Characteristics of patients misdiagnosed with Alzheimer's disease and their medication use: an analysis of the NACC-UDS database

AI-based differential diagnosis of dementia etiologies on multimodal data

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

Assessment of Preanalytical Cerebrospinal Fluid Handling and Storage Factors on Measurement of Aβ1-42, Aβ1-40, and pTau181 Using an Automated Chemiluminescent Platform

BACKGROUND

Standardizing cerebrospinal fluid (CSF) laboratory protocols will improve the reliability and availability of clinical biomarker testing required for prescription of novel Alzheimer disease (AD) therapies. This study evaluated several preanalytical handling and storage factors common to β-amyloid1-42 (Aβ1-42), β-amyloid1-40 (Aβ1-40), and phosphorylated tau (pTau181) concentrations including storage at different temperatures, extended cap contact, various mixing methods, and multiple freeze-thaw cycles.

METHODS

Aβ1-42, Aβ1-40, and pTau181 concentrations were measured using LUMIPULSE G1200 automated assays. Samples were collected in polypropylene tubes of various volumes. Sample cap-contact was evaluated by storing samples in upright and inverted positions at either 4°C for 1 week or -80°C for 1 month. To assess mixing methods, samples were freeze-thawed and mixed by inversion, vortex, horizontal roller, or unmixed prior to assay sampling. The impact of successive freeze-thaw cycles was assessed through freezing, thawing, and analyzing CSF samples.

RESULTS

Short-term storage at 4°C did not affect Aβ1-42, Aβ1-40, or pTau181 measurements in any tube type. Tube cap contact affected Aβ1-42 in 2.5 mL tubes and pTau181 levels in 10 mL tubes. No difference was observed between mixing methods. After 4 freeze-thaw cycles, Aβ1-42 significantly decreased but Aβ1-40 remained unchanged. Utilizing the Aβ1-42/Aβ1-40 ratio, Aβ1-42 values normalized, maintaining ratio values within ±5% of baseline measurements.

CONCLUSIONS

Storage of CSF at 4°C for 1 week or -80°C for 1 month did not significantly affect Aβ1-42, Aβ1-40, pTau181, or associated ratio measurements. Tube cap-contact impacted pTau181 and pTau181/Aβ1-42 values in larger tubes. Mixing methods are equivalent. The Aβ1-42/Aβ1-40 ratio compensates for freeze-thaw variability up to 4 cycles.

Alcohol Use Disorder and Dementia: A Review

PURPOSE

By 2040, 21.6% of Americans will be over age 65, and the population of those older than age 85 is estimated to reach 14.4 million. Although not causative, older age is a risk factor for dementia: every 5 years beyond age 65, the risk doubles; approximately one-third of those older than age 85 are diagnosed with dementia. As current alcohol consumption among older adults is significantly higher compared to previous generations, a pressing question is whether drinking alcohol increases the risk for Alzheimer's disease or other forms of dementia.

SEARCH METHODS

Databases explored included PubMed, Web of Science, and ScienceDirect. To accomplish this narrative review on the effects of alcohol consumption on dementia risk, the literature covered included clinical diagnoses, epidemiology, neuropsychology, postmortem pathology, neuroimaging and other biomarkers, and translational studies. Searches conducted between January 12 and August 1, 2023, included the following terms and combinations: "aging," "alcoholism," "alcohol use disorder (AUD)," "brain," "CNS," "dementia," "Wernicke," "Korsakoff," "Alzheimer," "vascular," "frontotemporal," "Lewy body," "clinical," "diagnosis," "epidemiology," "pathology," "autopsy," "postmortem," "histology," "cognitive," "motor," "neuropsychological," "magnetic resonance," "imaging," "PET," "ligand," "degeneration," "atrophy," "translational," "rodent," "rat," "mouse," "model," "amyloid," "neurofibrillary tangles," "α-synuclein," or "presenilin." When relevant, "species" (i.e., "humans" or "other animals") was selected as an additional filter. Review articles were avoided when possible.

SEARCH RESULTS

The two terms "alcoholism" and "aging" retrieved about 1,350 papers; adding phrases-for example, "postmortem" or "magnetic resonance"-limited the number to fewer than 100 papers. Using the traditional term, "alcoholism" with "dementia" resulted in 876 citations, but using the currently accepted term "alcohol use disorder (AUD)" with "dementia" produced only 87 papers. Similarly, whereas the terms "Alzheimer's" and "alcoholism" yielded 318 results, "Alzheimer's" and "alcohol use disorder (AUD)" returned only 40 citations. As pertinent postmortem pathology papers were published in the 1950s and recent animal models of Alzheimer's disease were created in the early 2000s, articles referenced span the years 1957 to 2024. In total, more than 5,000 articles were considered; about 400 are herein referenced.

DISCUSSION AND CONCLUSIONS

Chronic alcohol misuse accelerates brain aging and contributes to cognitive impairments, including those in the mnemonic domain. The consensus among studies from multiple disciplines, however, is that alcohol misuse can increase the risk for dementia, but not necessarily Alzheimer's disease. Key issues to consider include the reversibility of brain damage following abstinence from chronic alcohol misuse compared to the degenerative and progressive course of Alzheimer's disease, and the characteristic presence of protein inclusions in the brains of people with Alzheimer's disease, which are absent in the brains of those with AUD.

Intelligent decision support systems for dementia care: A scoping review

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.

Small RNA Changes in Plasma Have Potential for Early Diagnosis of Alzheimer's Disease before Symptom Onset

Alzheimer's disease (AD), due to its multifactorial nature and complex etiology, poses challenges for research, diagnosis, and treatment, and impacts millions worldwide. To address the need for minimally invasive, repeatable measures that aid in AD diagnosis and progression monitoring, studies leveraging RNAs associated with extracellular vesicles (EVs) in human biofluids have revealed AD-associated changes. However, the validation of AD biomarkers has suffered from the collection of samples from differing points in the disease time course or a lack of confirmed AD diagnoses. Here, we integrate clinical diagnosis and postmortem pathology data to form more accurate experimental groups and use small RNA sequencing to show that EVs from plasma can serve as a potential source of RNAs that reflect disease-related changes. Importantly, we demonstrated that these changes are identifiable in the EVs of preclinical patients, years before symptom manifestation, and that machine learning models based on differentially expressed RNAs can help predict disease conversion or progression. This research offers critical insight into early disease biomarkers and underscores the significance of accounting for disease progression and pathology in human AD studies.

Consumption of drugs for Alzheimer's disease on the Brazilian private market

OBJECTIVE

To analyze the consumption of drugs for Alzheimer's disease on the Brazilian private market and its geographical distribution from 2014 to 2020.

METHODS

National data from the Brazilian National System of Controlled Product Management were used, regarding sales of donepezil, galantamine, rivastigmine, and memantine from January 2014 to December 2020. Sales data were used as a proxy for drug consumption and expressed as defined daily dose/1,000 inhabitants/year at national, regional, federative unit and microregion levels.

RESULTS

Drug consumption went from 5,000 defined daily doses/1,000 inhabitants, in 2014, to more than 16,000/1,000 inhabitants, in 2020, and all federative units showed positive variation. The Brazilian Northeast had the highest cumulative consumption in the period but displayed microregional disparities while the North region had the lowest consumption. Donepezil and memantine were the most consumed drugs, with the highest growth in consumption from 2014 to 2020.

CONCLUSION

The consumption of medicines indicated to treat Alzheimer's disease tripled in Brazil between 2014 and 2020, which may relate to the increase in the prevalence of the disease in the country, greater access to health services, and inappropriate use. This challenges managers and healthcare providers due to population aging and the increased prevalence of chronic-degenerative diseases.

Genetic risk factor clustering within and across neurodegenerative diseases

Overlapping symptoms and co-pathologies are common in closely related neurodegenerative diseases (NDDs). Investigating genetic risk variants across these NDDs can give further insight into disease manifestations. In this study we have leveraged genome-wide single nucleotide polymorphisms and genome-wide association study summary statistics to cluster patients based on their genetic status across identified risk variants for five NDDs (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Lewy body dementia and frontotemporal dementia). The multi-disease and disease-specific clustering results presented here provide evidence that NDDs have more overlapping genetic aetiology than previously expected and how neurodegeneration should be viewed as a spectrum of symptomology. These clustering analyses also show potential subsets of patients with these diseases that are significantly depleted for any known common genetic risk factors suggesting environmental or other factors at work. Establishing that NDDs with overlapping pathologies share genetic risk loci, future research into how these variants might have different effects on downstream protein expression, pathology and NDD manifestation in general is important for refining and treating NDDs.

Neuropsychological Comparison of Patients With Alzheimer's Disease and Dementia With Lewy Bodies

BACKGROUND AND PURPOSE

This study aimed to determine the neuropsychological differences between patients with early-stage Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) with a Clinical Dementia Rating (CDR) score of ≤1.

METHODS

We examined 168 patients with AD (126 with CDR score=0.5, 42 with CDR score=1) and 169 patients with DLB (104 with CDR score=0.5, 65 with CDR score=1) whose diagnoses were supported by 18F-flobetaben positron-emission tomography (PET) and 18F-N-(3-fluoropropyl)-2β-carbon ethoxy-3β-(4-iodophenyl) nortropane PET. Neuropsychological test scores were compared after controlling for age, sex, and education duration. Using a cutoff motor score on the Unified Parkinson's Disease Rating Scale of 20, patients with AD were further divided into AD with parkinsonism (ADP+, n=86) and AD without parkinsonism (ADP-, n=82).

RESULTS

At CDR scores of both 0.5 and 1, the DLB group had lower scores on the attention (digit-span forward at CDR score=0.5 and backward at CDR score=1), visuospatial, and executive (color reading Stroop test at CDR score=0.5 and phonemic fluency test, Stroop tests, and digit symbol coding at CDR score=1) tests than the AD group, but higher scores on the memory tests. The ADP- and ADP+ subgroups had comparable scores on most neuropsychological tests, but the ADP+ subgroup had lower scores on the color reading Stroop test.

CONCLUSIONS

Patients with DLB had worse attention, visuospatial, and executive functions but better memory function than patients with AD. Parkinsonism was not uncommon in the patients with AD and could be related to attention and executive dysfunction.

Living With Frontotemporal Degeneration: Diagnostic Journey, Symptom Experiences, and Disease Impact

Frontotemporal degeneration (FTD) is an umbrella term encompassing a range of rare neurodegenerative disorders that cause progressive declines in cognition, behavior, and personality. Hearing directly from individuals living with FTD and their care partners is critical in optimizing care, identifying meaningful clinical trial endpoints, and improving research recruitment and retention. The current paper presents a subset of data from the FTD Insights Survey, chronicling the diagnostic journey, symptoms, and the impact of FTD on distress, quality of life, and independence, in the mild to moderate stages of the disease. Survey respondents included 219 individuals diagnosed with FTD and 437 current care partners, representing a range of FTD diagnoses. Around half of survey respondents reported seeing three or more doctors before an FTD diagnosis was given, and a range of prior diagnoses were noted. Most frequently endorsed symptoms tended to be consistent with clinical characteristics of the specific diagnosis, though there was significant variability in symptoms reported within diagnostic categories as well as considerable overlap in symptoms between diagnostic categories. Cognitive and language symptoms of FTD were generally most distressing to the person diagnosed, and a loss of independence was endorsed as affecting quality of life. The distinct perspectives of diagnosed persons and care partners regarding disease impact differed notably for bvFTD/Pick's disease. Participating independently in a range of activities, within the home, outside the home, and with other people, were reported as challenging for people living with FTD, underscoring the degree to which the lives of these individuals are affected even at the mild and moderate stages of disease. Overall, by heeding the perspectives of those living with FTD, we can begin to design more meaningful research studies, provide better care, and develop therapies that improve quality of life.

Retinal imaging biomarkers of Alzheimer's disease: A systematic review and meta-analysis of studies using brain amyloid beta status for case definition

Introduction

We performed a systematic review and meta-analysis of the association between retinal imaging parameters and Alzheimer's disease (AD).

Methods

PubMed, EMBASE, and Scopus were systematically searched for prospective and observational studies. Included studies had AD case definition based on brain amyloid beta (Aβ) status. Study quality assessment was performed. Random-effects meta-analyses of standardized mean difference, correlation, and diagnostic accuracy were conducted.

Results

Thirty-eight studies were included. There was weak evidence of peripapillary retinal nerve fiber layer thinning on optical coherence tomography (OCT) (p = 0.14, 11 studies, n = 828), increased foveal avascular zone area on OCT-angiography (p = 0.18, four studies, n = 207), and reduced arteriole and venule vessel fractal dimension on fundus photography (p < 0.001 and p = 0.08, respectively, three studies, n = 297) among AD cases.

Discussion

Retinal imaging parameters appear to be associated with AD. Small study sizes and heterogeneity in imaging methods and reporting make it difficult to determine utility of these changes as AD biomarkers.

Highlights

We performed a systematic review on retinal imaging and Alzheimer's disease (AD).We only included studies in which cases were based on brain amyloid beta status.Several retinal biomarkers were associated with AD but clinical utility is uncertain.Studies should focus on biomarker-defined AD and use standardized imaging methods.

Advancing Diagnostic Certainty in Alzheimer's Disease: A Synthesis of the Diagnostic Process

Changes in diagnostic certainty can be evaluated by assessing the impact of a diagnostic test in driving decision making. Diagnostic tests can be appraised using validated measures of accuracy, i.e., sensitivity, specificity, and positive or negative predictive values against a known reference standard. However, other less well formalized factors affect diagnostic certainty. These inputs are under-researched and more difficult to quantify. Clinicians assess the significance of available data in the context of their expertise, pre-diagnostic confidence, and background knowledge of populations and disease. Inherent qualities of the diagnostic test and an individual clinician's interpretation of the meaning of test results will also affect the subsequent level of diagnostic certainty. These factors are only infrequently considered alongside the diagnostic accuracy of a test. In this paper, we present a model of the different processes which can affect diagnostic certainty in Alzheimer's disease (AD). This model builds upon existing understanding and provides further insights into the complexity of diagnostic certainty in AD and how we might improve this.

Blood Biomarkers for the Diagnosis of Neurodegenerative Dementia: A Systematic Review

IMPORTANCE

Accurately diagnosing neurodegenerative dementia is often challenging due to overlapping clinical features. Disease specific biomarkers could enhance diagnostic accuracy. However, CSF analysis procedures and advanced imaging modalities are either invasive or high-priced, and routinely unavailable. Easily accessible disease biomarkers would be of utmost value for accurate differential diagnosis of dementia subtypes.

OBJECTIVE

To assess the diagnostic accuracy of blood-based biomarkers for the differential diagnosis of AD from Frontotemporal Lobar Degeneration (FTLD), or AD from Dementia with Lewy Bodies (DLB).

METHODS

Systematic review. Three databases (PubMed, Scopus, and Web of Science) were searched. Studies assessing blood-based biomarkers levels in AD versus FTLD, or AD versus DLB, and its diagnostic accuracy, were selected. When the same biomarker was assessed in three or more studies, a meta-analysis was performed. QUADAS-2 criteria were used for quality assessment.

RESULTS

Twenty studies were included in this analysis. Collectively, 905 AD patients were compared to 1262 FTLD patients, and 209 AD patients were compared to 246 DLB patients. Regarding biomarkers for AD versus FTLD, excellent discriminative accuracy (AUC >0.9) was found for p-tau181, p-tau217, synaptophysin, synaptopodin, GAP43 and calmodulin. Other biomarkers also demonstrated good accuracy (AUC = 0.8-0.9). For AD versus DLB distinction, only miR-21-5p and miR-451a achieved excellent accuracy (AUC >0.9).

CONCLUSION

Encouraging results were found for several biomarkers, alone or in combination. Prospective longitudinal designs and consensual protocols, comprising larger cohorts and homogeneous testing modalities across centres, are essential to validate the clinical value of blood biomarkers for the precise etiological diagnosis of dementia.

Alzheimer Hastalığını Hafif Bilişsel Bozukluktan Ayırmak İçin Basit Bir MRI Tabanlı Görsel Kılavuz

Objective: To distinguish between mild cognitive impairment (MCI) and Alzheimer’s disease (AD) by visual assessment of the length of the hippocampus in magnetic resonance imaging (MRI). Method: Consecutive patients diagnosed with MCI and AD were searched on the system retrospectively. MRI was rated for hippocampal atrophy defining with and without loss of hippocampal length. Patients with loss of hippocampal height were classified as having AD by the clinical investigator, and the diagnosis of the patients was checked on the system. Results: A total of 56 memory clinic patients with AD (n=14) and MCI (n=42) were included in the study. AD patients had significantly more hippocampal atrophy than MCI patients (𝜒2=6.222, df=0.13, 𝑝=0.013). Conclusion: There is a complex issue in the differential diagnosis between MCI and AD. A simple glace to the MRI may give a brief opinion to the physician in the clinic routine.

Digital twins to enable better precision and personalized dementia care

In this perspective paper, we want to highlight the potential benefits of incorporating digital twins to support better dementia care. In particular, we assert that, by doing so, it is possible to ensure greater precision regarding dementia care while simultaneously enhancing personalization. Digital twins have been used successfully in manufacturing to enable better prediction and tailoring of solutions to meet required needs, and thereby have enabled more effective and efficient deployment of resources. We develop a model for digital twin in the healthcare domain as a clinical decision support tool by extrapolating its current uses from the manufacturing domain. We illustrate the power of the developed model in the context of dementia. Given the rapid rise of chronic conditions and the pressures on healthcare delivery to provide high quality, cost-effective care anywhere and anytime, we assert that such an approach is consistent with a value-based healthcare philosophy and thus important as the numbers of people with dementia continues to grow exponentially and this pressing healthcare issue is yet to be optimally addressed. Further research and development in this rapidly evolving domain is a strategic priority for ensuring the delivery of superior dementia care.

Monoamine oxidase binding not expected to significantly affect [18F]flortaucipir PET interpretation

Purpose

[¹⁸F]-labeled positron emission tomography (PET) radioligands permit in vivo assessment of Alzheimer’s disease biomarkers, including aggregated neurofibrillary tau (NFT) with [¹⁸F]flortaucipir. Due to structural similarities of flortaucipir with some monoamine oxidase A (MAO-A) inhibitors, this study aimed to evaluate flortaucipir binding to MAO-A and MAO-B and any potential impact on PET interpretation.

Methods

[¹⁸F]Flortaucipir autoradiography was performed on frozen human brain tissue slices, and PET imaging was conducted in rats. Dissociation constants were determined by saturation binding, association and dissociation rates were measured by kinetic binding experiments, and IC₅₀ values were determined by competition binding.

Results

Under stringent wash conditions, specific [¹⁸F]flortaucipir binding was observed on tau NFT-rich Alzheimer’s disease tissue and not control tissue. In vivo PET experiments in rats revealed no evidence of [¹⁸F]flortaucipir binding to MAO-A; pre-treatment with MAO inhibitor pargyline did not impact uptake or wash-out of [¹⁸F]flortaucipir. [¹⁸F]Flortaucipir bound with low nanomolar affinity to human MAO-A in a microsomal preparation in vitro but with a fast dissociation rate relative to MAO-A ligand fluoroethyl-harmol, consistent with no observed in vivo binding in rats of [¹⁸F]flortaucipir to MAO-A. Direct binding of flortaucipir to human MAO-B was not detected in a microsomal preparation. A high concentration of flortaucipir (IC₅₀ of 1.3 μM) was found to block binding of the MAO-B ligand safinamide to MAO-B on microsomes suggesting that, at micromolar concentrations, flortaucipir weakly binds to MAO-B in vitro.

Conclusion

These data suggest neither MAO-A nor MAO-B binding will contribute significantly to the PET signal in cortical target areas relevant to the interpretation of [¹⁸F]flortaucipir.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05822-9.

Automated detection, selection and classification of hippocampal landmark points for the diagnosis of Alzheimer's disease

BACKGROUND AND OBJECTIVE

Alzheimer's disease (AD) is a neurodegenerative, progressive, and irreversible disease that accounts for up to 80% of all dementia cases. AD predominantly affects older adults, and its clinical diagnosis is a challenging evaluation process, with imprecision rates between 12 and 23%. Structural magnetic resonance (MR) imaging has been widely used in studies related to AD because this technique provides images with excellent anatomical details and information about structural changes induced by the disease in the brain. Current studies are focused on detecting AD in its initial stage, i.e., mild cognitive impairment (MCI), since treatments for preventing or delaying the onset of symptoms is more effective when administered at the early stages of the disease. This study proposes a new technique to perform MR image classification in AD diagnosis using discriminative hippocampal point landmarks among the cognitively normal (CN), MCI, and AD populations.

METHODS

Our approach, based on a two-level classification, first detects and selects discriminative landmark points from two diagnosis populations based on their matching distance compared to a probabilistic atlas of 3-D labeled landmark points. The points are classified using attributes computed in a spherical support region around each point using information from brain probability image tissues of gray matter, white matter, and cerebrospinal fluid as sources of information. Next, at the second level, the images are classified based on a quantitative evaluation obtained from the first-level classifier outputs.

RESULTS

For the CN×MCI experiment, we achieved an AUC of 0.83, an accuracy of 75.58%, with 72.9% of sensitivity and 77.81% of specificity. For the MCI×AD experiment, we achieved an AUC value of 0.73, an accuracy of 69.8%, a sensitivity of 74.09% and specificity of 64.57%. Finally, for the CN×AD, we achieved an AUC of 0.95, an accuracy of 89.24%, with 85.58% of sensitivity and 92.71% of specificity.

CONCLUSIONS

The obtained classification results are similar to (or even higher than) other studies that classify AD compared to CN individuals and comparable to those classified patients with MCI.

A combination of multiple autoantibodies is associated with the risk of Alzheimer’s disease and cognitive impairment

Autoantibodies are self-antigen reactive antibodies that play diverse roles in the normal immune system, tissue homeostasis, and autoimmune and neurodegenerative diseases. Anti-neuronal autoantibodies have been detected in neurodegenerative disease serum, with unclear significance. To identify diagnostic biomarkers of Alzheimer’s disease (AD), we analyzed serum autoantibody profiles of the HuProt proteome microarray using the discovery set of cognitively normal control (NC, n = 5) and AD (n = 5) subjects. Approximately 1.5-fold higher numbers of autoantibodies were detected in the AD group (98.0 ± 39.9/person) than the NC group (66.0 ± 39.6/person). Of the autoantigen candidates detected in the HuProt microarray, five autoantigens were finally selected for the ELISA-based validation experiment using the validation set including age- and gender-matched normal (NC, n = 44), mild cognitive impairment (MCI, n = 44) and AD (n = 44) subjects. The serum levels of four autoantibodies including anti-ATCAY, HIST1H3F, NME7 and PAIP2 IgG were significantly different among NC, MCI and/or AD groups. Specifically, the anti-ATCAY autoantibody level was significantly higher in the AD (p = 0.003) and MCI (p = 0.015) groups compared to the NC group. The anti-ATCAY autoantibody level was also significantly correlated with neuropsychological scores of MMSE (rs = − 0.229, p = 0.012), K-MoCA (rs = − 0.270, p = 0.003), and CDR scores (rs = 0.218, p = 0.016). In addition, a single or combined occurrence frequency of anti-ATCAY and anti-PAIP2 autoantibodies was significantly associated with the risk of MCI and AD. This study indicates that anti-ATCAY and anti-PAIP2 autoantibodies could be a potential diagnostic biomarker of AD.

Deep Learning With 18F-Fluorodeoxyglucose-PET Gives Valid Diagnoses for the Uncertain Cases in Memory Impairment of Alzheimer's Disease

Objectives: Neuropsychological tests are an important basis for the memory impairment diagnosis in Alzheimer’s disease (AD). However, multiple memory tests might be conflicting within-subjects and lead to uncertain diagnoses in some cases. This study proposed a framework to diagnose the uncertain cases of memory impairment.

Methods: We collected 2,386 samples including AD, mild cognitive impairment (MCI), and cognitive normal (CN) using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and three different neuropsychological tests (Mini-Mental State Examination, Alzheimer’s Disease Assessment Scale-Cognitive Subscale, and Clinical Dementia Rating) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A deep learning (DL) framework using FDG-PET was proposed to diagnose uncertain memory impairment cases that were conflicting between tests. Subsequent ANOVA, chi-squared, and t-test were used to explain the potential causes of uncertain cases.

Results: For certain cases in the testing set, the proposed DL framework outperformed other methods with 95.65% accuracy. For the uncertain cases, its positive diagnoses had a significant (p < 0.001) worse decline in memory function than negative diagnoses in a longitudinal study of 40 months on average. In the memory-impaired group, uncertain cases were mainly explained by an AD metabolism pattern but mild in extent (p < 0.05). In the healthy group, uncertain cases were mainly explained by a non-energetic mental state (p < 0.001) measured using a global deterioration scale (GDS), with a significant depression-related metabolism pattern detected (p < 0.05).

Conclusion: A DL framework for diagnosing uncertain cases of memory impairment is proposed. Proved by longitudinal tracing of its diagnoses, it showed clinical validity and had application potential. Its valid diagnoses also provided evidence and explanation of uncertain cases based on the neurodegeneration and depression mental state.

Shaping a data-driven era in dementia care pathway through computational neurology approaches

BACKGROUND

Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making.

MAIN BODY

Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations.

CONCLUSION

The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Association of Positive Delirium Screening with Incident Dementia in Skilled Nursing Facilities

BACKGROUND AND OBJECTIVE

Early detection of delirium in skilled nursing facilities (SNFs) is a priority. The extent to which delirium screening leads to a potentially inappropriate diagnosis of Alzheimer's disease and related dementia (ADRD) is unknown.

DESIGN

Nationwide retrospective cohort study from 2011 to 2013.

SETTING

An SNF.

PARTICIPANTS

A total of 1,175,550 Medicare enrollees who entered the SNF from a hospital and had no prior diagnosis of dementia.

EXPOSURE

A positive screen for delirium using the validated Confusion Assessment Method (CAM), performed as part of the federally mandated Minimum Data Set (MDS) assessment.

MEASUREMENTS

Incident all-cause dementia, ascertained through International Classification of Diseases, Ninth Revision (ICD-9), diagnosis in Medicare claims or active diagnoses in MDS.

RESULTS

Positive screening for delirium was identified in 7.7% of cases (n = 90,449), and most occurred within the first 7 days of SNF admission (62.5%). The overall incidence of ADRD was 6.3% (n = 73,542). Nearly all new diagnoses of ADRD (93.5%) occurred within the first 30 days of SNF admission. Patients who screened CAM positive for delirium had a nearly threefold increased risk of receiving an incident ADRD diagnosis on the same day (hazard ratio (HR) = 2.63; 95% confidence interval (CI) = 1.50-4.63). Among patients who screened CAM positive for delirium, those who were cognitively intact or had mild cognitive impairments were, on average, six times more likely to receive an incident ADRD diagnosis (HR = 6.64; 95% CI = 1.76-25.0) relative to those testing CAM negative.

CONCLUSION AND RELEVANCE

Among older adults not previously diagnosed with dementia, a positive screen for delirium was significantly associated with higher risk of ADRD diagnosis after admission to a SNF. This risk was highest for patients in the first days of their stay and with the least cognitive impairment, suggesting that the ADRD diagnosis was potentially inappropriate.

Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease

Alzheimer's Disease (AD) is the most common neurodegenerative disease, with 10% prevalence in the elder population. Conventional Machine Learning (ML) was proven effective in supporting the diagnosis of AD, while very few studies investigated the performance of deep learning and transfer learning in this complex task. In this paper, we evaluated the potential of ensemble transfer-learning techniques, pretrained on generic images and then transferred to structural brain MRI, for the early diagnosis and prognosis of AD, with respect to a fusion of conventional-ML approaches based on Support Vector Machine directly applied to structural brain MRI. Specifically, more than 600 subjects were obtained from the ADNI repository, including AD, Mild Cognitive Impaired converting to AD (MCIc), Mild Cognitive Impaired not converting to AD (MCInc), and cognitively-normal (CN) subjects. We used T1-weighted cerebral-MRI studies to train: (1) an ensemble of five transfer-learning architectures pretrained on generic images; (2) a 3D Convolutional Neutral Network (CNN) trained from scratch on MRI volumes; and (3) a fusion of two conventional-ML classifiers derived from different feature extraction/selection techniques coupled to SVM. The AD-vs-CN, MCIc-vs-CN, MCIc-vs-MCInc comparisons were investigated. The ensemble transfer-learning approach was able to effectively discriminate AD from CN with 90.2% AUC, MCIc from CN with 83.2% AUC, and MCIc from MCInc with 70.6% AUC, showing comparable or slightly lower results with the fusion of conventional-ML systems (AD from CN with 93.1% AUC, MCIc from CN with 89.6% AUC, and MCIc from MCInc with AUC in the range of 69.1-73.3%). The deep-learning network trained from scratch obtained lower performance than either the fusion of conventional-ML systems and the ensemble transfer-learning, due to the limited sample of images used for training. These results open new prospective on the use of transfer learning combined with neuroimages for the automatic early diagnosis and prognosis of AD, even if pretrained on generic images.

Age-stratified longitudinal study of Alzheimer's and vascular dementia patients

INTRODUCTION

Similar symptoms, comorbidities and suboptimal diagnostic tests make the distinction between different types of dementia difficult, although this is essential for improved work-up and treatment optimization.

METHODS

We calculated temporal disease trajectories of earlier multi-morbidities in Alzheimer's disease (AD) dementia and vascular dementia (VaD) patients using the Danish National Patient Registry covering all hospital encounters in Denmark (1994 to 2016). Subsequently, we reduced the comorbidity space dimensionality using a non-linear technique, uniform manifold approximation and projection.

RESULTS

We found 49,112 and 24,101 patients that were diagnosed with AD or VaD, respectively. Temporal disease trajectories showed very similar disease patterns before the dementia diagnosis. Stratifying patients by age and reducing the comorbidity space to two dimensions, showed better discrimination between AD and VaD patients in early-onset dementia.

DISCUSSION

Similar age-associated comorbidities, the phenomenon of mixed dementia, and misdiagnosis create great challenges in discriminating between classical subtypes of dementia.

Genetic architecture of common non-Alzheimer's disease dementias

Frontotemporal dementia (FTD), dementia with Lewy bodies (DLB) and vascular dementia (VaD) are the most common forms of dementia after Alzheimer’s disease (AD). The heterogeneity of these disorders and/or the clinical overlap with other diseases hinder the study of their genetic components. Even though Mendelian dementias are rare, the study of these forms of disease can have a significant impact in the lives of patients and families and have successfully brought to the fore many of the genes currently known to be involved in FTD and VaD, starting to give us a glimpse of the molecular mechanisms underlying these phenotypes. More recently, genome-wide association studies have also pointed to disease risk-associated loci. This has been particularly important for DLB where familial forms of disease are very rarely described. In this review we systematically describe the Mendelian and risk genes involved in these non-AD dementias in an effort to contribute to a better understanding of their genetic architecture, find differences and commonalities between different dementia phenotypes, and uncover areas that would benefit from more intense research endeavors.

Novel fluid biomarkers to differentiate frontotemporal dementia and dementia with Lewy bodies from Alzheimer's disease: A systematic review

RATIONALE

Frontotemporal dementia (FTD) and dementia with Lewy bodies (DLB) are two common forms of neurodegenerative dementia, subsequent to Alzheimer's disease (AD). AD is the only dementia that includes clinically validated cerebrospinal fluid (CSF) biomarkers in the diagnostic criteria. FTD and DLB often overlap with AD in their clinical and pathological features, making it challenging to differentiate between these conditions.

AIM

This systematic review aimed to identify if novel fluid biomarkers are useful in differentiating FTD and DLB from AD. Increasing the certainty of the differentiation between dementia subtypes would be advantageous clinically and in research.

METHODS

PubMed and Scopus were searched for studies that quantified and assessed diagnostic accuracy of novel fluid biomarkers in clinically diagnosed patients with FTD or DLB, in comparison to patients with AD. Meta-analyses were performed on biomarkers that were quantified in 3 studies or more.

RESULTS

The search strategy yielded 614 results, from which, 27 studies were included. When comparing bio-fluid levels in AD and FTD patients, neurofilament light chain (NfL) level was often higher in FTD, whilst brain soluble amyloid precursor protein β (sAPPβ) was higher in patients with AD. When comparing bio-fluid levels in AD and DLB patients, α-synuclein ensued heterogeneous findings, while the noradrenaline metabolite (MHPG) was found to be lower in DLB. Ratios of Aβ42/Aβ38 and Aβ42/Aβ40 were lower in AD than FTD and DLB and offered better diagnostic accuracy than raw amyloid-β (Aβ) concentrations.

CONCLUSIONS

Several promising novel biomarkers were highlighted in this review. Combinations of fluid biomarkers were more often useful than individual biomarkers in distinguishing subtypes of dementia. Considering the heterogeneity in methods and results between the studies, further validation, ideally with longitudinal prospective designs with large sample sizes and unified protocols, are fundamental before conclusions can be finalised.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Background

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.

Results

Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.

Conclusion

PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

Concordance of Clinical Alzheimer Diagnosis and Neuropathological Features at Autopsy

It remains unclear what clinical features inform the accuracy of a clinical diagnosis of Alzheimer disease (AD). Data were obtained from the National Alzheimer's Coordinating Center to compare clinical and neuropathologic features among participants who did or did not have Alzheimer disease neuropathologic changes (ADNC) at autopsy. Participants (1854) had a clinical Alzheimer dementia diagnosis and ADNC at autopsy (Confirmed-AD), 204 participants had an AD diagnosis and no ADNC (AD-Mimics), and 253 participants had no AD diagnosis and ADNC (Unidentified-AD). Compared to Confirmed-AD participants, AD-Mimics had less severe cognitive impairment, while Unidentified-AD participants displayed more parkinsonian signs, depression, and behavioral problems. This study highlights the importance of developing a complete panel of biomarkers as a tool to inform clinical diagnoses, as clinical phenotypes that are typically associated with diseases other than AD may result in inaccurate diagnoses.

Patient with frontal-variant syndrome in early-onset Alzheimer's disease

The clinical manifestation of frontal-variant Alzheimer’s disease (fvAD) is not typical, and it is difficult yet necessary to differentiate fvAD from frontal-variant frontal temporal dementia (fvFTD). We describe a patient with early-onset Alzheimer’s disease (AD) who presented with an fvFTD-like syndrome and apolipoprotein E ɛ3/ ɛ4 genotype. A brain amyloid imaging procedure, 11C-Pittsburgh compound B positron emission tomography (PET), supported the final diagnosis of AD. Our present case highlights the clinical variability that characterises early-onset AD. A multimodal approach is crucial when assessing rare forms of dementia.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

Background:

More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms.

Objective:

Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis.

Materials and Methods:

We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers).

Results and Conclusion:

We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

Validity of screening instruments for the detection of dementia and mild cognitive impairment in hospital inpatients: A systematic review of diagnostic accuracy studies

INTRODUCTION

As the population ages, Alzheimer's disease and other subtypes of dementia are becoming increasingly prevalent. However, in recent years, diagnosis has often been delayed or not made at all. Thus, improving the rate of diagnosis has become an integral part of national dementia strategies. Although screening for dementia remains controversial, the case is strong for screening for dementia and other forms of cognitive impairment in hospital inpatients. For this reason, the objective of this systematic review was to provide clinicians, who wish to implement screening, an up-to-date choice of cognitive tests with the most extensive evidence base for the use in elective hospital inpatients.

METHODS

For this systematic review, PubMed, PsycINFO and Cochrane Library were searched by using a multi-concept search strategy. The databases were accessed on April 10, 2019. All cross-sectional studies that utilized brief, multi-domain cognitive tests as index test and a reference standard diagnosis of dementia or mild cognitive impairment as comparator were included. Only studies conducted in the hospital setting, sampling from unselected, elective inpatients older than 64 were considered.

RESULTS

Six studies met the inclusion criteria, with a total of 2112 participants. Diagnostic accuracy data for the Six-Item Cognitive Impairment Test, Cognitive Performance Scale, Clock-Drawing Test, Mini-Mental Status Examination, and Time & Change test were extracted and descriptively analyzed. Clinical and methodological heterogeneity between the studies precluded performing a meta-analysis.

DISCUSSION

This review found only a small number of instruments and was not able to recommend a single best instrument for use in a hospital setting. Although it was not possible to estimate the pooled operating characteristics, the included description of instrument characteristics, the descriptive analysis of performance measures, and the critical evaluation of the reporting studies may contribute to clinician's choice of the screening instrument that fits best their purpose.

Investigating the Association Between Verbal Forgetting and Pathological Markers of Alzheimer's and Lewy Body Diseases

BACKGROUND

The percentage of verbal forgetting (VF%) measure of the Rey Auditory Verbal Learning Test (RAVLT) has been proposed to differentiate patients diagnosed clinically with Alzheimer's disease (AD) and dementia with Lewy bodies (DLB).

OBJECTIVE

To determine if VF% aligns with gold-standard biomarker and autopsy evidence of AD and DLB neuropathology.

METHODS

Clinical, cognitive, sociodemographic, and biomarker data were collected from 315 patients with baseline cognitive impairment and 485 normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). AD markers included reduced cerebrospinal fluid (CSF) amyloid-β, elevated total-tau and phosphorylated-tau, hippocampal atrophy, and the presence of amyloid plaques and neurofibrillary tangles at autopsy. DLB markers included reduced CSF α-synuclein, preserved hippocampus, atrophied putamen, occipital glucose metabolism, and the presence of Lewy bodies at autopsy. Cognitively impaired participants were classified as ADVF% (n = 190) or DLBVF% (n = 125) based on their RAVLT VF% scores using a 75% cut-off (≥75%  = ADVF%, <75%  = DLBVF%). Postmortem data were available for 13 ADVF% participants, 13 DLBVF% patients, and six healthy controls.

RESULTS

ADVF% and DLBVF% participants did not differ on CSF or neuroimaging biomarkers, with the exception of total tau levels which were higher in ADVF%. In the subset of participants with autopsy data, comorbid AD and DLB pathology was most frequent in ADVF% participants, and pure DLB pathology was most frequent in DLBVF% participants, however, these differences were not statistically significant.

CONCLUSION

The RAVLT VF% measure does not reliably align with AD and DLB neuropathology in ADNI participants.

AD molecular: PET amyloid imaging across the Alzheimer's disease spectrum: From disease mechanisms to prevention

The advent of amyloid-beta (Aβ) positron emission tomography (PET) imaging has transformed the field of Alzheimer's disease (AD) by enabling the quantification of cortical Aβ accumulation and propagation in vivo. This revolutionary tool has made it possible to measure direct associations between Aβ and other AD biomarkers, to identify factors that influence Aβ accumulation and to redefine entry criteria into clinical trials as well as measure drug target engagement. This chapter summarizes the main findings on the associations of Aβ with other biomarkers of disease progression across the AD spectrum. It discusses investigations of the timing at which Aβ pathology starts to accumulate, demonstrates the clinical utility of Aβ PET imaging and discusses some ethical implications. Finally, it presents genetic and potentially modifiable lifestyle factors that might influence Aβ accumulation and therefore be targets for AD prevention.

Post-Mortem MRI and Histopathology in Neurologic Disease: A Translational Approach

In this review, combined post-mortem brain magnetic resonance imaging (MRI) and histology studies are highlighted, illustrating the relevance of translational approaches to define novel MRI signatures of neuropathological lesions in neuroinflammatory and neurodegenerative disorders. Initial studies combining post-mortem MRI and histology have validated various MRI sequences, assessing their sensitivity and specificity as diagnostic biomarkers in neurologic disease. More recent studies have focused on defining new radiological (bio)markers and implementing them in the clinical (research) setting. By combining neurological and neuroanatomical expertise with radiological development and pathological validation, a cycle emerges that allows for the discovery of novel MRI biomarkers to be implemented in vivo. Examples of this cycle are presented for multiple sclerosis, Alzheimer's disease, Parkinson's disease, and traumatic brain injury. Some applications have been shown to be successful, while others require further validation. In conclusion, there is much to explore with post-mortem MRI and histology studies, which can eventually be of high relevance for clinical practice.

Mass spectrometry: A platform for biomarker discovery and validation for Alzheimer's and Parkinson's diseases

Accurate, reliable, and objective biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), and related age-associated neurodegenerative disorders are urgently needed to assist in both diagnosis, particularly at early stages, and monitoring of disease progression. Technological advancements in protein detection platforms over the last few decades have resulted in a plethora of reported molecular biomarker candidates for both AD and PD; however, very few of these candidates are developed beyond the discovery phase of the biomarker development pipeline, a reflection of the current bottleneck within the field. In this review, the expanded use of selected reaction monitoring (SRM) targeted mass spectrometry will be discussed in detail as a platform for systematic verification of large panels of protein biomarker candidates prior to costly validation testing. We also advocate for the coupling of discovery-based proteomics with modern targeted MS-based approaches (e.g., SRM) within a single study in future workflows to expedite biomarker development and validation for AD and PD. It is our hope that improving the efficiency within the biomarker development process by use of an SRM pipeline may ultimately hasten the development of biomarkers that both decrease misdiagnosis of AD and PD and ultimately lead to detection at early stages of disease and objective assessment of disease progression. This article is part of the special issue "Proteomics".

18F-labeled radiopharmaceuticals for the molecular neuroimaging of amyloid plaques in Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia in the elderly, with tremendous impact on the affected individuals and the society. Definitive diagnosis can be achieved only by post mortem examination. Clinical diagnosis criteria currently applied in clinical practice for AD often fail to accurately discriminate between AD and non-AD dementia with up to 40% of misdiagnosed patients. Several published papers demonstrated that the pre-clinical phase of AD is characterized by an early rise in beta-amyloid accumulation into inter-neuronal space, followed by a severe synaptic dysfunction. Thus, beta-amyloid protein, detected in the cerebrospinal fluid, has been considered a specific AD biomarker. Molecular imaging of beta-amyloid deposits, with positron emission tomography (PET) and ¹⁸F-labeled radiopharmaceuticals such as ¹⁸F-florbetapir, ¹⁸F-florbetaben, and ¹⁸F-flutemetamol, has emerged as potential powerful tool for aiding AD diagnosis. The aim of the present paper is to review the existing literature on the clinical use of these new amyloid tracers in order to delineate their diagnostic value and limitations.

Alzheimer's genetic risk is reduced in primary age-related tauopathy: a potential model of resistance?

Objective

Nearly all adults >50 years of age have evidence for neurofibrillary tau tangles (NFTs) and a significant proportion of individuals additionally develop amyloid plaques (Aβ) consistent with Alzheimer's disease (AD). In an effort to identify the independent genetic risk factors for NFTs and Aβ, we investigated genotypic frequencies of AD susceptibility loci between autopsy‐confirmed AD and primary age‐related tauopathy (PART), a neuropathological condition defined by characteristic neurofibrillary tau tangles (NFTs) with minimal or absent Aβ.

Methods

General linear models assessed the odds of AD (N = 1190) relative to PART (N = 376) neuropathologically confirmed cases from two independent series: the Penn Brain Bank (PENN; AD N = 312; PART N = 65) and National Alzheimer's Coordinating Center (NACC; AD N = 878; PART N = 311). We also evaluated the odds of Braak stage NFT burden.

Results

Three genotypes significantly associated with reduced AD risk relative to PART in the PENN (N = 377) and NACC (N = 1189) cohorts including APOE ε4, APOE ε2, and rs6656401 in the CR1 gene. The genotypes rs6733839 in the BIN1 gene and rs28834970 in the PTK2B gene approached significance in the PENN cohort and were significantly associated with reduced AD risk in the NACC cohort. In a combined cohort analysis (N = 1566), APOE ε4 dosage was highly associated with higher Braak stage of NFT burden in Probable PART and AD, but not Definite PART.

Interpretation

The presence of genotypic differences between PART and AD suggest that PART can provide a genetic model of NFT risk and potential Aβ resistance to inform disease‐modifying therapies.

Histopathology and Florbetaben PET in Patients Incorrectly Diagnosed with Alzheimer's Disease

Of 57 individuals diagnosed with Alzheimer's disease (AD) in a phase III study, 13 (23%) had amyloid-β (Aβ) levels on postmortem histopathology that did not explain the dementia. Based on postmortem histopathology, a wide range of different non-AD conditions was identified, including frontotemporal dementia, hippocampal sclerosis, and dementia with Lewy bodies. Of the histopathologically Aβ negative scored cases ante-mortem Florbetaben PET scans were classified as negative for Aβ in 11 patients based on visual analysis and in all 12 quantifiable cases based on composite standardized uptake value ratios. Thus, florbetaben PET can assist physicians in the differential diagnosis of neurodegenerative disorders by reliably excluding Aβ pathology.

The Clinical Use of Cerebrospinal Fluid Biomarkers for Alzheimer's Disease Diagnosis: The Italian Selfie

Although the use of cerebrospinal fluid (CSF) amyloid β1-42 (Aβ42), tau (T-tau), and phosphorylated tau (p-tau181) gives added diagnostic and prognostic values, the diffusion is still limited in clinical practice and only a restricted number of patients receive an integrated clinico-biological diagnosis. By a survey, we aimed to do a "selfie" of the use and diffusion of CSF biomarkers of dementia in Italy, the standardization of pre-analytical procedures, the harmonization of ranges, and the participation to Quality Control programs. An online questionnaire was sent to the members of SIBioC and SINdem-ITALPLANED and to main neurological clinics all over Italy. In Italy, 25 laboratories provide biomarkers analysis in addition to a network of 15 neighboring hospitals. In sum, 40 neurological centers require CSF analyses. 7/20 regions (35%) lack CSF laboratories. Standardization of pre-analytical procedures is present in 62.02% of the laboratories; only 56.00% of the laboratories participate in International Quality Control. There is no harmonization of cut-offs. In Italy, the use of CSF biomarkers is still limited in clinical practice. Standardization and harmonization of normal ranges are needed. To optimize and expand the use of CSF biomarkers, a cost-benefit analysis should be promoted by scientific societies and national health services.

Potential Factors Associated with Cognitive Improvement of Individuals Diagnosed with Mild Cognitive Impairment or Dementia in Longitudinal Studies

Despite the progressive nature of Alzheimer's disease and other dementias, it is observed that many individuals that are diagnosed with mild cognitive impairment (MCI) in one clinical assessment, may return back to normal cognition (CN) in a subsequent assessment. Less frequently, such 'back-transitions' are also observed in people that had already been diagnosed with later stages of dementia. In this study, an analysis was performed on two longitudinal cohort datasets provided by 1) the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 2) the National Alzheimer's Coordinating Centre (NACC). The focus is on the observed improvement of individuals' clinical condition recorded in these datasets to explore potential associations with different factors. It is shown that, in both datasets, transitions from MCI to CN are significantly associated with younger age, better cognitive function, and the absence of ApoE ɛ4 alleles. Better cognitive function and in some cases the absence of ApoE ɛ4 alleles are also significantly associated with transitions from types of dementia to less severe clinical states. The effect of gender and education is not clear-cut in these datasets, although highly educated people who reach MCI tend to be more likely to show an improvement in their clinical state. The potential effect of other factors such as changes in symptoms of depression is also discussed. Although improved clinical outcomes can be associated with many factors, better diagnostic tools are required to provide insight into whether such improvements are a result of misdiagnosis, and if they are not, whether they are linked to improvements in the underlying neuropathological condition.

Dementia registries around the globe and their applications: A systematic review

Patient registries are valuable tools helping to address significant challenges in research, care, and policy. Registries, well embedded in many fields of medicine and public health, are relatively new in dementia. This systematic review presents the current situation in regards to dementia registries worldwide. We identified 31 dementia registries operating on an international, national, or local level between 1986 and 2016. More than half of the registries aimed to conduct or facilitate research, including preclinical research registries and registries recruiting research volunteers. Other dementia registries collected epidemiological or quality of care data. We present evidence of practical and economic outcomes of registries for research, clinical practice and policy, and recommendations for future development. Global harmonization of recruitment methods and minimum data would facilitate international comparisons. Registries provide a positive return on investment; their establishment and maintenance require ongoing support by government, policy makers, research funding bodies, clinicians, and individuals with dementia and their caregivers.

The impact of β-amyloid positron emission tomography on the diagnostic and treatment decisions of dementia experts

Aim: Amyloid positron emission tomography (aPET) measurement of Alzheimer's disease (AD) pathology could improve the accurate diagnosis of cognitive disorders. Appropriate use criteria recommend that only dementia experts order aPET. Materials & methods: We surveyed 145 dementia experts about their current approaches to evaluation and treatment and the likely influence of aPET. Results: Experts expected aPET to alter diagnostic procedures and patient management and also increase diagnostic certainty. They anticipated confirming AD or altering pharmacological treatment following positive results more than excluding AD following negative results. Experts familiar with aPET reported changes that were more consistent with appropriate use criteria and published evidence. Conclusions: Knowledge about aPET strongly influenced effects on diagnostic certainty and changed clinical practice. Dementia experts may need additional training to achieve optimal benefit from aPET.

The Differential Effects of Alzheimer's Disease and Lewy Body Pathology on Cognitive Performance: a Meta-analysis

Differential diagnosis of Alzheimer's disease (AD) from normal aging and other dementia etiologies is imperative for disease specific treatment options and long-term care planning. Neuropathological confirmation is the gold standard for neurodegenerative disease diagnosis, yet most published studies examining the use of neuropsychological tests in the differential diagnosis of dementia rely upon clinical diagnostic outcomes. The present study undertook a meta-analytic review of the literature to identify cognitive tests and domains that allow for the differentiation of individuals with AD pathology from individuals with dementia with Lewy Bodies (DLB) pathology and pathology-free individuals. A comprehensive literature search yielded 14 studies that met the inclusion criteria for the present meta-analysis. Six studies comprised 222 decedents with AD compared to 433 normal controls, and eight studies comprised 431 cases of AD compared to 155 decedents with DLB. Results revealed that the effect of having neuropathologically confirmed AD versus DLB lowered performance in the memory domain, and having DLB decreased performance in the visuospatial domain. No single test differed significantly across the AD and DLB groups. For the AD and pathology free comparison, results indicated that that AD was associated with poorer performance on the memory and language domains. With respect to specific cognitive tests, AD produced lower scores on list learning tests, category fluency, and the Digit Symbol substitution test. The limited number of studies meeting inclusion criteria warrants formulation of guidelines for reporting in clinico-pathological studies; suggested guidelines are provided.

Advances in Aβ plaque detection and the value of knowing: overcoming challenges to improving patient outcomes in Alzheimer's disease

Clinical diagnosis of Alzheimer's disease (AD) can be challenging as numerous diseases mimic the characteristics of AD. In this light, recent guidelines developed by different associations and working groups point out the need for biomarkers to support AD diagnosis. This paper discusses 18F-labeled radiotracers (which are indicated for PET imaging of the brain) and ongoing clinical studies that aim to generate new evidence for the usage of amyloid imaging. In addition to their relatively long half-life, these agents are known for their high sensitivity and high negative predictive values for detection of neuritic Aβ plaques. Comparisons with other biomarkers are provided and implications of diagnostic disclosures discussed. Finally, recent data from clinical trials underscore the importance of amyloid PET for detecting, quantifying and monitoring Aβ plaque deposits.

Timely Diagnosis for Alzheimer's Disease: A Literature Review on Benefits and Challenges

Background:

Timely diagnosis of Alzheimer’s disease (AD) refers to a diagnosis at the stage when patients come to the attention of clinicians because of concerns about changes in cognition, behavior, or functioning and can be still free of dementia and functionally independent.

Objectives:

To comprehensively review existing scientific evidence on the benefits and potential challenges of making a timely diagnosis of AD.

Methods:

Relevant studies were identified by searching electronic databases (Medline, Embase) and bibliographies for studies published in English between 1 January 2000 and 2 June 2014 on the consequences of a timely diagnosis of AD.

Results:

Nine studies were identified that investigated the consequences of diagnosing AD at the initial stages; none were specifically focused on prodromal AD. A timely diagnosis potentially offers the opportunities of early intervention, implementation of coordinated care plans, better management of symptoms, patient safety, cost savings, and postponement of institutionalization. Barriers to making a timely diagnosis include stigma, suicide risk, lack of training, diagnostic uncertainty, shortage of specialized diagnostic services, and the reluctance of healthcare providers to make a diagnosis when no effective disease-modifying options are available.

Conclusions:

Despite its potential benefits, few published studies have explored the advantages or risks of a timely diagnosis of AD. In light of the cultural shift toward diagnosis at the initial stage of the disease continuum, when the patient does not yet have dementia, more investigations are needed to evaluate the benefits and address the barriers that may impede making a timely AD diagnosis.

MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases

Accurately distinguishing between different degenerative dementias during life is challenging but increasingly important with the prospect of disease-modifying therapies. Molecular biomarkers of dementia pathology are becoming available, but are not widely used in clinical practice. Conversely, structural neuroimaging is recommended in the evaluation of cognitive impairment. Visual assessment remains the primary method of scan interpretation, but in the absence of a structured approach, diagnostically relevant information may be under-utilized. This definitive, multi-centre study uses post-mortem confirmed cases as the gold standard to: (i) assess the reliability of six visual rating scales; (ii) determine their associated pattern of atrophy; (iii) compare their diagnostic value with expert scan assessment; and (iv) assess the accuracy of a machine learning approach based on multiple rating scales to predict underlying pathology. The study includes T ₁ -weighted images acquired in three European centres from 184 individuals with histopathologically confirmed dementia (101 patients with Alzheimer’s disease, 28 patients with dementia with Lewy bodies, 55 patients with frontotemporal lobar degeneration), and scans from 73 healthy controls. Six visual rating scales (medial temporal, posterior, anterior temporal, orbito-frontal, anterior cingulate and fronto-insula) were applied to 257 scans (two raters), and to a subset of 80 scans (three raters). Six experts also provided a diagnosis based on unstructured assessment of the 80-scan subset. The reliability and time taken to apply each scale was evaluated. Voxel-based morphometry was used to explore the relationship between each rating scale and the pattern of grey matter volume loss. Additionally, the performance of each scale to predict dementia pathology both individually and in combination was evaluated using a support vector classifier, which was compared with expert scan assessment to estimate clinical value. Reliability of scan assessment was generally good (intraclass correlation coefficient > 0.7), and average time to apply all six scales was <3 min. There was a very close association between the pattern of grey matter loss and the regions of interest each scale was designed to assess. Using automated classification based on all six rating scales, the accuracy (estimated using the area under the receiver-operator curves) for distinguishing each pathological group from controls ranged from 0.86–0.97; and from one another, 0.75–0.92. These results were substantially better than the accuracy of any single scale, at least as good as expert reads, and comparable to previous studies using molecular biomarkers. Visual rating scores from magnetic resonance images routinely acquired as part of the investigation of dementias, offer a practical, inexpensive means of improving diagnostic accuracy.

A comparison of cognitive functioning in long-term care and short-stay nursing home residents

It can be challenging to provide person-centred care for individuals with cognitive impairment if they are unable to communicate their needs to facility providers clearly. The high base rates of dementia and mild cognitive impairment (MCI) in US nursing homes is well documented; however, our understanding of the unique prevalence of cognitive levels in long-term care and short-stay residents is limited. Our aim is to determine whether there are significant differences in specific cognitive levels between these two groups. Long-term care and short-stay residents (N = 579) were randomly selected from 18 Maryland, US skilled nursing facilities; 345 met inclusion criteria for participation (mean age 79.41) and completed a cognitive test (Brief Cognitive Assessment Tool (BCAT)). Based on BCAT scores, 78.9 per cent of the long-term care residents had dementia compared to 61.4 per cent for short-stay residents. The proportions of MCI, mild, and moderate to severe dementia were significantly different between the two groups (p = 0.00). The odds of residents having moderate to severe dementia were 2.76 times greater for long-term care compared to short-stay residents. BCAT total and factor scores were significantly different between long-term care and short-stay nursing home residents (p < 0.001). We discuss the implications of these empirical findings in terms of facilitating person-centred care in nursing homes.

Piramal Imaging

Piramal Imaging, a division of Piramal Enterprises Ltd, is a global radiopharmaceutical company that is actively developing novel PET radiotracers for use in molecular imaging. The company focuses on developing innovative products that improve early detection and characterization of chronic and life-threatening diseases, leading to better therapeutic outcomes and improved quality of life.

Medical costs of Alzheimer's disease misdiagnosis among US Medicare beneficiaries

INTRODUCTION

Recent developments in diagnostic technology can support earlier, more accurate diagnosis of non-Alzheimer's disease (AD) dementias.

METHODS

To evaluate potential economic benefits of early rule-out of AD, annual medical resource use and costs for Medicare beneficiaries potentially misdiagnosed with AD prior to their diagnosis of vascular dementia (VD) or Parkinson's disease (PD) were compared with that of similar patients never diagnosed with AD.

RESULTS

Patients with prior AD diagnosis used substantially more medical services every year until their VD/PD diagnosis, resulting in incremental annual medical costs of approximately $9,500-$14,000. However, following their corrected diagnosis, medical costs converged with those of patients never diagnosed with AD.

DISCUSSION

The observed correlation between timing of correct diagnosis and subsequent reversal in excess costs is strongly suggestive of the role of misdiagnosis of AD - rather than AD comorbidity - in this patient population. Our findings suggest potential benefits from earlier, accurate diagnosis.