Diagnostic Blood Biomarkers in Alzheimer’s Disease

Amyloid-β and Phosphorylated Tau are the Key Biomarkers and Predictors of Alzheimer's Disease

Alzheimer's disease (AD) is a age-related neurodegenerative disease and is a major public health concern both in Texas, US and Worldwide. This neurodegenerative disease is mainly characterized by amyloid-beta (Aβ) and phosphorylated Tau (p-Tau) accumulation in the brains of patients with AD and increasing evidence suggests that these are key biomarkers in AD. Both Aβ and p-tau can be detected through various imaging techniques (such as positron emission tomography, PET) and cerebrospinal fluid (CSF) analysis. The presence of these biomarkers in individuals, who are asymptomatic or have mild cognitive impairment can indicate an increased risk of developing AD in the future. Furthermore, the combination of Aβ and p-tau biomarkers is often used for more accurate diagnosis and prediction of AD progression. Along with AD being a neurodegenerative disease, it is associated with other chronic conditions such as cardiovascular disease, obesity, depression, and diabetes because studies have shown that these comorbid conditions make people more vulnerable to AD. In the first part of this review, we discuss that biofluid-based biomarkers such as Aβ, p-Tau in cerebrospinal fluid (CSF) and Aβ & p-Tau in plasma could be used as an alternative sensitive technique to diagnose AD. In the second part, we discuss the underlying molecular mechanisms of chronic conditions linked with AD and how they affect the patients in clinical care.

Explaining the Variability of Alzheimer Disease Fluid Biomarker Concentrations in Memory Clinic Patients Without Dementia

BACKGROUND AND OBJECTIVES

Patients' comorbidities can affect Alzheimer disease (AD) blood biomarker concentrations. Because a limited number of factors have been explored to date, our aim was to assess the proportion of the variance in fluid biomarker levels explained by the clinical features of AD and by a large number of non-AD-related factors.

METHODS

MEMENTO enrolled 2,323 individuals with cognitive complaints or mild cognitive impairment in 26 French memory clinics. Baseline evaluation included clinical and neuropsychological assessments, brain MRI, amyloid-PET, CSF (optional), and blood sampling. Blood biomarker levels were determined using the Simoa-HDX analyzer. We performed linear regression analysis of the clinical features of AD (cognition, AD genetic risk score, and brain atrophy) to model biomarker concentrations. Next, we added covariates among routine biological tests, inflammatory markers, demographic and behavioral determinants, treatments, comorbidities, and preanalytical sample handling in final models using both stepwise selection processes and least absolute shrinkage and selection operator (LASSO).

RESULTS

In total, 2,257 participants were included in the analysis (median age 71.7, 61.8% women, 55.2% with high educational levels). For blood biomarkers, the proportion of variance explained by clinical features of AD was 13.7% for neurofilaments (NfL), 11.4% for p181-tau, 3.0% for Aβ-42/40, and 1.4% for total-tau. In final models accounting for non-AD-related factors, the variance was mainly explained by age, routine biological tests, inflammatory markers, and preanalytical sample handling. In CSF, the proportion of variance explained by clinical features of AD was 24.8% for NfL, 22.3% for Aβ-42/40, 19.8% for total-tau, and 17.2% for p181-tau. In contrast to blood biomarkers, the largest proportion of variance was explained by cognition after adjustment for covariates. The covariates that explained the largest proportion of variance were also the most frequently selected with LASSO. The performance of blood biomarkers for predicting A+ and T+ status (PET or CSF) remained unchanged after controlling for drivers of variance.

DISCUSSION

This comprehensive analysis demonstrated that the variance in AD blood biomarker concentrations was mainly explained by age, with minor contributions from cognition, brain atrophy, and genetics, conversely to CSF measures. These results challenge the use of blood biomarkers as isolated stand-alone biomarkers for AD.

The heritability of blood-based biomarkers related to risk of Alzheimer's disease in a population-based sample of early old-age men

INTRODUCTION

Despite their increased application, the heritability of Alzheimer's disease (AD)-related blood-based biomarkers remains unexplored.

METHODS

Plasma amyloid beta 40 (Aβ40), Aβ42, the Aβ42/40 ratio, total tau (t-tau), and neurofilament light (NfL) data came from 1035 men 60 to 73 years of age (μ = 67.0, SD = 2.6). Twin models were used to calculate heritability and the genetic and environmental correlations between them.

RESULTS

Additive genetics explained 44% to 52% of Aβ42, Aβ40, t-tau, and NfL. The Aβ42/40 ratio was not heritable. Aβ40 and Aβ42 were genetically near identical (rg  = 0.94). Both Aβ40 and Aβ42 were genetically correlated with NfL (rg  = 0.35 to 0.38), but genetically unrelated to t-tau.

DISCUSSION

Except for Aβ42/40, plasma biomarkers are heritable. Aβ40 and Aβ42 share mostly the same genetic influences, whereas genetic influences on plasma t-tau and NfL are largely unique in early old-age men. The absence of genetic associations between the Aβs and t-tau is not consistent with the amyloid cascade hypothesis.

The Predictive Ability of Blood Neurofilament Light Chain in Predicting Cognitive Decline in the Alzheimer's Disease Continuum: A Systematic Review and Meta-Analysis

Background

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset. Identifying candidate predictors to forecast AD dementia risk before disease onset is crucial for early diagnosis and treatment.

Objective

We aimed to assess the predictive ability of blood neurofilament light (NfL) chain in anticipating cognitive decline in the AD continuum.

Methods

We systematically searched PubMed, Web of Science, and Embase from inception until April 7, 2023. Longitudinal observational studies examining the association between baseline blood NfL and cognitive decline or clinical disease conversion were included based on inclusion/exclusion criteria. The final effect size was represented by adjusted hazard ratios (HR) or standardized beta (s.β) coefficients with a 95% confidence interval (CI).

Results

A total of 2,862 articles were identified, and 26 studies were included in this meta-analysis. The results indicated that baseline blood NfL could predict cognitive decline, with MMSE [s.β= -0.17, 95% CI (-0.26, -0.07)]; PACC [s.β= -0.09, 95% CI (-0.16, -0.03)]; ADAS-cog [s.β= 0.21, 95% CI (0.13, 0.29)]; CDR-SOB [s.β= 0.27, 95% CI (0.03, 0.50)]; Global cognitive composite [s.β= -0.05, 95% CI (-0.08, -0.01)]; Memory subdomain [s.β= -0.06, 95% CI (-0.09, -0.03)]; Language subdomain [s.β= -0.07, 95% CI (-0.10, -0.05)]; Executive function subdomain [s.β= -0.02, 95% CI (-0.03, -0.01)]; Visuospatial subdomain [s.β= -0.06, 95% CI (-0.08, -0.04)]. Additionally, baseline blood NfL could predict disease progression (conversion from CU/SCD/MCI to MCI/AD) in the AD continuum [Adjust HR = 1.32, 95% CI (1.12, 1.56)].

Conclusions

Baseline blood NfL demonstrated predictive capabilities for global cognition and its memory, language, executive function, visuospatial subdomains decline in the AD continuum. Moreover, it exhibited the potential to predict disease progression in non-AD dementia participants.

Biomarkers associated with the pathogenesis of Alzheimer's disease

Alzheimer's disease (AD) is a progressive degenerative neurological illness with insidious onset. Due to the complexity of the pathogenesis of AD and different pathological changes, the clinical phenotypes of dementia are diverse, and these pathological changes also interact with each other. Therefore, it is of great significance to search for biomarkers that can diagnose these pathological changes to improve the ability to monitor the course of disease and treat the disease. The pathological mechanism hypothesis with high recognition of AD mainly includes the accumulation of β-amyloid (Aβ) around neurons and hyperphosphorylation of tau protein, which results in the development of neuronal fiber tangles (NFTs) and mitochondrial dysfunction. AD is an irreversible disease; currently, there is no clinical cure or delay in the disease process of drugs, and there is a lack of effective early clinical diagnosis methods. AD patients, often in the dementia stages and moderate cognitive impairment, will seek medical treatment. Biomarkers can help diagnose the presence or absence of specific diseases and their pathological processes, so early screening and diagnosis are crucial for the prevention and therapy of AD in clinical practice. β-amyloid deposition (A), tau pathology (T), and neurodegeneration/neuronal damage (N), also known as the AT (N) biomarkers system, are widely validated core humoral markers for the diagnosis of AD. In this paper, the pathogenesis of AD related to AT (N) and the current research status of cerebrospinal fluid (CSF) and blood related biomarkers were reviewed. At the same time, the limitations of humoral markers in the diagnosis of AD were also discussed, and the future development of humoral markers for AD was prospected. In addition, the contents related to mitochondrial dysfunction, prion virology and intestinal microbiome related to AD are also described, so as to understand the pathogenesis of AD in many aspects and dimensions, so as to evaluate the pathological changes related to AD more comprehensively and accurately.

Correlation studies of Hippocampal Morphometry and Plasma NFL Levels in Cognitively Unimpaired Subjects

Alzheimer's disease(AD) is being the burden of society and family. Applying computing-aided strategies to reveal its pathology is one of the research highlights. Plasma neurofilament light (NFL) is an emerging noninvasive and economic biomarker for AD molecular pathology. It is valuable to reveal the correlations between the plasma NFL levels and neurodegeneration, especially hippcampal deformations at the preclinical stage. The negative correlation between plasma NFL levels and hippocampal volumes has been documented. However, the relationship between the plasma NFL levels and the hippocampal morphometry details at the preclinical stage is still elusive. This study seeks to demonstrate the capacity of our proposed surface-based hippocampal morphometry system to discern the plasma NFL positive (NFL+>41.9 pg/L) level and plasma NFL negative (NFL-<41.9pg/L) level and illustrate its superiority to the hippocampal volume measurement by drawing the cohort of 154 CU middle aged and elderly adults. We also apply this morphometry measure and a proposed sparse coding based classification algorithm to classify CU individuals with NFL+ and NFL- levels. Experimental results show that the proposed hippocampal morphometry system offers stronger statistical power to discriminate CU subjects with NFL+ and NFL- levels, comparing with the hippocampal volume measure. Furthermore, this system can discriminate plasma NFL levels in CU individuals (Accuracy=0.86). Both the group level and individual level analysis results indicate that the association between plasma NFL levels and the hippocampal shapes can be mapped at the preclinical stage.

Healthy brain aging and delayed dementia in Texas rural elderly

Healthy aging is the process of preserving and enhancing one's independence, physical and mental well-being, and overall quality of life. It involves the mental, emotional, and cognitive wellness. Although biological and genetic factors have a significant influence on the process of aging gracefully, other adjustable factors also play a crucial role. Adopting positive behaviors such as maintaining a nutritious and balanced diet, engaging in regular physical activity, effectively managing stress and anxiety, ensuring sufficient sleep, nurturing spiritual coping mechanisms, and prioritizing overall well-being from an early stage can collectively influence both lifespan and the quality of health during advanced years. We aim to explore the potential impacts of biological, psychosocial, and environmental factors on the process of healthy cognitive aging in individuals who exhibit healthy aging. Additionally, we plan to present initial findings that demonstrate how maintaining good cognitive health during aging could potentially postpone the emergence of neurodegenerative disorders. We hypothesize that there will be strong associations between biological, environmental, and social factors that cause some elderly to be superior in cognitive health than others. For preliminary data collection, we recruited 25 cognitively healthy individuals and 5 individuals with MCI/AD between the ages of 60-90 years. We conducted anthropometric measurements, and blood biomarker testing, administered surveys, and obtained structural brain magnetic resonance imaging (MRI) scans. The Montreal Cognitive Assessment (MoCA) scores and sub-scores for the healthy group were also reported. We found that at baseline, individuals exhibiting healthy cognitive aging, and those with MCI/AD had comparable measures of anthropometrics and blood biomarkers. The healthy group exhibited lower signs of brain volume loss and the ones observed were age-related. Moreover, within the healthy group, there was a significant correlation (p = 0.003) between age and MoCA scores. Conversely, within the individuals with MCI/AD, the MRI scans showed disease signs of grey and white matter and loss of cerebral volume. Healthy brain aging is a scientific area that remains under-explored. Our current study findings support our hypothesis. Future studies are required in diverse populations to determine the various biological, psychological, environmental, lifestyle, and social factors that contribute to it.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Plasma P-Tau181 for the Discrimination of Alzheimer’s Disease from Other Primary Dementing and/or Movement Disorders

Blood phospho-tau181 may offer a useful biomarker for Alzheimer’s disease. However, the use of either serum or plasma phospho-tau181 and their diagnostic value are currently under intense investigation. In a pilot study, we measured both serum and plasma phospho-tau181 (pT181-Tau) by single molecule array (Simoa) in a group of patients with Alzheimer’s disease and a mixed group of patients with other primary dementing and/or movement disorders. Classical cerebrospinal fluid biomarkers were also measured. Plasma (but not serum) pT181-Tau showed a significant increase in Alzheimer’s disease and correlated significantly with cerebrospinal fluid amyloid and pT181-Tau. Receiver operating curve analysis revealed a significant discrimination of Alzheimer’s from non-Alzheimer’s disease patients, with an area under the curve of 0.83 and an excellent sensitivity but a moderate specificity. Plasma pT181-Tau is not an established diagnostic biomarker for Alzheimer’s disease, but it could become one in the future, or it may serve as a screening tool for specific cases of patients or presymptomatic subjects.