Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review

Association of the inflammation-related proteome with dementia development at older age: results from a large, prospective, population-based cohort study

BACKGROUND

Chronic inflammation is a central feature of several forms of dementia. However, few details on the associations of blood-based inflammation-related proteins with dementia incidence have been explored yet.

METHODS

The Olink Target 96 Inflammation panel was measured in baseline serum samples (collected 07/2000-06/2002) of 1782 older adults from a German, population-based cohort study in a case-cohort design. Logistic regression models were used to assess the associations of biomarkers with all-cause dementia, Alzheimer's disease, and vascular dementia incidence.

RESULTS

During 17 years of follow-up, 504 participants were diagnosed with dementia, including 163 Alzheimer's disease and 195 vascular dementia cases. After correction for multiple testing, 58 out of 72 tested (80.6%) biomarkers were statistically significantly associated with all-cause dementia, 22 with Alzheimer's disease, and 33 with vascular dementia incidence. We identified four biomarker clusters, among which the strongest representatives, CX3CL1, EN-RAGE, LAP TGF-beta-1, and VEGF-A, were significantly associated with dementia endpoints independently from other inflammation-related proteins. CX3CL1 (odds ratio [95% confidence interval] per 1 standard deviation increase: 1.41 [1.24-1.60]) and EN-RAGE (1.41 [1.25-1.60]) were associated with all-cause dementia incidence, EN-RAGE (1.51 [1.25-1.83]) and LAP TGF-beta-1 (1.46 [1.21-1.76]) with Alzheimer's disease incidence, and VEGF-A (1.43 [1.20-1.70]) with vascular dementia incidence. All named associations were stronger among APOE ε4-negative subjects.

CONCLUSION

With this large, population-based cohort study, we show for the first time that the majority of inflammation-related proteins measured in blood samples are associated with total dementia incidence. Future studies should concentrate not only on single biomarkers but also on the complex relationships in biomarker clusters.

The trend of disruption in the functional brain network topology of Alzheimer's disease

Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach

BACKGROUND

Early and accurate diagnosis of Alzheimer's disease (AD) is essential for disease management and therapeutic choices that can delay disease progression. Machine learning (ML) approaches have been extensively used in attempts to develop algorithms for reliable early diagnosis of AD, although clinical usefulness, interpretability, and generalizability of the classifiers across datasets and MRI protocols remain limited.

METHODS

We report a multi-diagnostic and generalizable approach for mild cognitive impairment (MCI) and AD diagnosis using structural MRI and ML. Classifiers were trained and tested using subjects from the AD Neuroimaging Initiative (ADNI) database (n = 570) and the Open Access Series of Imaging Studies (OASIS) project database (n = 531). Several classifiers are compared and combined using voting for a decision. Additionally, we report tests of generalizability across datasets and protocols (IR-SPGR and MPRAGE), the impact of using graph theory measures on diagnostic classification performance, the relative importance of different brain regions on classification for better interpretability, and an evaluation of the potential for clinical applicability of the classifier.

RESULTS

Our "healthy controls (HC) vs. AD" classifier trained and tested on the combination of ADNI and OASIS datasets obtained a balanced accuracy (BAC) of 90.6% and a Matthew's correlation coefficient (MCC) of 0.811. Our "HC vs. MCI vs. AD" classifier trained and tested on the ADNI dataset obtained a 62.1% BAC (33.3% being the by-chance cut-off) and 0.438 MCC. Hippocampal features were the strongest contributors to the classification decisions (approx. 25-45%), followed by temporal (approx. 13%), cingulate, and frontal regions (approx. 8-13% each), which is consistent with our current understanding of AD and its progression. Classifiers generalized well across both datasets and protocols. Finally, using graph theory measures did not improve classification performance.

CONCLUSIONS

In sum, we present a diagnostic tool for MCI and AD trained using baseline scans and a follow-up diagnosis regardless of progression, which is multi-diagnostic, generalizable across independent data sources and acquisition protocols, and with transparently reported performance. Rated as potentially clinically applicable, our tool may be clinically useful to inform diagnostic decisions in dementia, if successful in real-world prospective clinical trials.

Alzheimer's disease: a scoping review of biomarker research and development for effective disease diagnosis

INTRODUCTION

Alzheimer's disease (AD) is regarded as the foremost reason for neurodegeneration that prominently affects the geriatric population. Characterized by extracellular accumulation of amyloid-beta (Aβ), intracellular aggregation of hyperphosphorylated tau (p-tau), and neuronal degeneration that causes impairment of memory and cognition. Amyloid/tau/neurodegeneration (ATN) classification is utilized for research purposes and involves amyloid, tau, and neuronal injury staging through MRI, PET scanning, and CSF protein concentration estimations. CSF sampling is invasive, and MRI and PET scanning requires sophisticated radiological facilities which limit its widespread diagnostic use. ATN classification lacks effectiveness in preclinical AD.

AREAS COVERED

This publication intends to collate and review the existing biomarker profile and the current research and development of a new arsenal of biomarkers for AD pathology from different biological samples, microRNA (miRNA), proteomics, metabolomics, artificial intelligence, and machine learning for AD screening, diagnosis, prognosis, and monitoring of AD treatments.

EXPERT OPINION

It is an accepted observation that AD-related pathological changes occur over a long period of time before the first symptoms are observed providing ample opportunity for detection of biological alterations in various biological samples that can aid in early diagnosis and modify treatment outcomes.

Neuronal ER-Signalosome Proteins as Early Biomarkers in Prodromal Alzheimer's Disease Independent of Amyloid-β Production and Tau Phosphorylation

There exists considerable interest to unveil preclinical period and prodromal stages of Alzheimer's disease (AD). The mild cognitive impairment (MCI) is characterized by significant memory and/or other cognitive domains impairments, and is often considered the prodromal phase of AD. The cerebrospinal fluid (CSF) levels of β-amyloid (βA), total tau (t-tau), and phosphorylated tau (p-tau) have been used as biomarkers of AD albeit their significance as indicators during early stages of AD remains far from accurate. The new biomarkers are being intensively sought as to allow identification of pathological processes underlying early stages of AD. Fifty-three participants (75.4 ± 8.3 years) were classified in three groups as cognitively normal healthy controls (HC), MCI, and subjective memory complaints (SMC). The subjects were subjected to a battery of neurocognitive tests and underwent lumbar puncture for CSF extraction. The CSF levels of estrogen-receptor (ER)-signalosome proteins, βA, t-tau and p-tau, were submitted to univariate, bivariate, and multivariate statistical analyses. We have found that the components of the ER-signalosome, namely, caveolin-1, flotilin-1, and estrogen receptor alpha (ERα), insulin growth factor-1 receptor β (IGF1Rβ), prion protein (PrP), and plasmalemmal voltage dependent anion channel 1 (VDAC) could be detected in the CSF from all subjects of the HC, MCI, and SMC groups. The six proteins appeared elevated in MCI and slightly increased in SMC subjects compared to HC, suggesting that signalosome proteins undergo very early modifications in nerve cells. Using a multivariate approach, we have found that the combination of ERα, IGF-1Rβ, and VDAC are the main determinants of group segregation with resolution enough to predict the MCI stage. The analyses of bivariate relationships indicated that collinearity of ER-signalosome proteins vary depending on the stage, with some pairs displaying opposed relationships between HC and MCI groups, and the SMC stage showing either no relationships or behaviors similar to either HC or MCI stages. The multinomial logistic regression models of changes in ER-signalosome proteins provide reliable predictive criteria, particularly for the MCI. Notably, most of the statistical analyses revealed no significant relationships or interactions with classical AD biomarkers at either disease stage. Finally, the multivariate functions were highly correlated with outcomes from neurocognitive tests for episodic memory. These results demonstrate that alterations in ER-signalosome might provide useful diagnostic information on preclinical stages of AD, independently from classical biomarkers.

Impaired Affordance Perception as the Basis of Tool Use Deficiency in Alzheimer’s Disease

The present study investigated whether defective affordance perception capacity underpins tool use deficits in patients with Alzheimer’s disease (AD). An affordance, a concept James Gibson introduced, scales environmental objects to an animal’s action capabilities, thus offering opportunities for action. Each man-made artifact carries both a primary affordance (its designed function) and secondary affordances. In Experiment 1, participants identified secondary affordances of objects as a measure of their ability to identify alternative uses of familiar tools. A single response Go/No-Go task was administered to 4 groups: AD, mild cognitive impairment (MCI), Parkinson’s disease (PD), and elderly controls (EC). Groups were matched for age and years of education. The AD group performed poorest, followed by MCI, and PD and EC. EC and PD groups’ results failed to reach statistical significance, and the AD group performed at chance. In Experiment 2, participants judged the physical properties of the same objects used in Experiment 1. Even AD patients performed reliably, ruling out a visual processing deficit as the basis for their poor performance in Experiment 1. Results suggest that degraded affordance detection capacity can differentiate AD from normal aging and other neurodegenerative disorders and could be an affordable marker for AD, even in the early stages of AD.

Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of Alzheimer's disease

The roles of brain regions activities and gene expressions in the development of Alzheimer's disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region-gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.

Exposing the Brain Proteomic Signatures of Alzheimer's Disease in Diverse Racial Groups: Leveraging Multiple Data Sets and Machine Learning

Recent studies have highlighted that the proteome can be used to identify potential biomarker candidates for Alzheimer's disease (AD) in diverse cohorts. Furthermore, the racial and ethnic background of participants is an important factor to consider to ensure the effectiveness of potential biomarkers for representative populations. A promising approach to survey potential biomarker candidates for diagnosing AD in diverse cohorts is the application of machine learning to proteomics data sets. Herein, we leveraged six existing bottom-up proteomics data sets, which included non-Hispanic White, African American/Black, and Hispanic participants, to study protein changes in AD and cognitively unimpaired participants. Machine learning models were applied to these data sets and resulted in the identification of amyloid-β precursor protein (APP) and heat shock protein β-1 (HSPB1) as two proteins that have high ability to distinguish AD; however, each protein's performance varied based upon the racial and ethnic background of the participants. HSPB1 particularly was helpful for generating high areas under the curve (AUCs) for African American/Black participants. Overall, HSPB1 improved the performance of the machine learning models when combined with APP and/or participant age and is a potential candidate that should be further explored in AD biomarker discovery efforts.

Assessment of a Plasma Amyloid Probability Score to Estimate Amyloid Positron Emission Tomography Findings Among Adults With Cognitive Impairment

IMPORTANCE

The diagnostic evaluation for Alzheimer disease may be improved by a blood-based diagnostic test identifying presence of brain amyloid plaque pathology.

OBJECTIVE

To determine the clinical performance associated with a diagnostic algorithm incorporating plasma amyloid-β (Aβ) 42:40 ratio, patient age, and apoE proteotype to identify brain amyloid status.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study includes analysis from 2 independent cross-sectional cohort studies: the discovery cohort of the Plasma Test for Amyloidosis Risk Screening (PARIS) study, a prospective add-on to the Imaging Dementia-Evidence for Amyloid Scanning study, including 249 patients from 2018 to 2019, and MissionAD, a dataset of 437 biobanked patient samples obtained at screenings during 2016 to 2019. Data were analyzed from May to November 2020.

EXPOSURES

Amyloid detected in blood and by positron emission tomography (PET) imaging.

MAIN OUTCOMES AND MEASURES

The main outcome was the diagnostic performance of plasma Aβ42:40 ratio, together with apoE proteotype and age, for identifying amyloid PET status, assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS

All 686 participants (mean [SD] age 73.2 [6.3] years; 368 [53.6%] men; 378 participants [55.1%] with amyloid PET findings) had symptoms of mild cognitive impairment or mild dementia. The AUC of plasma Aβ42:40 ratio for PARIS was 0.79 (95% CI, 0.73-0.85) and 0.86 (95% CI, 0.82-0.89) for MissionAD. Ratio cutoffs for Aβ42:40 based on the Youden index were similar between cohorts (PARIS: 0.089; MissionAD: 0.092). A logistic regression model (LRM) incorporating Aβ42:40 ratio, apoE proteotype, and age improved diagnostic performance within each cohort (PARIS: AUC, 0.86 [95% CI, 0.81-0.91]; MissionAD: AUC, 0.89 [95% CI, 0.86-0.92]), and overall accuracy was 78% (95% CI, 72%-83%) for PARIS and 83% (95% CI, 79%-86%) for MissionAD. The model developed on the prospectively collected samples from PARIS performed well on the MissionAD samples (AUC, 0.88 [95% CI, 0.84-0.91]; accuracy, 78% [95% CI, 74%-82%]). Training the LRM on combined cohorts yielded an AUC of 0.88 (95% CI, 0.85-0.91) and accuracy of 81% (95% CI, 78%-84%). The output of this LRM is the Amyloid Probability Score (APS). For clinical use, 2 APS cutoff values were established yielding 3 categories, with low, intermediate, and high likelihood of brain amyloid plaque pathology.

CONCLUSIONS AND RELEVANCE

These findings suggest that this blood biomarker test could allow for distinguishing individuals with brain amyloid-positive PET findings from individuals with amyloid-negative PET findings and serve as an aid for Alzheimer disease diagnosis.

The role of Memory Clinics in the assessment and management of dementia, now and into the future

PURPOSE OF REVIEW

The diagnosis of dementia is often delayed and many individuals with the disorder go undiagnosed or receive an inadequate assessment. Specialist Memory Clinics have long been at the centre of such assessments. Recent developments in biomarkers and disease-modifying treatments are likely increase the need for specialist assessments. It is therefore timely to review the role of Memory Clinics internationally.

RECENT FINDINGS

The timely diagnosis of dementia and cognitive decline is the main responsibility of Memory Clinics. These clinics can diagnose dementia up to 4 years earlier than primary care services, but their low numbers and capacity of leads to long waiting times and a focus on complex cases. Few clinics are resourced to offer specific postdiagnostic support services, and their roles in clinical trials and research have not been realised. They are often well integrated with primary care and aged care services.

SUMMARY

Memory Clinics play an important role in bridging the gap between research and clinical practice and providing high-quality assessment and care. Additional Memory Clinic services and greater harmonisation of their responsibilities and procedures are needed to exploit their full potential.

Brain CT can predict low lean mass in the elderly with cognitive impairment: a community-dwelling study

Background

The coexistence of sarcopenia and dementia in aging populations is not uncommon, and they may share common risk factors and pathophysiological pathways. This study aimed to evaluate the relationship between brain atrophy and low lean mass in the elderly with impaired cognitive function.

Methods

This cross-sectional study included 168 elderly patients who visited the multi-disciplinary dementia outpatient clinic at Kaohsiung Chang Gung Memorial Hospital for memory issues, between 2017 and 2019. The body composition was assessed by dual energy X-ray absorptiometry (DEXA) and CT based skeletal muscle index including L3 skeletal muscle index (L3SMI) and masseter muscle mass index (MSMI). The brain atrophy assessment was measured by CT based visual rating scale. Possible predictors of low lean mass in the elderly with cognitive impairement were identified by binary logistic regression. ROC curves were generated from binary logistic regression.

Results

Among the 81 participants, 43 (53%) remained at a normal appendicular skeletal muscle index (ASMI), whereas 38 (47%) showed low ASMI. Compared with the normal ASMI group, subjects with low ASMI exhibited significantly lower BMI, L3SMI, and MSMI (all p < 0.05), and showed significant brain atrophy as assessed by visual rating scale (p < 0.001). The accuracy of predictive models for low ASMI in the elderly with cognitive impairment were 0.875, (Area under curve (AUC) = 0.926, 95% confidence interval [CI] 0.844–0.972) in model 1 (combination of BMI, GCA and L3SMI) and 0.885, (Area under curve (AUC) = 0.931, [CI] 0.857–0.979) in model 2 (combination of BMI, GCA and MSMI).

Conclusions

Global cortical atrophy and body mass index combined with either L3 skeletal muscle index or masseter skeletal muscle index can predict low lean mass in the elderly with cognitive impairment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02626-8.

Clinical application of serological Alzheimer's disease diagnosis using a highly sensitive biosensor with hydrogel-enhanced dielectrophoretic force

Analysis of a ratio between amyloid beta 1-40 and 1-42 (Aβ_1-40 and Aβ_1-42) presented in plasm enables a highly accurate diagnosis of Alzheimer's disease (AD). However, the analysis of plasma Aβs is not routinely conducted because of the lack of Aβ detection techniques sensitive enough to specifically detect Aβ from thousands of biomaterials present in the plasma. We developed a hydrogel-patterned spiral microelectrode sensor combined with a hopping dielectrophoretic (DEP) force, combining the negative DEP and positive DEP forces, for Aβ detection. The hydrogel effectively increased the number of immobilized fragmented antibodies in the reaction region of the sensor and enabled size-exclusive passive filtration of non-specific plasma proteins from that region. The hopping DEP force further concentrated the Aβs and removed the non-specific plasma proteins. Consequently, our sensor achieved a limit of detection (LOD) of approximately ∼ 0.15 pg/mL for both Aβ_1-40 and Aβ_1-42 in the standard plasma. Finally, comparing the ratio between Aβ_1-40 and Aβ_1-42 signals, we distinguished AD patients from cognitively normal subjects with 95.83% accuracy and 92.31% precision (n = 24, p < 0.0001, One-way ANOVA).

Quantified Brain Magnetic Resonance Imaging Volumes Differentiate Behavioral Variant Frontotemporal Dementia from Early-Onset Alzheimer's Disease

BACKGROUND

The differentiation of behavioral variant frontotemporal dementia (bvFTD) from early-onset Alzheimer's disease (EOAD) by clinical criteria can be inaccurate. The volumetric quantification of clinically available magnetic resonance (MR) brain scans may facilitate early diagnosis of these neurodegenerative dementias.

OBJECTIVE

To determine if volumetric quantification of brain MR imaging can identify persons with bvFTD from EOAD.

METHODS

3D T1 MR brain scans of 20 persons with bvFTD and 45 with EOAD were compared using Neuroreader to measure subcortical, and lobar volumes, and Volbrain for hippocampal subfields. Analyses included: 1) discriminant analysis with leave one out cross-validation; 2) input of predicted probabilities from this process into a receiver operator characteristic (ROC) analysis; and 3) Automated linear regression to identify predictive regions.

RESULTS

Both groups were comparable in age and sex with no statistically significant differences in symptom duration. bvFTD had lower volume percentiles in frontal lobes, thalamus, and putamen. EOAD had lower parietal lobe volumes. ROC analyses showed 99.3% accuracy with Neuroreader percentiles and 80.2% with subfields. The parietal lobe was the most predictive percentile. Although there were differences in hippocampal (particularly left CA2-CA3) subfields, it did not add to the discriminant analysis.

CONCLUSION

Percentiles from an MR based volumetric quantification can help differentiate between bvFTD from EOAD in routine clinical care. Use of hippocampal subfield volumes does not enhance the diagnostic separation of these two early-onset dementias.

Recent Advances in the Application Peptide and Peptoid in Diagnosis Biomarkers of Alzheimer's Disease in Blood

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with irreversible damage of the brain and a continuous pathophysiological process. Early detection and accurate diagnosis are essential for the early intervention of AD. Precise detection of blood biomarkers related to AD could provide a shortcut to identifying early-stage patients before symptoms. In recent years, targeting peptides or peptoids have been chosen as recognition elements in nano-sensors or fluorescence detection to increase the targeting specificity, while peptide-based probes were also developed considering their specific advantages. Peptide-based sensors and probes have been developed according to different strategies, such as natural receptors, high-throughput screening, or artificial design for AD detection. This review will briefly summarize the recent developments and trends of AD diagnosis platforms based on peptide and peptoid as recognition elements and provide insights into the application of peptide and peptoid with different sources and characteristics in the diagnosis of AD biomarkers.

Detection of multiplex exosomal miRNAs for clinically accurate diagnosis of Alzheimer's disease using label-free plasmonic biosensor based on DNA-Assembled advanced plasmonic architecture

Alzheimer's disease (AD), the most common neurologic disorder, is characterized by progressive cognitive impairment. However, the low clinical significance of the currently used core AD biomarkers amyloid-beta and tau proteins remains a challenge. Recently, exosomes, found in human biological fluids, are gaining increasing attention because of their clinical significance in diagnosing of various diseases. In particular, blood-derived exosomal miRNAs are not only stable but also provide information regarding the different characteristics according to AD progression. However, quantitative and qualitative detection is difficult due to their characteristics, such as small size, low abundance, and high homology. Here, we present a DNA-assembled advanced plasmonic architecture (DAPA)-based plasmonic biosensor to accurately detect exosomal miRNAs in human serum. The designed nanoarchitecture possesses two narrow nanogaps that induce plasmon coupling; this significantly enhances its optical energy density, resulting in a 1.66-fold higher refractive-index (RI) sensitivity than nanorods at localized surface plasmon resonance (LSPR). Thus, the proposed biosensor is ultrasensitive and capable of selective single-nucleotide detection of exosomal miRNAs at the attomolar level. Furthermore, it identified AD patients from healthy controls by measuring the levels of exosomal miRNA-125b, miRNA-15a, and miRNA-361 in clinical serum samples. In particular, the combination of exosomal miRNA-125b and miRNA-361 showed the best diagnostic performance with a sensitivity of 91.67%, selectivity of 95.00%, and accuracy of 99.52%. These results demonstrate that our sensor can be clinically applied for AD diagnosis and has great potential to revolutionize the field of dementia research and treatment in the future.

The penalty of stress - Epichaperomes negatively reshaping the brain in neurodegenerative disorders

Adaptation to acute and chronic stress and/or persistent stressors is a subject of wide interest in central nervous system disorders. In this context, stress is an effector of change in organismal homeostasis and the response is generated when the brain perceives a potential threat. Herein, we discuss a nuanced and granular view whereby a wide variety of genotoxic and environmental stressors, including aging, genetic risk factors, environmental exposures, and age- and lifestyle-related changes, act as direct insults to cellular, as opposed to organismal, homeostasis. These two concepts of how stressors impact the central nervous system are not mutually exclusive. We discuss how maladaptive stressor-induced changes in protein connectivity through epichaperomes, disease-associated pathologic scaffolds composed of tightly bound chaperones, co-chaperones, and other factors, impact intracellular protein functionality altering phenotypes, that in turn disrupt and remodel brain networks ranging from intercellular to brain connectome levels. We provide an evidence-based view on how these maladaptive changes ranging from stressor to phenotype provide unique precision medicine opportunities for diagnostic and therapeutic development, especially in the context of neurodegenerative disorders including Alzheimer's disease where treatment options are currently limited.

Changes in LASSI-L performance over time among older adults with amnestic MCI and amyloid positivity: A preliminary study

There is a pressing need to develop measures that are sensitive to the earliest subtle cognitive changes of Alzheimer's disease (AD) to improve early detection and track disease progression. The Loewenstein-Acevedo Scales of Semantic Interference (LASSI-L) has been shown to successfully discriminate between cognitively unimpaired (CU) older adults and those with amnestic mild cognitive impairment (MCI) and to correlate with total and regional brain amyloid load. The present study investigated how the LASSI-L scores change over time among three distinct diagnostic groups. Eighty-six community-dwelling older adults underwent a baseline evaluation including: a clinical interview, a neuropsychological evaluation, Magnetic Resonance Imaging (MRI), and amyloid Positron Emission Tomography (PET). A follow up evaluation was conducted 12 months later. Initial mean values were calculated using one-way ANOVAs and chi-square analyses. Post-hoc comparisons were conducted using Tukey's Honestly Significant Difference (HSD). A 3 × 2 repeated measures analysis was utilized to examine differences in LASSI-L performance over time. The MCI amyloid positive group demonstrated a significantly greater decline in LASSI-L performance than the MCI amyloid negative and CU groups respectively. The scales that best differentiated the three groups included the Cued A2, which taps into maximum learning capacity, and Cued B2, which assesses the failure to recover from proactive semantic interference. Our findings further support the LASSI-L's discriminative validity.

Amyloid-β PET Classification on Cognitive Aging Stages Using the Centiloid Scale

PROPOSE

This study aims to explore the use of the Centiloid (CL) method in amyloid-β PET quantification to evaluate distinct cognitive aging stages, investigating subjects' mismatch classification using different cut-points for amyloid-β positivity.

PROCEDURES

The CL equation was applied in four groups of individuals: SuperAgers (SA), healthy age-matched controls (AC), healthy middle-aged controls (MC), and Alzheimer's disease (AD). The amyloid-β burden was calculated and compared between groups and quantitative variables. Three different cut-points (Jack CR, Wiste HJ, Weigand SD, et al., Alzheimer's Dement 13:205-216, 2017; Salvadó G, Molinuevo JL, Brugulat-Serrat A, et al., Alzheimer's Res Ther 11:27, 2019; and Amadoru S, Doré V, McLean CA, et al., Alzheimer's Res Ther 12:22, 2020) were applied in CL values to differentiate the earliest abnormal pathophysiological accumulation of Aβ and the established Aβ pathology.

RESULTS

The AD group exhibited a significantly increased Aβ burden compared to the MC, but not AC groups. Both healthy control (MC and AC) groups were not significantly different. Visually, the SA group showed a diverse distribution of CL values compared with MC; however, the difference was not significant. The CL values have a moderate and significant relationship between Aβ visual read, RAVLT DR and MMSE. Depending on the cut-point used, 10 CL, 19 CL, or 30 CL, 7.5% of our individuals had a different classification in the Aβ positivity. For the AC group, we obtained about 40 to 60% of the individuals classified as positive.

CONCLUSION

SuperAgers exhibited a similar Aβ load to AC and MC, differing in cognitive performance. Independently of cut-point used (10 CL, 19 CL, or 30 CL), three SA individuals were classified as Aβ positive, showing the duality between the individual's clinics and the biological definition of Alzheimer's. Different cut-points lead to Aβ positivity classification mismatch in individuals, and an extra care is needed for individuals who have a CL value between 10 and 30 CL.

DNA-Based Smart Reagent for Detecting Alzheimer's Associated MicroRNAs

Alzheimer's disease (AD) is the most common neurodegenerative disorder, with significant research efforts devoted to identifying new biomarkers for clinical diagnosis and treatment. MicroRNAs have emerged as likely disease regulators and biomarkers for AD, now implicated as having roles in several biological processes related to progression of the disease. In this work, we use the miRacles assay (microRNA activated conditional looping of engineered switches) for single-step detection of AD-related microRNAs. The technology is based on conformationally responsive DNA nanoswitches that loop upon recognition of a target microRNA and report their on/off status through an electrophoretic readout. Unlike many methods, our approach directly detects native microRNAs without amplification or labeling, eliminating the need for expensive enzymes, reagents, and equipment. For known AD-related microRNA miR-107, we demonstrated sensitivity of ∼8 fM, specificity among four similar microRNAs of the same family, and simultaneous multiplexed detection of those four microRNA targets. Toward clinical use, we screened 56 AD-related microRNAs and found four that showed detectable differences between total RNA extracts derived from human healthy and AD brain samples. In the context of AD, this "smart reagent" could facilitate biomarker discovery, accelerate efforts to understand the role of microRNAs in AD, and have clinical potential as a diagnostic or monitoring tool for validated biomarkers.

Brain metabolism in tau and amyloid mouse models of Alzheimer's disease: An MRI study

Alzheimer's disease (AD) is the leading cause of cognitive impairment and dementia in elderly individuals. According to the current biomarker framework for "unbiased descriptive classification", biomarkers of neurodegeneration, "N", constitute a critical component in the tri-category "A/T/N" system. Current biomarkers of neurodegeneration suffer from potential drawbacks such as requiring invasive lumbar puncture, involving ionizing radiation, or representing a late, irreversible marker. Recent human studies have suggested that reduced brain oxygen metabolism may be a new functional marker of neurodegeneration in AD, but the heterogeneity and the presence of mixed pathology in human patients did not allow a full understanding of the role of oxygen extraction and metabolism in AD. In this report, global brain oxygen metabolism and related physiological parameters were studied in two AD mouse models with relatively pure pathology, using advanced MRI techniques including T2 -relaxation-under-spin-tagging (TRUST) and phase contrast (PC) MRI. Additionally, regional cerebral blood flow (CBF) was determined with pseudocontinuous arterial spin labeling. Reduced global oxygen extraction fraction (by -18.7%, p = 0.008), unit-mass cerebral metabolic rate of oxygen (CMRO2 ) (by -17.4%, p = 0.04) and total CMRO2 (by -30.8%, p < 0.001) were observed in Tau4RΔK mice-referred to as the tau AD model-which manifested pronounced neurodegeneration, as measured by diminished brain volume (by -15.2%, p < 0.001). Global and regional CBF in these mice were not different from those of wild-type mice (p > 0.05), suggesting normal vascular function. By contrast, in B6;SJL-Tg [APPSWE]2576Kha (APP) mice-referred to as the amyloid AD model-no brain volume reduction, as well as relatively intact brain oxygen extraction and metabolism, were found (p > 0.05). Consistent with the imaging data, behavioral measures of walking distance were impaired in Tau4RΔK mice (p = 0.004), but not in APP mice (p = 0.88). Collectively, these findings support the hypothesis that noninvasive MRI measurement of brain oxygen metabolism may be a promising biomarker of neurodegeneration in AD.

EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline

No diagnostic or predictive instruments to help with early diagnosis and timely therapeutic intervention are available as yet for most neuro-psychiatric disorders. A quantum potential mean and variability score (qpmvs), to identify neuropsychiatric and neurocognitive disorders with high accuracy, based on routine EEG recordings, was developed. Information processing in the brain is assumed to involve integration of neuronal activity in various areas of the brain. Thus, the presumed quantum-like structure allows quantification of connectivity as a function of space and time (locality) as well as of instantaneous quantum-like effects in information space (non-locality). EEG signals reflect the holistic (nonseparable) function of the brain, including the highly ordered hierarchy of the brain, expressed by the quantum potential according to Bohmian mechanics, combined with dendrogram representation of data and p-adic numbers. Participants consisted of 230 participants including 28 with major depression, 42 with schizophrenia, 65 with cognitive impairment, and 95 controls. Routine EEG recordings were used for the calculation of qpmvs based on ultrametric analyses, closely coupled with p-adic numbers and quantum theory. Based on area under the curve, high accuracy was obtained in separating healthy controls from those diagnosed with schizophrenia (p<0.0001), depression (p<0.0001), Alzheimer's disease (AD; p<0.0001), and mild cognitive impairment (MCI; p<0.0001) as well as in differentiating participants with schizophrenia from those with depression (p<0.0001), AD (p<0.0001) or MCI (p<0.0001) and in differentiating people with depression from those with AD (p<0.0001) or MCI (p<0.0001). The novel EEG analytic algorithm (qpmvs) seems to be a useful and sufficiently accurate tool for diagnosis of neuropsychiatric and neurocognitive diseases and may be able to predict disease course and response to treatment.

Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer's disease (MCI and dementia) in primary care: a review and synthesis

The critical role of primary care clinicians (PCCs) in Alzheimer's disease (AD) prevention, diagnosis and management must evolve as new treatment paradigms and disease-modifying therapies (DMTs) emerge. Our understanding of AD has grown substantially: no longer conceptualized as a late-in-life syndrome of cognitive and functional impairments, we now recognize that AD pathology builds silently for decades before cognitive impairment is detectable. Clinically, AD first manifests subtly as mild cognitive impairment (MCI) due to AD before progressing to dementia. Emerging optimism for improved outcomes in AD stems from a focus on preventive interventions in midlife and timely, biomarker-confirmed diagnosis at early signs of cognitive deficits (i.e. MCI due to AD and mild AD dementia). A timely AD diagnosis is particularly important for optimizing patient care and enabling the appropriate use of anticipated DMTs. An accelerating challenge for PCCs and AD specialists will be to respond to innovations in diagnostics and therapy for AD in a system that is not currently well positioned to do so. To overcome these challenges, PCCs and AD specialists must collaborate closely to navigate and optimize dynamically evolving AD care in the face of new opportunities. In the spirit of this collaboration, we summarize here some prominent and influential models that inform our current understanding of AD. We also advocate for timely and accurate (i.e. biomarker-defined) diagnosis of early AD. In doing so, we consider evolving issues related to prevention, detecting emerging cognitive impairment and the role of biomarkers in the clinic.

Fractal motor activity regulation and sex differences in preclinical Alzheimer's disease pathology

INTRODUCTION

Degradation in fractal motor activity regulation (FMAR), a measure of multiscale self-similarity of motor control, occurs in aging and accelerates with clinical progression to Alzheimer's disease (AD). Whether FMAR changes occur during the pre-symptomatic phase of the disease in women and men remains unknown.

METHODS

FMAR was assessed in cognitively normal participants (n = 178) who underwent 7 to 14 days of home actigraphy. Preclinical AD pathology was determined by amyloid imaging-Pittsburgh compound B (PiB) and cerebrospinal fluid (CSF) phosphorylated-tau181 (p-tau) to amyloid beta 42 (Aβ42) ratio.

RESULTS

Degradation in daytime FMAR was overall significantly associated with preclinical amyloid plaque pathology via PiB+ imaging (beta coefficient β = 0.217, standard error [SE] = 0.101, P = .034) and increasing CSF tau181-Aβ42 ratio (β = 0.220, SE = 0.084, P = .009). In subset analysis by sex, the effect sizes were significant in women for PiB+ (β = 0.279, SE = 0.112, P = .015) and CSF (β = 0.245, SE = 0.094, P = .011) but not in men (both Ps > .05). These associations remained after inclusion of daily activity level, apolipoprotein E ε4 carrier status, and rest/activity patterns.

DISCUSSION

Changes in daytime FMAR from actigraphy appear to be present in women early in preclinical AD. This may be a combination of earlier pathology changes in females reflected in daytime FMAR, and a relatively underpowered male group. Further studies are warranted to test FMAR as an early noncognitive physiological biomarker that precedes the onset of cognitive symptoms.

Identifying and characterizing different stages toward Alzheimer's disease using ordered core features and machine learning

Based on the joint HCPMMP parcellation method we developed before, which divides the cortical brain into 360 regions, the concept of ordered core features (OCF) is first proposed to reveal the functional brain connectivity relationship among different cohorts of Alzheimer's disease (AD), late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI) and healthy controls (HC). A set of core network features that change significantly under the specifically progressive relationship were extracted and used as supervised machine learning classifiers. The network nodes in this set mainly locate in the frontal lobe and insular, forming a narrow band, which are responsible for cognitive impairment as suggested by previous finding. By using these features, the accuracy ranged from 86.0% to 95.5% in binary classification between any pair of cohorts, higher than 70.1%-91.0% when using all network features. In multi-group classification, the average accuracy was 75% or 78% for HC, EMCI, LMCI or EMCI, LMCI, AD against baseline of 33%, and 53.3% for HC, EMCI, LMCI and AD against baseline of 25%. In addition, the recognition rate was lower when combining EMCI and LMCI patients into one group of mild cognitive impairment (MCI) for classification, suggesting that there exists a big difference between early and late MCI patients. This finding supports the EMCI/LMCI inclusion criteria introduced by ADNI based on neuropsychological assessments.

In Vivo Amyloid, Neurodegeneration, and Verbal Learning in Late Middle-Aged Hispanics

BACKGROUND

The National Institute on Aging (NIA)/Alzheimer's Association (AA) 2018 framework conceptualizes Alzheimer's disease (AD) biologically. Evidence of brain amyloid by biomarkers defines AD pathologic change and the Alzheimer's continuum. The presence of tau or neurodegeneration in the absence of amyloid defines non-AD pathologic change.

OBJECTIVE

To examine the relation of in vivo amyloid and neurodegeneration with verbal learning, one of the cognitive abilities affected early in AD, in late middle age.

METHODS

This was a cross-sectional study of amyloid and neurodegeneration biomarkers in a community-based cohort of 350 late-middle aged Hispanics without dementia (mean age: 64.15±3.34; 72.0%women). Amyloid (A) was measured as global standardized uptake value ratio (SUVR) with 18F-Florbetaben positron emission tomography (PET). Neurodegeneration (N) was ascertained as cortical thickness (CT) in AD signature areas using brain magnetic resonance imaging. We examined A/N continuously, categorically, by A/N profiles, and profile categories. The amyloid threshold for positivity was defined using the K means method. The CT threshold was defined as 2 standard deviations below the mean CT. Verbal learning was ascertained using total recall and delayed recall in the Buschke Selective Reminding test (SRT).

RESULTS

Higher cortical thickness was associated with higher performance in SRT delayed recall. Amyloid SUVR was not related to SRT performance. The low CT category was associated with lower performance in SRT delayed recall, while Amyloid categories were not related to any SRT score. The non-AD pathologic change group (A-N+) performed worse in SRT delayed recall compared to the Normal A/N profile group (A-N-).

CONCLUSION

In late middle-aged Hispanics without dementia, non-AD pathologic change, but not the Alzheimer's continuum, was related to verbal learning.

Nanomedicine-based technologies and novel biomarkers for the diagnosis and treatment of Alzheimer's disease: from current to future challenges

Increasing life expectancy has led to an aging population, which has consequently increased the prevalence of dementia. Alzheimer's disease (AD), the most common form of dementia worldwide, is estimated to make up 50-80% of all cases. AD cases are expected to reach 131 million by 2050, and this increasing prevalence will critically burden economies and health systems in the next decades. There is currently no treatment that can stop or reverse disease progression. In addition, the late diagnosis of AD constitutes a major obstacle to effective disease management. Therefore, improved diagnostic tools and new treatments for AD are urgently needed. In this review, we investigate and describe both well-established and recently discovered AD biomarkers that could potentially be used to detect AD at early stages and allow the monitoring of disease progression. Proteins such as NfL, MMPs, p-tau217, YKL-40, SNAP-25, VCAM-1, and Ng / BACE are some of the most promising biomarkers because of their successful use as diagnostic tools. In addition, we explore the most recent molecular strategies for an AD therapeutic approach and nanomedicine-based technologies, used to both target drugs to the brain and serve as devices for tracking disease progression diagnostic biomarkers. State-of-the-art nanoparticles, such as polymeric, lipid, and metal-based, are being widely investigated for their potential to improve the effectiveness of both conventional drugs and novel compounds for treating AD. The most recent studies on these nanodevices are deeply explained and discussed in this review.

Can discourse processing performance serve as an early marker of Alzheimer’s disease and mild cognitive impairment? A systematic review of text comprehension

A number of linguistic and cognitive deficits have been reported during the course of Alzheimer’s disease (AD) and its preceding stage of mild cognitive impairment (MCI), with some deficits appearing years before onset of clinical symptoms. It continues to be a critical task to identify tools that may serve as an early marker of pathology that are also reliably able to distinguish AD from normal ageing. Given the limited success of classic psychometric cognitive testing, a novel approach in assessment is warranted. A potentially sensitive assessment paradigm is discourse processing. The aim of this review was to synthesize original research studies investigating comprehension of discourse in AD and MCI, and to evaluate the potential of this paradigm as a promising avenue for further research. A literature search targeting studies with AD or MCI groups over 60 years of age was conducted in PubMed, Web of Science, and PsycINFO databases. Eight articles with good quality were included in the review. Six measures of discourse comprehension—naming latency, summary, lesson, main idea, proportion of inferential clauses, true/false questions—were identified. All eight studies reported significant deficits in discourse comprehension in AD and MCI groups on five of the six measures, when compared to cognitively healthy older adults. Mixed results were observed for associations with commonly used cognitive measures. Given the consistent findings for discourse comprehension measures across all studies, we strongly recommend further research on its early predictive potential, and discuss different avenues for research.

Pre-Clinical Neuroprotective Evidences and Plausible Mechanisms of Sulforaphane in Alzheimer's Disease

Sulforaphane, a potent dietary bioactive agent obtainable from cruciferous vegetables, has been extensively studied for its effects in disease prevention and therapy. Sulforaphane potently induces transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2)-mediated expression of detoxification, anti-oxidation, and immune system-modulating enzymes, and possibly acts as an anti-carcinogenic agent. Several clinical trials are in progress to study the effect of diverse types of cruciferous vegetables and sulforaphane on prostate cancer, breast cancer, lung cancer, atopic asthmatics, skin aging, dermatitis, obesity, etc. Recently, the protective effects of sulforaphane on brain health were also considerably studied, where the studies have further extended to several neurological diseases, including Alzheimer’s disease (AD), Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, multiple sclerosis, autism spectrum disorder, and schizophrenia. Animal and cell studies that employ sulforaphane against memory impairment and AD-related pre-clinical biomarkers on amyloid-β, tau, inflammation, oxidative stress, and neurodegeneration are summarized, and plausible neuroprotective mechanisms of sulforaphane to help prevent AD are discussed. The increase in pre-clinical evidences consistently suggests that sulforaphane has a multi-faceted neuroprotective effect on AD pathophysiology. The anti-AD-like evidence of sulforaphane seen in cells and animals indicates the need to pursue sulforaphane research for relevant biomarkers in AD pre-symptomatic populations.

Advances in the development paradigm of biosample-based biosensors for early ultrasensitive detection of alzheimer's disease

This review highlights current developments, challenges, and future directions for the use of invasive and noninvasive biosample-based small biosensors for early diagnosis of Alzheimer's disease (AD) with biomarkers to incite a conceptual idea from a broad number of readers in this field. We provide the most promising concept about biosensors on the basis of detection scale (from femto to micro) using invasive and noninvasive biosamples such as cerebrospinal fluid (CSF), blood, urine, sweat, and tear. It also summarizes sensor types and detailed analyzing techniques for ultrasensitive detection of multiple target biomarkers (i.e., amyloid beta (Aβ) peptide, tau protein, Acetylcholine (Ach), microRNA137, etc.) of AD in terms of detection ranges and limit of detections (LODs). As the most significant disadvantage of CSF and blood-based detection of AD is associated with the invasiveness of sample collection which limits future strategy with home-based early screening of AD, we extensively reviewed the future trend of new noninvasive detection techniques (such as optical screening and bio-imaging process). To overcome the limitation of non-invasive biosamples with low concentrations of AD biomarkers, current efforts to enhance the sensitivity of biosensors and discover new types of biomarkers using non-invasive body fluids are presented. We also introduced future trends facing an infection point in early diagnosis of AD with simultaneous emergence of addressable innovative technologies.

The Role of Salivary Biomarkers in the Early Diagnosis of Alzheimer's Disease and Parkinson's Disease

Many neurodegenerative diseases present with progressive neuronal degeneration, which can lead to cognitive and motor impairment. Early screening and diagnosis of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are necessary to begin treatment before the onset of clinical symptoms and slow down the progression of the disease. Biomarkers have shown great potential as a diagnostic tool in the early diagnosis of many diseases, including AD and PD. However, screening for these biomarkers usually includes invasive, complex and expensive methods such as cerebrospinal fluid (CSF) sampling through a lumbar puncture. Researchers are continuously seeking to find a simpler and more reliable diagnostic tool that would be less invasive than CSF sampling. Saliva has been studied as a potential biological fluid that could be used in the diagnosis and early screening of neurodegenerative diseases. This review aims to provide an insight into the current literature concerning salivary biomarkers used in the diagnosis of AD and PD. The most commonly studied salivary biomarkers in AD are β-amyloid_1-42/1-40 and TAU protein, as well as α-synuclein and protein deglycase (DJ-1) in PD. Studies continue to be conducted on this subject and researchers are attempting to find correlations between specific biomarkers and early clinical symptoms, which could be key in creating new treatments for patients before the onset of symptoms.

Quantifying misfolded protein oligomers as drug targets and biomarkers in Alzheimer and Parkinson diseases

Protein misfolding and aggregation are characteristic of a wide range of neurodegenerative disorders, including Alzheimer and Parkinson diseases. A hallmark of these diseases is the aggregation of otherwise soluble and functional proteins into amyloid aggregates. Although for many decades such amyloid deposits have been thought to be responsible for disease progression, it is now increasingly recognized that the misfolded protein oligomers formed during aggregation are, instead, the main agents causing pathological processes. These oligomers are transient and heterogeneous, which makes it difficult to detect and quantify them, generating confusion about their exact role in disease. The lack of suitable methods to address these challenges has hampered efforts to investigate the molecular mechanisms of oligomer toxicity and to develop oligomer-based diagnostic and therapeutic tools to combat protein misfolding diseases. In this Review, we describe methods to quantify misfolded protein oligomers, with particular emphasis on diagnostic applications as disease biomarkers and on therapeutic applications as target biomarkers. The development of these methods is ongoing, and we discuss the challenges that remain to be addressed to establish measurement tools capable of overcoming existing limitations and to meet present needs.

Plasma and cerebrospinal fluid ABeta42 for the differential diagnosis of Alzheimer's disease dementia in participants diagnosed with any dementia subtype in a specialist care setting

BACKGROUND

Dementia is a syndrome that comprises many differing pathologies, including Alzheimer's disease dementia (ADD), vascular dementia (VaD) and frontotemporal dementia (FTD). People may benefit from knowing the type of dementia they live with, as this could inform prognosis and may allow for tailored treatment. Beta-amyloid (1-42) (ABeta42) is a protein which decreases in both the plasma and cerebrospinal fluid (CSF) of people living with ADD, when compared to people with no dementia. However, it is not clear if changes in ABeta42 are specific to ADD or if they are also seen in other types of dementia. It is possible that ABeta42 could help differentiate ADD from other dementia subtypes.

OBJECTIVES

To determine the accuracy of plasma and CSF ABeta42 for distinguishing ADD from other dementia subtypes in people who meet the criteria for a dementia syndrome.

SEARCH METHODS

We searched MEDLINE, and nine other databases up to 18 February 2020. We checked reference lists of any relevant systematic reviews to identify additional studies.

SELECTION CRITERIA

We considered cross-sectional studies that differentiated people with ADD from other dementia subtypes. Eligible studies required measurement of participant plasma or CSF ABeta42 levels and clinical assessment for dementia subtype.

DATA COLLECTION AND ANALYSIS

Seven review authors working independently screened the titles and abstracts generated by the searches. We collected data on study characteristics and test accuracy. We used the second version of the 'Quality Assessment of Diagnostic Accuracy Studies' (QUADAS-2) tool to assess internal and external validity of results. We extracted data into 2 x 2 tables, cross-tabulating index test results (ABeta42) with the reference standard (diagnostic criteria for each dementia subtype). We performed meta-analyses using bivariate, random-effects models. We calculated pooled estimates of sensitivity, specificity, positive predictive values, positive and negative likelihood ratios, and corresponding 95% confidence intervals (CIs). In the primary analysis, we assessed accuracy of plasma or CSF ABeta42 for distinguishing ADD from other mixed dementia types (non-ADD). We then assessed accuracy of ABeta42 for differentiating ADD from specific dementia types: VaD, FTD, dementia with Lewy bodies (DLB), alcohol-related cognitive disorder (ARCD), Creutzfeldt-Jakob disease (CJD) and normal pressure hydrocephalus (NPH). To determine test-positive cases, we used the ABeta42 thresholds employed in the respective primary studies. We then performed sensitivity analyses restricted to those studies that used common thresholds for ABeta42.

MAIN RESULTS

We identified 39 studies (5000 participants) that used CSF ABeta42 levels to differentiate ADD from other subtypes of dementia. No studies of plasma ABeta42 met the inclusion criteria. No studies were rated as low risk of bias across all QUADAS-2 domains. High risk of bias was found predominantly in the domains of patient selection (28 studies) and index test (25 studies). The pooled estimates for differentiating ADD from other dementia subtypes were as follows: ADD from non-ADD: sensitivity 79% (95% CI 0.73 to 0.85), specificity 60% (95% CI 0.52 to 0.67), 13 studies, 1704 participants, 880 participants with ADD; ADD from VaD: sensitivity 79% (95% CI 0.75 to 0.83), specificity 69% (95% CI 0.55 to 0.81), 11 studies, 1151 participants, 941 participants with ADD; ADD from FTD: sensitivity 85% (95% CI 0.79 to 0.89), specificity 72% (95% CI 0.55 to 0.84), 17 studies, 1948 participants, 1371 participants with ADD; ADD from DLB: sensitivity 76% (95% CI 0.69 to 0.82), specificity 67% (95% CI 0.52 to 0.79), nine studies, 1929 participants, 1521 participants with ADD. Across all dementia subtypes, sensitivity was greater than specificity, and the balance of sensitivity and specificity was dependent on the threshold used to define test positivity.

AUTHORS' CONCLUSIONS

Our review indicates that measuring ABeta42 levels in CSF may help differentiate ADD from other dementia subtypes, but the test is imperfect and tends to misdiagnose those with non-ADD as having ADD. We would caution against the use of CSF ABeta42 alone for dementia classification. However, ABeta42 may have value as an adjunct to a full clinical assessment, to aid dementia diagnosis.

Stereoscopic Depth Perception and Visuospatial Dysfunction in Alzheimer's Disease

With visuospatial dysfunction emerging as a potential marker that can detect Alzheimer's disease (AD) even in its earliest stages and with disturbance in stereopsis suspected to be the prime contributor to visuospatial deficits in AD, we assessed stereoscopic abilities of patients with AD and mild cognitive impairment (MCI). Whereas previous research assessing patients' stereoacuity has yielded mixed results, we assessed patients' capacity to process coarse disparities that can convey adequate depth information about objects in the environment. We produced two virtual cubes at two different distances from the observer by manipulating disparity type (absolute vs. relative), disparity direction (crossed vs. uncrossed) and disparity magnitude, then had participants judge the object that appeared closer to them. Two patient groups performed as well as, or even better than elderly controls, suggesting that AD patients' coarse disparity processing capacity is capable of supporting common tasks involving reaching, grasping, driving, and navigation. Results may help researchers narrow down the exact cause(s) of visuospatial deficits in AD and develop and validate measures to assess visuospatial dysfunction in clinical trials and disease diagnosis.

The Cholinergic System, the Adrenergic System and the Neuropathology of Alzheimer's Disease

Neurodegenerative diseases are a major public health problem worldwide with a wide spectrum of symptoms and physiological effects. It has been long reported that the dysregulation of the cholinergic system and the adrenergic system are linked to the etiology of Alzheimer’s disease. Cholinergic neurons are widely distributed in brain regions that play a role in cognitive functions and normal cholinergic signaling related to learning and memory is dependent on acetylcholine. The Locus Coeruleus norepinephrine (LC-NE) is the main noradrenergic nucleus that projects and supplies norepinephrine to different brain regions. Norepinephrine has been shown to be neuroprotective against neurodegeneration and plays a role in behavior and cognition. Cholinergic and adrenergic signaling are dysregulated in Alzheimer’s disease. The degeneration of cholinergic neurons in nucleus basalis of Meynert in the basal forebrain and the degeneration of LC-NE neurons were reported in Alzheimer’s disease. The aim of this review is to describe current literature on the role of the cholinergic system and the adrenergic system (LC-NE) in the pathology of Alzheimer’s disease and potential therapeutic implications.

Diagnosis of Alzheimer's Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

BACKGROUND

The present systematic review and meta-analysis of diagnostic test accuracy summarizes the last three decades in advances on diagnosis of Alzheimer's disease (AD) in developed and developing countries.

OBJECTIVE

To determine the accuracy of biomarkers in diagnostic tools in AD, for example, cerebrospinal fluid, positron emission tomography (PET), and magnetic resonance imaging (MRI), etc.

METHODS

The authors searched PubMed for published studies from 1990 to April 2020 on AD diagnostic biomarkers. 84 published studies were pooled and analyzed in this meta-analysis and diagnostic accuracy was compared by summary receiver operating characteristic statistics.

RESULTS

Overall, 84 studies met the criteria and were included in a meta-analysis. For EEG, the sensitivity ranged from 67 to 98%, with a median of 80%, 95% CI [75, 91], tau-PET diagnosis sensitivity ranged from 76 to 97%, with a median of 94%, 95% CI [76, 97]; and MRI sensitivity ranged from 41 to 99%, with a median of 84%, 95% CI [81, 87]. Our results showed that tau-PET diagnosis had higher performance as compared to other diagnostic methods in this meta-analysis.

CONCLUSION

Our findings showed an important discrepancy in diagnostic data for AD between developed and developing countries, which can impact global prevalence estimation and management of AD. Also, our analysis found a better performance for the tau-PET diagnostic over other methods to diagnose AD patients, but the expense of tau-PET scan seems to be the limiting factor in the diagnosis of AD in developing countries such as those found in Asia, Africa, and Latin America.

Millipore xMap® Luminex (HATMAG-68K): An Accurate and Cost-Effective Method for Evaluating Alzheimer's Biomarkers in Cerebrospinal Fluid

Background: Alzheimer's disease (AD) biomarkers are of great relevance in clinical research, especially after the AT(N) framework. They enable early diagnosis, disease staging and research with new promising drugs, monitoring therapeutic response. However, the high cost and low availability of the most well-known methods limits their use in low and medium-income countries. In this context, Millipore xMap^® Luminex may be a cost-effective alternative. In our study, using INNOTEST^® as reference, we assess the diagnostic accuracy of Millipore xMap^® and propose a cutoff point for AD. Methods: We performed lumbar puncture of seven older individuals with clinically defined AD, 17 with amnestic mild cognitive impairment (aMCI) and 11 without objective cognitive impairment-control group (CG). Cerebrospinal fluid (CSF) biomarkers concentrations for aB42, p-Tau, and t-Tau were measured by INNOTEST^® and Millipore xMap^®, and then the techniques were compared to assess the diagnostic accuracy of the new test and to define a cutoff. Results: INNOTEST^® and Millipore xMap^® measurements showed all correlations >0.8 for the same biomarker, except for t-Tau that was 0.66. Millipore xMap^® measurements showed a robust accuracy for all biomarkers, with AUC higher than 0.808 (t-Tau), and the best for Aβ42 (AUC = 0.952). The most accurate cutoffs were found at 1012.98 pg/ml (Aβ42), 64.54 pg/ml (p-tau), 3251.81 pg/ml (t-tau), 3.370 (t-Tau/Aβ42), and 0.059 (p-Tau/Aβ42). Conclusion: Given its good accuracy and cost-effectiveness, Milliplex xMap^® tests seems a reliable and promising tool, especially for low and middle-income countries.

Investigating Auditory Electrophysiological Measures of Participants with Mild Cognitive Impairment and Alzheimer's Disease: A Systematic Review and Meta-Analysis of Event-Related Potential Studies

BACKGROUND

Objectively measuring auditory functions has been proposed as an avenue in differentiating normal age-related cognitive dysfunction from Alzheimer's disease (AD) and its prodromal states. Previous research has suggested auditory event-related potentials (AERPs) to be non-invasive, cost-effective, and efficient biomarkers for the diagnosis of AD.

OBJECTIVE

The objective of this paper is to review the published literature on AERPs measures in older adults diagnosed with AD and those at higher risk of developing AD, i.e., mild cognitive impairment (MCI) and subjective cognitive decline.

METHODS

The search was performed on six major electronic databases (Ovid MEDLINE, OVID EMBASE, PsycINFO, PubMed, Scopus, and CINAHL Plus). Articles identified prior to 7 May 2019 were considered for this review. A random effects meta-analysis and analysis of between study heterogeneity was conducted using the Comprehensive Meta-Analysis software.

RESULTS

The search identified 1,076 articles; 74 articles met the full inclusion criteria and were included in the systematic review, and 47 articles were included into the analyses. Pooled analysis suggests that AD participants can be differentiated from controls due to significant delays in ABR, N100, P200, N200, and P300 latencies. P300 amplitude was significantly smaller in AD participants compared to controls. P300 latencies differed significantly between MCI participants and controls based on the pooled analysis.

CONCLUSION

The findings of this review indicate that some AERPs may be valuable biomarkers of AD. In conjunction with currently available clinical and neuropsychological assessments, AERPs can aid in screening and diagnosis of prodromal AD.

Comparing fluid biomarkers of Alzheimer's disease between African American or Black African and white groups: A systematic review and meta-analysis

RATIONALE

Biomarker research for Alzheimer's disease (AD) has grown rapidly in recent years, ensuing the integration of the AD fluid biomarker profile: Aβ1-42, t-tau, and p-tau₁₈₁, into clinical and research criteria. However, current insights of AD arise almost exclusively from studies on white individuals. Some studies have revealed that epidemiology, clinical features, and genetics of AD show variations between individuals from black and white backgrounds, conveying the importance of ethnoracial differences, and the possibility of such differences also influencing AD biomarker levels. This systematic review explored whether AD fluid biomarker levels differ between African American (AA) or Black African and white groups.

AIM

To compare AD fluid biomarkers (Aβ1-42, p-tau₁₈₁, and t-tau) levels between AA or Black Africans and white individuals.

METHOD

PubMed, Scopus, and other sources were explored for studies that quantified AD biomarkers in biological fluid from whites and AA or Black African groups. Meta-analyses were performed to find the standardized mean difference for biomarkers that were quantified in ≥3 studies.

RESULTS

Five studies were included; studies on Black Africans were not found. The meta-analyses found CSF t-tau and p-tau₁₈₁ were consistently lower in AA than white individuals, in samples with normal cognition or with mild cognitive impairment/dementia.

CONCLUSIONS

The meta-analyses found significant differences for CSF tau between AA and white individuals with normal cognition and within the dementia spectrum, expressing the importance of taking into account ethnoracial factors when interpreting CSF AD biomarkers levels. However, the generalisability of these differences is restricted by small samples' size, lack of unified methodologies and recruitment's biases within studies; further large multicentre studies with harmonized protocols and sufficient power are imperative to investigate the extent of ethnoracial differences across the spectrum of cognitive decline, with vaster efforts necessary to diversify recruitment.

Scope of β-Secretase (BACE1)-Targeted Therapy in Alzheimer's Disease: Emphasizing the Flavonoid Based Natural Scaffold for BACE1 Inhibition

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common form of dementia in the world. Studies report the presence of extracellular amyloid plaques consisting of β-amyloid peptide and intracellular tangles consisting of hyperphosphorylated tau proteins as the histopathological indicators of AD. The process of β-amyloid peptide generation by sequential cleavage of amyloid precursor protein by β-secretase (BACE1) and γ-secretase, followed by its aggregation to form amyloid plaques, is the mechanistic basis of the amyloid hypothesis. Other popular hypotheses related to the pathogenesis of AD include the tau hypothesis and the oxidative stress hypothesis. Various targets of the amyloid cascade are now in prime focus to develop drugs for AD. Many BACE1 inhibitors, β-amyloid aggregation inhibitors, and Aβ clearance strategies using monoclonal antibodies are in various stages of clinical trials. This review provides an in-depth evaluation of the role of BACE1 in disease pathogenesis and also highlights the therapeutic approaches developed to find more potent but less toxic inhibitors for BACE1, particularly emphasizing the natural scaffold as a nontoxic lead for BACE1 inhibition. Cellular targets and signaling cascades involving BACE1 have been highlighted to understand the physiological role of BACE1. This knowledge is extremely crucial to understand the toxicity evaluations for BACE1-targeted therapy. We have particularly highlighted the scope of flavonoids as a new generation of nontoxic BACE1 inhibitory scaffolds. The structure-activity relationship of BACE1 inhibition for this group of compounds has been highlighted to provide a guideline to design more selective highly potent inhibitors. The review aims to provide a holistic overview of BACE1-targeted therapy for AD that paves the way for future drug development.

Uncharacterized RNAs in Plasma of Alzheimer's Patients Are Associated with Cognitive Impairment and Show a Potential Diagnostic Power

Alzheimer’s disease (AD) diagnosis is actually based on clinical evaluation and brain-imaging tests, and it can often be confirmed only post-mortem. Therefore, new non-invasive molecular biomarkers are necessary to improve AD diagnosis. As circulating microRNA biomarkers have been proposed for many diseases, including AD, we aimed to identify new diagnostic non-small RNAs in AD. Whole transcriptome analysis was performed on plasma samples of five AD and five unaffected individuals (CTRL) using the Clariom D Pico Assay, followed by validation in real-time PCR on 37 AD patients and 37 CTRL. Six differentially expressed (DE) transcripts were identified: GS1-304P7.3 (upregulated), NONHSAT090268, TC0100011037, TC0400008478, TC1400008125, and UBE2V1 (downregulated). Peripheral blood mononuclear cells (PBMCs) may influence the expression of circulating RNAs and their analysis has been proposed to improve AD clinical management. Accordingly, DE transcript expression was also evaluated in PBMCs, showing no difference between AD and CTRL. ROC (receiver operating characteristic) curve analysis was performed to evaluate the diagnostic accuracy of each DE transcript and a signature including all of them. A correlation between cognitive impairment and GS1-304P7.3, NONHSAT090268, TC0100011037, and TC0400008478 was detected, suggesting a potential association between their extracellular abundance and AD clinical phenotype. Finally, this study identified six transcripts showing altered expression in the plasma of AD patients. Given the need for new, accurate blood biomarkers for AD diagnosis, these transcripts may be considered for further analyses in larger cohorts, also in combination with other biomarkers, aiming to identify specific RNA-based biomarkers to be eventually applied to clinical practice.

Body Fluid Biomarkers for Alzheimer's Disease-An Up-To-Date Overview

Neurodegeneration is a highly complex process which is associated with a variety of molecular mechanisms related to ageing. Among neurodegenerative disorders, Alzheimer's disease (AD) is the most common, affecting more than 45 million individuals. The underlying mechanisms involve amyloid plaques and neurofibrillary tangles (NFTs) deposition, which will subsequently lead to oxidative stress, chronic neuroinflammation, neuron dysfunction, and neurodegeneration. The current diagnosis methods are still limited in regard to the possibility of the accurate and early detection of the diseases. Therefore, research has shifted towards the identification of novel biomarkers and matrices as biomarker sources, beyond amyloid-β and tau protein levels within the cerebrospinal fluid (CSF), that could improve AD diagnosis. In this context, the aim of this paper is to provide an overview of both conventional and novel biomarkers for AD found within body fluids, including CSF, blood, saliva, urine, tears, and olfactory fluids.

Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series

Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three different sparsification methods were used. In addition to simple thresholding, spectral sparsification based on effective resistance and sparse autoencoder were performed in order to analyze the effect of sparsification routine on classification results. Also, instead of extracting common features, the correlation matrices were reshaped to a correlation vector and used as a feature vector to enter the classifier. Since the correlation matrix is symmetric, in another analysis half of the feature vector was used, moreover, the Genetic Algorithm (GA) also utilized for feature vector dimension reduction. The non-linear SVM classifier with a polynomial kernel applied. The results showed that the autoencoder sparsification method had the greatest discrimination power with the accuracy of 98.35% for classification when the feature vector was the full correlation matrix.

Urinary Apolipoprotein C3 Is a Potential Biomarker for Alzheimer's Disease

INTRODUCTION

Biomarkers of Alzheimer's disease (AD) that can easily be measured in routine health checkups are desirable. Urine is a source of biomarkers that can be collected easily and noninvasively. We previously reported on the comprehensive profile of the urinary proteome of AD patients and identified proteins estimated to be significantly increased or decreased in AD patients by a label-free quantification method. The present study aimed to validate urinary levels of proteins that significantly differed between AD and control samples from our proteomics study (i.e., apolipoprotein C3 [ApoC3], insulin-like growth factor-binding protein 3 [Igfbp3], and apolipoprotein D [ApoD]).

METHODS

Enzyme-linked immunosorbent assays (ELISAs) were performed using urine samples from the same patient and control groups analyzed in the previous proteomics study (18 AD and 18 controls, set 1) and urine samples from an independent group of AD patients and controls (13 AD, 5 mild cognitive impairment [MCI], and 32 controls) from the National Center for Geriatrics and Gerontology Biobank (set 2).

RESULTS

In set 1, the crude urinary levels of ApoD, Igfbp3, and creatinine-adjusted ApoD were significantly higher in the AD group relative to the control group (p = 0.003, p = 0.002, and p = 0.019, respectively), consistent with our previous proteomics results. In set 2, however, the crude urinary levels of Igfbp3 were significantly lower in the AD+MCI group than in the control group (p = 0.028), and the levels of ApoD and ApoC3 did not differ significantly compared to the control group. Combined analysis of all samples revealed creatinine-adjusted ApoC3 levels to be significantly higher in the AD+MCI group (p = 0.015) and the AD-only group (p = 0.011) relative to the control group.

CONCLUSION

ApoC3 may be a potential biomarker for AD, as validated by ELISA. Further analysis of ApoC3 as a urinary biomarker for AD is warranted.

Classification and Graphical Analysis of Alzheimer's Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype

Graphical, voxel, and region-based analysis has become a popular approach to studying neurodegenerative disorders such as Alzheimer's disease (AD) and its prodromal stage [mild cognitive impairment (MCI)]. These methods have been used previously for classification or discrimination of AD in subjects in a prodromal stage called stable MCI (MCIs), which does not convert to AD but remains stable over a period of time, and converting MCI (MCIc), which converts to AD, but the results reported across similar studies are often inconsistent. Furthermore, the classification accuracy for MCIs vs. MCIc is limited. In this study, we propose combining different neuroimaging modalities (sMRI, FDG-PET, AV45-PET, DTI, and rs-fMRI) with the apolipoprotein-E genotype to form a multimodal system for the discrimination of AD, and to increase the classification accuracy. Initially, we used two well-known analyses to extract features from each neuroimage for the discrimination of AD: whole-brain parcelation analysis (or region-based analysis), and voxel-wise analysis (or voxel-based morphometry). We also investigated graphical analysis (nodal and group) for all six binary classification groups (AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs). Data for a total of 129 subjects (33 AD, 30 MCIs, 31 MCIc, and 35 HCs) for each imaging modality were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) homepage. These data also include two APOE genotype data points for the subjects. Moreover, we used the 2-mm AICHA atlas with the NiftyReg registration toolbox to extract 384 brain regions from each PET (FDG and AV45) and sMRI image. For the rs-fMRI images, we used the DPARSF toolbox in MATLAB for the automatic extraction of data and the results for REHO, ALFF, and fALFF. We also used the pyClusterROI script for the automatic parcelation of each rs-fMRI image into 200 brain regions. For the DTI images, we used the FSL (Version 6.0) toolbox for the extraction of fractional anisotropy (FA) images to calculate a tract-based spatial statistic. Moreover, we used the PANDA toolbox to obtain 50 white-matter-region-parcellated FA images on the basis of the 2-mm JHU-ICBM-labeled template atlas. To integrate the different modalities and different complementary information into one form, and to optimize the classifier, we used the multiple kernel learning (MKL) framework. The obtained results indicated that our multimodal approach yields a significant improvement in accuracy over any single modality alone. The areas under the curve obtained by the proposed method were 97.78, 96.94, 95.56, 96.25, 96.67, and 96.59% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs binary classification, respectively. Our proposed multimodal method improved the classification result for MCIs vs. MCIc groups compared with the unimodal classification results. Our study found that the (left/right) precentral region was present in all six binary classification groups (this region can be considered the most significant region). Furthermore, using nodal network topology, we found that FDG, AV45-PET, and rs-fMRI were the most important neuroimages, and showed many affected regions relative to other modalities. We also compared our results with recently published results.

Novel MRI Techniques Identifying Vascular Leak and Paravascular Flow Reduction in Early Alzheimer Disease

With beta amyloid and tau antibody treatment trial failures, avenues directed to other facets of the disease pathophysiology are being explored to treat in the preclinical or early clinical state. Clear evidence of blood–brain barrier (BBB) breakdown occurring early in the AD process has recently been established. Likewise, the glymphatic system regulating water and solute inflow and outflow in parallel with the vascular system is affected causing delayed clearance of fluid waste. Its dysfunction as a component of AD along with BBB leak are reasonable candidates to explore for future treatments. Ideally, human medication trials require a minimally invasive method of quantifying both improvements in BBB integrity and glymphatic fluid clearance correlated with clinical outcomes. We will review the known physiology and anatomy of the BBB system, and its relationship to the glymphatic system and the microglial surveillance system. Dysfunction of this tripart system occurring in preclinical Alzheimer disease (AD) will be reviewed along with existing MRI tools for identifying altered flow dynamics useful for monitoring improved functionality with future treatments. High-resolution dynamic contrast enhanced MRI imaging demonstrating BBB leak and the recently reported non-invasive 3D PASL MRI pilot study demonstrating significant delay in glymphatic clearance in AD subjects appear to be the best candidates.