Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010

Current biomarkers and treatment strategies in Alzheimer disease: An overview and future perspectives

Alzheimer's disease (AD), a progressive degenerative disorder first identified by Alois Alzheimer in 1907, poses a significant public health challenge. Despite its prevalence and impact, there is currently no definitive ante mortem diagnosis for AD pathogenesis. By 2050, the United States may face a staggering 13.8 million AD patients. This review provides a concise summary of current AD biomarkers, available treatments, and potential future therapeutic approaches. The review begins by outlining existing drug targets and mechanisms in AD, along with a discussion of current treatment options. We explore various approaches targeting Amyloid β (Aβ), Tau Protein aggregation, Tau Kinases, Glycogen Synthase kinase-3β, CDK-5 inhibitors, Heat Shock Proteins (HSP), oxidative stress, inflammation, metals, Apolipoprotein E (ApoE) modulators, and Notch signaling. Additionally, we examine the historical use of Estradiol (E2) as an AD therapy, as well as the outcomes of Randomized Controlled Trials (RCTs) that evaluated antioxidants (e.g., vitamin E) and omega-3 polyunsaturated fatty acids as alternative treatment options. Notably, positive effects of docosahexaenoic acid nutriment in older adults with cognitive impairment or AD are highlighted. Furthermore, this review offers insights into ongoing clinical trials and potential therapies, shedding light on the dynamic research landscape in AD treatment.

Relationship Between Cerebrospinal Fluid Alzheimer's Disease Biomarker Values Measured via Lumipulse Assays and Conventional ELISA: Single-Center Experience and Systematic Review

Background

Although Lumipulse assays and conventional ELISA are strongly correlated, the precise relationship between their measured values remains undetermined.

Objective

To determine the relationship between Lumipulse and ELISA measurement values.

Methods

Patients who underwent cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarker measurements and consented to biobanking between December 2021 and June 2023 were included. The relationship between values measured via Lumipulse assays and conventional ELISA were evaluated by Passing-Bablok analyses for amyloid-β 1-42 (Aβ 42), total tau (t-tau), and phospho-tau 181 (p-tau 181). Studies using both assays were systematically searched for in PubMed and summarized after quality assessment.

Results

Regression line slopes and intercepts were 1.41 (1.23 to 1.60) and -77.8 (-198.4 to 44.5) for Aβ 42, 0.94 (0.88 to 1.01) and 98.2 (76.9 to 114.4) for t-tau, and 1.60 (1.43 to 1.75) and -21.1 (-26.9 to -15.6) for p-tau181. Spearman's correlation coefficients were 0.90, 0.95, and 0.95 for Aβ 42, t-tau, and p-tau181, respectively. We identified 13 other studies that included 2,117 patients in total. Aβ 42 slope varied among studies, suggesting inter-lab difference of ELISA. The slope and intercept of t-tau were approximately 1 and 0, respectively, suggesting small proportional and systematic differences. Conversely, the p-tau181 slope was significantly higher than 1, distributed between 1.5-2 in most studies, with intercepts significantly lower than 0, suggesting proportional and systematic differences.

Conclusions

We characterized different relationship between measurement values for each biomarker, which may be useful for understanding the differences in CSF biomarker measurement values on different platforms and for future global harmonization.

Using principal component analysis to determine which vestibular stimuli provide best biomarkers for separating Alzheimer's from mixed Alzheimer's disease

Alzheimer's disease (AD) is often mixed with cerebrovascular disease (AD-CVD). Heterogeneity of dementia etiology and the overlapping of neuropathological features of AD and AD-CVD make feature identification of the two challenging. Separation of AD from AD-CVD is important as the optimized treatment for each group may differ. Recent studies using vestibular responses recorded from electrovestibulography (EVestG™) have offered promising results for separating these two pathologies. An EVestG measurement records responses to several different physical stimuli (called tilts). In previous research, the number of EVestG features from different tilts was selected based on physiological intuition to classify AD from AD-CVD. As the number of potential characteristic features from all tilts can be very large, in this study, we used an algorithm based on principal component analysis (PCA) to rank the most effective vestibular stimuli for differentiating AD from AD-CVD. Analyses were performed on the EVestG signals of 28 individuals with AD and 24 with AD-CVD. The results of this study showed that tilts simulating the otolithic organs (utricle and saccule) generated the most characteristic features for separating AD from AD-CVD.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.

Performance of SOBA-AD blood test in discriminating Alzheimer's disease patients from cognitively unimpaired controls in two independent cohorts

Amyloid-beta (Aβ) toxic oligomers are critical early players in the molecular pathology of Alzheimer's disease (AD). We have developed a Soluble Oligomer Binding Assay (SOBA-AD) for detection of these Aβ oligomers that contain α-sheet secondary structure that discriminates plasma samples from patients on the AD continuum from non-AD controls. We tested 265 plasma samples from two independent cohorts to investigate the performance of SOBA-AD. Testing was performed at two different sites, with different personnel, reagents, and instrumentation. Across two cohorts, SOBA-AD discriminated AD patients from cognitively unimpaired (CU) subjects with 100% sensitivity, > 95% specificity, and > 98% area under the curve (AUC) (95% CI 0.95-1.00). A SOBA-AD positive readout, reflecting α-sheet toxic oligomer burden, was found in AD patients, and not in controls, providing separation of the two populations, aside from 5 SOBA-AD positive controls. Based on an earlier SOBA-AD study, the Aβ oligomers detected in these CU subjects may represent preclinical cases of AD. The results presented here support the value of SOBA-AD as a promising blood-based tool for the detection and confirmation of AD.

Point prevalence of epilepsy in dementia: A "real-world" estimate

OBJECTIVE

Several studies have demonstrated a higher frequency of seizures and epilepsy in Alzheimer's disease and other forms of dementia as compared with healthy elderly individuals. However, incidence and prevalence of epilepsy in the general population of dementia are unknown since most previous studies were performed in secondary-tertiary referral centres. In addition, all prior studies but one provided "period" rather than "point" prevalence estimates.

METHODS

We assessed point prevalence estimate of epileptic manifestations requiring antiepileptic medication in patients Alzheimer's disease, vascular dementia, and fronto-temporal dementia from a secondary clinical setting.

RESULTS

Point prevalence estimates were 6.4% (95% CI: 1.5 to 11.3) in Alzheimer's disease, 8.9% (95% CI: 1.4 to 16.4), in vascular dementia, and 6% (95% CI: 1.3 to 10.7) in fronto-temporal dementia, rates that were greater than those observed in the healthy elderly population. Regardless of the etiology of dementia, epilepsy was characterized by unprovoked seizures that lacked distinguishing clinical features.

SIGNIFICANCE

These findings support epilepsy as part of the spectrum of dementia. The similar point prevalence of definite epilepsy requiring AED treatment in Alzheimer's disease and non Alzheimer dementias raised the possibility of similar underlying mechanism of epileptogenesis. Although this was not a population-based study, accurate point prevalence data from clinic setting would be important to better define the burden of epilepsy in dementia and the demands on health services to manage the condition.

Clinical value of Alzheimer's disease biomarker testing

INTRODUCTION

In the Investigating the Impact of Alzheimer's Disease Diagnostics in British Columbia (IMPACT-AD BC) study, we aimed to understand how Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarker testing-used in medical care-impacted medical decision-making (medical utility), personal decision-making (personal utility), and health system economics.

METHODS

The study was designed as an observational, longitudinal cohort study. A total of 149 patients were enrolled between February 2019 and July 2021. Patients referred to memory clinics were approached to participate if their dementia specialist ordered AD CSF biomarker testing as part of their routine medical care, and the clinical scenario met the appropriate use criteria for lumbar puncture and AD CSF biomarker testing. For the medical utility pillar, detailed clinical management plans were collected via physician questionnaires pre- and post-biomarker disclosure.

RESULTS

Patients with completed management questionnaires (n = 142) had a median age of 64 (interquartile range: 59-69) years, 48% were female, and 60% had CSF biomarker profiles on the AD continuum. Clinical management changed in 89.4% of cases. AD biomarker testing was associated with decreased need for other diagnostic procedures, including brain imaging (-52.0%) and detailed neuropsychological assessments (-63.2%), increased referrals and counseling (57.0%), and guided AD-related drug prescriptions (+88.4% and -50.0% in biomarker-positive and -negative cases, respectively).

DISCUSSION

AD biomarker testing was associated with significant and positive changes in clinical management, including decreased health care resource use, therapy optimization, and increased patient and family member counseling. While certain changes in management were linked to the AD biomarker profile (e.g., referral to clinical trials), the majority of changes were independent of baseline clinical presentation and level of cognitive impairment, demonstrating a broad value for AD biomarker testing in individuals meeting the appropriate use criteria for testing.

Is Alzheimer disease a disease?

Dementia, a prevalent condition among older individuals, has profound societal implications. Extensive research has resulted in no cure for what is perceived as the most common dementing illness: Alzheimer disease (AD). AD is defined by specific brain abnormalities - amyloid-β plaques and tau protein neurofibrillary tangles - that are proposed to actively influence the neurodegenerative process. However, conclusive evidence of amyloid-β toxicity is lacking, the mechanisms leading to the accumulation of plaques and tangles are unknown, and removing amyloid-β has not halted neurodegeneration. So, the question remains, are we making progress towards a solution? The complexity of AD is underscored by numerous genetic and environmental risk factors, and diverse clinical presentations, suggesting that AD is more akin to a syndrome than to a traditional disease, with its pathological manifestation representing a convergence of pathogenic pathways. Therefore, a solution requires a multifaceted approach over a single 'silver bullet'. Improved recognition and classification of conditions that converge in plaques and tangle accumulation and their treatment requires the use of multiple strategies simultaneously.

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

Microglia measured by TSPO PET are associated with Alzheimer's disease pathology and mediate key steps in a disease progression model

INTRODUCTION

Evidence suggests microglial activation precedes regional tau and neurodegeneration in Alzheimer's disease (AD). We characterized microglia with translocator protein (TSPO) positron emission tomography (PET) within an AD progression model where global amyloid beta (Aβ) precedes local tau and neurodegeneration, resulting in cognitive impairment.

METHODS

Florbetaben, PBR28, and MK-6240 PET, T1 magnetic resonance imaging, and cognitive measures were performed in 19 cognitively unimpaired older adults and 22 patients with mild cognitive impairment or mild AD to examine associations among microglia activation, Aβ, tau, and cognition, adjusting for neurodegeneration. Mediation analyses evaluated the possible role of microglial activation along the AD progression model.

RESULTS

Higher PBR28 uptake was associated with higher Aβ, higher tau, and lower MMSE score, independent of neurodegeneration. PBR28 mediated associations between tau in early and middle Braak stages, between tau and neurodegeneration, and between neurodegeneration and cognition.

DISCUSSION

Microglia are associated with AD pathology and cognition and may mediate relationships between subsequent steps in AD progression.

Biomarker-based staging of Alzheimer disease: rationale and clinical applications

Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.

Neuropathology, Neuroimaging, and Fluid Biomarkers in Alzheimer's Disease

An improved understanding of the pathobiology of Alzheimer's disease (AD) should lead ultimately to an earlier and more accurate diagnosis of AD, providing the opportunity to intervene earlier in the disease process and to improve outcomes. The known hallmarks of Alzheimer's disease include amyloid-β plaques and neurofibrillary tau tangles. It is now clear that an imbalance between production and clearance of the amyloid beta protein and related Aβ peptides, especially Aβ42, is a very early, initiating factor in Alzheimer's disease (AD) pathogenesis, leading to aggregates of hyperphosphorylation and misfolded tau protein, inflammation, and neurodegeneration. In this article, we review how the AD diagnostic process has been transformed in recent decades by our ability to measure these various elements of the pathological cascade through the use of imaging and fluid biomarkers. The more recently developed plasma biomarkers, especially phosphorylated-tau217 (p-tau217), have utility for screening and diagnosis of the earliest stages of AD. These biomarkers can also be used to measure target engagement by disease-modifying therapies and the response to treatment.

Global cognitive trajectory patterns in Alzheimer's disease

OBJECTIVES

The literature on Alzheimer's disease (AD) provides little data about long-term cognitive course trajectories. We identify global cognitive outcome trajectories and associated predictor variables that may inform clinical research and care.

DESIGN

Data derived from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set were used to examine the cognitive course of persons with possible or probable AD, a Mini-Mental State Examination (MMSE) of ≥10, and complete annual assessments for 5 years.

SETTING

Thirty-six Alzheimer's Disease Research Centers.

PARTICIPANTS

Four hundred and fourteen persons.

MEASUREMENTS

We used a hybrid approach comprising qualitative analysis of MMSE trajectory graphs that were operationalized empirically and binary logistic regression analyses to assess 19 variables' associations with each trajectory. MMSE scores of ±3 points or greater were considered clinically meaningful.

RESULTS

Five distinct cognitive trajectories were identified: fast decliners (32.6%), slow decliners (30.7%), zigzag stable (15.9%), stable (15.9%), and improvers (4.8%). The decliner groups had three subtypes: curvilinear, zigzag, and late decline. The fast decliners were associated with female gender, lower baseline MMSE scores, a shorter illness duration, or receiving a cognitive enhancer. An early MMSE decline of ≥3 points predicted a worse outcome. A higher rate of traumatic brain injury, the absence of an ApoE ϵ4 allele, and male gender were the strongest predictors of favorable outcomes.

CONCLUSIONS

Our hybrid approach revealed five distinct cognitive trajectories and a variegated pattern within the decliners and stable/improvers that was more consistent with real-world clinical experience than prior statistically modeled studies. Future investigations need to determine the consistency of the distribution of these categories across settings.

Predictors and outcomes of fluctuations in the clinical dementia rating scale

INTRODUCTION

Reversion, or change in cognitive status from impaired to normal, is common in aging and dementia studies, but it remains unclear what factors predict reversion.

METHODS

We investigated whether reverters, defined as those who revert from a Clinical Dementia Rating® (CDR®) scale score of 0.5 to CDR 0) differed on cognition and biomarkers from unimpaired participants (always CDR 0) and impaired participants (converted to CDR > 0 and had no reversion events). Models evaluated relationships between biomarker status, apolipoprotein E (APOE) ε4 status, and cognition. Additional models described predictors of reversion and predictors of eventual progression to CDR > 0.

RESULTS

CDR reversion was associated with younger age, better cognition, and negative amyloid biomarker status. Reverters that eventually progressed to CDR > 0 had more visits, were older, and were more likely to have an APOE ε4 allele.

DISCUSSION

CDR reversion occupies a transitional phase in disease progression between cognitive normality and overt dementia. Reverters may be ideal candidates for secondary prevention Alzheimer's disease (AD) trials.

HIGHLIGHTS

Reverters had more longitudinal cognitive decline than those who remained cognitively normal. Predictors of reversion: younger age, better cognition, and negative amyloid biomarker status. Reverting from CDR 0.5 to 0 is a risk factor for future conversion to CDR > 0. CDR reversion may be a transitional phase in Alzheimer's Disease progression. CDR reverters may be ideal for Alzheimer's disease secondary prevention trials.

New horizons in the diagnosis and management of Alzheimer's Disease in older adults

Alzheimer's Disease (ad) is the most common cause of dementia, and in addition to cognitive decline, it directly contributes to physical frailty, falls, incontinence, institutionalisation and polypharmacy in older adults. Increasing availability of clinically validated biomarkers including cerebrospinal fluid and positron emission tomography to assess both amyloid and tau pathology has led to a reconceptualisation of ad as a clinical-biological diagnosis, rather than one based purely on clinical phenotype. However, co-pathology is frequent in older adults which influence the accuracy of biomarker interpretation. Importantly, some older adults with positive amyloid or tau pathological biomarkers may never experience cognitive impairment or dementia. These strides towards achieving an accurate clinical-biological diagnosis are occurring alongside recent positive phase 3 trial results reporting statistically significant effects of anti-amyloid Disease-Modifying Therapies (DMTs) on disease severity in early ad. However, the real-world clinical benefit of these DMTs is not clear and concerns remain regarding how trial results will translate to real-world clinical populations, potential adverse effects (including amyloid-related imaging abnormalities), which can be severe and healthcare systems readiness to afford and deliver potential DMTs to appropriate populations. Here, we review recent advances in both clinical-biological diagnostic classification and future treatment in older adults living with ad. Advocating for access to both more accurate clinical-biological diagnosis and potential DMTs must be done so in a holistic and gerontologically attuned fashion, with geriatricians advocating for enhanced multi-component and multi-disciplinary care for all older adults with ad. This includes those across the ad severity spectrum including older adults potentially ineligible for emerging DMTs.

Demographic, clinical, biomarker, and neuropathological correlates of posterior cortical atrophy: an international cohort study and individual participant data meta-analysis

BACKGROUND

Posterior cortical atrophy is a rare syndrome characterised by early, prominent, and progressive impairment in visuoperceptual and visuospatial processing. The disorder has been associated with underlying neuropathological features of Alzheimer's disease, but large-scale biomarker and neuropathological studies are scarce. We aimed to describe demographic, clinical, biomarker, and neuropathological correlates of posterior cortical atrophy in a large international cohort.

METHODS

We searched PubMed between database inception and Aug 1, 2021, for all published research studies on posterior cortical atrophy and related terms. We identified research centres from these studies and requested deidentified, individual participant data (published and unpublished) that had been obtained at the first diagnostic visit from the corresponding authors of the studies or heads of the research centres. Inclusion criteria were a clinical diagnosis of posterior cortical atrophy as defined by the local centre and availability of Alzheimer's disease biomarkers (PET or CSF), or a diagnosis made at autopsy. Not all individuals with posterior cortical atrophy fulfilled consensus criteria, being diagnosed using centre-specific procedures or before development of consensus criteria. We obtained demographic, clinical, biofluid, neuroimaging, and neuropathological data. Mean values for continuous variables were combined using the inverse variance meta-analysis method; only research centres with more than one participant for a variable were included. Pooled proportions were calculated for binary variables using a restricted maximum likelihood model. Heterogeneity was quantified using I2.

FINDINGS

We identified 55 research centres from 1353 papers, with 29 centres responding to our request. An additional seven centres were recruited by advertising via the Alzheimer's Association. We obtained data for 1092 individuals who were evaluated at 36 research centres in 16 countries, the other sites having not responded to our initial invitation to participate to the study. Mean age at symptom onset was 59·4 years (95% CI 58·9-59·8; I2=77%), 60% (56-64; I2=35%) were women, and 80% (72-89; I2=98%) presented with posterior cortical atrophy pure syndrome. Amyloid β in CSF (536 participants from 28 centres) was positive in 81% (95% CI 75-87; I2=78%), whereas phosphorylated tau in CSF (503 participants from 29 centres) was positive in 65% (56-75; I2=87%). Amyloid-PET (299 participants from 24 centres) was positive in 94% (95% CI 90-97; I2=15%), whereas tau-PET (170 participants from 13 centres) was positive in 97% (93-100; I2=12%). At autopsy (145 participants from 13 centres), the most frequent neuropathological diagnosis was Alzheimer's disease (94%, 95% CI 90-97; I2=0%), with common co-pathologies of cerebral amyloid angiopathy (71%, 54-88; I2=89%), Lewy body disease (44%, 25-62; I2=77%), and cerebrovascular injury (42%, 24-60; I2=88%).

INTERPRETATION

These data indicate that posterior cortical atrophy typically presents as a pure, young-onset dementia syndrome that is highly specific for underlying Alzheimer's disease pathology. Further work is needed to understand what drives cognitive vulnerability and progression rates by investigating the contribution of sex, genetics, premorbid cognitive strengths and weaknesses, and brain network integrity.

FUNDING

None.

Convolutional neural network based data interpretable framework for Alzheimer's treatment planning

Alzheimer's disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction. In this study, we developed a framework that utilizes multimodal data (tabular data, magnetic resonance imaging (MRI) images, and genetic information) to classify AD. As part of the pre-processing phase, we generated a knowledge graph from the tabular data and MRI images. We employed graph neural networks for knowledge graph creation, and region-based convolutional neural network approach for image-to-knowledge graph generation. Additionally, we integrated various explainable AI (XAI) techniques to interpret and elucidate the prediction outcomes derived from multimodal data. Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images. We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided. Genetic expression values play a crucial role in AD analysis. We used a graphical gene tree to identify genes associated with the disease. Moreover, a dashboard was designed to display XAI outcomes, enabling experts and medical professionals to easily comprehend the prediction results.

A multiclass extreme gradient boosting model for evaluation of transcriptomic biomarkers in Alzheimer's disease prediction

BACKGROUND

Patients with young-onset Alzheimer's disease (AD) (before the age of 50 years old) often lack obvious imaging changes and amyloid protein deposition, which can lead to misdiagnosis with other cognitive impairments. Considering the association between immunological dysfunction and progression of neurodegenerative disease, recent research has focused on identifying blood transcriptomic signatures for precise prediction of AD.

METHODS

In this study, we extracted blood biomarkers from large-scale transcriptomics to construct multiclass eXtreme Gradient Boosting models (XGBoost), and evaluated their performance in distinguishing AD from cognitive normal (CN) and mild cognitive impairment (MCI).

RESULTS

Independent testing with external dataset revealed that the combination of blood transcriptomic signatures achieved an area under the receiver operating characteristic curve (AUC of ROC) of 0.81 for multiclass classification (sensitivity = 0.81; specificity = 0.63), 0.83 for classification of AD vs. CN (sensitivity = 0.72; specificity = 0.73), and 0.85 for classification of AD vs. MCI (sensitivity = 0.77; specificity = 0.73). These candidate signatures were significantly enriched in 62 chromosome regions, such as Chr.19p12-19p13.3, Chr.1p22.1-1p31.1, and Chr.1q21.2-1p23.1 (adjusted p < 0.05), and significantly overrepresented by 26 transcription factors, including E2F2, FOXO3, and GATA1 (adjusted p < 0.05). Biological analysis of these signatures pointed to systemic dysregulation of immune responses, hematopoiesis, exocytosis, and neuronal support in neurodegenerative disease (adjusted p < 0.05).

CONCLUSIONS

Blood transcriptomic biomarkers hold great promise in clinical use for the accurate assessment and prediction of AD.

The fusion of multi-omics profile and multimodal EEG data contributes to the personalized diagnostic strategy for neurocognitive disorders

BACKGROUND

The increasing prevalence of neurocognitive disorders (NCDs) in the aging population worldwide has become a significant concern due to subjectivity of evaluations and the lack of precise diagnostic methods and specific indicators. Developing personalized diagnostic strategies for NCDs has therefore become a priority.

RESULTS

Multimodal electroencephalography (EEG) data of a matched cohort of normal aging (NA) and NCDs seniors were recorded, and their faecal samples and urine exosomes were collected to identify multi-omics signatures and metabolic pathways in NCDs by integrating metagenomics, proteomics, and metabolomics analysis. Additionally, experimental verification of multi-omics signatures was carried out in aged mice using faecal microbiota transplantation (FMT). We found that NCDs seniors had low EEG power spectral density and identified specific microbiota, including Ruminococcus gnavus, Enterocloster bolteae, Lachnoclostridium sp. YL 32, and metabolites, including L-tryptophan, L-glutamic acid, gamma-aminobutyric acid (GABA), and fatty acid esters of hydroxy fatty acids (FAHFAs), as well as disturbed biosynthesis of aromatic amino acids and TCA cycle dysfunction, validated in aged mice. Finally, we employed a support vector machine (SVM) algorithm to construct a machine learning model to classify NA and NCDs groups based on the fusion of EEG data and multi-omics profiles and the model demonstrated 92.69% accuracy in classifying NA and NCDs groups.

CONCLUSIONS

Our study highlights the potential of multi-omics profiling and EEG data fusion in personalized diagnosis of NCDs, with the potential to improve diagnostic precision and provide insights into the underlying mechanisms of NCDs. Video Abstract.

rTMS concurrent with cognitive training rewires AD brain by enhancing GM-WM functional connectivity: a preliminary study

Repetitive transcranial magnetic stimulation (rTMS) and cognitive training for patients with Alzheimer's disease (AD) can change functional connectivity (FC) within gray matter (GM). However, the role of white matter (WM) and changes of GM-WM FC under these therapies are still unclear. To clarify this problem, we applied 40 Hz rTMS over angular gyrus (AG) concurrent with cognitive training to 15 mild-moderate AD patients and analyzed the resting-state functional magnetic resonance imaging before and after treatment. Through AG-based FC analysis, corona radiata and superior longitudinal fasciculus (SLF) were identified as activated WM tracts. Compared with the GM results with AG as seed, more GM regions were found with activated WM tracts as seeds. The averaged FC, fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) of the above GM regions had stronger clinical correlations (r/P = 0.363/0.048 vs 0.299/0.108, 0.351/0.057 vs 0.267/0.153, 0.420/0.021 vs 0.408/0.025, for FC/fALFF/ReHo, respectively) and better classification performance to distinguish pre-/post-treatment groups (AUC = 0.91 vs 0.88, 0.65 vs 0.63, 0.87 vs 0.82, for FC/fALFF/ReHo, respectively). Our results indicated that rTMS concurrent with cognitive training could rewire brain network by enhancing GM-WM FC in AD, and corona radiata and SLF played an important role in this process.

Assessing the lack of diversity in genetics research across neurodegenerative diseases: a systematic review of the GWAS Catalog and literature

Importance

The under-representation of participants with non-European ancestry in genome-wide association studies (GWAS) is a critical issue that has significant implications, including hindering the progress of precision medicine initiatives. This issue is particularly significant in the context of neurodegenerative diseases (NDDs), where current therapeutic approaches have shown limited success. Addressing this under-representation is crucial to harnessing the full potential of genomic medicine in underserved communities and improving outcomes for NDD patients.

Objective

Our primary objective was to assess the representation of non-European ancestry participants in genetic discovery efforts related to NDDs. We aimed to quantify the extent of inclusion of diverse ancestry groups in NDD studies and determine the number of associated loci identified in more inclusive studies. Specifically, we sought to highlight the disparities in research efforts and outcomes between studies predominantly involving European ancestry participants and those deliberately targeting non-European or multi-ancestry populations across NDDs.

Evidence Review

We conducted a systematic review utilizing existing GWAS results and publications to assess the inclusion of diverse ancestry groups in neurodegeneration and neurogenetics studies. Our search encompassed studies published up to the end of 2022, with a focus on identifying research that deliberately included non-European or multi-ancestry cohorts. We employed rigorous methods for the inclusion of identified articles and quality assessment.

Findings

Our review identified a total of 123 NDD GWAS. Strikingly, 82% of these studies predominantly featured participants of European ancestry. Endeavors specifically targeting non-European or multi-ancestry populations across NDDs identified only 52 risk loci. This contrasts with predominantly European studies, which reported over 90 risk loci for a single disease. Encouragingly, over 65% of these discoveries occurred in 2020 or later, indicating a recent increase in studies deliberately including non-European cohorts.

Conclusions and relevance

Our findings underscore the pressing need for increased diversity in neurodegenerative research. The significant under-representation of non-European ancestry participants in NDD GWAS limits our understanding of the genetic underpinnings of these diseases. To advance the field of neurodegenerative research and develop more effective therapies, it is imperative that future investigations prioritize and harness the genomic diversity present within and across global populations.

Elevated Amyloid-β PET Scan and Cognitive and Functional Decline in Mild Cognitive Impairment and Dementia of Uncertain Etiology

BACKGROUND

Elevated amyloid-β (Aβ) on positron emission tomography (PET) scan is used to aid diagnosis of Alzheimer's disease (AD), but many prior studies have focused on patients with a typical AD phenotype such as amnestic mild cognitive impairment (MCI). Little is known about whether elevated Aβ on PET scan predicts rate of cognitive and functional decline among those with MCI or dementia that is clinically less typical of early AD, thus leading to etiologic uncertainty.

OBJECTIVE

We aimed to investigate whether elevated Aβ on PET scan predicts cognitive and functional decline over an 18-month period in those with MCI or dementia of uncertain etiology.

METHODS

In 1,028 individuals with MCI or dementia of uncertain etiology, we evaluated the association between elevated Aβ on PET scan and change on a telephone cognitive status measure administered to the participant and change in everyday function as reported by their care partner.

RESULTS

Individuals with either MCI or dementia and elevated Aβ (66.6% of the sample) showed greater cognitive decline compared to those without elevated Aβ on PET scan, whose cognition was relatively stable over 18 months. Those with either MCI or dementia and elevated Aβ were also reported to have greater functional decline compared to those without elevated Aβ, even though the latter group showed significant care partner-reported functional decline over time.

CONCLUSIONS

Elevated Aβ on PET scan can be helpful in predicting rates of both cognitive and functional decline, even among cognitively impaired individuals with atypical presentations of AD.

CERAD-NAB and flexible battery based neuropsychological differentiation of Alzheimer's dementia and depression using machine learning approaches

Depression (DEP) and dementia of the Alzheimer's type (DAT) represent the most common neuropsychiatric disorders in elderly patients. Accurate differential diagnosis is indispensable to ensure appropriate treatment. However, DEP can yet mimic cognitive symptoms of DAT and patients with DAT often also present with depressive symptoms, impeding correct diagnosis. Machine learning (ML) approaches could eventually improve this discrimination using neuropsychological test data, but evidence is still missing. We therefore employed Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) and conventional Logistic Regression (LR) to retrospectively predict the diagnoses of 189 elderly patients (68 DEP and 121 DAT) based on either the well-established Consortium to Establish a Registry for Alzheimer's Disease neuropsychological assessment battery (CERAD-NAB) or a flexible battery approach (FLEXBAT). The best performing combination consisted of FLEXBAT and NB, correctly classifying 87.0% of patients as either DAT or DEP. However, all accuracies were similar across algorithms and test batteries (83.0% - 87.0%). Accordingly, our study is the first to show that common ML algorithms with their default parameters can accurately differentiate between patients clinically diagnosed with DAT or DEP using neuropsychological test data, but do not necessarily outperform conventional LR.

Diagnosis Framework for Probable Alzheimer's Disease and Mild Cognitive Impairment Based on Multi-Dimensional Emotion Features

BACKGROUND

Emotion and cognition are intercorrelated. Impaired emotion is common in populations with Alzheimer's disease (AD) and mild cognitive impairment (MCI), showing promises as an early detection approach.

OBJECTIVE

We aim to develop a novel automatic classification tool based on emotion features and machine learning.

METHODS

Older adults aged 60 years or over were recruited among residents in the long-term care facilities and the community. Participants included healthy control participants with normal cognition (HC, n = 26), patients with MCI (n = 23), and patients with probable AD (n = 30). Participants watched emotional film clips while multi-dimensional emotion data were collected, including mental features of Self-Assessment Manikin (SAM), physiological features of electrodermal activity (EDA), and facial expressions. Emotional features of EDA and facial expression were abstracted by using continuous decomposition analysis and EomNet, respectively. Bidirectional long short-term memory (Bi-LSTM) was used to train classification model. Hybrid fusion was used, including early feature fusion and late decision fusion. Data from 79 participants were utilized into deep machine learning analysis and hybrid fusion method.

RESULTS

By combining multiple emotion features, the model's performance of AUC value was highest in classification between HC and probable AD (AUC = 0.92), intermediate between MCI and probable AD (AUC = 0.88), and lowest between HC and MCI (AUC = 0.82).

CONCLUSIONS

Our method demonstrated an excellent predictive power to differentiate HC/MCI/AD by fusion of multiple emotion features. The proposed model provides a cost-effective and automated method that can assist in detecting probable AD and MCI from normal aging.

Plasma cell-free RNA profiling of Vietnamese Alzheimer's patients reveals a linkage with chronic inflammation and apoptosis: a pilot study

Introduction

Circulating cell-free RNA (cfRNA) is a potential hallmark for early diagnosis of Alzheimer's Disease (AD) as it construes the genetic expression level, giving insights into the pathological progress from the outset. Profiles of cfRNA in Caucasian AD patients have been investigated thoroughly, yet there was no report exploring cfRNAs in the ASEAN groups. This study examined the gap, expecting to support the development of point-of-care AD diagnosis.

Methods

cfRNA profiles were characterized from 20 Vietnamese plasma samples (10 probable AD and 10 age-matched controls). RNA reads were subjected to differential expression (DE) analysis. Weighted gene correlation network analysis (WGCNA) was performed to identify gene modules that were significantly co-expressed. These modules' expression profiles were then correlated with AD status to identify relevant modules. Genes with the highest intramodular connectivity (module membership) were selected as hub genes. Transcript counts of differentially expressed genes were correlated with key AD measures-MMSE and MTA scores-to identify potential biomarkers.

Results

136 genes were identified as significant AD hallmarks (p < 0.05), with 52 downregulated and 84 upregulated in the AD cohort. 45.6% of these genes are highly expressed in the hippocampus, cerebellum, and cerebral cortex. Notably, all markers related to chronic inflammation were upregulated, and there was a significant shift in all apoptotic markers. Three co-expressed modules were found to be significantly correlated with Alzheimer's status (p < 0.05; R2> 0.5). Functional enrichment analysis on these modules reveals an association with focal adhesion, nucleocytoplasmic transport, and metal ion response leading to apoptosis, suggesting the potential participation of these pathways in AD pathology. 47 significant hub genes were found to be differentially expressed genes with the highest connectivity. Six significant hub genes (CREB1, YTHDC1, IL1RL1, PHACTR2, ANKRD36B, RNF213) were found to be significantly correlated with MTA and MMSE scores. Other significant transcripts (XRN1, UBB, CHP1, THBS1, S100A9) were found to be involved in inflammation and neuronal death. Overall, we have identified candidate transcripts in plasma cf-RNA that are differentially expressed and are implicated in inflammation and apoptosis, which can jumpstart further investigations into applying cf-RNA as an AD biomarker in Vietnam and ASEAN countries.

SNAP25 is a potential target for early stage Alzheimer's disease and Parkinson's disease

BACKGROUND

Alzheimer's disease (AD) and Parkinson's disease (PD), two common irreversible neurodegenerative diseases, share similar early stage syndromes, such as olfaction dysfunction. Yet, the potential comorbidity mechanism of AD and PD was not fully elucidated.

METHODS

The gene expression profiles of GSE5281 and GSE8397 were downloaded from the Gene Expression Omnibus (GEO) database. We utilized a series of bioinformatics analyses to screen the overlapped differentially expressed genes (DEGs). The hub genes were further identified by the plugin CytoHubba of Cytoscape and validated in the hippocampus (HIP) samples of APP/PS-1 transgenic mice and the substantial nigra (SN) samples of A53T transgenic mice by real-time quantitative polymerase chain reaction (RT-qPCR). Meanwhile, the expression of the target genes in the olfactory epithelium/bulb was detected by RT-qPCR. Finally, molecular docking was used to screen potential compounds for the target gene.

RESULTS

One hundred seventy-four overlapped DEGs were identified in AD and PD. Five of the top ten enrichment pathways mainly focused on the synapse. Five hub genes were identified and further validated. As a common factor in AD and PD, the changes of synaptosomal-associated protein 25 (SNAP25) mRNA in olfactory epithelium/bulb were significantly decreased and had a strong association with those in the HIP and SN samples. Pazopanib was the optimal compound targeting SNAP25, with a binding energy of - 9.2 kcal/mol.

CONCLUSIONS

Our results provided a theoretical basis for understanding the comorbidity mechanism of AD and PD and highlighted that SNAP25 in the olfactory epithelium may serve as a potential target for early detection and intervention in both AD and PD.

Alzheimer's disease cortical morphological phenotypes are associated with TOMM40'523-APOE haplotypes

Both the APOE ε4 and TOMM40 rs10524523 ("523") genes have been associated with risk for Alzheimer's disease (AD) and neuroimaging biomarkers of AD. No studies have investigated the relationship of TOMM40'523-APOE ε4 on the structural complexity of the brain in AD individuals. We quantified brain morphology and multiple cortical attributes in individuals with mild cognitive impairment (MCI) and AD, then tested whether APOE ε4 or TOMM40 poly-T genotypes were related to AD morphological biomarkers in cognitively unimpaired (CU) and MCI/AD individuals. We identified several AD-specific phenotypes in brain morphology and found that TOMM40 poly-T short alleles are associated with early, AD-specific brain morphological differences in healthy aging. We observed decreased cortical thickness, sulcal depth, and fractal dimension in CU individuals with the poly-T short alleles. Moreover, in MCI/AD participants, the APOE ε4 (TOMM40 L) individuals had a higher rate of gene-related morphological markers indicative of AD. Our data suggest that TOMM40'523 is associated with early brain structure variations in the precuneus, temporal, and limbic cortices.

Design and feasibility of an Alzheimer's disease blood test study in a diverse community-based population

INTRODUCTION

Alzheimer's disease (AD) blood tests are likely to become increasingly important in clinical practice, but they need to be evaluated in diverse groups before use in the general population.

METHODS

This study enrolled a community-based sample of older adults in the St. Louis, Missouri, USA area. Participants completed a blood draw, Eight-Item Informant Interview to Differentiate Aging and Dementia (AD8® ), Montreal Cognitive Assessment (MoCA), and survey about their perceptions of the blood test. A subset of participants completed additional blood collection, amyloid positron emission tomography (PET), magnetic resonance imaging (MRI), and Clinical Dementia Rating (CDR® ).

RESULTS

Of the 859 participants enrolled in this ongoing study, 20.6% self-identified as Black or African American. The AD8 and MoCA correlated moderately with the CDR. The blood test was well accepted by the cohort, but it was perceived more positively by White and highly educated individuals.

DISCUSSION

Studying an AD blood test in a diverse population is feasible and may accelerate accurate diagnosis and implementation of effective treatments.

HIGHLIGHTS

A diverse group of older adults was recruited to evaluate a blood amyloid test. The enrollment rate was high and the blood test was well accepted by participants. Cognitive impairment screens have moderate performance in a diverse population. Alzheimer's disease blood tests are likely to be feasible for use in real-world settings.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease

OBJECTIVE

A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial.

METHODS

We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aβ) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aβ-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aβ-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls.

RESULTS

The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively.

CONCLUSION

The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.

Evaluating the Diagnostic Value of Electrovestibulography (EVestG) in Alzheimer's Patients with Mixed Pathology: A Pilot Study

Background and Objectives: Diagnosis of dementia subtypes caused by different brain pathophysiologies, particularly Alzheimer's disease (AD) from AD mixed with levels of cerebrovascular disease (CVD) symptomology (AD-CVD), is challenging due to overlapping symptoms. In this pilot study, the potential of Electrovestibulography (EVestG) for identifying AD, AD-CVD, and healthy control populations was investigated. Materials and Methods: A novel hierarchical multiclass diagnostic algorithm based on the outcomes of its lower levels of binary classifications was developed using data of 16 patients with AD, 13 with AD-CVD, and 24 healthy age-matched controls, and then evaluated on a blind testing dataset made up of a new population of 12 patients diagnosed with AD, 9 with AD-CVD, and 8 healthy controls. Multivariate analysis was run to test the between population differences while controlling for sex and age covariates. Results: The accuracies of the multiclass diagnostic algorithm were found to be 85.7% and 79.6% for the training and blind testing datasets, respectively. While a statistically significant difference was found between the populations after accounting for sex and age, no significant effect was found for sex or age covariates. The best characteristic EVestG features were extracted from the upright sitting and supine up/down stimulus responses. Conclusions: Two EVestG movements (stimuli) and their most informative features that are best selective of the above-populations' separations were identified, and a hierarchy diagnostic algorithm was developed for three-way classification. Given that the two stimuli predominantly stimulate the otholithic organs, physiological and experimental evidence supportive of the results are presented. Disruptions of inhibition associated with GABAergic activity might be responsible for the changes in the EVestG features.

Exploring shared features in neurodegenerative diseases

This scientific commentary refers to ‘Genetic risk factor clustering within and across neurodegenerative diseases’ by Koretsky et al. (https://doi.org/10.1093/brain/awad161).

Alzheimer Disease Biomarkers: Moving from CSF to Plasma for Reliable Detection of Amyloid and tau Pathology

BACKGROUND

Development of validated biomarkers to detect early Alzheimer disease (AD) neuropathology is needed for therapeutic AD trials. Abnormal concentrations of "core" AD biomarkers, cerebrospinal fluid (CSF) amyloid beta1-42, total tau, and phosphorylated tau correlate well with neuroimaging biomarkers and autopsy findings. Nevertheless, given the limitations of established CSF and neuroimaging biomarkers, accelerated development of blood-based AD biomarkers is underway.

CONTENT

Here we describe the clinical significance of CSF and plasma AD biomarkers to detect disease pathology throughout the Alzheimer continuum and correlate with imaging biomarkers. Use of the AT(N) classification by CSF and imaging biomarkers provides a more objective biologically based diagnosis of AD than clinical diagnosis alone. Significant progress in measuring CSF AD biomarkers using extensively validated highly automated assay systems has facilitated their transition from research use only to approved in vitro diagnostics tests for clinical use. We summarize development of plasma AD biomarkers as screening tools for enrollment and monitoring participants in therapeutic trials and ultimately in clinical care. Finally, we discuss the challenges for AD biomarkers use in clinical trials and precision medicine, emphasizing the possible ethnocultural differences in the levels of AD biomarkers.

SUMMARY

CSF AD biomarker measurements using fully automated analytical platforms is possible. Building on this experience, validated blood-based biomarker tests are being implemented on highly automated immunoassay and mass spectrometry platforms. The progress made developing analytically and clinically validated plasma AD biomarkers within the AT(N) classification scheme can accelerate use of AD biomarkers in therapeutic trials and routine clinical practice.

Characteristics of amnestic patients with hypometabolism patterns suggestive of Lewy body pathology

A clinical diagnosis of Alzheimer's disease dementia (ADD) encompasses considerable pathological and clinical heterogeneity. While Alzheimer's disease patients typically show a characteristic temporo-parietal pattern of glucose hypometabolism on 18F-fluorodeoxyglucose (FDG)-PET imaging, previous studies have identified a subset of patients showing a distinct posterior-occipital hypometabolism pattern associated with Lewy body pathology. Here, we aimed to improve the understanding of the clinical relevance of these posterior-occipital FDG-PET patterns in patients with Alzheimer's disease-like amnestic presentations. Our study included 1214 patients with clinical diagnoses of ADD (n = 305) or amnestic mild cognitive impairment (aMCI, n = 909) from the Alzheimer's Disease Neuroimaging Initiative, who had FDG-PET scans available. Individual FDG-PET scans were classified as being suggestive of Alzheimer's (AD-like) or Lewy body (LB-like) pathology by using a logistic regression classifier trained on a separate set of patients with autopsy-confirmed Alzheimer's disease or Lewy body pathology. AD- and LB-like subgroups were compared on amyloid-β and tau-PET, domain-specific cognitive profiles (memory versus executive function performance), as well as the presence of hallucinations and their evolution over follow-up (≈6 years for aMCI, ≈3 years for ADD). Around 12% of the aMCI and ADD patients were classified as LB-like. For both aMCI and ADD patients, the LB-like group showed significantly lower regional tau-PET burden than the AD-like subgroup, but amyloid-β load was only significantly lower in the aMCI LB-like subgroup. LB- and AD-like subgroups did not significantly differ in global cognition (aMCI: d = 0.15, P = 0.16; ADD: d = 0.02, P = 0.90), but LB-like patients exhibited a more dysexecutive cognitive profile relative to the memory deficit (aMCI: d = 0.35, P = 0.01; ADD: d = 0.85 P < 0.001), and had a significantly higher risk of developing hallucinations over follow-up [aMCI: hazard ratio = 1.8, 95% confidence interval = (1.29, 3.04), P = 0.02; ADD: hazard ratio = 2.2, 95% confidence interval = (1.53, 4.06) P = 0.01]. In summary, a sizeable group of clinically diagnosed ADD and aMCI patients exhibit posterior-occipital FDG-PET patterns typically associated with Lewy body pathology, and these also show less abnormal Alzheimer's disease biomarkers as well as specific clinical features typically associated with dementia with Lewy bodies.

Equivalence of plasma p-tau217 with cerebrospinal fluid in the diagnosis of Alzheimer's disease

INTRODUCTION

Plasma biomarkers are promising tools for Alzheimer's disease (AD) diagnosis, but comparisons with more established biomarkers are needed.

METHODS

We assessed the diagnostic performance of p-tau181 , p-tau217 , and p-tau231 in plasma and CSF in 174 individuals evaluated by dementia specialists and assessed with amyloid-PET and tau-PET. Receiver operating characteristic (ROC) analyses assessed the performance of plasma and CSF biomarkers to identify amyloid-PET and tau-PET positivity.

RESULTS

Plasma p-tau biomarkers had lower dynamic ranges and effect sizes compared to CSF p-tau. Plasma p-tau181 (AUC = 76%) and p-tau231 (AUC = 82%) assessments performed inferior to CSF p-tau181 (AUC = 87%) and p-tau231 (AUC = 95%) for amyloid-PET positivity. However, plasma p-tau217 (AUC = 91%) had diagnostic performance indistinguishable from CSF (AUC = 94%) for amyloid-PET positivity.

DISCUSSION

Plasma and CSF p-tau217 had equivalent diagnostic performance for biomarker-defined AD. Our results suggest that plasma p-tau217 may help reduce the need for invasive lumbar punctures without compromising accuracy in the identification of AD.

HIGHLIGHTS

p-tau217 in plasma performed equivalent to p-tau217 in CSF for the diagnosis of AD, suggesting the increased accessibility of plasma p-tau217 is not offset by lower accuracy. p-tau biomarkers in plasma had lower mean fold-changes between amyloid-PET negative and positive groups than p-tau biomarkers in CSF. CSF p-tau biomarkers had greater effect sizes than plasma p-tau biomarkers when differentiating between amyloid-PET positive and negative groups. Plasma p-tau181 and plasma p-tau231 performed worse than p-tau181 and p-tau231 in CSF for AD diagnosis.

Factors affecting progression of non-Alzheimer dementia: a retrospective analysis with long-term follow-up

Background

Non-Alzheimer's dementias, including vascular dementia (VaD), frontotemporal dementia (FTD), Lewy body dementia (LBD), and Parkinson's disease dementia (PDD), possess unique characteristics and prognostic factors that remain poorly understood. This study aims to investigate the temporal course of these subtypes and identify the impact of functional, neuropsychiatric, and comorbid medical conditions on prognosis. Additionally, the relationship between hippocampal atrophy, white matter intensities, and disease progression will be examined, along with the identification of key covariates influencing slow or fast progression in non-Alzheimer's dementias.

Methods

A total of 196 patients with non-Alzheimer's dementias who underwent at least three comprehensive evaluations were included, with proportions of VaD, FTD, LBD, and PDD being 50, 19.39, 19.90, and 10.71%, respectively. Patient demographics, comorbidities, neuropsychiatric and neuroimaging parameters, and global evaluation were analyzed using appropriate statistical methods. The study followed patients for a mean duration of 62.57 ± 33.45 months (ranging from 11 to 198 months).

Results

The results from three different visits for each non-AD dementia case demonstrated significant differences in various measures across visits, including functional capacity (BDLAS), cognition (MMSE), and other neuropsychological tests. Notably, certain genotypes and hippocampal atrophy grades were more prevalent in specific subtypes. The results indicate that Fazekas grading and hippocampal atrophy were significant predictors of disease progression, while epilepsy, extrapyramidal symptoms, thyroid dysfunction, coronary artery disease, diabetes mellitus, hypertension, stroke, hyperlipidemia, sleep disorders, smoking, and family history of dementia were not significant predictors. BDLAS and EDLAS scores at the first and second visits showed significant associations with disease progression, while scores at the third visit did not. Group-based trajectory analysis revealed that non-AD cases separated into two reliable subgroups with slow/fast prognosis, showing high reliability (Entropy = 0.790, 51.8 vs. 48.2%).

Conclusion

This study provides valuable insights into the temporal course and prognostic factors of non-Alzheimer's dementias. The findings underscore the importance of considering functional, neuropsychological, and comorbid medical conditions in understanding disease progression. The significant associations between hippocampal atrophy, white matter intensities, and prognosis highlight potential avenues for further research and therapeutic interventions.

Plasma neurofilament light chain associated with impaired regional cerebral blood flow in healthy individuals

Background: Recent findings suggest that the plasma axonal structural protein, neurofilament light (NFL) chain, may serve as a potential blood biomarker for early signs of neurodegenerative diseases, such as Alzheimer's disease (AD). Given the need for early detection of neurodegenerative disorders, the current study investigated the associations between regional cerebral blood flow (rCBF) in brain regions associated with neurodegenerative disorders and memory function with plasma NFL in AD, mild cognitive impairment (MCI), and healthy controls (HCs). Methods: We recruited 29 AD, 76 MCI, and 39 HCs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database in the current cross-sectional study. We used Pearson's correlation models adjusted for the effect of age, sex, and APOE genotype to investigate the association between plasma NFL and rCBF. Results: We found non-significant differences in age (F(2, 141) = 1.304; P = 0.275) and years of education (F(2, 141) = 0.013; P = 0.987). Additionally, we found significant differences between groups in terms of MMSE scores (F(2, 141) = 100.953; P < 0.001). Despite the observation of significantly reduced rCBF in AD and MCI groups versus HCs, we did not detect significant differences in plasma NFL between these groups. We found significant negative associations between plasma NFL and rCBF in various AD-related regions, these findings were only observed after analyses in all participants, and were observed in HCs alone and no significant associations were observed in the AD or MCI groups. Conclusion: These outcomes add to our current understanding surrounding the use of rCBF and plasma NFL biomarkers as tools for early detection and diagnosis of neurodegenerative diseases. A conclusion might be that the association between NFL and impaired rCBF exists before the clinical symptoms appear. Further longitudinal studies with a large sample size should be performed to examine the correlation between plasma NFL and rCBF in order to understand these complex relationships.

Heterogeneity of Alzheimer's disease identified by neuropsychological test profiling

Alzheimer's disease (AD) is a highly heterogeneous disorder. Untangling this variability could lead to personalized treatments and improve participant recruitment for clinical trials. We investigated the cognitive subgroups by using a data-driven clustering technique in an AD cohort. People with mild-moderate probable AD from Taiwan was included. Neuropsychological test results from the Cognitive Abilities Screening Instrument were clustered using nonnegative matrix factorization. We identified two clusters in 112 patients with predominant deficits in memory (62.5%) and non-memory (37.5%) cognitive domains, respectively. The memory group performed worse in short-term memory and orientation and better in attention than the non-memory group. At baseline, patients in the memory group had worse global cognitive status and dementia severity. Linear mixed effect model did not reveal difference in disease trajectory within 3 years of follow-up between the two clusters. Our results provide insights into the cognitive heterogeneity in probable AD in an Asian population.

Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

Neuroinflammation in a Mouse Model of Alzheimer's Disease versus Auditory Dysfunction: Machine Learning Interpretation and Analysis

Background

Auditory dysfunction, including central auditory hyperactivity, hearing loss and hearing in noise deficits, has been reported in 5xFAD Alzheimer's disease (AD) mice, suggesting a causal relationship between amyloidosis and auditory dysfunction. Central auditory hyperactivity correlated in time with small amounts of plaque deposition in the inferior colliculus and medial geniculate body, which are the auditory midbrain and thalamus, respectively. Neuroinflammation has been associated with excitation to inhibition imbalance in the central nervous system, and therefore has been proposed as a link between central auditory hyperactivity and AD in our previous report. However, neuroinflammation in the auditory pathway has not been investigated in mouse amyloidosis models.

Methods

Machine learning was used to classify the previously obtained auditory brainstem responses (ABRs) from 5xFAD mice and their wild type (WT) littermates. Neuroinflammation was assessed in six auditory-related regions of the cortex, thalamus, and brainstem. Cochlear pathology was assessed in cryosection and whole mount. Behavioral changes were assessed with fear conditioning, open field testing and novel objection recognition.

Results

Reliable machine learning classification of 5xFAD and WT littermate ABRs were achieved for 6M and 12M, but not 3M. The top features for accurate classification at 6 months of age were characteristics of Waves IV and V. Microglial and astrocytic activation were pronounced in 5xFAD inferior colliculus and medial geniculate body at 6 months, two neural centers that are thought to contribute to these waves. Lower regions of the brainstem were unaffected, and cortical auditory centers also displayed inflammation beginning at 6 months. No losses were seen in numbers of spiral ganglion neurons (SGNs), auditory synapses, or efferent synapses in the cochlea. 5xFAD mice had reduced responses to tones in fear conditioning compared to WT littermates beginning at 6 months.

Conclusions

Serial use of ABR in early AD patients represents a promising approach for early and inexpensive detection of neuroinflammation in higher auditory brainstem processing centers. As changes in auditory processing are strongly linked to AD progression, central auditory hyperactivity may serve as a biomarker for AD progression and/or stratify AD patients into distinct populations.

Lower mortality risk in APOE4 carriers with normal cognitive ageing

Abnormal cognitive ageing, including dementia, poses serious challenges to health and social systems in ageing populations. As such, characterizing factors associated with abnormal cognitive ageing and developing needed preventive measures are of great importance. The ε4 allele of the Apolipoprotein E gene (APOE4) is a well-known genetic risk factor for late-onset Alzheimer's disease. APOE4 carriers are also at elevated risk of cardiovascular diseases which are associated with increased risk of cognitive impairment. On the other hand, APOE4 is known to be associated with reduced risk of multiple common types of cancer-a major age-related disease and leading cause of mortality. We conducted the first-ever study of APOE4's opposing effects on cognitive decline and mortality using competing risk models considering two types of death-death with high-amounts versus low-amounts of autopsy-assessed Alzheimer's neuropathology. We observed that APOE4 was associated with decreased mortality risk in people who died with low amounts of Alzheimer's-type neuropathology, but APOE4 was associated with increased mortality risk in people who died with high amounts of Alzheimer's-type neuropathology, a major risk factor of cognitive impairment. Possible preventive measures of abnormal cognitive ageing are also discussed.

APOE-ε4 and BIN1 increase risk of Alzheimer's disease pathology but not specifically of Lewy body pathology

Lewy body (LB) pathology commonly occurs in individuals with Alzheimer's disease (AD) pathology. However, it remains unclear which genetic risk factors underlie AD pathology, LB pathology, or AD-LB co-pathology. Notably, whether APOE-ε4 affects risk of LB pathology independently from AD pathology is controversial. We adapted criteria from the literature to classify 4,985 subjects from the National Alzheimer's Coordinating Center (NACC) and the Rush University Medical Center as AD-LB co-pathology (AD+LB+), sole AD pathology (AD+LB-), sole LB pathology (AD-LB+), or no pathology (AD-LB-). We performed a meta-analysis of a genome-wide association study (GWAS) per subpopulation (NACC/Rush) for each disease phenotype compared to the control group (AD-LB-), and compared the AD+LB+ to AD+LB- groups. APOE-ε4 was significantly associated with risk of AD+LB- and AD+LB+ compared to AD-LB-. However, APOE-ε4 was not associated with risk of AD-LB+ compared to AD-LB- or risk of AD+LB+ compared to AD+LB-. Associations at the BIN1 locus exhibited qualitatively similar results. These results suggest that APOE-ε4 is a risk factor for AD pathology, but not for LB pathology when decoupled from AD pathology. The same holds for BIN1 risk variants. These findings, in the largest AD-LB neuropathology GWAS to date, distinguish the genetic risk factors for sole and dual AD-LB pathology phenotypes. Our GWAS meta-analysis summary statistics, derived from phenotypes based on postmortem pathologic evaluation, may provide more accurate disease-specific polygenic risk scores compared to GWAS based on clinical diagnoses, which are likely confounded by undetected dual pathology and clinical misdiagnoses of dementia type.

The effect of herpes zoster vaccination on the occurrence of deaths due to dementia in England and Wales

Background

The United Kingdom (UK) has used date of birth-based eligibility rules for live-attenuated herpes zoster (HZ) vaccination that have led to large differences in HZ vaccination coverage between individuals who differed in their age by merely a few days. Using this unique natural randomization, we have recently provided evidence from Welsh electronic health record data that HZ vaccination caused a reduction in new dementia diagnoses over a seven-year period. Based on this, we hypothesized that HZ vaccination may have slowed the dementia disease process more generally and, thus, already reduced deaths with dementia as their underlying cause even though the UK's HZ vaccination program commenced as recently as September 2013. Using country-wide death certificate data for England and Wales, this study, therefore, aimed to determine whether eligibility for HZ vaccination caused a reduction in deaths due to dementia over a nine-year follow-up period.

Methods

Adults who had their 80th birthday shortly before September 1 2013 were ineligible for HZ vaccination in the UK's National Health Service and remained ineligible for life, whereas those who had their 80th birthday shortly after September 1 2013 (i.e., born on or after September 2 1933) were eligible for one year. Akin to a randomized trial, this date-of-birth threshold generated birth cohorts who are likely exchangeable in observed and unobserved characteristics except for a small difference in age and a large difference in HZ vaccination uptake. We used country-wide data from death certificates in England and Wales on underlying causes of death from September 1 2004 to August 31 2022 by ICD-10 code and month of birth. Our analysis compared the percentage of the population with a death due to dementia among the month-of-birth cohorts around the September 2 1933 eligibility threshold using a regression discontinuity design. The primary analyses used the maximal available follow-up period of nine years.

Results

The study population included 5,077,426 adults born between September 1 1925 and August 31 1941 who were alive at the start of the HZ vaccination program. The month-of-birth cohorts around the September 2 1933 eligibility cutoff were well balanced in their occurrence of all-cause and cause-specific deaths (including deaths due to dementia) prior to the start of the vaccination program. We estimated that over a nine-year follow-up period, eligibility for HZ vaccination reduced the percentage of the population with a death due to dementia by 0.38 (95% CI: 0.08 to 0.68, p=0.012) percentage points, corresponding to a relative reduction of 4.8%. As in our prior analysis, this effect was stronger among women (-0.62 [95% CI: -1.06 to -0.19] percentage points, p=0.004) than among men (-0.11 [95% CI: -0.51 to 0.28] percentage points, p=0.574). The reduction in deaths due to dementia likely resulted in an increase in remaining life expectancy because we found that HZ vaccination eligibility reduced all-cause mortality but had no effect on deaths not due to dementia. An effect on deaths due to dementia at the September 2 date-of-birth eligibility threshold existed only since the year in which the HZ vaccination program was implemented.

Conclusions

Our findings indicate that HZ vaccination improved cognitive function at a fairly advanced stage of the dementia disease process because most individuals whose underlying cause of death was dementia during our nine-year follow-up period were likely already living with dementia at the start of the HZ vaccination program. By using a different population, type of data, and outcome than our prior study in Welsh electronic health record data, this analysis adds to the evidence base that HZ vaccination slows, or potentially even prevents, the natural history of dementia.

Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status

BACKGROUND

Plasma biomarkers have emerged as promising screening tools for Alzheimer's disease (AD) because of their potential to detect amyloid β (Aβ) accumulation in the brain. One such candidate is the plasma Aβ42/40 ratio (Aβ42/40). Unlike previous research that used traditional immunoassay, recent studies that measured plasma Aβ42/40 using fully automated platforms reported promising results. However, its utility should be confirmed using a broader patient population, focusing on the potential for early detection.

METHODS

We recruited 174 participants, including healthy controls (HC) and patients with clinical diagnoses of AD, frontotemporal lobar degeneration, dementia with Lewy bodies/Parkinson's disease, mild cognitive impairment (MCI), and others, from a university memory clinic. We examined the performance of plasma Aβ42/40, measured using the fully automated high-sensitivity chemiluminescence enzyme (HISCL) immunoassay, in detecting amyloid-positron emission tomography (PET)-derived Aβ pathology. We also compared its performance with that of Simoa-based plasma phosphorylated tau at residue 181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL).

RESULTS

Using the best cut-off derived from the Youden Index, plasma Aβ42/40 yielded an area under the receiver operating characteristic curve (AUC) of 0.949 in distinguishing visually assessed 18F-Florbetaben amyloid PET positivity. The plasma Aβ42/40 had a significantly superior AUC than p-tau181, GFAP, and NfL in the 167 participants with measurements for all four biomarkers. Next, we analyzed 99 participants, including only the HC and those with MCI, and discovered that plasma Aβ42/40 outperformed the other plasma biomarkers, suggesting its ability to detect early amyloid accumulation. Using the Centiloid scale (CL), Spearman's rank correlation coefficient between plasma Aβ42/40 and CL was -0.767. Among the 15 participants falling within the CL values indicative of potential future amyloid accumulation (CL between 13.5 and 35.7), plasma Aβ42/40 categorized 61.5% (8/13) as Aβ-positive, whereas visual assessment of amyloid PET identified 20% (3/15) as positive.

CONCLUSION

Plasma Aβ42/40 measured using the fully automated HISCL platform showed excellent performance in identifying Aβ accumulation in the brain in a well-characterized cohort. This equipment may be useful for screening amyloid pathology because it has the potential to detect early amyloid pathology and is readily applied in clinical settings.

Pitfalls of predicting age-related traits by polygenic risk scores

Polygenic risk scores (PRS) are a method increasingly used to capture the combined effect of genome-wide significant variants and those which individually do not show genome-wide significant association but are likely to contribute to the risk of developing diseases. However, their practical use incurs complications and inconsistencies that so far limit their clinical applicability. The aims of the present review are to discuss the PRS for age-related diseases and to highlight pitfalls and limitations of PRS prediction accuracy due to ageing and mortality effects. We argue that the PRS is widely used but the individual's PRS values differ substantially depending on the number of genetic variants included, the discovery GWAS and the method employed to generate them. Moreover, for neurodegenerative disorders, although an individual's genetics do not change with age, the actual score depends on the age of the sample used in the discovery GWAS and is likely to reflect the individual's disease risk at this particular age. Improvement of PRS prediction accuracy for neurodegenerative disorders will come from two sides, both the precision of clinical diagnoses, and a careful attention to the age distribution in the underlying samples and validation of the prediction in longitudinal studies.

Cognitivism ageing: The Alzheimer conundrum as switched ontology & the potential for a new materialist dementia

Following recent regulatory approvals for anti-Alzheimer's monoclonal antibodies, this paper considers the contemporary role of cognitivism in defining the ontological commitments of dementia research, as well as movements away from cognitivism under the umbrella of 4E cognitive science. 4E cognitive theories, extending cognition into bodies, their environs, and active relations between the two, share potentially fruitful affinities with new materialisms which focus on the co-constitution of matter in intra-action. These semi-overlapping conceptual positions furnish some opportunity for an ontological alternative to longstanding cognitivist commitments, particularly to the isolated brain as a material catalyst for commercial interventions. After outlining mainstream cognitivism and its shortcomings, I explore 4E and new materialism as possibly transformative conceptual schemas for dementia research, a field for which cognitivist imaginings of cognitive decline in later life have profound and often regrettable ramifications. To realise this new materialist dementia, I sketch out a cognitive ontology based on Barad's agential realism. This facilitates a reassessment of the biggest conundrum in dementia research - the lack of neat correlation between (apparently material) neuropathology and (apparently immaterial) cognitive impairment - alongside the continued failure of efforts to develop effective interventions. It also gives social researchers working on cognitive decline in later life an opportunity to reappraise the nature of social science as a response to such phenomena. If cognition and cognitive ageing are reimagined as an emergent characteristic of intra-acting matter, then new materialist social science might be at least as conducive to salutogenic interventions as the neuropsychiatric technoscience that dominates the contemporary dementia research economy despite continual failures. I argue that a new materialist cognitive ontology could help us think beyond an ageing cognitivism and, by extension, beyond the Alzheimer conundrum.

The Early-Onset Alzheimer's Disease Whole-Genome Sequencing Project: Study design and methodology

INTRODUCTION

Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations, resulting in a lack of understanding of its molecular etiology.

METHODS

Whole-genome sequencing and harmonization of clinical, neuropathological, and biomarker data of over 5000 EOAD cases of diverse ancestries.

RESULTS

A publicly available genomics resource for EOAD with extensive harmonized phenotypes. Primary analysis will (1) identify novel EOAD risk loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits.

DISCUSSION

This novel resource complements over 50,000 control and late-onset AD samples generated through the Alzheimer's Disease Sequencing Project (ADSP). The harmonized EOAD/ADSP joint call will be available through upcoming ADSP data releases and will allow for additional analyses across the full onset range.

HIGHLIGHTS

Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations. This results in a significant lack of understanding of the molecular etiology of this devastating form of the disease. The Early-Onset Alzheimer's Disease Whole-genome Sequencing Project is a collaborative initiative to generate a large-scale genomics resource for early-onset Alzheimer's disease with extensive harmonized phenotype data. Primary analyses are designed to (1) identify novel EOAD risk and protective loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits. The harmonized genomic and phenotypic data from this initiative will be available through NIAGADS.

Implication of the cooking oil-peroxidation product "hydroxynonenal" for Alzheimer's disease

Aldehyde dehydrogenase 2 (ALDH2) is a mitochondrial enzyme that reduces cell injuries via detoxification of lipid-peroxidation product, 4-hydroxy-2-nonenal (hydroxynonenal). It is generated exogenously via deep-frying of linoleic acid-rich cooking oils and/or endogenously via oxidation of fatty acids involved in biomembranes. Although its toxicity for human health is widely accepted, the underlying mechanism long remained unknown. In 1998, Yamashima et al. have formulated the "calpain-cathepsin hypothesis" as a molecular mechanism of ischemic neuronal death. Subsequently, they found that calpain cleaves Hsp70.1 which became vulnerable after the hydroxynonenal-induced carbonylation at the key site Arg469. Since it is the pivotal aberration that induces lysosomal membrane rupture, they suggested that neuronal death in Alzheimer's disease similarly occurs by chronic ischemia via the calpain-cathepsin cascade triggered by hydroxynonenal. For nearly three decades, amyloid β (Aβ) peptide was thought to be a root substance of Alzheimer's disease. However, because of both the insignificant correlations between Aβ depositions and occurrence of neuronal death or dementia, and the negative results of anti-Aβ medicines tested so far in the patients with Alzheimer's disease, the strength of the "amyloid cascade hypothesis" has been weakened. Recent works have suggested that hydroxynonenal is a mediator of programmed cell death not only in the brain, but also in the liver, pancreas, heart, etc. Increment of hydroxynonenal was considered an early event in the development of Alzheimer's disease. This review aims at suggesting ways out of the tunnel, focusing on the implication of hydroxynonenal in this disease. Herein, the mechanism of Alzheimer neuronal death is discussed by focusing on Hsp70.1 with a dual function as chaperone protein and lysosomal stabilizer. We suggest that Aβ is not a culprit of Alzheimer's disease, but merely a byproduct of autophagy/lysosomal failure resulting from hydroxynonenal-induced Hsp70.1 disorder. Enhancing ALDH2 activity to detoxify hydroxynonenal emerges as a promising strategy for preventing and treating Alzheimer's disease.

A how-to guide for a precision medicine approach to the diagnosis and treatment of Alzheimer's disease

Article purpose

The clinical approach to Alzheimer's disease (AD) is challenging, particularly in high-functioning individuals. Accurate diagnosis is crucial, especially given the significant side effects, including brain hemorrhage, of newer monoclonal antibodies approved for treating earlier stages of Alzheimer's. Although early treatment is more effective, early diagnosis is also more difficult. Several clinical mimickers of AD exist either separately, or in conjunction with AD pathology, adding to the diagnostic complexity. To illustrate the clinical decision-making process, this study includes de-identified cases and reviews of the underlying etiology and pathology of Alzheimer's and available therapies to exemplify diagnostic and treatment subtleties.

Problem

The clinical presentation of Alzheimer's is complex and varied. Multiple other primary brain pathologies present with clinical phenotypes that can be difficult to distinguish from AD. Furthermore, Alzheimer's rarely exists in isolation, as almost all patients also show evidence of other primary brain pathologies, including Lewy body disease and argyrophilic grain disease. The phenotype and progression of AD can vary based on the brain regions affected by pathology, the coexistence and severity of other brain pathologies, the presence and severity of systemic comorbidities such as cardiac disease, the common co-occurrence with psychiatric diagnoses, and genetic risk factors. Additionally, symptoms and progression are influenced by an individual's brain reserve and cognitive reserve, as well as the timing of the diagnosis, which depends on the demographics of both the patient and the diagnosing physician, as well as the availability of biomarkers.

Methods

The optimal clinical and biomarker strategy for accurately diagnosing AD, common neuropathologic co-morbidities and mimickers, and available medication and non-medication-based treatments are discussed. Real-life examples of cognitive loss illustrate the diagnostic and treatment decision-making process as well as illustrative treatment responses.

Implications

AD is best considered a syndromic disorder, influenced by a multitude of patient and environmental characteristics. Additionally, AD existing alone is a unicorn, as there are nearly always coexisting other brain pathologies. Accurate diagnosis with biomarkers is essential. Treatment response is affected by the variables involved, and the effective treatment of Alzheimer's disease, as well as its prevention, requires an individualized, precision medicine strategy.