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Alzheimer’s disease

increase of dementia patients

The rapid increase of the elderly population will accelerate an increase in patients with dementia. The number of dementia patients (including Alzheimer’s diseases patients) is expected to exceed 1 million in 2024, 2 million in 2039, and 3 million in 2050. Dementia is a disease that lasts for a long time, so the prevalence does not easily drop, and the number of dementia patients continues to increase cumulatively as the life expectancy increases. The annual management cost per dementia patient is 20.74 million won, and the national dementia management cost is 14.6 trillion won. (June 2019, Central Dementia Center, Ministry of Health and Welfare)

Alzheimer’s disease (AD) is the most common cause of dementia, accounting for approximately 70% of cases. Although extensive efforts to overcome this disease have been made in the research field, there are no disease-modifying therapeutics of AD. Therefore, early diagnosis and subsequent intervention are important for the current clinical strategy of AD. In this respect, we are studying risk predictors and biomarkers for early diagnosis, as well as the prevention and treatment of AD.

Prediction model for assessing the risk of conversion to dementia in patients with mild cognitive impairment (MCI)

MCI is considered to be a transitional state between normal aging and AD. Because not all patients with MCI develop AD dementia, finding predictors for AD progression in MCI patients is important for clinical treatment. To develop a risk prediction model, we analysed cohort-based pathology-related amyloid biomarkers. We recruited 20 patients with AD, 40 patients with MCI, and 60 normal control subjects. All participants were subjected to detailed clinical examinations including amyloid positron emission tomography (PET) imaging, and they provided blood samples. The collected resources were deposited in the National Biobank of Korea to be used for the development and standardization of early AD diagnosis.

Prediction model for assessing the risk of conversion to dementia in patients with mild cognitive impairment

Figure 1. Prediction model for assessing the risk of conversion to dementia in patients with mild cognitive impairment

There were two research achievements in this study.
First, we created a prediction model (a nomogram), which can predict the conversion to dementia in patients with MCI. We then performed neuropsychological tests on 338 patients with MCI. The risk scores of dementia progression were obtained according to the following four criteria: (1) the modality of involved memory dysfunction, (2) the severity of memory dysfunction, (3) the multiplicity of involved cognitive domains, and (4) age. With this prediction model, we could calculate the risk scores of conversions to dementia in patients with MCI using neuropsychological profiles (Jang et al., Journal of Alzheimer’s Disease, 2017; 60(4):1579-1587).

Second, we identified reliable reference regions for amyloid PET imaging analysis in subcortical vascular dementia (SVaD) using Centiloid scores. Centiloid scores were investigated in several reference regions including cerebellar grey (CG), whole cerebellum (WC), WC with brainstem, pons, and white matter (WM). Differences of scores between the normal subjects and patients with SVaD in each region as well as the variability of the scores were examined. CG, WC, and WC with brainstem, but not WM or pons, were reliable reference regions for amyloid imaging analysis in SVaD. These results may contribute to making an early diagnosis of dementia and standardization of the diagnosis.

Discovery of blood-based biomarkers for early diagnosis of AD

Our research was extended to find blood-based biomarkers for patients with AD.
Angiogenesis-related factors including vascular endothelial growth factor (VEGF) might be involved in the pathogenesis of AD. Soluble forms of the VEGF receptor are likely to be intrinsic negative counterparts of VEGF. Therefore, we measured the plasma levels of VEGF and its two soluble receptors (sVEGFR1 and sVEGFR2) in 120 control subjects, 75 patients with MCI, and 76 patients with AD using an enzyme-linked immunosorbent assay. Plasma levels of VEGF in patients with AD were higher than those in healthy control subjects. However, plasma levels of sVEGFR1 and sVEGFR2 were lower in patients with AD than in healthy control subjects.
Levels of VEGFR2 mRNA significantly declined in human umbilical vein endothelial cells treated with amyloid-beta. Protein levels of VEGFR2 also declined in the brains of AD model mice. In addition, we showed that the expression of sVEGFR2 and VEGFR2 was decreased by transfection with the Notch intracellular domain. These results indicate that alterations of VEGF and levels of its two receptors might be associated with those at risk for AD (Cho et al., Scientific Reports 2017; 7:17713).

Gene expression profiles reflect the biologically diverse activities of cells under specific cell environments. Using the transcriptional response of cultured cells to blood composition, we developed a litmus gene assay to discriminate blood samples reflecting different sample qualities or disease conditions. This cell-based litmus gene assay identified six genes (CCL20, CEMIP, IL1B, IL8, PRG2, and PTGS2) as potential biomarkers of plasma quality control (Patent Number: 10-1781960). We also applied the litmus gene assay to discovering blooddriven biomarkers of AD and identified the SPC25 gene as an AD biomarker. The SPC25 gene expression level significantly increased in the cell-based assay using serum samples from patients with MCI. This indicates the effectiveness and potential of a litmus gene assay to detect the orchestrated effects of circulating systemic factors, leading to a successful diagnosis of AD and MCI. This method is broadly applicable to the diagnosis of disease subtypes or pathophysiological stages of complex diseases and tumours (Shim et al., Scientific Reports 2017; 7:16848).