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Background: We previously demonstrated the validity of a regression model that included ethnicity as a novel predictor for predicting normative brain volumes in old age. The model was optimized using brain volumes measured with a standard tool FreeSurfer. Objective: Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations. Methods: Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59–89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers. Results: In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001). Conclusions: Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

The gut–brain axis (GBA) plays a significant role in various neurodegenerative disorders, such as Alzheimer's disease (AD), and the gut microbiome (GM) can bidirectionally communicate with the brain through the GBA. Thus, recent evidence indicates that the GM may affect the pathological features and the progression of AD in humans. The aim of our study was to elucidate the impact of probiotics on the pathological features of AD in a 5xFAD model. Probiotics (Bifidobacterium lactis, Levilactobacillus brevis, and Limosilactobacillus fermentum) were orally administered in 5xFAD mice to modify the GM composition. Additionally, freeze-dried food containing phosphatidylserine was used as the positive control. Behavioral pathogenesis was assessed through the cross maze and Morris water maze tests. Our findings revealed that probiotic administration resulted in significant improvements in spatial and recognition memories. Furthermore, the neuroprotective effects of probiotics were substantiated by a reduction in amyloid-β accumulation in critical brain regions. Microglial activation in 5xFAD mice was also attenuated by probiotics in the hippocampus and cerebral cortex. Moreover, elevated tau phosphorylation in 5xFAD mice was ameliorated in the probiotics-treated group. The results highlight the potential use of probiotics as a neuroprotective intervention in AD. [ABSTRACT FROM AUTHOR]

The diagnosis of Alzheimer's disease (AD) needs to be improved. We investigated if hippocampal subfield volume measured by structural imaging, could supply information, so that the diagnosis of AD could be improved. In this study, subjects were classified based on clinical, neuropsychological, and amyloid positivity or negativity using PET scans. Data from 478 elderly Korean subjects grouped as cognitively unimpaired β-amyloid-negative (NC), cognitively unimpaired β-amyloid-positive (aAD), mild cognitively impaired β-amyloid-positive (pAD), mild cognitively impaired—specific variations not due to dementia β-amyloid-negative (CIND), severe cognitive impairment β-amyloid-positive (ADD+) and severe cognitive impairment β-amyloid-negative (ADD-) were used. NC and aAD groups did not show significant volume differences in any subfields. The CIND did not show significant volume differences when compared with either the NC or the aAD (except L-HATA). However, pAD showed significant volume differences in Sub, PrS, ML, Tail, GCMLDG, CA1, CA4, HATA, and CA3 when compared with the NC and aAD. The pAD group also showed significant differences in the hippocampal tail, CA1, CA4, molecular layer, granule cells/molecular layer/dentate gyrus, and CA3 when compared with the CIND group. The ADD- group had significantly larger volumes than the ADD+ group in the bilateral tail, SUB, PrS, and left ML. The results suggest that early amyloid depositions in cognitive normal stages are not accompanied by significant bilateral subfield volume atrophy. There might be intense and accelerated subfield volume atrophy in the later stages associated with the cognitive impairment in the pAD stage, which subsequently could drive the progression to AD dementia. Early subfield volume atrophy associated with the β-amyloid burden may be characterized by more symmetrical atrophy in CA regions than in other subfields. We conclude that the hippocampal subfield volumetric differences from structural imaging show promise for improving the diagnosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder influenced by age, sex, genetic factors, immune alterations, and infections. Multiple lines of evidence suggest that changes in antibody response are linked to AD pathology. To elucidate the mechanisms underlying AD development, we investigated antibodies that target autoimmune epitopes using high-resolution epitope microarrays. Our study compared two groups: individuals with AD (n = 19) and non-demented (ND) controls (n = 19). To validate the results, we measured antibody levels in plasma samples from AD patients (n = 96), mild cognitive impairment (MCI; n = 91), and ND controls (n = 97). To further explore the invlovement of EBV, we performed epitope masking immunofluorescence microscopy analysis and tests to induce lytic replication using the B95–8 cell line. In this study, we analyzed high-resolution epitope-specific serum antibody levels in AD, revealing significant disparities in antibodies targeting multiple epitopes between the AD and control groups. Particularly noteworthy was the significant down-regulation of antibody (anti-DG#29) targeting an epitope of Epstein-Barr virus nuclear antigen 1 (EBNA1). This down-regulation increased AD risk in female patients (odds ratio up to 6.6), but not in male patients. Our investigation further revealed that the down-regulation of the antibody (anti-DG#29) is associated with EBV reactivation in AD, as indicated by the analysis of EBV VCA IgG or IgM levels. Additionally, our data demonstrated that the epitope region on EBNA1 for the antibody is hidden during the EBV lytic reactivation of B95–8 cells. Our findings suggest a potential relationship of EBV in the development of AD in female. Moreover, we propose that antibodies targeting the epitope (DG#29) of EBNA1 could serve as valuable indicators of AD risk in female. [ABSTRACT FROM AUTHOR]

Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. Yet, the complexity of diagnosing AD and measuring its invasiveness poses an obstacle. To this end, blood-based biomarkers could mitigate the inconveniences that impede an accurate diagnosis. We developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring–mass spectrometry, a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control, asymptomatic AD, prodromal AD), and AD dementia. Multimarker models were developed using 10 protein biomarkers and apolipoprotein E genotypes for amyloid beta and 10 biomarkers with Korean Mini-Mental Status Examination (K-MMSE) score for predicting Alzheimer disease progression. The accuracies for the AD classification model and AD progression monitoring model were 84.9% (95% CI 82.8 to 87.0) and 79.1% (95% CI 77.8 to 80.5), respectively. The models were more accurate in diagnosing AD, compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD. [ABSTRACT FROM AUTHOR]

The present study reports two novel genome-wide significant loci for late-onset Alzheimer's disease (LOAD) identified from APOE ε4 non-carrier subjects of East Asian origin. A genome-wide association study of Alzheimer's disease was performed in 2,291 Korean seniors in the discovery phase, from the Gwangju Alzheimer' and Related Dementias (GARD) cohort study. The study was replicated in a Japanese cohort of 1,956 subjects that suggested two novel susceptible SNPs in two genes: LRIG1 and CACNA1A. This study demonstrates that the discovery of AD-associated variants is feasible in non-European ethnic groups using samples comprising fewer subjects from the more homogeneous genetic background. [ABSTRACT FROM AUTHOR]

Brain aging is becoming an increasingly important topic, and the norms of brain structures are essential for diagnosing neurodegenerative diseases. However, previous studies of the aging brain have mostly focused on Caucasians, not East Asians. The aim of this paper was to examine ethnic differences in the aging process of brain structures or to determine to what extent ethnicity affects the normative values of lobar and subcortical volumes in clinically normal elderly and the diagnosis in multi-racial patients with Alzheimer's disease (AD). Lobar and subcortical volumes were measured using FreeSurfer from MRI data of 1,686 normal Koreans (age range 59–89) and 851 Caucasian, non-Hispanic subjects in the ADNI and OASIS datasets. The regression models were designed to predict brain volumes, including ethnicity, age, sex, intracranial volume (ICV), magnetic field strength (MFS), and MRI scanner manufacturers as independent variables. Ethnicity had a significant effect for all lobar (|β| > 0.20, p < 0.001) and subcortical regions (|β| > 0.08, p < 0.001) except left pallidus and bilateral ventricles. To demonstrate the validity of the z-score for AD diagnosis, 420 patients and 420 normal controls were selected evenly from the Korean and Caucasian datasets. The four validation groups divided by race and diagnosis were matched on age and sex using a propensity score matching. We analyzed whether and to what extent the ethnicity adjustment improved the diagnostic power of the logistic regression model that was built using the only z-scores of six regions: bilateral temporal cortices, hippocampi, and amygdalae. The performance of the classifier after ethnicity adjustment was significantly improved compared with the classifier before ethnicity adjustment (ΔAUC = 0.10, D = 7.80, p < 0.001; AUC comparison test using bootstrap). Korean AD dementia patients may not be classified by Caucasian norms of brain volumes because the brain regions vulnerable to AD dementia are bigger in normal Korean elderly peoples. Therefore, ethnicity is an essential factor in establishing normative data for regional volumes in brain aging and applying it to the diagnosis of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

This paper presents an efficient computer-aided diagnosis (CAD) approach for the automatic detection of Alzheimer's disease in patients' T1 MRI scans using the voxel-based morphometry (VBM) analysis of the region of interest (ROI) in the brain. The idea is to generate a normal distribution of feature vectors from ROIs then later use for classification via Bayesian regularized neural network (BR-NN). The first dataset consists of the magnetic resonance imaging (MRI) of 74 Alzheimer's disease (AD), 42 mild cognitive impairment (MCI), and 74 control normal (CN) from the ADNI1 dataset. The other dataset consists of the MRI of 42 Alzheimer's disease dementia (ADD), 42 normal controls (NCs), and 39 MCI due to AD (mAD) from our GARD2 database. We aim to create a generalized network to distinguish normal individuals (CN/NC) from dementia patients AD/ADD and MCI/mAD. Our performance relies on our feature extraction process and data smoothing process. Here the key process is to generate a Statistical Parametric Mapping (SPM) t-map image from VBM analysis and obtain the region of interest (ROI) that shows the optimistic result after two-sample t-tests for a smaller value of p < 0.001(AD vs. CN). The result was overwhelming for the distinction between AD/ADD and CN/NC, thus validating our idea for discriminative MRI features. Further, we compared our performance with other recent state-of-the-art methods, and it is comparatively better in many cases. We have experimented with two datasets to validate the process. To validate the network generalization, BR-NN is trained from 70% of the ADNI dataset and tested on 30% of the ADNI, 100% of the GARD dataset, and vice versa. Additionally, we identified the brain anatomical ROIs that may be relatively responsible for brain atrophy during the AD diagnosis. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is one of the most common neurodegenerative illnesses (dementia) among the elderly. Recently, researchers have developed a new method for the instinctive analysis of AD based on machine learning and its subfield, deep learning. Recent state-of-the-art techniques consider multimodal diagnosis, which has been shown to achieve high accuracy compared to a unimodal prognosis. Furthermore, many studies have used structural magnetic resonance imaging (MRI) to measure brain volumes and the volume of subregions, as well as to search for diffuse changes in white/gray matter in the brain. In this study, T1-weighted structural MRI was used for the early classification of AD. MRI results in high-intensity visible features, making preprocessing and segmentation easy. To use this image modality, we acquired four types of datasets from each dataset's server. In this work, we downloaded 326 subjects from the National Research Center for Dementia homepage, 123 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) homepage, 121 subjects from the Alzheimer's Disease Repository Without Borders homepage, and 131 subjects from the National Alzheimer's Coordinating Center homepage. In our experiment, we used the multiatlas label propagation with expectation–maximization-based refinement segmentation method. We segmented the images into 138 anatomical morphometry images (in which 40 features belonged to subcortical volumes and the remaining 98 features belonged to cortical thickness). The entire dataset was split into a 70 : 30 (training and testing) ratio before classifying the data. A principal component analysis was used for dimensionality reduction. Then, the support vector machine radial basis function classifier was used for classification between two groups—AD versus health control (HC) and early mild cognitive impairment (MCI) (EMCI) versus late MCI (LMCI). The proposed method performed very well for all four types of dataset. For instance, for the AD versus HC group, the classifier achieved an area under curve (AUC) of more than 89% for each dataset. For the EMCI versus LMCI group, the classifier achieved an AUC of more than 80% for every dataset. Moreover, we also calculated Cohen kappa and Jaccard index statistical values for all datasets to evaluate the classification reliability. Finally, we compared our results with those of recently published state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Cerebrospinal fluid (CSF) amyloid-β1-42 (Aβ1-42), total tau protein (t-Tau), and phosphorylated Tau (p-Tau) are ATN biomarkers for Alzheimer's disease (AD) and reflect pathogenic changes in the brain. CSF biomarkers of AD are considered for inclusion in the diagnostic criteria for research and clinical purposes to reduce the uncertainty of clinical diagnosis and to distinguish among AD stages.Objective: This study aims to compare two commercially available analytical platforms with respect to accuracy and the potential for early diagnosis of AD.Methods: A total of 211 CSF samples from healthy control (HC) and AD subjects were analyzed using two analytical platforms, INNOTEST ELISA and INNOBIA AlzBio3 xMAP kits. The accuracy of diagnosis and AUC values distinguishing study groups were compared between the two analytical platforms.Results: The absolute values for Aβ1-42, t-Tau, and p-Tau181 levels differed between the two platforms. The Aβ1-42 levels decreased, while t-Tau and p-Tau levels increased according to the AD stages. The AUC of Aβ1-42 and t-Tau, which distinguish the early stages of AD (preclinical and prodromal AD), were similar between the two platforms, whereas there were significant differences in p-Tau AUC values. CSF p-Tau using the INNOBIA was highly accurate for distinguishing both preclinical AD (AUC = 0.826, cut-off score≥38.89) and prodromal AD (AUC = 0.862, cut-off score≥41.88) from HC.Conclusion: The accuracy of CSF p-Tau levels in the preclinical and prodromal AD is higher for the INNOBIA than the INNOTEST, and the early stage AD can be accurately distinguished from HC. [ABSTRACT FROM AUTHOR]

In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer’s disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: Relatively little attention has been paid to the meaning of reversion from mild cognitive impairment (MCI) to cognitively normal (CN), compared to MCI progression studies. The purpose of the study was to investigate the characteristics contributing to reversion from MCI to CN and to identify the associated factors with such reversion. Patients and methods: We retrospectively identified 200 individuals who initially diagnosed as MCI and completed the second visit from the National Research Center for Dementia (NRCD) registry in Korea. Participants underwent comprehensive clinical and neuropsychological assessments. Factors associated with reversion were examined by a independent-samples t-test, χ2 test, and logistic regression. Longitudinal change was examined by a repeated measures analysis of variance (rANOVA). Results: Based on the second assessment, 78 (39%) individuals were found to have reverted to CN (rMCI) and 118 (59%) remained with MCI (sMCI). Four (2%) progressed to Alzheimer's disease dementia and they were excluded from further analysis. Over a wide range of socio-demographic, clinical, and neuropsychological variables, group difference was significant only in neuropsychological tests of cognitive control. Both groups showed improvement in several neuropsychological tests, implying a practice effect, but the rMCI group showed greater improvement. Conclusion: Reversion from MCI to CN might not be a false-positive error but a true recovery from cognitive impairment. Our results suggest that cognitive control ability may be a characteristic favorable for the restoration of cognitive function. Therefore, assessment of cognitive control might facilitate the development of appropriate interventions for MCI as well as prognosis evaluation. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a common neurodegenerative disease with an often seen prodromal mild cognitive impairment (MCI) phase, where memory loss is the main complaint progressively worsening with behavior issues and poor self-care. However, not all patients clinically diagnosed with MCI progress to the AD. Currently, several high-dimensional classification techniques have been developed to automatically distinguish among AD, MCI, and healthy control (HC) patients based on T1-weighted MRI. However, these method features are based on wavelets, contourlets, gray-level co-occurrence matrix, etc., rather than using clinical features which helps doctors to understand the pathological mechanism of the AD. In this study, a new approach is proposed using cortical thickness and subcortical volume for distinguishing binary and tertiary classification of the National Research Center for Dementia dataset (NRCD), which consists of 326 subjects. Five classification experiments are performed: binary classification, i.e., AD vs HC, HC vs mAD (MCI due to the AD), and mAD vs aAD (asymptomatic AD), and tertiary classification, i.e., AD vs HC vs mAD and AD vs HC vs aAD using cortical and subcortical features. Datasets were divided in a 70/30 ratio, and later, 70% were used for training and the remaining 30% were used to get an unbiased estimation performance of the suggested methods. For dimensionality reduction purpose, principal component analysis (PCA) was used. After that, the output of PCA was passed to various types of classifiers, namely, softmax, support vector machine (SVM), k-nearest neighbors, and naïve Bayes (NB) to check the performance of the model. Experiments on the NRCD dataset demonstrated that the softmax classifier is best suited for the AD vs HC classification with an F1 score of 99.06, whereas for other groups, the SVM classifier is best suited for the HC vs mAD, mAD vs aAD, and AD vs HC vs mAD classifications with the F1 scores being 99.51, 97.5, and 99.99, respectively. In addition, for the AD vs HC vs aAD classification, NB performed well with an F1 score of 95.88. In addition, to check our proposed model efficiency, we have also used the OASIS dataset for comparing with 9 state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Being a carrier of the apolipoprotein E (APOE) ε4 allele is a clear risk factor for development of Alzheimer’s disease (AD). On some neurocognitive tests, there are smaller differences between carriers and noncarriers, while other tests show larger differences. Aims: We explore whether the size of the difference between carriers and noncarriers is a function of how well the tests measure general intelligence, so whether there are Jensen effects. Methods: We used the method of correlated vectors on 441 Korean older adults at risk for AD and 44 with AD. Results: Correlations between APOE carriership and test scores ranged from −.05 to .11 (normal), and −.23 to .54 (AD). The differences between carriers and noncarriers were Jensen effects: r = .31 and r = .54, respectively. Conclusion: A composite neurocognitive score may show a clearer contrast between APOE carriers and noncarriers than a large number of scores of single neurocognitive tests. [ABSTRACT FROM AUTHOR]

Potential biomarkers for Alzheimer's disease (AD) include amyloid β1–42 (Aβ1–42), t-Tau, p-Tau181, neurofilament light chain (NFL), and neuroimaging biomarkers. Their combined use is useful for diagnosing and monitoring the progress of AD. Therefore, further development of a combination of these biomarkers is essential. We investigated whether plasma NFL/Aβ1–42 can serve as a plasma-based primary screening biomarker reflecting brain neurodegeneration and amyloid pathology in AD for monitoring disease progression and early diagnosis. We measured the NFL and Aβ1–42 concentrations in the CSF and plasma samples and performed correlation analysis to evaluate the utility of these biomarkers in the early diagnosis and monitoring of AD spectrum disease progression. Pearson's correlation analysis was used to analyse the associations between the fluid biomarkers and neuroimaging data. The study included 136 participants, classified into five groups: 28 cognitively normal individuals, 23 patients with preclinical AD, 22 amyloid-negative patients with amnestic mild cognitive impairment, 32 patients with prodromal AD, and 31 patients with AD dementia. With disease progression, the NFL concentrations increased and Aβ1–42 concentrations decreased. The plasma and CSF NFL/Aβ1–42 were strongly correlated (r = 0.558). Plasma NFL/Aβ1–42 was strongly correlated with hippocampal volume/intracranial volume (r = 0.409). In early AD, plasma NFL/Aβ1–42 was associated with higher diagnostic accuracy than the individual biomarkers. Moreover, in preclinical AD, plasma NFL/Aβ1–42 changed more rapidly than the CSF t-Tau or p-Tau181 concentrations. Our findings highlight the utility of plasma NFL/Aβ1–42 as a non-invasive plasma-based biomarker for early diagnosis and monitoring of AD spectrum disease progression. [ABSTRACT FROM AUTHOR]

Lipocalin-2 (LCN2) is an inflammatory protein with diverse functions in the brain. Although many studies have investigated the mechanism of LCN2 in brain injuries, the effect of LCN2 on amyloid-toxicity-related memory deficits in a mouse model of Alzheimer's disease (AD) has been less studied. We investigated the role of LCN2 in human AD patients using a mouse model of AD. We created an AD mouse model by injecting amyloid-beta oligomer (AβO) into the hippocampus. In this model, animals exhibited impaired learning and memory. We found LCN2 upregulation in the human brain frontal lobe, as well as a positive correlation between white matter ischemic changes and serum LCN2. We also found increased astrocytic LCN2, microglia activation, iron accumulation, and blood–brain barrier disruption in AβO-treated hippocampi. These findings suggest that LCN2 is involved in a variety of amyloid toxicity mechanisms, especially neuroinflammation and oxidative stress. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common form of dementia in the elderly population, but its underlying cause has not been fully elucidated. Recent studies have shown that microRNAs (miRNAs) play important roles in regulating the expression levels of genes associated with AD development. In this study, we analyzed miRNAs in plasma and cerebrospinal fluid (CSF) from AD patients and cognitively normal (including amyloid positive) individuals. miR-1273g-3p was identified as an AD-associated miRNA and found to be elevated in the CSF of early-stage AD patients. The overexpression of miR-1273g-3p enhanced amyloid beta (Aβ) production by inducing oxidative stress and mitochondrial impairments in AD model cell lines. A biotin-streptavidin pull-down assay demonstrated that miR-1273g-3p primarily interacts with mitochondrial genes, and that their expression is downregulated by miR-1273g-3p. In particular, the miR-1273g-3p-target gene TIMM13 showed reduced expression in brain tissues from human AD patients. These results suggest that miR-1273g-3p expression in an early stage of AD notably contributes to Aβ production and mitochondrial impairments. Thus, miR-1273g-3p might be a biomarker for early diagnosis of AD and a potential therapeutic target to prevent AD progression. [ABSTRACT FROM AUTHOR]

Variants in the APOE gene region may explain ethnic differences in the association of Alzheimer's disease (AD) with ε4. Ethnic differences in allele frequencies for three APOE region SNPs (single nucleotide polymorphisms) were identified and tested for association in 19,398 East Asians (EastA), including Koreans and Japanese, 15,836 European ancestry (EuroA) individuals, and 4985 African Americans, and with brain imaging measures of cortical atrophy in sub-samples of Koreans and EuroAs. Among ε4/ε4 individuals, AD risk increased substantially in a dose-dependent manner with the number of APOE promoter SNP rs405509 T alleles in EastAs (TT: OR (odds ratio) = 27.02, p = 8.80 × 10−94; GT: OR = 15.87, p = 2.62 × 10−9) and EuroAs (TT: OR = 18.13, p = 2.69 × 10−108; GT: OR = 12.63, p = 3.44 × 10−64), and rs405509-T homozygotes had a younger onset and more severe cortical atrophy than those with G-allele. Functional experiments using APOE promoter fragments demonstrated that TT lowered APOE expression in human brain and serum. The modifying effect of rs405509 genotype explained much of the ethnic variability in the AD/ε4 association, and increasing APOE expression might lower AD risk among ε4 homozygotes. [ABSTRACT FROM AUTHOR]

Background: We previously demonstrated the validity of a regression model that included ethnicity as a novel predictor for predicting normative brain volumes in old age. The model was optimized using brain volumes measured with a standard tool FreeSurfer. Objective: Here we further verified the prediction model using newly estimated brain volumes from Neuro I, a quantitative brain analysis system developed for Korean populations. Methods: Lobar and subcortical volumes were estimated from MRI images of 1,629 normal Korean and 786 Caucasian subjects (age range 59–89) and were predicted in linear regression from ethnicity, age, sex, intracranial volume, magnetic field strength, and scanner manufacturers. Results: In the regression model predicting the new volumes, ethnicity was again a substantial predictor in most regions. Additionally, the model-based z-scores of regions were calculated for 428 AD patients and the matched controls, and then employed for diagnostic classification. When the AD classifier adopted the z-scores adjusted for ethnicity, the diagnostic accuracy has noticeably improved (AUC = 0.85, ΔAUC = + 0.04, D = 4.10, p < 0.001). Conclusions: Our results suggest that the prediction model remains robust across different measurement tool, and ethnicity significantly contributes to the establishment of norms for brain volumes and the development of a diagnostic system for neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is one of the most common neurodegenerative disorders in elderly people. It is diagnosed by detecting amyloid beta (Aβ) protein in cerebrospinal fluid (CSF) obtained by lumbar puncture or through expensive positron emission tomography (PET) imaging. Although blood-based diagnosis of AD offers a less invasive and cost-effective alternative, the quantification of Aβ is technically challenging due to its low abundance in peripheral blood. To address this, we developed a compact yet highly sensitive microwell-based electrochemical sensor with a densely packed microelectrode array (20 by 20) for enhancing sensitivity. Employing microwells on the working and counter electrodes minimized the leakage current from the metallic conductors into the assay medium, refining the signal fidelity. We achieved a detection limit <10 fg/mL for Aβ by elevating the signal-to-noise ratio, thus capable of AD biomarker quantification. Moreover, the microwell structure maintained the performance irrespective of variations in bead number, indicative of the sensor's robustness. The sensor's efficacy was validated through the analysis of Aβ concentrations in plasma samples from 96 subjects, revealing a significant distinction between AD patients and healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.85. Consequently, our novel microwell-based electrochemical biosensor represents a highly sensitive platform for detecting scant blood-based biomarkers, including Aβ, offering substantial potential for advancing AD diagnostics. [ABSTRACT FROM AUTHOR]

Background: Subjective memory complaints (SMC) and depressive symptoms (SDS) are common in the elderly population. However, the relationship among SMC, SDS, and cognitive function remains unclear. We investigated these associations in the elderly from cognitively normal (CN), pre-mild cognitive impairment (MCI), and amnestic MCI (aMCI) groups.Methods: Participants (CN, 299; pre-MCI, 106; aMCI, 267) underwent comprehensive clinical and neuropsychological assessment. and self-report SMC and SDS questionnaires. SMC and SDS were administered in a self-report format. For each neuropsychological test z-score, stepwise multiple linear regressions were performed to assess the relative contribution of SMC, SDS, and their interactions.Results: SMC are associated with lower objective memory, while SDS are associated with lower psychomotor speed. Interactions between SMC and SDS were significant for tests of memory, executive function, psychomotor speed, and global cognition. Additional analyses revealed that SDS moderated the SMC-cognition relationship such that only individuals with higher SDS showed significant SMC-cognition associations.Limitations: Due to the cross-sectional design, associations among SMC, SDS, and cognitive function was rather weak, albeit significant. Additionally, future biomarker studies, such as those assessing amyloid burden, are needed to explore the mechanisms underlying the relationship among SMC, SDS, and cognitive function.Conclusion: Early identification of individuals at risk for developing abnormal cognitive changes is critical. Our findings from the study involving a large sample of carefully selected participants suggest that SMC and SDS could be used as early detection markers of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

The early diagnosis of Alzheimer's Disease (AD) plays a central role in the treatment of AD. Particularly, identifying the preclinical AD (pAD) stage could be crucial for timely treatment in the elderly. However, screening participants with pAD requires a series of psychological and neurological examinations. Thus, an efficient diagnostic tool is needed. Here, we recruited 91 elderly participants and collected 1 min of resting-state electroencephalography data to classify participants as normal aging or diagnosed with pAD. We used deep neural networks (Deep ConvNet, EEGNet, EEG-TCNet, and cascade CRNN) in the within- and cross-subject paradigms for classification and found individual variations of classification accuracy in the cross-subject paradigm. Further, we proposed an individualized diagnostic strategy to identify neurophysiological similarities across participants and the proposed approach considering individual characteristics improved the diagnostic performance by approximately 20%. Our findings suggest that considering individual characteristics would be a breakthrough in diagnosing AD using deep neural networks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Amyloid-β increases the acetylcholine-induced intracellular calcium load in SH-SY5Y cells. • Phospholipase C-β1 expression is upregulated in amyloid-β-treated SH-SY5Y cells and 5× familiar Alzheimer's disease (5×FAD) mice brains. • Overexpression of phospholipase C-β1 elevates the calcium release from endoplasmic reticulum in SH-SY5Y cells. • Knockdown of phospholipase C-β1 abrogates the calcium overload induced by amyloid-β in SH-SY5Y cells. Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Amyloid-β (Aβ) has long been considered a key cause of neurodegeneration in the AD brain. Although the mechanisms underlying Aβ-induced neurodegeneration are not fully understood, a number of recent studies have suggested that intracellular calcium overload mediates this process. In this study, we focused on the cellular function of phospholipase C-β1 (PLCB1), which regulates calcium signaling by mediating hydrolysis of phosphatidylinositol 4,5-bisphosphate through G-protein coupled receptor pathways. First, we confirmed that acetylcholine-induced calcium release from intracellular stores of SH-SY5Y cells was significantly increased with Aβ 42 oligomer treatment. We further found that PLCB1 expression was upregulated in Aβ 42 -treated cells, and PLCB1 overexpression in SH-SY5Y cells elicited the calcium overload observed in Aβ-treated cells. In addition, Aβ 42 oligomer-induced calcium overload in SH-SY5Y cells was alleviated by knockdown of PLCB1, indicating that PLCB1 plays an essential role in the neurotoxic process initiated by Aβ. The elevation of PLCB1 expression was confirmed in the brain tissues from the 5× familial AD (5×FAD) model mice. These findings suggest that PLCB1 may represent a potential therapeutic target for protecting neuronal cells against excitotoxicity in AD progression. [ABSTRACT FROM AUTHOR]