Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

INTRODUCTION: Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. METHODS: We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N = 878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). RESULTS: Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. DISCUSSION: Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. HIGHLIGHTS: Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

Subsequent correlated changes in complement component 3 and amyloid beta oligomers in the blood of patients with Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) involves the complement cascade, with complement component 3 (C3) playing a key role. However, the relationship between C3 and amyloid beta (Aß) in blood is limited. METHODS: Plasma C3 and Aß oligomerization tendency (AßOt) were measured in 35 AD patients and 62 healthy controls. Correlations with cerebrospinal fluid (CSF) biomarkers, cognitive impairment, and amyloid positron emission tomography (PET) were analyzed. Differences between biomarkers were compared in groups classified by concordances of biomarkers. RESULTS: Plasma C3 and AßOt were elevated in AD patients and in CSF or amyloid PET-positive groups. Weak positive correlation was found between C3 and AßOt, while both had strong negative correlations with CSF Aß42 and cognitive performance. Abnormalities were observed for AßOt and CSF Aß42 followed by C3 changes. DISCUSSION: Increased plasma C3 in AD are associated with amyloid pathology, possibly reflecting a defense response for Aß clearance. Further studies on Aß-binding proteins will enhance understanding of Aß mechanisms in blood.

Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-ß oligomerization data.

BACKGROUND: The tendency of amyloid-ß to form oligomers in the blood as measured with Multimer Detection System-Oligomeric Amyloid-ß (MDS-OAß) is a valuable biomarker for Alzheimer's disease and has been verified with heparin-based plasma. The objective of this study was to evaluate the performance of ethylenediaminetetraacetic acid (EDTA)-based MDS-OAß and to develop machine learning algorithms to predict amyloid positron emission tomography (PET) positivity. METHODS: The performance of EDTA-based MDS-OAß in predicting PET positivity was evaluated in 312 individuals with various machine learning models. The models with various combinations of features (i.e., MDS-OAß level, age, apolipoprotein E4 alleles, and Mini-Mental Status Examination [MMSE] score) were tested 50 times on each dataset. RESULTS: The random forest model best-predicted amyloid PET positivity based on MDS-OAß combined with other features with an accuracy of 77.14 ± 4.21% and an F1 of 85.44 ± 3.10%. The order of significance of predictive features was MDS-OAß, MMSE, Age, and APOE. The Support Vector Machine using the MDS-OAß value only showed an accuracy of 71.09 ± 3.27% and F-1 value of 80.18 ± 2.70%. CONCLUSIONS: The Random Forest model using EDTA-based MDS-OAß combined with the MMSE and apolipoprotein E status can be used to prescreen for amyloid PET positivity.

Fibrinogen Levels and Cognitive Profile Differences in Patients with Mild Cognitive Impairment.

BACKGROUND: Fibrinogen is considered a marker of vascular pathology, indicating a weakened blood-brain barrier, and has a causative role in neuroinflammation and neurodegeneration. Little is known about the relationship between fibrinogen levels and cognitive function in patients with mild cognitive impairment (MCI). We aimed to investigate differences in cognitive profiles according to plasma fibrinogen levels in patients with MCI and the influence of plasma fibrinogen levels on cognitive decline. METHODS: This retrospective cohort study included 643 patients with MCI: 323 patients in the high fibrinogen (high fib) group and 320 patients in the low fibrinogen (low fib) group. A multiple linear regression model was performed to compare cognitive test performance between groups. The Cox proportional hazard model was used to analyze the hazard ratio of fibrinogen level for disease progression. RESULTS: The high fib group demonstrated poorer performance in attention, executive function, and confrontation naming than the low fib group. After adjustment for APOE genotype, the high fib group was associated with poor attention and executive function. After adjustment for vascular risk factors including body mass index, hypertension, diabetes mellitus, dyslipidemia, and smoking history, the high fib group showed declined attention and confrontation naming ability. High fibrinogen levels did not predict disease progression to CDR 1. CONCLUSION: High plasma fibrinogen levels were associated with poor performance in attention in patients with MCI, regardless of APOE genotype or vascular risk factors.

Predictors of pre-operative cognitive impairment in meningioma patients over 60 years old.

BACKGROUND: The aim of this study was to assess the cognitive function of patients over 60 years old with meningioma using a domain-specific neuropsychological test and to investigate the relevant factors affecting pre-operative cognitive decline in different subdomains. METHODS: We retrospectively investigated 46 intracranial meningioma patients between the ages of 60 and 85 years. All patients underwent brain MRI and neuropsychological test. Tumor size, location, peritumoral edema, and medial temporal atrophy (MTA) were examined to determine the association with cognitive impairment. We performed a logistic regression analysis to examine the odds ratios (ORs) for cognitive decline of four subdomains: verbal memory, language, visuospatial, and executive functions. RESULTS: Tumor size and age were associated with executive dysfunction (OR 1.083, 95% confidence interval (CI) 1.006-1.166, and OR 1.209, 95% CI 1.018-1.436, respectively). There was no statistically significant association in other cognitive domains (language, verbal memory, and visuospatial function) with variables in regression analysis. CONCLUSIONS: We conclude that tumor size and age are positively related with executive function in pre-operative meningioma patients over 60 years old.

Coping with Dementia in the Middle of the COVID-19 Pandemic.

Multiple neurological complications have been associated with the coronavirus disease-19 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2. This is a narrative review to gather information on all aspects of COVID-19 in elderly patients with cognitive impairment. First, the following three mechanisms have been proposed to underlie the neurological complications associated with COVID-19: 1) direct invasion, 2) immune and inflammatory reaction, and 3) hypoxic brain damage by COVID-19. Next, because the elderly dementia patient population is particularly vulnerable to COVID-19, we discussed risk factors and difficulties associated with cognitive disorders in this vulnerable population. We also reviewed the effects of the patient living environment in COVID-19 cases that required intensive care unit (ICU) care. Furthermore, we analyzed the impact of stringent social restrictions and COVID-19 pandemic-mediated policies on dementia patients and care providers. Finally, we provided the following strategies for working with elderly dementia patients: general preventive methods; dementia care at home and nursing facilities according to the activities of daily living and dementia characteristics; ICU care after COVID-19 infection; and public health care system and government response. We propose that longitudinal follow-up studies are needed to fully examine COVID-19 associated neurological complications, such as dementia, and the efficacy of telemedicine/telehealth care programs.

A pathogenic PSEN1 Trp165Cys mutation associated with early-onset Alzheimer's disease.

BACKGROUND: Presenilin-1 (PSEN1) is one of the causative genes for early onset Alzheimer's disease (EOAD). Recently, emerging studies reported several novel PSEN1 mutations among Asian. We describe a male with EOAD had a pathogenic PSEN1 mutation. CASE PRESENTATION: A 53-year-old male presented with memory decline, followed by difficulty in finding ways. Patient had positive family history, since his mother and one of his brother was also affected with dementia. Brain magnetic resonance imaging (MRI) scan showed mild degree of atrophy of bilateral hippocampus and parietal lobe. 18F-Florbetaben-PET (FBB-PET) revealed increased amyloid deposition in bilateral frontal, parietal, temporal lobe and precuneus. Whole exome analysis revealed a heterozygous, probably pathogenic PSEN1 (c.695G > T, p.W165C) mutation. Interestingly, Trp165Cys mutation is located in trans membrane (TM)-III region, which is conserved between PSEN1/PSEN2. In vitro studies revealed that PSEN1 Trp165Cys could result in disturbances in amyloid metabolism. This prediction was confirmed by structure predictions and previous in vitro studies that the p.Trp165Cys could result in decreased Aß42/Aß40 ratios. CONCLUSION: We report a case of EOAD having a pathogenic PSEN1 (Trp165Cys) confirmed with in silico and in vitro predictions.

Branch-duct intraductal papillary mucinous neoplasm and embolic stroke of unknown cause.

BACKGROUND: In many embolic strokes, the specific embolic source might be uncertain. When we found asymptomatic branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs) in patients with ischemic stroke of unknown cause, most of gastroenterologists might undervalue it as a potential thromboembolic sources. METHODS: We report 4 patients with embolic stroke of unknown cause and incidental BD-IPMNs. RESULTS: Magnetic resonance imaging revealed multiple embolic infarctions. Recommended cardiac examinations, such as 24-hour Holter monitoring and transthoracic and transesophageal echocardiography, were normal. Other cancer markers and molecular markers of hypercoagulopathy were normal. On abdominal computed tomography scan, performed to detect hidden malignancy, a low attenuated lesion with a diameter of less than 3 cm was observed in the pancreas. CONCLUSION: Although BD-IPMNs are associated with a lower rate of malignancy than main-duct IPMNs, the BD-IPMNs had some malignant potential and frequently coexist with extrapancreatic or intrapancreatic cancers. Therefore, we suggest that, in some cases, the BD-IPMNs might be considered as a thromboembolic source.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease.

BACKGROUND: Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers. METHODS: We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification. RESULTS: Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs. CONCLUSIONS: Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Systemic immune-inflammation index as a predictor of early stroke progression/recurrence in acute atherosclerotic ischemic stroke.

OBJECTIVES: Although the systemic immune-inflammatory index (SII) has recently been correlated with stroke severity and functional outcome, the underlying pathogenesis remains largely unknown. The objective of this study was to explore whether SII could predict early neurologic deterioration (END) in different etiologies of acute ischemic stroke. MATERIALS AND METHODS: From January 2019 to December 2021, a total of 697 consecutive patients with acute ischemic stroke, admitted within 72 hours from stroke onset, were prospectively enrolled. The patients were categorized into 4 groups based on quartiles of SII, calculated as platelets multiplied by neutrophils divided by lymphocytes. END and stroke progression/recurrence were assessed during the first 7 days after stroke onset using predetermined definitions. Logistic regression analysis was conducted to evaluate the association between SII and END, while considering the variation in association across stroke etiologies. RESULTS: END occurred in 135 patients: 24 (3.4%) for Group I, 25 (3.6%) for Group II, 33 (4.7%) for Group III, and 53 (7.6%) for Group IV. Among the END subtypes, stroke progression/recurrence stroke was the most prevalent. In the logistic regression model, the adjusted odds ratios (ORs) of END and stroke progression/recurrence for group IV were 2.51 (95% CI, 1.27-4.95) and 1.98 (95% CI, 1.03-3.89), respectively. Among the stroke etiologies, group IV showed a significant increase in END (OR 4.24; 95% CI, 1.42-12.64) and stroke progression/recurrence (OR 4.13; 95% CI, 1.39-12.27) specifically in case of large artery atherosclerosis. CONCLUSIONS: SII independently predicts early stroke progression/recurrence in patients with acute atherosclerotic ischemic stroke.

Characteristics of discordance between amyloid positron emission tomography and plasma amyloid-ß 42/40 positivity.

Various plasma biomarkers for amyloid-ß (Aß) have shown high predictability of amyloid PET positivity. However, the characteristics of discordance between amyloid PET and plasma Aß42/40 positivity are poorly understood. Thorough interpretation of discordant cases is vital as Aß plasma biomarker is imminent to integrate into clinical guidelines. We aimed to determine the characteristics of discordant groups between amyloid PET and plasma Aß42/40 positivity, and inter-assays variability depending on plasma assays. We compared tau burden measured by PET, brain volume assessed by MRI, cross-sectional cognitive function, longitudinal cognitive decline and polygenic risk score (PRS) between PET/plasma groups (PET-/plasma-, PET-/plasma+, PET+/plasma-, PET+/plasma+) using Alzheimer's Disease Neuroimaging Initiative database. Additionally, we investigated inter-assays variability between immunoprecipitation followed by mass spectrometry method developed at Washington University (IP-MS-WashU) and Elecsys immunoassay from Roche (IA-Elc). PET+/plasma+ was significantly associated with higher tau burden assessed by PET in entorhinal, Braak III/IV, and Braak V/VI regions, and with decreased volume of hippocampal and precuneus regions compared to PET-/plasma-. PET+/plasma+ showed poor performances in global cognition, memory, executive and daily-life function, and rapid cognitive decline. PET+/plasma+ was related to high PRS. The PET-/plasma+ showed intermediate changes between PET-/plasma- and PET+/plasma+ in terms of tau burden, hippocampal and precuneus volume, cross-sectional and longitudinal cognition, and PRS. PET+/plasma- represented heterogeneous characteristics with most prominent variability depending on plasma assays. Moreover, IP-MS-WashU showed more linear association between amyloid PET standardized uptake value ratio and plasma Aß42/40 than IA-Elc. IA-Elc showed more plasma Aß42/40 positivity in the amyloid PET-negative stage than IP-MS-WashU. Characteristics of PET-/plasma+ support plasma biomarkers as early biomarker of amyloidopathy prior to amyloid PET. Various plasma biomarker assays might be applied distinctively to detect different target subjects or disease stages.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease.

Background: Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers. Methods: We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification. Results: Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules, and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and apoE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs. Conclusions: Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Subclinical Hypothyroidism and Cognitive Impairment.

BACKGROUND: Although thyroid dysfunction has been considered as a cause of reversible cognitive impairment, association between subclinical hypothyroidism and cognitive impairment is controversial. OBJECTIVE: We compared cognitive profiles of patients in an euthyroid or subclinical hypothyroid (sHypo) state, as well as their disease progression from mild cognitive impairment (MCI) to dementia within 3 years. METHODS: We included 2,181 patients in a euthyroid and 284 in a sHypo state over 60 years of age who underwent an extensive cognitive assessment at Seoul National University Bundang Hospital but were not prescribed levothyroxine, methimazole, carbimazole, or propylthiouracil. After propensity score matching for age, sex, and education level, 1,118 patients in a euthyroid and 283 patients in a sHypo state were included. Attention, language, memory, visuocontructive, and executive functions were compared between the groups using Student's t-test or the Mann-Whitney U test. To investigate the association between disease progression and subclinical hypothyroidism, a Cox regression analyses was performed in 379 patients with MCI. Patients with thyroid-stimulating hormone levels over 10 mlU/L was classified as the "sHypo10", and hazard ratios for sHypo or sHypo10 were assessed. RESULTS: There was no difference in attention, language, memory, visuoconstructive, and executive functions between the patient groups. Progression from MCI to dementia was not associated with sHypo or sHypo10. CONCLUSION: There was no difference in cognitive profile between euthyroid and sHypo patients, and no association between subclinical hypothyroidism and disease progression. This might suggest a clue of strategies regarding hormone therapy in subclinical hypothyroidism with cognitive impairment.

Integration of amyloid-ß oligomerization tendency as a plasma biomarker in Alzheimer's disease diagnosis.

Introduction: There has been significant development in blood-based biomarkers targeting amyloidopathy of Alzheimer's disease (AD). However, the guidelines for integrating such biomarkers into AD diagnosis are still inadequate. Multimer Detection System-Oligomeric Amyloid-ß (MDS-OAß) as a plasma biomarker detecting oligomerization tendency is available in the clinical practice. Main text: We suggest how to interpret the results of plasma biomarker for amyloidopathy using MDS-OAß with neuropsychological test, brain magnetic resonance imaging (MRI), and amyloid PET for AD diagnosis. Combination of each test result differentiates various stages of AD, other neurodegenerative diseases, or cognitive impairment due to the causes other than neurodegeneration. Discussion: A systematic interpretation strategy could support accurate diagnosis and staging of AD. Moreover, comprehensive use of biomarkers that target amyloidopathy such as amyloid PET on brain amyloid plaque and MDS-OAß on amyloid-ß oligomerization tendency can complement to gain advanced insights on amyloid-ß dynamics in AD.

Immunity gene IFITM3 variant: Relation to cognition and Alzheimer's disease pathology.

Introduction: We investigated single-nucleotide polymorphisms (SNPs) in IFITM3, an innate immunity gene and modulator of amyloid beta in Alzheimer's disease (AD), for association with cognition and AD biomarkers. Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1565) and AddNeuroMed (N = 633) as discovery and replication samples, respectively. We performed gene-based association analysis of SNPs in IFITM3 with cognitive performance and SNP-based association analysis with cognitive decline and amyloid, tau, and neurodegeneration biomarkers for AD. Results: Gene-based association analysis showed that IFITM3 was significantly associated with cognitive performance. Particularly, rs10751647 in IFITM3 was associated with less cognitive decline, less amyloid and tau burden, and less brain atrophy in ADNI. The association of rs10751647 with cognitive decline and brain atrophy was replicated in AddNeuroMed. Discussion: This suggests that rs10751647 in IFITM3 is associated with less vulnerability for cognitive decline and AD biomarkers, providing mechanistic insight regarding involvement of immunity and infection in AD. Highlights: IFITM3 is significantly associated with cognitive performance.rs10751647 in IFITM3 is associated with cognitive decline rates with replication.rs10751647 is associated with amyloid beta load, cerebrospinal fluid phosphorylated tau levels, and brain atrophy.rs10751647 is associated with IFITM3 expression levels in blood and brain.rs10751647 in IFITM3 is related to less vulnerability to Alzheimer's disease pathogenesis.

Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms.

Background and Objective: Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. Methods: We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. Results: Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. Conclusions: Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.

Change in cognitive function according to cholinesterase inhibitor use and amyloid PET positivity in patients with mild cognitive impairment.

BACKGROUND: Cholinesterase inhibitors (ChEIs) are an FDA-approved symptomatic treatment for patients with Alzheimer's disease (AD). Its efficacy in patients with mild cognitive impairment (MCI), however, is controversial. Nonetheless, ChEIs have often been used in patients with MCI. From the perspective that ChEIs were developed based on the pathomechanism of AD, the effect of ChEIs in MCI patients could be different depending on the amyloid burden. In this retrospective observational study, we aimed to investigate the influence of ChEIs and amyloid burden on cognitive change for 1 year in patients with MCI. METHODS: We included 111 patients with MCI with a Clinical Dementia Rating (CDR) score of 0.5, a 1-year follow-up cognitive assessment, and amyloid positron emission tomography (PET) performed within 6 months before or after the baseline cognitive assessment (73 ChEI users and 38 ChEI non-users) from the Neurocognitive Behavior Center of Seoul National University Bundang Hospital. Additionally, those who had a positive amyloid PET scan more than 6 months before the baseline cognitive assessment and those who had a negative amyloid PET scan more than 6 months after the 1-year follow-up cognitive assessment were also included. Among the total 111 patients, 25 ChEI users and 25 ChEI non-users were matched by baseline Mini-Mental State Examination (MMSE) score, age, educational level, CDR Sum of Boxes, and amyloid PET positivity using propensity score matching. Multiple linear regression analysis was performed to assess the influence of ChEI use and amyloid PET positivity on cognitive change for 1 year. Univariate and multivariate logistic regression analyses were performed to evaluate the association between ChEI use and disease progression to CDR 1 at the 1-year follow-up visit. RESULTS: ChEI use or non-use was not associated with cognitive change for 1 year. Amyloid PET positivity or negativity did not change this non-association. Furthermore, progression to CDR 1 was related to low baseline MMSE score (OR 0.606, CI 0.381-0.873), but not with ChEI use or non-use, and not with amyloid PET result. CONCLUSION: ChEI use or non-use was not related to cognitive change at a 1-year follow-up visit in patients with or without amyloid burden. In addition, ChEI use or non-use could not predict disease progression to CDR 1 at 1-year follow-up visit.

Differences between memory encoding and retrieval failure in mild cognitive impairment: results from quantitative electroencephalography and magnetic resonance volumetry.

BACKGROUND: The memory impairments in mild cognitive impairment (MCI) can be classified into encoding (EF) and retrieval (RF) failure, which can be affected by underlying pathomechanism. We explored the differences structurally and functionally. METHODS: We compared quantitative electroencephalography (qEEG) power spectra and connectivity between 87 MCI patients with EF and 78 MCI with RF using iSyncBrain® (iMediSync Inc., Republic of Korea) ( https://isyncbrain.com/ ). Voxel-based morphometric analysis of the gray matter (GM) in the MCI groups and 71 cognitive normal controls was also done using the Computational Anatomy Toolbox 12 ( http://www.neuro.uni-jena.de/cat/ ). RESULTS: qEEG showed higher frontal theta and lower beta2 band power, and higher theta connectivity in the EF. There was no statistically significant difference in GM volume between the EF and RF. However, when compared to normal control, GM volume reductions due to EF in the left thalamus and bilateral hippocampi and reductions due to RF in the left thalamus, right superior frontal lobe, right superior temporal lobe, and right middle cingulum were observed (p < 0.05, family-wise error correction). CONCLUSIONS: MCI differs functionally and structurally according to their specific memory impairments. The EF findings are structurally and functionally more consistent with the prodromal Alzheimer's disease stage than the RF findings. Since this study is a cross-sectional study, prospective follow-up studies are needed to investigate whether different types of memory impairments can predict the underlying pathology of amnestic MCI. Additionally, insufficient sample size may lead to ambiguous statistical findings in direct comparisons, and a larger patient cohort could more robustly identify differences in GM volume reductions between the EF and the RF group.

Dysregulated expression levels of APH1B in peripheral blood are associated with brain atrophy and amyloid-ß deposition in Alzheimer's disease.

BACKGROUND: The interaction between the brain and periphery might play a crucial role in the development of Alzheimer's disease (AD). METHODS: Using blood transcriptomic profile data from two independent AD cohorts, we performed expression quantitative trait locus (cis-eQTL) analysis of 29 significant genetic loci from a recent large-scale genome-wide association study to investigate the effects of the AD genetic variants on gene expression levels and identify their potential target genes. We then performed differential gene expression analysis of identified AD target genes and linear regression analysis to evaluate the association of differentially expressed genes with neuroimaging biomarkers. RESULTS: A cis-eQTL analysis identified and replicated significant associations in seven genes (APH1B, BIN1, FCER1G, GATS, MS4A6A, RABEP1, TRIM4). APH1B expression levels in the blood increased in AD and were associated with entorhinal cortical thickness and global cortical amyloid-ß deposition. CONCLUSION: An integrative analysis of genetics, blood-based transcriptomic profiles, and imaging biomarkers suggests that APH1B expression levels in the blood might play a role in the pathogenesis of AD.