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Background: Gold standard dementia assessments are rarely available in large real‐world datasets, leaving researchers to choose among methods with imperfect but acceptable accuracy to identify nursing home (NH) residents with dementia. In healthcare claims, options include claims‐based diagnosis algorithms, diagnosis indicators, and cognitive function measures in the Minimum Data Set (MDS), but few studies have compared these. We evaluated the proportion of NH residents identified with possible dementia and concordance of these three. Methods: Using a 20% random sample of 2018–2019 Medicare beneficiaries, we identified MDS admission assessments for non‐skilled NH stays among individuals with continuous enrollment in Medicare Parts A, B, and D. Dementia was identified using: (1) Chronic Conditions Warehouse (CCW) claims‐based algorithm for Alzheimer's disease and non‐Alzheimer's dementia; (2) MDS active diagnosis indicators for Alzheimer's disease and non‐Alzheimer's dementias; and (3) the MDS Cognitive Function Scale (CFS) (at least mild cognitive impairment). We compared the proportion of admissions with evidence of possible dementia using each criterion and calculated the sensitivity, specificity, and agreement of the CCW claims definition and MDS indicators for identifying any impairment on the CFS. Results: Among 346,013 non‐SNF NH admissions between 2018 and 2019, 57.2% met criteria for at least one definition (44.7% CFS, 40.7% CCW algorithm, 26.0% MDS indicators). The MDS CFS uniquely identified the greatest proportion with evidence of dementia. The CCW claims algorithm had 63.7% sensitivity and 78.1% specificity for identifying any cognitive impairment on the CFS. Active diagnosis indicators from the MDS had lower sensitivity (47.0%), but higher specificity (91.0%). Conclusions: Claims‐ and MDS‐based methods for identifying NH residents with possible dementia have only partial overlap in the cohorts they identify, and neither is an obvious gold standard. Future studies should seek to determine whether additional functional assessments from the MDS or prescriptions can improve identification of possible dementia in this population. [ABSTRACT FROM AUTHOR]

Objectives Early diagnosis of Alzheimer's disease (AD) using brain scans and other biomarker tests will be essential to increasing the benefits of emerging disease-modifying therapies, but AD biomarkers may have unintended negative consequences on stigma. We examined how a brain scan result affects AD diagnosis confidence and AD stigma. Methods The study used a vignette-based experiment with a 2 × 2 × 3 factorial design of main effects: a brain scan result as positive or negative, treatment availability and symptom stage. We sampled 1,283 adults ages 65 and older between June 11and July 3, 2019. Participants (1) rated their confidence in an AD diagnosis in each of four medical evaluations that varied in number and type of diagnostic tools and (2) read a vignette about a fictional patient with varied characteristics before completing the Modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). We examined mean diagnosis confidence by medical evaluation type. We conducted between-group comparisons of diagnosis confidence and FS-ADS scores in the positive versus negative brain scan result conditions and, in the positive condition, by symptom stage and treatment availability. Results A positive versus negative test result corresponds with higher confidence in an AD diagnosis independent of medical evaluation type (all p  < .001). A positive result correlates with stronger reactions on 6 of 7 FS-ADS domains (all p  < .001). Discussion A positive biomarker result heightens AD diagnosis confidence but also correlates with more AD stigma. Our findings inform strategies to promote early diagnosis and clinical discussions with individuals undergoing AD biomarker testing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common type of dementia in the elderly. The ApoE ε4 gene is the main genetic risk factors for sporadic AD, and is associated with the changes in the amyloid (3- protein (Aβ) and tau protein, the core pathological features of AD. ApoE ε4 has great potential as a genetic biomarker for AD. Clinical studies have shown the important role of peripheral blood ApoE ε4 in AD risk assessment and disease detection. But in the current clinical practice, there are many weak points of insufficient understanding and insufficient attention about the clinical use of ApoE ε4. The importance of ApoE ε4 is highlighted especially with the development of high-quality clinical drug trials or the arrival of clinical drug therapy targeting Aβ for AD. So far, there is still a lack of Chinese expert consensus on the standardized application of ApoE ε4 in AD. Given that, this article systematically summarizes the current domestic and international research on the application of ApoE ε4 in AD. The consensus is written and aimed to fully reflect the clinical application value of ApoE ε4 in AD, and improve the diagnosis and treatment level of AD, and guide further clinical research. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common fatal neurodegenerative disease among the elderly worldwide, characterized by memory and cognitive impairment. The identification of biomarkers for AD is crucial and urgent to facilitate the diagnosis and intervention. The aim of this study was to evaluate the diagnostic value of acyl-Coenzyme A thioesterase 7 (ACOT7) as a serum biomarker for the prediction of AD. In our study, we observed a significant increase in ACOT7 expression in patients (n = 366) with AD and animal (n = 8-12) models of AD, compared to the control group. A significant negative correlation was found between ACOT7 levels and Mini-Mental State Examination (MMSE) scores (r = -0.85; p < 0.001). The analysis of the receiver operating characteristic curve (ROC) showed that the area under the curve (AUC) for ACOT7 was 0.83 (95% confidence intervals: 0.80-0.86). The optimal cut-off point of 62.5 pg./mL was selected with the highest sum of sensitivity and specificity. The diagnostic accuracy of serum ACOT7 for AD was 77% (95% confidence intervals: 72-82%), with a sensitivity of 80% (95% confidence intervals: 75-84%) and a specificity of 74% (95% confidence intervals: 69-79%). Moreover, the ROC analysis showed that the AUC of Aβ42/40 ratio is 0.70, and the diagnostic accuracy was 72%, with 69% sensitivity and 76% specificity. Compared with the AD traditional marker Aβ42/40 ratio, ACOT7 shows better superiority as a new serum candidate biomarker of AD. By suppressing the ACOT7 gene, our study provides evidence of the involvement of ACOT7 in the metabolism of amyloid precursor protein (APP), resulting in alterations in the expression levels of Aβ42, BACE1 and βCTF. ACOT7 has the ability to modulate the amyloidogenic pathway of APP metabolism, while it does not have an impact on the non-amyloidogenic pathway. In conclusion, the findings of our study suggest that serum ACOT7 may serve as a promising and non-invasive biomarker for AD. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Methods: Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. Results: On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aß42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aß42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors. Conclusions: Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions. [ABSTRACT FROM AUTHOR]

Background: Drivers with dementia are at a higher risk of motor vehicle accidents. The characteristics of driving behaviour of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) have not been fully elucidated. We investigated driving ability and its relationship with cognitive function and magnetic resonance imaging (MRI) morphometry indicators. Methods: The driving abilities of 19 patients with AD and 11 with amnestic MCI (aMCI) were evaluated using a driving simulator. The association between each driving ability parameter and the Mini‐Mental State Examination (MMSE) score or voxel‐based specific regional analysis system for AD (VSRAD) was assessed. Results: Patients with AD made a significantly higher number of operational errors than those with aMCI in attention allocation in the complex task test (P = 0.0008). The number of operational errors in attention allocation in the complex task test significantly and negatively correlated with MMSE scores in all participants (r = −0.4354, P = 0.0162). The decision time in the selective reaction test significantly and positively correlated with the severity and extent of medial temporal structural atrophy (r = 0.4807, P = 0.0372; r = 0.4862, P = 0.0348; respectively). Conclusion: An increase in the operational errors for attention allocation in the complex task test could be a potential indicator of progression from aMCI to AD. Atrophy of the medial temporal structures could be a potential predictor of impaired judgement in driving performance in aMCI and AD. A driving simulator could be useful for evaluating the driving abilities of individuals with aMCI and AD. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD), a form of dementia prevalent in older age, is a neurodegenerative condition impacting brain nerve cells. Early-stage AD symptoms are often subtle, complicating timely diagnosis. Early detection allows for intervention, slowing disease progression and facilitating appropriate treatments. Deep learning methods, particularly gradient-based images, prove promising for early Alzheimer's detection in Magnetic Resonance (MR) imaging. Gradient-based images, highlighting details in low-intensity images and enhancing contrast, play a vital role in determining structures' location, shape, and size, notably in techniques like Magnetic Resonance Imaging (MRI). This study aims to boost model performance in early AD detection by applying the Gradient filter before training deep learning models on diverse-angle and constant-density brain MRI images. The dataset comprises three categories representing early-stage AD, including images of Mild cognitive impairment and healthy individuals. Original and gradient-filtered image subsets were inputted into deep learning models. Results indicate superior performance of gradient-based images, with the Densenet201 deep learning model achieving the highest accuracy at 98.63%. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disorder that affects millions of individuals worldwide. Researchers have conducted numerous studies to find accurate biomarkers for early AD diagnosis and develop more effective treatments. The main pathological hallmarks of AD are amyloid beta and Tau proteins. Other biomarkers, such as DNA, RNA, and proteins, can also be helpful in early AD diagnosis. To diagnose and treat AD promptly, it is essential to accurately measure the concentration of biomarkers in the cerebrospinal fluid or blood. However, due to the low concentrations of these biomarkers in the body, highly sensitive analytical techniques are required. To date, sensors have become increasingly important due to their high sensitivity, swift detection, and adaptable manipulation features. These qualities make them an excellent substitute for conventional instruments. Nanomaterials are commonly employed in sensors to amplify signals and improve sensitivity. This review paper summarized the integration of nanomaterials in optical sensor systems, including colorimetric, fluorescent, and surface-enhanced Raman scattering sensors for AD biomarkers detection. [ABSTRACT FROM AUTHOR]

Background: Electrophysiological biomarkers are being examined as potential diagnostic measures of cognitive impairment and its manifestations for psychiatric nurses' use in the care of Alzheimer's disease (AD). However, there is no integrative review describing the themes from the current research about electrophysiological biomarkers and the developing relationship among the themes. Characterizing this developing relationship is imperative for any possible integration of biomarkers into the care of AD by psychiatric nurses. Objective: The purpose of this integrative review is to identify themes from the current research about electrophysiological biomarkers of AD and the developing relationship among the themes, the conceivable relational premise for psychiatric nurses to integrate electrophysiological biomarkers into the screening, assessment, diagnosis, and treatment of AD for the care of persons with AD. Methods: A literature search was executed with PUBMED (accessing Medline and Elsevier) and CINAHL databases that focused on studies about electrophysiological biomarkers of AD from 2015 to 2022. Twenty-seven peer-reviewed studies met this review's inclusion criteria. Results: Five themes emerged: (1) assessing/screening, (2) assessment differential, (3) diagnosing, (4) diagnostic accuracy, and (5) treating. These themes related sequentially and linearly, establishing a developing relationship about the risk, the onset, and the progression of AD. Discussion: Electrophysiological biomarkers associated to cognitive impairment in AD, supporting the accepted understanding of the symptoms of AD. Changes in behavior and functioning were not examined, limiting the possible integration of electrophysiological biomarkers. Further investigations are warranted with an expansion of the clinical symptoms and diverse study populations. [ABSTRACT FROM AUTHOR]

Introduction: Discourse is one of the main linguistic aspects affected by Alzheimer's disease (AD), and its relationship with memory needs to be further studied, mainly in low education and low socioeconomic status groups. The present study aimed to investigate differences in the recall of short narratives between participants with mild AD and a control group (CG) of typical older adults (CG) with the use of automatic assessment. Methods: Seventeen older adults diagnosed with AD (mean age 76.41, mean education 5.82) and 34 typical older adults (mean age 74.26, mean education 7.09) were asked to listen to and then retell a short story. Syntactic, lexical, and semantic features were assessed via the NILC-Metrix software, and the features were correlated with episodic, working, and semantic memory assessment. Results: Differences were found in 7 of the 34 features assessed. Syntactically, the group diagnosed with AD produced narratives with fewer sentences, fewer words per sentence, and lower Yngve depth scores. Lexically, the AD group produced narratives with fewer words and prepositions per sentence. Semantically, the narratives produced by the AD group featured words with a lower mean age of acquisition and lower Brunét's index scores. For the CG, episodic memory performance correlated with the ratio of conjunctions. No other significant correlation was found for semantic and working memory in the CG. No correlation was found between memory performance and linguistic features for the AD group. Discussion: The automatic assessment of linguistic features showed impaired narrative recall in participants diagnosed with AD relative to healthy controls at the syntactic, lexical, and semantic levels of discourse. These findings corroborate previous literature showing a decline in discourse production performance resulting from cognitive impairment in AD. Conclusion: The assessment of linguistic performance through a narrative recall task provides valuable insights into cognitive decline related to AD. [ABSTRACT FROM AUTHOR]

Objectives: The implementation of disease‐modifying treatments for Alzheimer's Disease (AD) will require cost‐effective diagnostic processes. As part of The Precision Medicine In AD consortium (PMI‐AD) project, the aim is to analyze the baseline costs of diagnosing early AD at memory clinics in Norway, Slovenia, and the Netherlands. Methods: The costs of cognitive testing and a clinical examination, apolipoprotein E, magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), positron emission tomography and blood‐based biomarkers (BBM), which are used in different combinations in the three countries, were analyzed. Standardized unit costs, adjusted for GDP per capita and based on Swedish conditions were applied. The costs were expressed in euros (€) as of 2019. A diagnostic set comprising clinical examination, cognitive testing, MRI and CSF was defined as the gold standard, with MRI mainly used as an exclusion filter. Results: Cost data were available for 994 persons in Norway, 169 in Slovenia and 1015 in the Netherlands. The mean diagnostic costs were 1478 (95% confidence interval 1433–1523) € in Norway, 851 (731–970) € in Slovenia and 1184 (1135–1232) € in the Netherlands. Norway had the highest unit costs but also the greatest use of tests. With a uniform diagnostic test set applied, the diagnostic costs were 1264 (1238–1291) €, in Norway, 843 (771–914) € in Slovenia and 1184 (1156–1213) € in the Netherlands. There were no major cost differences between the final set of diagnoses. Conclusions: The total costs for setting a diagnosis of AD varied somewhat in the three countries, depending on unit costs and use of tests. These costs are relatively low in comparison to the societal costs of AD. Key points: The introduction of blood‐based biomarkers for detecting Alzheimer's Disease (AD) can change the diagnostic process.Given the expected demands for treatment of cognitive disorders such as AD, the costs and capacity of the diagnostic work‐up are of great importance for care funders and planners.Our study incorporating diagnostic costs from three distinct European regions provides valuable inputs for cost‐effectiveness studies. [ABSTRACT FROM AUTHOR]

The Scottish Intercollegiate Guidelines Network (SIGN) have recently published their guideline SIGN168 on 'Assessment, Diagnosis, Care, and Support for People with Dementia and their Carers'. The guideline makes evidence-based recommendations for best practice in the assessment, care and support of adults living with dementia. Topics featured in this guideline are limited to those prioritised by stakeholders, especially people with lived and living experience, and those not well covered under pre-existing guidance. We summarise the guideline recommendations related to identification and diagnosis of dementia, investigative procedures, postdiagnostic support living with dementia, including non-pharmacological approaches for distressed behaviours, using technology to support people with dementia, grief and dementia and changing needs of people with dementia. The guideline content is summarised as officially published, with additional commentary in the final section. [ABSTRACT FROM AUTHOR]

Background: Changes in everyday functioning constitute a clinically meaningful outcome, even in the early stages of Alzheimer’s disease. Performance-based assessments of everyday functioning might help uncover these early changes. We aimed to investigate how changes over time in everyday functioning relate to tau and amyloid in cognitively unimpaired older adults. Methods: Seventy-six cognitively unimpaired participants (72 ± 6 years old, 61% female) completed multiple Harvard Automated Phone Task (APT) assessments over 2.0 ± 0.9 years. The Harvard APT consists of three tasks, performed through an automated phone system, in which participants refill a prescription (APT-Script), select a new primary care physician (APT-PCP), and transfer money to pay a bill (APT-Bank). Participants underwent Pittsburgh compound-B and flortaucipir positron emission tomography scans at baseline. We computed distribution volume ratios for a cortical amyloid aggregate and standardized uptake volume ratios for medial temporal and neocortical tau regions. In separate linear mixed models, baseline amyloid by time and tau by time interactions were used to predict longitudinal changes in performance on the Harvard APT tasks. Three-way amyloid by tau by time interactions were also investigated. Lastly, we examined associations between tau and change in Harvard APT scores in exploratory voxel-wise whole-brain analyses. All models were adjusted for age, sex, and education. Results: Amyloid [unstandardized partial regression coefficient estimate (β) =-0.007, 95% confidence interval (95% CI) = (-0.013,-0.001)], and medial temporal tau [β =-0.013, 95% CI = (-0.022,-0.004)] were associated with change over time in years on APT-PCP only, i.e., higher baseline amyloid and higher baseline tau were associated with steeper rate of decline of APT-PCP. Voxel-wise analyses showed widespread associations between tau and change in APT-PCP scores over time. Conclusion: Even among cognitively unimpaired older adults, changes over time in the performance of cognitively complex everyday activities relate to cortical amyloid and widespread cerebral tau burden at baseline. These findings support the link between Alzheimer’s disease pathology and function and highlight the importance of measuring everyday functioning in preclinical disease stages. [ABSTRACT FROM AUTHOR]

Soluble Triggering Receptor Expressed on Myeloid Cells 2 (sTREM2) plays a crucial role in the pathogenesis of Alzheimer’s disease (AD). This review comprehensively examines sTREM2’s involvement in AD, focusing on its regulatory functions in microglial responses, neuroinflammation, and interactions with key pathological processes. We discuss the dynamic changes in sTREM2 levels in cerebrospinal fluid and plasma throughout AD progression, highlighting its potential as a therapeutic target. Furthermore, we explore the impact of genetic variants on sTREM2 expression and its interplay with other AD risk genes. The evidence presented in this review suggests that modulating sTREM2 activity could influence AD trajectory, making it a promising avenue for future research and drug development. By providing a holistic understanding of sTREM2’s multifaceted role in AD, this review aims to guide future studies and inspire novel therapeutic strategies. [ABSTRACT FROM AUTHOR]

Introduction: Mild cognitive impairment (MCI) is an important stage in Alzheimer's disease (AD) research, focusing on early pathogenic factors and mechanisms. Examining MCI patient subtypes and identifying their cognitive and neuropathological patterns as the disease progresses can enhance our understanding of the heterogeneous disease progression in the early stages of AD. However, few studies have thoroughly analyzed the subtypes of MCI, such as the cortical atrophy, and disease development characteristics of each subtype. Methods: In this study, 396 individuals with MCI, 228 cognitive normal (CN) participants, and 192 AD patients were selected from ADNI database, and a semi-supervised mixture expert algorithm (MOE) with multiple classification boundaries was constructed to define AD subtypes. Moreover, the subtypes of MCI were obtained by using the multivariate linear boundary mapping of support vector machine (SVM). Then, the gray matter atrophy regions and severity of each MCI subtype were analyzed and the features of each subtype in demography, pathology, cognition, and disease progression were explored combining the longitudinal data collected for 2 years and analyzed important factors that cause conversion of MCI were analyzed. Results: Three MCI subtypes were defined by MOE algorithm, and the three subtypes exhibited their own features in cortical atrophy. Nearly one-third of patients diagnosed with MCI have almost no significant difference in cerebral cortex from the normal aging population, and their conversion rate to AD are the lowest. The subtype characterized by severe atrophy in temporal lobe and frontal lobe have a faster decline rate in many cognitive manifestations than the subtype featured with diffuse atrophy in the whole cortex. APOE e4 is an important factor that cause the conversion of MCI to AD. Conclusion: It was proved through the data-driven method that MCI collected by ADNI baseline presented different subtype features. The characteristics and disease development trajectories among subtypes can help to improve the prediction of clinical progress in the future and also provide necessary clues to solve the classification accuracy of MCI. [ABSTRACT FROM AUTHOR]

Background: Now, there are no sensitive biomarkers for improving Alzheimer's disease (AD) and comorbid Parkinson's disease (PD). The aim of the present study was to analyze differentially expressed genes (DEGs) in brain tissue from AD and PD patients via bioinformatics analysis, as well as to explore precise diagnostic and therapeutic targets for AD and comorbid PD. Methods: GFE122063 and GSE7621 data sets from GEO in NCBI, were used to screen differentially expressed genes (DEGs) for AD and PD, and identify the intersected genes, respectively. Intersected genes were analyzed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, STRING site and Cytoscape were used to construct a protein-protein interaction (PPI) network, CytoNCA algorithm to analyze and evaluate centrality, Mcode plug-in to analyze module, and Cytohubba to screen key genes. Combined GO-KEGG enrichment analysis with Cytoscape algorithm to screen the key gene in AD complicated with PD. Then, the DEGs for AD and PD were imported into the Association Map (CMap) online platform to screen out the top 10 small molecule drugs, and using molecular docking techniques to evaluate the interactions between small molecule drugs and key genes receptors. Results: In total, 231 upregulated genes and 300 downregulated genes were identified. GO analysis revealed that the DEGs were highly enriched in signal transduction, and KEGG analysis revealed that the DEGs were associated with the MAPK and PI3K-Akt signaling pathways. Epidermal growth factor receptor (EGFR) was identified as a potential receptor gene in AD and comorbid PD. EGFR was upregulated in both AD and PD, and the proteins that interact with EGFR were enriched in the Ras/Raf/MAPK and PI3K/Akt signaling pathways. Semagacestat was identified as a drug with therapeutic potential for treating AD complicated with PD. There was a high binding affinity between semagacestat and EGFRNTD, with seven hydrogen bonds and one hydrophobic bond. Discussion: Semagacestat may improve the health of patients with AD complicated with PD through the regulation of the Ras/Raf/MAPK and PI3K/ Akt signaling pathways by EGFR, providing evidence supporting the structural modification of semagacestat to develop a more effective drug for treating AD complicated with PD. [ABSTRACT FROM AUTHOR]

The entorhinal cortex (EC) stands out as a critical brain region affected in the early phases of Alzheimer's disease (AD), with some of the disease's pathological processes originating from this area, making it one of the most crucial brain regions in AD. Recent research highlights disruptions in the brain's network activity, characterized by heightened excitability and irregular oscillations, may contribute to cognitive impairment. These disruptions are proposed not only as potential therapeutic targets but also as early biomarkers for AD. In this paper, we will begin with a review of the anatomy and function of EC, highlighting its selective vulnerability in AD. Subsequently, we will discuss the disruption of EC network activity, exploring changes in excitability and neuronal oscillations in this region during AD and hypothesize that, considering the advancements in neuromodulation techniques, addressing the disturbances in the network activity of the EC could offer fresh insights for both the diagnosis and treatment of AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common neurodegenerative disease in older age. Pathophysiological changes begin in the brains of affected individuals many years before any clinical signs are observed. Although brain imaging and neurophysiological analyzes are useful to reveal anatomical and functional changes in patients whose diagnosis of AD is considered based on clinical examination, their contribution to the diagnosis is quite limited, particularly in the early stages of the disease. Some biological markers are important as laboratory support in the early diagnosis of AD. Biomarkers are objectively measurable and evaluable indicators that serve to identify normal biological processes, pathological processes, and therapeutic response rates. Biomarkers have the potential to predict the likelihood of disease, assist in early diagnosis, and contribute to monitoring treatment effectiveness. This article aimed to provide information about the use of proximity extension assay technology in biomarker studies in AD. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to examine the differences in upper limb apraxia assessments and neuropsychological profiles of patients diagnosed with Alzheimer's disease (AD) dementia and mild cognitive impairment (MCI) and healthy controls. Patients and methods: A total of 53 participants were included in the retrospective study, including nine patients with MCI, 23 patients diagnosed with AD, and 21 healthy patients equivalent in age and education level. The participants' data were collected between July 2021 and December 2022. A 12-question mini-test taken from the Test of Upper Limb Apraxia (TULIA) was used in the apraxia evaluation. Individuals' upper limb apraxia evaluations were compared according to diagnostic groups, and their neuropsychological profiles were also examined. Results: Apraxia was found to be associated with impairments in memory retrieval function, executive dysfunction, and decrease in object naming performance. Significant differences were observed between diagnostic groups in both apraxia assessment and neuropsychological tests. Conclusion: The findings indicate that the cognitive profile that emerges with the combined use of upper extremity apraxia assessment and related neuropsychological tests may serve as a marker and guide in the planning and correct execution of treatment in the transition to Alzheimer-type dementia, similar to other neuropsychological tests. [ABSTRACT FROM AUTHOR]

More than 16 million Americans living with cognitive impairment warrant a diagnostic evaluation to determine the cause of this disorder. The recent availability of disease-modifying therapies for Alzheimer's disease (AD) is expected to significantly drive demand for such diagnostic testing. Accurate, accessible, and affordable methods are needed. Blood biomarkers (BBMs) offer advantages over usual care amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers in these regards. This study used a budget impact model to assess the economic utility of the PrecivityAD® blood test, a clinically validated BBM test for the evaluation of brain amyloid, a pathological hallmark of AD. The model compared 2 scenarios: (1) baseline testing involving usual care practice, and (2) early use of a BBM test before usual care CSF and PET biomarker use. At a modest 40% adoption rate, the BBM test scenario had comparable sensitivity and specificity to the usual care scenario and showed net savings in the diagnostic work-up of $3.57 million or $0.30 per member per month in a 1 million member population, translating to over $1B when extrapolated to the US population as a whole and representing a 11.4% cost reduction. Savings were driven by reductions in the frequency and need for CSF and PET testing. Additionally, BBM testing was associated with a cost savings of $643 per AD case identified. Use of the PrecivityAD blood test in the clinical care pathway may prevent unnecessary testing, provide cost savings, and reduce the burden on both patients and health plans. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To better understand the areas of risk that may become apparent across the life course of dementia • To recognise the ethical issues posed by trying to eliminate the risk of harm for people living with dementia • To consider how the principles of safeguarding apply to risk management in dementia care. What constitutes a risk for a person living with dementia may be perceived and prioritised differently by nurses from varying clinical backgrounds. Furthermore, risk may be perceived differently according to the context. This article outlines some of the social, psychological and physical risk factors relevant to people living with dementia across the life course of the condition. It is important that nurses understand their role in identifying, assessing and managing risk and are aware of the resources, policies, legislation and processes designed to support decision-making and minimise the risk of harm to people living with dementia, their families and carers. The authors hope that this article will support nurses to become more confident in identifying risk while encouraging them to adopt a proactive and person-centred approach to risk assessment and management. [ABSTRACT FROM AUTHOR]

Racially and ethnically diverse populations have recently contributed to the majority of rural and small-town growth. Consequently, the disproportionately high risk and prevalence of Alzheimer's disease and related dementias (ADRD) among rural and minoritized older residents will likely increase. To address this threat, we tested the hypotheses that (1) a faith-based, resident-led approach would increase basic ADRD knowledge and diagnosis, and (2) older age, female gender, lower educational levels, and more years lived rural would predict number of referrals, new dementia diagnoses, and treatment. An adaptation of Schoenberg's Faith Moves Mountains model, previously successful in detection and management of other chronic illnesses in rural settings, guided this community-based participatory research. Local faith community members were trained as research assistants to recruit, administer surveys, conduct brief memory assessments, teach brain health strategies, and follow-up with residents. Outreaches were offered virtually during the pandemic, then in-person monthly at rotating church sites, and repeated ∼1 year later. This rural sample was racially and ethnically diverse (74.5% non-White), with 28% reporting eight or less years of formal education. Findings included that referrals and years lived rural were significant and positive predictors of new ADRD treatments [(b = 3.74, χ2(1, n = 235) = 13.01, p < 0.001); (b = 0.02, χ2(1, n = 235 = 3.93, p = 0.048)], respectively, regardless of participant characteristics. Resident-led action research in rural, diverse, faith communities is a successful approach to increasing ADRD disease knowledge, detection, diagnosis, and treatment. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) presents a significant global health challenge, affecting over 6 million individuals. This underscores the urgent need for early detection to improve treatment effectiveness and disease management. AD is characterized by the buildup of amyloid beta (Aβ) plaques and neurofibrillary tangles, resulting in progressive and irreversible neuronal degeneration. However, current diagnostic methods, like imaging scans, are often inaccessible and costly, limiting their usefulness for early detection. This study investigates the potential of nasal secretion analysis as a non-invasive diagnostic method for AD. Using Bicinchoninic acid (BCA) and Enzyme-Linked Immunosorbent Assay (ELISA) assays, we examined protein levels, focusing mainly on amyloid beta 42/40 (Aβ 42/40) and Interleukin-6 (IL-6) in nasal secretion samples. Blood and brain samples were also collected for comparison, providing insights into systemic biomarkers reflecting overall pathological processes and direct examination of AD-related neuropathology, respectively. Our findings highlight the promise of nasal secretion analysis as a diagnostic tool for AD, emphasizing the importance of Aβ 42/40 and IL-6. ELISA results revealed increased levels of Aβ 42/40 in nasal secretions from AD mice, exhibiting an average OD450 signal of 0.496, in contrast to healthy cohorts where the signal was 0.4895 for young mice and 0.46 for aged mice. Additionally, elevated levels of IL-6 were identified in nasal secretions of AD mice, averaging 0.1395 pg/mL, compared to healthy cohorts, where young mice averaged 0.0998 pg/mL and aged mice had 0.11 pg/mL. This underscores IL-6's potential as a biomarker for AD pathology and contributes to understanding its dynamics in AD. Moreover, the study's BCA assay results indicated a concentration of proteins in the fluid, expanding the potential for future research to discover more protein biomarkers. This study aims to facilitate further research to validate the diagnostic accuracy of nasal secretion analysis and its comparative effectiveness with existing methods. These advancements hold promise for early AD detection and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Abstract The newly proposed revised criteria for diagnosis and staging of Alzheimer's disease (AD) by the Alzheimer's Association (AA) Workgroup represent a significant milestone in the field. These criteria offer objective measures for diagnosing and staging biological AD, bridging the gap between research and clinical care. Although implementation feasibility may vary across regions and settings, improving the availability and accuracy of biomarkers, especially plasma biomarkers, is expected to enhance the applicability of these criteria in clinical practice. The Fall 2023 Alzheimer's Association Research Roundtable (AARR) meeting served as a forum for gathering industry perspectives and feedback on these revised criteria, ensuring that the new criteria inform research, clinical trial design, and clinical care. In this article, we outline a summary of the newly proposed “Revised Criteria for Diagnosis and Staging of AD: AA Workgroup” and provide highlights from the AARR meeting in fall 2023. Highlights The Alzheimer's Association Research Roundtable (AARR) convened leaders from industry, academia, and government, to review the Revised Criteria for Diagnosis and Staging of AD: AA Workgroup, and gather industry perspectives and feedback on these revised criteria before its publication. The newly proposed revised criteria for diagnosis and staging of Alzheimer's disease (AD) by the AA's Workgroup represent a significant milestone, offering objective measures for the biological and staging of AD and bridging the gap between research and clinical care. Improving the availability and accuracy of biomarkers, especially blood‐based biomarkers (BBMs) is expected to improve clinical research and enhance the applicability of these criteria in clinical practice.

The article focuses on the accelerated progression of early-onset Alzheimer's disease (EOAD) due to cerebral amyloid angiopathy (CAA) in a 61-year-old woman. Topics include the patient's initial diagnosis of mild cognitive impairment, the role of CAA in cognitive decline and Alzheimer's disease, and the importance of monitoring risk factors like alcohol consumption and hypertension in EOAD cases.

Objective: Early Alzheimer's disease (AD) diagnosis remains challenging, necessitating specific biomarkers for timely detection. This study aimed to identify such biomarkers and explore their associations with cognitive decline. Methods: A cohort of 1759 individuals across cognitive aging stages, including healthy controls (HC), mild cognitive impairment (MCI), and AD, was examined. Utilizing nine biomarkers from structural MRI (sMRI), diffusion tensor imaging (DTI), and positron emission tomography (PET), predictions were made for Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale Sum of Boxes (CDRSB), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS). Biomarkers included four sMRI (e.g., average thickness [ATH]), four DTI (e.g., mean diffusivity [MD]), and one PET Amyloid-β (Aβ) measure. Ensemble regression tree (ERT) technique with bagging and random forest approaches were applied in four groups (HC/MCI, HC/AD, MCI/AD, and HC/MCI/AD). Results: Aβ emerged as a robust predictor of cognitive scores, particularly in late-stage AD. Volumetric measures, notably ATH, consistently correlated with cognitive scores across early and late disease stages. Additionally, ADAS demonstrated links to various neuroimaging biomarkers in all subject groups, highlighting its efficacy in monitoring brain changes throughout disease progression. ERT identified key brain regions associated with cognitive scores, such as the right transverse temporal region for Aβ, left and right entorhinal cortex, left inferior temporal gyrus, and left middle temporal gyrus for ATH, and the left uncinate fasciculus for MD. Conclusion: This study underscores the importance of an interdisciplinary approach in understanding AD mechanisms, offering potential contributions to early biomarker development. [ABSTRACT FROM AUTHOR]

Neurodegenerative disorders such as Alzheimer's disease (AD) represent an increasingly significant public health concern. As clinical diagnosis faces challenges, biomarkers are becoming increasingly important in research, trials, and patient assessments. While biomarkers like amyloid‐β peptide, tau proteins, CSF levels (Aβ, tau, and p‐tau), and neuroimaging techniques are commonly used in AD diagnosis, they are often limited and invasive in monitoring and diagnosis. For this reason, blood‐based biomarkers are the optimal choice for detecting neurodegeneration in brain diseases due to their noninvasiveness, affordability, reliability, and consistency. This literature review focuses on plasma neurofilament light (NfL) and CSF NfL as blood‐based biomarkers used in recent AD diagnosis. The findings revealed that the core CSF biomarkers of neurodegeneration (T‐tau, P‐tau, and Aβ42), CSF NFL, and plasma T‐tau were strongly associated with Alzheimer's disease, and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Elevated levels of plasma and cerebrospinal fluid NfL were linked to decreased [18F]FDG uptake in corresponding brain areas. In participants with Aβ positivity (Aβ+), NfL correlated with reduced metabolism in regions susceptible to Alzheimer's disease. In addition, CSF NfL levels correlate with brain atrophy and predict cognitive changes, while plasma total tau does not. Plasma P‐tau, especially in combination with Aβ42/Aβ40, is promising for symptomatic AD stages. Though not AD‐exclusive, blood NfL holds promise for neurodegeneration detection and assessing treatment efficacy. Given the consistent levels of T‐tau, P‐tau, Aβ42, and NFL in CSF, their incorporation into both clinical practice and research is highly recommended. [ABSTRACT FROM AUTHOR]

Background: Abnormal cerebrospinal fluid (CSF)/serum albumin ratio (Qalb) levels have been observed in patients with cognitive impairment. Few studies have specifically focused on Lewy Body Disease (LBD), and the results were controversial. Thus, we conducted this systematic review and meta-analysis to investigate Qalb levels in patients with LBD by including data from different studies. Method: We systematically searched PubMed, Embase, Cochrane Library, and Web of Science databases for a collection of studies containing studies comparing Qalb levels in patients with LBD and healthy controls (including healthy controls and other dementia subtypes). In the initial search, 86 relevant papers were retrieved. Standardized mean differences (SMD) in Qalb levels were calculated using a random effects model. Results: A total of 13 eligible studies were included. Mean Qalb levels were significantly higher in patients with LBD compared to healthy older adults [standardized mean difference (SMD): 2.95, 95% confidence interval (CI): 0.89-5.00, Z = 2.81, p = 0.005]; and were significantly higher in patients with LBD than in patients with Alzheimer's disease (AD) (SMD: 1.13, 95% CI: 0.42-1.83, Z = 3.15, p = 0.002);whereas mean Qalb levels were significantly higher in patients with frontotemporal lobar degeneration (FTLD) compared to those with AD (SMD: 1.13, 95% CI, 0.14-2.13, Z = 2.24, p = 0.03). Conclusion: Qalb levels were significantly elevated in LBD patients compared with normal older adults and were higher than those in AD patients and FTLD patients, which helped in the differential diagnosis of LBD from other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Background: Diabetes type II is one of the most serious health challenges as it affects both human and material resources. Due to its complication, there were varying degrees of disability, a decline in survival, a drop in living standards, and an increase in the financial load on the individual and family. Aim of study: evaluate the effect of health education program on elderly with diabetes type II for early detection of Alzheimer disease (Diabetes type III). Subjects and Method:-Study design: A quasi-experimental study Subjects: A convenience sample of 105 elderly with diabetes type II data Tools collection: Tool (I): A structured schedule. Part I: Bio-socio-demographic features of elderly with type II diabetes. Part II: Medical history. Part III: Knowledge of the elderly patient about type II diabetes (Diabetic Knowledge Test DKT). Tool (II): Diabetes Self-Management Questionnaire (DSMQ) Tool III: King’s Health Questionnaire (KHQ) Tool (IV): Mini – Mental State Examination (MMSE). Tool (V): physical assessment. Results: Greater than two-thirds of the studied elderly has lower level of knowledge about diabetes before intervention while two-thirds of elderly acquired high level of knowledge about it after intervention. Most of the studied elderly had low level of knowledge about Alzheimer disease before intervention while almost threequarters of them had high level of knowledge about it after intervention. Conclusion and recommendation: greater incidence of minor cognitive impairment going undetected in type II diabetics. Efforts should be done to enhance knowledge and practical skills of the diabetic patients and their family. [ABSTRACT FROM AUTHOR]

Introduction: Low educational attainment is a potential risk factor for Alzheimer's disease (AD) development. Alpha-secretase ADAM10 plays a central role in AD pathology, attenuating the formation of beta-amyloid peptides and, therefore, their aggregation into senile plaques. This study seeks to investigate ADAM10 as a blood-based biomarker in mild cognitive impairment (MCI) and AD in a diverse group of community-dwelling older adults, focusing on those with limited educational attainment. Methods: Participants were recruited from public health services. Cognition was evaluated using Mini-Mental State Examination (MMSE) and Addenbrooke's Cognitive Examination – Revised (ACE-R) batteries. Blood samples were collected to analyze plasma ADAM10 levels. A logistic regression was conducted to verify the influence of plasma ADAM10 on the AD diagnosis. Results: Significant differences in age, years of education, prescribed medications, and cognitive test scores were found between the MCI and AD groups. Regarding cognitive performance, both ACE-R and MMSE scores displayed significant differences between groups, with post hoc analyses highlighting these distinctions, particularly between AD and cognitively unimpaired individuals. Elevated plasma ADAM10 levels were associated with a 4.5-fold increase in the likelihood of a diagnosis of MCI and a 5.9-fold increase in the likelihood of a diagnosis of AD. These findings suggest ADAM10 levels in plasma as a valuable biomarker for assessing cognitive status in older individuals with low education attainment. Conclusion: This study underscores the potential utility of plasma ADAM10 levels as a blood-based biomarker for cognitive status, especially in individuals with low educational backgrounds, shedding light on their relevance in AD development and diagnosis. [ABSTRACT FROM AUTHOR]

The worldwide incidence of cognitive impairment is escalating, yet no effective solutions for these afflictions have been discovered. Consequently, the importance of early identification and immediate intervention is heightened. Advanced eye movements--a form of voluntary eye movements that includes anti-saccades, memory-guided saccades, predictive saccades, pro-saccades and gap/overlap saccades, mediated by the cerebral cortex and subcortical pathways reflect cognitive levels and functions across different domains. In view of their objectivity, reproducibility, and non-invasive characteristics, advanced eye movement examination possesses significant prospective utility across a wide range of cognitive impairment. This paper extensively reviews various models associated with advanced eye movement examinations and their current applications in cognitive impairment such as Alzheimer's disease, Lewy body dementia and frontotemporal dementia. Advanced eye movement examination can serve as a biomarker for early screening diagnosis and research on cognitive impairment. In the future, combining advanced eye movement examination with neuropsychological scale assessment and other diagnostic methods may contribute to further early identification of these types of diseases. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease, with a complex pathogenesis and an irreversible course. Therefore, the early diagnosis of AD is particularly important for the intervention, prevention, and treatment of the disease. Based on the different pathophysiological mechanisms of AD, the research progress of biofluid biomarkers are classified and reviewed. In the end, the challenges and perspectives of future research are proposed. [ABSTRACT FROM AUTHOR]

Introduction: Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring. Methods: A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated. Results: The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups. Discussion: This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), referring to a gradual deterioration in cognitive function, including memory loss and impaired thinking skills, has emerged as a substantial worldwide challenge with profound social and economic implications. As the prevalence of AD continues to rise and the population ages, there is an imperative demand for innovative imaging techniques to help improve our understanding of these complex conditions. Photoacoustic (PA) imaging forms a hybrid imaging modality by integrating the high-contrast of optical imaging and deep-penetration of ultrasound imaging. PA imaging enables the visualization and characterization of tissue structures and multifunctional information at high resolution and, has demonstrated promising preliminary results in the study and diagnosis of AD. This review endeavors to offer a thorough overview of the current applications and potential of PA imaging on AD diagnosis and treatment. Firstly, the structural, functional, molecular parameter changes associated with AD-related brain imaging captured by PA imaging will be summarized, shaping the diagnostic standpoint of this review. Then, the therapeutic methods aimed at AD is discussed further. Lastly, the potential solutions and clinical applications to expand the extent of PA imaging into deeper AD scenarios is proposed. While certain aspects might not be fully covered, this mini-review provides valuable insights into AD diagnosis and treatment through the utilization of innovative tissue photothermal effects. We hope that it will spark further exploration in this field, fostering improved and earlier theranostics for AD. [ABSTRACT FROM AUTHOR]

Aim: To determine the effectiveness of a caregiver education (needs tailored) and telephone follow‐up intervention for caregivers of people with moderate stage Alzheimer's disease on caregiver burden, caregiving impact on life, and patients' neuropsychiatric symptoms, dependence on activities of daily living. Methods: This quasi‐experimental study sampled caregivers of people with moderate stage Alzheimer's. Caregivers in the intervention group received education and telephone follow‐up over 12 weeks, while the control group received routine care. Caregivers were assessed for burden, changes in life, and patients for neuropsychiatric symptoms, and dependence on daily living activities. Results: The caregiver burden, life changes, distress, and patients' neuropsychiatric symptom scores showed apparent trend toward betterment, but no statistically significant differences were found in study outcomes between the two groups (P >.05). Conclusion: The caregiver need‐based, structured education and telephone follow‐up intervention was not empirically effective. With the promising effect from this study, managing behavioral symptoms with need‐based, structured, and skill‐oriented training has the potential to alleviate the burden on caregivers. [ABSTRACT FROM AUTHOR]

BACKGROUND: Epidemiological evidence suggests air pollution adversely affects cognition and increases the risk of Alzheimer’s disease (AD), but little is known about the biological effects of fine particulate matter (PM2.5, particulate matter with aerodynamic diameter ≤2.5 μm) on early predictors of future disease risk. OBJECTIVES: We investigated the association between 1-, 3-, and 5-y exposure to ambient and traffic-related PM2.5 and cerebrospinal fluid (CSF) biomarkers of AD. METHODS: We conducted a cross-sectional analysis using data from 1,113 cognitively healthy adults (45-75 y of age) from the Emory Healthy Brain Study in Georgia in the United States. CSF biomarker concentrations of Aβ42, tTau, and pTau, were collected at enrollment (2016-2020) and analyzed with the Roche Elecsys system. Annual ambient and traffic-related residential PM2.5 concentrations were estimated at a 1-km and 250-m resolution, respectively, and computed for each participant's geocoded address, using three exposure time periods based on specimen collection date. Associations between PM2.5 and CSF biomarker concentrations, considering continuous and dichotomous (dichotomized at clinical cutoffs) outcomes, were estimated with multiple linear/logistic regression, respectively, controlling for potential confounders (age, gender, race, ethnicity, body mass index, and neighborhood socioeconomic status). RESULTS: Interquartile range (IQR; IQR=0.845) increases in 1-y [β:-0.101; 95% confidence interval (CI): -0.18, -0.02] and 3-y (β:-0.078; 95% CI: -0.15, -0.00) ambient PM2.5 exposures were negatively associated with Aβ42 CSF concentrations. Associations between ambient PM2.5 and Aβ42 were similar for 5- y estimates (β:-0.076; 95% CI: -0.160, 0.005). Dichotomized CSF variables revealed similar associations between ambient PM2.5 and Aβ42. Associations with traffic-related PM2.5 were similar but not significant. Associations between PM2.5 exposures and tTau, pTau tTau/Aβ42, or pTau/Aβ42 levels were mainly null. CONCLUSION: In our study, consistent trends were found between 1-y PM2.5 exposure and decreased CSF Aβ42, which suggests an accumulation of amyloid plaques in the brain and an increased risk of developing AD. [ABSTRACT FROM AUTHOR]

Objectives: We previously analysed the preparedness to deliver a disease-modifying Alzheimer's treatment in the United Kingdom and predicted substantial wait times. This study updates the prediction for the National Health Service (NHS) in England, using an improved model and newer data. Methods: We reviewed published data on capacity for diagnosis of cognitive impairment combined with expert input and constructed a model for wait times to access from 2023 to 2043. The model tracks patients from initial evaluation in primary care, cognitive testing by a dementia specialist, confirmatory biomarker testing with positron emission tomography (PET) scans or examination of cerebrospinal fluid and infusion delivery. Capacity for specialist visits and PET scans are assumed to be capacity constrained, and cerebrospinal fluid testing and infusion delivery to be scalable. Results: Capacity constraints were projected to result in substantial wait times: patients referred to specialists based on a brief cognitive test, which is the current standard of care, would expect an overall initial wait times of 56 months in 2023, increasing to 129 months in 2029 and then falling slowly to around 100 months. Use of a blood test for the confirmation of Alzheimer's pathology as an additional triage step, would reduce wait times to around 17 to 25 months. Discussion: The NHS England lacks capacity to provide timely access to a disease-modifying treatment, which is estimated to result in significant wait times and potentially avoidable disease progression. Better diagnostic tools at initial evaluation may reduce delays. [ABSTRACT FROM AUTHOR]

Purpose: Single-subject voxel-based morphometry (VBM) compares an individual T1-weighted MRI to a sample of normal MRI in a normative database (NDB) to detect regional atrophy. Outliers in the NDB might result in reduced sensitivity of VBM. The primary aim of the current study was to propose a method for outlier removal ("NDB cleaning") and to test its impact on the performance of VBM for detection of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). Methods: T1-weighted MRI of 81 patients with biomarker-confirmed AD (n = 51) or FTLD (n = 30) and 37 healthy subjects with simultaneous FDG-PET/MRI were included as test dataset. Two different NDBs were used: a scanner-specific NDB (37 healthy controls from the test dataset) and a non-scanner-specific NDB comprising 164 normal T1-weighted MRI from 164 different MRI scanners. Three different quality metrics based on leave-one-out testing of the scans in the NDB were implemented. A scan was removed if it was an outlier with respect to one or more quality metrics. VBM maps generated with and without NDB cleaning were assessed visually for the presence of AD or FTLD. Results: Specificity of visual interpretation of the VBM maps for detection of AD or FTLD was 100% in all settings. Sensitivity was increased by NDB cleaning with both NDBs. The effect was statistically significant for the multiple-scanner NDB (from 0.47 [95%-CI 0.36–0.58] to 0.61 [0.49–0.71]). Conclusion: NDB cleaning has the potential to improve the sensitivity of VBM for the detection of AD or FTLD without increasing the risk of false positive findings. [ABSTRACT FROM AUTHOR]

Purpose: To assess the performance of the inferior lateral ventricle (ILV) to hippocampal (Hip) volume ratio on brain MRI, for Alzheimer's disease (AD) diagnostics, comparing it to individual automated ILV and hippocampal volumes, and visual medial temporal lobe atrophy (MTA) consensus ratings. Methods: One-hundred-twelve subjects (mean age ± SD, 66.85 ± 13.64 years) with varying degrees of cognitive decline underwent MRI using a Philips Ingenia 3T. The MTA scale by Scheltens, rated on coronal 3D T1-weighted images, was determined by three experienced radiologists, blinded to diagnosis and sex. Automated volumetry was computed by icobrain dm (v. 5.10) for total, left, right hippocampal, and ILV volumes. The ILV/Hip ratio, defined as the percentage ratio between ILV and hippocampal volumes, was calculated and compared against a normative reference population (n = 1903). Inter-rater agreement, association, classification accuracy, and clinical interpretability on patient level were reported. Results: Visual MTA scores showed excellent inter-rater agreement. Ordinal logistic regression and correlation analyses demonstrated robust associations between automated brain segmentations and visual MTA ratings, with the ILV/Hip ratio consistently outperforming individual hippocampal and ILV volumes. Pairwise classification accuracy showed good performance without statistically significant differences between the ILV/Hip ratio and visual MTA across disease stages, indicating potential interchangeability. Comparison to the normative population and clinical interpretability assessments showed commensurability in classifying MTA "severity" between visual MTA and ILV/Hip ratio measurements. Conclusion: The ILV/Hip ratio shows the highest correlation to visual MTA, in comparison to automated individual ILV and hippocampal volumes, offering standardized measures for diagnostic support in different stages of cognitive decline. [ABSTRACT FROM AUTHOR]

Background and Objectives Persons with dementia and their care partners have known risks for loneliness and social isolation throughout the disease trajectory, yet little is described about social lives in a population heterogeneous for disease stage, syndrome type, and setting. Research Design and Methods We conducted a secondary analysis of qualitative interviews from multiple studies to triangulate responses from a cohort of persons with dementia (n  = 24), and active (n  = 33) or bereaved (n  = 15) care partners diverse in setting, dementia type and stage, and life experience. Interviews explored challenges related to social lives and were analyzed thematically. Results Persons with dementia were on average 80 years old (range: 67–94), 38% female, and 78% diagnosed with Alzheimer's dementia; care partners were on average 67 years old (range: 40–87) and 69% female. We identified 3 primary themes. First, dyads lost external social networks due to complex factors, including discomfort of surrounding social networks, caregiving responsibilities, and progressive cognitive deficits. Second, care partners described disruptions of meaningful dyadic relationships due to progressive cognitive and functional deficits, leading to loneliness and anticipatory grief. Third, adaptive strategies centered on care partners facilitating shared social activities and programs addressing caregiver burden. An overarching theme of disease–course accumulation of barriers to social interactions and constant adaptations was present in all themes. Discussion and Implications Isolation and loneliness are a shared experience and source of distress for persons with dementia and care partners. Results can inform interventions tailored to individual needs and disease stages of dyads that enhance social connectedness. [ABSTRACT FROM AUTHOR]

The precise identification of Alzheimer's disease and other prevalent neurodegenerative diseases remains a difficult issue that requires the development of early detection of the disease and inexpensive biomarkers that can replace the present cerebrospinal fluid and imaging biomarkers. Blood biomarkers, such as amyloid and neurofilament light, have been emphasized as an important and practical tool in a testing or examination procedure thanks to advancements in ultra‐sensitive detection techniques. Although saliva is not currently being researched for neurodegenerative diseases, it is an important source of biomarkers that can be used for the identification of diseases and has some advantages over other biofluids. While this may be true for most people, getting saliva from elderly people presents some significant challenges. In this overview, we will first discuss how saliva is created and how aging‐related illnesses may affect the amount and kind of saliva produced. The findings support the use of salivary amyloid protein, tau species, and novel biomarkers in the diagnosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is considered a promising tool to aid and accelerate diagnosis in various medical areas, including neuroimaging. However, its success is set back by the lack of large-scale public datasets. Indeed, medical institutions possess a large amount of data; however, open-sourcing is prevented by the legal requirements to protect the patient’s privacy. Federated Learning (FL) is a viable alternative that can overcome this issue. This work proposes training an ML model for Alzheimer’s Disease (AD) detection based on structural MRI (sMRI) data in a federated setting. We implement two aggregation algorithms, Federated Averaging (FedAvg) and Secure Aggregation (SecAgg), and compare their performance with the centralized ML model training. We simulate heterogeneous environments and explore the impact of demographical (sex, age, and diagnosis) and imbalanced data distributions. The simulated heterogeneous environments allow us to observe these statistical dierences’ eect on the ML models trained using FL and highlight the importance of studying such dierences when training ML models for AD detection. Moreover, as part of the evaluation, we demonstrate the increased privacy guarantees of FL with SecAgg via simulated membership inference attacks. [ABSTRACT FROM AUTHOR]

Alzheimer disease (AD) is the most common cause of dementia worldwide. Its clinical manifestations include a progressive loss of memory and other cognitive domains, as well as brain atrophy. An elevated homocysteine level (>15 µmol/L), known as hyperhomocysteinemia, is also an attributing risk factor for AD, vascular pathologies, and brain atrophy. Neuroimaging studies including T2-weighted magnetic resonance imaging scans revealed white matter hyperintensities in the periventricular and deep white matter, enlarged ventricles, widened sulci, and decreased white matter mass, which are features of aging, as well as cerebrovascular changes. This case series investigated changes in biochemical marker levels including serum homocysteine, folate, and vitamin B12, and the degree of atrophic variations in cortical-subcortical white matter in AD. The present study hypothesized that serum homocysteine levels might be used as a surrogate marker to screen for AD at an earlier stage. [ABSTRACT FROM AUTHOR]

The nervous system possesses bidirectional, sophisticated and delicate communications with the immune system. These neuroimmune interactions play a vitally important role in the initiation and development of many disorders, especially neurodegenerative diseases. Although scientific advancements have made tremendous progress in this field during the last few years, neuroimmune communications are still far from being elucidated. By organizing recent research, in this review, we discuss the local and intersystem neuroimmune interactions and their roles in Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. Unveiling these will help us gain a better understanding of the process of interplay inside the body and how the organism maintains homeostasis. It will also facilitate a view of the diseases from a holistic, pluralistic and interconnected perspective, thus providing a basis of developing novel and effective methods to diagnose, intervene and treat diseases. [ABSTRACT FROM AUTHOR]

Background: Here, we aimed to investigate the roles of long‐term potentiation‐like (LTP‐like) plasticity using intermittent theta burst (iTBS) protocol and resting motor threshold (rMT) in the differential diagnosis of Alzheimer's disease (AD), diffuse dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Method: We enrolled 21 subjects with AD, 28 subjects with DLB, 14 subjects with FTD, and 33 elderly subjects with normal cognitive functions into the study. We recorded rMT and percentage amplitude change of motor evoked potentials (MEPs) after the iTBS protocol in each group. Results: In patients with AD and DLB, the percentage amplitude change of MEPs, and rMTs were significantly lower than in healthy subjects. However, no significant difference was observed in individuals with FTD. Conclusion: Our findings showed that transcranial magnetic stimulation measures, particularly rMTs and LTP‐like plasticity, may be potential biomarkers to distinguish between different dementia subtypes. Impaired motor cortical excitability and synaptic plasticity were more prominent in AD and DLB than in FTD. This aligns with the evidence that cortical motor networks are usually spared in FTDs in early‐to‐middle stages. [ABSTRACT FROM AUTHOR]

Introduction: Cognitive screening measures are an established part of a dementia assessment and often include measures of subjective difficulties, e.g., Everyday Memory Questionnaire (EMQ), informant-rated difficulty, Cambridge Behavioural Inventory – Revised (CBI-R), and objective cognitive assessments like Addenbrooke's Cognitive Examination (ACE-III). While these measures have validity for the purpose of diagnosing dementia, in clinical practice they are often used outside of their evidence base for the purpose of cognitive re-assessment. The current study sought to evaluate the psychometric properties for the repeated use of these assessments. Methods: This study used a longitudinal design, which included 49 healthy controls, 33 people with subjective memory difficulties, and 10 people with Alzheimer's disease (AD) being assessed twice, with approximately a 1-year interval between assessments. Results: The EMQ has adequate re-test reliability (r = 0.78), but people with an AD diagnosis rated their memory as better than those with SMD, making it unsuitable as a measure. The CBI-R had moderate re-test reliability (r = 0.62). However, deterioration on the CBI-R was not useful for diagnosing AD. The ACE-III has high re-test reliability (r = 0.89). A change of five was associated with reasonable classification accuracy for identifying AD and achieved statistical significance. Conclusion: Using a 1-year interval, of the three measures used in this study, only the ACE-III total score may be a useful measure of change over time, although it should be applied cautiously due to the lack of representativeness of the sample, and change scores should always be triangulated with other forms of evidence of deterioration. [ABSTRACT FROM AUTHOR]