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BACKGROUND: As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans‐ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). METHODS: The GIRA model was created to include polygenic risk score calculated from the AD genome‐wide association study loci, the apolipoprotein E haplotypes, and non‐genetic covariates including age, sex, and the first three principal components of population substructure. RESULTS: We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. CONCLUSIONS: As the initial effort by the IHCC to leverage existing large‐scale datasets in a collaborative setting with DAC, we developed a trans‐ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications. [ABSTRACT FROM AUTHOR]

There is a proven correlation between the severity of dementia and reduced brain volumes. Several studies have attempted to use activity data to estimate brain volume as a means of detecting reduction early; however, raw activity data are not directly interpretable and are unstructured, making them challenging to utilize. Furthermore, in the previous research, brain volume estimates were limited to total brain volume and the investigators were unable to detect reductions in specific regions of the brain that are typically used to characterize disease progression. We aimed to evaluate volume prediction of 116 brain regions through activity data obtained combining time-frequency domain- and unsupervised deep learning-based feature extraction methods. We developed a feature extraction model based on unsupervised deep learning using activity data from the National Health and Nutrition Examination Survey (NHANES) dataset (n = 14,482). Then, we applied the model and the time-frequency domain feature extraction method to the activity data of the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study (BICWALZS) datasets (n = 177) to extract activity features. Brain volumes were calculated from the brain magnetic resonance imaging of the BICWALZS dataset and anatomically subdivided into 116 regions. Finally, we fitted linear regression models to estimate each regional volume of the 116 brain areas based on the extracted activity features. Regression models were statistically significant for each region, with an average correlation coefficient of 0.990 ± 0.006. In all brain regions, the correlation was > 0.964. Particularly, regions of the temporal lobe that exhibit characteristic atrophy in the early stages of Alzheimer's disease showed the highest correlation (0.995). Through a combined deep learning-time-frequency domain feature extraction method, we could extract activity features based solely on the activity dataset, without including clinical variables. The findings of this study indicate the possibility of using activity data for the detection of neurological disorders such as Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: In many high-income Western countries, the prevalence of dementia had been reduced over the past decades.Objective: We investigated whether the prevalence of all-cause dementia, Alzheimer's disease, vascular dementia, and mild cognitive impairment (MCI) had changed in Korea from 2008 to 2017.Methods: Nationwide Survey on Dementia Epidemiology of Korea (NaSDEK) in 2008 and 2017 was conducted on representative elderly populations that were randomly sampled across South Korea. Both surveys employed a two-stage design (screening and diagnostic phases) and diagnosed dementia and MCI according to the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders and the consensus criteria from the International Working Group, respectively. The numbers of participants aged 65 years or older in the screening and diagnostic phases were 6,141 and 1,673 in the NaSDEK 2008 and 2,972 and 474 in the NaSDEK 2017, respectively.Results: The age- and sex-standardized prevalence of all-cause dementia and Alzheimer's disease showed nonsignificant decrease (12.3% to 9.8%, odds ratio [OR] = 0.89, 95% confidence interval [CI] = 0.54-1.48 for all-cause dementia; 7.6% to 6.8%, OR [95% CI] = 0.91 [0.58-1.42] for Alzheimer's disease). Vascular dementia decreased in the young-old population aged less than 75 years (2.7% to 0.001%, OR [95% CI] = 0.04 [0.01-0.15]) and in women (1.9% to 0.5%, OR [95% CI] = 0.27 [0.10-0.72]) while MCI remained stable (25.3% to 26.2%, OR [95% CI] = 1.08 [0.67-1.73]).Conclusion: We found that the prevalence of dementia in Korea showed a nonsignificant decrease between 2008 and 2017. [ABSTRACT FROM AUTHOR]

For the machine learning-based prediction of the conversion from mild cognitive impairment to Alzheimer's disease, the collection of sufficient data to train a model is required, which involves a lot of time and expense. When data is not enough, combining public and in-house data may be appropriate by applying domain adaptation that alleviates inter-site heterogeneity. Existing methods simultaneously transform in-house and public data to represent them into a common feature space, and then train a classifier using labels in public data. However, this procedure causes the time- and cost-consuming re-training of classifier whenever in-house data changes, and also inheres the risk of information loss in public data. Motivated by this, we propose a method that only transforms in-house data while preserving public data, namely one-way domain adaptation. The proposed method represents in-house data similar with public data by matching the data distribution and the connectivity between brain regions with mean vectors and covariance matrices, respectively. Then, the pre-trained classifier in public data is applied to predict AD conversion for in-house data. The experiments, which use the Australian Imaging Biomarkers and Lifestyle Study of Aging and the Open Access Series of Imaging Studies as the in-house data and the Alzheimer's Disease Neuroimaging Initiative as the public data, show the effectiveness and efficiency of the proposed method, improving prediction performance about 34.8% on average without labels in the in-house datasets. [ABSTRACT FROM AUTHOR]

Background: Sex effects on the progression of Alzheimer's disease (AD) have received less attention than other demographic factors, including onset age and education.Objective: The aim of this study was to investigate whether sex affected cortical thinning in the disease progression of AD.Methods: We prospectively recruited 36 patients with early-stage AD and 14 people with normal cognition. All subjects were assessed with magnetic resonance imaging at baseline, Year 1, Year 3, and Year 5. We performed cortical thickness analyses using surface-based morphometry on magnetic resonance imaging.Results: Women with AD showed more rapid cortical thinning in the left dorsolateral frontal cortex, left superior temporal gyrus, bilateral temporo-parietal association cortices, bilateral anterior cingulate gyri, bilateral medial frontal cortices, and bilateral occipital cortices over 5 years than men with AD, even though there was no difference in cortical thickness at baseline. In contrast, there were no regions of significantly more rapid atrophy in men with AD.Conclusions: Our findings suggest that women deteriorate faster than men in the progression of AD. [ABSTRACT FROM AUTHOR]

Background: It is unclear how brain reserve interacts with gender and apolipoprotein E4 (APOE4) genotype, and how this influences the progression of Alzheimer's disease (AD). The association between intracranial volume (ICV) and progression to AD in subjects with mild cognitive impairment (MCI), and differences according to gender and APOE4 genotype, was investigated. Methods: Data from subjects initially diagnosed with MCI and at least two visits were downloaded from the ADNI database. Those who progressed to AD were defined as converters. The longitudinal influence of ICV was determined by survival analysis. The time of conversion from MCI to AD was set as a fiducial point, as all converters would be at a similar disease stage then, and longitudinal trajectories of brain atrophy and cognitive decline around that point were compared using linear mixed models. Results: Large ICV increased the risk of conversion to AD in males (HR: 4.24, 95% confidence interval (CI): 1.17-15.40) and APOE4 non-carriers (HR: 10.00, 95% CI: 1.34-74.53), but not in females or APOE4 carriers. Cognitive decline and brain atrophy progressed at a faster rate in males with large ICV than in those with small ICV during the two years before and after the time of conversion. Conclusions: Large ICV increased the risk of conversion to AD in males and APOE4 non-carriers with MCI. This may be due to its influence on disease trajectory, which shortens the duration of the MCI stage. A longitudinal model of progression trajectory is proposed. [ABSTRACT FROM AUTHOR]

Background/Objectives Although hypertension is known to be a risk factor for AD, the effects of hypertension on brain function in AD patients are not well understood. We investigated alterations in resting-state functional connectivity according to the presence of hypertension in AD patients by using a method of correlation analysis based on a seed region in the posterior cingulate cortex (PCC). We also determined whether differences in resting-state connectivity were associated with gray matter atrophy. Methods Thirty-seven AD patients (18 patients with hypertension and 19 patients without hypertension) underwent the resting-state functional magnetic resonance imaging. We obtained the PCC maps by a temporal correlation method, to identify alterations in the functional connectivity of the PCC in hypertensive group relative to non-hypertensive group. Voxel-based morphometry analysis was also applied to adjust the confounding effect of gray matter atrophy. Results We detected a decreased connectivity to the PCC in the regions of subgenual anterior cingulated cortex (ACC) in hypertensive group relative to non-hypertensive group. However, we observed a pattern of increased connectivity between the PCC and the left inferior parietal cortex in hypertensive group. After correction for gray matter atrophy, all detected regions still remained significant. Conclusions Altered connectivity in AD patients with hypertension suggests the possibility that hypertension impairs resting-state functional connectivity of the AD brain, inducing a compensational process outside the impaired networks or disequilibrium in brain connectivity. This finding may account for an additional contribution of hypertension to the pathophysiology of AD. [ABSTRACT FROM AUTHOR]

Although Alzheimer's disease (AD) is the most common neurodegenerative disease, there are still no drugs available to treat or prevent AD effectively. Here, we examined changes in levels of selected proteins implicated in the pathogenesis of AD using plasma samples of control subjects and patients with cognition impairment. To precisely categorize the disease, fifty-six participants were examined with clinical cognitive tests, amyloid positron emission tomography (PET) scan, and white matter hyperintensities scored by magnetic resonance imaging. Plasma cathepsin D levels of the subjects were examined by immunoblotting and enzyme-linked immunosorbent assay (ELISA). Correlation of plasma cathepsin D levels with AD-related factors and clinical characteristics were examined by statistical analysis. By analyzing quantitative immunoblot and ELISA, we found that the plasma level of cathepsin D, a major lysosomal protease, was decreased in the group with amyloid plaque deposition at the brain compared to the control group. The level of plasma cathepsin D was negatively correlated with clinical dementia rating scale sum of boxes (CDR-SB) scores. In addition, our integrated multivariable logistic regression model suggests the high performance of plasma cathepsin D level for discriminating AD from non-AD. These results suggest that the plasma cathepsin D level could be developed as a diagnostic biomarker candidate for AD. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) have been proposed as a promising biomarker for various diseases including Alzheimer's disease (AD). More attention has recently been focused on the diagnosis and treatment at earlier stage of mild cognitive impairment (MCI) for preventing its progression to AD. To identify potential pathologic markers for Aβ(+)MCI (Alzheimer's pathologic change with MCI), we investigated miRNA expression profiles in the platelets from patients with Aβ(+)MCI, in comparison with those from Aβ(−)MCI (Non-Alzheimer's pathologic change with MCI) and CNI (cognitively normal individuals). We found that let-7i-5p, miR-125a, miR-1233-5p, and miR-6787-5p were significantly downregulated, while miR-6880-5p expression was upregulated. Of these, only miR-1233-5p was significantly downregulated by Aβ treatment in both human platelets and their precursor megakaryocytes (MEG-01 cells). We explored the role of miRNAs by using miRNA mimics or inhibitors, and found that the diminished level of miR-1233-5p was associated with Aβ-induced increase in the expression of P-selectin and cell adhesion to fibronectin. Our results further indicated that Aβ-induced increase in platelet/MEG adhesion to fibronectin is likely mediated via P-selectin. In conclusion, this study suggests the downregulation of platelet-derived miR-1233-5p as a pathologic marker for Aβ(+)MCI. [ABSTRACT FROM AUTHOR]

Background: The leading causes of death among the elderly with cognitive impairment are unknown. This study aims to estimate the suicide and accidental death rates on the basis of a clinical case registry of patients diagnosed with cognitive impairment. Methods: The target sample consisted of 10,169 patients diagnosed with dementia or mild cognitive impairment (MCI), who were evaluated at the Clinical Research Center for Dementia of Korea (CREDOS) from January 2005 to December 2013. Information about whether the patients had died from suicide or in any kind of accident by December 31, 2016, was obtained from the database of the National Statistical Office (NSO). The standardized mortality ratio (SMR) and Cox-regression analysis were performed for evaluating the risk of suicide and accidental death as identified by the ICD-10. Results: The average of the Clinical Dementia Rating Scale (CDR) score (0.68 vs 0.93) was lower, and the age at the time of study registration (71.42 vs 75.68 years) was younger in the suicidal death group, as compared to the accidental death group. The overall SMR for accidental death in cognitively impaired patients (1.44, 95% CI 1.22–1.71) was significantly higher than the general population. Later onset (1.43, 95% CI 1.20–1.71) and older age (2.21, 95% CI 1.04–4.68) increased the risk of accidental death in cognitively impaired patients. According to the dementia subtypes, the SMR for accidental death was higher in both Alzheimer's disease (1.72, 95% CI 1.36–2.14) and vascular dementia (2.14, 95% CI 1.27–3.38). Additionally, the SMR for accidental death showed an increasing tendency as the CDR score increased (mild 1.80, 95% CI 1.32–2.42, moderate 1.86, 95% CI 1.07–3.03, severe 3.32, 95% CI 1.08–7.76). Unemployment increased the risks of both suicide (3.71, 95% CI 1.54–8.95) and accidental death (2.09, 95% CI 1.20–3.63). Conclusions: Among people with cognitive impairment, the risk of death by suicide did not increase, whereas that of accidental death increased significantly. Preventive strategies for premature mortality in those with cognitive impairment should be implemented from the early stages and should include careful evaluation of the individual risk factors for each type of death. [ABSTRACT FROM AUTHOR]

We investigated which factors might explain the differences between the frequencies of brain β-amyloid (Aβ) deposition in Korean and European cognitively normal individuals (CNs). We recruited 434 Korean CNs from the Samsung Medical Center (SMC) and 323 European CNs from the US Alzheimer's Disease Neuroimaging Initiative (ADNI). The Korean CNs showed lower education duration (11.8 ± 4.8 years vs. 16.8 ± 2.5 years, p < 0.001) than the European CNs. The frequency of Aβ (+) was higher in the European CNs (32.8%) than in the Korean CNs (20.0%; p < 0.001). In the SMC genome-wide association study (GWAS), 10 variants (including rs7481773 on chromosome 11, located near the brain-derived neurotrophic factor gene) exceeded the genome-wide significance level (p < 5 × 10−8). Especially, rs7481773 carriers showed more rapid decline in memory function than non–carriers (p = 0.048). However, this association was not observed in the ADNI GWAS. Our findings suggested that the different frequencies of Aβ (+) between CN Koreans and Europeans might be related to decreased cognitive reserve or genetic factors. [ABSTRACT FROM AUTHOR]