Aβ and tau structure-based biomarkers for a blood- and CSF-based two-step recruitment strategy to identify patients with dementia due to Alzheimer's disease

Photophysics of Molecular Probes for Amyloid-β Detection: Computational Insights into the Roles of Probe Linker and Functional Groups

In this computational study, density functional theory (DFT) and time-dependent DFT methods (TD-DFT) were employed to study the optical properties of six families of molecules with donor (D), bridge (B), and acceptor (A) fragments that have potential for use as fluorescent molecular probes for the early detection of Alzheimer's disease. After validating our computational method against experimental data, using X-ray and absorption data, the equilibrium geometries and wave functions of the ground and first singlet excited states were systematically studied. Our simulations demonstrate that the S1 states of these rod-like D-B-A fluorescent probes are twisted intramolecular charge transfer states with a predominant highest occupied molecular orbital-least unoccupied molecular orbital (HOMO-LUMO) character, the former localized primarily at the donor, whereas the latter at the acceptor site. Moreover, the influence of the bridge, donor, and acceptor fragments on molecules' absorption energies is explored, highlighting the influence of double and triple bonds and some specific modifications on the acceptor side, including the addition of electronegative atoms, pyranone derivatives, and their functionalization. By having the absorption energies of 324 probes in hand, machine learning models were trained to predict the absorption energies of molecules. The models were found to be predictive, which suggests a potential that predictive models for other crucial properties, such as emission and quantum yield, can also be trained if suitable training data sets are made available.

Probing Chemical Complexity of Amyloid Plaques in Alzheimer's Disease Mice using Hyperspectral Raman Imaging

One of the distinctive pathological features of Alzheimer's disease (AD) is the deposition of amyloid plaques within the brain of affected individuals. These plaques have traditionally been investigated using labeling techniques such as immunohistochemical imaging. However, the use of labeling can disrupt the structural integrity of the molecules being analyzed. Hence, it is imperative to employ label-free imaging methods for noninvasive examination of amyloid deposits in their native form, thereby providing more relevant information pertaining to AD. This study presents compelling evidence that label-free and nondestructive confocal Raman imaging is a highly effective approach for the identification and chemical characterization of amyloid plaques within cortical regions of an arcAβ mouse model of AD. Furthermore, this investigation elucidates how the spatial correlation of Raman signals can be exploited to identify robust Raman marker bands and discern proteins and lipids from amyloid plaques. Finally, this study uncovers the existence of distinct types of amyloid plaques in the arcAβ mouse brain, exhibiting significant disparities in terms of not only shape and size but also molecular composition.

Translating blood biomarkers into clinical practice for Alzheimer's disease: Challenges and perspectives

Early and accurate diagnosis of Alzheimer's disease (AD) in clinical practice is urgent with advances in AD treatment. Blood biomarker assays are preferential diagnostic tools for widespread clinical use with the advantages of being less invasive, cost effective, and easily accessible, and they have shown good performance in research cohorts. However, in community-based populations with maximum heterogeneity, great challenges are still faced in diagnosing AD based on blood biomarkers in terms of accuracy and robustness. Here, we analyze these challenges, including the confounding impact of systemic and biological factors, small changes in blood biomarkers, and difficulty in detecting early changes. Furthermore, we provide perspectives on several potential strategies to overcome these challenges for blood biomarkers to bridge the gap from research to clinical practice.

Artificial intelligence-coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases

Diagnosis of neurodegenerative disorders (NDDs) including Parkinson's disease and Alzheimer's disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progression of NDDs, thus underscoring the need for structural biomarker-based diagnostics. We developed an immunoassay-coupled nanoplasmonic infrared metasurface sensor that detects proteins linked to NDDs, such as alpha-synuclein, with specificity and differentiates the distinct structural species using their unique absorption signatures. We augmented the sensor with an artificial neural network enabling unprecedented quantitative prediction of oligomeric and fibrillar protein aggregates in their mixture. The microfluidic integrated sensor can retrieve time-resolved absorbance fingerprints in the presence of a complex biomatrix and is capable of multiplexing for the simultaneous monitoring of multiple pathology-associated biomarkers. Thus, our sensor is a promising candidate for the clinical diagnosis of NDDs, disease monitoring, and evaluation of novel therapies.

Dumbbell Aptamer Sensor Based on Dual Biomarkers for Early Detection of Alzheimer's Disease

Finding a timely, sensitive, and noninvasive detection method has become an urgent need for asymptomatic early diagnosis of Alzheimer's disease (AD). MicroRNA-193b (miR-193b) and Aβ42 oligomers (AβO42) in neurogenic exosomes were confirmed to reflect pathological changes in the AD early stage. The combination of two biomarkers is promising for the earlier detection of AD. In this study, a detection system based on the principle of the entropy-driven strand displacement reaction (ESDR) was developed, including a dumbbell detection probe (H), an indicator probe (R), and graphene oxide (GO). In the detection system, the two hairpins of H were opened by the interaction of miR-193b (T1) and AβO42 (T2) with the aptamer. Then R hybridized with H and began to displace T, initiating the next round of ESDR to achieve sensitive detection of T. GO specifically adsorbed free R and quenched the fluorescence, further reducing the intensity of the background signal. Both of these points provided the system with a more sensitive analytical performance. The detection limit of miR-193b was 77 pM and the detection limit of AβO42 was 53 pM. This sensor detected the change of "one increase (AβO42) and one decrease (miR-193b)" in the exosome sample. Additionally, results showed that this detection system could distinguish the model of early AD from the non-AD control, which was sufficient for earlier and more sensitive detection of AD. This strategy has strong specificity, high sensitivity, and easy operation, which provides broad prospects for the early diagnosis of AD.

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies

High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

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.

Amyloid-β Oligomers: Multiple Moving Targets

Alzheimer’s Disease (AD) is a neurodegenerative disorder that is characterized clinically by progressive cognitive decline and pathologically by the β-sheet rich fibril plaque deposition of the amyloid-β (Aβ) peptide in the brain. While plaques are a hallmark of AD, plaque burden is not correlated with cognitive impairment. Instead, Aβ oligomers formed during the aggregation process represent the main agents of neurotoxicity, which occurs 10–20 years before patients begin to show symptoms. These oligomers are dynamic in nature and represented by a heterogeneous distribution of aggregates ranging from low- to high-molecular weight, some of which are toxic while others are not. A major difficulty in determining the pathological mechanism(s) of Aβ, developing reliable diagnostic markers for early-stage detection, as well as effective therapeutics for AD are the differentiation and characterization of oligomers formed throughout disease propagation based on their molecular features, effects on biological function, and relevance to disease propagation and pathology. Thus, it is critical to methodically identify the mechanisms of Aβ aggregation and toxicity, as well as describe the roles of different oligomers and aggregates in disease progression and molecular pathology. Here, we describe a variety of biophysical techniques used to isolate and characterize a range of Aβ oligomer populations, as well as discuss proposed mechanisms of toxicity and therapeutic interventions aimed at specific assemblies formed during the aggregation process. The approaches being used to map the misfolding and aggregation of Aβ are like what was done during the fundamental early studies, mapping protein folding pathways using combinations of biophysical techniques in concert with protein engineering. Such information is critical to the design and molecular engineering of future diagnostics and therapeutics for AD.

Potential Protein Blood-Based Biomarkers in Different Types of Dementia: A Therapeutic Overview

Abstract:

Biomarkers capable of identifying and distinguishing types of dementia such as Alzheimer's disease (AD), Parkinson's disease dementia (PDD), Lewy body dementia (LBD), and frontotemporal dementia (FTD) have been become increasingly relentless. Studies of possible biomarker proteins in the blood that can help formulate new diagnostic proposals and therapeutic visions of different types of dementia are needed. However, due to several limitations of these biomarkers, especially in discerning dementia, their clinical applications are still undetermined. Thus, the updating of biomarker blood proteins that can help in the diagnosis and discrimination of these main dementia conditions is essential to enable new pharmacological and clinical management strategies, with specificities for each type of dementia. To review the literature concerning protein blood-based AD and non-AD biomarkers as new pharmacological targets and/or therapeutic strategies. Recent findings for protein-based AD, PDD, LBD, and FTD biomarkers are focused on in this review. Protein biomarkers were classified according to the pathophysiology of the dementia types. The diagnosis and distinction of dementia through protein biomarkers is still a challenge. The lack of exclusive biomarkers for each type of dementia highlights the need for further studies in this field. Only after this, blood biomarkers may have a valid use in clinical practice as they are promising to help in diagnosis and in the differentiation of diseases.

Diagnostic Accuracy of Blood-Based Biomarker Panels: A Systematic Review

Background

Because of high prevalence of Alzheimer's disease (AD) in low- and middle-income countries (LMICs), there is an urgent need for inexpensive and minimally invasive diagnostic tests to detect biomarkers in the earliest and asymptomatic stages of the disease. Blood-based biomarkers are predicted to have the most impact for use as a screening tool and predict the onset of AD, especially in LMICs. Furthermore, it has been suggested that panels of markers may perform better than single protein candidates.

Methods

Medline/Pubmed was searched to identify current relevant studies published from January 2016 to December 2020. We included all full-text articles examining blood-based biomarkers as a set of protein markers or panels to aid in AD's early diagnosis, prognosis, and characterization.

Results

Seventy-six articles met the inclusion criteria for systematic review. Majority of the studies reported plasma and serum as the main source for biomarker determination in blood. Protein-based biomarker panels were reported to aid in AD diagnosis and prognosis with better accuracy than individual biomarkers. Conventional (amyloid-beta and tau) and neuroinflammatory biomarkers, such as amyloid beta-42, amyloid beta-40, total tau, phosphorylated tau-181, and other tau isoforms, were the most represented. We found the combination of amyloid beta-42/amyloid beta-40 ratio and APOEε4 status to be most represented with high accuracy for predicting amyloid beta-positron emission tomography status.

Conclusion

Assessment of Alzheimer's disease biomarkers in blood as a non-invasive and cost-effective alternative will potentially contribute to early diagnosis and improvement of therapeutic interventions. Given the heterogeneous nature of AD, combination of markers seems to perform better in the diagnosis and prognosis of the disease than individual biomarkers.

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.

Refining the amyloid β peptide and oligomer fingerprint ambiguities in Alzheimer's disease: Mass spectrometric molecular characterization in brain, cerebrospinal fluid, blood, and plasma

Since its discovery, amyloid-β (Aβ) has been the principal target of investigation of in Alzheimer's disease (AD). Over the years however, no clear correlation was found between the Aβ plaque burden and location, and AD-associated neurodegeneration and cognitive decline. Instead, diagnostic potential of specific Aβ peptides and/or their ratio, was established. For instance, a selective reduction in the concentration of the aggregation-prone 42 amino acid-long Aβ peptide (Aβ42) in cerebrospinal fluid (CSF) was put forward as reflective of Aβ peptide aggregation in the brain. With time, Aβ oligomers-the proposed toxic Aβ intermediates-have emerged as potential drivers of synaptic dysfunction and neurodegeneration in the disease process. Oligomers are commonly agreed upon to come in different shapes and sizes, and are very poorly characterized when it comes to their composition and their "toxic" properties. The concept of structural polymorphism-a diversity in conformational organization of amyloid aggregates-that depends on the Aβ peptide backbone, makes the characterization of Aβ aggregates and their role in AD progression challenging. In this review, we revisit the history of Aβ discovery and initial characterization and highlight the crucial role mass spectrometry (MS) has played in this process. We critically review the common knowledge gaps in the molecular identity of the Aβ peptide, and how MS is aiding the characterization of higher order Aβ assemblies. Finally, we go on to present recent advances in MS approaches for characterization of Aβ as single peptides and oligomers, and convey our optimism, as to how MS holds a promise for paving the way for progress toward a more comprehensive understanding of Aβ in AD research.

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer's Disease

The early diagnosis of Alzheimer's disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer's patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer's disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

Biomarkers for evaluating the effects of exercise interventions in patients with MCI or dementia: A systematic review and meta-analysis

OBJECTIVES

To summarize the biomarkers for evaluating the effects of exercise interventions in patients with cognitive impairment associated with aging, as well as their responses to exercise interventions.

DESIGN

A systematic review and meta-analysis METHODS: We systematically searched different electronic database, including PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, PsycINFO, SPORTDiscus up to April 2020. Clinical controlled trials with exercise interventions in patients with cognitive impairment were included. The main outcomes included all the biomarkers used to evaluate the effects of exercise interventions. If data for certain biomarkers was enough (more than 2 studies), meta-analyses would be performed to estimate the effect sizes by calculating the standard mean differences (SMDs) and 95% confidence intervals (CIs).

RESULTS

Finally, we included 33 articles from 26 trials. The biomarkers included neurotrophic factors, inflammatory factors, oxidative stress markers, neuropathological hallmarks, metabolic biomarkers and genotypes. The meta-analyses indicated that exercise significantly decreased the levels of IL-6 (SMD = -0.45; 95% CI: -0.72, -0.18) and low-density lipoprotein (SMD = -0.26; 95% CI: -0.50, -0.01). Subgroup analyses showed that aerobic exercise also could decrease the levels of TNF-α (SMD = -1.21; 95% CI: -2.29, -0.14). There were some important cognition-related biomarkers which were rarely measured, such as Aβ, tau and IGF-1.

CONCLUSION

Regular exercise showed positive effects on reducing inflammation and regulating lipid metabolism. But the available evidence is limited and more studies with different exercise interventions should be conducted to test the effects of exercise on other important cognition-related biomarkers in patients with cognitive dysfunction.

Plasma Amyloid Concentration in Alzheimer's Disease: Performance of a High-Throughput Amyloid Assay in Distinguishing Alzheimer's Disease Cases from Controls

Background:

Collection of cerebrospinal fluid (CSF) for measurement of amyloid-β (Aβ) species is a gold standard in Alzheimer’s disease (AD) diagnosis, but has risks. Thus, establishing a low-risk blood Aβ test with high AD sensitivity and specificity is of outmost interest.

Objective:

We evaluated the ability of a commercially available plasma Aβ assay to distinguish AD patients from biomarker-healthy controls.

Method:

In a case-control design, we examined plasma samples from 44 AD patients (A + N+) and 49 controls (A–N–) from a memory clinic. AD was diagnosed using a combination of neuropsychological examination, CSF biomarker analysis and brain imaging. Total Aβ₄₀ and total Aβ₄₂ in plasma were measured through enzyme-linked immunosorbent assay (ELISA) technology using ABtest40 and ABtest42 test kits (Araclon Biotech Ltd.). Receiver operating characteristic (ROC) analyses with outcome AD were performed, and sensitivity and specificity were calculated.

Results:

Plasma Aβ_42/40 was weakly positively correlated with CSF Aβ_42/40 (Spearman’s rho 0.22; p = 0.037). Plasma Aβ_42/40 alone was not able to statistically significantly distinguish between AD patients and controls (AUC 0.58; 95% CI 0.46, 0.70). At a cut-point of 0.076 maximizing sensitivity and specificity, plasma Aβ_42/40 had a sensitivity of 61.2% and a specificity of 63.6%.

Conclusion:

In this sample, the high-throughput blood Aβ assay was not able to distinguish well between AD patients and controls. Whether or not the assay may be useful in large-scale epidemiological settings remains to be seen.

Genetic predisposition, Aβ misfolding in blood plasma, and Alzheimer's disease

Alzheimer's disease is highly heritable and characterized by amyloid plaques and tau tangles in the brain. The aim of this study was to investigate the association between genetic predisposition, Aβ misfolding in blood plasma, a unique marker of Alzheimer associated neuropathological changes, and Alzheimer's disease occurrence within 14 years. Within a German community-based cohort, two polygenic risk scores (clinical Alzheimer's disease and Aβ42 based) were calculated, APOE genotype was determined, and Aβ misfolding in blood plasma was measured by immuno-infrared sensor in 59 participants diagnosed with Alzheimer's disease during 14 years of follow-up and 581 participants without dementia diagnosis. Associations between each genetic marker and Aβ misfolding were assessed through logistic regression and the ability of each genetic marker and Aβ misfolding to predict Alzheimer's disease was determined. The Alzheimer's disease polygenic risk score and APOE ε4 presence were associated to Aβ misfolding (odds ratio, 95% confidence interval: per standard deviation increase of score: 1.25, 1.03-1.51; APOE ε4 presence: 1.61, 1.04-2.49). No association was evident for the Aβ polygenic risk score. All genetic markers were predictive of Alzheimer's disease diagnosis albeit much less so than Aβ misfolding (areas under the curve: Aβ polygenic risk score: 0.55; AD polygenic risk score: 0.59; APOE ε4: 0.63; Aβ misfolding: 0.84). Clinical Alzheimer's genetic risk was associated to early pathological changes (Aβ misfolding) measured in blood, however, predicted Alzheimer's disease less accurately than Aβ misfolding itself. Genetic predisposition may provide information regarding disease initiation, while Aβ misfolding could be important in clinical risk prediction.

Multimodal, label-free fluorescence and Raman imaging of amyloid deposits in snap-frozen Alzheimer's disease human brain tissue

Alzheimer's disease (AD) neuropathology is characterized by hyperphosphorylated tau containing neurofibrillary tangles and amyloid-beta (Aβ) plaques. Normally these hallmarks are studied by (immuno-) histological techniques requiring chemical pretreatment and indirect labelling. Label-free imaging enables one to visualize normal tissue and pathology in its native form. Therefore, these techniques could contribute to a better understanding of the disease. Here, we present a comprehensive study of high-resolution fluorescence imaging (before and after staining) and spectroscopic modalities (Raman mapping under pre-resonance conditions and stimulated Raman scattering (SRS)) of amyloid deposits in snap-frozen AD human brain tissue. We performed fluorescence and spectroscopic imaging and subsequent thioflavin-S staining of the same tissue slices to provide direct confirmation of plaque location and correlation of spectroscopic biomarkers with plaque morphology; differences were observed between cored and fibrillar plaques. The SRS results showed a protein peak shift towards the β-sheet structure in cored amyloid deposits. In the Raman maps recorded with 532 nm excitation we identified the presence of carotenoids as a unique marker to differentiate between a cored amyloid plaque area versus a non-plaque area without prior knowledge of their location. The observed presence of carotenoids suggests a distinct neuroinflammatory response to misfolded protein accumulations.

Recent technological and scientific developments concerning the use of infrared spectroscopy for point-of-care applications

In this contribution we review selected point-of-care applications of infrared spectroscopy and the technological innovations they are based on. After a short introduction summarizing the general idea behind point-of-care applications we introduce the reader to important infrared spectroscopy sensing principles on a very basic level. We discuss the role of optical components like quantum cascade lasers, supercontinuum sources, waveguides and how they are potentially going to revolutionize point-of-care applications. First, we focus on the technological solutions of some principal problems like increasing the pathlength in a transmission cell to enhance the sensitivity for solutes in aqueous solutions and discuss indirect methods which circumvent the problem of low transmittance. In the second part we show how the technological progress of the last decades enabled scientific progress leading to selected concrete and outstanding point-of-care solutions and applications based on infrared spectroscopy. These include the detection and quantification of malaria parasitemia, early recognition of Alzheimer's disease long before the onset of clinical symptoms and a non-invasive method for testing the blood glucose content. The selected examples demonstrate and showcase that infrared spectroscopy is on the way to become an indispensable technique for point-of-care applications.

TDP-43 as structure-based biomarker in amyotrophic lateral sclerosis

Pathologic alterations of Transactivation response DNA‐binding protein 43 kilo Dalton (TDP‐43) are a major hallmark of amyotrophic lateral sclerosis (ALS). In this pilot study, we analyzed the secondary structure distribution of TDP‐43 in cerebrospinal fluid of ALS patients (n = 36) compared to Parkinson´s disease patients (PD; n = 30) and further controls (Ctrl; n = 24) using the immuno‐infrared sensor technology. ALS patients could be discriminated from PD and Ctrl with a sensitivity/specificity of 89 %/77 % and 89 %/83 %, respectively. Our findings demonstrate that TDP‐43 misfolding measured by the immuno‐infrared sensor technology has the potential to serve as a biomarker candidate for ALS.

Alteration of Metabolic Profile and Potential Biomarkers in the Plasma of Alzheimer's Disease

The expending of elderly population worldwide has resulted in a dramatic rise in the incidence of chronic diseases such as Alzheimer's disease (AD). Inadequate understanding of the mechanisms underlying AD has hampered the development of efficient tools for definitive diagnosis and curative interventions. Previous studies have attempted to discover reliable biomarkers of AD, but these biomarkers can only be measured through invasive (neuropathological markers in cerebrospinal fluid) or expensive (positron emission tomography scanning or magnetic resonance imaging) techniques. Metabolomics is a high-throughput technology that can detect and catalog large numbers of small metabolites and may be a useful tool for characterization of AD and identification of biomarkers. In this study, we used ultra-performance liquid chromatography-mass spectrometry based untargeted metabolomics to measure the concentrations of plasma metabolites in a cohort of subjects with AD (n=44) and cognitively normal controls (Ctrl, n=94). The AD group showed marked reductions in levels of polyunsaturated fatty acids, acyl-carnitines, degradation products of tryptophan, and elevated levels of bile acids compared to the Ctrl group. We then validated the results using an independent cohort that included subjects with AD (n=30), mild cognitive impairment (MCI, n=13), healthy controls (n=43), and non-AD neurological disease controls (NDC, n=31). We identified five metabolites comprising cholic acid, chenodeoxycholic acid, allocholic acid, indolelactic acid, and tryptophan that were able to distinguish patients with AD from both Ctrl and NDC with satisfactory sensitivity and specificity. The concentrations of these metabolites were significantly correlated with disease severity. Our results also suggested that altered bile acid profiles in AD and MCI might indicate early risk for the development of AD. These findings may allow for development of new approaches for diagnosis of AD and may provide novel insights into AD pathogenesis.

Blood-based Biomarkers of Alzheimer's Disease: The Long and Winding Road

BACKGROUND

Blood-based biomarkers can be very useful in formulating new diagnostic and treatment proposals in the field of dementia, especially in Alzheimer's disease (AD). However, due to the influence of several factors on the reproducibility and reliability of these markers, their clinical use is still very uncertain. Thus, up-to-date knowledge about the main blood biomarkers that are currently being studied is extremely important in order to discover clinically useful and applicable tools, which could also be used as novel pharmacological strategies for the AD treatment.

METHODS

A narrative review was performed based on the current candidates of blood-based biomarkers for AD to show the main results from different studies, focusing on their clinical applicability and association with AD pathogenesis.

OBJECTIVE

The aim of this paper was to carry out a literature review on the major blood-based biomarkers for AD, connecting them with the pathophysiology of the disease.

RESULTS

Recent advances in the search of blood-based AD biomarkers were summarized in this review. The biomarkers were classified according to the topics related to the main hallmarks of the disease such as inflammation, amyloid, and tau deposition, synaptic degeneration and oxidative stress. Moreover, molecules involved in the regulation of proteins related to these hallmarks were described, such as non-coding RNAs, neurotrophins, growth factors and metabolites. Cells or cellular components with the potential to be considered as blood-based AD biomarkers were described in a separate topic.

CONCLUSION

A series of limitations undermine new discoveries on blood-based AD biomarkers. The lack of reproducibility of findings due to the small size and heterogeneity of the study population, different analytical methods and other assay conditions make longitudinal studies necessary in this field to validate these structures, especially when considering a clinical evaluation that includes a broad panel of these potential and promising blood-based biomarkers.

Fluid Candidate Biomarkers for Alzheimer's Disease: A Precision Medicine Approach

A plethora of dynamic pathophysiological mechanisms underpins highly heterogeneous phenotypes in the field of dementia, particularly in Alzheimer's disease (AD). In such a faceted scenario, a biomarker-guided approach, through the implementation of specific fluid biomarkers individually reflecting distinct molecular pathways in the brain, may help establish a proper clinical diagnosis, even in its preclinical stages. Recently, ultrasensitive assays may detect different neurodegenerative mechanisms in blood earlier. ß-amyloid (Aß) peptides, phosphorylated-tau (p-tau), and neurofilament light chain (NFL) measured in blood are gaining momentum as candidate biomarkers for AD. P-tau is currently the more convincing plasma biomarker for the diagnostic workup of AD. The clinical role of plasma Aβ peptides should be better elucidated with further studies that also compare the accuracy of the different ultrasensitive techniques. Blood NFL is promising as a proxy of neurodegeneration process tout court. Protein misfolding amplification assays can accurately detect α-synuclein in cerebrospinal fluid (CSF), thus representing advancement in the pathologic stratification of AD. In CSF, neurogranin and YKL-40 are further candidate biomarkers tracking synaptic disruption and neuroinflammation, which are additional key pathophysiological pathways related to AD genesis. Advanced statistical analysis using clinical scores and biomarker data to bring together individuals with AD from large heterogeneous cohorts into consistent clusters may promote the discovery of pathophysiological causes and detection of tailored treatments.

Is Alzheimer's Disease a Liver Disease of the Brain?

Clinical specialization is not only a force for progress, but it has also led to the fragmentation of medical knowledge. The focus of research in the field of Alzheimer's disease (AD) is neurobiology, while hepatologists focus on liver diseases and lipid specialists on atherosclerosis. This article on AD focuses on the role of the liver and lipid homeostasis in the development of AD. Amyloid-β (Aβ) deposits accumulate as plaques in the brain of an AD patient long before cognitive decline is evident. Aβ generation is a normal physiological process; the steady-state level of Aβ in the brain is determined by balance between Aβ production and its clearance. We present evidence suggesting that the liver is the origin of brain Aβ deposits and that it is involved in peripheral clearance of circulating Aβ in the blood. Hence the liver could be targeted to decrease Aβ production or increase peripheral clearance.

Relationship of amyloid-β1-42 in blood and brain amyloid: Ginkgo Evaluation of Memory Study

A blood test that predicts the extent of amyloid plaques in the brain and risk of Alzheimer's disease would have important benefits for the early identification of higher risk of dementia and Alzheimer's disease and the evaluation of new preventative therapies. The goal of this study was to determine whether plasma levels of amyloid-β1-42, 1-40 and the amyloid-β1-42/1-40 ratio among participants in the Pittsburgh centre of the Ginkgo Evaluation of Memory Study were related to the extent of brain fibrillar amyloid plaques measured in 2009 using Pittsburgh compound-B PET imaging, hippocampal volume, cortical thickness in the temporal lobe and white matter lesions. There were 194 participants who had Pittsburgh compound-B measurements in 2009 with the mean age of 85 years; 96% were white and 60% men. Pittsburgh compound-B positivity was defined as a standardized uptake value ratio of ≥1.57. Amyloid-β in blood was measured using a sandwich enzyme-linked immunosorbent assay developed by Eli Lilly and modified at the University of Vermont. All participants were nondemented as of 2008 at the time of study close out. The study sample included 160 with blood samples drawn in 2000-02 and 133 from 2009 and also had brain amyloid measured in 2009. All blood samples were analysed at the same time in 2009. Plasma amyloid-β1-42 was inversely related to the percent Pittsburgh compound-B positive (standardized uptake value ratio ≥1.57), β -0.04, P = 0.005. Practically all participants who were apolipoprotein-E4 positive at older ages were also Pittsburgh compound-B positive for fibrillar amyloid. Among apolipoprotein-E4-negative participants, quartiles of amyloid-β1-42 were inversely related to Pittsburgh compound-B positivity. In multiple regression models, plasma amyloid-β1-42 measured in 2000-02 or 2009 were significantly and inversely related to Pittsburgh compound-B positivity as was the amyloid-β1-42/1-40 ratio. There was a 4-fold increase in the odds ratio for the presence of Pittsburgh compound-B positivity in the brain in 2009 for the first quartile of amyloid-β1-42 as compared with the fourth quartile in the multiple logistic model. This is one of the first longitudinal studies to evaluate the relationship between amyloid-β1-42 in the blood and the extent of brain amyloid deposition measured by PET imaging using Pittsburgh compound-B. Our findings showed that remote and recent low plasma amyloid-β1-42 levels were inversely associated with brain amyloid deposition in cognitively normal individuals. However, changes in plasma amyloid-β1-42 over time (8 years) were small and not related to the amount of Pittsburgh compound-B.

[New biomarkers for Alzheimer's disease in cerebrospinal fluid and blood]

In accordance with the current German dementia guidelines, the dementia biomarkers amyloid beta 42, the tau peptides total tau and phosphorylated tau 181 are recommended for cerebrospinal fluid (CSF)-based diagnostics of dementia. Several studies have clearly shown that determination of the amyloid beta 42 to amyloid beta 40 peptide ratio is superior to the interpretation of amyloid beta 42 alone and should be implemented in the clinical work-up; however, in recent years different studies have presented many other innovative CSF and blood-based biomarkers. Besides CSF-based neurochemical diagnostics of dementia promising novel protocols for the detection of amyloid beta peptides in blood have meanwhile been published, which can currently be used in clinical studies for blood-based early diagnostics of Alzheimer's dementia. Following further validation and assay optimization these blood assays should be available for routine diagnostics in the near future.

Reversible Immuno-Infrared Sensor for the Detection of Alzheimer's Disease Related Biomarkers

The development of biosensors for medical purposes is a growing field. An immuno-infrared biosensor for the preclinical detection of Alzheimer's disease (AD) in body fluids was developed. The key element of this sensor is an ATR crystal with chemically modified surface to catch the biomarker out of the body fluid. So far, the immuno-infrared sensor can be used only once and requires time-consuming steps of sensor exchange, sensor cleaning, and novel surface functionalization. Here, we developed an immuno-infrared sensor providing a reusable surface and showcase its performance by the detection of the AD biomarker proteins Aβ and Tau in human cerebrospinal fluid (CSF). The sensor surface is covalently coated with the immunoglobulin binding proteins Protein A or Protein G. These were employed for noncovalent immobilization of antibodies and the subsequent immobilization and analysis of their antigens. The reversible antibody immobilization can be repeated several times with the same or different antibodies. Further, the more specific binding of the antibody via its Fc region instead of the conventional NHS coupling leads to a 3-4-fold higher antigen binding capacity of the antibody. Thus, the throughput, sensitivity, and automation capacity of the immuno-infrared biosensor are significantly increased as compared to former immuno-infrared assays. This immuno-sensor can be used with any antibody that binds to Protein A or Protein G.