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BACKGROUND AND PURPOSE: Amyloid ß (Aß), a major biomarker of Alzheimer's disease, leads to tau accumulation, neurodegeneration and cognitive decline. Modelling the trajectory of Aß accumulation in cognitively unimpaired (CU) individuals is crucial, as treatments targeting Aß are anticipated. The evolution of Aß levels was investigated to determine whether it could lead to classification into different groups by studying longitudinal Aß changes in older CU individuals, and differences between the groups were compared. METHODS: A total of 297 CU participants were included from the Alzheimer's Disease Neuroimaging Initiative database, and these participants underwent apolipoprotein E (APOE) genotyping, neuropsychological testing, brain magnetic resonance imaging, and an average of 3.03 follow-up 18F-florbetapir positron emission tomography scans. Distinct Aß trajectory patterns were classified using latent class growth analysis, and longitudinal cognitive performances across these patterns were assessed with a linear mixed effects model. RESULTS: The optimal model consisted of three classes, with a high entropy value of 0.947. The classes were designated as follows: class 1, non-accumulation group (n = 197); class 2, late accumulation group (n = 70); and class 3, early accumulation group (n = 30). The late accumulation and early accumulation groups had more APOE ε4 carriers than the non-accumulation group. The longitudinal analysis of cognitive performance revealed that the early accumulation group showed the steepest decline (modified Preclinical Alzheimer's Cognitive Composite with digit symbol substitution [mPACCdigit], p < 0.001; modified Preclinical Alzheimer's Cognitive Composite with trails B [mPACCtrailsB], p < 0.001) and the late accumulation group showed a steeper decline (mPACCdigit, p = 0.014; mPACCtrailsB, p = 0.007) compared to the non-accumulation group. CONCLUSIONS: Our study showed the heterogeneity of Aß accumulation trajectories in CU older individuals. The prognoses for cognitive decline differ according to the Aß trajectory patterns.

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INTRODUCTION: We investigated distinctive factors associated with cognitive reserve (CR) based on education level. METHODS: Among 1247 participants who underwent neuropsychological assessment, amyloid positron emission tomography, and brain magnetic resonance imaging, 336 participants with low education (≤6 years) and 697 with high education (≥12 years) were selected. CR was measured as the difference between the predicted and observed value of cognitive function based on cortical thickness. Multiple linear regression was conducted in each group after controlling for age and sex. RESULTS: In the low-education group, low literacy, long sleep duration(>8 h/day), and diabetes were negatively associated with CR, whereas cognitive and physical activity were positively associated with CR. In the high-education group, cognitive activity was positively related to CR, whereas low literacy, long sleep duration (> 8 h/day), and depression were negatively related to CR. DISCUSSION: This study provides insights into different strategies for enhancing CR based on educational background. HIGHLIGHTS: Factors associated with cognitive reserve (CR) varied according to the education level. Diabetes and physical activity were associated with CR in the low-education group. Depression was related to CR in the high-education group. Low literacy, sleep duration, and cognitive activity were associated with CR in both groups. Dementia-prevention strategies should be tailored according to educational level.

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There is an emerging body of evidence concerning the neurological effect of air pollutants on cognitive function and increased risk of neurodegeneration. Although previous studies have suggested that polycyclic aromatic hydrocarbons (PAHs) are neurotoxic, the effect of PAHs exposure on neurodegeneration remains unclear. This study aimed to investigate the association between PAH exposure and the risk of developing amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). For this matched case-control cross-sectional study, we recruited patients aged ≥50 years diagnosed with aMCI and AD from the Samsung Medical Center, Seoul, Korea, between 2014 and 2019. For each patient, we randomly selected four cognitively healthy controls through frequency matching based on sex, age group, and education level. Urinary levels of four PAH metabolites, 1-hydroxypyrene (1-OHP), 1-hydroxyphenanthrene (1-OHPhe), 2-hydroxyfluorene (2-OHFlu), and 2-naphthol (2-NAP), were measured. A conditional logistic regression model was used to evaluate the association, adjusting for potential confounders. A total of 212 patients with aMCI with 848 matched controls, and 267 patients with AD with 1068 matched controls were included in the analyses to estimate the risk of PAH exposure. We found that elevated urinary levels of PAH metabolites (specifically, 1-OHP and 2-NAP) were significantly associated with an increased risk of aMCI and AD. An increase of one unit in log-transformed level of urinary 1-OHP was associated with a 1.15- and 1.16-times higher risk of aMCI and AD, respectively. An increase of one unit in log-transformed level of urinary 2-NAP was associated with a 1.11- and 1.13-times higher risk of aMCI and AD, respectively. These findings indicate that PAH exposure may increase the risk of aMCI and AD, especially for the elderly population. Considering the widespread distribution of PAHs in the environment, reducing PAH exposure may be an effective strategy for the prevention of neurodegenerative diseases.

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PURPOSE: Although rapid cognitive decline (RCD) is an important unfavorable prognostic factor, not much is known about it, especially in amyloid-negative individuals. The purpose of this study was to investigate risk factors for RCD in amyloid-negative individuals. PATIENTS AND METHODS: We retrospectively enrolled 741 individuals who were either cognitively unimpaired or had early-stage cognitive ability loss and who underwent 18F-florbetaben (FBB) (n = 402) or 18F-flutemetamol (FMM) (n = 339) PET/CT. Based on visual and semiquantitative (SUV ratio [SUVR]-based) analysis, the following amyloid-negative groups were established: visual-negative FBB (n = 232), visual-negative FMM (n = 161), SUVR-negative FBB (n = 104), and SUVR-negative FMM (n = 101). Univariable and multivariable logistic regression analyses were performed for RCD using 5 SUVRs, 5 cortical thicknesses, and 5 neuropsychological domains and clinico-demographic factors. RESULTS: In the amyloid-negative groups, a decline in language function was commonly identified as a significant risk factor for RCD (P = 0.0044 in the visual-negative FBB group, P = 0.0487 in the visual-negative FMM group, P = 0.0031 in the SUVR-negative FBB group, and P = 0.0030 in the SUVR-negative FMM group). In addition, declines in frontal/executive function, frontal SUVR, and parietal SUVR; a longer duration of education; and mild cognitive decline in the amyloid-negative groups were also significant risk factors for RCD. CONCLUSIONS: Even in amyloid-negative individuals without cognitive impairment or with early-stage cognitive ability loss, those with decreased language and frontal/executive functions on neuropsychological testing are at risk of progression to RCD.

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Background: Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Methods: Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Results: Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Conclusion: Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.

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BACKGROUND: Increasing evidence supports the association between body mass index (BMI), Alzheimer's disease, and vascular markers. Recently, metabolically unhealthy conditions have been reported to affect the expression of these markers. We aimed to investigate the effects of BMI status on Alzheimer's and vascular markers in relation to metabolic health status. METHODS: We recruited 1,736 Asians without dementia (71.6 ± 8.0 years). Participants were categorized into underweight, normal weight, or obese groups based on their BMI. Each group was further divided into metabolically healthy (MH) and unhealthy (MU) groups based on the International Diabetes Foundation definition of metabolic syndrome. The main outcome was Aß positivity, defined as a Centiloid value of 20.0 or above and the presence of vascular markers, defined as severe white matter hyperintensities (WMH). Logistic regression analyses were performed for Aß positivity and severe WMH with BMI status or interaction terms between BMI and metabolic health status as predictors. Mediation analyses were performed with hippocampal volume (HV) and baseline Mini-Mental State Examination (MMSE) scores as the outcomes, and linear mixed models were performed for longitudinal change in MMSE scores. RESULTS: Being underweight increased the risk of Aß positivity (odds ratio [OR] = 2.37, 95% confidence interval [CI] 1.13-4.98), whereas obesity decreased Aß positivity risk (OR = 0.63, 95% CI 0.50-0.80). Especially, obesity decreased the risk of Aß positivity (OR = 0.38, 95% CI 0.26-0.56) in the MH group, but not in the MU group. Obesity increased the risk of severe WMH (OR = 1.69, 1.16-2.47). Decreased Aß positivity mediate the relationship between obesity and higher HV and MMSE scores, particularly in the MH group. Obesity demonstrated a slower decline in MMSE (ß = 1.423, p = 0.037) compared to being normal weight, especially in the MH group. CONCLUSIONS: Our findings provide new evidence that metabolic health has a significant effect on the relationship between obesity and Alzheimer's markers, which, in turn, lead to better clinical outcomes.

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Anticancer drugs may affect the incidence of dementia by modulating the common pathophysiology between cancer and dementia. However, there is a paucity of research that focused on anticancer drugs with different mechanisms of action and their associations with subtypes of dementia. Therefore, we aimed to investigate the incidence of dementia according to various groups of anticancer drugs. From the Korea National Health Insurance Service database, our retrospective population-based cohort study enrolled 116,506 cancer patients aged 65 years and older who received anticancer drugs between January 1, 2008 and December 31, 2018. The hazard ratio was determined using Cox proportional hazards regression models, comparing each group of anticancer drugs to all other anticancer drugs, after adjusting for covariates. Antimetabolites (HR = 0.91; 95% CI 0.84-0.97) and molecular targeted therapies (HR = 0.60; 95% CI 0.49-0.74) were associated with a decreased incidence of dementia of the Alzheimer type (DAT), but not with vascular dementia. Among molecular targeted therapies, epidermal growth factor receptor inhibitors (HR = 0.60; 95% CI 0.46-0.79) and multikinase inhibitors (HR = 0.49; 95% CI 0.27-0.89) were associated with a low incidence of DAT only. Our findings highlight the potential for targeted repurposing of anticancer drugs to prevent dementia.

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The frequency of the apolipoprotein E ɛ4 allele and vascular risk factors differs among ethnic groups. We aimed to assess the combined effects of apolipoprotein E ɛ4 and vascular risk factors on brain age in Korean and UK cognitively unimpaired populations. We also aimed to determine the differences in the combined effects between the two populations. We enrolled 2314 cognitively unimpaired individuals aged ≥45 years from Korea and 6942 cognitively unimpaired individuals from the UK, who were matched using propensity scores. Brain age was defined using the brain age index. The apolipoprotein E genotype (ɛ4 carriers, ɛ2 carriers and ɛ3/ɛ3 homozygotes) and vascular risk factors (age, hypertension and diabetes) were considered predictors. Apolipoprotein E ɛ4 carriers in the Korean (ß = 0.511, P = 0.012) and UK (ß = 0.302, P = 0.006) groups had higher brain age index values. The adverse effects of the apolipoprotein E genotype on brain age index values increased with age in the Korean group alone (ɛ2 carriers × age, ß = 0.085, P = 0.009; ɛ4 carriers × age, ß = 0.100, P < 0.001). The apolipoprotein E genotype, age and ethnicity showed a three-way interaction with the brain age index (ɛ2 carriers × age × ethnicity, ß = 0.091, P = 0.022; ɛ4 carriers × age × ethnicity, ß = 0.093, P = 0.003). The effects of apolipoprotein E on the brain age index values were more pronounced in individuals with hypertension in the Korean group alone (ɛ4 carriers × hypertension, ß = 0.777, P = 0.038). The apolipoprotein E genotype, age and ethnicity showed a three-way interaction with the brain age index (ɛ4 carriers × hypertension × ethnicity, ß=1.091, P = 0.014). We highlight the ethnic differences in the combined effects of the apolipoprotein E ɛ4 genotype and vascular risk factors on accelerated brain age. These findings emphasize the need for ethnicity-specific strategies to mitigate apolipoprotein E ɛ4-related brain aging in cognitively unimpaired individuals.

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INTRODUCTION: This study aimed to explore the potential of whole brain white matter patterns as novel neuroimaging biomarkers for assessing cognitive impairment and disability in older adults. METHODS: We conducted an in-depth analysis of magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans in 454 participants, focusing on white matter patterns and white matter inter-subject variability (WM-ISV). RESULTS: The white matter pattern ensemble model, combining MRI and amyloid PET, demonstrated a significantly higher classification performance for cognitive impairment and disability. Participants with Alzheimer's disease (AD) exhibited higher WM-ISV than participants with subjective cognitive decline, mild cognitive impairment, and vascular dementia. Furthermore, WM-ISV correlated significantly with blood-based biomarkers (such as glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]), and cognitive function and disability scores. DISCUSSION: Our results suggest that white matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making and determining cognitive impairment and disability. HIGHLIGHTS: The ensemble model combined both magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) and demonstrated a significantly higher classification performance for cognitive impairment and disability. Alzheimer's disease (AD) revealed a notably higher heterogeneity compared to that in subjective cognitive decline, mild cognitive impairment, or vascular dementia. White matter inter-subject variability (WM-ISV) was significantly correlated with blood-based biomarkers (glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]) and with the polygenic risk score for AD. White matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making processes and determining cognitive impairment and disability.

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BACKGROUND: Risk factors for cardiovascular disease, including elevated blood pressure, are known to increase risk of Alzheimer's disease. There has been increasing awareness of the relationship between long-term blood pressure (BP) patterns and their effects on the brain. We aimed to investigate the association of repeated BP measurements with Alzheimer's and vascular disease markers. METHODS: We recruited 1,952 participants without dementia between August 2015 and February 2022. During serial clinic visits, we assessed both systolic BP (SBP) and diastolic BP (DBP), and visit-to-visit BP variability (BPV) was quantified from repeated measurements. In order to investigate the relationship of mean SBP (or DBP) with Alzheimer's and vascular markers and cognition, we performed multiple linear and logistic regression analyses after controlling for potential confounders (Model 1). Next, we investigated the relationship of with variation of SBP (or DBP) with the aforementioned variables by adding it into Model 1 (Model 2). In addition, mediation analyses were conducted to determine mediation effects of Alzheimer's and vascular makers on the relationship between BP parameters and cognitive impairment. RESULTS: High Aß uptake was associated with greater mean SBP (ß = 1.049, 95% confidence interval 1.016-1.083). High vascular burden was positively associated with mean SBP (odds ratio = 1.293, 95% CI 1.015-1.647) and mean DBP (1.390, 1.098-1.757). High tau uptake was related to greater systolic BPV (0.094, 0.001-0.187) and diastolic BPV (0.096, 0.007-0.184). High Aß uptake partially mediated the relationship between mean SBP and the Mini-Mental State Examination (MMSE) scores. Hippocampal atrophy mediated the relationship between diastolic BPV and MMSE scores. CONCLUSIONS: Each BP parameter affects Alzheimer's and vascular disease markers differently, which in turn leads to cognitive impairment. Therefore, it is necessary to appropriately control specific BP parameters to prevent the development of dementia. Furthermore, a better understanding of pathways from specific BP parameters to cognitive impairments might enable us to select the managements targeting the specific BP parameters to prevent dementia effectively.

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BACKGROUND: Overactive bladder (OAB) is a common condition in middle-aged and older women. It has been reported to be potentially linked to cognitive decline, particularly in older adults. This study investigated the association between OAB symptoms and cognitive impairment in middle-aged women. MATERIALS AND METHODS: This cross-sectional study had a sample of 1652 women (mean age 49.3 ± 2.8 years) who were not taking medication for either urinary tract infection or OAB. OAB symptoms and cognitive function were evaluated by self-administered questionnaires: the Overactive Bladder Symptom Score and the Alzheimer's disease 8. Logistic regression models estimated prevalence ratios (PRs) with 95 % confidence intervals (CI) for cognitive impairment according to the presence/absence of OAB. Mediation analyses assessed the impact of poor sleep quality on this association. RESULTS: Cognitive impairment was more prevalent in women with OAB than in those without OAB (multivariable-adjusted PR: 1.88 [95 % CI: 1.52-2.24]). Women experiencing nocturia (≥twice a night), urinary urgency at least once a week, and urgency urinary incontinence at least once a week had multivariable-adjusted PRs (95 % CI) for cognitive impairment of 2.08 (1.50-2.65), 2.12 (1.66-2.58), and 1.75 (1.17-2.34), respectively. Poor sleep quality mediated 10.81 % [95 % CI: 4.55-19.44 %] of the relationship between OAB and cognitive impairment. CONCLUSIONS: Among middle-aged women not taking OAB medications, OAB symptoms were associated with cognitive impairment, partly because of poor sleep quality. Further research is needed to determine whether early screening of patients with OAB can help identify those susceptible to cognitive impairment associated with OAB medication and if preventive measures should be targeted at this group.

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Background: Amyloid-ß (Aß) commonly coexists and impacts prognosis in subcortical vascular cognitive impairment (SVCI). Objective: This study aimed to examine the differences in clinical and neuroimaging variables between Aß-positive and Aß-negative SVCI and to propose a prediction model for Aß positivity in clinically diagnosed SVCI patients. Methods: A total of 130 patients with SVCI were included in model development, and a separate cohort of 70 SVCI patients was used in external validation. The variables for the prediction model were selected by comparing the characteristics of the Aß-negative and Aß-positive SVCI groups. The final model was determined using a stepwise method. The model performance was evaluated using the receiver operating characteristic (ROC) curve and a calibration curve. A nomogram was used for visualization. Results: Among 130 SVCI patients, 70 (53.8%) were Aß-positive. The Aß-positive SVCI group was characterized by older age, tendency to be in the dementia stage, a higher prevalence of APOEɛ4, a lower prevalence of lacune, and more severe medial temporal atrophy (MTA). The final prediction model, which excluded MTA grade following the stepwise method for variable selection, demonstrated good accuracy in distinguishing between Aß-positive and Aß-negative SVCI, with an area under the curve (AUC) of 0.80. The external validation demonstrated an AUC of 0.71. Conclusions: The findings suggest that older age, dementia stage, APOEɛ4 carrier, and absence of lacunes may be predictive of Aß positivity in clinically diagnosed SVCI patients.

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Importance: Repetitive transcranial magnetic stimulation (rTMS) has emerged as a safe and promising intervention for Alzheimer disease (AD). Objective: To investigate the effect of a 4-week personalized hippocampal network-targeted rTMS on cognitive and functional performance, as well as functional connectivity in AD. Design, Setting, and Participants: This randomized clinical trial, which was sham-controlled and masked to participants and evaluators, was conducted between May 2020 and April 2022 at a single Korean memory clinic. Eligible participants were between ages 55 and 90 years and had confirmed early AD with evidence of an amyloid biomarker. Participants who met the inclusion criteria were randomly assigned to receive hippocampal network-targeted rTMS or sham stimulation. Participants received 4-week rTMS treatment, with assessment conducted at weeks 4 and 8. Data were analyzed between April 2022 and January 2024. Interventions: Each patient received 20 sessions of personalized rTMS targeting the left parietal area, functionally connected to the hippocampus, based on fMRI connectivity analysis over 4 weeks. The sham group underwent the same procedure, excluding actual magnetic stimulation. A personalized 3-dimensional printed frame to fix the TMS coil to the optimal target site was produced. Main Outcomes and Measures: The primary outcome was the change in the AD Assessment Scale-Cognitive Subscale test (ADAS-Cog) after 8 weeks from baseline. Secondary outcomes included changes in the Clinical Dementia Rating-Sum of Boxes (CDR-SOB) and Seoul-Instrumental Activity Daily Living (S-IADL) scales, as well as resting-state fMRI connectivity between the hippocampus and cortical areas. Results: Among 30 participants (18 in the rTMS group; 12 in the sham group) who completed the 8-week trial, the mean (SD) age was 69.8 (9.1) years; 18 (60%) were female. As the primary outcome, the change in ADAS-Cog at the eighth week was significantly different between the rTMS and sham groups (coefficient [SE], -5.2 [1.6]; P = .002). The change in CDR-SOB (-4.5 [1.4]; P = .007) and S-IADL (1.7 [0.7]; P = .004) were significantly different between the groups favoring rTMS groups. The fMRI connectivity analysis revealed that rTMS increased the functional connectivity between the hippocampus and precuneus, with its changes associated with improvements in ADAS-Cog (r = -0.57; P = .005). Conclusions and Relevance: This randomized clinical trial demonstrated the positive effects of rTMS on cognitive and functional performance, and the plastic changes in the hippocampal-cortical network. Our results support the consideration of rTMS as a potential treatment for AD. Trial Registration: ClinicalTrials.gov Identifier: NCT04260724.

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Objectives: Accurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI. Methods: We recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-ß (Aß) positron emission tomography (PET) scans. We devised a novel deep learning architecture by leveraging an attention mechanism in a recurrent neural network. We trained a predictive model by inputting age, gender, education, apolipoprotein E genotype, neuropsychological test scores, and brain MRI and amyloid PET features. Cognitive outcomes and MRI features of an MCI subject were predicted using the proposed network. Results: The proposed predictive model demonstrated good prediction performance (AUC = 0.814 ± 0.035) in five-fold cross-validation, along with reliable prediction in cognitive decline and MRI markers over time. Faster cognitive decline and brain atrophy in larger regions were forecasted in patients with Aß (+) than with Aß (-). Conclusion: The proposed method provides effective and accurate means for predicting the progression of individuals within a specific period. This model could assist clinicians in identifying subjects at a higher risk of rapid cognitive decline by predicting future cognitive decline and MRI marker changes over time for patients with MCI. Future studies should validate and refine the proposed predictive model further to improve clinical decision-making.

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We examined whether there were differences in the presence of centrum semiovale-enlarged perivascular spaces (CSO-ePVS) and basal ganglia-ePVS (BG-ePVS) among patients with Alzheimer disease-related cognitive impairment (ADCI) based on their age of onset. Out of a total of 239 patients with cognitive impairment, 155 with positive amyloid-PET results were included. Among these, 43 had early-onset ADCI (EOADCI) and 112 had late-onset ADCI (LOADCI). Patients with LOADCI exhibited a higher prevalence of hypertension, lacunes, white matter hyperintensities, and BG-ePVS than those with EOADCI. BG-ePVS showed a significant correlation with age at the onset and the number of lacunes, whereas CSO-ePVS did not exhibit any association. The higher prevalence of BG-ePVS in patients with LOADCI might be attributable to vascular risk factors (hypertension) and cerebral small vessel disease (CSVD). These findings support the hypothesis that BG-ePVS is associated with CSVD and vascular risk factors, whereas CSO-ePVS is associated with cerebral amyloid angiopathy.

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BACKGROUND: Early-onset dementia (EOD, onset age < 65) and late-onset dementia (LOD, onset age ≥ 65) exhibit distinct features. Understanding the risk factors for dementia development and mortality in EOD and LOD respectively is crucial for personalized care. While risk factors are known for LOD development and mortality, their impact on EOD remains unclear. We aimed to investigate how hypertension, diabetes mellitus, hyperlipidemia, atrial fibrillation, and osteoporosis influence the development and mortality of EOD and LOD, respectively. METHODS: Using the Korean National Health Insurance Service (NHIS) database, we collected 546,709 dementia-free individuals and followed up for 11 years. In the two study groups, the Younger group (< 65 years old) and the Older group (≥ 65 years old), we applied Cox proportional hazard models to assess risk factors for development of EOD and LOD, respectively. Then, we assessed risk factors for mortality among EOD and LOD. RESULTS: Diabetes mellitus and osteoporosis increased the risk of EOD and LOD development. Hypertension increased the risk of EOD, while atrial fibrillation increased the risk of LOD. Conversely, hyperlipidemia exhibited a protective effect against LOD development. Additionally, diabetes mellitus increased mortality in EOD and LOD. Hypertension and atrial fibrillation increased mortality in LOD, while hyperlipidemia decreased mortality in EOD and LOD. CONCLUSIONS: Risk factors influencing dementia development and mortality differed in EOD and LOD. Targeted public health interventions addressing age-related risk factors may reduce dementia incidence and mortality.

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Previous studies on Alzheimer's disease-type cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI) has rarely explored spatiotemporal heterogeneity. This study aims to identify distinct spatiotemporal cortical atrophy patterns in ADCI and SVCI. 1,338 participants (713 ADCI, 208 SVCI, and 417 cognitively unimpaired elders) underwent brain magnetic resonance imaging (MRI), amyloid positron emission tomography, and neuropsychological tests. Using MRI, this study measures cortical thickness in five brain regions (medial temporal, inferior temporal, posterior medial parietal, lateral parietal, and frontal areas) and utilizes the Subtype and Stage Inference (SuStaIn) model to predict the most probable subtype and stage for each participant. SuStaIn identifies two distinct cortical thinning patterns in ADCI (medial temporal: 65.8%, diffuse: 34.2%) and SVCI (frontotemporal: 47.1%, parietal: 52.9%) patients. The medial temporal subtype of ADCI shows a faster decline in attention, visuospatial, visual memory, and frontal/executive domains than the diffuse subtype (p-value < 0.01). However, there are no significant differences in longitudinal cognitive outcomes between the two subtypes of SVCI. Our study provides valuable insights into the distinct spatiotemporal patterns of cortical thinning in patients with ADCI and SVCI, suggesting the potential for individualized therapeutic and preventive strategies to improve clinical outcomes.

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Educational attainment (EduYears), a heritable trait often used as a proxy for cognitive ability, is associated with various health and social outcomes. Previous genome-wide association studies (GWASs) on EduYears have been focused on samples of European (EUR) genetic ancestries. Here we present the first large-scale GWAS of EduYears in people of East Asian (EAS) ancestry (n = 176,400) and conduct a cross-ancestry meta-analysis with EduYears GWAS in people of EUR ancestry (n = 766,345). EduYears showed a high genetic correlation and power-adjusted transferability ratio between EAS and EUR. We also found similar functional enrichment, gene expression enrichment and cross-trait genetic correlations between two populations. Cross-ancestry fine-mapping identified refined credible sets with a higher posterior inclusion probability than single population fine-mapping. Polygenic prediction analysis in four independent EAS and EUR cohorts demonstrated transferability between populations. Our study supports the need for further research on diverse ancestries to increase our understanding of the genetic basis of educational attainment.

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We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer's disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer's disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of [Formula: see text]%, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer's diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).

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