonance imaging (MRI), Diffusion tensor image (DTI), 18 F-2-fluro-D-deoxy-glucose-positron emission tomography (18F-FDG-PET)，Amyloid-PET and tau-PET. To exploring neuroelectrophysiology biomarkers collect the data on polysomnography, resting state electroencephalogram, and evoked potentials (P1, N1, P2, N2, etc.). ELISA, SIMOA and other analytical methods were used to detect the contents related to MCI conversion to dementia in the blood, cerebrospinal fluid, urine, saliva and feces. Using statistic and machine learning methods, the biomarkers and their combinations from MCI transmit to dementia could be obtained, and it can contribute to the construction of a risk prediction model and early warning evaluation system of MCI transmit to dementia.