Promising Blood Biomarkers for Clinical Use in Alzheimer's Disease: A Focused Update

Alzheimer's disease approaches - Focusing on pathology, biomarkers and clinical trial candidates

The strategy for the development of new drugs for Alzheimer's disease (AD) recognizes that an effective therapy requires early therapeutic intervention and a multifactorial approach that considers the individual initiators of AD development. Current knowledge of AD includes the understanding of pathophysiology, risk factors, biomarkers, and the evolving patterns of biomarker abnormalities. This knowledge is essential in identifying potential molecular targets for new drug development. This review summarizes promising AD drug candidates, many of which are currently in phase 2 or 3 clinical trials. New agents are classified according to the Common Alzheimer's Disease Research Ontology (CADRO). The main targets of new drugs for AD are processes related to amyloid beta and tau neurotoxicity, neurotransmission, inflammation, metabolism and bioenergetics, synaptic plasticity, and oxidative stress. These interventions are aimed at preventing disease onset and slowing or eliminating disease progression. The efficacy of pharmacotherapy may be enhanced by combining these drugs with other treatments, antioxidants, and dietary supplements. Ongoing research into AD pathophysiology, risk factors, biomarkers, and the dynamics of biomarker abnormalities may contribute to the understanding of AD and offer hope for effective therapeutic strategies in the near future.

Cost-effectiveness comparison between blood biomarkers and conventional tests in Alzheimer's disease diagnosis

Dementia management has evolved with drugs such as lecanemab, shifting management from palliative care to early diagnosis and intervention. However, the administration of these drugs presents challenges owing to the invasiveness, high cost and limited availability of amyloid-PET and cerebrospinal fluid tests for guiding drug administration. Our manuscript explores the potential of less invasive blood biomarkers as a diagnostic method, with a cost-effectiveness analysis and a comparison with traditional tests. Our findings suggest that blood biomarkers are a cost-effective alternative, but with lower accuracy, indicating the need for multiple specific biomarkers for precision. This underscores the importance of future research on new blood biomarkers and their clinical efficacy.

Definition of a Threshold for the Plasma Aβ42/Aβ40 Ratio Measured by Single-Molecule Array to Predict the Amyloid Status of Individuals without Dementia

Alzheimer's disease (AD) is characterized by amyloid beta (Aβ) plaques and hyperphosphorylated tau in the brain. Aβ plaques precede cognitive impairments and can be detected through amyloid-positron emission tomography (PET) or in cerebrospinal fluid (CSF). Assessing the plasma Aβ42/Aβ40 ratio seems promising for non-invasive and cost-effective detection of brain Aβ accumulation. This approach involves some challenges, including the accuracy of blood-based biomarker measurements and the establishment of clear, standardized thresholds to categorize the risk of developing brain amyloid pathology. Plasma Aβ42/Aβ40 ratio was measured in 277 volunteers without dementia, 70 AD patients and 18 non-AD patients using single-molecule array. Patients (n = 88) and some volunteers (n = 66) were subject to evaluation of amyloid status by CSF Aβ quantification or PET analysis. Thresholds of plasma Aβ42/Aβ40 ratio were determined based on a Gaussian mixture model, a decision tree, and the Youden's index. The 0.0472 threshold, the one with the highest sensitivity, was retained for general population without dementia screening, and the 0.0450 threshold was retained for research and clinical trials recruitment, aiming to minimize the need for CSF or PET analyses to identify amyloid-positive individuals. These findings offer a promising step towards a cost-effective method for identifying individuals at risk of developing AD.

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.

Biomarkers as Endpoints in Clinical Trials for Alzheimer's Disease

Background

Alzheimer's disease (AD) is a neurodegenerative disease that imposes economic and societal burden. Biomarkers have played a crucial role in the recent approval of aducanumab and lecanemab as disease-modifying therapies which marked a significant milestone for the treatment of AD. The inclusion of biomarkers in AD trials facilitates precise diagnosis, monitors safety, demonstrates target engagement, and supports disease modification.

Objective

This study analyzed the utilization state and trends of biomarkers as endpoints in AD trials.

Methods

In this retrospective study, trials were collected by searching clinicaltrials.gov using the term "Alzheimer". Primary and secondary outcomes were analyzed separately for each phase.

Results

Among the 1,048 analyzed trials, 313 (29.87%) adopted biomarkers as primary endpoints and 364 (34.73%) as secondary endpoints, mainly in phases 1 and 2. The top three biomarkers adopted as primary endpoints in phases 1, 2, and 3 were amyloid-PET, tau-PET, and MRI. The top three biomarkers adopted as secondary endpoints, in phase 1, were cerebrospinal fluid (CSF) amyloid-β (Aβ), blood Aβ and amyloid-PET; in phase 2, they were MRI, CSF Aβ, and CSF phospho-tau; and in phase 3, they were amyloid PET, MRI, and blood Aβ. There was a statistically significant increase in the adoption of biomarkers as primary endpoints in phase 2 trials (p = 0.001) and secondary endpoints in phase 3 trials (p = 0.001).

Conclusions

The growing recognition of the importance of biomarkers in AD trial' design and drug development is evident by the significant steady increase in biomarkers' utilization in phases 2 and 3.

Predicting amyloid PET positivity using plasma p-tau181 and other blood-based biomarkers

Introduction

This study aimed to determine the efficacy of combining plasma phosphorylated tau (p-tau)181, amyloid beta (Aβ)42/Aβ40, neurofilament light (NfL), and apolipoprotein E (APOE) genotypes for detecting positive amyloid positron emission tomography (PET), which is little known in the Asian population, in two independent cohorts.

Methods

Biomarkers were measured using a single-molecule array (Simoa) in a cohort study (Asan). All participants underwent amyloid PET. Significant changes in the area under the curve (AUC) and Akaike Information Criterion values were considered to determine the best model. The generalizability of this model was tested using another cohort (KBASE-V).

Results

In the Asan cohort, after adjusting for age and sex, p-tau181 (AUC = 0.854) or APOE ε4 status (AUC = 0.769) distinguished Aβ status with high accuracy. Combining them or adding NfL and Aβ42/40 improved model fitness. The best-fit model included the plasma p-tau181, APOE ε4, NfL and Aβ42/40. The models established from the Asan cohort were tested in the KBASE-V cohort. Additionally, in the KBASE-V cohort, these three biomarker models had similar AUC in cognitively unimpaired (AUC = 0.768) and mild cognitive impairment (MCI) (AUC = 0.997) participants.

Conclusions

Plasma p-tau181 showed a high performance in determining Aβ-PET positivity. Adding plasma NfL and APOE ε4 status improved the model fit without significant improvement in AUC.

Predicting cognitive stage transition using p-tau181, Centiloid, and other measures

BACKGROUND

A combination of plasma phospho-tau (p-tau), amyloid beta (Aβ)-positron emission tomography (PET), brain magnetic resonance imaging, cognitive function tests, and other biomarkers might predict future cognitive decline. This study aimed to investigate the efficacy of combining these biomarkers in predicting future cognitive stage transitions within 3 years.

METHODS

Among the participants in the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE-V) study, 49 mild cognitive impairment (MCI) and 113 cognitively unimpaired (CU) participants with Aβ-PET and brain imaging data were analyzed.

RESULTS

Older age, increased plasma p-tau181, Aβ-PET positivity, and decreased semantic fluency were independently associated with cognitive stage transitions. Combining age, p-tau181, the Centiloid scale, semantic fluency, and hippocampal volume produced high predictive value in predicting future cognitive stage transition (area under the curve = 0.879).

CONCLUSIONS

Plasma p-tau181 and Centiloid scale alone or in combination with other biomarkers, might predict future cognitive stage transition in non-dementia patients.

HIGHLIGHTS

-Plasma p-tau181 and Centiloid scale might predict future cognitive stage transition. -Combining them or adding other biomarkers increased the predictive value. -Factors that independently associated with cognitive stage transition were demonstrated.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Perspective on the current state of the LRRK2 field

Almost 2 decades after linking LRRK2 to Parkinson's disease, a vibrant research field has developed around the study of this gene and its protein product. Recent studies have begun to elucidate molecular structures of LRRK2 and its complexes, and our understanding of LRRK2 has continued to grow, affirming decisions made years ago to therapeutically target this enzyme for PD. Markers of LRRK2 activity, with potential to monitor disease progression or treatment efficacy, are also under development. Interestingly, there is a growing understanding of the role of LRRK2 outside of the central nervous system in peripheral tissues such as gut and immune cells that may also contribute to LRRK2 mediated pathology. In this perspective, our goal is to take stock of LRRK2 research by discussing the current state of knowledge and critical open questions in the field.

Cognitive Activity Is Associated with Cognitive Function over Time in a Diverse Group of Older Adults, Independent of Baseline Biomarkers

BACKGROUND

More frequent engagement in cognitive activity is associated with better cognitive function in older adults, but the mechanism of action is not fully understood. Debate remains whether increased cognitive activity provides a meaningful benefit for cognitive health or if decreased cognitive activity represents a prodrome of cognitive impairment. Neurological biomarkers provide a novel way to examine this relationship in the context of cognitive aging.

METHODS

We examined the association of self-reported cognitive activity, cognitive function, and concentrations of three biomarkers in community-dwelling participants of a longitudinal, population-based study. Cognitive activity was measured at baseline by asking participants to rate the frequency of 7 activities: (1) viewing television, (2) listening to the radio, (3) visiting a museum, (4) playing games, such as cards, checkers, crosswords, or other puzzles or games, (5) reading books, (6) reading magazines, and (7) reading newspapers. Cognitive function was measured with a battery of four tests (Mini-Mental State Examination, Digit Symbol Test, and the immediate and delayed recall of the East Boston Test) averaged into a composite score. At baseline, we evaluated the concentration of total tau (tau), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP).

RESULTS

The study sample comprised 1,168 older participants, primarily non-Hispanic Blacks (60%) and women (63%). At baseline, they were an average of 77 years old with 12.6 years of education. Mixed-effects models showed that cognitive activity was associated with better cognitive functioning at baseline and over time. These relationships remained after each biomarker was added to the model. Over an average of 6.4 years of follow-up, cognitive activity was associated with cognitive decline in the model with tau (estimate = 0.0123; p value = 0.03) and was mildly attenuated in the models with NfL (estimate = 0.0110; p value = 0.06) and GFAP (estimate = 0.0111; p value = 0.06). Biomarkers did not modify the association between cognitive activity and cognitive function over time.

CONCLUSION

The benefits of cognitive activity on cognition appear to be independent of biomarkers: tau, NfL, and GFAP, measured at baseline. More frequent cognitive activity may benefit the cognitive health of older adults with a wide range of potential disease risk and presentations.

Cognitive Impairment and Dementia Data Model: Quality Evaluation and Improvements

Recently, datasets with various factors and indicators of cognitive diseases have been available for clinical research. Although the transformation of information to a particular data model is straightforward, many challenges arise if data from different repositories have to be integrated. Since each data source keeps entities with different names and relationships at different levels of granularity and format, the information can be partially lost or not properly presented. It is therefore important to have a common data model that provides a unified description of different factors and indicators related to cognitive diseases. Thus, in our previous work, we proposed a hierarchical cognitive impairment and dementia data model that keeps the semantics of the data in a human-readable format and accelerates the interoperability of clinical datasets. It defines data entities, their attributes and relationships related to diagnosis and treatment. This paper extends our previous work by evaluating and improving the data model by adapting the methodology proposed by D. Moody and G. Shanks. The completeness, simplicity, correctness and integrity of the data model are assessed and based on the results a new, improved version of the model is generated. The understandability of the improved model is evaluated using an online questionnaire. Simplicity and integrity are also considered as well as the factors that may influence the flexibility of the data model.

Differential proteomic profile of lumbar and ventricular cerebrospinal fluid

BACKGROUND

Pathological cerebral conditions may manifest in altered composition of the cerebrospinal fluid (CSF). Although diagnostic CSF analysis seeks to establish pathological disturbances in the brain proper, CSF is generally sampled from the lumbar compartment for reasons of technical ease and ethical considerations. We here aimed to compare the molecular composition of CSF obtained from the ventricular versus the lumbar CSF compartments to establish a relevance for employing lumbar CSF as a proxy for the CSF bathing the brain tissue.

METHODS

CSF was collected from 46 patients with idiopathic normal pressure hydrocephalus (iNPH) patients during their diagnostic workup (lumbar samples) and in connection with their subsequent CSF diversion shunt surgery (ventricular samples). The mass-spectrometry-based proteomic profile was determined in these samples and in addition, selected biomarkers were quantified with ELISA (S100B, neurofilament light (NfL), amyloid-β (Aβ₄₀, Aβ₄₂), and total tau (T-tau) and phosphorylated tau (P-tau) forms). The latter analysis was extended to include paired porcine samples obtained from the lumbar compartment and the cerebromedullary cistern closely related to the ventricles.

RESULTS

In total 1231 proteins were detected in the human CSF. Of these, 216 distributed equally in the two CSF compartments, whereas 22 were preferentially (or solely) present in the ventricular CSF and four in the lumbar CSF. The selected biomarkers of neurodegeneration and Alzheimer's disease displayed differential distribution, some with higher (S100B, T-tau, and P-tau) and some with lower (NfL, Aβ₄₀, Aβ₄₂) levels in the ventricular compartment. In the porcine samples, all biomarkers were most abundant in the lumbar CSF.

CONCLUSIONS

The overall proteomic profile differs between the ventricular and the lumbar CSF compartments, and so does the distribution of clinically employed biomarkers. However, for a range of CSF proteins and biomarkers, one can reliably employ lumbar CSF as a proxy for ventricular CSF if or a lumbar/cranial index for the particular molecule has been established. It is therefore important to verify the compartmental preference of the proteins or biomarkers of interest prior to extrapolating from lumbar CSF to that of the ventricular fluid bordering the brain.

Linking the Amyloid, Tau, and Mitochondrial Hypotheses of Alzheimer's Disease and Identifying Promising Drug Targets

Damage or loss of brain cells and impaired neurochemistry, neurogenesis, and synaptic and nonsynaptic plasticity of the brain lead to dementia in neurodegenerative diseases, such as Alzheimer's disease (AD). Injury to synapses and neurons and accumulation of extracellular amyloid plaques and intracellular neurofibrillary tangles are considered the main morphological and neuropathological features of AD. Age, genetic and epigenetic factors, environmental stressors, and lifestyle contribute to the risk of AD onset and progression. These risk factors are associated with structural and functional changes in the brain, leading to cognitive decline. Biomarkers of AD reflect or cause specific changes in brain function, especially changes in pathways associated with neurotransmission, neuroinflammation, bioenergetics, apoptosis, and oxidative and nitrosative stress. Even in the initial stages, AD is associated with Aβ neurotoxicity, mitochondrial dysfunction, and tau neurotoxicity. The integrative amyloid-tau-mitochondrial hypothesis assumes that the primary cause of AD is the neurotoxicity of Aβ oligomers and tau oligomers, mitochondrial dysfunction, and their mutual synergy. For the development of new efficient AD drugs, targeting the elimination of neurotoxicity, mutual potentiation of effects, and unwanted protein interactions of risk factors and biomarkers (mainly Aβ oligomers, tau oligomers, and mitochondrial dysfunction) in the early stage of the disease seems promising.

Applications of CRISPR-Cas9 in Alzheimer's Disease and Related Disorders

Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease represent some of the most prevalent neurodegenerative disorders afflicting millions of people worldwide. Unfortunately, there is a lack of efficacious treatments to cure or stop the progression of these disorders. While the causes of such a lack of therapies can be attributed to various reasons, the disappointing results of recent clinical trials suggest the need for novel and innovative approaches. Since its discovery, there has been a growing excitement around the potential for CRISPR-Cas9 mediated gene editing to identify novel mechanistic insights into disease pathogenesis and to mediate accurate gene therapy. To this end, the literature is rich with experiments aimed at generating novel models of these disorders and offering proof-of-concept studies in preclinical animal models validating the great potential and versatility of this gene-editing system. In this review, we provide an overview of how the CRISPR-Cas9 systems have been used in these neurodegenerative disorders.