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INTRODUCTION Cerebral amyloid angiopathy (CAA) and Alzheimer's disease (AD) both involve amyloid beta (Aβ) accumulation in vessels and brain parenchyma, respectively. As Aβ‐targeting therapies emerge, CAA draws attention due to its link with amyloid‐related imaging abnormalities (ARIA), underscoring the need for biomarkers beyond magnetic resonance imaging (MRI). METHODS CAA polygenic risk scores (CAA‐PRS) were generated in 105 ADNI participants, and their predictive ability for pathological CAA was evaluated, including in subgroups with high amyloid burden or without MRI‐visible CAA markers. RESULTS CAA‐PRS was significantly associated with pathological CAA (OR = 1.766, p < 0.001), with an AUC of 0.783 overall and 0.766 (OR = 1.780, p = 0.002) in those with high amyloid burden. A marginal association was observed in MRI‐negative individuals. DISCUSSION CAA‐PRS may serve as a complementary biomarker to imaging for identifying high‐risk individuals with CAA, particularly in the context of Aβ‐targeting therapies and ARIA risk. Highlights CAA‐PRS is generated. CAA‐PRS is associated with pathologically confirmed CAA. CAA‐PRS is associated with CAA in individuals with high amyloid burden.

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 (Aβ) in blood is limited. METHODS Plasma C3 and Aβ oligomerization tendency (AβOt) 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 AβOt were elevated in AD patients and in CSF or amyloid PET–positive groups. Weak positive correlation was found between C3 and AβOt, while both had strong negative correlations with CSF Aβ42 and cognitive performance. Abnormalities were observed for AβOt and CSF Aβ42 followed by C3 changes. DISCUSSION Increased plasma C3 in AD are associated with amyloid pathology, possibly reflecting a defense response for Aβ clearance. Further studies on Aβ‐binding proteins will enhance understanding of Aβ mechanisms in blood.

Background Cholinesterase inhibitors (ChEIs) are prescribed for Alzheimer’s disease (AD) and sometimes for mild cognitive impairment (MCI) without knowing underlying pathologies and its effect on cognition. We investigated the frequency of ChEI prescriptions in amyloid-negative MCI and their association with cognitive changes in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Methods We included participants with amyloid positron emission tomography (PET)-negative MCI from the ADNI. We analyzed the associations of ChEI use with cognitive changes, brain volume, and cerebrospinal fluid (CSF) total tau (t-tau), hyperphosphorylated tau 181 (p-tau 181 ), and p-tau 181 /t-tau ratio. Results ChEIs were prescribed in 27.4% of amyloid PET-negative MCI and were associated with faster cognitive decline, reduced baseline hippocampal volume and entorhinal cortical thickness, and a longitudinal decrease in the frontal lobe cortical thickness. Conclusions The association between ChEI use and accelerated cognitive decline may stem from underlying pathologies involving reduced hippocampal volume, entorhinal cortical thickness and faster frontal lobe atrophy. We suggest that ChEI use in amyloid PET-negative MCI patients might need further consideration, and studies investigating the causality between ChEI use and cognitive decline are warranted in the future.

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.

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.

Background The Clock Drawing Test (CDT) and Rey–Osterrieth Complex Figure Test (RCFT) are widely used as a part of neuropsychological test batteries to assess cognitive function. Our objective was to confirm the prediction accuracies of the RCFT-copy and CDT for cognitive impairment (CI) using convolutional neural network algorithms as a screening tool. Methods The CDT and RCFT-copy data were obtained from patients aged 60–80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. Convolutional neural network algorithms using TensorFlow (ver. 2.3.0) on the Colab cloud platform ( www.colab.research.google.com ) were used for preprocessing and modeling. We measured the prediction accuracy of each drawing test 10 times using this dataset with the following classes: normal cognition (NC) vs. mildly impaired cognition (MI), NC vs. severely impaired cognition (SI), and NC vs. CI (MI + SI). Results The accuracy of the CDT was better for differentiating MI (CDT, 78.04 ± 2.75; RCFT-copy, not being trained) and SI from NC (CDT, 91.45 ± 0.83; RCFT-copy, 90.27 ± 1.52); however, the RCFT-copy was better at predicting CI (CDT, 77.37 ± 1.77; RCFT, 83.52 ± 1.41). The accuracy for a 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found between them. Conclusions The two drawing tests showed good performance for predicting severe impairment of cognition; however, a drawing test alone is not enough to predict overall CI. There are some limitations to our study: the sample size was small, all the participants did not perform both the CDT and RCFT-copy, and only the copy condition of the RCFT was used. Algorithms involving memory performance and longitudinal changes are worth future exploration. These results may contribute to improved home-based healthcare delivery.

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. 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. 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. SII independently predicts early stroke progression/recurrence in patients with acute atherosclerotic ischemic stroke. •SII may serve as a predictor of stroke progression and recurrence in acute ischemic stroke.•SII could predict the occurrence of early neurologic deterioration, especially in atherosclerotic etiology.•SII was not a prominent predictor in other stroke etiologies including cardioembolism.•SII is easily accessible and cost-effective.

INTRODUCTION The molecular mechanisms underlying early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) remain incompletely understood, particularly in Asian populations. METHODS RNA‐sequencing was carried out on blood samples from 248 participants in the Seoul National University Bundang Hospital cohort to perform differential gene expression (DGE) and weighted gene co‐expression network analysis. Findings were replicated in an independent Korean cohort (N = 275). RESULTS DGE analysis identified 18 and 88 dysregulated genes in EOAD and LOAD, respectively. Network analysis identified a LOAD‐associated module showing a significant enrichment in pathways related to mitophagy, 5′ adenosine monophosphate‐activated protein kinase signaling, and ubiquitin‐mediated proteolysis. In the replication cohort, downregulation of SMOX and PLVAP in LOAD was replicated, and the LOAD‐associated module was highly preserved. In addition, SMOX and PLVAP were associated with brain amyloid beta deposition. DISCUSSION Our findings suggest distinct molecular signatures for EOAD and LOAD in a Korean population, providing deeper understanding of their diagnostic potential and molecular mechanisms. Highlights Analysis identified 18 and 88 dysregulated genes in early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD), respectively. Expression levels of SMOX and PLVAP were downregulated in LOAD. Expression levels of SMOX and PLVAP were associated with brain amyloid beta deposition. Pathways including mitophagy and 5′ adenosine monophosphate‐activated protein kinase signaling were enriched in a LOAD module. A LOAD module was highly preserved across two independent cohorts.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, predominantly affecting the aging population. Early detection through biomarkers is essential for early intervention. Recent sub‐classification of AD through extensive cerebrospinal fluid (CSF) proteomic analyses revealed distinct characteristics of each subtype, necessitating tailored therapeutic strategies. While CSF proteomics has identified potential biomarkers, the need for non‐invasive and cost‐effective substitutions highlights the importance of blood‐based biomarkers (BBMs). This review is a comprehensive review that categorizes potential BBMs based on neuronal hyperplasticity (subtype 1), underlining their role in refining subtype classification and enabling precision medicine. Early AD is often marked by cortical and hippocampal hyperactivity, followed by hypoactivity during later stages of neurodegeneration. While the exact mechanisms remain unclear, factors like Ca2+, glutamate, amyloid beta, tau, genetic factors, and impaired glial function play a role. Advancements in blood‐based diagnostics would improve detection, individual treatment strategies, and evaluation of therapeutic response, eventually reducing the burden of AD on health‐care systems. Highlights Alzheimer's disease (AD; subtype 1) exhibits neuronal hyperplasticity, mild cortical atrophy, and moderate microglial activation. The neuronal hyperplasticity subtype of AD is characterized by an upregulation of synaptic and plasticity‐related proteins, distinguishing it from other AD subtypes. Identifying biomarkers specific to neuronal hyperplasticity would enable real‐time monitoring of therapeutic responses, allowing for individualized therapy as opposed to a “one‐size‐fits‐all” strategy. The treatments based on neuronal hyperactivity reduction, restoration of synaptic plasticity, and anti‐inflammation/metabolic dysfunction would be useful in this AD subtype. Blood‐based biomarkers offer a cost‐effective and accessible alternative to cerebrospinal fluid and neuroimaging methods.

MicroRNAs (miRNAs), small and highly conserved non-coding RNA molecules, have emerged as promising molecular biomarkers due to their regulatory roles in gene expression and stability in blood. We used measurements of 64 plasma miRNAs from 145 participants in the Alzheimer's disease Neuroimaging Initiative cohort, including 74 probable AD patients and 71 cognitively normal (CN) older adults. We performed principal component analysis (PCA) with factor rotation for dimension reduction to identify AD-associated principal components (PCs) and their key miRNAs with factor loadings higher than 0.8. We investigated their association with amyloid/tau/neurodegeneration (A/T/N) biomarkers and cognition. After identifying the candidate target genes of key miRNAs, we performed pathway enrichment analysis. We conducted mediation analyses to assess the effect of the associations between miRNAs and A/T/N biomarkers on AD diagnosis and cognition. Finally, we used a machine learning approach to evaluate the performance of key miRNAs for AD classification. PCA identified one PC as significantly associated with AD. The PC was also significantly associated with CSF p-tau levels, hippocampal volume, and cognition. Two key miRNAs (miR-423-5p and miR-92a-3p) in the PC were associated with AD. Lower levels of miR-423-5p and miR-92a-3p were associated with reduced hippocampal volume and worse cognition, and lower levels of miR-423-5p were associated with higher brain amyloid deposition. Pathway enrichment analysis identified several significant biological processes, including memory, protein phosphorylation, and the phosphatidylinositol-3-phosphate biosynthetic process. Mediation analysis revealed that miR-423-5p, but not miR-92a-3p, had indirect effects on AD diagnosis and memory performance through brain amyloid deposition and brain atrophy. Machine learning analysis demonstrated that incorporating two key miRNAs improved the performance of demographic information for AD classification. Plasma miR-423-5p and miR-92a-3p are implicated in AD pathology and cognitive decline, providing insights into their roles in disease mechanisms. This study suggests the potential of these miRNAs as blood-based molecular biomarkers for AD.