Comparing biological markers of Alzheimer's disease across blood fraction and platforms: Comparing apples to oranges

In a circuit necessary for cognition and emotional affect, Alzheimer's-like pathology associates with neuroinflammation, cognitive and motivational deficits in the young adult TgF344-AD rat

In addition to extracellular amyloid plaques, intracellular neurofibrillary tau tangles, and inflammation, cognitive and emotional affect perturbations are characteristic of Alzheimer's disease (AD). The cognitive and emotional domains impaired by AD include several forms of decision making (such as intertemporal choice), blunted motivation (increased apathy), and impaired executive function (such as working memory and cognitive flexibility). However, the interaction between these domains of the mind and their supporting neurobiological substrates at prodromal stages of AD, or whether these interactions can be predictive of AD severity (individual variability), remain unclear. In this study, we employed a battery of cognitive and emotional tests in the young adult (5-7 mo) transgenic Fisher-344 AD (TgF344-AD; TgAD) rat model of AD. We also assessed whether markers of inflammation or AD-like pathology in the prelimbic cortex (PrL) of the medial prefrontal cortex (mPFC), basolateral amygdala (BLA), or nucleus accumbens (NAc), all structures that directly support the aforementioned behaviors, were predictive of behavioral deficits. We found TgAD rats displayed maladaptive decision making, greater apathy, and impaired working memory that was indeed predicted by AD-like pathology in the relevant brain structures, even at an early age. Moreover, we report that the BLA is an early epicenter of inflammation, and notably, AD-like pathology in the PrL, BLA, and NAc was predictive of BLA inflammation. These results suggest that operant-based battery testing may be sensitive enough to determine pathology trajectories, including neuroinflammation, from early stages of AD.

Identification of immune-related candidate biomarkers in plasma of patients with sporadic vestibular schwannoma

Vestibular schwannoma (VS) is an intracranial tumor arising from neoplastic Schwann cells and typically presenting with hearing loss. The traditional belief that hearing deficit is caused by physical expansion of the VS, compressing the auditory nerve, does not explain the common clinical finding that patients with small tumors can have profound hearing loss, suggesting that tumor-secreted factors could influence hearing ability in VS patients. We conducted profiling of patients' plasma for 66 immune-related factors in patients with sporadic VS (N > 170) and identified and validated candidate biomarkers associated with tumor size (S100B) and hearing (MCP-3). We further identified a nine-biomarker panel (TNR-R2, MIF, CD30, MCP-3, IL-2R, BLC, TWEAK, eotaxin, and S100B) with outstanding discriminatory ability for VS. These findings revealed possible therapeutic targets for VS, providing a unique diagnostic tool that may predict hearing change and tumor growth in VS patients, and may inform the timing of tumor resection to preserve hearing.

Influence of preanalytical and analytical factors on the quantification of six regulatory serum proteins

Background: We analyzed differences in protein concentrations in human blood serum depending on the tube material and the immunoassay platform used. Materials & methods: Blood samples from study participants were collected in glass and polypropylene tubes (n = 292). Serum concentrations of six proteins (BDNF, IGF-1, VEGF-A, TGF-β1, MCP-1 and IL-18) were assessed by using ELISAs (all biomarkers), as well as a novel fully automated immunoassay platform (all but IGF-1, n = 211). Bland-Altman analyses were conducted to investigate intrasample variability of protein concentrations. Results: Tube comparison resulted in mean biases of between -0.45 and -70.64%. Platform comparison revealed mean biases of between 21.04 and -128.10%. Conclusion: Protein concentrations can vary significantly depending on the types of tube and immunoassay used, with protein-specific differences.

Association of inflammation and cognition in the elderly: A systematic review and meta-analysis

Background

The development of mild cognitive impairment (MCI) and Alzheimer's disease (AD) may be associated with an inflammatory process. Inflammatory cytokines may be a surrogate for systemic inflammation leading to worsening neurological function. We aim to investigate the association between cognitive impairment and inflammation by pooling and analyzing the data from previously published studies.

Methods

We performed a systematic literature search on MEDLINE, PubMed, Embase, Web of Science, and Scopus for prospective longitudinal and cross-sectional studies evaluating the relationship between inflammation and cognitive functions.

Results

A total of 79 articles were included in our systematic review and meta-analysis. Pooled estimates from cross-sectional studies have demonstrated an increased level of C-reactive protein (CRP) [Hedges's g 0.35, 95% CI (0.16, 0.55), p < 0.05], IL-1β [0.94, 95% CI (-0.04, 1.92), p < 0.05], interleukin-6 (IL-6) [0.46, 95% CI (0.05, 0.88), p < 0.005], TNF alpha [0.22, 95% CI (-0.24, 0.68), p < 0.05], sTNFR-1 [0.74, 95% CI (0.46, 1.02), p < 0.05] in AD compared to controls. Similarly, higher levels of IL-1β [0.17, 95% CI (0.05, 0.28), p < 0.05], IL-6 [0.13, 95% CI (0.08, 0.18), p < 0.005], TNF alpha [0.28, 95% CI (0.07, 0.49), p < 0.05], sTNFR-1 [0.21, 95% CI (0.05, 0.48), p < 0.05] was also observed in MCI vs. control samples. The data from longitudinal studies suggested that levels of IL-6 significantly increased the risk of cognitive decline [OR = 1.34, 95% CI (1.13, 1.56)]. However, intermediate levels of IL-6 had no significant effect on the final clinical endpoint [OR = 1.06, 95% CI (0.8, 1.32)].

Conclusion

The data from cross-sectional studies suggest a higher level of inflammatory cytokines in AD and MCI as compared to controls. Moreover, data from longitudinal studies suggest that the risk of cognitive deterioration may increase by high IL-6 levels. According to our analysis, CRP, antichymotrypsin (ACT), Albumin, and tumor necrosis factor (TNF) alpha may not be good surrogates for neurological degeneration over time.

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.

Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer's Disease

The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, Friedreich's ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

Synergistic influence of cytokine gene polymorphisms over the risk of dementia: A multifactor dimensionality reduction analysis

OBJECTIVE

Evidence supports the important role of neuroinflammation in some types of dementia. This study aimed to evaluate the effect of epistasis of gene cytokines such as interleukin (IL)-α, IL-6, tumor necrosis factor (TNFα), and interferon-gamma (IFN-γ) on the susceptibility to the development of dementia.

MATERIALS AND METHODS

In the study, 221 patients diagnosed with dementia and 710 controls were included. The multifactor-dimensionality reduction (MDR) analysis was performed to identify the epistasis between SNP located in genes of IL-α (rs1800587), IL-6 (rs1800796), TNFα (rs361525 and rs1800629), and IFNγ (rs2069705). The best risk prediction model was identified based on precision and cross-validation consistency.

RESULTS

Multifactor-dimensionality reduction analysis detected a significant model with the genes TNFα, IFNγ, IL1α, and IL6 (prediction success: 72%, p < 0.0001). When risk factors were analyzed with these polymorphisms, the model achieved a similar prediction for dementia as the genes-only model.

CONCLUSION

These data indicate that gene-gene interactions form significant models to identify populations susceptible to dementia.

Plasma biomarkers predict cognitive trajectories in an ethnoracially and clinically diverse cohort: Mediation with hippocampal volume

Introduction

We examine whether the association between key plasma biomarkers (amyloid β [aβ] 42/40, total tau (t-tau), neurofilament light [NfL]) and cognitive trajectories (executive function [EF] and episodic memory [EM]) is mediated through neurodegeneration.

Methods

All participants were recruited from the University of California, Davis-Alzheimer's Disease Research Center (n = 473; baseline age range = 49-95 years, 60% women). We applied an accelerated longitudinal design to test latent growth models for EF and EM, and path and mediation analyses. Age was centered at 75 years, and all models were adjusted for sex, education, and ethnicity.

Results

HV differentially mediated the association aβ 42/40 and NfL on EF and EM level and change. Hippocampal volume (HV) did not mediate the association between t-tau and cognitive performance.

Discussion

Neurodegeneration as represented with HV selectively mediates the association between key non-invasive plasma biomarkers and cognitive trajectories in an ethnoracially and clinically diverse community-based sample.

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.

Plasma microRNA vary in association with the progression of Alzheimer's disease

Introduction

Early intervention in Alzheimer's disease (AD) requires the development of an easily administered test that is able to identify those at risk. Focusing on microRNA robustly detected in plasma and standardizing the analysis strategy, we sought to identify disease‐stage specific biomarkers.

Methods

Using TaqMan microfluidics arrays and a statistical consensus approach, we assessed plasma levels of 185 neurodegeneration‐related microRNA, in cohorts of cognitively normal amyloid β‐positive (CN‐Aβ+), mild cognitive impairment (MCI), and Alzheimer's disease (AD) participants, relative to their respective controls.

Results

Distinct disease stage microRNA biomarkers were identified, shown to predict membership of the groups (area under the curve [AUC] >0.8) and were altered dynamically with AD progression in a longitudinal study. Bioinformatics demonstrated that these microRNA target known AD‐related pathways, such as the Phosphoinositide 3‐kinase (PI3K‐Akt) signalling pathway. Furthermore, a significant correlation was found between miR‐27a‐3p, miR‐27b‐3p, and miR‐324‐5p and amyloid beta load.

Discussion

Our results show that microRNA signatures alter throughout the progression of AD, reflect the underlying disease pathology, and may prove to be useful diagnostic markers.

Characterizing plasma NfL in a community-dwelling multi-ethnic cohort: Results from the HABLE study

INTRODUCTION

No large-scale characterizations of neurofilament light chain (NfL) have been conducted in diverse populations.

METHODS

Baseline data were analyzed among n = 890 Mexican Americans and n = 813 non-Hispanic Whites from the multi-ethnic Health & Aging Brain among Latino Elders (HABLE) study. Plasma NfL was measured on the Simoa platform.

RESULTS

In unadjusted models, NfL was significantly associated with age (P < .001), hypertension (P  < .001), dyslipidemia (P = .02), and diabetes (P  < .001). Covarying for age and sex, NfL was associated with neurodegeneration (P  < .001) and global amyloid burden levels (P = .02) in a subset with available data. NfL levels were significantly associated with diagnostic groups (Normal Cognition [NC], mild cognitive impairment [MCI], Dementia; P  < .001); however, there was no cut-score that yielded acceptable diagnostic accuracy. NfL levels produced a sensitivity of 0.60 and specificity of 0.78 with negative predictive value of 89% for detecting amyloid positivity.

DISCUSSION

Plasma NfL levels are significantly impacted by age and medical co-morbidities that are common among older adults, which complicate its utility as a diagnostic biomarker.

Characteristics of Dysregulated Proinflammatory Cytokines and Cognitive Dysfunction in Late-Life Depression and Amnestic Mild Cognitive Impairment

Background

Late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are two different diseases associated with a high risk of developing Alzheimer's disease (AD). Both diseases are accompanied by dysregulation of inflammation. However, the differences and similarities of peripheral inflammatory parameters in these two diseases are not well understood.

Methods

We used Luminex assays to measure 29 cytokines simultaneously in the plasma of two large cohorts of subjects at high risk for AD (23 LLD and 23 aMCI) and 23 normal controls (NCs) in the community. Demographics and lifestyle factors were also collected. Cognitive function was evaluated with the Chinese versions of the Montreal Cognitive Assessment (C-MoCA) and neuropsychological test battery (NTB).

Results

We observed a remarkably increased level of IL-6 in the plasma and reduced levels of chemokines (CXCL11 and CCL13) in the LLD group compared with the aMCI group. The LLD group also showed lower levels of CXCL16 than the NC group. Furthermore, altered cytokine levels were associated with abnormal results in neuropsychological testing and Geriatric Depression Scale scores in both the LLD and aMCI groups. Notably, combinations of cytokines (IL-6 and CCL13) and two subitems of C-MoCA (orientation and short-term memory) generated the best area under the receiver operating characteristic curve (AUROC = 0.974).

Conclusion

A novel model based on proinflammatory cytokines and brief screening tests performs with fair accuracy in the discrimination between LLD and aMCI. These findings will give clues to provide new therapeutic targets for interventions or markers for two diseases with similar predementia syndromes.

Interbatch Reliability of Blood-Based Cytokine and Chemokine Measurements in Community-Dwelling Older Adults: A Cross-Sectional Study

Blood-based inflammatory markers hold considerable promise for diagnosis and prognostication of age-related neurodegenerative disease, though a paucity of research has empirically tested how reliably they can be measured across different experimental runs ("batches"). We quantified the interbatch reliability of 13 cytokines and chemokines in a cross-sectional study of 92 community-dwelling older adults (mean age = 74; 48% female). Plasma aliquots from the same blood draw were parallelly processed in 2 separate batches using the same analytic platform and procedures (high-performance electrochemiluminescence by Meso Scale Discovery). Interbatch correlations (Pearson's r) ranged from small and nonsignificant (r = .13 for macrophage inflammatory protein-1 alpha [MIP-1α]) to very large (r > .90 for interferon gamma [IFNγ], interleukin-10 [IL-10], interferon gamma-induced protein 10 [IP-10], MIP-1β, thymus and activation-regulated chemokine [TARC]) with most markers falling somewhere in between (.67 ≤ r ≤ .90 for IL-6, tumor necrosis factor alpha [TNF-α], Eotaxin, Eotaxin-3, monocyte chemoattractant protein-1 [MCP-1], MCP-4, macrophage-derived chemokine [MDC]). All markers, except for IL-6 and MCP-4, showed significant differences in absolute values between batches, with discrepancies ranging in effect size (Cohen's d) from small to moderate (0.2 ≤ |d| ≤ 0.5 for IL-10, IP-10, MDC) to large or very large (0.68 ≤ |d| ≤ 1.5 for IFNγ, TNF-α, Eotaxin, Eotaxin-3, MCP-1, MIP-1α, MIP-1β, TARC). Relatively consistent associations with external variables of interest (age, sex, systolic blood pressure, body mass index, cognition) were observed across batches. Taken together, our results suggest heterogeneity in measurement reliability of blood-based cytokines and chemokines, with some analytes outperforming others. Future work is needed to evaluate the generalizability of these findings while identifying potential sources of batch effect measurement error.

The Health & Aging Brain among Latino Elders (HABLE) study methods and participant characteristics

INTRODUCTION

Mexican Americans remain severely underrepresented in Alzheimer's disease (AD) research. The Health & Aging Brain among Latino Elders (HABLE) study was created to fill important gaps in the existing literature.

METHODS

Community-dwelling Mexican Americans and non-Hispanic White adults and elders (age 50 and above) were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing, and 3T magnetic resonance imaging (MRI) of the brain. Amyloid and tau positron emission tomography (PET) scans were added at visit 2. Blood samples were stored in the Biorepository.

RESULTS

Data was examined from n = 1705 participants. Significant group differences were found in medical, demographic, and sociocultural factors. Cerebral amyloid and neurodegeneration imaging markers were significantly different between Mexican Americans and non-Hispanic Whites.

DISCUSSION

The current data provide strong support for continued investigations that examine the risk factors for and biomarkers of AD among diverse populations.

Effect of mindfulness intervention versus health education program on salivary Aβ-42 levels in community-dwelling older adults with mild cognitive impairment: A randomized controlled trial

BACKGROUND

Few randomized controlled trials have investigated the effects of mindfulness intervention on older adults diagnosed with mild cognitive impairment (MCI). Specifically, scarce literature exists on the potential benefits of mindfulness intervention on biomarkers representing AD hallmarks. Our previous studies showed the potential of Mindful Awareness Practice (MAP) in improving multiple biomarkers of gut microbiota, systemic inflammation, and synaptic functions. Extending these findings, in this study, we conducted analysis on bio-banked saliva samples, examining whether MAP improved salivary amyloid beta-42 (Aβ-42) levels in community-dwelling older adults diagnosed with MCI. We also explored the moderating role of education level, an indicator of cognitive reserve, on intervention effect.

METHODS

A total of 55 community-dwelling older adults diagnosed with MCI were randomized into either the treatment arm, MAP, or the active control arm, the health education program (HEP). Interventions were performed for a total of nine months. Field and laboratory investigators who were blinded to the treatment allocations collected saliva samples at baseline, 3-month, and 9-month follow-ups. Salivary Aβ-42 levels were quantified using a commercial assay. Linear-mixed models were used to examine the effect of MAP on salivary Aβ-42 levels.

RESULTS

Compared to the HEP arm, MAP participants had no significantly modified Aβ-42 levels throughout the 9-month intervention period, regardless of subgroup analyses stratified by either sex or MCI-subtypes (amnestic and non-amnestic). Exploring the moderating effect of education, participants in the HEP arm with higher education levels had significantly lower salivary Aβ-42 at 3-month time-point.

DISCUSSION

Taken together with our previous findings and other mindfulness interventional studies failing to find a significant effect on peripheral Aβ-42, we conclude the non-significant effects of mindfulness intervention on ameliorating peripheral Aβ-42 levels. Conversely, participants in the HEP arm with higher cognitive reserve had significantly improved salivary Aβ-42, highlighting the role of cognitive reserve in moderating treatment response in MCI.

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.

Proteomic profiles for Alzheimer's disease and mild cognitive impairment among adults with Down syndrome spanning serum and plasma: An Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study

INTRODUCTION

Previously generated serum and plasma proteomic profiles were examined among adults with Down syndrome (DS) to determine whether these profiles could discriminate those with mild cognitive impairment (MCI-DS) and Alzheimer's disease (DS-AD) from those cognitively stable (CS).

METHODS

Data were analyzed on n = 305 (n = 225 CS; n = 44 MCI-DS; n = 36 DS-AD) enrolled in the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS).

RESULTS

Distinguishing MCI-DS from CS, the serum profile produced an area under the curve (AUC) = 0.95 (sensitivity [SN] = 0.91; specificity [SP] = 0.99) and an AUC = 0.98 (SN = 0.96; SP = 0.97) for plasma when using an optimized cut-off score. Distinguishing DS-AD from CS, the serum profile produced an AUC = 0.93 (SN = 0.81; SP = 0.99) and an AUC = 0.95 (SN = 0.86; SP = 1.0) for plasma when using an optimized cut-off score. AUC remained unchanged to slightly improved when age and sex were included. Eotaxin3, interleukin (IL)-10, C-reactive protein, IL-18, serum amyloid A , and FABP3 correlated fractions at r2 > = 0.90.

DISCUSSION

Proteomic profiles showed excellent detection accuracy for MCI-DS and DS-AD.

Cross-platform comparison of highly sensitive immunoassay technologies for cytokine markers: Platform performance in post-traumatic stress disorder and Parkinson's disease

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Cross-platform comparisons were conducted across five leading immunoassay platforms.

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Plasma and serum were obtained from healthy controls and clinical populations.

•

Analytic parameters included sensitivity, precision, and performance correlation.

•

Platform performance was highly variable, particularly for low-abundant cytokines.

•

Findings highlight certain immunoassays should be prioritized in future research.

There is mounting evidence of systemic inflammation in post-traumatic stress disorder (PTSD) and Parkinson’s disease (PD), yet inconsistency and a lack of replicability in findings of putative biological markers have delayed progress in this space. Variability in performance between platforms may contribute to the lack of consensus in the biomarker literature, as has been seen for a number of psychiatric disorders, including PTSD. Thus, there is a need for high-performance, scalable, and validated platforms for the discovery and development of biomarkers of inflammation for use in drug development and as clinical diagnostics. To identify the best platform for use in future biomarker discovery efforts, we conducted a comprehensive cross-platform and cross-assay evaluation across five leading platform technologies. This initial assessment focused on four cytokines that have been implicated PTSD – interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ. To assess platform performance and understand likely measurements in individuals with brain disorders, serum and plasma samples were obtained from individuals with PTSD (n = 13) or Parkinson’s Disease (n = 14) as well as healthy controls (n = 5). We compared platform performance across a number of common analytic parameters, including assay precision, sensitivity, frequency of endogenous analyte detection (FEAD), correlation between platforms, and parallelism in measurement of cytokines using a serial dilution series. The single molecule array (Simoa™) ultra-sensitive platform (Quanterix), MESO V-Plex (Mesoscale Discovery), and Luminex xMAP® (Myriad) were conducted by their respective vendors, while Luminex® and Quantikine® high-sensitivity ELISA assays were evaluated by R&D System’s Biomarker Testing Services. The assay with the highest sensitivity in detecting endogenous analytes across all analytes and clinical populations (i.e. the highest FEAD), was the Simoa™ platform. In contrast, more variable performance was observed for MESO V-plex, R&D Luminex® and Quantikine®, while Myriad’s Luminex xMAP® exhibited low FEAD across all analytes and samples. Simoa™ also demonstrated high precision in detecting endogenous cytokines, as reflected in < 20 percent coefficient of variance (%CV) across replicate runs for samples from the healthy controls, PTSD patients, and PD patients. In contrast, MESO V-Plex, R&D Luminex® and Quantikine® had variable performance in terms of precision across cytokines. Myriad Luminex xMAP® could not be included in precision estimates because the vendor did not run samples in duplicate. For cross-platform performance comparisons, the highest cross-platform correlations were observed for IL-6 such that all platforms – except for Myriad’s Luminex xMAP® – had strong correlations with one another in measurements of IL-6 (r range = 0.59 – 0.86). For the other cytokines, there was low to no correlation across platforms, such that reported measurements of IL-1β, TNF-α, and IFN-γ varied across assays. Taken together, these findings provide novel evidence that the choice of immunoassay could greatly impact reported cytokine findings. The current study provides crucial information on the variability in performance between platforms and across immunoassays that may help inform the selection of assay in future research studies. Further, the results emphasize the need for performing comparative evaluations of immunoassays as new technologies emerge over time, particularly given the lack of reference standards for the quantitative assessments of cytokines.

Combining Select Blood-Based Biomarkers with Neuropsychological Assessment to Detect Mild Cognitive Impairment among Mexican Americans

BACKGROUND

Recent work has supported use of blood-based biomarkers in detection of amnestic mild cognitive impairment (MCI). Inclusion of neuropsychological measures has shown promise in enhancing utility of biomarkers to detect disease.

OBJECTIVE

The present study sought to develop cognitive-biomarker profiles for detection of MCI.

METHODS

Data were analyzed on 463 participants (normal control n = 378; MCI n = 85) from HABLE. Random forest analyses determined proteomic profile of MCI. Separate linear regression analyses determined variance accounted for by select biomarkers per neuropsychological measure. When neuropsychological measure with the least shared variance was identified, it was then combined with select biomarkers to create a biomarker-cognitive profile.

RESULTS

The biomarker-cognitive profile was 90% accurate in detecting MCI. Among amnestic MCI cases, the detection accuracy of the biomarker-cognitive profile was 92% and increased to 94% with demographic variables.

CONCLUSION

The biomarker-cognitive profile for MCI was highly accurate in its detection with use of only five biomarkers.

Advance in Plasma AD Core Biomarker Development: Current Findings from Immunomagnetic Reduction-Based SQUID Technology

New super-sensitive biomarker assay platforms for measuring Alzheimer's disease (AD) core pathological markers in plasma have recently been developed and tested. Research findings from these technologies offer promising evidence for identifying the earliest stages of AD and correlating them with brain pathological progression. Here, we review findings using immunomagnetic reduction, one of these ultrasensitive technologies. The principles, technology and assays developed, along with selected published findings will be discussed. The major findings from this technology were significant increases of amyloid beta (Aβ) 42 and total tau (t-tau) levels in subjects clinically diagnosed with early AD when compared with cognitively normal control (NC) subjects. The composite marker of the product of Aβ42 and t-tau discriminated subjects with early AD from NC subjects with high accuracy. The potential of this technology for the purpose of early or preclinical disease stage detection has yet to be explored in subjects who have also been assessed with brain imaging and cerebrospinal fluid AD core biomarker measurements.

Blood-based protein predictors of dementia severity as measured by δ: Replication across biofluids and cohorts

INTRODUCTION

Dementia severity can be empirically described by the latent dementia phenotype "δ" and its various composite "homologs". We have explored δ's blood-based protein biomarkers in the Texas Alzheimer's Research and Care Consortium (TARCC) study. However, it would be convenient to replicate those associations in the Alzheimer's Disease Neuroimaging Initiative (ADNI). To this end, we recently engineered a δ homolog from observed cognitive performance measures common to both projects (i.e., "dT2A").

METHODS

We used nine rationally chosen peripheral blood-based protein biomarkers as indicators of a latent variable "INFLAMMATION". We then associated that construct with dT2A in structural equation models adjusted for age, gender, depressive symptoms, and apolipoprotein E (APOE) ε4 allelic burden. Significant factor loadings and INFLAMMATION's association with dT2A were confirmed in random splits of TARCC's relatively large sample, and across biofluids in the ADNI.

RESULTS

Nine proteins measured in serum (TARCC) or plasma (ADNI) explained ≅10% of dT2A's variance in both samples, independently of age, APOE, education, and gender. All loaded significantly on INFLAMMATION, and positively or negatively, depending on their known roles are PRO- or ANTI-inflammatory proteins, respectively. The parameters of interest were confirmed across random 50% splits of the TARCC's sample, and replicated across biofluids in the ADNI.

DISCUSSION

These results suggest that SEM can be used to replicate biomarker findings across samples and biofluids, and that a substantial fraction of dementia's variance is attributable to peripheral blood-based protein levels.

Proteins and microRNAs are differentially expressed in tear fluid from patients with Alzheimer's disease

Alzheimer's disease (AD) is characterized by a progressive loss of neurons and cognitive functions. Therefore, early diagnosis of AD is critical. The development of practical and non-invasive diagnostic tests for AD remains, however, an unmet need. In the present proof-of-concept study we investigated tear fluid as a novel source of disease-specific protein and microRNA-based biomarkers for AD development using samples from patients with mild cognitive impairment (MCI) and AD. Tear protein content was evaluated via liquid chromatography-mass spectrometry and microRNA content was profiled using a genome-wide high-throughput PCR-based platform. These complementary approaches identified enrichment of specific proteins and microRNAs in tear fluid of AD patients. In particular, we identified elongation initiation factor 4E (eIF4E) as a unique protein present only in AD samples. Total microRNA abundance was found to be higher in tears from AD patients. Among individual microRNAs, microRNA-200b-5p was identified as a potential biomarker for AD with elevated levels present in AD tear fluid samples compared to controls. Our study suggests that tears may be a useful novel source of biomarkers for AD and that the identification and verification of biomarkers within tears may allow for the development of a non-invasive and cost-effective diagnostic test for AD.

Metabolomics Analyses of Saliva Detect Novel Biomarkers of Alzheimer's Disease

Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer's disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

Definition and quantification of six immune- and neuroregulatory serum proteins in healthy and demented elderly

Aim: Blood-based biomarkers related to immune- and neuroregulatory processes may be indicative of dementia but lack standardization and proof-of-principle studies. Materials & methods: The blood serum collection protocol as well as the analytic procedure to quantify the markers BDNF, IGF-1, VEGF, TGF-β 1, MCP-1 and IL-18 in blood serum were standardized and their concentrations were compared between groups of 81 Alzheimer’s disease patients and 79 healthy controls. Results: Applying standardized methods, results for the quantification of the six markers in blood serum are stable and their concentrations significantly differ for all analytes except VEGF between patients diagnosed with Alzheimer’s disease and healthy controls. Conclusion: Analyzing a panel of six markers in blood serum under standardized conditions may serve as a diagnostic tool in primary dementia care in the future.

Levels of α-2 Macroglobulin in cognitively normal Mexican- Americans with Subjective Cognitive Decline: A HABLE Study

BACKGROUND

The presence of Subjective Cognitive Decline (SCD) in the absence of objective change and the inflammatory biomarker Alpha 2 Macroglobulin (A2M) have both been implicated in preclinical Alzheimer's disease. Mexican Americans are population with high rates of cardiovascular and inflammatory disorders.

OBJECTIVES

The current study investigated the levels of A2M in cognitively normal Mexican Americans with and without complaints of cognitive decline.

METHOD

293 (243 females, 50 males) community-based cognitively normal older Mexican Americans from the ongoing Health and Aging Brain among Latino Elders (HABLE) study were grouped based on subjective cognitive decline and blood samples were assayed by electrochemiluminescence to determine levels of A2M.

RESULTS

Participants with SCD had significantly higher levels of A2M than those without SCD. Females with SCD had a significantly higher level of A2M.

CONCLUSIONS

Results suggest that higher levels of A2M, a marker of neuronal injury, may be involved in subtle changes in cognitive functioning recognizable to persons reporting SCD but too subtle to be objectively measured. Longitudinal research is needed to assess the impact of SDC and A2M in progression to MCI and dementia in Mexican Americans.

A δ Homolog for Dementia Case Finding with Replication in the Alzheimer's Disease Neuroimaging Initiative

Dementia can be empirically described by the latent dementia phenotype "δ" and its various composite "homologs". We have explored δ's blood-based protein biomarkers in the Texas Alzheimer's Research and Care Consortium (TARCC) study. However, it would be convenient to replicate those associations in the Alzheimer's Disease Neuroimaging Initiative (ADNI). To this end, we have engineered a δ homolog from observed cognitive performance measures common to both projects. Our findings were replicated in randomly selected 50% splits of TARCC data (Group 1, N = 1,747; Group 2, N = 1,755), and then independently in ADNI (N = 1,737). The new δ homolog, i.e., "dT2A" (d-TARCC to ADNI), fit the data of both studies well, and was strongly correlated with dementia severity, as rated by the Clinical Dementia Rating Scale "sum of boxes" (TARCC: r = 0.99, p < 0.001; ADNI: r = 0.96, p < 0.001). dT2A achieved an area under the receiver operating characteristic curve of 0.981 (0.976-0.985) for the discrimination of Alzheimer's disease from normal controls in TARCC, and 0.988 (0.983-0.993) in ADNI. dT2A is the 12th δ homolog published to date, and opens the door to independent replications across these and similar studies.

Omics-based Biomarkers for the Early Alzheimer Disease Diagnosis and Reliable Therapeutic Targets Development

BACKGROUND

Alzheimer's disease (AD), the most common cause of dementia in adulthood, has great medical, social, and economic impact worldwide. Available treatments result in symptomatic relief, and most of them are indicated from the early stages of the disease. Therefore, there is an increasing body of research developing accurate and early diagnoses, as well as diseasemodifying therapies.

OBJECTIVE

Advancing the knowledge of AD physiopathological mechanisms, improving early diagnosis and developing effective treatments from omics-based biomarkers.

METHODS

Studies using omics technologies to detect early AD, were reviewed with a particular focus on the metabolites/lipids, micro-RNAs and proteins, which are identified as potential biomarkers in non-invasive samples.

RESULTS

This review summarizes recent research on metabolomics/lipidomics, epigenomics and proteomics, applied to early AD detection. Main research lines are the study of metabolites from pathways, such as lipid, amino acid and neurotransmitter metabolisms, cholesterol biosynthesis, and Krebs and urea cycles. In addition, some microRNAs and proteins (microglobulins, interleukins), related to a common network with amyloid precursor protein and tau, have been also identified as potential biomarkers. Nevertheless, the reproducibility of results among studies is not good enough and a standard methodological approach is needed in order to obtain accurate information.

CONCLUSION

The assessment of metabolomic/lipidomic, epigenomic and proteomic changes associated with AD to identify early biomarkers in non-invasive samples from well-defined participants groups will potentially allow the advancement in the early diagnosis and improvement of therapeutic interventions.

A comparison of biofluid cytokine markers across platform technologies: Correspondence or divergence?

BACKGROUND

Quantification of biofluid cytokines is a rapidly growing area of translational research. However, comparability across the expanding number of available assay platforms for detection of the same proteins remains to be determined. We aimed to directly compare a panel of commonly measured cytokines in plasma of typically aging adults across two high sensitivity quantification platforms, Meso Scale Discovery high performance electrochemiluminiscence (HPE) and single-molecule immunosorbent assays (Simoa) by Quanterix.

METHODS

57 community-dwelling older adults completed a blood draw, neuropsychological assessment, and brain MRI as part of a healthy brain aging study. Plasma samples from the same draw dates were analyzed for IL-10, IP-10, IL-6, TNFα, and IL-1β on HPE and Simoa, separately. Reliable detectability (coefficient of variance (CV) < 20% and outliers 3 interquartiles above the median removed), intra-assay precision, absolute concentrations, reproducibility across platforms, and concurrent associations with external variables of interest (e.g., demographics, peripheral markers of vascular health, and brain health) were examined.

RESULTS

The proportion of cytokines reliably measured on HPE (87.7-93.0%) and Simoa (75.4-93.0%) did not differ (ps > 0.32), with the exception of IL-1β which was only reliably measured using Simoa (68.4%). On average, CVs were acceptable at <8% across both platforms. Absolute measured concentrations were higher using Simoa for IL-10, IL-6, and TNFα (ps < 0.05). HPE and Simoa shared only small-to-moderate proportions of variance with one another on the same cytokine proteins (range: r = 0.26 for IL-10 to r = 0.64 for IL-6), though platform agreement did not dependent on cytokine concentrations. Cytokine ratios within each platform demonstrated similar relative patterns of up- and down-regulation across HPE and Simoa, though still significantly differed (ps < 0.001). Supporting concurrent validity, all 95% confidence intervals of the correlations between cytokines and external variables overlapped between the two platforms. Moreover, most associations were in expected directions and consistently so across platforms (e.g., IL-6 and TNFα), though with several notable exceptions for IP-10 and IL-10.

CONCLUSIONS

HPE and Simoa showed comparable detectability and intra-assay precision measuring a panel of commonly examined cytokine proteins, with the exception of IL-1β which was not reliably detected on HPE. However, Simoa demonstrated overall higher concentrations and the two platforms did not show agreement when directly compared against one another. Relative cytokine ratios and associations demonstrated similar patterns across platforms. Absolute cytokine concentrations may not be directly comparable across platforms, may be analyte dependent, and interpretation may be best limited to discussion of relative associations.

Elevated plasma ferritin in elderly individuals with high neocortical amyloid-β load

Ferritin, an iron storage and regulation protein, has been associated with Alzheimer's disease (AD); however, it has not been investigated in preclinical AD, detected by neocortical amyloid-β load (NAL), before cognitive impairment. Cross-sectional analyses were carried out for plasma and serum ferritin in participants in the Kerr Anglican Retirement Village Initiative in Aging Health cohort. Subjects were aged 65-90 years and were categorized into high and low NAL groups via positron emission tomography using a standard uptake value ratio cutoff=1.35. Ferritin was significantly elevated in participants with high NAL compared with those with low NAL, adjusted for covariates age, sex, apolipoprotein E ɛ4 carriage and levels of C-reactive protein (an inflammation marker). Ferritin was also observed to correlate positively with NAL. A receiver operating characteristic curve based on a logistic regression of the same covariates, the base model, distinguished high from low NAL (area under the curve (AUC)=0.766), but was outperformed when plasma ferritin was added to the base model (AUC=0.810), such that at 75% sensitivity, the specificity increased from 62 to 71% on adding ferritin to the base model, indicating that ferritin is a statistically significant additional predictor of NAL over and above the base model. However, ferritin's contribution alone is relatively minor compared with the base model. The current findings suggest that impaired iron mobilization is an early event in AD pathogenesis. Observations from the present study highlight ferritin's potential to contribute to a blood biomarker panel for preclinical AD.

Characterization and clinical use of inflammatory cerebrospinal fluid protein markers in Alzheimer's disease

Background

Neuroinflammation has gained increasing attention as a potential contributing factor in Alzheimer’s disease (AD) pathology. A clinical cerebrospinal fluid biomarker capable of monitoring this process during the course of the disease has yet to emerge, chiefly owing to contradictory research findings. In this study, we sought to clarify the utility of inflammatory biomarkers in diagnostic procedures of AD in three steps: (1) to screen for proteins that are robustly detectable in cerebrospinal fluid; (2) based on this analysis, to explore any associations between the analytically robust markers and salient pathological features of AD; and (3) to determine the discriminative power of these markers in the clinical diagnosis of AD.

Methods

From a total of 46 proteins, 15 that were robustly detectable in cerebrospinal fluid were identified. A subsequent analysis of these markers in a cohort of 399 patients (nondemented subjects, patients with mild cognitive impairment [MCI], and patients with AD, supplemented by smaller cohorts of other diseases) was conducted. Fluid biomarker data were related to AD pathology and neuropsychological markers and adjusted for confounders such as age, sex, apolipoprotein E genotype, and biobank storage time.

Results

Cerebrospinal fluid levels of C-reactive protein and soluble TREM2 differed between nondemented subjects, patients with MCI, or patients with AD and were associated with amyloid and tau pathology. Several markers were associated with tau pathology only or with other neurodegenerative diseases. Correlations between neuropsychological performance and inflammatory markers were weak, but they were most prominent in AD and for the most challenging cognitive tests. All investigated covariates had significant influence, with varying effects across the markers. Still, none of the markers achieved discriminative power of more than 70% to distinguish between patient groups defined by clinical or neuropathological categories.

Conclusions

Basic analytical considerations proved indispensable for this type of study because only one-third of the tested markers were robustly detectable in cerebrospinal fluid. Detectable inflammatory protein markers were associated in multiple ways with AD pathology. Yet, even significantly associated markers were not powerful enough in terms of effect strength, sensitivity, and specificity, and hence they were not suited for direct use in clinical diagnostic practice. Targets other than those most commonly considered in this field of research might provide results with better clinical applicability.

Electronic supplementary material

The online version of this article (10.1186/s13195-018-0353-3) contains supplementary material, which is available to authorized users.

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Mast Cell Activation in Brain Injury, Stress, and Post-traumatic Stress Disorder and Alzheimer's Disease Pathogenesis

Mast cells are localized throughout the body and mediate allergic, immune, and inflammatory reactions. They are heterogeneous, tissue-resident, long-lived, and granulated cells. Mast cells increase their numbers in specific site in the body by proliferation, increased recruitment, increased survival, and increased rate of maturation from its progenitors. Mast cells are implicated in brain injuries, neuropsychiatric disorders, stress, neuroinflammation, and neurodegeneration. Brain mast cells are the first responders before microglia in the brain injuries since mast cells can release prestored mediators. Mast cells also can detect amyloid plaque formation during Alzheimer's disease (AD) pathogenesis. Stress conditions activate mast cells to release prestored and newly synthesized inflammatory mediators and induce increased blood-brain barrier permeability, recruitment of immune and inflammatory cells into the brain and neuroinflammation. Stress induces the release of corticotropin-releasing hormone (CRH) from paraventricular nucleus of hypothalamus and mast cells. CRH activates glial cells and mast cells through CRH receptors and releases neuroinflammatory mediators. Stress also increases proinflammatory mediator release in the peripheral systems that can induce and augment neuroinflammation. Post-traumatic stress disorder (PTSD) is a traumatic-chronic stress related mental dysfunction. Currently there is no specific therapy to treat PTSD since its disease mechanisms are not yet clearly understood. Moreover, recent reports indicate that PTSD could induce and augment neuroinflammation and neurodegeneration in the pathogenesis of neurodegenerative diseases. Mast cells play a crucial role in the peripheral inflammation as well as in neuroinflammation due to brain injuries, stress, depression, and PTSD. Therefore, mast cells activation in brain injury, stress, and PTSD may accelerate the pathogenesis of neuroinflammatory and neurodegenerative diseases including AD. This review focusses on how mast cells in brain injuries, stress, and PTSD may promote the pathogenesis of AD. We suggest that inhibition of mast cells activation and brain cells associated inflammatory pathways in the brain injuries, stress, and PTSD can be explored as a new therapeutic target to delay or prevent the pathogenesis and severity of AD.

Age-related neurodegenerative disease associated pathways identified in retinal and vitreous proteome from human glaucoma eyes

Glaucoma is a chronic disease that shares many similarities with other neurodegenerative disorders of the central nervous system. This study was designed to evaluate the association between glaucoma and other neurodegenerative disorders by investigating glaucoma-associated protein changes in the retina and vitreous humour. The multiplexed Tandem Mass Tag based proteomics (TMT-MS3) was carried out on retinal tissue and vitreous humour fluid collected from glaucoma patients and age-matched controls followed by functional pathway and protein network interaction analysis. About 5000 proteins were quantified from retinal tissue and vitreous fluid of glaucoma and control eyes. Of the differentially regulated proteins, 122 were found linked with pathophysiology of Alzheimer’s disease (AD). Pathway analyses of differentially regulated proteins indicate defects in mitochondrial oxidative phosphorylation machinery. The classical complement pathway associated proteins were activated in the glaucoma samples suggesting an innate inflammatory response. The majority of common differentially regulated proteins in both tissues were members of functional protein networks associated brain changes in AD and other chronic degenerative conditions. Identification of previously reported and novel pathways in glaucoma that overlap with other CNS neurodegenerative disorders promises to provide renewed understanding of the aetiology and pathogenesis of age related neurodegenerative diseases.

Amyloid Beta and Tau as Alzheimer's Disease Blood Biomarkers: Promise From New Technologies

The utility of the levels of amyloid beta (Aβ) peptide and tau in blood for diagnosis, drug development, and assessment of clinical trials for Alzheimer's disease (AD) has not been established. The lack of availability of ultra-sensitive assays is one critical issue that has impeded progress. The levels of Aβ species and tau in plasma and serum are much lower than levels in cerebrospinal fluid. Furthermore, plasma or serum contain high levels of assay-interfering factors, resulting in difficulties in the commonly used singulex or multiplex ELISA platforms. In this review, we focus on two modern immune-complex-based technologies that show promise to advance this field. These innovative technologies are immunomagnetic reduction technology and single molecule array technology. We describe the technologies and discuss the published studies using these technologies. Currently, the potential of utilizing these technologies to advance Aβ and tau as blood-based biomarkers for AD requires further validation using already collected large sets of samples, as well as new cohorts and population-based longitudinal studies.

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.

PRECISION MEDICINE - The Golden Gate for Detection, Treatment and Prevention of Alzheimer's Disease

During this decade, breakthrough conceptual shifts have commenced to emerge in the field of Alzheimer's disease (AD) recognizing risk factors and the non-linear dynamic continuum of complex pathophysiologies amongst a wide dimensional spectrum of multi-factorial brain proteinopathies/neurodegenerative diseases. As is the case in most fields of medicine, substantial advancements in detecting, treating and preventing AD will likely evolve from the generation and implementation of a systematic precision medicine strategy. This approach will likely be based on the success found from more advanced research fields, such as oncology. Precision medicine will require integration and transfertilization across fragmented specialities of medicine and direct reintegration of Neuroscience, Neurology and Psychiatry into a continuum of medical sciences away from the silo approach. Precision medicine is biomarker-guided medicine on systems-levels that takes into account methodological advancements and discoveries of the comprehensive pathophysiological profiles of complex multi-factorial neurodegenerative diseases, such as late-onset sporadic AD. This will allow identifying and characterizing the disease processes at the asymptomatic preclinical stage, where pathophysiological and topographical abnormalities precede overt clinical symptoms by many years to decades. In this respect, the uncharted territory of the AD preclinical stage has become a major research challenge as the field postulates that early biomarker guided customized interventions may offer the best chance of therapeutic success. Clarification and practical operationalization is needed for comprehensive dissection and classification of interacting and converging disease mechanisms, description of genomic and epigenetic drivers, natural history trajectories through space and time, surrogate biomarkers and indicators of risk and progression, as well as considerations about the regulatory, ethical, political and societal consequences of early detection at asymptomatic stages. In this scenario, the integrated roles of genome sequencing, investigations of comprehensive fluid-based biomarkers and multimodal neuroimaging will be of key importance for the identification of distinct molecular mechanisms and signaling pathways in subsets of asymptomatic people at greatest risk for progression to clinical milestones due to those specific pathways. The precision medicine strategy facilitates a paradigm shift in Neuroscience and AD research and development away from the classical "one-size-fits-all" approach in drug discovery towards biomarker guided "molecularly" tailored therapy for truly effective treatment and prevention options. After the long and winding decade of failed therapy trials progress towards the holistic systems-based strategy of precision medicine may finally turn into the new age of scientific and medical success curbing the global AD epidemic.