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

Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review

Prediction models have been developed to identify mild cognitive impairment (MCI) cases likely to convert to dementia. This systematic review summarizes multi-source prediction models for MCI to dementia conversion. PubMed and Embase were searched for model development and validation studies from inception up to January 18 2024. Models were assessed for included predictors, predictive performance, risk of bias, and generalizability. 62 studies were included: 41 machine learning models, 11 regression models, and 5 disease state indexes. The number of predictors in the models ranged from 2 to 60; magnetic resonance imaging (MRI) and cognitive scores were the most common sources. Performance measures indicate reasonable predictive capabilities (area under the curve [AUC] range: 0.58-0.98, accuracy range: 66.1-96.3%); however, most studies are at high risk of bias and 47 studies lack external validation. Currently, no highly valid prediction model is available for MCI to dementia conversion risk due to limited generalizability and high risk of bias in most studies. HIGHLIGHTS: Numerous models have been developed to predict the likelihood of conversion to dementia in individuals with MCI. Prediction models seem to have a reasonably good performance in predicting conversion to dementia, however, external validation and generalizability is often lacking. There is no prediction model available with a low risk for bias and that has been externally validated to accurately predict the risk of MCI to dementia conversion. For MCI to dementia conversion prediction models, more emphasis should be directed towards external validation, generalizability, and clinical applicability.

Plasma protein risk scores for mild cognitive impairment and Alzheimer's disease in the Framingham heart study

INTRODUCTION

It is unclear whether aggregated plasma protein risk scores (PPRSs) could be useful in predicting the risks of mild cognitive impairment (MCI) and Alzheimer's disease (AD).

METHODS

The Cox proportional hazard model with the Least Absolute Shrinkage and Selection Operator penalty was used to build the PPRSs for MCI and AD in 1515 Framingham Heart Study Generation 2 with 1128 proteins measured in plasma at exam 5 (cognitively normal [CN] = 1258, MCI = 129, AD = 128).

RESULTS

MCI PPRS had a hazard ratio (HR) of 6.97 [5.34, 9.12], with a discriminating power (C-index = 82.52%). AD PPRS had a HR of 5.74 [4.67, 7.05] (C-index = 88.15%). Both PPRSs were also significantly associated with cognitive changes, brain atrophy, and plasma AD biomarkers. Proteins in the MCI and AD PPRSs were involved in several pathways related to leukocyte, chemotaxis, immunity, inflammation, and cellular migration.

DISCUSSION

This study suggests that PPRSs serve well to predict the risk of developing MCI and AD as well as cognitive changes and AD-related pathogenesis in the brain.

HIGHLIGHTS

PPRSs were developed for the risk of AD and AD preclinical stage, MCI. PPRSs were developed for MCI and AD associated with cognitive changes, loss of brain volume, and increasing level of plasma AD biomarkers. Leukocyte, chemotaxis, immunity, inflammation, and cellular migration enriched in proteins were identified as being involved in MCI and AD PPRSs.

Quality of Life in Mild Cognitive Impairment and Mild Dementia Associated with Alzheimer's Disease: A Systematic Review

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) have a profound impact on patients' quality of life (QoL), with progressive declines occurring as the disease advances. This systematic review aims to summarize the published evidence on patient-reported outcomes (PROs) in individuals with MCI due to AD and mild AD dementia. Comprehensive searches were conducted across five major databases to identify studies reporting on utility values, disutilities, and QoL measures in these patient populations. A total of 23 studies were included that utilized various QoL assessment tools, including EQ-5D (n = 14), SF-36/SF-12 (n = 4), and QOL-AD (n = 11). Reported EQ-5D scores ranged from 0.81 to 0.92 for patients with MCI and from 0.67 to 0.85 for those with mild AD, indicating a noticeable decline in QoL as the disease progresses. QOL-AD scores ranged from 33.8 to 42.5 for MCI and from 32.4 to 38.1 for mild AD, equally reflecting the greater impairment in QoL with disease advancement. Interventions were generally associated with smaller declines in PROs compared to placebo, suggesting a positive impact of treatment in mitigating QoL deterioration. The findings underscore the significant QoL differences between MCI and mild AD, emphasizing the potential benefit of early intervention to preserve QoL and delay disease progression. This review highlights the importance of continued research to better understand QoL in patients with MCI and mild AD dementia, particularly in terms of capturing comprehensive patient-reported outcomes and evaluating the effectiveness of interventions over time. These findings can contribute to a more informed approach in clinical practice and support decision-making in the management of early-stage AD.

Image-and-Label Conditioning Latent Diffusion Model: Synthesizing A$\beta$-PET From MRI for Detecting Amyloid Status

Deposition of $\beta$-amyloid (A$\beta$), which is generally observed by A$\beta$-PET, is an important biomarker to evaluate subjects with early-onset dementia. However, acquisition of A$\beta$-PET usually suffers from high expense and radiation hazards, making A$\beta$-PET not commonly used as MRI. As A$\beta$-PET scans are only used to determine whether A$\beta$ deposition is positive or not, it is highly valuable to capture the underlying relationship between A$\beta$ deposition and other neuroimages (i.e., MRI) and detect amyloid status based on other neuroimages to reduce necessity of acquiring A$\beta$-PET. To this end, we propose an image-and-label conditioning latent diffusion model (IL-CLDM) to synthesize A$\beta$-PET scans from MRI scans by enhancing critical shared information to finally achieve MRI-based A$\beta$ classification. Specifically, two conditioning modules are introduced to enable IL-CLDM to implicitly learn joint image synthesis and diagnosis: 1) an image conditioning module, to extract meaningful features from source MRI scans to provide structural information, and 2) a label conditioning module, to guide the alignment of generated scans to the diagnosed label. Experiments on a clinical dataset of 510 subjects demonstrate that our proposed IL-CLDM achieves image quality superior to five widely used models, and our synthesized A$\beta$-PET scans (by IL-CLDM) can significantly help classification of A$\beta$ as positive or negative.

LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment

Alzheimer's disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, given the limitations of current diagnostic biomarkers and the need for non-invasive, accessible tools. This study aims to address these gaps by exploring driving performance as a novel, non-invasive biomarker for MCI detection. Using the LiveDrive AI system, equipped with multimodal sensing (MMS) technology and a driving performance assessment strategy, the proposed work analyzes the predictive capacity of driving patterns in indicating cognitive decline. Machine learning models, trained on an expert-annotated in-house dataset, were employed to detect MCI status from driving performance. Key findings demonstrate the feasibility of using nuanced driving features, such as velocity and acceleration during turning, as indicators of cognitive decline. This approach holds promise for integration into smartphone or car applications, enabling real-time, continuous cognitive health monitoring. The implications of this work suggest a transformative step towards scalable, real-world solutions for early AD diagnosis, with the potential to improve patient outcomes and disease management.

Improving estimation efficiency for survival data analysis by integrating a coarsened time-to-event outcome from an external study

In the era of big data, increasing availability of data makes combining different data sources to obtain more accurate estimations a popular topic. However, the development of data integration is often hindered by the heterogeneity in data forms across studies. In this paper, we focus on a case in survival analysis where we have primary study data with a continuous time-to-event outcome and complete covariate measurements, while the data from an external study contain an outcome observed at regular intervals, and only a subset of covariates is measured. To incorporate external information while accounting for the different data forms, we posit working models and obtain informative weights by empirical likelihood, which will be used to construct a weighted estimator in the main analysis. We have established the theory demonstrating that the new estimator has higher estimation efficiency compared to the conventional ones, and this advantage is robust to working model misspecification, as confirmed in our simulation studies. To assess its utility, we apply our method to accommodate data from the National Alzheimer's Coordinating Center to improve the analysis of the Alzheimer's Disease Neuroimaging Initiative Phase 1 study.

Operationalizing selection criteria for clinical trials in Alzheimer's disease: Biomarker and clinical considerations: Proceedings from the Alzheimer's Association Research Roundtable (AARR) Fall 2021 meeting

The design of clinical trials in Alzheimer's disease (AD) must consider the development of new plasma, cerebrospinal fluid (CSF), and imaging biomarkers. They must also define clinically meaningful outcomes for patients and set endpoints that measure these outcomes accurately. With the accelerated United States Food and Drug Administration (FDA) approval of the first anti-amyloid, disease-modifying treatment for AD, a monoclonal antibody called aducanumab, the landscape of clinical trial design is evolving. Enrolment in clinical trials may be impacted by the availability of this and other treatments, and trial design must take into consideration that patients may desire a disease-modifying treatment rather than potentially being randomized to the placebo arm. The Alzheimer's Association Research Roundtable (AARR) Fall 2021 meeting discussed the consideration of well-defined AD staging criteria in protocol design and how they influence more standardized inclusion/exclusion criteria for trials, as well as what constitutes meaningful differentiation between the stages. Discussion explored the current state of knowledge regarding biomarkers and how they can inform AD staging criteria, as many trials are now designed based on specific biomarker features, further underscoring the importance of coordinating AD staging criteria and biomarkers. The relationship between cognition and biomarkers has been studied and this must continue as trials move forward. Researchers, patients, clinicians, regulatory scientists, and payers discussed the state of the field as well as the future of symptomatic Alzheimer's disease clinical trials.

Highlights

The Alzheimer's Association Research Roundtable (AARR) convened leaders from academia and industry as well as patients, care partners, clinicians, regulators, and payers to discuss the topic of operationalizing selection criteria for clinical trials and the role of biomarkers.Well-defined Alzheimer's disease (AD) staging criteria are an important consideration in study protocol design.Staging criteria and biomarkers must be coordinated to yield high-quality clinical trial results that have meaning for patients with AD by selecting a population most likely to benefit from a specific treatment.

Glutamate: Molecular Mechanisms and Signaling Pathway in Alzheimer's Disease, a Potential Therapeutic Target

Gamma-glutamate is an important excitatory neurotransmitter in the central nervous system (CNS), which plays an important role in transmitting synapses, plasticity, and other brain activities. Nevertheless, alterations in the glutamatergic signaling pathway are now accepted as a central element in Alzheimer's disease (AD) pathophysiology. One of the most prevalent types of dementia in older adults is AD, a progressive neurodegenerative illness brought on by a persistent decline in cognitive function. Since AD has been shown to be multifactorial, a variety of pharmaceutical targets may be used to treat the condition. N-methyl-D-aspartic acid receptor (NMDAR) antagonists and acetylcholinesterase inhibitors (AChEIs) are two drug classes that the Food and Drug Administration has authorized for the treatment of AD. The AChEIs approved to treat AD are galantamine, donepezil, and rivastigmine. However, memantine is the only non-competitive NMDAR antagonist that has been authorized for the treatment of AD. This review aims to outline the involvement of glutamate (GLU) at the molecular level and the signaling pathways that are associated with AD to demonstrate the drug target therapeutic potential of glutamate and its receptor. We will also consider the opinion of the leading authorities working in this area, the drawback of the existing therapeutic strategies, and the direction for the further investigation.

Association of glial fibrillary acid protein, Alzheimer's disease pathology and cognitive decline

Increasing evidence shows that neuroinflammation is a possible modulator of tau spread effects on cognitive impairment in Alzheimer's disease. In this context, plasma levels of the glial fibrillary acidic protein (GFAP) have been suggested to have a robust association with Alzheimer's disease pathophysiology. This study aims to assess the correlation between plasma GFAP and Alzheimer's disease pathology, and their synergistic effect on cognitive performance and decline. A cohort of 122 memory clinic subjects with amyloid and tau PET, MRI scans, plasma GFAP and Mini-Mental State Examination (MMSE) was included in the study. A subsample of 94 subjects had a follow-up MMSE score at ≥1 year after baseline. Regional and voxel-based correlations between Alzheimer's disease biomarkers and plasma GFAP were assessed. Mediation analyses were performed to evaluate the effects of plasma GFAP on the association between amyloid and tau PET and between tau PET and cognitive impairment and decline. GFAP was associated with increased tau PET ligand uptake in the lateral temporal and inferior temporal lobes in a strong left-sided pattern independently of age, sex, education, amyloid and APOE status (β = 0.001, P < 0.01). The annual rate of MMSE change was significantly and independently correlated with both GFAP (β = 0.006, P < 0.01) and global tau standardized uptake value ratio (β = 4.33, P < 0.01), but not with amyloid burden. Partial mediation effects of GFAP were found on the association between amyloid and tau pathology (13.7%) and between tau pathology and cognitive decline (17.4%), but not on global cognition at baseline. Neuroinflammation measured by circulating GFAP is independently associated with tau Alzheimer's disease pathology and with cognitive decline, suggesting neuroinflammation as a potential target for future disease-modifying trials targeting tau pathology.

Guidelines for the use and interpretation of Alzheimer's disease biomarkers in clinical practice in Brazil: recommendations from the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology

In recent years, the diagnostic accuracy of Alzheimer's disease has been enhanced by the development of different types of biomarkers that indicate the presence of neuropathological processes. In addition to improving patient selection for clinical trials, biomarkers can assess the effects of new treatments on pathological processes. However, there is concern about the indiscriminate and poorly supported use of biomarkers, especially in asymptomatic individuals or those with subjective cognitive decline. Difficulties interpreting these tests, high costs, and unequal access make this scenario even more challenging in healthcare. This article presents the recommendations from the Scientific Department of Cognitive Neurology and Aging of the Brazilian Academy of Neurology (Departamento Científico de Neurologia Cognitiva e Envelhecimento da Academia Brasileira de Neurologia) regarding the rational use and interpretation of Alzheimer's disease biomarkers in clinical practice. The clinical diagnosis of cognitive-behavioral syndrome is recommended as the initial step to guide the request for biomarkers.

Cognitive status and demographics modify the association between subjective cognition and amyloid

OBJECTIVE

This study examined the effect of cognitive status, education, and sex on the association between subjective cognitive decline (SCD) and Alzheimer's disease (AD) biomarkers in non-demented older adults.

METHODS

Vanderbilt Memory and Aging Project participants (n = 129), dementia or stroke free, completed fasting lumbar puncture, SCD assessment, and cognitive assessment. Cerebrospinal fluid (CSF) biomarkers for AD were analyzed. Linear regression models related SCD to CSF AD biomarkers and follow-up models assessed interactions of SCD × cognitive status, sex, reading level, and education on AD biomarkers.

RESULTS

In main effect models, higher SCD was associated with more amyloidosis (p-values <0.004). SCD was not associated with tau, p-tau, or neurofilament light (NFL) levels (p-values >0.38). SCD score interacted with cognitive status (p < 0.02), sex (p = 0.03), and education (p-values <0.005) on amyloidosis. In stratified models, higher SCD was associated with more amyloid in cognitively unimpaired (p-values <0.003), men (p = 0.0003), and higher education. No SCD score × reading-level interaction was found (p-values >0.51) though SCD related to amyloid markers in the higher reading-level group (p-values <0.004).

INTERPRETATION

Higher SCD was associated with greater cerebral amyloid accumulation, one of the earliest pathological AD changes. SCD appears most useful in detecting early AD-related brain changes prior to objective cognitive impairment, in men, and those with higher quantity and quality of education and highlight the importance of considering these factors.

Blood DNA methylomic signatures associated with CSF biomarkers of Alzheimer's disease in the EMIF-AD study

INTRODUCTION

We investigated blood DNA methylation patterns associated with 15 well-established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration.

METHODS

We assessed DNA methylation in 885 blood samples from the European Medical Information Framework for Alzheimer's Disease (EMIF-AD) study using the EPIC array.

RESULTS

We identified Bonferroni-significant differential methylation associated with CSF YKL-40 (five loci) and neurofilament light chain (NfL; seven loci) levels, with two of the loci associated with CSF YKL-40 levels correlating with plasma YKL-40 levels. A co-localization analysis showed shared genetic variants underlying YKL-40 DNA methylation and CSF protein levels, with evidence that DNA methylation mediates the association between genotype and protein levels. Weighted gene correlation network analysis identified two modules of co-methylated loci correlated with several amyloid measures and enriched in pathways associated with lipoproteins and development.

DISCUSSION

We conducted the most comprehensive epigenome-wide association study (EWAS) of AD-relevant CSF biomarkers to date. Future work should explore the relationship between YKL-40 genotype, DNA methylation, and protein levels in the brain.

HIGHLIGHTS

Blood DNA methylation was assessed in the EMIF-AD MBD study. Epigenome-wide association studies (EWASs) were performed for 15 Alzheimer's disease (AD)-relevant cerebrospinal fluid (CSF) biomarker measures. Five Bonferroni-significant loci were associated with YKL-40 levels and seven with neurofilament light chain (NfL). DNA methylation in YKL-40 co-localized with previously reported genetic variation. DNA methylation potentially mediates the effect of single-nucleotide polymorphisms (SNPs) in YKL-40 on CSF protein levels.

Biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is among the most common neurodegenerative disorders. AD is characterized by deposition of neurofibrillary tangles and amyloid plaques, leading to associated secondary pathologies, progressive neurodegeneration, and eventually death. Currently used diagnostics are largely image-based, lack accuracy and do not detect early disease, ie, prior to onset of symptoms, thus limiting treatment options and outcomes. Although biomarkers such as amyloid-β and tau protein in cerebrospinal fluid have gained much attention, these are generally limited to disease progression. Unfortunately, identification of biomarkers for early and accurate diagnosis remains a challenge. As such, body fluids such as sweat, serum, saliva, mucosa, tears, and urine are under investigation as alternative sources for biomarkers that can aid in early disease detection. This review focuses on biomarkers identified through proteomics in various biofluids and their potential for early and accurate diagnosis of AD.

Modern scores for traditional tests - Review of the diagnostic potential of scores derived from word list learning tests in mild cognitive impairment and early Alzheimer's Disease

Episodic memory impairment is one of the early hallmarks in Alzheimer's Disease. In the clinical diagnosis and research, episodic memory impairment is typically assessed using word lists that are repeatedly presented to and recalled by the participant across several trials. Until recently, total learning scores, which consist of the total number of words that are recalled by participants, were almost exclusively used for diagnostic purposes. The present review aims at summarizing evidence on additional scores derived from the learning trials which have recently been investigated more frequently regarding their diagnostic potential. These scores reflect item acquisition, error frequencies, strategy use, intertrial fluctuations, and recall consistency. Evidence was summarized regarding the effects of clinical status on these scores. Preclinical, mild cognitive impairment and mild Alzheimer's Disease stages were associated with a pattern of reduced item acquisition, more errors, less strategy use, and reduced access of items, indicating slowed and erroneous encoding. Practical implications and limitations of the present research will be discussed.

Double-Edged Sword: A Positive Brain Scan Result Heightens Confidence in an Alzheimer's Diagnosis But Also Leads to Higher Stigma Among Older Adults in a Vignette-Based Experiment

OBJECTIVES

Early diagnosis of Alzheimer's disease (AD) using brain scans and other biomarker tests will be essential to increasing the benefits of emerging disease-modifying therapies, but AD biomarkers may have unintended negative consequences on stigma. We examined how a brain scan result affects AD diagnosis confidence and AD stigma.

METHODS

The study used a vignette-based experiment with a 2 × 2 × 3 factorial design of main effects: a brain scan result as positive or negative, treatment availability and symptom stage. We sampled 1,283 adults ages 65 and older between June 11and July 3, 2019. Participants (1) rated their confidence in an AD diagnosis in each of four medical evaluations that varied in number and type of diagnostic tools and (2) read a vignette about a fictional patient with varied characteristics before completing the Modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). We examined mean diagnosis confidence by medical evaluation type. We conducted between-group comparisons of diagnosis confidence and FS-ADS scores in the positive versus negative brain scan result conditions and, in the positive condition, by symptom stage and treatment availability.

RESULTS

A positive versus negative test result corresponds with higher confidence in an AD diagnosis independent of medical evaluation type (all p < .001). A positive result correlates with stronger reactions on 6 of 7 FS-ADS domains (all p < .001).

DISCUSSION

A positive biomarker result heightens AD diagnosis confidence but also correlates with more AD stigma. Our findings inform strategies to promote early diagnosis and clinical discussions with individuals undergoing AD biomarker testing.

Nomination of a novel plasma protein biomarker panel capable of classifying Alzheimer's disease dementia with high accuracy in an African American cohort

Introduction

African Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimer's disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD.

Methods

Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in a cohort of AA clinically diagnosed as AD dementia (n=183) or cognitively unimpaired (CU, n=145). Machine learning approaches were implemented to identify the set of plasma proteins that yields the best classification accuracy.

Results

A plasma protein panel achieved an area under the curve (AUC) of 0.91 to classify AD dementia vs CU. The reproducibility of this finding was observed in the ANMerge plasma and AMP-AD Diversity brain datasets (AUC=0.83; AUC=0.94).

Discussion

This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning approaches. Our findings also highlight the potential importance of the matrisome and cerebrovascular dysfunction in AD pathophysiology.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Multimodal subtypes identified in Alzheimer's Disease Neuroimaging Initiative participants by missing-data-enabled subtype and stage inference

Alzheimer's disease is a highly heterogeneous disease in which different biomarkers are dynamic over different windows of the decades-long pathophysiological processes, and potentially have distinct involvement in different subgroups. Subtype and Stage Inference is an unsupervised learning algorithm that disentangles the phenotypic heterogeneity and temporal progression of disease biomarkers, providing disease insight and quantitative estimates of individual subtype and stage. However, a key limitation of Subtype and Stage Inference is that it requires a complete set of biomarkers for each subject, reducing the number of datapoints available for model fitting and limiting applications of Subtype and Stage Inference to modalities that are widely collected, e.g. volumetric biomarkers derived from structural MRI. In this study, we adapted the Subtype and Stage Inference algorithm to handle missing data, enabling the application of Subtype and Stage Inference to multimodal data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid and cognitive tests) from 789 participants in the Alzheimer's Disease Neuroimaging Initiative. Missing-data Subtype and Stage Inference identified five subtypes having distinct progression patterns, which we describe by the earliest unique abnormality as 'Typical AD with Early Tau', 'Typical AD with Late Tau', 'Cortical', 'Cognitive' and 'Subcortical'. These new multimodal subtypes were differentially associated with age, years of education, Apolipoprotein E (APOE4) status, white matter hyperintensity burden and the rate of conversion from mild cognitive impairment to Alzheimer's disease, with the 'Cognitive' subtype showing the fastest clinical progression, and the 'Subcortical' subtype the slowest. Overall, we demonstrate that missing-data Subtype and Stage Inference reveals a finer landscape of Alzheimer's disease subtypes, each of which are associated with different risk factors. Missing-data Subtype and Stage Inference has broad utility, enabling the prediction of progression in a much wider set of individuals, rather than being restricted to those with complete data.

Profiling lecanemab as a treatment option for Alzheimer's disease

INTRODUCTION

In July 2023, the U.S. Food and Drug Administration (FDA) granted full approval to lecanemab for the treatment of mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and mild AD dementia. Considering the limited treatment options for AD, the approval of lecanemab offers hope and opens the door for other disease-modifying therapies in the pipeline.

AREAS COVERED

In this review, the authors summarize the FDA treatment guidelines, other anti-amyloid agents, and drug information relevant to prescribers, such as pharmacology and pharmacokinetics. Relevant clinical trial outcomes are discussed along with their significance and controversies.

EXPERT OPINION

While questions remain about the magnitude of lecanemab's clinical impact, its approval signifies major progress in addressing the underlying pathology of AD. The authors have confidence in lecanemab as a promising treatment option and foresee exciting advancements on the 5-year horizon. Yet, further research is needed regarding trials beyond 18 months, post-marketing surveillance, and lecanameb in combination with existing treatments and lifestyle interventions.

Predicting amyloid positivity from FDG-PET images using radiomics: A parsimonious model

BACKGROUND AND OBJECTIVE

Amyloid plaques are one of the physical hallmarks of Alzheimer's disease. The objective of this study is to predict amyloid positivity non-invasively from FDG-PET images using a radiomics approach.

METHODS

We obtained FDG-PET images of 301 individuals from various groups, including control normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD), from the ADNI database. Following the utilization of the CSF Aβ1-42 (192) and Standardized Uptake Value Ratio (SUVR) (1.11) thresholds derived from Florbetapir scans, the subjects were categorized into two categories: those with a positive amyloid status (n = 185) and those with a negative amyloid status (n = 116). The process of segmenting the entire brain into 95 classes using the DKT-atlas was utilized. Following that, we obtained 120 characteristics for each of the 95 regions of interest (ROIs). We employed eight feature selection methods to analyze the features. Additionally, we utilized eight different classifiers on the 20 most significant features extracted from each feature selection method. Finally, in order to improve interpretability, we selected the most important features and ROIs.

RESULT

We found that the GNB classifier and the LASSO feature selection method had the best performance with an average accuracy of (AUC=0.924) while using 18 features on 15 ROIs. We were then able to reduce the model to three regions (Hippocampus, inferior parietal, and isthmus cingulate) and three gray-level based features (AUC=0.853).

CONCLUSION

The FDG-PET images which serve to study metabolic activity can be used to predict amyloid positivity without the use of invasive methods or another PET tracer and study. The proposed method has superior prediction accuracy with respect to similar studies reported in the literature using other imaging modalities. Only three brain regions had a high impact on amyloid positivity results.

Implementation of an ultra-sensitive microwell-based electrochemical sensor for the detection of Alzheimer's disease

Alzheimer's Disease (AD) is one of the most common neurodegenerative disorders in elderly people. It is diagnosed by detecting amyloid beta (Aβ) protein in cerebrospinal fluid (CSF) obtained by lumbar puncture or through expensive positron emission tomography (PET) imaging. Although blood-based diagnosis of AD offers a less invasive and cost-effective alternative, the quantification of Aβ is technically challenging due to its low abundance in peripheral blood. To address this, we developed a compact yet highly sensitive microwell-based electrochemical sensor with a densely packed microelectrode array (20 by 20) for enhancing sensitivity. Employing microwells on the working and counter electrodes minimized the leakage current from the metallic conductors into the assay medium, refining the signal fidelity. We achieved a detection limit <10 fg/mL for Aβ by elevating the signal-to-noise ratio, thus capable of AD biomarker quantification. Moreover, the microwell structure maintained the performance irrespective of variations in bead number, indicative of the sensor's robustness. The sensor's efficacy was validated through the analysis of Aβ concentrations in plasma samples from 96 subjects, revealing a significant distinction between AD patients and healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.85. Consequently, our novel microwell-based electrochemical biosensor represents a highly sensitive platform for detecting scant blood-based biomarkers, including Aβ, offering substantial potential for advancing AD diagnostics.

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

Executive function measures of participants with mild cognitive impairment: Systematic review and meta-analysis of event-related potential studies

OBJECTIVE

Objective measurements of executive functions using event-related potential (ERP) may be used as markers for differentiating healthy controls (HC) from patients with mild cognitive impairment (MCI). ERP is non-invasive, cost-effective, and affordable. Older adults with MCI demonstrate deteriorated executive function, serving as a potentially valid neurophysiological marker for identifying MCI. We aimed to review published ERP studies on executive function in older adults with MCI and summarize the performance differences by component between healthy older adults and older adults with MCI.

METHODS

Eight electronic databases (Web of Science, PubMed, ScienceDirect, American Psychological Association PsycNet, Cochrane Library, Scopus, Embase, and Ovid) were searched for the study. Articles published from January 1 to December 31, 2022, were considered for this review. A random-effects meta-analysis and between-study heterogeneity analysis were conducted using Comprehensive Meta-Analysis V3.0 software.

RESULTS

We identified 7829 articles of which 28 met the full inclusion criteria and were included in the systematic review and analyses. Our pooled analysis suggested that participants with MCI can be differentiated from HC by significant P200, P300, and N200 latencies. The P100 and P300 amplitudes were significantly smaller in participants with MCI when compared with those in the HCs, and the patients with MCI showed increased N200 amplitudes. Our findings provide new insights into potential electrophysiological biomarkers for diagnosing MCI.

Heart Rate Variability and Cognition: A Narrative Systematic Review of Longitudinal Studies

BACKGROUND

Heart rate variability (HRV) is a reliable and convenient method to assess autonomic function. Cross-sectional studies have established a link between HRV and cognition. Longitudinal studies are an emerging area of research with important clinical implications in terms of the predictive value of HRV for future cognition and in terms of the potential causal relationship between HRV and cognition. However, they have not yet been the objective of a systematic review. Therefore, the aim of this systematic review was to investigate the association between HRV and cognition in longitudinal studies.

METHODS

The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Embase, PsycINFO and PubMed databases were searched from the earliest available date to 26 June 2023. Studies were included if they involved adult human subjects and evaluated the longitudinal association between HRV and cognition. The risk of bias was assessed with the Newcastle-Ottawa Scale for Cohort Studies. The results were presented narratively.

RESULTS

Of 14,359 records screened, 12 studies were included in this systematic review, with a total of 24,390 participants. Two thirds of the studies were published from 2020 onwards. All studies found a longitudinal relationship between HRV and cognition. There was a consistent association between higher parasympathetic nervous system (PNS) activity and better cognition, and some association between higher sympathetic nervous system activity and worse cognition. Also, higher PNS activity persistently predicted better executive functioning, while data on episodic memory and language were more scant and/or controversial.

CONCLUSIONS

Our results support the role of HRV as a biomarker of future cognition and, potentially, as a therapeutic target to improve cognition. They will need confirmation by further, more comprehensive studies also including unequivocal non-HRV sympathetic measures and meta-analyses.

Sleep Biomarkers for Predicting Cognitive Decline and Alzheimer's Disease: A Systematic Review of Longitudinal Studies

BACKGROUND

Sleep disturbances are considered a hallmark of dementia, and strong evidence supports the association between alterations in sleep parameters and cognitive decline in patients with mild cognitive impairment and Alzheimer's disease (AD).

OBJECTIVE

This systematic review aims to summarize the existing evidence on the longitudinal association between sleep parameters and cognitive decline, with the goal of identifying potential sleep biomarkers of AD-related neurodegeneration.

METHODS

Literature search was conducted in PubMed, Web of Science, and Scopus databases from inception to 28 March 2023. Longitudinal studies investigating the association between baseline objectively-measured sleep parameters and cognitive decline were assessed for eligibility.

RESULTS

Seventeen studies were included in the qualitative synthesis. Sleep fragmentation, reduced sleep efficiency, reduced REM sleep, increased light sleep, and sleep-disordered breathing were identified as predictors of cognitive decline. Sleep duration exhibited a U-shaped relation with subsequent neurodegeneration. Additionally, several sleep microstructural parameters were associated with cognitive decline, although inconsistencies were observed across studies.

CONCLUSIONS

These findings suggest that sleep alterations hold promise as early biomarker of cognitive decline, but the current evidence is limited due to substantial methodological heterogeneity among studies. Further research is necessary to identify the most reliable sleep parameters for predicting cognitive impairment and AD, and to investigate interventions targeting sleep that can assist clinicians in the early recognition and treatment of cognitive decline. Standardized procedures for longitudinal studies evaluating sleep and cognition should be developed and the use of continuous sleep monitoring techniques, such as actigraphy or EEG headband, might be encouraged.

Speech-Based Digital Biomarkers for Alzheimer's Research

Digital biomarkers are of growing interest in the field of Alzheimer's Disease (AD) research. Digital biomarker data arising from digital health tools holds various potential benefits: more objective and more accurate assessment of patients' symptoms and remote collection of signals in real-world scenarios but also multimodal variance for prediction models of individual disease progression. Speech can be collected at minimal patient burden and provides rich data for assessing multiple aspects of AD pathology including cognition. However, the operations around collecting, preparing, and validly interpreting speech data within the context of clinical research on AD remains complex and sometimes challenging. Through a dedicated pipeline of speech collection tools, preprocessing steps and algorithms, precise qualification and quantification of an AD patient's pathology can be achieved from their speech. The aim of this chapter is to describe the methods that are needed to create speech collection scenarios that result in valuable speech-based digital biomarkers for clinical research.

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.

Comparison of Commonly Measured Plasma and Cerebrospinal Fluid Proteins and Their Significance for the Characterization of Cognitive Impairment Status

BACKGROUND

Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer's disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI).

OBJECTIVE

This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status.

METHODS

This cohort study included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification.

RESULTS

We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOEϵ4 genotype.

CONCLUSIONS

This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.

Optimized Pre-analytical Handling Protocol for Blood-Based Biomarkers of Alzheimer's Disease

The therapeutic management of patients with Alzheimer's disease (AD) has been hindered by poor diagnostic accuracy. As such, there is an unmet clinical need for tools that can detect and diagnose the disease in its early stages. Compared with cerebrospinal fluid (CSF)-based biomarkers or positron emission tomography (PET), the use of reliable blood-based biomarkers could offer an accessible and minimally invasive method of streamlining diagnosis in the clinical setting. However, the influence of pre-analytical processing and sample handling parameters on the accurate measurement of protein biomarkers is well established, especially for AD CSF-based biomarkers. In this chapter, we provide recommendations for an optimal sample handling protocol for the analysis of blood-based biomarkers specifically for amyloid pathology in AD.

The Potential of a Stratified Approach to Drug Repurposing in Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods.

Ketone bodies mediate alterations in brain energy metabolism and biomarkers of Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia. AD is a progressive neurodegenerative disorder characterized by cognitive dysfunction, including learning and memory deficits, and behavioral changes. Neuropathology hallmarks of AD such as amyloid beta (Aβ) plaques and neurofibrillary tangles containing the neuron-specific protein tau is associated with changes in fluid biomarkers including Aβ, phosphorylated tau (p-tau)-181, p-tau 231, p-tau 217, glial fibrillary acidic protein (GFAP), and neurofilament light (NFL). Another pathological feature of AD is neural damage and hyperactivation of astrocytes, that can cause increased pro-inflammatory mediators and oxidative stress. In addition, reduced brain glucose metabolism and mitochondrial dysfunction appears up to 15 years before the onset of clinical AD symptoms. As glucose utilization is compromised in the brain of patients with AD, ketone bodies (KBs) may serve as an alternative source of energy. KBs are generated from the β-oxidation of fatty acids, which are enhanced following consumption of ketogenic diets with high fat, moderate protein, and low carbohydrate. KBs have been shown to cross the blood brain barrier to improve brain energy metabolism. This review comprehensively summarizes the current literature on how increasing KBs support brain energy metabolism. In addition, for the first time, this review discusses the effects of ketogenic diet on the putative AD biomarkers such as Aβ, tau (mainly p-tau 181), GFAP, and NFL, and discusses the role of KBs on neuroinflammation, oxidative stress, and mitochondrial metabolism.

Sex and Education Modify the Association Between Subjective Cognitive Decline and Amyloid Pathology

Background

Subjective cognitive decline (SCD) may be an early risk factor for dementia, particularly in highly educated individuals and women. This study examined the effect of education and sex on the association between SCD and Alzheimer's disease (AD) biomarkers in non-demented older adults.

Method

Vanderbilt Memory and Aging Project participants free of clinical dementia or stroke (n=156, 72±6 years, 37% mild cognitive impairment, 33% female) completed fasting lumbar puncture, SCD assessment, and Wide Range Achievement Test-III Reading subtest to assess reading level at baseline as a a proxy for educational quality. Cerebrospinal fluid (CSF) biomarkers for AD (β-amyloid 42 (Aβ42), Aβ42/40 ratio, phosphorylated tau (p-tau), tau, and neurofilament light (NfL)) were analyzed in batch. Linear mixed effects models related SCD to CSF AD biomarkers and follow-up models assessed SCD x sex, SCD x reading level , and SCD x education interactions on AD biomarkers.

Result

In main effect models, higher SCD was associated with lower Aβ42 and Aβ42/40 ratio (p-values<0.004). SCD was not associated with tau, p-tau, or NfL levels ( p- values>0.38). SCD score interacted with sex on Aβ42/40 ratio ( p =0.03) but no other biomarkers ( p -values>0.10). In stratified models, higher SCD was associated with lower Aβ42/40 ratio in men ( p =0.0003) but not in women ( p =0.48). SCD score interacted with education on Aβ42 ( p =0.005) and Aβ42/40 ratio ( p =0.001) such that higher education was associated with a stronger negative association between SCD and amyloid levels. No SCD score x reading level interaction was found (p-values> 0.51) though significant associations between SCD and amyloid markers were seen in the higher reading level group (p-values<0.004) but not the lower reading level group (p-values>0.12) when stratified by a median split in reading level.

Conclusion

Among community-dwelling older adults free of clinical dementia, higher SCD was associated with greater cerebral amyloid accumulation, one of the earliest pathological AD changes. SCD appears most useful in detecting early AD-related brain changes in men and individuals with higher quantity and quality of education. SCD was not associated with CSF markers of tau pathology or neurodegeneration. These findings suggest that considering sex and education is important when assessing SCD in older adults.

Novel fluid biomarkers for mild cognitive impairment: A systematic review and meta-analysis

Mild cognitive impairment (MCI) is a well-established prodromal stage of dementia (e.g., Alzheimer's disease) that is often accompanied by early signs of neurodegeneration. To facilitate a better characterization of the underlying pathophysiology, we assessed the available literature to evaluate potential fluid biomarkers in MCI. Peer-reviewed articles that measured cerebrospinal fluid (CSF) and/or peripheral biomarkers of neuronal injury (total-tau [T-tau], neurofilament light chain [NfL], heart-type fatty acid binding protein [HFABP], neuron-specific enolase, ubiquitin C-terminal hydrolase L1) and/or astroglial pathology (glial fibrillary acidic protein [GFAP], S100 calcium-binding protein B) in MCI and healthy controls were assessed. Group differences were summarized by standardized mean differences (SMDs) and 95% confidence intervals calculated using a random-effects model. Heterogeneity was quantified using I2. A total of 107 studies were included in the meta-analysis and 10 studies were qualitatively reviewed. In CSF, concentrations of NfL (SMD = 0.69 [0.56, 0.83]), GFAP (SMD = 0.41 [0.07, 0.75]), and HFABP (SMD = 0.57 [0.26, 0.89]) were elevated in MCI. In blood, increased concentrations of T-tau (SMD = 0.19 [0.09, 0.29]), NfL (SMD = 0.41 [0.32, 0.49]), and GFAP (SMD = 0.39 [0.23, 0.55]) were found in MCI. Heterogeneity that was identified in all comparisons was explored using meta-regression and subgroup analysis. Elevated NfL and GFAP can be detected in both CSF and peripheral blood. Monitoring these biomarkers in clinical settings may provide important insight into underlying neurodegenerative processes in MCI.

Decision making in clinical trials: Interim analyses, innovative design, and biomarkers

The efficient and accurate execution of clinical trials testing novel treatments for Alzheimer's disease (AD) is a critical component of the field's collective efforts to develop effective disease-modifying treatments for AD. The lengthy and heterogeneous nature of clinical progression in AD contributes to the challenges inherent in demonstrating a clinically meaningful benefit of any potential new AD therapy. The failure of many large and expensive clinical trials to date has prompted a focus on optimizing all aspects of decision making, to not only expedite the development of new treatments, but also maximize the value of the information that each clinical trial yields, so that all future clinical trials (including those that are negative) will contribute toward advancing the field. To address this important topic the Alzheimer's Association Research Roundtable convened December 1-2, 2020. The goals focused around identifying new directions and actionable steps to enhance clinical trial decision making in planned future studies.

ATN profile classification across two independent prospective cohorts

Background

The ATN model represents a research framework used to describe in subjects the presence or absence of Alzheimer's disease (AD) pathology through biomarkers. The aim of this study was to describe the prevalence of different ATN profiles using quantitative imaging biomarkers in two independent cohorts, and to evaluate the pertinence of ATN biomarkers to identify comparable populations across independent cohorts.

Methods

A total of 172 subjects from the Geneva Memory Clinic and 113 volunteers from a study on healthy aging at the University Hospital of Zurich underwent amyloid (A) and tau (T) PET, as well as T1-weigthed MRI scans using site-specific protocols. Subjects were classified by cognition (cognitively unimpaired, CU, or impaired, CI) based on clinical assessment by experts. Amyloid data converted into the standardized centiloid scale, tau PET data normalized to cerebellar uptake, and hippocampal volume expressed as a ratio over total intracranial volume ratio were considered as biomarkers for A, T, and neurodegeneration (N), respectively. Positivity for each biomarker was defined based on previously published thresholds. Subjects were then classified according to the ATN model. Differences among profiles were tested using Kruskal-Wallis ANOVA, and between cohorts using Wilcoxon tests.

Results

Twenty-nine percent of subjects from the Geneva cohorts were classified with a normal (A-T-N-) profile, while the Zurich cohort included 64% of subjects in the same category. Meanwhile, 63% of the Geneva and 16% of the Zurich cohort were classified within the AD continuum (being A+ regardless of other biomarkers' statuses). Within cohorts, ATN profiles were significantly different for age and mini-mental state examination scores, but not for years of education. Age was not significantly different between cohorts. In general, imaging A and T biomarkers were significantly different between cohorts, but they were no longer significantly different when stratifying the cohorts by ATN profile. N was not significantly different between cohorts.

Conclusion

Stratifying subjects into ATN profiles provides comparable groups of subjects even when individual recruitment followed different criteria.

Cerebrospinal fluid microRNAs as potential biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is the leading form of dementia worldwide, but its early detection and diagnosis remain a challenge. MicroRNAs (miRNAs) are a group of small endogenous RNA molecules that regulate mRNA expression. Recent evidence suggests miRNAs play an important role in the five major hallmarks of AD pathophysiology: amyloidogenesis, tauopathy, neuroinflammation, synaptic dysfunction, and neuronal death. Compared to traditional biomarkers of AD, miRNAs display a greater degree of stability in cerebrospinal fluid. Moreover, aberrant changes in miRNA expression can be measured over time to monitor and guide patient treatment. Specific miRNA profiles and combinations may also be used to distinguish AD subjects from normal controls and other causes of dementia. Because of these properties, miRNAs are now being considered as promising and potential biomarkers of AD. This review comprehensively summarizes the diagnostic potential and regulatory roles miRNAs play in AD.

Analytical Validation of a Novel MicroRNA Panel for Risk Stratification of Cognitive Impairment

We have been developing a novel approach to identify cognitive impairment-related biomarkers by profiling brain-enriched and inflammation-associated microRNA (miRNA) in plasma specimens of cognitively unimpaired and cognitively impaired patients. Here, we present an analytical validation of the novel miRNA panel, CogniMIR^®, using two competing quantitative PCR technologies for the expression analysis of 24 target miRNAs. Total RNA from the plasma specimens was isolated using the MagMAX mirVana Kit, and RT-qPCR was performed using stem-loop-based TaqMan and LNA-based qPCR assays. Evaluation of RNA dilution series for our target 24 miRNAs, performed by two operators on two different days, demonstrated that all CogniMIR^® panel miRNAs can be reliably and consistently detected by both qPCR technologies, with sample input as low as 20 copies in a qPCR reaction. Intra-run and inter-run repeatability and reproducibility analyses using RNA specimens demonstrated that both operators generated repeatable and consistent Cts, with R² values of 0.94 to 0.99 and 0.96 to 0.97, respectively. The study results clearly indicate the suitability of miRNA profiling of plasma specimens using either of the qPCR technologies. However, the LNA-based qPCR technology appears to be more operationally friendly and better suited for a CAP/CLIA-certified clinical laboratory.

Does adding MRI and CSF-based biomarkers improve cognitive status classification based on cognitive performance questionnaires?

BACKGROUND

Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown.

METHODS

Data come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence.

RESULTS

Our analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence.

CONCLUSION

MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection.

Testing times for dementia: a community survey identifying contemporary barriers to risk reduction and screening

BACKGROUND

Advances in pharmacological and non-pharmacological dementia interventions may mean future dementia prevention incorporates a combination of targeted screening and lifestyle modifications. Elucidating potential barriers which may prevent community engagement with dementia prevention initiatives is important to maximise the accessibility and feasibility of these initiatives across the lifespan.

METHODS

Six hundred seven adults aged over 18 years completed a 54-item, multiple-choice survey exploring contemporary attitudes towards, and barriers to, dementia risk reduction and screening relative to other common health conditions. Participants were sourced from Australia's largest, paid, data analytics service (ORIMA).

RESULTS

Finances (p = .009), poor motivation (p = .043), and time (p ≤ .0001) emerged as significant perceived barriers to dementia risk reduction behaviours. Lack of time was more likely to be reported by younger, relative to older, participants (p ≤ .0001), while females were more likely than males to report financial (p = .019) and motivational (p = .043) factors. Binary logistic regression revealed willingness to undertake dementia testing modalities was significantly influenced by gender (genetic testing, p = .012; saliva, p = .038, modifiable risk factors p = .003), age (cognitive testing, p ≤ .0001; blood, p = .010), and socio-economic group (retinal imaging, p = .042; modifiable risk-factor screening, p = .019). Over 65% of respondents felt adequately informed about risk reduction for at least one non-dementia health condition, compared to 30.5% for dementia.

CONCLUSIONS

This study found perceived barriers to dementia risk reduction behaviours, and the willingness to engage in various dementia testing modalities, was significantly associated with socio-demographic factors across the lifespan. These findings provide valuable insight regarding the accessibility and feasibility of potential methods for identifying those most at risk of developing dementia, as well as the need to better promote and support wide-scale engagement in dementia risk reduction behaviours across the lifespan.

Sex Differences between Neuronal Loss and the Early Onset of Amyloid Deposits and Behavioral Consequences in 5xFAD Transgenic Mouse as a Model for Alzheimer's Disease

A promising direction in the research on Alzheimer's Disease (AD) is the identification of biomarkers that better inform the disease progression of AD. However, the performance of amyloid-based biomarkers in predicting cognitive performance has been shown to be suboptimal. We hypothesise that neuronal loss could better inform cognitive impairment. We have utilised the 5xFAD transgenic mouse model that displays AD pathology at an early phase, already fully manifested after 6 months. We have evaluated the relationships between cognitive impairment, amyloid deposition, and neuronal loss in the hippocampus in both male and female mice. We observed the onset of disease characterized by the emergence of cognitive impairment in 6-month-old 5xFAD mice coinciding with the emergence of neuronal loss in the subiculum, but not amyloid pathology. We also showed that female mice exhibited significantly increased amyloid deposition in the hippocampus and entorhinal cortex, highlighting sex-related differences in the amyloid pathology of this model. Therefore, parameters based on neuronal loss might more accurately reflect disease onset and progression compared to amyloid-based biomarkers in AD patients. Moreover, sex-related differences should be considered in studies involving 5xFAD mouse models.

Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts

BACKGROUND

Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed.

OBJECTIVE

Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations.

METHODS

Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort.

RESULTS

The algorithms achieved a performance of AUC  0.73 and AUC  0.77 in the respective training cohorts and AUC  0.81 in the unseen validation cohorts.

CONCLUSION

The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.

Efficacy and safety of the novel GlyT1 inhibitor BI 425809 in Alzheimer's dementia: a randomized controlled trial

BACKGROUND

This phase II proof-of-concept study assessed the efficacy and safety of BI 425809, a novel selective glycine transporter-1 inhibitor, for the treatment of cognitive impairment associated with probable Alzheimer's disease dementia.

METHODS

This 12-week, multicenter, double-blind, placebo-controlled, parallel-group study randomized (1:1:1:1:1) patients with mild-to-moderate probable Alzheimer's disease dementia to BI 425809 2, 5, 10, and 25 mg or placebo once daily. The primary efficacy endpoint was the change from baseline in Alzheimer's Disease Assessment Scale-Cognitive Subscale 11-item total score after 12 weeks of treatment. Safety was also assessed.

RESULTS

Six hundred and ten male and female patients were randomized to BI 425809 2 mg (n = 123), 5 mg (n = 122), 10 mg (n = 122), and 25 mg (n = 123) or placebo (n = 120). Approximately 47% (n = 286) were male; the mean (standard deviation) age was 72.9 (7.7) years. Treatment compliance was above 97% for all dose groups. The Mini-Mental State Examination category on the median score was < 22 in 47% (n = 287) of patients and ≥ 22 in 53% (n = 322) of patients. No significant, non-flat dose-response relationship was detected for the primary endpoint (adjusted p-value > 0.76 for all models). BI 425809 was generally well-tolerated. Overall, 47.9% (n = 292) of patients reported at least one adverse event during the trial; the frequency of patients with investigator-defined drug-related adverse events was similar in all treatment groups, ranging from 15.4 to 19.5% across the BI 425809 treatment groups and 15.8% for placebo.

CONCLUSIONS

No clinically meaningful changes from baseline were observed following treatment with BI 425809 in patients with mild-to-moderate probable Alzheimer's disease dementia.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02788513 (1346-0023). Registered on June 2, 2016. EU Clinical Trials Register 2015-005438-24. Registered on May 6, 2016.

Impact of pre-analytical sample handling factors on plasma biomarkers of Alzheimer's disease

An unmet need exists for reliable plasma biomarkers of amyloid pathology, in the clinical laboratory setting, to streamline diagnosis of Alzheimer's disease (AD). For routine clinical use, a biomarker must provide robust and reliable results under pre-analytical sample handling conditions. We investigated the impact of different pre-analytical sample handling procedures on the levels of seven plasma biomarkers in development for potential routine use in AD. Using (1) fresh (never frozen) and (2) previously frozen plasma, we evaluated the effects of (A) storage time and temperature, (B) freeze/thaw (F/T) cycles, (C) anticoagulants, (D) tube transfer, and (E) plastic tube types. Blood samples were prospectively collected from patients with cognitive impairment undergoing investigation in a memory clinic. β-amyloid 1-40 (Aβ40), β-amyloid 1-42 (Aβ42), apolipoprotein E4, glial fibrillary acidic protein, neurofilament light chain, phosphorylated-tau (phospho-tau) 181, and phospho-tau-217 were measured using Elecsys^® plasma prototype immunoassays. Recovery signals for each plasma biomarker and sample handling parameter were calculated. For all plasma biomarkers measured, pre-analytical effects were comparable between fresh (never frozen) and previously frozen samples. All plasma biomarkers tested were stable for ≤24 h at 4°C when stored as whole blood and ethylenediaminetetraacetic acid (EDTA) plasma. Recovery signals were acceptable for up to five tube transfers, or two F/T cycles, and in both polypropylene and low-density polyethylene tubes. For all plasma biomarkers except Aβ42 and Aβ40, analyte levels were largely comparable between EDTA, lithium heparin, and sodium citrate tubes. Aβ42 and Aβ40 were most sensitive to pre-analytical handling, and the effects could only be partially compensated by the Aβ42/Aβ40 ratio. We provide recommendations for an optimal sample handling protocol for analysis of plasma biomarkers for amyloid pathology AD, to improve the reproducibility of future studies on plasma biomarkers assays and for potential use in routine clinical practice.

Early Detection of Alzheimer's Disease-Related Pathology Using a Multi-Disease Diagnostic Platform Employing Autoantibodies as Blood-Based Biomarkers

BACKGROUND

Evidence for the universal presence of IgG autoantibodies in blood and their potential utility for the diagnosis of Alzheimer's disease (AD) and other neurodegenerative diseases has been extensively demonstrated by our laboratory. The fact that AD-related neuropathological changes in the brain can begin more than a decade before tell-tale symptoms emerge has made it difficult to develop diagnostic tests useful for detecting the earliest stages of AD pathogenesis.

OBJECTIVE

To determine the utility of a panel of autoantibodies for detecting the presence of AD-related pathology along the early AD continuum, including at pre-symptomatic [an average of 4 years before the transition to mild cognitive impairment (MCI)/AD)], prodromal AD (MCI), and mild-moderate AD stages.

METHODS

A total of 328 serum samples from multiple cohorts, including ADNI subjects with confirmed pre-symptomatic, prodromal, and mild-moderate AD, were screened using Luminex xMAP® technology to predict the probability of the presence of AD-related pathology. A panel of eight autoantibodies with age as a covariate was evaluated using randomForest and receiver operating characteristic (ROC) curves.

RESULTS

Autoantibody biomarkers alone predicted the probability of the presence of AD-related pathology with 81.0% accuracy and an area under the curve (AUC) of 0.84 (95% CI = 0.78-0.91). Inclusion of age as a parameter to the model improved the AUC (0.96; 95% CI = 0.93-0.99) and overall accuracy (93.0%).

CONCLUSION

Blood-based autoantibodies can be used as an accurate, non-invasive, inexpensive, and widely accessible diagnostic screener for detecting AD-related pathology at pre-symptomatic and prodromal AD stages that could aid clinicians in diagnosing AD.

Biomarkers and Risk Assessment of Alzheimer's Disease in Low- and Middle-Income Countries

Dementia is a chronic syndrome which is common among the elderly and is associated with significant morbidity and mortality for patients and their caregivers. Alzheimer's disease (AD), the most common form of clinical dementia, is biologically characterized by the deposition of amyloid-β plaques and neurofibrillary tangles in the brain. The onset of AD begins decades before manifestation of symptoms and clinical diagnosis, underlining the need to shift from clinical diagnosis of AD to a more objective diagnosis using biomarkers. Having performed a literature search of original articles and reviews on PubMed and Google Scholar, we present this review detailing the existing biomarkers and risk assessment tools for AD. The prevalence of dementia in low- and middle-income countries (LMICs) is predicted to increase over the next couple of years. Thus, we aimed to identify potential biomarkers that may be appropriate for use in LMICs, considering the following factors: sensitivity, specificity, invasiveness, and affordability of the biomarkers. We also explored risk assessment tools and the potential use of artificial intelligence/machine learning solutions for diagnosing, assessing risks, and monitoring the progression of AD in low-resource settings. Routine use of AD biomarkers has yet to gain sufficient ground in clinical settings. Therefore, clinical diagnosis of AD will remain the mainstay in LMICs for the foreseeable future. Efforts should be made towards the development of low-cost, easily administered risk assessment tools to identify individuals who are at risk of AD in the population. We recommend that stakeholders invest in education, research and development targeted towards effective risk assessment and management.

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review

Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well as our ability to detect the disease. Accordingly, a large amount of research has been invested into innovative diagnostic methods for AD. Functional MRI (fMRI) is a form of neuroimaging technology that has been used to diagnose AD; however, fMRI is incredibly noisy, complex, and thus lacks clinical use. Nonetheless, recent innovations in deep learning technology could enable the simplified and streamlined analysis of fMRI. Deep learning is a form of artificial intelligence that uses computer algorithms based on human neural networks to solve complex problems. For example, in fMRI research, deep learning models can automatically denoise images and classify AD by detecting patterns in participants' brain scans. In this systematic review, we investigate how fMRI (specifically resting-state fMRI) and deep learning methods are used to diagnose AD. In turn, we outline the common deep neural network, preprocessing, and classification methods used in the literature. We also discuss the accuracy, strengths, limitations, and future direction of fMRI deep learning methods. In turn, we aim to summarize the current field for new researchers, suggest specific areas for future research, and highlight the potential of fMRI to aid AD diagnoses.

Current and Future Trends in Biomarkers for the Early Detection of Alzheimer's Disease in Asia: Expert Opinion

Alzheimer's disease (AD) poses a substantial healthcare burden in the rapidly aging Asian population. Early diagnosis of AD, by means of biomarkers, can lead to interventions that might alter the course of the disease. The amyloid, tau, and neurodegeneration (AT[N]) framework, which classifies biomarkers by their core pathophysiological features, is a biomarker measure of amyloid plaques and neurofibrillary tangles. Our current AD biomarker armamentarium, comprising neuroimaging biomarkers and cerebrospinal fluid biomarkers, while clinically useful, may be invasive and expensive and hence not readily available to patients. Several studies have also investigated the use of blood-based measures of established core markers for detection of AD, such as amyloid-β and phosphorylated tau. Furthermore, novel non-invasive peripheral biomarkers and digital biomarkers could potentially expand access to early AD diagnosis to patients in Asia. Despite the multiplicity of established and potential biomarkers in AD, a regional framework for their optimal use to guide early AD diagnosis remains lacking. A group of experts from five regions in Asia gathered at a meeting in March 2021 to review the current evidence on biomarkers in AD diagnosis and discuss best practice around their use, with the goal of developing practical guidance that can be implemented easily by clinicians in Asia to support the early diagnosis of AD. This article summarizes recent key evidence on AD biomarkers and consolidates the experts' insights into the current and future use of these biomarkers for the screening and early diagnosis of AD in Asia.

Longitudinal characterization of retinal vasculature alterations with optical coherence tomography angiography in a mouse model of tauopathy

Tauopathies are a family of neurodegenerative diseases which predominately afflict the rapidly growing aging population suffering from various brain disorders including Alzheimer's disease, frontotemporal dementia with parkinsonism-17 and Pick disease. As the only visually accessible region of the central nervous system, in recent years, the retina has attracted extensive attention for its potential as a target for visualizing and quantifying emerging biomarkers of neurodegenerative diseases. Our previous study has found that retinal vascular inflammation and leakage occur at the very early stage of tauopathic mouse model. Here, we aimed to non-invasively visualize age-dependent alterations of retinal vasculature assessing the potential for using changes in retinal vasculature as the biomarker for the early diagnosis of tauopathy. Optical coherence tomography angiography (OCTA), a non-invasive depth-resolved high-resolution imaging technique was used to visualize and quantify tauopathy-induced alterations of retinal vasculature in P301S transgenic mice overexpressing the P301S mutant form of human tau and age-matched wild type littermate mice at 3, 6 and 10 months of age. We observed significant alterations of vascular features in the intermediate capillary plexus (ICP) and deep capillary plexus (DCP) but not in the superficial vascular complex (SVC) of P301S mice at early stages of tauopathy. With aging, alterations of vascular features in P301S mice became more prominent in all three vascular plexuses. Staining of retinal vasculature in flatmounts and trypsin digests of P301S mice at 10 months of age revealed decreased vessel density and increased acellular capillary formation, indicating that vascular degeneration also occurs during tauopathy. Overall, our results demonstrate that the changes in retinal vascular features accelerate during the progression of tauopathy. Vessels in the ICP and DCP may be more susceptible to tauopathy than vessels in the SVC. Since changes in retinal vasculature often precede tau pathology in the brain, non-invasive identification of retinal vascular alterations with OCTA may be a useful biomarker for the early diagnosis of tauopathy and monitoring its progression.

Memory markers in the continuum of the Alzheimer's clinical syndrome

BACKGROUND

The individual and complementary value of the Visual Short-Term Memory Binding Test (VSTMBT) and the Free and Cued Selective Reminding Test (FCSRT) as markers to trace the AD continuum was investigated. It was hypothesised that the VSTMBT would be an early indicator while the FCSRT would inform on imminent progression.

METHODS

Healthy older adults (n=70) and patients with mild cognitive impairment (MCI) (n=80) were recruited and followed up between 2012 and 2017. Participants with at least two assessment points entered the study. Using baseline and follow-up assessments four groups were defined: Older adults who were healthy (HOA), with very mild cognitive but not functional impairment (eMCI), and with MCI who did and did not convert to dementia (MCI converters and non-converters).

RESULTS

Only the VSTMBT predicted group membership in the very early stages (HOA vs eMCI). As the disease progressed, the FCSRT became a strong predictor excluding the VSTMB from the models. Their complementary value was high during the mid-prodromal stages and decreased in stages closer to dementia.

DISCUSSION

The study supports the notion that neuropsychological assessment for AD needs to abandon the notion of one-size-fits-all. A memory toolkit for AD needs to consider tools that are early indicators and tools that suggest imminent progression. The VSTMBT and the FSCRT are such tools.

Learning Cognitive-Test-Based Interpretable Rules for Prediction and Early Diagnosis of Dementia Using Neural Networks

Background: Accurate, cheap, and easy to promote methods for dementia prediction and early diagnosis are urgently needed in low- and middle-income countries. Integrating various cognitive tests using machine learning provides promising solutions. However, most effective machine learning models are black-box models that are hard to understand for doctors and could hide potential biases and risks. Objective: To apply cognitive-test-based machine learning models in practical dementia prediction and diagnosis by ensuring both interpretability and accuracy. Methods: We design a framework adopting Rule-based Representation Learner (RRL) to build interpretable diagnostic rules based on the cognitive tests selected by doctors. According to the visualization and test results, doctors can easily select the final rules after analysis and trade-off. Our framework is verified on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 606) and Peking Union Medical College Hospital (PUMCH) dataset (n = 375). Results: The predictive or diagnostic rules learned by RRL offer a better trade-off between accuracy and model interpretability than other representative machine learning models. For mild cognitive impairment (MCI) conversion prediction, the cognitive-test-based rules achieve an average area under the curve (AUC) of 0.904 on ADNI. For dementia diagnosis on subjects with a normal Mini-Mental State Exam (MMSE) score, the learned rules achieve an AUC of 0.863 on PUMCH. The visualization analyses also verify the good interpretability of the learned rules. Conclusion: With the help of doctors and RRL, we can obtain predictive and diagnostic rules for dementia with high accuracy and good interpretability even if only cognitive tests are used.