Nanoparticle-based immunomagnetic assay of plasma biomarkers for differentiating dementia and prodromal states of Alzheimer's disease - A cross-validation study

Longitudinal assessment of plasma biomarkers for early detection of cognitive changes in subjective cognitive decline

Background

Individuals experiencing subjective cognitive decline (SCD) are at an increased risk of developing mild cognitive impairment and dementia. Early identification of SCD and neurodegenerative diseases using biomarkers may help clinical decision-making and improve prognosis. However, few cross-sectional and longitudinal studies have explored plasma biomarkers in individuals with SCD using immunomagnetic reduction.

Objective

To identify plasma biomarkers for SCD.

Methods

Fifty-two participants [38 with SCD, 14 healthy controls (HCs)] underwent baseline assessments, including measurements of plasma Aβ42, Aβ40, t-tau, p-tau, and α-synuclein using immunomagnetic reduction (IMR) assays, cognitive tests and the Mini-Mental State Examination (MMSE). Following initial cross-sectional analysis, 39 individuals (29 with SCD, 10 HCs) entered a longitudinal phase for reassessment of these biomarkers and the MMSE. Biomarker outcomes across different individual categories were primarily assessed using the area under the receiver operating characteristic (ROC) curve. The SCD subgroup with an MMSE decline over one point was compared to those without such a decline.

Results

Higher baseline plasma Aβ1-42 levels significantly discriminated participants with SCD from HCs, with an acceptable area under the ROC curve (AUC) of 67.5% [95% confidence interval (CI), 52.7-80.0%]. However, follow-up and changes in MMSE and IMR data did not significantly differ between the SCD and HC groups (p > 0.05). Furthermore, lower baseline plasma Aβ1-42 levels were able to discriminate SCD subgroups with and without cognitive decline with a satisfied performance (AUC, 75.0%; 95% CI, 55.6-89.1%). At last, the changes in t-tau and Aβ42 × t-tau could differentiate between the two SCD subgroups (p < 0.05).

Conclusion

Baseline plasma Aβ42 may help identify people with SCD and predict SCD progression. The role of plasma Aβ42 levels as well as their upward trends from baseline in cases of SCD that progress to mild cognitive impairment and Alzheimer's disease require further investigation.

Machine Learning-Based Classification of Subjective Cognitive Decline, Mild Cognitive Impairment, and Alzheimer's Dementia Using Neuroimage and Plasma Biomarkers

Alzheimer's disease (AD) progresses relentlessly from the preclinical to the dementia stage. The process begins decades before the diagnosis of dementia. Therefore, it is crucial to detect early manifestations to prevent cognitive decline. Recent advances in artificial intelligence help tackle the complex high-dimensional data encountered in clinical decision-making. In total, we recruited 206 subjects, including 69 cognitively unimpaired, 40 subjective cognitive decline (SCD), 34 mild cognitive impairment (MCI), and 63 AD dementia (ADD). We included 3 demographic, 1 clinical, 18 brain-image, and 3 plasma biomarker (Aß_1-42, Aß_1-40, and tau protein) features. We employed the linear discriminant analysis method for feature extraction to make data more distinguishable after dimension reduction. The sequential forward selection method was used for feature selection to identify the 12 best features for machine learning classifiers. We used both random forest and support vector machine as classifiers. The area under the receiver operative curve (AUROC) was close to 0.9 between diseased (combining ADD and MCI) and the controls. AUROC was higher than 0.85 between SCD and controls, 0.90 between MCI and SCD, and above 0.85 between ADD and MCI. We can differentiate between adjacent phases of the AD spectrum with blood biomarkers and brain MR images with the help of machine learning algorithms.

Fluorescent aptasensor based on conformational switch-induced hybridization for facile detection of β-amyloid oligomers

Aβ oligomers (AβO) are a dominant biomarker for early Alzheimer's disease diagnosis. A fluorescent aptasensor coupled with conformational switch-induced hybridization was established to detect AβO. The fluorescent aptasensor is based on the interaction of fluorophore-labeled AβO-specific aptamer (FAM-Apt) against its partly complementary DNA sequence on the surface of magnetic beads (cDNA-MBs). Once the FAM-Apt binds to AβO, the conformational switch of FAM-Apt increases the tendency to be captured by cDNA-MBs. This causes a descending fluorescence of supernatant, which can be utilized to determine the levels of AβO. Thus, the base-pair matching above 12 between FAM-Apt and cDNA-MBs with increasing hybridizing free energies reached the ascending fluorescent signal equilibrium. The optimized aptasensor showed linearity from 1.7 ng mL^-1 to 85.1 (R = 0.9977) with good recoveries (79.27-109.17%) in plasma. Furthermore, the established aptasensor possesses rational selectivity in the presence of monomeric Aβ, fibrotic Aβ, and interferences. Therefore, the developed aptasensor is capable of quantifying AβO in human plasma and possesses the potential to apply in clinical cases.

Comparing the effects of olfactory-based sensory stimulation and board game training on cognition, emotion, and blood biomarkers among individuals with dementia: A pilot randomized controlled trial

Olfactory dysfunction can indicate early cognitive decline and is associated with dementia symptoms. We developed an olfactory-based sensory stimulation program and investigated its effects on cognition and emotion, and board game training were used as a comparison. In this parallel design pilot study, 30 participants with mild to moderate dementia were equal randomly assigned to the control (CONT), olfactory stimulation with cognitive training (OS), and board game (BG) groups. Two participants were withdrawn from CONT and OS groups, respectively. The intervention was a 12-week program with one 30-min session twice a week. We employed a blood-based biomarker technique and several cognitive and psychological tests to measure basal and after-intervention values. No significant differences were observed between the groups after intervention, as measured using the Mini-Mental State Examination, Loewenstein Occupational Therapy Cognitive Assessment (LOTCA), Top International Biotech Smell Identification Test, and Geriatric Depression Scale (GDS). The results showed that the OS group had a lower plasma Tau level than the other groups following intervention, whereas the CONT group had a significantly increased plasma amyloid ß1-42 level. OS participants had a lower concentration ratio of plasma Tau and amyloid Aß1-42 and showed more stable or improved cognition, olfactory function, and mood state. Both the OS and BG groups had a higher percentage of participants with stable or improved cognition and emotion. Taken together, these results suggest that olfactory-based sensory stimulation can be a beneficial intervention for patients with dementia.

A Review of Application of Aβ42/40 Ratio in Diagnosis and Prognosis of Alzheimer’s Disease

The number of patients with Alzheimer’s disease (AD) and non-Alzheimer’s disease (non-AD) has drastically increased over recent decades. The amyloid cascade hypothesis attributes a vital role to amyloid-β protein (Aβ) in the pathogenesis of AD. As the main pathological hallmark of AD, amyloid plaques consist of merely the 42 and 40 amino acid variants of Aβ (Aβ 42 and Aβ 40). The cerebrospinal fluid (CSF) biomarker Aβ 42/40 has been extensively investigated and eventually integrated into important diagnostic tools to support the clinical diagnosis of AD. With the development of highly sensitive assays and technologies, blood-based Aβ 42/40, which was obtained using a minimally invasive and cost-effective method, has been proven to be abnormal in synchrony with CSF biomarker values. This paper presents the recent progress of the CSF Aβ 42/40 ratio and plasma Aβ 42/40 for AD as well as their potential clinical application as diagnostic markers or screening tools for dementia.

A Profile of Nanoparticle-Based Plasma Neurodegenerative Biomarkers for Cognitive Function Among Patients Undergoing Hemodialysis

Purpose

This study aimed to compare the plasma levels of nanoparticle-based neurodegenerative biomarkers between hemodialysis (HD) participants with grossly normal cognitive function and healthy controls.

Patients and Methods

A cohort of participants undergoing maintenance HD and healthy controls were enrolled for comparison between July and October 2021. The immunomagnetic reduction method was used to measure plasma neurodegenerative biomarkers Aβ_1-40, Aβ_1-42, tau protein, and neurofilament light chain (NfL). The clinical dementia rating (CDR) was used to evaluate cognitive function. A receiver operating characteristic curve was used to discriminate between HD participants and healthy controls.

Results

There were 52 and 18 participants in the HD and healthy control groups, respectively. The mean age of the HD participants was 62 years, and that of the healthy controls was 57 years. The mean HD vintage in the HD cohort was 11.8 years. HD participants demonstrated significantly higher plasma levels of Aβ_1-42, tau protein, Aβ_1-42 × tau, and NfL and Aβ_1-42/Aβ_1-40 ratio and significantly lower plasma Aβ_1-40 levels than healthy controls. The measured plasma biomarkers could not discriminate between CDR0 and CDR0.5 HD participants. The area under the curve of the study biomarkers to discriminate HD participants from healthy controls ranged from 0.987 (Aβ_1-42 × tau) to 0.889 (NfL).

Conclusion

The plasma levels of nanoparticle-based neurodegenerative biomarkers were higher in HD participants with grossly normal cognitive function than in healthy controls. These findings imply that neurodegenerative changes appear in HD participants. A profile of plasma neurodegenerative biomarkers could be considered a potential surrogate for evaluating long-term cognitive function in HD participants.

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

Alzheimer’s disease (AD) is the most-common cause of neurodegenerative dementia, and it is characterized by abnormal amyloid and tau accumulation, which indicates neurodegeneration. AD has mostly been diagnosed clinically. However, ligand-specific positron emission tomography (PET) imaging, such as amyloid PET, and cerebrospinal fluid (CSF) biomarkers are needed to accurately diagnose AD, since they supplement the shortcomings of clinical diagnoses. Using biomarkers that represent the pathology of AD is essential (particularly when disease-modifying treatment is available) to identify the corresponding pathology of targeted therapy and for monitoring the treatment response. Although imaging and CSF biomarkers are useful, their widespread use is restricted by their high cost and the discomfort during the lumbar puncture, respectively. Recent advances in AD blood biomarkers shed light on their future use for clinical purposes. The amyloid β (Aβ)42/Aβ40 ratio and the concentrations of phosphorylated tau at threonine 181 and at threonine 217, and of neurofilament light in the blood were found to represent the pathology of Aβ, tau, and neurodegeneration in the brain when using automatic electrochemiluminescence technologies, single-molecule arrays, immunoprecipitation coupled with mass spectrometry, etc. These blood biomarkers are imminently expected to be incorporated into clinical practice to predict, diagnose, and determine the stage of AD. In this review we focus on advancements in the measurement technologies for blood biomarkers and the promising biomarkers that are approaching clinical application. We also discuss the current limitations, the needed further investigations, and the perspectives on their use.

Effect of Time to Detection on the Measured Concentrations of Blood Proteins Associated with Alzheimer’s Disease

Background

For assays using immunomagnetic reduction, a reagent composed of antibody-functionalized magnetic nanoparticles is dispersed in phosphate-buffered saline solution. The real-time signals of alternating-current (ac) magnetic susceptibility, χ_ac, of the reagent are subsequently recorded after mixing the reagent with a biofluid sample. After mixing the reagent and sample, the reduction in χ_ac of the mixture is calculated and used to quantify the concentration of the target biomarker in the sample. The reduction does not occur immediately but rather occurs at some time after mixing. This observation implies that the time elapsed before recording the real-time signals of χ_ac of a reagent-sample mixture needs to be investigated to ensure that the signals are fully recorded. In this work, the effect of time to detection on the measured concentrations of proteins in human plasma after mixing the reagent and sample is examined.

Methods

The proteins analyzed are related to Alzheimer's disease: amyloid β 1–40, amyloid β 1–42, and Tau protein. The investigated times to detection after the mixing the reagent and sample are 0, 20, 30, 40, and 120 min.

Results

The results show that the recording of real-time signals of χ_ac should be conducted within 20 min after mixing the reagent and sample.

Biofluid Biomarkers of Alzheimer’s Disease: Progress, Problems, and Perspectives

Since the establishment of the biomarker-based A-T-N (Amyloid/Tau/Neurodegeneration) framework in Alzheimer’s disease (AD), the diagnosis of AD has become more precise, and cerebrospinal fluid tests and positron emission tomography examinations based on this framework have become widely accepted. However, the A-T-N framework does not encompass the whole spectrum of AD pathologies, and problems with invasiveness and high cost limit the application of the above diagnostic methods aimed at the central nervous system. Therefore, we suggest the addition of an “X” to the A-T-N framework and a focus on peripheral biomarkers in the diagnosis of AD. In this review, we retrospectively describe the recent progress in biomarkers based on the A-T-N-X framework, analyze the problems, and present our perspectives on the diagnosis of AD.

Evidence of plasma biomarkers indicating high risk of dementia in cognitively normal subjects

Subjects with comorbidities are at risk for neurodegeneration. There is a lack of a direct relationship between comorbidities and neurodegeneration. In this study, immunomagnetic reduction (IMR) assays were utilized to assay plasma Aβ_1-42 and total tau protein (T-Tau) levels in poststroke (PS, n = 27), family history of Alzheimer's disease (ADFH, n = 35), diabetes (n = 21), end-stage renal disease (ESRD, n = 41), obstructive sleep apnea (OSA, n = 20), Alzheimer's disease (AD, n = 65). Thirty-seven healthy controls (HCs) were enrolled. The measured concentrations of plasma Aβ_1-42 were 14.26 ± 1.42, 15.43 ± 1.76, 15.52 ± 1.60, 16.15 ± 1.05, 16.52 ± 0.59, 15.97 ± 0.54 and 20.06 ± 3.09 pg/mL in HC, PS, ADFH, diabetes, ESRD, OSA and AD groups, respectively. The corresponding concentrations of plasma T-Tau were 15.13 ± 3.62, 19.29 ± 8.01, 17.93 ± 6.26, 19.74 ± 2.92, 21.54 ± 2.72, 20.17 ± 2.77 and 41.24 ± 14.64 pg/mL. The plasma levels of Aβ_1-42 and T-Tau in were significantly higher in the PS, ADFH, diabetes, ESRD and OSA groups than controls (Aβ_1-42 in PS: 15.43 ± 1.76 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.005; T-Tau in PS: 19.29 ± 8.01 vs. 15.13 ± 3.62 pg/mL, p < 0.005, Aβ_1-42 in ADFH: 15.52 ± 1.60 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in ADFH: 17.93 ± 6.26 vs. 15.13 ± 3.62 pg/mL, p < 0.005, Aβ_1-42 in diabetes: 16.15 ± 1.05 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in diabetes: 19.74 ± 2.92 vs. 15.13 ± 3.62 pg/mL, p < 0.001, Aβ_1-42 in ESRD: 16.52 ± 0.59 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in ESRD: 21.54 ± 2.72 vs. 15.13 ± 3.62 pg/mL, p < 0.001, Aβ_1-42 in OSA: 15.97 ± 0.54 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in OSA: 20.17 ± 2.77 vs. 15.13 ± 3.62 pg/mL, p < 0.001). This evidence indicates the high risk for dementia in these groups from the perspective of plasma biomarkers.

Assessment of High Risk for Alzheimer's Disease Using Plasma Biomarkers in Subjects with Normal Cognition in Taiwan: A Preliminary Study

Background

In Alzheimer's disease (AD), cognitive impairment begins 10-15 years later than neurodegeneration in the brain. Plasma biomarkers are promising candidates for assessing neurodegeneration in people with normal cognition. It has been reported that subjects with the concentration of plasma amyloid-β 1-42×total tau protein higher than 455 pg²/ml² are assessed as having a high risk of amnesic mild impairment or AD, denoted as high risk of AD (HRAD).

Objective

The prevalence of high-risk for dementia in cognitively normal controls is explored by assaying plasma biomarkers.

Methods

422 subjects with normal cognition were enrolled around Taiwan. Plasma Aβ_1-40, Aβ_1-42, and T-Tau levels were assayed using immunomagnetic reduction to assess the risk of dementia.

Results

The results showed that 4.6% of young adults (age: 20-44 years), 8.5% of middle-aged adults (age: 45-64 years), and 7.3% of elderly adults (age: 65-90 years) had HRAD. The percentage of individuals with HRAD dramatically increased in middle-aged and elderly adults compared to young adults.

Conclusion

The percentage of HRAD in cognitively normal subjects are approximately 10%, which reveals that the potentially public-health problem of AD in normal population. Although the subject having abnormal levels of Aβ or tau is not definitely going on to develop cognitive declines or AD, the risk of suffering cognitive impairment in future is relatively high. Suitable managements are suggested for these high-risk cognitively normal population. Worth noting, attention should be paid to preventing cognitive impairment due to AD, not only in elderly adults but also middle-aged adults.

Blood-Based ATN Biomarkers of Alzheimer's Disease: A Meta-Analysis

BACKGROUND

The Amyloid Tau Neurodegeneration (ATN) framework was proposed to define the biological state underpinning Alzheimer's disease (AD). Blood-based biomarkers offer a scalable alternative to the costly and invasive currently available biomarkers.

OBJECTIVE

In this meta-analysis we sought to assess the diagnostic performance of plasma amyloid (Aβ40, Aβ42, Aβ42/40 ratio), tangle (p-tau181), and neurodegeneration (total tau [t-tau], neurofilament light [NfL]) biomarkers.

METHODS

Electronic databases were screened for studies reporting biomarker concentrations for AD and control cohorts. Biomarker performance was examined by random-effect meta-analyses based on the ratio between biomarker concentrations in patients and controls.

RESULTS

83 studies published between 1996 and 2020 were included in the analyses. Aβ42/40 ratio as well as Aβ42 discriminated AD patients from controls when using novel platforms such as immunomagnetic reduction (IMR). We found significant differences in ptau-181 concentration for studies based on single molecule array (Simoa), but not for studies based on IMR or ELISA. T-tau was significantly different between AD patients and control in IMR and Simoa but not in ELISA-based studies. In contrast, NfL differentiated between groups across platforms. Exosome studies showed strong separation between patients and controls for Aβ42, t-tau, and p-tau181.

CONCLUSION

Currently available assays for sampling plasma ATN biomarkers appear to differentiate between AD patients and controls. Novel assay methodologies have given the field a significant boost for testing these biomarkers, such as IMR for Aβ, Simoa for p-tau181. Enriching samples through extracellular vesicles shows promise but requires further validation.

Investigation of the Number of Tests Required for Assaying Plasma Biomarkers Associated with Alzheimer's Disease Using Immunomagnetic Reduction

INTRODUCTION

Concentrations of plasma biomarkers associated with Alzheimer's disease have been reported to be as low as several tens of picograms/milliliter (pg/ml). However, in assays measuring these biomarkers, it is likely that repeated measurements are necessary to obtain reliable values.

METHODS

We performed assays as a single test or as duplicate, quadruplicate, fivefold and tenfold repeated tests, on samples spiked with different concentrations of amyloid β 1-40 (Aβ_1-40; 1-1000 pg/ml), Aβ_1-42 (1-30,000 pg/ml) and total Tau protein (T-Tau; 0.1-1000 pg/ml), with the aim to to calculate the coefficients of variation (CVs).

RESULTS

The results demonstrated common changes in the CVs with changes in the number of tests for a given sample: the CVs decreased with increases in the number of tests from one to ten. All CV values were distributed within the range of 0.35 to 15.5%; as such, the CV values were all lower than the acceptable value of 20%.

CONCLUSION

Based on this study, a single assay of Aβ_1-40, Aβ_1-42 and T-Tau, respectively, provides reliable results in terms of the measurement of that plasma biomarker.

The validation status of blood biomarkers of amyloid and phospho-tau assessed with the 5-phase development framework for AD biomarkers

PURPOSE

The development of blood biomarkers that reflect Alzheimer's disease (AD) pathophysiology (phosphorylated tau and amyloid-β) has offered potential as scalable tests for dementia differential diagnosis and early detection. In 2019, the Geneva AD Biomarker Roadmap Initiative included blood biomarkers in the systematic validation of AD biomarkers.

METHODS

A panel of experts convened in November 2019 at a two-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of blood biomarkers was assessed based on the Biomarker Roadmap methodology and discussed fully during the workshop which also evaluated cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers.

RESULTS

Plasma p-tau has shown analytical validity (phase 2 primary aim 1) and first evidence of clinical validity (phase 3 primary aim 1), whereas the maturity level for Aβ remains to be partially achieved. Full and partial achievement has been assigned to p-tau and Aβ, respectively, in their associations to ante-mortem measures (phase 2 secondary aim 2). However, only preliminary evidence exists for the influence of covariates, assay comparison and cut-off criteria.

CONCLUSIONS

Despite the relative infancy of blood biomarkers, in comparison to CSF biomarkers, much has already been achieved for phases 1 through 3 - with p-tau having greater success in detecting AD and predicting disease progression. However, sufficient data about the effect of covariates on the biomarker measurement is lacking. No phase 4 (real-world performance) or phase 5 (assessment of impact/cost) aim has been tested, thus not achieved.

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.