Stages Of Alzheimer's Disease Research Articles

Correlations between plasma markers and brain Aβ deposition across the AD continuum: Evidence from SILCODE.

Previous studies have found that Alzheimer's disease (AD)-related plasma markers are associated with amyloid beta (Aβ) deposition, but the change of this association in different Aβ pathological stages remains unclear. Data were obtained from the SILCODE. According to the standardized uptake value ratio (SUVR) and Aβ stage classification, correlation analysis was performed among plasma biomarkers, and voxel/SUVR values in the regions of interest (ROI) and clinical scale information, respectively. Mediation analysis was used to study the possible pathways. The proportion of cognitively normal (CN) and subjective cognitive decline (SCD) was the highest in stages A0 to 1, while in stages A2 to 4, the proportion of mild cognitive impairment (MCI) and AD increased. Plasma phosphorylated tau (p-tau)181 and glial fibrillary acidic protein (GFAP) levels were significantly lower in stage A0 compared to the later phases. Two pathways demonstrated fully mediated effects: positron emission tomography (PET) SUVR-plasma p-tau181-Mini-Mental State Examination (MMSE) and PET SUVR-plasma GFAP-MMSE. This study demonstrated the role of plasma biomarkers in the early stage of AD, especially in SCD, from both the clinical diagnosis and Aβ stage dimensions. Plasma ptau181 and GFAP level serve as indicators of early Alzheimer's disease and the pathologic Aβ staging classification. A possible ceiling effect of GFAP was observed in the mid-to-late stages of the AD course. This study confirms the role of AD plasma markers in promoting Aβ deposition at an early stage, particularly in females with subjective cognitive decline(SCD). The overlapping brain regions of plasma p-tau181, GFAP, and neurofilament light for Aβ deposition in the brain in early AD were distributed across various regions, including the posterior cingulate gyrus, rectus gyrus, and inferior temporal gyrus.

Enhancing Classification of Alzheimer’s Disease using Spatial Attention Mechanism

Aim This study aims to enhance the precision of Alzheimer's disease (AD) detection by integrating Spatial Attention Mechanism into a Convolutional Neural Network (CNN) architecture. Background Alzheimer's disease is a progressive neurodegenerative disorder characterized by abnormal protein deposits in the brain, leading to nerve cell loss and posing a significant global health challenge. Early and accurate detection is crucial for disease management and treatment due to the lack of a cure and the disease's severe progression. Objective The objective of this research is to improve the accuracy of Alzheimer's disease classification using MRI data by implementing a Spatial Attention Mechanism in a CNN architecture. Methods The study utilized T1-weighted MRI data from the OASIS 1 and OASIS 2 datasets. The key innovation is the Spatial Attention layer incorporated within a CNN model, which computes the average of each channel in the input feature map. This layer guides subsequent layers to focus on critical brain regions, enhancing the model's accuracy in differentiating between Alzheimer's disease stages. Results The model achieved a validation accuracy of 99.69% with a sensitivity and specificity of 1.0000, demonstrating its reliability in distinguishing between different stages of Alzheimer's disease. The adaptability of the Spatial Attention layer allows the model to assign higher weights to crucial brain regions, improving its discriminative power. Conclusion The integration of the Spatial Attention Mechanism into the CNN architecture significantly contributes to the early detection of Alzheimer's disease, enabling timely interventions. This innovative approach has the potential to revolutionize Alzheimer's diagnosis by enhancing accuracy and offering a robust solution for classification.

Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer's disease.

Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.

ErbB4 deficiency exacerbates olfactory dysfunction in an early-stage Alzheimer's disease mouse model.

Olfactory dysfunction is increasingly recognized as an early indicator of Alzheimer's disease (AD). Aberrations in GABAergic function and the excitatory/inhibitory (E/I) balance within the olfactory bulb (OB) have been implicated in olfactory impairment during the initial stages of AD. While the neuregulin 1 (NRG1)/ErbB4 signaling pathway is known to regulate GABAergic transmission in the brain and is associated with various neuropsychiatric disorders, its specific role in early AD-related olfactory impairment remains incompletely understood. This study demonstrated that olfactory dysfunction preceded cognitive decline in young adult APP/PS1 mice and was characterized by reduced levels of NRG1 and ErbB4 in the OB. Further investigation revealed that deletion of ErbB4 in parvalbumin interneurons reduced GABAergic transmission and increased hyperexcitability in mitral and tufted cells (M/Ts) in the OB, thereby accelerating olfactory dysfunction in young adult APP/PS1 mice. Additionally, ErbB4 deficiency was associated with increased accumulation of Aβ and BACE1-mediated cleavage of APP, along with enhanced CDK5 signaling in the OB. NRG1 infusion into the OB was found to enhance GABAergic transmission in M/Ts and alleviate olfactory dysfunction in young adult APP/PS1 mice. These findings underscore the critical role of NRG1/ErbB4 signaling in regulating GABAergic transmission and E/I balance within the OB, contributing to olfactory impairment in young adult APP/PS1 mice, and provide novel insights for early intervention strategies in AD. This work has shown that ErbB4 deficiency increased the burden of Aβ, impaired GABAergic transmission, and disrupted the E/I balance of mitral and tufted cells (M/Ts) in the OB, ultimately resulting in olfactory dysfunction in young adult APP/PS1 mice. NRG1 could enhance GABAergic transmission, rescue E/I imbalance in M/Ts, and alleviate olfactory dysfunction in young adult APP/PS1 mice. OB: olfactory bulb, E/I: excitation/inhibition, Pr: probability of release, PV: parvalbumin interneurons, Aβ: β-amyloid, GABA: gamma-aminobutyric acid.

Revisiting which language declines more in Spanish-English bilinguals with Alzheimer's disease: Longitudinal decline patterns on the multilingual naming test

Theories of bilingual language production predict that bilinguals with Alzheimer's disease (AD) should exhibit one of two decline patterns. Either parallel decline of both languages (if decline reflects damage to semantic representations that are accessed by both languages), or asymmetrical decline, with greater decline of the nondominant language (if decline reflects reduced ability to resolve competition from the dominant language with disease progression). Only two previous studies examined decline longitudinally with one showing parallel, and the other asymmetrical, decline. We examined decline over 2–7 years (3.9 on average) in Spanish-English bilinguals (N = 23). Logistic regression revealed a parallel decline pattern at one year from baseline, but an asymmetrical decline pattern over the longer decline period, with greater decline of the nondominant language (when calculating predicted probabilities of a correct response). The asymmetrical decline pattern was significantly greater for the nondominant language only when including item-difficulty in the model. Exploratory analyses across dominance groups looking at proportional decline relative to initial naming accuracy further suggested that decline of the nondominant language may be more precipitous if that language was acquired later in life, but the critical interaction needed to support this possibility was not statistically significant in a logistic regression analysis. These results suggest that accessibility of the nondominant language may initially be more resilient in early versus more advanced AD, and that AD affects shared semantic representations before executive control declines to a point where the ability to name pictures in single-language testing block is disrupted. Additional work is needed to determine if asymmetrical decline patterns are magnified by late age of acquisition of the nondominant language, and if more subtle impairments to executive control underlie impairments to language switching that occur in the earliest stages of AD (even preclinically).

Novel Leptin-Based Therapeutic Strategies to Limit Synaptic Dysfunction in Alzheimer's Disease.

Accumulation of hyper-phosphorylated tau and amyloid beta (Aβ) are key pathological hallmarks of Alzheimer's disease (AD). Increasing evidence indicates that in the early pre-clinical stages of AD, phosphorylation and build-up of tau drives impairments in hippocampal excitatory synaptic function, which ultimately leads to cognitive deficits. Consequently, limiting tau-related synaptic abnormalities may have beneficial effects in AD. There is now significant evidence that the hippocampus is an important brain target for the endocrine hormone leptin and that leptin has pro-cognitive properties, as activation of synaptic leptin receptors markedly influences higher cognitive processes including learning and memory. Clinical studies have identified a link between the circulating leptin levels and the risk of AD, such that AD risk is elevated when leptin levels fall outwith the physiological range. This has fuelled interest in targeting the leptin system therapeutically. Accumulating evidence supports this possibility, as numerous studies have shown that leptin has protective effects in a variety of models of AD. Recent findings have demonstrated that leptin has beneficial effects in the preclinical stages of AD, as leptin prevents the early synaptic impairments driven by tau protein and amyloid β. Here we review recent findings that implicate the leptin system as a potential novel therapeutic target in AD.

Diffusion model enables quantitative CBF analysis of Alzheimer's Disease.

Cerebral blood flow (CBF) measured by arterial spin labeling (ASL) is a promising biomarker for Alzheimer's Disease (AD). ASL data from multiple vendors were included in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. However, the M0 images were missing in Siemens ASL data, prohibiting CBF quantification. Here, we utilized a generative diffusion model to impute the missing M0 and validated generated CBF data with acquired data from GE. A conditional latent diffusion model was trained to generate the M0 image and validate it on an in-house dataset (N=55) based on image similarity metrics, accuracy of CBF quantification, and consistency with the physical model. This model was then applied to the ADNI dataset (Siemens: N=211) to impute the missing M0 for CBF calculation. We further compared the imputed data (Siemens) and acquired data (GE) regarding regional CBF differences by AD stages, their classification accuracy for AD prediction, and CBF trajectory slopes estimated by a mixed effect model. The trained diffusion model generated the M0 image with high fidelity (Structural similarity index, SSIM=0.924±0.019; peak signal-to-noise ratio, PSNR=33.348±1.831) and caused minimal bias in CBF values (mean difference in whole brain is 1.07±2.12ml/100g/min). Both generated and acquired CBF data showed similar differentiation patterns by AD stages, similar classification performance, and decreasing slopes with AD progression in specific AD-related regions. Generated CBF data also improved accuracy in classifying AD stages compared to qualitative perfusion data. This study shows the potential of diffusion models for imputing missing modalities for large-scale studies of CBF variation with AD.

Neuroimaging biomarkers and CSF sTREM2 levels in Alzheimer’s disease: a longitudinal study

Understanding the exact pathophysiological mechanisms underlying the involvement of triggering receptor expressed on myeloid cells 2 (TREM2) related microglia activation is crucial for the development of clinical trials targeting microglia activation at different stages of Alzheimer’s disease (AD). Given the contradictory findings in the literature, it is imperative to investigate the longitudinal alterations in cerebrospinal fluid (CSF) soluble TREM2 (sTREM2) levels as a marker for microglia activation, and its potential association with AD biomarkers, in order to address the current knowledge gap. In this study, we aimed to assess the longitudinal changes in CSF sTREM2 levels within the framework of the A/T/N classification system for AD biomarkers and to explore potential associations with AD pathological features, including the presence of amyloid-beta (Aβ) plaques and tau aggregates. The baseline and longitudinal (any available follow-up visit) CSF sTREM2 levels and processed tau-PET and Aβ-PET data of 1001 subjects were recruited from the ADNI database. The participants were classified into four groups based on the A/T/N framework: A+ /TN+ , A+ /TN− , A− /TN+ , and A− /TN− . Linear regression analyses were conducted to assess the relationship between CSF sTREM2 with cognitive performance, tau and Aβ-PET adjusting for age, gender, education, and APOE ε4 status. Based on our analysis there was a significant difference in baseline and rate of change of CSF sTREM2 between ATN groups. While there was no association between baseline CSF sTREM2 and cognitive performance (ADNI-mem), we found that the rate of change of CSF sTREM2 is significantly associated with cognitive performance in the entire cohort but not the ATN groups. We found that the baseline CSF sTREM2 is significantly associated with baseline tau-PET and Aβ-PET rate of change only in the A+ /TN+ group. A significant association was found between the rate of change of CSF sTREM2 and the tau- and Aβ-PET rate of change only in the A+ /TN− group. Our study suggests that the TREM2-related microglia activation and their relations with AD markers and cognitive performance vary the in presence or absence of Aβ and tau pathology. Furthermore, our findings revealed that a faster increase in the level of CSF sTREM2 might attenuate future Aβ plaque formation and tau aggregate accumulation only in the presence of Aβ pathology.

Ensemble ResDenseNet: Alzheimer’s disease staging from brain MRI using deep weighted ensemble transfer learning

Alzheimer’s disease (AD) is a persistent neurological disorder with almost no current cure, although available medications can mitigate its progression. The timely identification and staging of AD is pivotal in impeding and managing its advancement. This study aims to develop a comprehensive framework for the early detection of AD and the classification of medical images across four AD stages. We have proposed a weighted ensemble deep transfer learning framework using two pretrained CNN architectures, namely ResNet152V2 and DenseNet201. Additionally, gradient-weighted class activation mapping (Grad-CAM) is utilized to enhance interpretability by capturing gradients associated with AD in brain MRI images, propagating them to the final convolutional layer. This technique narrows the interpretability gap in deep learning models, enhancing accessibility and understanding of their decision-making processes, particularly in the context of AD diagnosis. Two different MRI datasets have been used to train and evaluate the performances of the proposed framework focusing on generalization capability with diverse data. The performances of the framework are analyzed meticulously under different case studies. The experimental results demonstrate that the weighted ensemble architecture exhibits superior performance characteristics over base models showing accuracy of 98%, which is compatible with related state of the art algorithms.

Activation of the hypoxia response in the aging cerebrovasculature protects males against cognitive impairment.

Alzheimer's disease (AD) is a neurodegenerative disorder with a distinct sex bias. Age-related vascular alterations, a hallmark of AD onset and progression, are consistently associated with sexual dimorphism. Here, we conducted an integrative meta-analysis of 335,803 single-nucleus transcriptomes and 667 bulk transcriptomes from the vascular system in AD and normal aging to address the underlying sex-dependent vascular aging in AD. All vascular cell types in male AD patients exhibited an activated hypoxia response and downstream signaling pathways including angiogenesis. The female AD vasculature is characterized by increased antigen presentation and decreased angiogenesis. We further confirmed that these sex-biased alterations in the cerebral vascular emerged and were primarily determined in the early stages of AD. Sex-stratified analysis of normal vascular aging revealed that angiogenesis and various stress-response genes were downregulated concurrently with female aging. Conversely, the hypoxia response increased steadily in males upon aging. An investigation of upstream driver transcription factors (TFs) revealed that altered communication between estrogen receptor alpha (ESR1) and hypoxia induced factors during menopause contributes to the inhibition of angiogenesis during normal female vascular aging. Additionally, inhibition of CREB1, a TF that targets estrogen, is also related to female AD. Overall, our study revealed a distinct cerebral vascular profile in females and males, and revealed novel targets for precision medicine therapy for AD.

Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)

Cognitive disorders affect various cognitive functions that can have a substantial impact on individual’s daily life. Alzheimer’s disease (AD) is one of such well-known cognitive disorders. Early detection and treatment of cognitive diseases using artificial intelligence can help contain them. However, the complex spatial relationships and long-range dependencies found in medical imaging data present challenges in achieving the objective. Moreover, for a few years, the application of transformers in imaging has emerged as a promising area of research. A reason can be transformer’s impressive capabilities of tackling spatial relationships and long-range dependency challenges in two ways, i.e., (1) using their self-attention mechanism to generate comprehensive features, and (2) capture complex patterns by incorporating global context and long-range dependencies. In this work, a Bi-Vision Transformer (BiViT) architecture is proposed for classifying different stages of AD, and multiple types of cognitive disorders from 2-dimensional MRI imaging data. More specifically, the transformer is composed of two novel modules, namely Mutual Latent Fusion (MLF) and Parallel Coupled Encoding Strategy (PCES), for effective feature learning. Two different datasets have been used to evaluate the performance of proposed BiViT-based architecture. The first dataset contain several classes such as mild or moderate demented stages of the AD. The other dataset is composed of samples from patients with AD and different cognitive disorders such as mild, early, or moderate impairments. For comprehensive comparison, a multiple transfer learning algorithm and a deep autoencoder have been each trained on both datasets. The results show that the proposed BiViT-based model achieves an accuracy of 96.38% on the AD dataset. However, when applied to cognitive disease data, the accuracy slightly decreases below 96% which can be resulted due to smaller amount of data and imbalance in data distribution. Nevertheless, given the results, it can be hypothesized that the proposed algorithm can perform better if the imbalanced distribution and limited availability problems in data can be addressed.Graphical abstract

Assessing Alzheimer's disease via plasma extracellular vesicle-derived mRNA.

Alzheimer's disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern. Recently it has been revealed that extracellular vesicles (EVs) play a critical role in AD pathogenesis and progression. Their stability and presence in various biofluids, such as blood, offer a minimally invasive window for monitoring AD-related changes. We analyzed plasma EV-derived messenger RNA (mRNA) from 82 human subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls. With next-generation sequencing, we profiled differentially expressed genes (DEGs), identifying those associated with AD. Based on DEGs identified in both the MCI and AD groups, a diagnostic model was established based on machine learning, demonstrating an average diagnostic accuracy of over 98% and showed a strong correlation with different AD stages. mRNA derived from plasma EVs shows significant promise as a non-invasive biomarker for the early detection and continuous monitoring of AD. The study conducted next-generation sequencing (NGS) of mRNA derived from human plasma extracellular vesicles (EVs) to assess Alzheimer's disease (AD).Profiling of plasma EV-derived mRNA shows a significantly enriched AD pathway, indicating its potential for AD-related studies.The AD-prediction model achieved a receiver-operating characteristic area under the curve (ROC-AUC) of more than 0.98, with strong correlation to the established Clinical Dementia Rating (CDR).

Volumetric and functional connectivity changes of the thalamic nuclei in different stages of Alzheimer’s disease

ObjectiveMemory processes known to be impaired in Alzheimer’s disease (AD) are maintained by a large-scale neurocognitive network with subcortical components, including the thalamus. Therefore, we aimed to examine the volumetric and functional changes of the thalamic nuclei at different scales across AD stages. MethodsMRI data of patients diagnosed with 20 AD dementia (ADD), 30 amnestic mild cognitive impairment (MCI), and 30 subjective cognitive impairment (SCI) were used. Volumetric and functional connectivity analyzes were performed by dividing the thalamus into anterior, medial, posterior, lateral and intralaminar nucleus groups and their specific subnuclei. ResultsIn the course of AD, the volume of the medial group nuclei, especially the mediodorsal medial magnocellular (MDm) nucleus, decreases. Medial group nuclei and MDm functional connectivity with frontal areas were decreased both in ADD and MCI compared to SCI group, while both of them increased their functional connectivity with visual areas in the ADD group compared to the MCI group. ConclusionsOur study suggests that the medial group of the thalamus, and specifically the MDm, may be affected in AD. SignificanceSpecific thalamic nuclei may be a critical anatomical region for investigating structural and functional changes in AD.

Structural Brain Differences in the Alzheimer’s Disease Continuum: Insights Into the Heterogeneity From a Large Multisite Neuroimaging Consortium

BackgroundNeurodegenerative diseases require collaborative, multisite research to comprehensively grasp their complex and diverse pathological progression; however, there is caution in aggregating global data due to data heterogeneity. In the current study, we investigated brain structure across stages of Alzheimer’s disease (AD) and how relationships vary across sources of heterogeneity. MethodsUsing 6 international datasets (n > 27,000), associations of structural neuroimaging markers were investigated in relation to the AD continuum via meta-analysis. We investigated whether associations varied across elements of magnetic resonance imagingacquisition, study design, and populations. ResultsModest differences in associations were found depending on how data were acquired; however, patterns were similar. Preliminary results suggested that neuroimaging marker–AD relationships differ across ethnic groups. ConclusionsDiversity in data offers unique insights into the neural substrate of AD; however, harmonized processing and transparency of data collection are needed. Global collaborations should embrace the inherent heterogeneity that exists in the data and quantify its contribution to research findings at the meta-analytical stage.

Brain regions differences in amyloid-β and gene expression in early APP/PS1 mice and identification of Npas4 as a key molecule in Alzheimer's disease.

Distinct brain regions are differentially affected during the various stages of Alzheimer's disease (AD). While the hippocampus and cortex are known to play significant roles, the involvement of the cerebellum has received less attention. Understanding the changes in diverse brain regions is essential to unravel the neuropathological mechanism in early-stage AD. Our research aimed to explore and compare amyloid-β (Aβ) pathology and gene expression profiles across the hippocampus, cortex, and cerebellum in the early stages of the Amyloid Precursor Protein/Presenilin-1 (APP/PS1) mouse model. By 7 months of age, significant Aβ plaque accumulation was observed in the hippocampus and cortex of APP/PS1 mice, while no such deposits were found in the cerebellum. Gene expression analysis revealed predominant effects on immune response pathways in the hippocampus and cortex. Even in the absence of Aβ deposition, notable gene expression changes were observed in the cerebellum of APP/PS1 mice. Intriguingly, Neuronal PAS Domain protein 4 (Npas4) expression was consistently down-regulated across all brain regions, independent of Aβ plaque presence. Our findings reveal distinct transcriptomic alterations and Aβ pathology in select cerebral regions during the initial phase of AD. Notably, the diminished expression of the Npas4 across three brain regions implies that Npas4 could play a pivotal role in the early pathogenesis of AD.

Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers.

Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.

The neurophysiological mechanisms of medial prefrontal-perirhinal cortex circuit mediating temporal order memory decline in early stage of AD rats

The temporal component of episodic memory has been recognized as a sensitive behavioral marker in early stage of Alzheimer's disease (AD) patients. However, parallel studies in AD animals are currently lacking, and the underlying neural circuit mechanisms remain poorly understood. Using a novel AppNL-G-F knock-in (APP-KI) rat model, the developmental changes of temporal order memory (TOM) and the relationship with medial prefrontal cortex and perirhinal cortex (mPFC-PRH) circuit were determined through in vivo electrophysiology and microimaging technique. We observed a deficit in TOM performance during the object temporal order memory task (OTOMT) in APP-KI rats at 6 month old, which was not evident at 3 or 4 months of age. Alongside behavioral changes, we identified a gradually extensive and aggravated regional activation and functional alterations in the mPFC and PRH during the performance of OTOMT, which occurred prior to the onset of TOM deficits. Moreover, coherence analysis showed that the functional connectivity between the mPFC and PRH could predict the extent of future behavioral performance. Further analysis revealed that the aberrant mPFC-PRH interaction mainly attributed to the progressive deterioration of synaptic transmission, information flow and network coordination from mPFC to PRH, suggesting the mPFC dysfunction maybe the key area of origin underlying the early changes of TOM. These findings identify a pivotal role of the mPFC-PRH circuit in mediating the TOM deficits in the early stage of AD, which holds promising clinical translational value and offers potential early biological markers for predicting AD memory progression.

Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup.

The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step-by-step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. HIGHLIGHTS: We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early-changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later-changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.

Detection of cerebrospinal fluid biomarkers changes of Alzheimer's disease using a cognitive stress test in persons with subjective cognitive decline and mild cognitive impairment.

Timely and accurate diagnosis of the earliest manifestations of Alzheimer's disease (AD) is critically important. Cognitive challenge tests such as the Loewenstein Acevedo Scales for Semantic Interference and Learning (LASSI-L) have shown favorable diagnostic properties in a number of previous investigations using amyloid or FDG PET. However, no studies have examined LASSI-L performance against cerebrospinal fluid biomarkers of AD, which can be affected before the distribution of fibrillar amyloid and other changes that can be observed in brain neuroimaging. Therefore, we aimed to evaluate the relationship between LASSI-L scores and CSF biomarkers and the capacity of the cognitive challenge test to detect the presence of amyloid and tau deposition in patients with subjective cognitive decline and amnestic mild cognitive impairment (MCI). One hundred and seventy-nine patients consulting for memory loss without functional impairment were enrolled. Patients were examined using comprehensive neuropsychological assessment, the LASSI-L, and cerebrospinal fluid (CSF) biomarkers (Aβ1-42/Aβ1-40 and ptau181). Means comparisons, correlations, effect sizes, and ROC curves were calculated. LASSI-L scores were significantly associated with CSF biomarkers Aβ1-42/Aβ1-40 in patients diagnosed with MCI and subjective cognitive decline, especially those scores evaluating the capacity to recover from proactive semantic interference effects and delayed recall. A logistic regression model for the entire sample including LASSI-L and age showed an accuracy of 0.749 and an area under the curve of 0.785 to detect abnormal amyloid deposition. Our study supports the biological validity of the LASSI-L and its semantic interference paradigm in the context of the early stages of AD. These findings emphasize the utility and the convenience of including sensitive cognitive challenge tests in the assessment of patients with suspicion of early stages of AD.

Plasma Insulin Predicts Early Amyloid-β Pathology Changes in Alzheimer's Disease.

Evidence suggests that type 2 diabetes (T2D) is an independent risk factor for Alzheimer's disease (AD), sharing similar pathophysiological traits like impaired insulin signaling. To test the association between plasma insulin and cerebrospinal fluid (CSF) AD pathology. A total of 304 participants were included in the Alzheimer's Disease Neuroimaging Initiative, assessing plasma insulin and CSF AD pathology. We explored the cross-sectional and longitudinal associations between plasma insulin and AD pathology and compared their associations across different AD clinical and pathological stages. In the non-demented group, amyloid-β (Aβ)+ participants (e.g., as reflected by CSF Aβ42) exhibited significantly lower plasma insulin levels compared to non-demented Aβ-participants (p < 0.001). This reduction in plasma insulin was more evident in the A+T+ group (as shown by CSF Aβ42 and pTau181 levels) when compared to the A-T- group within the non-dementia group (p = 0.002). Additionally, higher plasma insulin levels were consistently associated with more normal CSF Aβ42 levels (p < 0.001) across all participants. This association was particularly significant in the Aβ-group (p = 0.002) and among non-demented individuals (p < 0.001). Notably, baseline plasma insulin was significantly correlated with longitudinal changes in CSF Aβ42 (p = 0.006), whereas baseline CSF Aβ42 did not show a similar correlation with changes in plasma insulin over time. These findings suggest an association between plasma insulin and early Aβ pathology in the early stages of AD, indicating that plasma insulin may be a potential predictor of changes in early Aβ pathology.