Iron is crucial for brain function, but excessive iron is neurotoxic. Abnormally high brain iron accumulation is one of the pathogenic factors in Alzheimer's disease (AD). Therefore, understanding the mechanistic basis of iron dyshomeostasis in AD is vital for disease mitigation. Calcium, another essential bioelement involved in cell signaling, also exhibits dysregulated homeostasis in AD. Calcium ion (Ca2+) signaling can influence iron homeostasis through multiple effectors. Our previous studies identified Ca2+/calmodulin (CAM)-dependent protein kinase kinase 2 (CAMKK2) as a regulator of transferrin (TF)-bound iron trafficking through the TF receptor (TFRC). Given CAMKK2's high expression in brain cells, it was hypothesized that abnormal CAMKK2-TF/TFRC signaling may underlie excessive iron deposition in AD brains. This study aims to retrospectively investigate CAMKK2, TF, TFRC proteins, and iron content in temporal cortex tissues from AD patients and cognitively normal (CN) individuals, postmortem. Postmortem temporal cortex tissues from 74 AD patients, 27 Parkinson's disease (PD) patients, and 17 CN individuals were analyzed for CAMKK2, TF, and TFRC protein levels by Western blotting. Additionally, prefrontal/temporal cortex tissues from 30 CN individuals of various ages were examined for age-related effects. Iron content in cortical tissues was measured using a colorimetric assay. CAMKK2, TF, and TFRC levels were significantly decreased in AD patients' temporal cortices compared to CN individuals, independent of age or postmortem interval-related changes. PD patients' also exhibited similar reductions in CAMKK2/TF/TFRC levels. The increased iron content in AD brains was significantly correlated with reduced TF/TFRC protein levels. Building on the previous identification of CAMKK2 as a regulator of TF/TFRC trafficking and iron homeostasis, the findings from this study suggest that downregulation of CAMKK2 in AD cortices may disrupt TF/TFRC signaling and contribute to iron overloading and neurodegeneration through iron-induced toxicity. The decreased levels of TF/TFRC and increased iron in AD brains may result from enhanced clearance or post-trafficking degradation of TF/TFRC due to CAMKK2 downregulation. Restoring CAMKK2 levels in the AD brain could offer a novel therapeutic approach to reestablish iron homeostasis. Further studies are needed to explore the pathways linking CAMKK2 and iron dysregulation in AD and other neurodegenerative diseases.

BACKGROUNDLimited research has explored the effect of cardiovascular risk and amyloid interplay on cognitive decline in East Asians.METHODSVascular burden was quantified using Framingham's General Cardiovascular Risk Score (FRS) in 526 Korean Brain Aging Study (KBASE) participants. Cognitive differences in groups stratified by FRS and amyloid positivity were assessed at baseline and longitudinally.RESULTSBaseline analyses revealed that amyloid‐negative (Aβ–) cognitively normal (CN) individuals with high FRS had lower cognition compared to Aβ– CN individuals with low FRS (p < 0.0001). Longitudinally, amyloid pathology predominantly drove cognitive decline, while FRS alone had negligible effects on cognition in CN and mild cognitive impairment (MCI) groups.CONCLUSIONOur findings indicate that managing vascular risk may be crucial in preserving cognition in Aβ– individuals early on and before the clinical manifestation of dementia. Within the CN and MCI groups, irrespective of FRS status, amyloid‐positive individuals had worse cognitive performance than Aβ– individuals.HighlightsVascular risk significantly affects cognition in amyloid‐negative older Koreans.Amyloid‐negative CN older adults with high vascular risk had lower baseline cognition.Amyloid pathology drives cognitive decline in CN and MCI, regardless of vascular risk.The study underscores the impact of vascular health on the AD disease spectrum.

Cerebrospinal fluid biomarkers are challenging to use for diagnosing mild cognitive impairment (MCI) in large populations, and there is an urgent need for new blood biomarkers. The aim of this study is to investigate whether astrocyte activation is correlated with hippocampal atrophy, and to assess the potential of glial fibrillary acidic protein (GFAP) as a biomarker for diagnosing MCI among community-dwelling older individuals. This cross-sectional study included 107 older adults. The levels of GFAP in serum were measured, and the volumetric assessment of gray matter within hippocampal subregions was conducted using Voxel-Based Morphometry (VBM). The relationship between hippocampal subregion volume and blood biomarkers were analyzed using partial correlation. The effectiveness of blood biomarkers in differentiating MCI was assessed using a receiver operating characteristic (ROC) curve. We found that serum GFAP levels were significantly elevated in the MCI group compared to the cognitively normal (CN) group. Additionally, individuals with MCI exhibited a reduction gray matter volume in specific hippocampal subregions. Notably, the right dentate gyrus (DG) and right cornu ammonis (CA) subregions were found to be effective for distinguishing MCI patients from CN individuals. Serum levels of GFAP demonstrate a sensitivity of 65.9% and a specificity of 75.6% in differentiating patients with MCI from CN individuals. Specific atrophy within hippocampal subregions has been observed in the brains of community-dwelling elderly individuals. Elevated levels of circulating GFAP may serve as a sensitive peripheral biomarker indicative of hippocampal-specific cognitive alterations in patients with MCI.

The pathological effects of amyloid β oligomers (Aβo) may be mediated through the metabotropic glutamate receptor subtype 5 (mGluR5), leading to synaptic loss in Alzheimer's disease (AD). Positron emission tomography (PET) studies of mGluR5 using [18F]FPEB indicate a reduction of receptor binding that is focused in the medial temporal lobe in AD. Synaptic loss due to AD measured through synaptic vesicle glycoprotein 2A (SV2A) quantification with [11C]UCB-J PET is also focused in the medial temporal lobe, but with clear widespread reductions is commonly AD-affected neocortical regions. In this study, we used [18F]FPEB and [11C]UCB-J PET to investigate the relationship between mGluR5 and synaptic density in early AD. Fifteen amyloid positive participants with early AD and 12 amyloid negative, cognitively normal (CN) participants underwent PET scans with both [18F]FPEB to measure mGluR5 and [11C]UCB-J to measure synaptic density. Parametric DVR images using equilibrium methods were generated from dynamic. For [18F]FPEB PET, DVR was calculated using equilibrium methods and a cerebellum reference region. For [11C]UCB-J PET, DVR was calculated with a simplified reference tissue model - 2 and a whole cerebellum reference region.. A strong positive correlation between mGluR5 and synaptic density was present in the hippocampus for participants with AD (r = 0.81, p < 0.001) and in the CN group (r = 0.74, p = 0.005). In the entorhinal cortex, there was a strong positive correlation between mGluR5 and synaptic in the AD group (r = 0.85, p <0.001), but a weaker non-significant correlation in the CN group (r = 0.36, p = 0.245). Exploratory analyses within and between other brain regions suggested significant positive correlations between mGluR5 in the medial temporal lobe and synaptic density in a broader set of commonly AD-affected regions. Medial temporal loss of mGluR5 in AD is associated with synaptic loss in both medial temporal regions and more broadly in association cortical regions, indicating that mGluR5 mediated Aβo toxicity may lead to early synaptic loss more broadly in AD-affected networks. In CN individuals, an isolated strong association between lower mGluR5 and lower synaptic density may indicate non-AD related synaptic loss.

Tau pathology mediated the plasma biomarkers and cognitive function in patients with mild cognitive impairment

Specific and cumulative infection burden and mild cognitive impairment and dementia: A population-based study

BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the ‘severity index’ generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI).MethodsWe used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk “disease-like” or low-risk “CN-like”. Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data.ResultsIn predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57).ConclusionWhen predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

BackgroundThe worldwide trend of demographic aging highlights the progress made in healthcare, albeit with health challenges like Alzheimer’s Disease (AD), prevalent in individuals aged 65 and above. Its early detection at the mild cognitive impairment (MCI) stage is crucial. Event-related potentials (ERPs) obtained by averaging EEG segments responded to repeated events are vital for cognitive impairment research. Consequently, examining intra-trial ERP variability is vital for comprehending fluctuations within psychophysiological processes of interest. This study aimed to investigate cognitive deficiencies and instability in MCI using ERP variability and its asymmetry from a prefrontal two-channel EEG device.MethodsIn this study, ERP variability for both target and non-target responses was examined using the response variance curve (RVC) in a sample comprising 481 participants with MCI and 1,043 age-matched healthy individuals. The participants engaged in auditory selective attention tasks. Cognitive decline was assessed using the Seoul Neuropsychological Screening Battery (SNSB) and the Mini-Mental State Examination (MMSE). The research employed various statistical methods, including independent t-tests, and univariate and multiple logistic regression analyses. These analyses were conducted to investigate group differences and explore the relationships between neuropsychological test results, ERP variability and its asymmetry measures, and the prevalence of MCI.ResultsOur results showed that patients with MCI exhibited unstable cognitive processing, characterized by increased ERP variability compared to cognitively normal (CN) adults. Multiple logistic regression analyses confirmed the association between ERP variability in the target and non-target responses with MCI prevalence, independent of demographic and neuropsychological factors.DiscussionThe unstable cognitive processing in the MCI group compared to the CN individuals implies abnormal neurological changes and reduced and (or) unstable attentional maintenance during cognitive processing. Consequently, utilizing ERP variability measures from a portable EEG device could serve as a valuable addition to the conventional ERP measures of latency and amplitude. This approach holds significant promise for identifying mild cognitive deficits and neural alterations in individuals with MCI.

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

IntroductionPosterior Cortical Atrophy (PCA) is a syndrome characterized by a progressive decline in higher-order visuospatial processing, leading to symptoms such as space perception deficit, simultanagnosia, and object perception impairment. While PCA is primarily known for its impact on visuospatial abilities, recent studies have documented language abnormalities in PCA patients. This study aims to delineate the nature and origin of language impairments in PCA, hypothesizing that language deficits reflect the visuospatial processing impairments of the disease.MethodsWe compared the language samples of 25 patients with PCA with age-matched cognitively normal (CN) individuals across two distinct tasks: a visually-dependent picture description and a visually-independent job description task. We extracted word frequency, word utterance latency, and spatial relational words for this comparison. We then conducted an in-depth analysis of the language used in the picture description task to identify specific linguistic indicators that reflect the visuospatial processing deficits of PCA.ResultsPatients with PCA showed significant language deficits in the visually-dependent task, characterized by higher word frequency, prolonged utterance latency, and fewer spatial relational words, but not in the visually-independent task. An in-depth analysis of the picture description task further showed that PCA patients struggled to identify certain visual elements as well as the overall theme of the picture. A predictive model based on these language features distinguished PCA patients from CN individuals with high classification accuracy.DiscussionThe findings indicate that language is a sensitive behavioral construct to detect visuospatial processing abnormalities of PCA. These insights offer theoretical and clinical avenues for understanding and managing PCA, underscoring language as a crucial marker for the visuospatial deficits of this atypical variant of Alzheimer’s disease.

Background:11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) regulates intracellular cortisol and its inhibition by the small molecule inhibitor, Xanamem™, may provide a disease-modifying strategy for Alzheimer’s disease (AD). Animal models suggest a range of 30–60% enzyme inhibition may suffice to provide neuroprotection.Objective:To determine the regional brain occupancy of 11β-HSD1 by Xanamem™ in cognitively normal participants (CN) and mild cognitive impairment (MCI)/mild AD patients to investigate potential dosing ranges for future efficacy studies.Methods:Seventeen MCI/AD and 23 CN were included. Regional brain time-activity curves (TAC), standardized uptake values (SUV40–60) and volume of distribution (VT) from Logan plot with image derived input function from 11C-TARACT positron emission tomography (PET) were used to assess the degree of 11β-HSD1 occupancy by increasing doses of Xanamem™ (5 mg, 10 mg, 20 mg or 30 mg daily for 7 days).Results:All measures showed high 11β-HSD1 occupancy with Xanamem to similar degree in CN and MCI/AD. The dose-response relationship was relatively flat above 5 mg. Respective median (interquartile range [Q1-Q3]) 11β-HSD1 occupancy in the MCI/AD and CN groups after treatment with 10 mg Xanamem were 80% [79–81%] and 75% [71–76%] in the neocortex, 69% [64–70%] and 61% [52–63%] in the medial temporal lobe, 80% [79–80%] and 73% [68–73%] in the basal ganglia, and 71% [67–75%] and 66% [62–68%] in the cerebellum.Conclusions:TAC, SUV40–60, and VT measures indicate Xanamem achieves high target occupancy levels with near saturation at 10 mg daily. These data support exploration of doses of≤10 mg daily in future clinical studies.

Introduction: Alzheimer’s, a prevalent degenerative dementia, succeeds Mild Cognitive Impairment (MCI), a variable precursor. The need to distinguish between them has grown with the advent of new disease-modifying treatments. Entorhinal cortex neurons collect sensory inputs from primary and association cortices, transmitting them to the hippocampus via the Perforant pathway-a White Matter (WM) tract in the parahippocampal region. Aim: To demonstrate the significance of Structural Magnetic Resonance Imaging (MRI) in distinguishing between individuals with Alzheimer’s Disease (AD) and those with MCI compared to a control group (CN). Materials and Methods: The present cross-sectional study was conducted in the Department of Radiodiagnosis at ACS Medical College and Hospital in Chennai, Tamil Nadu, India, from June 2022 to December 2022. Participants ranging in age from 45 to 82 years, underwent clinical evaluations and were subsequently classified based on their Mini-Mental State Examination (MMSE) scores. Those with MMSE scores below nine were diagnosed with AD, scores between 18 and 23 were indicative of MCI, and scores ranging from 24 to 30 signified inclusion in the healthy control group. The sample involved 30 healthy controls, 20 individuals with MCI, and 30 patients previously diagnosed with AD. Each participant underwent a comprehensive MRI scan. The diagnosis of AD and MCI was made using a novel technique that elaborates the dimensions of the parahippocampus on oblique coronal T1-weighted images. Results: The mean age of the participants was 65±5.5 years, ranging from 45 to 82 years. Among the 80 cases, 32 (40%) were males, and 48 (60%) were females. Ratios of the parahippocampal region were categorised as follows: ≤0.30 for AD, 0.31-0.39 for MCI, and ≥0.40 for cognitively normal individuals (CN). Patients with AD displayed ratios &lt;0.3, corresponding to MMSE scores &lt;9; those with MCI had ratios between 0.31 and 0.39, aligned with MMSE scores 18-23; while normal controls showed ratios ≥0.40, corresponding to MMSE scores 24-30. Conclusion: Using structural MRI to assess parahippocampal dimensions, effectively identifies neurological biomarkers linked to AD and MCI. This comprehensive approach not only enhances diagnostic precision but also lays the groundwork for pioneering treatment strategies aimed at tackling these cognitive conditions.

Amyloid β‐specific T cell response is enhanced in individuals with mild cognitive impairment

Identification of leukocyte surface biomarkers associated with late‐onset Alzheimer’s disease

Motor Sequence Learning in Mild Cognitive Impairment and Alzheimer’s Disease and its Relationship to Biomarkers

Medial temporal lobe gray matter microstructure alterations provide improved sensitivity to detect neurodegeneration associated with Alzheimer’s disease biomarkers

A multi‐site study of Alzheimer’s disease neuroimaging and cognitive biomarkers in 2819 cognitively normal individuals

Men show increased brain aging with respect to women among Cognitively Normal Individuals

Medial temporal lobe gray matter microstructure alterations provide improved sensitivity to detect neurodegeneration associated with Alzheimer’s disease biomarkers

Machine learning composite of remote cognitive tests for screening of preclinical AD cohorts