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INTRODUCTION: We investigated the prevalence of amyloid beta (Aβ) positivity (+) and cognitive trajectories in Koreans and non‐Hispanic Whites (NHWs). METHODS: We included 5121 Koreans from multiple centers across South Korea and 929 NHWs from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent Aβ positron emission tomography and were categorized into cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia stages. Age, sex, education, and apolipoprotein E. genotype were adjusted using multivariable logistic regression and stabilized inverse probability of treatment weights based on the propensity scores to mitigate imbalances in these variables. RESULTS: The prevalence of Aβ+ was lower in CU Koreans than in CU NHWs (adjusted odds ratio 0.60). Aβ+ Koreans showed a faster cognitive decline than Aβ+ NHWs in the CU (B = −0.314, p =.004) and MCI stages (B = −0.385, p <.001). DISCUSSION: Ethnic characteristics of Aβ biomarkers should be considered in research and clinical application of Aβ‐targeted therapies in diverse populations. Highlights: Koreans have a lower prevalence of Aβ positivity compared to NHWs in the CU stage.The effects of Alzheimer's risk factors on Aβ positivity differ between Koreans and NHWs.Aβ‐positive (Aβ+) Koreans show faster cognitive decline than Aβ+ NHWs in the CU and MCI stages. [ABSTRACT FROM AUTHOR]

Background: Mild cognitive impairment (MCI), especially the amnestic form, is considered a transitional state between normal aging and Alzheimer's disease (AD). However, not all amnestic MCI (aMCI) patients progress to AD. Thus, developing a predictive model for AD progression in aMCI patients has been considered important. Prametric methods such as logistic regression have been developed, but it is difficult to reflect complex patterns such as nonlinear relationships and interactions between variables. This study aims to improve the predictive power of aMCI patients' conversion to dementia by using an interpretable machine learning (IML) model and to identify factors that increase the risk of individual dementia conversion in each patient. Method: We obtained 760 aMCI patients, who had been followed at least three years after baseline neuropsychological tests from Samsung Medical Center from 2007 to 2019. We used neuropsychological tests and apolipoprotein E (APOE) genotype data for developing predictive models. Model building and model validation data sets were composed of 565 and 140 patients, respectively. For global interpretation, four models of logistic regression, random forest, support vector machine, and extreme gradient boost were compared. For local interpretation, individual conditional expectations (ICE) and SHapley Additive exPlanations (SHAP) were used to analyze individual patients. Result: Among 4 models, the extreme gradient boost model showed the best performance with an AUC of 0.83, an accuracy of 0.76, and an f1‐score of 0.65. The variables such as RCFT‐DR, CDR‐SOB, age, K‐MMSE, COWAT‐animal, education, SVLT‐DR, RCFT‐copy time, and APOE genotype were important features for creating the model. Through ICE and SHAP analysis, it was also possible to interpret which variables acted as strong factors for each patient. Conclusion: The present study developed a model for predicting dementia conversion in aMCI patients using IML technique. This predictive IML model is expected to be useful in clinical practice and research field as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient. [ABSTRACT FROM AUTHOR]

Background: Accumulation of β‐amyloid (Aβ) plaques and neurofibrillary tangles are core components of Alzheimer's disease (AD) pathogenesis. Longitudinal in vivo observations of AD pathologic changes using amyloid and tau positron emission tomography (PET) enable us to obtain AD biomarkers' hypothetical trajectory curves. However, the difference in disease progression by striatal Aβ deposition, sex, and apolipoprotein E (APOE) genotype has not been explained with trajectory curves. The aim of this study is to develop the trajectory in accumulation of cortical and striatal Aβ and tau deposits. We also tried to investigate the differences in sex and APOE genotype on trajectory curves. Method: We obtained 817 subjects from the Alzheimer's Disease Neuroimaging Initiative database, who showed either 18F‐florbetapir (AV45) PET positivity at least once or AV45 PET negativity with normal cognition. We Z‐transformed each value based on Aβ‐negative, cognitively normal subjects. Instead of obtaining a single rate value (∆Z/∆t) per subject, we calculated the slope for every interval of PET scans. For each biomarker, we fitted a penalized spline to model the slope of Z value as a nonlinear function of baseline Z value. Using the fitted splines, we obtained the predicted Z curves as a function of time and developed trajectory curves. Result: Among the subjects, 534 subjects were available for AV45 PET analysis and 163 subjects underwent 18F‐flortaucipir PET. While cortical Aβ accumulation rate increased until Z value reached 4.33 and decreased subsequently, ∆Z/∆t for the striatal Aβ and tau stayed more constant. Time from Z=0 to Z=2 was the longest for tau PET (22.6 years) and shortest for cortical Aβ (14.9 years). For cortical Aβ, Z values of males reached 1.7 years earlier than females. However, for striatal Aβ, males took 2.8 more years than females to reach Z=2. Also, APOE ɛ4 carriers showed faster progression for all biomarkers. Conclusion: This study shows that Aβ accumulates in the striatum after cortex and then tau protein, which is consistent with the amyloid cascade hypothesis. The trajectory curves differ according to sex and APOE genotype. Therefore, prognosis and treatment should be approached differently in clinical practice with respect to striatal involvement, sex, and APOE genotype. [ABSTRACT FROM AUTHOR]

Background: Early detection of β‐amyloid (Aβ) accumulation, a major biomarker for Alzheimer's disease (AD), has become important. While Aβ deposition can be detected by positron emission tomography (PET), practical fluid Aβ biomarker is also being developed recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of fluid Aβ levels for amyloid PET positivity. Methods: We recruited 488 participants who underwent both plasma Aβ and Aβ PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) Aβ and Aβ PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest‐MS, an antibody‐free liquid chromatography‐differential mobility spectrometry‐triple quadrupole mass spectrometry method and INNOTEST enzyme‐linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma Aβ and CSF Aβ, respectively, logistic regression and receiver operating characteristic analyses were performed. Results: When predicting Aβ PET status, both plasma Aβ42/40 ratio and CSF Aβ42 showed high accuracy (plasma Aβ area under the curve (AUC) 0.814; CSF Aβ AUC 0.848). In the plasma Aβ models, the AUC values were higher than plasma Aβ alone model, when the models were combined with either cognitive stage (p < 0.001) or APOE genotype (p = 0.011). On the other hand, there was no difference between the CSF Aβ models, when these variables were added. Conclusions: Plasma Aβ might be a useful predictor of Aβ PET status as much as CSF Aβ, particularly when considered with clinical information. [ABSTRACT FROM AUTHOR]

Background: Among the various risk factors for Alzheimer disease (AD), apolipoprotein E (APOE) genotype ε4 is known to be a strong factor. On the other hand, ε2 is known to have a protective effect. It is not yet clear how amyloid positivity according to the APOE genotype differs by diagnostic criteria. Also, there were no studies showing the ethnic difference in the effects of APOE genotype on amyloid‐beta (Aβ) between American and Asian. The aim of this study was to investigate how Aβ positivity (Aβ+) according to APOE genotypes differs by ethnicity and cognition. Method: We recruited 4,203 Korean subjects from multi‐centers in South Korea and 1,069 subjects from Alzheimer's Disease Neuroimaging Initiative database. All subjects underwent neuropsychological tests, APOE genotype tests, and amyloid PET (FMM/FBB/AV45) scans. Subjects with the ε2/ε4 genotype were excluded due to the opposing effects of the ε4 and ε2 alleles. The degree of influence on amyloid positivity by each factor was analyzed by logistic regression. Result: The frequency of Aβ+ in normal cognition (NC) group was higher in ADNI than in Korean group (p <0.001). Amnestic mild cognitive impairment and AD dementia (ADD) groups showed no difference in the frequency of Aβ+ between ADNI and Korean groups. Relative to APOE ε3/ε3 group, APOE ε4 carriers showed higher frequent Aβ+ regardless of cognitive status and ethnicity (Figure). ADNI and Korean groups showed APOE ε2 carriers less frequent Aβ+ in ADD but not in NC relative to APOE ε3/ε3 group. Conclusion: Our findings suggested that there were no differences in effects of APOE ε4 genotype on Aβ+ between European and Korean. These findings will be helpful in predicting amyloid positivity and AD intervention according to APOE genotype and ethnicity. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is characterized by amyloid beta (Aβ) aggregation [A] and tau aggregation reflecting neurofibrillary tangles [T]. These AD markers lead to neurodegeneration [N] and cognitive impairments. Recently, the ATN research framework of was presented by National Institute on Aging and Alzheimer's Association. Cerebral small vessel disease (CSVD, [V]), which is one of most important cause of cognitive impairments, has an additive or synergistic effect on cognitive impairments with AD markers. In the present study, we investigated the frequency of ATV abnormalities in participants with varying degree of AD and CSVD markers. We also determined whether CSVD might synergistically affect cognitive decline with AD markers. Method: We consecutively recruited 195 subjects (105 AD‐related cognitive impairment subjects, 86 subcortical vascular cognitive impairment subjects) from Samsung Medical Center. [A] and [T] positivities were determined by cut‐off values of Aβ and tau uptakes on PET using iterative outlier methods. [V] was assessed by the presence of severe white matter hyperintensity. All subjects underwent neuropsychological tests including Mini‐Mental State Exam (MMSE) and clinical dementia rating‐sum of boxes (CDR‐SOB). A linear mixed effect model was used to compare cognitive changes over time. Result: The number of subjects of A‐T‐, A+T‐, and A+T+ groups were 16 (15.1%), 29 (27.4%), and 56 (52.8%) in the V‐ group; and 46 (51.7%), 33 (37.1%), and 8 (9.0%) in the V+ group. The linear mixed effects model analysis showed that A+/T+ were associated with worse cognitive decline, and its impact is higher in the V+ group than in the V‐ group (Figure). Conclusion: Our findings suggested that there were interactive effects of AD markers and CSVD on cognitive decline. These findings demonstrate the importance of vascular factor management in patients with cognitive impairment and suggest that the ATV framework is useful in predicting cognitive decline in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Amyloid‐β (Aβ) accumulation, a major biomarker in Alzheimer's disease (AD), induces tau accumulation, eventually resulting in cognitive decline. However, Aβ trajectory in cognitively normal participants has not been extensively investigated, yet. Modeling of Aβ trajectory remains an important goal, as future treatments targeting Aβ are expected to be developed. In the present study, we therefore investigated whether they might be divided into the normal aging group and the pathological group with increased amyloid. We also compared the differences between the non‐accumulating group and accumulating group. Method: We included 297 subjects from the Alzheimer's Disease Neuroimaging Initiative database, who showed normal cognition at baseline. All subjects underwent neuropsychological test, apolipoprotein E (APOE) genotype test, brain MRI, and an average of 3.03 follow‐up 18F‐florbetapir (AV45) PET scans. The original dataset was divided by 6:4 to create a training set and a validation set. Latent Class Growth Analysis was used for statistical analysis. Result: Based on the information criterion, the model consisting of three classes was found to be the best with a high entropy of 0.979: class 1 (non‐accumulating group; n = 117, 65.7%), class 2 (accumulating group; n = 46, 25.8%), and class 3 (high accumulation group; n = 15, 8.4%) (Figure). The accumulating group had more APOE ε4 carriers and a higher tau burden than the non‐accumulating group (Table). Conclusion: Our study showed that there is the heterogeneity of Aβ trajectories according to aging in the normal cognitive elderly. Heterogeneity of Aβ accumulation and early detection of accumulating group may be helpful in early diagnosis and treatment of AD. [ABSTRACT FROM AUTHOR]

Background: Early detection of amyloid beta (Aβ) accumulation has become important as Aβ accumulation begins before clinical symptoms appear. Previous longitudinal studies of Aβ‐negative (Aβ‐) people have shown that some of them convert to amyloid‐positive (Aβ+) and others remain amyloid‐negative. The clinical effects of conversion to Aβ+ have not been extensively investigated, yet. The aim of the study is to investigate the longitudinal cognitive changes according to conversion to Aβ+. Method: We recruited subjects from 62 Korean subjects from Samsung Medical Center and 275 the Alzheimer's Disease Neuroimaging Initiative database that were followed for more than three years. All subjects were amyloid PET negative and showed normal cognitive function at baseline. They were followed for more than three years and underwent amyloid PET and clinical dementia rating‐sum of boxes (CDR‐SOB). Longitudinal cognition changes according to conversion to Aβ+ were analyzed using a linear mixed effect model. Result: Twelve (4.4%) of Aβ‐ ADNI participants and three (4.8%) of Aβ‐ Korean participants were converted to Aβ+. The ADNI and Korean subjects showed that CDR‐SOB was significantly deteriorated in the converter group than in the non‐converter group (p = 0.034 for ADNI; p = 0.022 for Korean). Conclusion: Our findings revealed that conversion to Aβ+ predicted worse clinical outcomes, suggesting that it is important to recognize potential candidates for primary prevention of AD. [ABSTRACT FROM AUTHOR]