Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer's disease in individuals with subjective cognitive decline

Present and Future of Blood-Based Biomarkers of Alzheimer's Disease: Beyond the Classics

The field of blood-based biomarkers for Alzheimer's disease (AD) has advanced at an incredible pace, especially after the development of sensitive analytic platforms that can facilitate large-scale screening. Such screening will be important when more sophisticated diagnostic methods are scarce and expensive. Thus, blood-based biomarkers can potentially reduce diagnosis inequities among populations from different socioeconomic contexts. This large-scale screening can be performed so that older adults at risk of cognitive decline assessed using these methods can then undergo more complete assessments with classic biomarkers, increasing diagnosis efficiency and reducing costs to the health systems. Blood-based biomarkers can also aid in assessing the effect of new disease-modifying treatments. This paper reviews recent advances in the area, focusing on the following leading candidates for blood-based biomarkers: amyloid-beta (Aβ), phosphorylated tau isoforms (p-tau), neurofilament light (NfL), and glial fibrillary acidic (GFAP) proteins, as well as on new candidates, Neuron-Derived Exosomes contents (NDEs) and Transactive response DNA-binding protein-43 (TDP-43), based on data from longitudinal observational cohort studies. The underlying challenges of validating and incorporating these biomarkers into routine clinical practice and primary care settings are also discussed. Importantly, challenges related to the underrepresentation of ethnic minorities and socioeconomically disadvantaged persons must be considered. If these challenges are overcome, a new time of cost-effective blood-based biomarkers for AD could represent the future of clinical procedures in the field and, together with continued prevention strategies, the beginning of an era with a lower incidence of dementia worldwide.

Application of blood-based biomarkers of Alzheimer's disease in clinical practice: Recommendations from Taiwan Dementia Society

Blood-based biomarkers (BBM) are potentially powerful tools that assist in the biological diagnosis of Alzheimer's disease (AD) in vivo with minimal invasiveness, relatively low cost, and good accessibility. This review summarizes current evidence for using BBMs in AD, focusing on amyloid, tau, and biomarkers for neurodegeneration. Blood-based phosphorylated tau and the Aβ42/Aβ40 ratio showed consistent concordance with brain pathology measured by CSF or PET in the research setting. In addition, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are neurodegenerative biomarkers that show the potential to assist in the differential diagnosis of AD. Other pathology-specific biomarkers, such as α-synuclein and TAR DNA-binding protein 43 (TDP-43), can potentially detect AD concurrent pathology. Based on current evidence, the working group from the Taiwan Dementia Society (TDS) achieved consensus recommendations on the appropriate use of BBMs for AD in clinical practice. BBMs may assist clinical diagnosis and prognosis in AD subjects with cognitive symptoms; however, the results should be interpreted by dementia specialists and combining biochemical, neuropsychological, and neuroimaging information. Further studies are needed to evaluate BBMs' real-world performance and potential impact on clinical decision-making.

Predictors of progression from subjective cognitive decline to objective cognitive impairment: A systematic review and meta-analysis of longitudinal studies

BACKGROUND

Subjective cognitive decline is one of the first symptoms of dementia. With increasing awareness of brain health and a rising prevalence of dementia, a growing number of individuals seek medical assistance for purely subjective cognitive decline. However, only individuals with specific characteristics tend to experience clinical progression.

OBJECTIVES

This study aims to summarize the predictors of objective cognitive impairment in individuals with subjective cognitive decline and to identify those at higher risk of clinical progression.

DESIGN

Systematic review and meta-analysis.

METHODS

We systematically searched 11 electronic databases from inception to February 1, 2023, for longitudinal studies investigating factors associated with the clinical progression of subjective cognitive decline. Effect sizes were pooled using fixed-effects and random-effects models. Leveraging the results of the meta-analysis, we developed two risk prediction models for objective cognitive impairment.

RESULTS

Forty-six cohort studies were included in the systematic review, of which 28 met the meta-analysis criteria. Fifteen predictors were identified, including 4 biomarkers (amyloid β deposition, lower Hulstaert Formula scores, apolipoprotein e4, and hippocampus atrophy), four epidemiological factors (older age at baseline, impaired instrumental activity of daily living, depression, and anxiety), and seven neuropsychological factors (participants in clinical settings, older age at onset, stable symptom, concerns, cognitive decline confirmed by informant, severe symptoms, and poor performance on Trail Making Test B). Based on the meta-analysis results, we developed two risk prediction models. The first model (Model1) incorporates epidemiological and neuropsychological factors, distinguishing individuals with low and medium risk. The second model (Model2) includes additional biomarkers to enhance predictive performance and identify individuals at high risk.

CONCLUSIONS

This study provides a comprehensive characterization of individuals undergoing clinical progression from subjective cognitive decline to mild cognitive impairment or dementia. The developed models support the prediction of progression risk in both memory clinic and community settings, aiding in the early identification of individuals at risk of disease conversion and facilitating the translation of evidence into clinical practice.

REGISTRATION

The systematic review and meta-analysis have been registered in PROSPERO (CRD 42023392476).

TWEETABLE ABSTRACT

Factors for predicting progression from subjective cognitive decline to objective cognitive impairment: evidence from longitudinal studies.

Plasma biomarkers predict Alzheimer's disease before clinical onset in Chinese cohorts

Plasma amyloid-β (Aβ)42, phosphorylated tau (p-tau)181, and neurofilament light chain (NfL) are promising biomarkers of Alzheimer's disease (AD). However, whether these biomarkers can predict AD in Chinese populations is yet to be fully explored. We therefore tested the performance of these plasma biomarkers in 126 participants with preclinical AD and 123 controls with 8-10 years of follow-up from the China Cognition and Aging Study. Plasma Aβ42, p-tau181, and NfL were significantly correlated with cerebrospinal fluid counterparts and significantly altered in participants with preclinical AD. Combining plasma Aβ42, p-tau181, and NfL successfully discriminated preclinical AD from controls. These findings were validated in a replication cohort including 51 familial AD mutation carriers and 52 non-carriers from the Chinese Familial Alzheimer's Disease Network. Here we show that plasma Aβ42, p-tau181, and NfL may be useful for predicting AD 8 years before clinical onset in Chinese populations.

PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer's disease

Developing reliable biomarkers is important for screening Alzheimer's disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET. 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ +, 160 Aβ -), 115 MCI(54 Aβ +, 61Aβ -). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI + SCD (33 Aβ +, 43 Aβ -). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aβ +, 24 Aβ -). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aβ +, 19 Aβ -). Similar trends of EEG power have been observed from the group comparison between Aβ + and Aβ -, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended.

Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

BACKGROUND

Histologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD.

METHODS

This was an individual participant data meta-analysis of the prognostic performance of histologically assessed fibrosis stage (F0-4), liver stiffness measured by vibration-controlled transient elastography (LSM-VCTE), fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) in patients with NAFLD. The literature was searched for a previously published systematic review on the diagnostic accuracy of imaging and simple non-invasive tests and updated to Jan 12, 2022 for this study. Studies were identified through PubMed/MEDLINE, EMBASE, and CENTRAL, and authors were contacted for individual participant data, including outcome data, with a minimum of 12 months of follow-up. The primary outcome was a composite endpoint of all-cause mortality, hepatocellular carcinoma, liver transplantation, or cirrhosis complications (ie, ascites, variceal bleeding, hepatic encephalopathy, or progression to a MELD score ≥15). We calculated aggregated survival curves for trichotomised groups and compared them using stratified log-rank tests (histology: F0-2 vs F3 vs F4; LSM: <10 vs 10 to <20 vs ≥20 kPa; FIB-4: <1·3 vs 1·3 to ≤2·67 vs >2·67; NFS: <-1·455 vs -1·455 to ≤0·676 vs >0·676), calculated areas under the time-dependent receiver operating characteristic curves (tAUC), and performed Cox proportional-hazards regression to adjust for confounding. This study was registered with PROSPERO, CRD42022312226.

FINDINGS

Of 65 eligible studies, we included data on 2518 patients with biopsy-proven NAFLD from 25 studies (1126 [44·7%] were female, median age was 54 years [IQR 44-63), and 1161 [46·1%] had type 2 diabetes). After a median follow-up of 57 months [IQR 33-91], the composite endpoint was observed in 145 (5·8%) patients. Stratified log-rank tests showed significant differences between the trichotomised patient groups (p<0·0001 for all comparisons). The tAUC at 5 years were 0·72 (95% CI 0·62-0·81) for histology, 0·76 (0·70-0·83) for LSM-VCTE, 0·74 (0·64-0·82) for FIB-4, and 0·70 (0·63-0·80) for NFS. All index tests were significant predictors of the primary outcome after adjustment for confounders in the Cox regression.

INTERPRETATION

Simple non-invasive tests performed as well as histologically assessed fibrosis in predicting clinical outcomes in patients with NAFLD and could be considered as alternatives to liver biopsy in some cases.

FUNDING

Innovative Medicines Initiative 2.

Progress in blood biomarkers of subjective cognitive decline in preclinical Alzheimer's disease

ABSTRACT

Alzheimer's disease (AD) is a neurodegenerative disease that gradually impairs cognitive functions. Recently, there has been a conceptual shift toward AD to view the disease as a continuum. Since AD is currently incurable, effective intervention to delay or prevent pathological cognitive decline may best target the early stages of symptomatic disease, such as subjective cognitive decline (SCD), in which cognitive function remains relatively intact. Diagnostic methods for identifying AD, such as cerebrospinal fluid biomarkers and positron emission tomography, are invasive and expensive. Therefore, it is imperative to develop blood biomarkers that are sensitive, less invasive, easier to access, and more cost effective for AD diagnosis. This review aimed to summarize the current data on whether individuals with SCD differ reliably and effectively in subjective and objective performances compared to cognitively normal elderly individuals, and to find one or more convenient and accessible blood biomarkers so that researchers can identify SCD patients with preclinical AD in the population as soon as possible. Owing to the heterogeneity and complicated pathogenesis of AD, it is difficult to make reliable diagnoses using only a single blood marker. This review provides an overview of the progress achieved to date with the use of SCD blood biomarkers in patients with preclinical AD, highlighting the key areas of application and current challenges.

Nicotinic acetylcholine receptor subtype expression, function, and pharmacology: Therapeutic potential of α-conotoxins

The pentameric nicotinic acetylcholine receptors (nAChRs) are typically classed as muscle- or neuronal-type, however, the latter has also been reported in non-neuronal cells. Given their broad distribution, nAChRs mediate numerous physiological and pathological processes including synaptic transmission, presynaptic modulation of transmitter release, neuropathic pain, inflammation, and cancer. There are 17 different nAChR subunits and combinations of these subunits produce subtypes with diverse pharmacological properties. The expression and role of some nAChR subtypes have been extensively deciphered with the aid of knock-out models. Many nAChR subtypes expressed in heterologous systems are selectively targeted by the disulfide-rich α-conotoxins. α-Conotoxins are small peptides isolated from the venom of cone snails, and a number of them have potential pharmaceutical value.

The association of subjective sleep characteristics and plasma biomarkers of Alzheimer's disease pathology in older cognitively unimpaired adults with higher amyloid-β burden

We aimed to investigate the association of subjective sleep characteristics and plasma Alzheimer's disease (AD) biomarkers in older cognitively unimpaired adults with higher amyloid-β (Aβ) burden. Unimpaired cognition was determined by education-adjusted performance for the Mini-Mental State Examination and exclusion of dementia and mild cognitive impairment via standardized neuropsychological tests. We used Pittsburgh Sleep Quality Index (PSQI) to assess subjective sleep quality. The participants also underwent examination of plasma AD biomarkers and ¹⁸F-florbetapir PET scan. Correlation and multiple linear regression analyses were used to investigate the association between subjective sleep characteristics and AD biomarkers. A total of 335 participants were included and 114 were Aβ-PET positive. Multivariable regression analysis showed sleep duration > 8 h and sleep disturbance were associated with Aβ deposition in total participants. Two multiple linear regression models were applied and the results revealed in participants with Aβ-PET (+), falling asleep at ≥ 22:00 to ≤ 23:00 was associated with higher levels of Aβ42 and Aβ42/40. Other associations with higher Aβ42/40 and standard uptake value ratio contained sleep efficiency value, sleep efficiency ≥ 75%, no/mild daytime dysfunction and PSQI score ≤ 5. Higher p-Tau-181 level was associated with sleep latency > 30 min in Aβ-PET (+) group and moderate/severe sleep disturbance in Aβ-PET (-) group. Our data suggests sleep duration ≤ 8 h and no/mild sleep disturbance may be related to less Aβ burden. In participants with Aβ deposition, falling asleep at 22:00 to 23:00, higher sleep efficiency (at least ≥ 75%), no/mild daytime dysfunction, sleep latency ≤ 30 min, and good sleep quality may help improve AD pathology.

Association of plasma biomarkers, p-tau181, glial fibrillary acidic protein, and neurofilament light, with intermediate and long-term clinical Alzheimer's disease risk: Results from a prospective cohort followed over 17 years

INTRODUCTION

Blood biomarkers for Alzheimer's disease (AD) are the future of AD risk assessment. The aim of this study was to determine the association between plasma-measured phosphorylated tau (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) levels and risk of clinical AD incidence with consideration to the impact of cardiovascular health.

METHODS

Within a community-based cohort, biomarker levels were measured at baseline using single molecule array technology in 768 participants (aged 50-75) followed over 17 years. Associations among biomarkers and AD, vascular dementia, and mixed dementia incidence were assessed.

RESULTS

GFAP was associated with clinical AD incidence even more than a decade before diagnosis (9-17 years), while p-tau181 and NfL were associated with more intermediate AD risk (within 9 years). Significant interaction was detected between cardiovascular health and p-tau181/NfL.

DISCUSSION

GFAP may be an early AD biomarker increasing before p-tau181 and NfL and the effect modifying role of cardiovascular health should be considered in biomarker risk stratification.

Plasma amyloid beta 40/42, phosphorylated tau 181, and neurofilament light are associated with cognitive impairment and neuropathological changes among World Trade Center responders: A prospective cohort study of exposures and cognitive aging at midlife

Introduction

World Trade Center (WTC) responders are experiencing a high risk of mild cognitive impairment (MCI) and dementia, though the etiology remains inadequately characterized. This study investigated whether WTC exposures and chronic post-traumatic stress disorder (PTSD) were correlated with plasma biomarkers characteristic of Alzheimer's disease (AD) neuropathology.

Methods

Eligible participants included WTC-exposed individuals with a baseline cognitive assessment and available plasma sample. We examined levels of the amyloid beta (Aβ)40/42 ratio, phosphorylated tau 181 (p-tau181), and neurofilament light chain (NfL) and associations with a WTC exposures (duration on site ≥15 weeks, dust cloud), the PTSD Symptom Checklist for Diagnostic and Statistical Manual of Mental Disorders, 4th edition PTSD, and classification of amyloid/tau/neurodegeneration (AT[N]) profiles. Multinomial logistic regressions assessed whether biomarkers predicted increased risk of MCI or dementia.

Results

Of 1179 eligible responders, 93.0% were male, mean (standard deviation) age 56.6 years (7.8). Aβ40/42, p-tau181, and NfL intercorrelated and increased with age. In subgroup analyses of responders with available neuroimaging data (n = 75), Aβ40/42 and p-tau181 were further associated with decreased hippocampal volume (Spearman's ρ = -0.3). Overall, 58.08% of responders with dementia had ≥1 elevated biomarker, and 3.45% had elevations across all biomarkers. In total, 248 (21.05%) had MCI and 70 (5.94%) had dementia. Increased risk of dementia was associated with plasma AT(N) profile T+ or A+N+. Exposure on site ≥15 weeks was independently associated with T+ (adjusted risk ratio [aRR] = 1.03 [1.01-1.05], P = 0.009), and T+N+ profile (aRR = 2.34 [1.12-4.87]). The presence of PTSD was independently associated with risk of A+ (aRR = 1.77 [1.11-2.82]).

Discussion

WTC exposures and chronic PTSD are associated with plasma biomarkers consistent with neurodegenerative disease.

ATR-FTIR Biosensors for Antibody Detection and Analysis

Quality control of drug products is of paramount importance in the pharmaceutical world. It ensures product safety, efficiency, and consistency. In the case of complex biomolecules such as therapeutic proteins, small variations in bioprocess parameters can induce substantial variations in terms of structure, impacting the drug product quality. Conditions for obtaining highly reproducible grafting of 11-mercaptoundecanoic acid were determined. On that basis, we developed an easy-to-use, cost effective, and timesaving biosensor based on ATR-FTIR spectroscopy able to detect immunoglobulins during their production. A germanium crystal, used as an internal reflection element (IRE) for FTIR spectroscopy, was covalently coated with immunoglobulin-binding proteins. This thereby functionalized surface could bind only immunoglobulins present in complex media such as culture media or biopharmaceutical products. The potential subsequent analysis of their structure by ATR-FTIR spectroscopy makes this biosensor a powerful tool to monitor the production of biotherapeutics and assess important critical quality attributes (CQAs) such as high-order structure and aggregation level.

Amyloid-beta misfolding and GFAP predict risk of clinical Alzheimer's disease diagnosis within 17 years

INTRODUCTION

Blood-based biomarkers for Alzheimer's disease (AD) are urgently needed. Here, four plasma biomarkers were measured at baseline in a community-based cohort followed over 17 years, and the association with clinical AD risk was determined.

METHODS

Amyloid beta (Aβ) misfolding status as a structure-based biomarker as well as phosphorylated tau 181 (P-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) concentration levels were determined at baseline in heparin plasma from 68 participants who were diagnosed with AD and 240 controls without dementia diagnosis throughout follow-up.

RESULTS

Aβ misfolding exhibited high disease prediction accuracy of AD diagnosis within 17 years. Among the concentration markers, GFAP showed the best performance, followed by NfL and P-tau181. The combination of Aβ misfolding and GFAP increased the accuracy.

DISCUSSION

Aβ misfolding and GFAP showed a strong ability to predict clinical AD risk and may be important early AD risk markers. Aβ misfolding illustrated its potential as a prescreening tool for AD risk stratification in older adults.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ_1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Fluid Biomarkers in Alzheimer’s Disease and Other Neurodegenerative Disorders: Toward Integrative Diagnostic Frameworks and Tailored Treatments

The diagnosis of neurodegenerative diseases (NDDs) represents an increasing social burden, with the unsolved issue of disease-modifying therapies (DMTs). The failure of clinical trials treating Alzheimer′s Disease (AD) so far highlighted the need for a different approach in drug design and patient selection. Identifying subjects in the prodromal or early symptomatic phase is critical to slow down neurodegeneration, but the implementation of screening programs with this aim will have an ethical and social aftermath. Novel minimally invasive candidate biomarkers (derived from blood, saliva, olfactory brush) or classical cerebrospinal fluid (CSF) biomarkers have been developed in research settings to stratify patients with NDDs. Misfolded protein accumulation, neuroinflammation, and synaptic loss are the pathophysiological hallmarks detected by these biomarkers to refine diagnosis, prognosis, and target engagement of drugs in clinical trials. We reviewed fluid biomarkers of NDDs, considering their potential role as screening, diagnostic, or prognostic tool, and their present-day use in clinical trials (phase II and III). A special focus will be dedicated to novel techniques for the detection of misfolded proteins. Eventually, an applicative diagnostic algorithm will be proposed to translate the research data in clinical practice and select prodromal or early patients to be enrolled in the appropriate DMTs trials for NDDs.

Novel Ultrasensitive Detection Technologies for the Identification of Early and Minimally Invasive Alzheimer's Disease Blood Biomarkers

BACKGROUND

Single molecule array (SIMOA) and other ultrasensitive detection technologies have allowed the determination of blood-based biomarkers of Alzheimer's disease (AD) for diagnosis and monitoring, thereby opening up a promising field of research.

OBJECTIVE

To review the published bibliography on plasma biomarkers in AD using new ultrasensitive techniques.

METHODS

A systematic review of the PubMed database was carried out to identify reports on the use of blood-based ultrasensitive technology to identify biomarkers for AD.

RESULTS

Based on this search, 86 works were included and classified according to the biomarker determined. First, plasma amyloid-β showed satisfactory accuracy as an AD biomarker in patients with a high risk of developing dementia. Second, plasma t-Tau displayed good sensitivity in detecting different neurodegenerative diseases. Third, plasma p-Tau was highly specific for AD. Fourth, plasma NfL was highly sensitive for distinguishing between patients with neurodegenerative diseases and healthy controls. In general, the simultaneous determination of several biomarkers facilitated greater accuracy in diagnosing AD (Aβ42/Aβ40, p-Tau181/217).

CONCLUSION

The recent development of ultrasensitive technology allows the determination of blood-based biomarkers with high sensitivity, thus facilitating the early detection of AD through the analysis of easily obtained biological samples. In short, as a result of this knowledge, pre-symptomatic and early AD diagnosis may be possible, and the recruitment process for future clinical trials could be more precise. However, further studies are necessary to standardize levels of blood-based biomarkers in the general population and thus achieve reproducible results among different laboratories.

Blood-based biomarkers for Alzheimer's disease

Neurodegenerative disorders such as Alzheimer's disease (AD) represent a mounting public health challenge. As these diseases are difficult to diagnose clinically, biomarkers of underlying pathophysiology are playing an ever-increasing role in research, clinical trials, and in the clinical work-up of patients. Though cerebrospinal fluid (CSF) and positron emission tomography (PET)-based measures are available, their use is not widespread due to limitations, including high costs and perceived invasiveness. As a result of rapid advances in the development of ultra-sensitive assays, the levels of pathological brain- and AD-related proteins can now be measured in blood, with recent work showing promising results. Plasma P-tau appears to be the best candidate marker during symptomatic AD (i.e., prodromal AD and AD dementia) and preclinical AD when combined with Aβ42/Aβ40. Though not AD-specific, blood NfL appears promising for the detection of neurodegeneration and could potentially be used to detect the effects of disease-modifying therapies. This review provides an overview of the progress achieved thus far using AD blood-based biomarkers, highlighting key areas of application and unmet challenges.

Sleep Quality Improvement Enhances Neuropsychological Recovery and Reduces Blood Aβ42/40 Ratio in Patients with Mild–Moderate Cognitive Impairment

Background and objectives: Alzheimer’s disease is a progressive brain degeneration and is associated with a high prevalence of sleep disorders. Amyloid β peptide-42/40 (Aβ_42/40) and Tau-pT181 are the core biomarkers in cerebrospinal fluid and blood. Accumulated data from studies in mouse models and humans demonstrated an aberrant elevation of these biomarkers due to sleep disturbance, especially sleep-disordered breathing (SDB). However, it is not clear if sleep quality improvement reduces the blood levels of Ab_42/40 ratio and Tau-pT181 in Alzheimer’s disease patients. Materials and Methods: In this prospective study, a longitudinal analysis was conducted on 64 patients with mild–moderate cognition impairment (MCI) due to Alzheimer’s disease accompanied by SDB. Another 33 MCI cases without sleep-disordered breathing were included as the control group. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) score system. Neuropsychological assessments were conducted using the Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Clinical Dementia Rating (CDR), 24-h Hamilton Rating Scale for Depression (HRSD-24), and Hamilton Anxiety Rating Scale (HAMA) scoring systems. Aβ₄₂, Aβ_40, and Tau-pT181 protein levels in blood specimens were measured using ELISA assays. All patients received donepezil treatment for Alzheimer’s disease. SDB was managed with continuous pressure ventilation. Results: A significant correlation was found among PSQI, HRSD-24, HAMA, Aβ_42/40 ratio, and Tau-pT181 level in all cases. In addition, a very strong and negative correlation was discovered between education level and dementia onset age. Compared to patients without SDB (33 non-SD cases), patients with SDB (64 SD cases) showed a significantly lower HRSD-24 score and a higher Aβ_42/40 ratio Tau-pT181 level. Sleep treatment for patients with SDB significantly improved all neuropsychological scores, Aβ_42/40 ratio, and Tau-pT181 levels. However, 11 patients did not completely recover from a sleep disorder (PSQI > 5 post-treatment). In this subgroup of patients, although HAMA score and Tau-pT181 levels were significantly reduced, MoCA and HRSD-24 scores, as well as Aβ_42/40 ratio, were not significantly improved. ROC analysis found that the blood Aβ_42/40 ratio held the highest significance in predicting sleep disorder occurrence. Conclusions: This is the first clinical study on sleep quality improvement in Alzheimer’s disease patients. Sleep quality score was associated with patient depression and anxiety scores, as well as Aβ_42/40 ratio and Tau-pT181 levels. A complete recovery is critical for fully improving all neuropsychological assessments, Aβ_42/40 ratio, and Tau-pT181 levels. Blood Aβ_42/40 ratio is a feasible prognostic factor for predicting sleep quality.

Blood-based biomarkers for Alzheimer's disease: towards clinical implementation

For many years, blood-based biomarkers for Alzheimer's disease seemed unattainable, but recent results have shown that they could become a reality. Convincing data generated with new high-sensitivity assays have emerged with remarkable consistency across different cohorts, but also independent of the precise analytical method used. Concentrations in blood of amyloid and phosphorylated tau proteins associate with the corresponding concentrations in CSF and with amyloid-PET or tau-PET scans. Moreover, other blood-based biomarkers of neurodegeneration, such as neurofilament light chain and glial fibrillary acidic protein, appear to provide information on disease progression and potential for monitoring treatment effects. Now the question emerges of when and how we can bring these biomarkers to clinical practice. This step would pave the way for blood-based biomarkers to support the diagnosis of, and development of treatments for, Alzheimer's disease and other dementias.

Machine Learning to Predict Brain Amyloid Pathology in Pre-dementia Alzheimer’s Disease Using QEEG Features and Genetic Algorithm Heuristic

The use of positron emission tomography (PET) as the initial or sole biomarker of β-amyloid (Aβ) brain pathology may inhibit Alzheimer’s disease (AD) drug development and clinical use due to cost, access, and tolerability. We developed a qEEG-ML algorithm to predict Aβ pathology among subjective cognitive decline (SCD) and mild cognitive impairment (MCI) patients, and validated it using Aβ PET. We compared QEEG data between patients with MCI and those with SCD with and without PET-confirmed beta-amyloid plaque. We compared resting-state eyes-closed electroencephalograms (EEG) patterns between the amyloid positive and negative groups using relative power measures from 19 channels (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), divided into eight frequency bands, delta (1–4 Hz), theta (4–8 Hz), alpha 1 (8–10 Hz), alpha 2 (10–12 Hz), beta 1 (12–15 Hz), beta 2 (15–20 Hz), beta 3 (20–30 Hz), and gamma (30–45 Hz) calculated by FFT and denoised by iSyncBrain^®. The resulting 152 features were analyzed using a genetic algorithm strategy to identify optimal feature combinations and maximize classification accuracy. Guided by gene modeling methods, we treated each channel and frequency band of EEG power as a gene and modeled it with every possible combination within a given dimension. We then collected the models that showed the best performance and identified the genes that appeared most frequently in the superior models. By repeating this process, we converged on a model that approximates the optimum. We found that the average performance increased as this iterative development of the genetic algorithm progressed. We ultimately achieved 85.7% sensitivity, 89.3% specificity, and 88.6% accuracy in SCD amyloid positive/negative classification, and 83.3% sensitivity, 85.7% specificity, and 84.6% accuracy in MCI amyloid positive/negative classification.

Aβ misfolding in blood plasma is inversely associated with body mass index even in middle adulthood

Background

To understand the potential for early intervention and prevention measures in Alzheimer’s disease, the association between risk factors and early pathological change needs to be assessed. Hence, the aim of this study was to determine whether risk factors of Alzheimer’s clinical syndrome (clinical AD), such as body mass index (BMI), are associated with Aβ misfolding in blood, a strong risk marker for AD among older adults.

Methods

Information on risk factors and blood samples were collected at baseline in the ESTHER study, a population-based cohort study of older adults (age 50–75 years) in Germany. Aβ misfolding in blood plasma was analyzed using an immuno-infrared-sensor in a total of 872 participants in a nested case-control design among incident dementia cases and matched controls. Associations between risk factors and Aβ misfolding were assessed by multiple logistic regression. For comparison, the association between the risk factors and AD incidence during 17 years of follow-up was investigated in parallel among 5987 cohort participants.

Results

An inverse association with Aβ misfolding was seen for BMI at age 50 based on reported weight history (aOR 0.64, 95% CI 0.43–0.96, p = 0.03). Similar but not statistically significant associations were seen for BMI at baseline (i.e., mean age 68) and at age 40. No statistically significant associations with Aβ misfolding were found for other risk factors, such as diabetes, smoking, and physical activity. On the other hand, low physical activity was associated with a significantly reduced risk of developing clinical AD compared to physical inactivity.

Conclusions

Our results support that AD pathology may be detectable and associated with reduced weight even in middle adulthood, many years before clinical diagnosis of AD. Physical activity might reduce the risk of onset of AD symptoms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00889-2.

Ultrasensitive techniques and protein misfolding amplification assays for biomarker-guided reconceptualization of Alzheimer's and other neurodegenerative diseases

INTRODUCTION

The clinical validation and qualification of biomarkers reflecting the complex pathophysiology of neurodegenerative diseases (NDDs) is a fundamental challenge for current drug discovery and development and next-generation clinical practice. Novel ultrasensitive detection techniques and protein misfolding amplification assays hold the potential to optimize and accelerate this process.

AREAS COVERED

Here we perform a PubMed-based state of the art review and perspective report on blood-based ultrasensitive detection techniques and protein misfolding amplification assays for biomarkers discovery and development in NDDs.

EXPERT OPINION

Ultrasensitive assays represent innovative solutions for blood-based assessments during the entire Alzheimer's disease (AD) biological and clinical continuum, for contexts of use (COU) such as prediction, detection, early diagnosis, and prognosis of AD. Moreover, cerebrospinal fluid (CSF)-based misfolding amplification assays show encouraging performance in detecting α-synucleinopathies in prodromal or at-high-risk individuals and may serve as tools for patients' stratification by the presence of α-synuclein pathology. Further clinical research will help overcome current methodological limitations, also through exploring multiple accessible bodily matrices. Eventually, integrative longitudinal studies will support precise definitions for appropriate COU across NDDs.

State-of-the-Art Methods and Emerging Fluid Biomarkers in the Diagnostics of Dementia-A Short Review and Diagnostic Algorithm

The most common neurodegenerative dementias include Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). The correct etiology-based diagnosis is pivotal for clinical management of these diseases as well as for the suitable timing and choosing the accurate disease-modifying therapies when these become available. Enzyme-linked immunosorbent assay (ELISA)-based methods, detecting altered levels of cerebrospinal fluid (CSF) Tau, phosphorylated Tau, and Aβ-42 in AD, allowed the wide use of this set of biomarkers in clinical practice. These analyses demonstrate a high diagnostic accuracy in AD but suffer from a relatively restricted usefulness due to invasiveness and lack of prognostic value. In recent years, the development of novel advanced techniques has offered new state-of-the-art opportunities in biomarker discovery. These include single molecule array technology (SIMOA), a tool for non-invasive analysis of ultra-low levels of central nervous system-derived molecules from biofluids, such as CSF or blood, and real-time quaking (RT-QuIC), developed to analyze misfolded proteins. In the present review, we describe the history of methods used in the fluid biomarker analyses of dementia, discuss specific emerging biomarkers with translational potential for clinical use, and suggest an algorithm for the use of new non-invasive blood biomarkers in clinical practice.