Biological marker candidates of Alzheimer's disease in blood, plasma, and serum

Older Amyloid Beta as a Candidate Blood Biomarker of Early Cognitive Decline in the Elderly-A Preliminary Study

(1) Background/Objectives: The pathogenic process of Alzheimer's disease (AD) is known to begin decades before its clinical onset. This period, although imperceptible to the patient, encompasses a gradual neuronal loss. The first symptoms of dementia, often classified as mild cognitive impairment (MCI), in many cases converts into incipient AD, but can also remain stable or even reverse to cognitive norm. An easy and fast blood-based method of identifying patients at risk of conversion to AD would allow for the application of disease-altering therapies. This preliminary study focuses on the identification and assessment of the relationship between plasma amyloid beta (Aβ) and cognitive performance in older Polish adults with respect to its adequacy as a biomarker of an early cognitive deterioration. (2) Methods: The preliminary research sample consisted of 230 participants, 109 females and 121 males, aged 65 plus. The association between plasma Aβ concentrations with cognitive status, gender, and age were assessed. The analyses were conducted in three categories of cognitive performance: cognitive norm, mild cognitive impairment, and mild dementia, based on results of the Mini-Mental State Examination (MMSE) and functional tests. (3) Results: No significant differences in plasma Aβ levels for different cognitive statuses were identified. No significant differences were found in Aβ levels based on age or gender. (4) Conclusions: In order to thoroughly explore the power of research on plasma Aβ with respect to early cognitive deterioration, further prospective studies are required.

Novel plasma protein biomarkers: A time-dependent predictive model for Alzheimer's disease

BACKGROUND

The accurate prediction of Alzheimer's disease (AD) is crucial for the efficient management of its progression. The objective of this research was to construct a new risk predictive model utilizing novel plasma protein biomarkers for predicting AD incidence in the future and analyze their potential biological correlation with AD incidence.

METHODS

A cohort of 440 participants aged 60 years and older from the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal cohort was utilized. The baseline plasma proteomics data was employed to conduct Cox regression, LASSO regression, and cross-validation to identify plasma protein signatures predictive of AD risk. Subsequently, a multivariable Cox proportional hazards model based on these signatures was constructed. The performance of the risk prediction model was evaluated using time-dependent receiver operating characteristic (t-ROC) curves and Kaplan-Meier curves. Additionally, we analyzed the correlations between protein signature expression in plasma and predicted AD risk, the time of AD onset, the expression of protein signatures in cerebrospinal fluid (CSF), the expression of CSF and plasma biomarkers, and APOE ε4 genotypes. Colocalization and Mendelian randomization analyses was conducted to investigate the association between protein features and AD risk. GEO database was utilized to analyze the differential expression of protein features in the blood and brain of AD patients.

RESULTS

We identified seven protein signatures (APOE, CGA, CRP, CCL26, CCL20, NRCAM, and PYY) that independently predicted AD incidence in the future. The risk prediction model demonstrated area under the ROC curve (AUC) values of 0.77, 0.76, and 0.77 for predicting AD incidence at 4, 6, and 8 years, respectively. Furthermore, the model remained stable in the range of the 3rd to the 12th year (ROC ≥ 0.74). The low-risk group, as defined by the model, exhibited a significantly later AD onset compared to the high-risk group (P < 0.0001). Moreover, all protein signatures exhibited significant correlations with AD risk (P < 0.001) and the time of AD onset (P < 0.01). There was no strong correlation between the protein expression levels in plasma and CSF, as well as AD CSF biomarkers. APOE, CGA, and CRP exhibited significantly lower expression levels in APOE ε4 positive individuals (P < 0.05). Additionally, colocalization analysis reveals a significant association between AD and SNP loci in APOE. Mendelian randomization analysis shows a negative correlation between NRCAM and AD risk. Transcriptomic analysis indicates a significant downregulation of NRCAM and PYY in the peripheral blood of AD patients (P < 0.01), while APOE, CGA, and NRCAM are significantly downregulated in the brains of AD patients (P < 0.0001).

CONCLUSION

Our research has successfully identified protein signatures in plasma as potential risk biomarkers that can independently predict AD onset in the future. Notably, this risk prediction model has demonstrated commendable predictive performance and stability over time. These findings underscore the promising utility of plasma protein signatures in dynamically predicting the risk of AD, thereby facilitating early screening and intervention strategies.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection.

RESULTS

This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1-42 (Aβ 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach.

SIGNIFICANCE

This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1-42, particularly in areas with low resources and limited communication infrastructure.

Sensitivity of the African neuropsychology battery memory subtests and learning slopes in discriminating APOE 4 and amyloid pathology in adult individuals in the Democratic Republic of Congo

Background

The current study examined the sensitivity of two memory subtests and their corresponding learning slope metrics derived from the African Neuropsychology Battery (ANB) to detect amyloid pathology and APOEε4 status in adults from Kinshasa, the Democratic Republic of the Congo.

Methods

85 participants were classified for the presence of β-amyloid pathology and based on allelic presence of APOEε4 using Simoa. All participants were screened using CSID and AQ, underwent verbal and visuospatial memory testing from ANB, and provided blood samples for plasma Aβ42, Aβ40, and APOE proteotype. Pearson correlation, linear and logistic regression were conducted to compare amyloid pathology and APOEε4 status with derived learning scores, including initial learning, raw learning score, learning over trials, and learning ratio.

Results

Our sample included 35 amyloid positive and 44 amyloid negative individuals as well as 42 without and 39 with APOEε4. All ROC AUC ranges for the prediction of amyloid pathology based on learning scores were low, ranging between 0.56-0.70 (95% CI ranging from 0.44-0.82). The sensitivity of all the scores ranged between 54.3-88.6, with some learning metrics demonstrating good sensitivity. Regarding APOEε4 prediction, all AUC values ranged between 0.60-0.69, with all sensitivity measures ranging between 53.8-89.7. There were minimal differences in the AUC values across learning slope metrics, largely due to the lack of ceiling effects in this sample.

Discussion

This study demonstrates that some ANB memory subtests and learning slope metrics can discriminate those that are normal from those with amyloid pathology and those with and without APOEε4, consistent with findings reported in Western populations.

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

Comparison of Commonly Measured Plasma and Cerebrospinal Fluid Proteins and Their Significance for the Characterization of Cognitive Impairment Status

BACKGROUND

Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer's disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI).

OBJECTIVE

This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status.

METHODS

This cohort study included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification.

RESULTS

We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOEϵ4 genotype.

CONCLUSIONS

This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.

Comorbidities Incorporated to Improve Prediction for Prevalent Mild Cognitive Impairment and Alzheimer's Disease in the HABS-HD Study

BACKGROUND

Blood biomarkers have the potential to transform Alzheimer's disease (AD) diagnosis and monitoring, yet their integration with common medical comorbidities remains insufficiently explored.

OBJECTIVE

This study aims to enhance blood biomarkers' sensitivity, specificity, and predictive performance by incorporating comorbidities. We assess this integration's efficacy in diagnostic classification using machine learning, hypothesizing that it can identify a confident set of predictive features.

METHODS

We analyzed data from 1,705 participants in the Health and Aging Brain Study-Health Disparities, including 116 AD patients, 261 with mild cognitive impairment, and 1,328 cognitively normal controls. Blood samples were assayed using electrochemiluminescence and single molecule array technology, alongside comorbidity data gathered through clinical interviews and medical records. We visually explored blood biomarker and comorbidity characteristics, developed a Feature Importance and SVM-based Leave-One-Out Recursive Feature Elimination (FI-SVM-RFE-LOO) method to optimize feature selection, and compared four models: Biomarker Only, Comorbidity Only, Biomarker and Comorbidity, and Feature-Selected Biomarker and Comorbidity.

RESULTS

The combination model incorporating 17 blood biomarkers and 12 comorbidity variables outperformed single-modal models, with NPV12 at 92.78%, AUC at 67.59%, and Sensitivity at 65.70%. Feature selection led to 22 chosen features, resulting in the highest performance, with NPV12 at 93.76%, AUC at 69.22%, and Sensitivity at 70.69%. Additionally, interpretative machine learning highlighted factors contributing to improved prediction performance.

CONCLUSIONS

In conclusion, combining feature-selected biomarkers and comorbidities enhances prediction performance, while feature selection optimizes their integration. These findings hold promise for understanding AD pathophysiology and advancing preventive treatments.

Parkinson's Disease Blood Test for Primary Care

Background

A blood-test that could serve as a potential first step in a multi-tiered neurodiagnostic process for ruling out Parkinson's disease (PD) in primary care settings would be of tremendous value. This study therefore sought to conduct a large-scale cross-validation of our Parkinson's disease Blood Test (PDBT) for use in primary care settings.

Methods

Serum samples were analyzed from 846 PD and 2291 volunteer controls. Proteomic assays were run on a multiplex biomarker assay platform using Electrochemiluminescence (ECL). Diagnostic accuracy statistics were generated using area under the receiver operating characteristic curve (AUC), Sensitivity (SN), Specificity (SP) and Negative Predictive Value (NPV).

Results

In the training set, the PDBT reached an AUC of 0.98 when distinguishing PD cases from controls with a SN of 0.84 and SP of 0.98. When applied to the test set, the PDBT yielded an AUC of 0.96, SN of 0.79 and SP of 0.97. The PDBT obtained a negative predictive value of 99% for a 2% base rate.

Conclusion

The PDBT was highly successful in discriminating PD patients from control cases and has great potential for providing primary care providers with a rapid, scalable and cost-effective tool for screening out PD.

The Influence of Orthopedic Surgery on Circulating Metabolite Levels, and their Associations with the Incidence of Postoperative Delirium

The mechanisms underlying the occurrence of postoperative delirium development are unclear and measurement of plasma metabolites may improve understanding of its causes. Participants (n = 54) matched for age and gender were sampled from an observational cohort study investigating postoperative delirium. Participants were ≥65 years without a diagnosis of dementia and presented for primary elective hip or knee arthroplasty. Plasma samples collected pre- and postoperatively were grouped as either control (n = 26, aged: 75.8 ± 5.2) or delirium (n = 28, aged: 76.2 ± 5.7). Widespread changes in plasma metabolite levels occurred following surgery. The only metabolites significantly differing between corresponding control and delirium samples were ornithine and spermine. In delirium cases, ornithine was 17.6% higher preoperatively, and spermine was 12.0% higher postoperatively. Changes were not associated with various perioperative factors. In binary logistic regression modeling, these two metabolites did not confer a significantly increased risk of delirium. These findings support the hypothesis that disturbed polyamine metabolism is an underlying factor in delirium that warrants further investigation.

Recent Advances in the Application Peptide and Peptoid in Diagnosis Biomarkers of Alzheimer's Disease in Blood

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with irreversible damage of the brain and a continuous pathophysiological process. Early detection and accurate diagnosis are essential for the early intervention of AD. Precise detection of blood biomarkers related to AD could provide a shortcut to identifying early-stage patients before symptoms. In recent years, targeting peptides or peptoids have been chosen as recognition elements in nano-sensors or fluorescence detection to increase the targeting specificity, while peptide-based probes were also developed considering their specific advantages. Peptide-based sensors and probes have been developed according to different strategies, such as natural receptors, high-throughput screening, or artificial design for AD detection. This review will briefly summarize the recent developments and trends of AD diagnosis platforms based on peptide and peptoid as recognition elements and provide insights into the application of peptide and peptoid with different sources and characteristics in the diagnosis of AD biomarkers.

A tacrine-tetrahydroquinoline heterodimer potently inhibits acetylcholinesterase activity and enhances neurotransmission in mice

Cholinergic neurons are ubiquitous and involved in various higher brain functions including learning and memory. Patients with Alzheimer's disease exhibit significant dysfunction and loss of cholinergic neurons. Meanwhile, such cholinergic deficits can be potentially relieved pharmacologically by increasing acetylcholine. Acetylcholinesterase (AChE) inhibitors have been used to improve cholinergic transmission in the brain for two decades and have proven effective for alleviating symptoms in the early stages of Alzheimer's disease. Therefore, the search for AChE inhibitors for drug development is ongoing. The enzymatic pocket of AChE has long been the target of several drug designs over the last two decades. The peripheral and catalytic sites of AChE are simultaneously bound by several dimeric molecules, enabling more-efficient inhibition. Here, we used 6-chlorotacrine and the tetrahydroquinolone moiety of huperzine A to design and synthesize a series of heterodimers that inhibit AChE at nanomolar potency. Specifically, compound 7b inhibits AChE with an IC₅₀ < 1 nM and spares butyrylcholinesterase. Administration of 7b to mouse brain slices restores synaptic activity impaired by pirenzepine, a muscarinic M1-selective antagonist. Moreover, oral administration of 7b to C57BL/6 mice enhances hippocampal long-term potentiation in a dose-dependent manner and is detectable in the brain tissue. All these data supported that 7b is a potential cognitive enhancer and is worth for further exploration.

Towards point-of-care diagnosis of Alzheimer's disease: Multi-analyte based portable chemiresistive platform for simultaneous detection of β-amyloid (1-40) and (1-42) in plasma

Label-free simultaneous detection of Alzheimer's disease (AD) specific biomarkers Aβ40 and Aβ42 peptides on a single platform using polypyrrole nanoparticle-based chemiresistive biosensors is reported here. The proposed interdigitated-microelectrode based inexpensive multisensor-platform can concurrently detect Aβ40 and Aβ42 in spiked-plasma in the range of 10-14 - 10-6 g/mL (with LoDs being 5.71 and 9.09 fg/mL, respectively), enabling the estimation of diagnostically significant Aβ42/Aβ40 ratio. A detailed study has been undertaken here to record the individual sensor responses against spiked-plasma samples with varying amounts and proportions of the two target peptides, towards enabling disease-progression monitoring using the Aβ-ratio. As compared to the existing cost-ineffective brain-imaging techniques such as PET and MRI, and the high-risk CSF based invasive AD biomarkers detecting procedures, the proposed approach offers a viable alternative for affordable point-of-care AD diagnostics, with possible usage in performance evaluation of therapeutic drugs. Towards point-of-care applications, the portable readout used in this work was conjugated with an android-based mobile app for data-acquisition and analysis.

Curiosity-Based Interventions Increase Everyday Functioning Score But Not Serum BDNF Levels in a Cohort of Healthy Older Adults

An enriched environment is effective in stimulating learning and memory in animal models as well as in humans. Environmental enrichment increases brain-derived neurotrophic factor (BDNF) in aged rats and reduces levels of Alzheimer-related proteins in the blood, including amyloid-β (Aβ) peptides and misfolded toxic forms of tau. To address whether stimulation of curiosity, which is a form of enrichment, may provide a buffer against Alzheimer's disease (AD), we measured levels of biomarkers associated with AD at baseline and after a 6-week intervention in older adults (>65 years of age) randomized to one of three different intervention conditions. Specifically, in this pilot study, we tested the effectiveness of a traditional, structured learning environment compared to a self-motivated learning environment designed to stimulate curiosity. There were no significant differences from baseline to post-intervention in any of the groups for Aβ42/Aβ40 ratio or t-tau (total-tau) plasma levels. Serum BDNF levels decreased significantly in the control group. Interestingly, individuals who had the lowest serum BDNF levels at baseline experienced significantly higher increases in BDNF over the course of the 6-week intervention compared to individuals with higher serum BDNF levels at baseline. As expected, older individuals had lower MoCA scores. Years of education correlated negatively with Aβ levels, suggesting a protective effect of education on levels of this toxic protein. ECog scores were negatively correlated with BDNF levels, suggesting that better performance on the ECog questionnaire was associated with higher BDNF levels. Collectively, these findings did not suggest that a 6-week cognitive training intervention focused on curiosity resulted in significant alterations in blood biomarkers but showed interesting correlations between cognitive scores and BDNF levels, further supporting the role of this trophic factor in brain health in older adults.

RRY Inhibits Amyloid-β1-42 Peptide Aggregation and Neurotoxicity

BACKGROUND

Current understanding of amyloid-β protein (Aβ) aggregation and toxicity provides an extensive list of drugs for treating Alzheimer's disease (AD); however, one of the most promising strategies for its treatment has been tri-peptides.

OBJECTIVE

The aim of this study is to examine those tri-peptides, such as Arg-Arg-Try (RRY), which have the potential of Aβ1-42 aggregating inhibition and Aβ clearance.

METHODS

In the present study, in silico, in vitro, and in vivo studies were integrated for screening tri-peptides binding to Aβ, then evaluating its inhibition of aggregation of Aβ, and finally its rescuing cognitive deficit.

RESULTS

In the in silico simulations, molecular docking and molecular dynamics determined that seven top-ranking tri-peptides could bind to Aβ1-42 and form stable complexes. Circular dichroism, ThT assay, and transmission electron microscope indicated the seven tri-peptides might inhibit the aggregation of Aβ1-42 in vitro. In the in vivo studies, Morris water maze, ELISA, and Diolistic staining were used, and data showed that RRY was capable of rescuing the Aβ1-42-induced cognitive deficit, reducing the Aβ1-42 load and increasing the dendritic spines in the transgenic mouse model.

CONCLUSION

Such converging outcomes from three consecutive studies lead us to conclude that RRY is a preferred inhibitor of Aβ1-42 aggregation and treatment for Aβ-induced cognitive deficit.

Recursive Support Vector Machine Biomarker Selection for Alzheimer's Disease

BACKGROUND

There is a need for more reliable diagnostic tools for the early detection of Alzheimer's disease (AD). This can be a challenge due to a number of factors and logistics making machine learning a viable option.

OBJECTIVE

In this paper, we present on a Support Vector Machine Leave-One-Out Recursive Feature Elimination and Cross Validation (SVM-RFE-LOO) algorithm for use in the early detection of AD and show how the SVM-RFE-LOO method can be used for both classification and prediction of AD.

METHODS

Data were analyzed on n = 300 participants (n = 150 AD; n = 150 cognitively normal controls). Serum samples were assayed via a multi-plex biomarker assay platform using electrochemiluminescence (ECL).

RESULTS

The SVM-RFE-LOO method reduced the number of features in the model from 21 to 16 biomarkers and achieved an area under the curve (AUC) of 0.980 with a sensitivity of 94.0% and a specificity of 93.3%. When the classification and prediction performance of SVM-RFE-LOO was compared to that of SVM and SVM-RFE, we found similar performance across the models; however, the SVM-RFE-LOO method utilized fewer markers.

CONCLUSION

We found that 1) the SVM-RFE-LOO is suitable for analyzing noisy high-throughput proteomic data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance. Our recursive feature elimination model can serve as a general model for biomarker discovery in other diseases.

Analysis of the Human Plasma Proteome Using Multi-Nanoparticle Protein Corona for Detection of Alzheimer's Disease

As the population affected by Alzheimer's disease (AD) grows, so does the need for a noninvasive and accurate diagnostic tool. Current research reveals that AD pathogenesis begins as early as decades before clinical symptoms. The unique properties of nanoparticles (NPs) may be exploited to develop noninvasive diagnostics for early detection of AD. After exposure of NPs to biological fluids, the NP surface is altered by an unbiased but selective and reproducible adsorption of biomolecules commonly referred to as the biomolecular corona or protein corona (PC). The discovery that the plasma proteome may be differentially altered during health and disease leads to the concept of disease-specific PCs. Herein, the disease-specific PCs formed around NPs in a multi-NPs platform are employed to successfully identify subtle changes in plasma protein patterns and detect AD (>92% specificity and ≈100% sensitivity). Similar discrimination power is achieved using banked plasma samples from a cohort of patients several years prior to their diagnosis with AD. With the nanoplatform's analytic ability to analyze pathological proteomic changes into a disease-specific identifier, this promising, noninvasive technology with implications for early detection and intervention could benefit not only patients with AD but other diseases as well.

A Pilot randomized clinical trial of adapted tango to improve cognition and psychosocial function in African American women with family history of Alzheimer's disease (ACT trial)

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the adapted tango intervention was very well received as demonstrated by minimal participant attrition, satisfaction questionnaires that indicated high satisfaction, and anecdotal reports.

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Adapted tango intervention may be helpful in controlling or reducing markers of inflammation in AA women with a parental history of AD.

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Participants in tango demonstrated improvements in whole-body spatial cognition and short-term and working memory, and reduced deterioration of executive function.

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Although our tango group did not show large positive effect in cumulative caregiver burden post intervention, the large positive effect in role Captivity, caregiver confidence, and deprivation of intimate exchange displays some of the positive effects of this dance intervention for the caregivers.

Alzheimer's disease (AD) is a devastating, progressive neurodegenerative disease resulting in memory loss and a severe reduction in the ability to perform activities of daily living. Ethnicity-related genetic factors promoting the development of dementias among African Americans (AA) and increased risk among women for developing AD indicates that AA women with a parental history of AD are at great risk for developing AD. This phase I study assessed the impact of a 12 week, 20-lesson adapted Argentine Tango intervention (n = 24) to a no-contact control group (n = 10) on measures of plasma inflammatory markers, cognition, and motor and psychosocial performance in middle-aged AA woman at increased risk for AD by virtue of parental history. Some woman (n = 16) were also caregivers; thus, the impact of the intervention on caregiving burden was examined in this subset. Preliminary analysis of efficacy was conducted with significance tests on biomarkers and key measures of cognition, including visuospatial and executive function, balance, and strength. After 12 weeks, Tango participants had significantly decreased inflammatory cytokine, including reductions in IL-7 (p = 0.003), IFN-γ (p = 0.011), TNFα (p = 0.011), and MCP-1 (p = 0.042) compared to controls. Large effects were noted for the Tango group on tests of executive functioning (d = 0.89), and inhibition (p = 0.031). Participants in Tango improved in dynamic and static balance (p = 0.018) and functional lower body strength (p = 0.023). Secondary assessment revealed trends favoring the intervention group were noted in spatial cognition and executive function. Moderate effects were noted in caregiving burden measures among the subset of caregivers. These data demonstrate substantial reductions in inflammatory biomarkers along with cognitive and motor improvements through a non-pharmacologic, affordable intervention among a small, well-characterized cohort of AA women with a parental history of AD.

Use of Fourier-Transform Infrared Spectroscopy (FT-IR) for Monitoring Experimental Helicobacter pylori Infection and Related Inflammatory Response in Guinea Pig Model

Infections due to Gram-negative bacteria Helicobacter pylori may result in humans having gastritis, gastric or duodenal ulcer, and even gastric cancer. Investigation of quantitative changes of soluble biomarkers, correlating with H. pylori infection, is a promising tool for monitoring the course of infection and inflammatory response. The aim of this study was to determine, using an experimental model of H. pylori infection in guinea pigs, the specific characteristics of infrared spectra (IR) of sera from H. pylori infected (40) vs. uninfected (20) guinea pigs. The H. pylori status was confirmed by histological, molecular, and serological examination. The IR spectra were measured using a Fourier-transform (FT)-IR spectrometer Spectrum 400 (PerkinElmer) within the range of wavenumbers 3000–750 cm⁻¹ and converted to first derivative spectra. Ten wavenumbers correlated with H. pylori infection, based on the chi-square test, were selected for a K-nearest neighbors (k-NN) algorithm. The wavenumbers correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and in the W4 window related to nucleic acids and hydrocarbons. The k-NN for detection of H. pylori infection has been developed based on chemometric data. Using this model, animals were classified as infected with H. pylori with 100% specificity and 97% sensitivity. To summarize, the IR spectroscopy and k-NN algorithm are useful for monitoring experimental H. pylori infection and related inflammatory response in guinea pig model and may be considered for application in humans.

Beyond cells: The extracellular circulating 20S proteasomes

Accumulating evidence arising from numerous clinical studies indicate that assembled and functional 20S proteasome complexes circulate freely in plasma. Elevated levels of this core proteolytic complex have been found in the plasma of patients suffering from blood, skin and solid cancers, autoimmune disorders, trauma and sepsis. Moreover, in various diseases, there is a positive correlation between circulating 20S proteasome (c20S) levels and treatment efficacy and survival rates, suggesting the involvement of this under-studied c20S complex in pathophysiology. However, many aspects of this system remain enigmatic, as we still do not know the origin, biological role or mechanisms of extracellular transport and regulation of c20S proteasomes. In this review, we provide an overview of the current understanding of the c20S proteasome system and discuss the remaining gaps in knowledge.

Fibrinogen isoforms as potential blood-based biomarkers of Alzheimer's disease using a proteomics approach

Objective: Alzheimer's disease (AD), the commonest form of dementia which is characterized by progressive decline in cognitive function, can only be definitively diagnosed after death. Although biomarkers may aid diagnosis, currently available AD biomarkers, which are predominantly based on cerebrospinal fluid and neuroimaging facilities, are either invasive or costly. Blood-based biomarkers for AD diagnosis are highly sought after due to its practicality at the clinic. This study was undertaken to determine the differential protein expression in plasma amongst Malaysian AD, mild cognitive impairment (MCI) and non-AD individuals. Methods: A proteomic approach which utilized two-dimensional differential in gel electrophoresis (2 D DIGE) was performed for blood samples from 15 AD, 14 MCI and 15 non-AD individuals. Results: Mass spectrometry (MS)-based protein identification via MALDI ToF/ToF showed that fibrinogen-β-chain (spot 64) and fibrinogen-γ-chain (spot 91) with differential expression ratio >1.5 were significantly upregulated (p < 0.05) in AD patients when compared to non-AD individuals. Further data analysis using Pearson correlation found that the upregulated fibrinogen-γ-chain was weakly but significantly (p < 0.05) and inversely correlated with cognitive decline. Conclusion: Fibrinogen isoforms may play important roles in the vascular pathology of AD as well as neuroinflammation. As such, fibrinogen appears to be a promising blood-based biomarker for AD. Further validation of the present findings in larger population is now warranted.

Proteomics as a reliable approach for discovery of blood-based Alzheimer's disease biomarkers: A systematic review and meta-analysis

In order to gauge the impact of proteomics in discovery of Alzheimer's disease (AD) blood-based biomarkers, this study had systematically reviewed articles published between 1984-2019. Articles that fulfilled the inclusion criteria were assessed for risk of bias. A meta-analysis was performed for replicable candidate biomarkers (CB). Of the 1651 articles that were identified, 17 case-control and two cohort studies, as well as three combined case-control and longitudinal designs were shortlisted. A total of 207 AD and mild cognitive impairment (MCI) CB were discovered, with 48 reported in >2 studies. This review highlights six CB, namely alpha-2-macroglobulin (α2M)_ps, pancreatic polypeptide (PP)_ps, apolipoprotein A-1 (ApoA-1)_ps, afamin_p, insulin growth factor binding protein-2 (IGFBP-2)_ps and fibrinogen-γ-chain_p, all of which exhibited consistent pattern of regulation in >three independent cohorts. They are involved in AD pathogenesis via amyloid-beta (Aβ), neurofibrillary tangles, diabetes and cardiovascular diseases (CVD). Meta-analysis indicated that ApoA-1_ps was significantly downregulated in AD (SMD = -1.52, 95% CI: -1.89, -1.16, p < 0.00001), with low inter-study heterogeneity (I² = 0%, p = 0.59). α2M_ps was significantly upregulated in AD (SMD = 0.83, 95% CI: 0.05, 1.62, p = 0.04), with moderate inter-study heterogeneity (I² = 41%, p = 0.19). Both CB are involved in Aβ formation. These findings provide important insights into blood-based AD biomarkers discovery via proteomics.

Enlightening the advancements in electrochemical bioanalysis for the diagnosis of Alzheimer's disease and other neurodegenerative disorders

Neurodegenerative disorders (NDD), and particularly Alzheimer's disease (AD), are one of the greatest challenges facing our current medicine and society because of its increasing incidence and the high burden imposed both on patients' families and health systems. Despite this, their accurate diagnosis, mostly conducted by cerebrospinal fluid (CSF) analysis or neuroimaging techniques, costly, time-consuming, and unaffordable for most of the population, remains a complex task. In this situation, electrochemical biosensors are flourishing as promising alternative tools for the simple, fast, and low-cost diagnosis of NDD/AD. This review article provides the relevant clinical details of NDD/AD along with the closely related genetic (genetic mutations, polymorphisms of ApoE and specific miRNAs) and proteomic (amyloid-β peptides, total and phosphorylated tau protein) biomarkers circulating mostly in CSF. In addition, the article systematically enlightens a general view of the electrochemical affinity biosensors (mostly aptasensors and immunosensors) reported in the past two years for the determination of such biomarkers. The different developed strategies, analytical performances and applications are comprehensively discussed. Recent advancements in signal amplification methodologies involving smart designs and the use of nanomaterials and rational surface chemistries, as well as the challenges that must be struggled and the prospects in electrochemical affinity biosensing to bring more accessibility to NDD/AD diagnosis, prognosis, and follow-up, are also pointed out.

Disposable immunoplatforms for the simultaneous determination of biomarkers for neurodegenerative disorders using poly(amidoamine) dendrimer/gold nanoparticle nanocomposite

Early diagnosis in primary care settings can increase access to therapies and their efficiency as well as reduce health care costs. In this context, we report in this paper the development of a disposable immunoplatform for the rapid and simultaneous determination of two protein biomarkers recently reported to be involved in the pathological process of neurodegenerative disorders (NDD), tau protein (tau), and TAR DNA-binding protein 43 (TDP-43). The methodology involves implementation of a sandwich-type immunoassay on the surface of dual screen-printed carbon electrodes (dSPCEs) electrochemically grafted with p-aminobenzoic acid (p-ABA), which allows the covalent immobilization of a gold nanoparticle-poly(amidoamine) (PAMAM) dendrimer nanocomposite (3D-Au-PAMAM). This scaffold was employed for the immobilization of the capture antibodies (CAbs). Detector antibodies labeled with horseradish peroxidase (HRP) and amperometric detection at - 0.20 V (vs. Ag pseudo-reference electrode) using the H₂O₂/hydroquinone (HQ) system were used. The developed methodology exhibits high sensitivity and selectivity for determining the target proteins, with detection limits of 2.3 and 12.8 pg mL^-1 for tau and TDP-43, respectively. The simultaneous determination of tau and TDP-43 was accomplished in raw plasma samples and brain tissue extracts from healthy individuals and NDD-diagnosed patients. The analysis can be performed in just 1 h using a simple one-step assay protocol and small sample amounts (5 μL plasma and 2.5 μg brain tissue extracts). Graphical abstract.

Innovative IgG Biomarkers Based on Phage Display Microbial Amyloid Mimotope for State and Stage Diagnosis in Alzheimer's Disease

An innovative approach to identify new conformational antigens of Aβ_1–42 recognized by IgG autoantibodies as biomarkers of state and stage in Alzheimer’s disease (AD) patients is described. In particular, through the use of bioinformatics modeling, conformational similarities between several Aβ_1–42 forms and other amyloid-like proteins with F1 capsular antigen (Caf1) of Yersinia pestis were first found. pVIII M13 phage display libraries were then screened against YPF19, anti-Caf1 monoclonal antibody, and IgGs of AD patients, in alternate biopanning cycles of a so-called “double binding” selection. From the selected phage clones, one, termed 12III1, was found to be able to prevent in vitro Aβ_1–42-induced cytotoxicity in SH-SY5Y cells, as well as to promote disaggregation of preformed fibrils, to a greater extent with respect to wild-type phage (pC89). IgG levels detected by 12III1 provided a significant level of discrimination between diseased and nondemented subjects, as well as a good correlation with the state progression of the disease. These results give significant impact in AD state and stage diagnosis, paving the way for the development not only for an innovative blood diagnostic assay for AD precise diagnosis, progressive clinical assessment, and screening but also for new effective treatments.

Medical Histories of Control Subjects Influence the Biomarker Potential of Plasma Aβ in Alzheimer's Disease: a Meta-analysis

Whether blood amyloid-β (Aβ) could be a peripheral biomarker of Alzheimer's disease (AD) remains in dispute. In the present study, we conducted a meta-analysis with 19 citations searched from Embase, PubMed, and the Cochrane Library database. Weighted mean difference (WMD) with 95% confidence intervals (CIs) was used to estimate the effect size. We firstly analyzed the plasma Aβ₄₀, Aβ₄₂, and Aβ₄₂/Aβ₄₀ ratio in AD and control group subjects. However, only a lower level of plasma Aβ₄₂ was figured out in AD group subjects with weak statistical significance (WMD 1.82; 95% CI 0.59, 3.06; P = 0.004; I² = 84%). We considered that the medical histories of control subjects could influence the biomarker ability of plasma Aβ. Therefore, subgroup analyses were then carried out based on a new recruiting criterion for control subjects, defining as no afflictions of any Aβ-related diseases. Surprisingly, AD group subjects showed a significant decrease in plasma Aβ₄₂/Aβ₄₀ ratio with low heterogeneity among studies (WMD 0.02; 95% CI 0.02, 0.02; P < 0.00001; I² = 0%). Moreover, not only the Aβ₄₂/Aβ₄₀ ratio but also Aβ₄₂ and Aβ₄₀ were indifferent between AD and pseudo-control subjects which might be afflicted with Aβ-related diseases. This meta-analysis demonstrated that medical histories of control subjects were interference factors impeding plasma Aβ to be a biomarker of AD.

A proteomic signature for dementia with Lewy bodies

INTRODUCTION

We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease would distinguish patients with Lewy body disease from normal controls, and if it would distinguish dementia with Lewy bodies (DLB) from Parkinson's disease (PD).

METHODS

Stored plasma samples were obtained from 145 patients (DLB n = 57, PD without dementia n = 32, normal controls n = 56) enrolled from patients seen in the Behavioral Neurology or Movement Disorders clinics at the Mayo Clinic, Florida. Proteomic assays were conducted and analyzed as per our previously published protocols.

RESULTS

In the first step, the proteomic profile distinguished the DLB-PD group from controls with a diagnostic accuracy of 0.97, sensitivity of 0.91, and specificity of 0.86. In the second step, the proteomic profile distinguished the DLB from PD groups with a diagnostic accuracy of 0.92, sensitivity of 0.94, and specificity of 0.88.

DISCUSSION

These data provide evidence of the potential utility of a multitiered blood-based proteomic screening method for detecting DLB and distinguishing DLB from PD.

Colorimetric Paper-Based Immunosensor for Simultaneous Determination of Fetuin B and Clusterin toward Early Alzheimer's Diagnosis

Alzheimer's disease is a devastating condition characterized by a progressive and slow brain decay in elders. Here, we developed a paper-based lateral flow immunoassay for simultaneous and fast determination of Alzheimer's blood biomarkers, fetuin B and clusterin. Selective antibodies to targeted biomarkers were immobilized on gold nanoparticles (AuNPs) and deposited on paper pads. After adding the sample on the paper-based device, the biofluid laterally flows toward the selective antibody, permitting AuNP-Ab accumulation on the test zone, which causes a color change from white to pink. Image analysis was performed using a customized algorithm for the automatic recognition of the area of analysis and color clustering. Colorimetric detection was compared to electrochemical methods for the precise quantification of biomarkers. The best performance was found for the color parameter "L*". Good linearity (R² = 0.988 and 0.998) and reproducibility (%RSD = 2.79% and 1.82%, N = 3) were demonstrated for the quantification of fetuin B and clusterin, respectively. Furthermore, the specificity of the immunosensor was tested on mixtures of proteins, showing negligible cross-reactivity and good performance in complex environments. We believe that our biosensor has a potential for early-stage diagnosis of Alzheimer's disease and toward a better understanding of Alzheimer's developing mechanisms.

Identification of Optimum Panel of Blood-based Biomarkers for Alzheimer's Disease Diagnosis Using Machine Learning

With the increasing number of people living with Alzheimer's disease (AD), there is a need for low-cost and easy to use methods to detect AD early to facilitate access to appropriate care pathways. Neuroimaging biomarkers (such as those based on PET and MRI) and biochemical biomarkers (such as those based on CSF) are recommended by international guidelines to facilitate diagnosis. However, neuroimaging is expensive and may not be widely available and CSF testing is invasive. Bloodbased biomarkers offer the potential for the development of a low-cost and more time efficient tool to detect AD to complement CSF and neuroimaging as blood is much easier to obtain. Although no single blood biomarker is yet able to detect AD, combinations of biomarkers (also called panels) have shown good results. However, a large number of biomarkers are often needed to achieve a satisfactory detection performance. In addition, it is difficult to reproduce reported results within and across different study cohorts because of data overfitting and lack of access to the datasets used in the studies. In this study, our focus is to identify an optimum panel (in terms of the least number of blood biomarkers to meet the specified diagnostic performance of 80% sensitivity and specificity) based on a widely accessible data set, and to demonstrate a testing methodology that reinforces reproducibility of results. Realizing a panel with reduced number of markers will have significant impact on the complexity and cost of diagnosis and potential development of cost-effective point of care devices.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

The effectiveness and safety of Kami Guibi-tang for mild cognitive impairment: study protocol of a pilot, randomized, placebo-controlled, double-blind trial

Background

Mild cognitive impairment (MCI) is an intermediate phase between normal aging and dementia. Since a majority of amnestic MCI (aMCI) cases progress to Alzheimer’s disease (AD), it is considered the prodromal stage of AD and, therefore, a treatment target for the prevention of further cognitive decline. However, there is no approved treatment for MCI at present. Kami Guibi-tang (KGT) is a herbal drug used in Korean medicine to treat amnesia, insomnia, loss of appetite, and depression. We will explore the effectiveness and safety of KGT in amnestic MCI in this trial.

Methods/design

The study will be a single-center, randomized, placebo-controlled, double-blind trial. Eligible participants diagnosed with amnestic MCI will be randomly allocated to a treatment or control group. Participants will take KGT or placebo granules, three times a day, for 24 weeks. The primary outcomes will be changes in Seoul Neuropsychological Screening Battery (SNSB) scores, and magnetic resonance imaging (MRI) measurements including those of brain metabolites, neurotransmitters, and cerebral blood flow. The secondary outcomes will include the safety assessment, measured by changes in blood chemistry, changes in blood protein and cholesterol levels related to AD pathology, and a comparison of MRI changes between the two groups, using age and genotype as covariates.

Discussion

This study will be the first clinical trial to identify the therapeutic potential of Kami Guibi-tang for amnestic MCI. The findings will provide insight into the feasibility of large-scale trials to gather evidence for KGT as a treatment for MCI.

Trial registration

Korean Clinical Trial Registry, ID: KCT0002407. Registered on 30 March 2017.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3567-1) contains supplementary material, which is available to authorized users.

Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study

Introduction

We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases.

Methods

Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms.

Results

The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal.

Discussion

These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.

A Nanoplasmonic Biosensor for Ultrasensitive Detection of Alzheimer's Disease Biomarker Using a Chaotropic Agent

Blood-based diagnosis (hemodiagnosis) of Alzheimer's disease (AD) is emerging as a promising alternative to cerebrospinal-fluid-based methods because blood contains various kinds of AD biomarkers, including amyloid beta 1-40, 1-42, and τ (tau) protein. However, with current technology, the accuracy of the blood-plasma-based methods is relatively low compared to the traditional methods because the concentration of AD biomarkers in blood plasma is incredibly low, and diverse interference is present in blood plasma, which hinders precise detection. Here, we suggest a nanoplasmonic biosensor using gold nanorods with a chaotropic agent for precise ultrasensitive detecting of Alzheimer's disease biomarkers in human plasma. This nanoplasmonic biosensor is based on the localized surface plasmon resonance (LSPR), which is extremely sensitive to the point where it can respond to an insignificant change of the refractive index around the gold nanoparticles. Also, using guanidine hydrochloride as a chaotropic agent, we can overcome the obstacles of blood-based AD diagnostics. In more detail, this agent interrupts the network between water molecules and weakens the hydrophobic interactions between proteins, remarkably improving detection capabilities to target τ protein. By reducing the overlapping ranges between protein levels in an age-matched control and AD patients' plasma, this system can accurately diagnose AD patients. This platform also can analyze disease from mild cognitive impairment using standardized blood biomarker tau protein, which is related to Alzheimer's disease. As a result, our platform can be applied to clinical trials, and thus it has excellent potential in the medical field.

Revealing heterogeneity of brain imaging phenotypes in Alzheimer's disease based on unsupervised clustering of blood marker profiles

Alzheimer's disease (AD) affects millions of people and is a major rising problem in health care worldwide. Recent research suggests that AD could have different subtypes, presenting differences in how the disease develops. Characterizing those subtypes could be key to deepen the understanding of this complex disease. In this paper, we used a multivariate, non-supervised clustering method over blood-based markers to find subgroups of patients defined by distinctive blood marker profiles. Our analysis on ADNI database identified 4 possible subgroups, each with a different blood profile. More importantly, we show that subgroups with different profiles have a different relationship between brain phenotypes detected in magnetic resonance imaging and disease condition.

Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.

Biomarkers in Neurodegenerative Diseases

The past decade has seen tremendous efforts in biomarker discovery and validation for neurodegenerative diseases. The source and type of biomarkers has continued to grow for central nervous system diseases, from biofluid-based biomarkers (blood or cerebrospinal fluid (CSF)), to nucleic acids, tissue, and imaging. While DNA remains a predominant biomarker used to identify familial forms of neurodegenerative diseases, various types of RNA have more recently been linked to familial and sporadic forms of neurodegenerative diseases during the past few years. Imaging approaches continue to evolve and are making major contributions to target engagement and early diagnostic biomarkers. Incorporation of biomarkers into drug development and clinical trials for neurodegenerative diseases promises to aid in the development and demonstration of target engagement and drug efficacy for neurologic disorders. This review will focus on recent advancements in developing biomarkers for clinical utility in Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).

Serum Ethanolamine Plasmalogen and Urine Myo-Inositol as Cognitive Decline Markers

Recent studies have suggested that metabolic disorders, particularly type 2 diabetes mellitus (T2DM), and dementia, including Alzheimer's disease (AD), were linked at the clinical and molecular levels. Brain insulin deficiency and resistance may be key events in AD pathology mechanistically linking AD to T2DM. Ethanolamine plasmalogens (PlsEtns) are abundant in the brain and play essential roles in neuronal function and myelin formation. As such, PlsEtn deficiency may be pathologically relevant in some neurodegenerative disorders such as AD. Decreased brain PlsEtn associated with dementia may reflect serum PlsEtn deficiency. We hypothesized that myo-inositol plays a role in myelin formation through its facilitation of PlsEtn biosynthesis. Excessive urinary myo-inositol (UMI) loss would likely result in PlsEtn deficiency potentially leading to demyelinating diseases such as dementia. Accordingly, measurement of both serum PlsEtn and baseline UMI excretion could improve the detection of cognitive impairment (CI) in a more specific and reliable manner. To verify our hypothesis, we conducted a clinical observational study of memory clinic outpatients (MCO) and cognitively normal elderly (NE) for nearly 4.5years. We demonstrated that serum PlsEtn concentration associated with UMI excretion was useful for predicting advancing dementia in patients with mild CI. Because hyperglycemia and associated insulin resistance might be a leading cause of increased baseline UMI excretion, serum PlsEtn quantitation would be useful in detecting CI among the elderly with hyperglycemia. Our findings suggest that myo-inositol is a novel candidate molecule linking T2DM to AD.

Evolving Relevance of Neuroproteomics in Alzheimer's Disease

Substantial progress in the understanding of the biology of Alzheimer's disease (AD) has been achieved over the past decades. The early detection and diagnosis of AD and other age-related neurodegenerative diseases, however, remain a challenging scientific frontier. Therefore, the comprehensive discovery (relating to all individual, converging or diverging biochemical disease mechanisms), development, validation, and qualification of standardized biological markers with diagnostic and prognostic functions with a precise performance profile regarding specificity, sensitivity, and positive and negative predictive value are warranted.Methodological innovations in the area of exploratory high-throughput technologies, such as sequencing, microarrays, and mass spectrometry-based analyses of proteins/peptides, have led to the generation of large global molecular datasets from a multiplicity of biological systems, such as biological fluids, cells, tissues, and organs. Such methodological progress has shifted the attention to the execution of hypothesis-independent comprehensive exploratory analyses (opposed to the classical hypothesis-driven candidate approach), with the aim of fully understanding the biological systems in physiology and disease as a whole. The systems biology paradigm integrates experimental biology with accurate and rigorous computational modelling to describe and foresee the dynamic features of biological systems. The use of dynamically evolving technological platforms, including mass spectrometry, in the area of proteomics has enabled to rush the process of biomarker discovery and validation for refining significantly the diagnosis of AD. Currently, proteomics-which is part of the systems biology paradigm-is designated as one of the dominant matured sciences needed for the effective exploratory discovery of prospective biomarker candidates expected to play an effective role in aiding the early detection, diagnosis, prognosis, and therapy development in AD.

Development of a Label-Free Immunosensor for Clusterin Detection as an Alzheimer's Biomarker

Clusterin (CLU) has been associated with the clinical progression of Alzheimer's disease (AD) and described as a potential AD biomarker in blood plasma. Due to the enormous attention given to cerebrospinal fluid (CSF) biomarkers for the past couple of decades, recently found blood-based AD biomarkers like CLU have not yet been reported for biosensors. Herein, we report the electrochemical detection of CLU for the first time using a screen-printed carbon electrode (SPCE) modified with 1-pyrenebutyric acid N-hydroxysuccinimide ester (Pyr-NHS) and decorated with specific anti-CLU antibody fragments. This bifunctional linker molecule contains succinylimide ester to bind protein at one end while its pyrene moiety attaches to the carbon surface by means of π-π stacking. Cyclic voltammetric and square wave voltammetric studies showed the limit of detection down to 1 pg/mL and a linear concentration range of 1-100 pg/mL with good sensitivity. Detection of CLU in spiked human plasma was demonstrated with satisfactory recovery percentages to that of the calibration data. The proposed method facilitates the cost-effective and viable production of label-free point-of-care devices for the clinical diagnosis of AD.

Fluid and imaging biomarkers for Alzheimer's disease: Where we stand and where to head to

There is increasing evidence that a number of potentially informative biomarkers for Alzheimer disease (AD) can improve the accuracy of diagnosing this form of dementia, especially when used as a panel of diagnostic assays and interpreted in the context of neuroimaging and clinical data. Moreover, by combining the power of CSF biomarkers with neuroimaging techniques to visualize Aβ deposits (or neurodegenerative lesions), it might be possible to better identify individuals at greatest risk for developing MCI and converting to AD. The objective of this article was to review recent progress in selected imaging and chemical biomarkers for prediction, early diagnosis and progression of AD. We present our view point of a scenario that places CSF and imaging markers on the verge of general utility based on accuracy levels that already match (or even surpass) current clinical precision.

A Decade of Blood Biomarkers for Alzheimer's Disease Research: An Evolving Field, Improving Study Designs, and the Challenge of Replication

Blood-based biomarkers represent a less invasive and potentially cheaper approach for aiding Alzheimer's disease (AD) detection compared with cerebrospinal fluid and some neuroimaging biomarkers. Acknowledging that many in the field have made great progress, here we review some of the work that our group has pursued to identify and validate blood-based proteomic biomarkers through both case control and AD pathology endophenotype-based approaches. Our focus is primarily to identify a minimally invasive and hopefully cost-effective blood-based biomarker to reduce screen failure in clinical trials where participants have prodromal or even pre-clinical disease. We summarize some of the key findings and approaches taken in these biomarker studies, while addressing the main challenges, including that of limited replication in the field, and discuss opportunities for biomarker development.

Metallomics Applied to the Study of Neurodegenerative and Mental Diseases

Biochemical imbalances, provoked by aging or a secondary illness, might directly affect the brain, causing severe problems, such as loss of memory or alteration of behavior patterns. Brain disorders are usually classified as injuries (such as stroke, hematomas, and concussions), tumors, and neurodegenerative (such as Parkinson's and Alzheimer's diseases) and mental (such as depression, bipolar disorder, schizophrenia) diseases. As the pathophysiology of these illnesses is not completely established and multiple factors are involved, metallomics, a bioanalytical strategy that allows the detection of metal ions and metalloproteins in diverse biological matrices, is of extreme relevance in identifying which elements are affected by a disease and/or treatment. Thus, determining which element ions suffer disturbances in their homeostasis during the disease progress is relevant to understand the biochemical changes and propose new drug targets. In addition, it is well known that oxidative stress plays an important role in the development of pathological neurodegenerative and mental diseases, which may be caused by metal ion dyshomeostasis, so it is also important to understand endogenous antioxidant metalloprotein and metalloenzyme mechanisms in this regard. In this context, recent applications of metallomics in the study of neurodegenerative and mental disorders are discussed in this chapter, as well as future trends in this research area.

Platelet phosphorylated TDP-43: an exploratory study for a peripheral surrogate biomarker development for Alzheimer's disease

Aim:

Alzheimer's disease (AD) and other forms of dementia create a noncurable disease population in world's societies. To develop a blood-based biomarker is important so that the remedial or disease-altering therapeutic intervention for AD patients would be available at the early stage.

Materials & methods:

TDP-43 levels were analyzed in postmortem brain tissue and platelets of AD and control subjects.

Results:

We observed an increased TDP-43 (<60%) in postmortem AD brain regions and similar trends were also observed in patient's platelets.

Conclusion:

Platelet TDP-43 could be used as a surrogate biomarker that is measurable, reproducible and sensitive for screening the patients with some early clinical signs of AD and can be used to monitor disease prognosis.

In this study, we explore to identify an Alzheimer's disease (AD)-selective phospho-specific antibody that recognizes the diseased form of TDP-43 protein in patient's blood-derived platelets. Our results suggest that selective antiphosphorylated TDP-43 antibody discriminates AD from non-demented controls and patients with amyotrophic lateral sclerosis. Therefore, platelet screening with a selective antibody could potentially be a useful tool for diagnostic purposes for AD.

FTIR and Raman Spectroscopy Applied to Dementia Diagnosis Through Analysis of Biological Fluids

To date, it is still difficult to perform an early and accurate diagnosis of dementia, therefore significant research has focused on finding new dementia biomarkers that can aid in this respect. There is an urgent need for non-invasive, rapid, and relatively inexpensive procedures for early diagnostics. Studies have demonstrated that of spectroscopic techniques, such as Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy could be a useful and accurate procedure to diagnose dementia. Given that several biochemical mechanisms related to neurodegeneration and dementia can lead to changes in plasma components and others peripheral body fluids; blood-based samples coupled to spectroscopic analyses can be used as a simple and less invasive approach.

Alzheimer's Disease: Biomarkers in the Genome, Blood, and Cerebrospinal Fluid

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that slowly destroys memory and thinking skills, resulting in behavioral changes. It is estimated that nearly 36 million are affected globally with numbers reaching 115 million by 2050. AD can only be definitively diagnosed at autopsy since its manifestations of senile plaques and neurofibrillary tangles throughout the brain cannot yet be fully captured with current imaging technologies. Current AD therapeutics have also been suboptimal. Besides identifying markers that distinguish AD from controls, there has been a recent drive to identify better biomarkers that can predict the rates of cognitive decline and neocortical amyloid burden in those who exhibit preclinical, prodromal, or clinical AD. This review covers biomarkers of three main types: genes, cerebrospinal fluid-derived, and blood-derived biomarkers. Looking ahead, cutting-edge OMICs technologies, including proteomics and metabolomics, ought to be fully tapped in order to mine even better biomarkers for AD that are more predictive.

Relationship between cytokine levels in the cerebrospinal fluid and 11C-Pittsburgh compound B retention in patients with mild cognitive impairment

AIM

In the present study, we examined the relationship between cytokine levels in the cerebrospinal fluid (CSF) and ¹¹ C-Pittsburgh compound B (PiB) retention in patients with mild cognitive impairment.

METHODS

A total of 33 participants (12 men and 21 women; mean age 76.5 years) with mild cognitive impairment underwent neuropsychological assessments, PiB positron emission tomography and analysis of cytokine levels in the CSF. The CSF levels of 48 cytokines and growth factors were measured using multiplex immunoassays. PiB retention was assessed based on a standardized uptake value ratio. Mild cognitive impairment participants were classified as PiB-positive and PiB-negative, with a cut-off level of 1.4. We compared the CSF cytokine levels and Alzheimer's disease biomarkers, including β-amyloid 1-42, total tau and tau phosphorylated at threonine 181, between the two subgroups, and evaluated the correlation between PiB retention or CSF Alzheimer's disease biomarkers and CSF cytokine levels.

RESULTS

Cytokine levels in the CSF did not differ between the two subgroups. Macrophage inflammatory protein-1β levels in the CSF significantly correlated with PiB retention only in the PiB-positive subgroup, whereas stem cell growth factor-β levels significantly correlated with PiB retention in the PiB-negative subgroup. Furthermore, stem cell growth factor-β levels significantly correlated with total tau and tau phosphorylated at threonine 181 levels in only the PiB-negative subgroup.

CONCLUSION

The present findings suggest that macrophage inflammatory protein-1β and stem cell growth factor-β are associated with chronic inflammatory processes accompanied by amyloid deposition or AD pathophysiology. Geriatr Gerontol Int 2017; 17: 1907-1913.

Biomarkers for the Early Detection and Progression of Alzheimer's Disease

The recent failures of potential disease-modifying drugs for Alzheimer's disease (AD) may reflect the fact that the enrolled participants in clinical trials are already too advanced to derive a clinical benefit. Thus, well-validated biomarkers for the early detection and accurate diagnosis of the preclinical stages of AD will be crucial for therapeutic advancement. The combinatorial use of biomarkers derived from biological fluids, such as cerebrospinal fluid (CSF), with advanced molecular imaging and neuropsychological testing may eventually achieve the diagnostic sensitivity and specificity necessary to identify people in the earliest stages of the disease when drug modification is most likely possible. In this regard, positive amyloid or tau tracer retention on positron emission tomography imaging, low CSF concentrations of the amyloid-β 1-42 peptide, high CSF concentrations in total tau and phospho-tau, mesial temporal lobe atrophy on magnetic resonance imaging, and temporoparietal/precuneus hypometabolism or hypoperfusion on 18F-fluorodeoxyglucose positron emission tomography have all emerged as biomarkers for the progression to AD. However, the ultimate AD biomarker panel will likely involve the inclusion of novel CSF and blood biomarkers more precisely associated with confirmed pathophysiologic mechanisms to improve its reliability for detecting preclinical AD. This review highlights advancements in biological fluid and imaging biomarkers that are moving the field towards achieving the goal of a preclinical detection of AD.

Blood-based biomarkers in Alzheimer disease: Current state of the science and a novel collaborative paradigm for advancing from discovery to clinic

The last decade has seen a substantial increase in research focused on the identification of blood-based biomarkers that have utility in Alzheimer's disease (AD). Blood-based biomarkers have significant advantages of being time- and cost-efficient as well as reduced invasiveness and increased patient acceptance. Despite these advantages and increased research efforts, the field has been hampered by lack of reproducibility and an unclear path for moving basic discovery toward clinical utilization. Here we reviewed the recent literature on blood-based biomarkers in AD to provide a current state of the art. In addition, a collaborative model is proposed that leverages academic and industry strengths to facilitate the field in moving past discovery only work and toward clinical use. Key resources are provided. This new public-private partnership model is intended to circumvent the traditional handoff model and provide a clear and useful paradigm for the advancement of biomarker science in AD and other neurodegenerative diseases.