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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Studies on Biomarkers for Mild Cognitive Impairment Conversion to Dementia

Conditions
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Biomarkers, MCI Conversion to Dementia

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Male or female patients aged ≥50 and ≤85 years; Meet the diagnostic criteria for dementia or MCI; ③ Neuropsychological score: MMSE 15-28 points, CDR≤1 point; ④ The patients and their families were informed and signed the informed consent. Exclusion Criteria: There are other neurological diseases that can cause brain dysfunction (such as depression, brain tumors, Parkinson's disease disease, metabolic encephalopathy, encephalitis, multiple sclerosis, epilepsy, traumatic brain injury, normal intracranial pressure hydrocephalus, etc.); There are other systemic diseases that can cause cognitive impairment (such as hepatic insufficiency, renal insufficiency, Thyroid dysfunction, severe anemia, folic acid or vitamin B12 deficiency, syphilis, HIV infection, alcohol and drug abuse, etc.); Suffering from a disease that cannot cooperate with the completion of cognitive examination; ④ There are contraindications to nuclear magnetic resonance; There is mental and neurodevelopmental delay; ⑥ refuse to draw blood; ⑦ Refuse to sign the informed consent.

Ages Eligible for Study
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Minimum Age: 50 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| MCI progression<br>MCI-P (Compared with the baseline, MMSE score declined > 4 points per year) | |
| MCI stabilization<br>MCI-S (Compared with the baseline, MMSE score decreased < 4 points per year） | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Rate of change in global cognition as measured by Clinical Dementia Rating (CDR). | Assess statistically significant difference in score between MCI-P and MCI-S using the neuropsychological scales CDR. CDR, a multidimensional scale for dementia severity, which scored 0-3, with higher scores indicating worse functioning. | 5 years | 
| Rate of change in global cognition as measured by Mini-Mental State Examination (MMSE). | Assess statistically significant difference in score between MCI-P and MCI-S using the neuropsychological scales MMSE. MMSE scores range from 0-30, with higher scores representing better cognitive function. | 5 years | 
| Rate of change in global cognition as measured by Montreal Cognitive Assessment (MoCA). | Assess statistically significant difference in score between MCI-P and MCI-S using the neuropsychological scales MoCA. MoCA scores range from 0-30, with higher scores representing better cognitive function. | 5 years | 
| Rate of change in the severity of cognitive impairment as assessed by Alzheimer's Disease Assessment Scale-Cognitive section (ADAS-cog). | Assess statistically significant difference in score between MCI-P and MCI-S using the neuropsychological scales ADAS-cog. ADAS-cog scores range from 0-70, with higher scores indicating better global cognitive function. | 5 years | 
| Rate of change in memory function as assessed by World Health Organization-Un-iversity of California, Los Angeles, auditory verbal learning test (WHO-UCLA AVLT). | Assess statistically significant difference in score between MCI-P and MCI-S using the neuropsychological scales like WHO-UCLA AVLT. WHO-UCLA AVLT scores depend on the number of correct words, which ranges from 0-15, with higher scores representing better memory function. | 5 years | 
| Rate of change in language function as assessed by Boston Naming Test (BNT). | Assess statistically significant difference in score between MCI-P and MCI-S using the neuropsychological scales like BNT. BNT scores range from 0-30, with higher scores representing better language function. | 5 years | 
| Rate of change in psychobehavioral symptoms as assessed by Neuropsychiatric Inventory (NPI). | Assess statistically significant difference in score between MCI-P and MCI-S using the neuropsychological scales like NPI. Patient assessment grading scores range from 0-144 in NPI, and caregivers distress grading scores range from 0-60, with 0 representing the best. | 5 years | 
| Rate of change in activities of daily living as assessed by Alzheimer's Disease Cooperative Study-Activity of Daily Living (ADCS-ADL). | Assess statistically significant difference between in score MCI-P and MCI-S using the neuropsychological scales like ADCS-ADL. ADCS-ADL scores range from 0-54, with higher scores indicating better completion ability. | 5 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Rate of change in differential protein content as assessed by CSF and blood samples. | Assess statistic difference in protein content between MCI-P and MCI-S using CSF and blood samples through proteomics DIA technology analysis. | 5 years | 
| Rate of change in small molecule metabolite as assessed by stool and urine. | Assess statistically significant difference in small molecule metabolite between MCI-P and MCI-S using stool and urine through untargeted metabolomics (LC-MS) analysis. | 5 years | 
| Rate of change in metagenomes as assessed by stool samples. | Assess statistically significant difference in transcription between MCI-P and MCI-S using stool samples through DNA high-throughput sequencing analysis. | 5 years | 
| Rate of change in classical AD protein biomarkers as assessed by CSF and blood samples. | Assess statistically significant difference in content between MCI-P and MCI-S using CSF and blood samples, through detecting the classical AD marker content. | 5 years | 
| Rate of change in sleep apnea hypopnea as assessed by Standard night Polysomnography. |  | 5 years | 
| Rate of change in brain resting state activity as assessed by Resting state electroencephalogram (EEG). |  | 5 years | 
| Rate of change in brain stem damage as assessed by Evoked potentials (EPs). |  | 5 years | 
| Rates of change in brain structure using brain structure magnetic resonance imaging (sMRI). |  | 5 years | 
| Rates of change in brain function characteristics using brain functional MRI (fMRI). |  | 5 years | 
| Rates of change in white matter fiber bundle using Diffusion tensor image (DTI). |  | 5 years | 
| Rates of change in glucose metabolism as measured by 18F-FDG-PET. |  | 5 years | 
| Rates of change in amyloid deposition as measured by amyloid-PET |  | 5 years | 
| Rates of change in tau deposition as measured by tau-PET. |  | 5 years |