A blood screening test for Alzheimer's disease

Alzheimer's disease approaches - Focusing on pathology, biomarkers and clinical trial candidates

The strategy for the development of new drugs for Alzheimer's disease (AD) recognizes that an effective therapy requires early therapeutic intervention and a multifactorial approach that considers the individual initiators of AD development. Current knowledge of AD includes the understanding of pathophysiology, risk factors, biomarkers, and the evolving patterns of biomarker abnormalities. This knowledge is essential in identifying potential molecular targets for new drug development. This review summarizes promising AD drug candidates, many of which are currently in phase 2 or 3 clinical trials. New agents are classified according to the Common Alzheimer's Disease Research Ontology (CADRO). The main targets of new drugs for AD are processes related to amyloid beta and tau neurotoxicity, neurotransmission, inflammation, metabolism and bioenergetics, synaptic plasticity, and oxidative stress. These interventions are aimed at preventing disease onset and slowing or eliminating disease progression. The efficacy of pharmacotherapy may be enhanced by combining these drugs with other treatments, antioxidants, and dietary supplements. Ongoing research into AD pathophysiology, risk factors, biomarkers, and the dynamics of biomarker abnormalities may contribute to the understanding of AD and offer hope for effective therapeutic strategies in the near future.

Plasma Aβ42/Aβ40 and phospho-tau217 concentration ratios increase the accuracy of amyloid PET classification in preclinical Alzheimer's disease

INTRODUCTION

Incorporating blood-based Alzheimer's disease biomarkers such as tau and amyloid beta (Aβ) into screening algorithms may improve screening efficiency.

METHODS

Plasma Aβ, phosphorylated tau (p-tau)181, and p-tau217 concentration levels from AHEAD 3-45 study participants were measured using mass spectrometry. Tau concentration ratios for each proteoform were calculated to normalize for inter-individual differences. Receiver operating characteristic (ROC) curve analysis was performed for each biomarker against amyloid positivity, defined by > 20 Centiloids. Mixture of experts analysis assessed the value of including tau concentration ratios into the existing predictive algorithm for amyloid positron emission tomography status.

RESULTS

The area under the receiver operating curve (AUC) was 0.87 for Aβ42/Aβ40, 0.74 for phosphorylated variant p-tau181 ratio (p-tau181/np-tau181), and 0.92 for phosphorylated variant p-tau217 ratio (p-tau217/np-tau217). The Plasma Predicted Centiloid (PPC), a predictive model including p-tau217/np-tau217, Aβ42/Aβ40, age, and apolipoprotein E improved AUC to 0.95.

DISCUSSION

Including plasma p-tau217/np-tau217 along with Aβ42/Aβ40 in predictive algorithms may streamline screening preclinical individuals into anti-amyloid clinical trials.

CLINICALTRIALS

gov Identifier: NCT04468659 HIGHLIGHTS: The addition of plasma phosphorylated variant p-tau217 ratio (p-tau217/np-tau217) significantly improved plasma biomarker algorithms for identifying preclinical amyloid positron emission tomography positivity. Prediction performance at higher NAV Centiloid levels was improved with p-tau217/np-tau217. All models generated for this study are incorporated into the Plasma Predicted Centiloid (PPC) app for public use.

Longitudinal Phospho-tau217 Predicts Amyloid Positron Emission Tomography in Asymptomatic Alzheimer's Disease

BACKGROUND

Blood-based AD biomarkers such as plasma P-tau217 are increasingly used in clinical trials as a screening tool.

OBJECTIVES

To assess the utility of an electrochemiluminescence (ECL) immunoassay in predicting brain amyloid PET status in cognitively unimpaired individuals.

SETTING

Plasma samples collected at baseline, week 12, and week 240 or endpoint originated from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) trial and the companion Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) study.

PARTICIPANTS

Both A4 and LEARN enrolled eligible cognitively unimpaired persons 65 to 85 years. Individuals with elevated brain amyloid PET levels were eligible for the A4 Study, while those without elevated brain amyloid PET levels were eligible for the LEARN Study.

INTERVENTION

Participants in the A4 Study received intravenous solanezumab (up to 1600 mg) or placebo every 4 weeks. The LEARN Study is an observational study without intervention.

MEASUREMENTS

Plasma P-tau217 concentration levels from A4 Study participants were measured using an ECL immunoassay. Receiver Operating Characteristic (ROC) curve analysis was performed for each biomarker against amyloid positivity, defined by ≥22 CL and ≥ 33 CL.

RESULTS

Receiver operating characteristic curve (ROC) analysis indicates high diagnostic value of P-tau217 in individuals with amyloid PET ≥ 20 (Area under the ROC (AUROC): 0.87) and ≥ 33 CL (AUROC: 0.89). Repeated testing with the placebo group taken 12 weeks apart (range: 68 to 143 days) and the LEARN participants taken between 1.4 and 1.75 years resulted in a strong positive correlation (Corr. 0.91 (0.90 to 0.92)).

CONCLUSION

An ECL immunoassay testing plasma P-tau217 accurately predicts amyloid PET positivity in cognitively unimpaired individuals. Our future analyses aim to determine if use of this assay may reduce the screening burden of preclinical individuals into anti-amyloid clinical trials.

Digital phenotyping: An equal opportunity approach to reducing disparities in Alzheimer's disease and related dementia research

A rapidly aging world population is fueling a concomitant increase in Alzheimer's disease (AD) and related dementias (ADRD). Scientific inquiry, however, has largely focused on White populations in Australia, the European Union, and North America. As such, there is an incomplete understanding of AD in other populations. In this perspective, we describe research efforts and challenges of cohort studies from three regions of the world: Central America, East Africa, and East Asia. These cohorts are engaging with the Davos Alzheimer's Collaborative (DAC), a global partnership that brings together cohorts from around the world to advance understanding of AD. Each cohort is poised to leverage the widespread use of mobile devices to integrate digital phenotyping into current methodologies and mitigate the lack of representativeness in AD research of racial and ethnic minorities across the globe. In addition to methods that these three cohorts are already using, DAC has developed a digital phenotyping protocol that can collect ADRD-related data remotely via smartphone and/or in clinic via a tablet to generate a common data elements digital dataset that can be harmonized with additional clinical and molecular data being collected at each cohort site and when combined across cohorts and made accessible can provide a global data resource that is more racially/ethnically represented of the world population.

Blood-based biomarkers in Alzheimer's disease: Future directions for implementation

INTRODUCTION

Disease-modifying therapies (DMTs) for Alzheimer's disease (AD) will increase diagnostic demand. A non-invasive blood-based biomarker (BBBM) test for detection of amyloid-β pathology may reduce diagnostic barriers and facilitate DMT initiation.

OBJECTIVE

To explore heterogeneity in AD care pathways and potential role of BBBM tests.

METHODS

Survey of 213 healthcare professionals/payers in US/China/UK/Germany/Spain/France and two advisory boards (US/Europe).

RESULTS

Current diagnostic pathways are heterogeneous, meaning many AD patients are missed while low-risk patients undergo unnecessary procedures. Confirmatory amyloid testing (cerebrospinal fluid biomarkers/positron emission tomography) is utilized in few patients, resulting in diagnostic/treatment delays. A high negative-predictive-value test could streamline the diagnostic pathway by reducing unnecessary procedures in low-risk patients; supporting confirmatory testing where needed. Imminent approval of DMTs will increase need for fast and reliable AD diagnostic tests.

DISCUSSION

An easy-to-use, accurate, non-invasive BBBM test for amyloid pathology could guide diagnostic procedures or referral, streamlining early diagnosis and DMT initiation.

Highlights

This study explored AD care pathways and how BBBM may meet diagnostic demandsCurrent diagnostic pathways are heterogeneous, with country and setting variationsMany AD patients are missed, while low-risk patients undergo unnecessary proceduresAn easy-to-use, accurate, non-invasive BBBM test for amyloid pathology is neededThis test could streamline early diagnosis of amyloid pathology and DMT initiation.

Evaluation of Blood-Based Plasma Biomarkers as Potential Markers of Amyloid Burden in Preclinical Alzheimer's Disease

BACKGROUND

Participant eligibility for the A4 Study was determined by amyloid PET imaging. Given the disadvantages of amyloid PET imaging in accessibility and cost, blood-based biomarkers may serve as a sufficient biomarker and more cost-effective screening tool for patient enrollment into preclinical AD trials.

OBJECTIVE

To determine if a blood-based screening test can adequately identify amyloid burden in participants screened into a preclinical AD trial.

METHODS

In this cross-sectional study, 224 participants from the A4 Study received an amyloid PET scan (18Florbetapir) within 90 days of blood sample collection. Blood samples from all study participants were processed within 2 h after phlebotomy. Plasma amyloid measures were quantified by Shimazdu and C2 N Diagnostics using mass spectrometry-based platforms. A corresponding subset of blood samples (n = 100) was processed within 24 h after phlebotomy and analyzed by C2 N.

RESULTS

Plasma Aβ42/Aβ40 demonstrated the highest association for Aβ accumulation in the brain with an AUC 0.76 (95%CI = 0.69, 0.82) at C2 N and 0.80 (95%CI = 0.75, 0.86) at Shimadzu. Blood samples processed to plasma within 2 h after phlebotomy provided a better prediction of amyloid PET status than blood samples processed within 24 h (AUC 0.80 versus 0.64; p < 0.001). Age, sex, and APOE ɛ4 carrier status did not the diagnostic performance of plasma Aβ42/Aβ40 to predict amyloid PET positivity in A4 Study participants.

CONCLUSION

Plasma Aβ42/Aβ40 may serve as a potential biomarker for predicting elevated amyloid in the brain. Utilizing blood testing over PET imaging may improve screening efficiency into clinical trials.

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.

Medical comorbidities and ethnicity impact plasma Alzheimer's disease biomarkers: Important considerations for clinical trials and practice

INTRODUCTION

Despite the clinical implementation, there remain significant gaps in our knowledge regarding the impact of race/ethnicity or common medical comorbidity on plasma Alzheimer's disease (AD) biomarkers.

METHODS

Plasma biomarkers of amyloid beta (Aβ)_40, Aβ₄₂ , total tau, and neurofilament light chain (NfL) were measured across cognitively normal Mexican Americans (n = 445) and non-Hispanic Whites (n = 520).

RESULTS

Dyslipidemia was associated with elevated Aβ₄₀ (P = .01) and Aβ₄₂ (P = .001) while hypertension was associated with elevated Aβ₄₀ (P = .003), Aβ₄₂ (P < .001), and total tau (P = .002) levels. Diabetes was associated with higher Aβ₄₀ (P < .001), Aβ₄₂ (P < .001), total tau (P < .001), and NfL (P < .001) levels. Chronic kidney disease (CKD) was associated with elevations in Aβ₄₀ (P < .001), Aβ₄₂ (P < .001), total tau (P < .001), and NfL (P < .001) levels. Mexican Americans had significantly lower Aβ₄₀ (P < .001) and higher total tau (P = .005) levels.

DISCUSSION

Plasma AD biomarkers vary significantly in association with common medical comorbidities as well as ethnicity. These findings are important for those using these biomarkers in clinical practice and clinical trials.

Application of Structural Retinal Biomarkers to Detect Cognitive Impairment in a Primary Care Setting

Background

Despite the diagnostic accuracy of advanced neurodiagnostic procedures, the detection of Alzheimer's disease (AD) remains poor in primary care. There is an urgent need for screening tools to aid in the detection of early AD.

Objective

This study examines the predictive ability of structural retinal biomarkers in detecting cognitive impairment in a primary care setting.

Methods

Participants were recruited from Alzheimer's Disease in Primary Care (ADPC) study. As part of the ADPC Retinal Biomarker Study (ADPC RBS), visual acuity, an ocular history questionnaire, eye pressure, optical coherence tomography (OCT) imaging, and fundus imaging was performed.

Results

Data were examined on n = 91 participants. The top biomarkers for predicting cognitive impairment included the inferior quadrant of the outer retinal layers, all four quadrants of the peripapillary retinal nerve fiber layer, and the inferior quadrant of the macular retinal nerve fiber layer.

Conclusion

The current data provides strong support for continued investigation into structural retinal biomarkers, particularly the retinal nerve fiber layer, as screening tools for AD.

Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks

BACKGROUND AND OBJECTIVE

Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD.

METHODS

We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis.

RESULTS

Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway.

CONCLUSION

Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.

Linking the Amyloid, Tau, and Mitochondrial Hypotheses of Alzheimer's Disease and Identifying Promising Drug Targets

Damage or loss of brain cells and impaired neurochemistry, neurogenesis, and synaptic and nonsynaptic plasticity of the brain lead to dementia in neurodegenerative diseases, such as Alzheimer's disease (AD). Injury to synapses and neurons and accumulation of extracellular amyloid plaques and intracellular neurofibrillary tangles are considered the main morphological and neuropathological features of AD. Age, genetic and epigenetic factors, environmental stressors, and lifestyle contribute to the risk of AD onset and progression. These risk factors are associated with structural and functional changes in the brain, leading to cognitive decline. Biomarkers of AD reflect or cause specific changes in brain function, especially changes in pathways associated with neurotransmission, neuroinflammation, bioenergetics, apoptosis, and oxidative and nitrosative stress. Even in the initial stages, AD is associated with Aβ neurotoxicity, mitochondrial dysfunction, and tau neurotoxicity. The integrative amyloid-tau-mitochondrial hypothesis assumes that the primary cause of AD is the neurotoxicity of Aβ oligomers and tau oligomers, mitochondrial dysfunction, and their mutual synergy. For the development of new efficient AD drugs, targeting the elimination of neurotoxicity, mutual potentiation of effects, and unwanted protein interactions of risk factors and biomarkers (mainly Aβ oligomers, tau oligomers, and mitochondrial dysfunction) in the early stage of the disease seems promising.

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.

Probing the proteome to explore potential correlates of increased Alzheimer's-related cerebrovascular disease in adults with Down syndrome

Cerebrovascular disease is associated with symptoms and pathogenesis of Alzheimer's disease (AD) among adults with Down syndrome (DS). The cause of increased dementia-related cerebrovascular disease in DS is unknown. We explored whether protein markers of neuroinflammation are associated with markers of cerebrovascular disease among adults with DS. Participants from the Alzheimer's disease in Down syndrome (ADDS) study with magnetic resonance imaging (MRI) scans and blood biomarker data were included. Support vector machine (SVM) analyses examined the relationship of blood-based proteomic biomarkers with MRI-defined cerebrovascular disease among participants characterized as having cognitive decline (n = 36, mean age ± SD = 53 ± 6.2) and as being cognitively stable (n = 78, mean age = 49 ± 6.4). Inflammatory and AD markers were associated with cerebrovascular disease, particularly among symptomatic individuals. The pattern suggested relatively greater inflammatory involvement among cognitively stable individuals and greater AD involvement among those with cognitively decline. The findings help to generate hypotheses that both inflammatory and AD markers are implicated in cerebrovascular disease among those with DS and point to potential mechanistic pathways for further examination.

Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease

Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers with feature selection could improve prediction performance for AD. 150 D patients and 150 normal controls (NCs) were enrolled for a serum test, and 100 patients and 100 NCs were enrolled for the plasma test. Among these, 79 ADs and 65 NCs had serum and plasma samples in common. A 10 times repeated 5-fold cross-validation model and a feature selection method were used to overcome the overfitting problem when serum and plasma biomarkers were combined. First, we tested to see if simply adding serum and plasma biomarkers improved prediction performance but also caused overfitting. Then we employed a feature selection algorithm we developed to overcome the overfitting problem. Lastly, we tested the prediction performance in a 10 times repeated 5-fold cross validation model for training and testing sets. We found that the combined biomarkers improved AD prediction but also caused overfitting. A further feature selection based on the combination of serum and plasma biomarkers solved the problem and produced an even higher prediction performance than either serum or plasma biomarkers on their own. The combined feature-selected serum–plasma biomarkers may have critical implications for understanding the pathophysiology of AD and for developing preventative treatments.

Characterizing Plasma Biomarkers of Alzheimer's in a Diverse Community-Based Cohort: A Cross-Sectional Study of the HAB-HD Cohort

Background

Due to their low cost, less invasive nature, and ready availability, plasma biomarkers of Alzheimer's disease have been proposed as one-time screening tools for clinical trials and research. The impact of ethnoracial factors on these biomarkers has received little attention. The current cross-sectional study investigated the levels of Aβ₄₀, Aβ₄₂, total tau (t tau), and neurofilament light (NfL) across diagnoses for each of the three major ethnoracial groups in the United States in a community-based cohort of older adults.

Methods

A total of 1,862 participants (852 Mexican Americans (MAs); 775 non-Hispanic Whites (NHWs), and 235 African Americans (AAs)) drawn from The Health & Aging Brain Study—Health Disparities (HABS-HD) study were included. Diagnoses were assigned using an algorithm (decision tree) verified by consensus review. Plasma samples were assayed using Simoa technology. Levels of each biomarker were compared for the three ethnoracial groups across cognitive diagnoses using ANOVA covarying sex and age.

Results

Significant differences were found across the groups at each level of cognitive impairment. Cognitively unimpaired (CU) AA had significantly lower levels of each of the biomarkers than cognitively unimpaired MA or NHW and NHW had higher levels of Aβ₄₀, and NfL than the other two groups. MA had higher t tau than AA or NHW. Mild cognitive impairment (MCI) group NHW had the highest levels on all the biomarkers and AA had the lowest. NHW and MA have higher levels of Aβ₄₀, Aβ₄₂, and t tau there was no difference between the groups for Aβ₄₂. NHW had significantly higher levels of Aβ₄₀, t tau, and NfL than AA. AA had a higher Aβ₄₂/Aβ₄₀ ratio than either NHW or MA for CU MCI.

Conclusions

The use of plasma biomarkers of cognitive decline is promising given their advantages over other biomarkers such as CSF and imaging but as the current research shows, ethnoracial differences must be considered to enhance accuracy and utility. Developing ethnoracial-specific cut points and establishing normative ranges by assay platform for each of the biomarkers are needed. Longitudinal research to assess changes in biomarkers during a cognitive decline is ongoing.

Proteomic Markers and Early Prediction of Alzheimer's Disease

Alzheimer's disease (AD) is the most common socially significant neurodegenerative pathology, which currently affects more than 30 million elderly people worldwide. Since the number of patients grows every year and may exceed 115 million by 2050, and due to the lack of effective therapies, early prediction of AD remains a global challenge, solution of which can contribute to the timely appointment of a preventive therapy in order to avoid irreversible changes in the brain. To date, clinical assays for the markers of amyloidosis in cerebrospinal fluid (CSF) have been developed, which, in conjunction with the brain MRI and PET studies, are used either to confirm the diagnosis based on obligate clinical criteria or to predict the risk of AD developing at the stage of mild cognitive impairment (MCI). However, the problem of predicting AD at the asymptomatic stage remains unresolved. In this regard, the search for new protein markers and studies of proteomic changes in CSF and blood plasma are of particular interest and may consequentially identify particular pathways involved in the pathogenesis of AD. Studies of specific proteomic changes in blood plasma deserve special attention and are of increasing interest due to the much less invasive method of sample collection as compared to CSF, which is important when choosing the object for large-scale screening. This review briefly summarizes the current knowledge on proteomic markers of AD and considers the prospects of developing reliable methods for early identification of AD risk factors based on the proteomic profile.

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.

Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study

Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learning is an efficient analytical strategy to simultaneously identify multiple candidate biomarkers for dementia. We aimed to identify important candidate blood biomarkers for dementia using three machine learning models. We included 1642 (mean 69 ± 6 yr, 53% women) dementia-free Framingham Offspring Cohort participants attending examination, 7 who had available blood biomarker data. We developed three machine learning models, support vector machine (SVM), eXtreme gradient boosting of decision trees (XGB), and artificial neural network (ANN), to identify candidate biomarkers for incident dementia. Over a mean 12 ± 5 yr follow-up, 243 (14.8%) participants developed dementia. In multivariable models including all 38 available biomarkers, the XGB model demonstrated the strongest predictive accuracy for incident dementia (AUC 0.74 ± 0.01), followed by ANN (AUC 0.72 ± 0.01), and SVM (AUC 0.69 ± 0.01). Stepwise feature elimination by random sampling identified a subset of the nine most highly informative biomarkers. Machine learning models confined to these nine biomarkers showed improved model predictive accuracy for dementia (XGB, AUC 0.76 ± 0.01; ANN, AUC 0.75 ± 0.004; SVM, AUC 0.73 ± 0.01). A parsimonious panel of nine candidate biomarkers were identified which showed moderately good predictive accuracy for incident dementia, although our results require external validation.

Electrochemical Detection of Alzheimer’s Disease Biomarker, β-Secretase Enzyme (BACE1), With One-Step Synthesized Reduced Graphene Oxide

β-Secretase1 (BACE1) catalyzes the rate-limiting step in the generation of amyloid-β peptides, that is, the principal component involved in the pathology of Alzheimer’s disease (AD). Recent research studies show correlation between blood and cerebrospinal fluid (CSF) levels of BACE1 with the pathophysiology of AD. In this study, we report one-step synthesized reduced graphene oxide (rGO), activated via carbodiimide chemistry, conjugated with BACE1 antibody (Ab), and immobilized on fluorine-doped tin oxide (FTO) electrodes for rapid detection of BACE1 antigen (Ag) for AD diagnosis. The synthesis and fabrication steps were characterized using different types of spectroscopic, X-ray analytic, microscopic, and voltametric techniques. Various parameters including nanomaterial/Ab concentration, response time, pH, temperature, and rate of scan were standardized for maximum current output using the modified electrode. Final validation was performed via detection of BACE1 Ag ranging from 1 fM to 1 µM, with a detection limit of 0.64 fM in buffer samples and 1 fM in spiked serum samples, as well as negligible cross-reactivity with neurofilament Ag in buffer, spiked serum, and spiked artificial CSF. The proposed immunosensor gave a quick result in 30 s, and good repeatability and storage stability for a month, making it a promising candidate for sensitive, specific, and early diagnosis of AD. Thus, the fabricated electrochemical biosensor for BACE-1 detection improves detection performance compared to existing sensors as well as reduces detection time and cost, signifying its potential in early diagnosis of AD in clinical samples.

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.

A blood screening tool for detecting mild cognitive impairment and Alzheimer's disease among community-dwelling Mexican Americans and non-Hispanic Whites: A method for increasing representation of diverse populations in clinical research

INTRODUCTION

Representation of Mexican Americans in Alzheimer's disease (AD) clinical research has been extremely poor.

METHODS

Data were examined from the ongoing community-based, multi-ethnic Health & Aging Brain among Latino Elders (HABLE) study. Participants underwent functional exams, clinical labs, neuropsychological testing, and 3T magnetic resonance imaging of the brain. Fasting proteomic markers were examined for predicting mild cognitive impairment (MCI) and AD using support vector machine models.

RESULTS

Data were examined from n = 1649 participants (Mexican American n = 866; non-Hispanic White n = 783). Proteomic profiles were highly accurate in detecting MCI (area under the curve [AUC] = 0.91) and dementia (AUC = 0.95). The proteomic profiles varied significantly between ethnic groups and disease state. Negative predictive value was excellent for ruling out MCI and dementia across ethnic groups.

DISCUSSION

A blood-based screening tool can serve as a method for increasing access to state-of-the-art AD clinical research by bridging between community-based and clinic-based settings.

Proteomic Profiles of Neurodegeneration Among Mexican Americans and Non-Hispanic Whites in the HABS-HD Study

BACKGROUND

Hispanics are expected to experience the largest increase in Alzheimer's disease (AD) and AD related dementias over the next several decades. However, few studies have examined biomarkers of AD among Mexican Americans, the largest segment of the U.S. Hispanic population.

OBJECTIVE

We sought to examine proteomic profiles of an MRI-based marker of neurodegeneration from the AT(N) framework among a multi-ethnic, community-dwelling cohort.

METHODS

Community-dwelling Mexican Americans and non-Hispanic white adults and elders were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing, and 3T MRI of the brain. A neurodegeneration MRI meta-ROI biomarker for the AT(N) framework was calculated.

RESULTS

Data was examined from n = 1,291 participants. Proteomic profiles were highly accurate for detecting neurodegeneration (i.e., N+) among both Mexican Americans (AUC = 1.0) and non-Hispanic whites (AUC = 0.98). The proteomic profile of N + was different between ethnic groups. Further analyses revealed that the proteomic profiles of N + varied by diagnostic status (control, MCI, dementia) and ethnicity (Mexican American versus non-Hispanic whites) though diagnostic accuracy was high for all classifications.

CONCLUSION

A proteomic profile of neurodegeneration has tremendous value and point towards novel diagnostic and intervention opportunities. The current findings demonstrate that the underlying biological factors associated with neurodegeneration are different between Mexican Americans versus non-Hispanic whites as well as at different levels of disease progression.

Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer's Disease With High Performance Computing

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer's disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.

Analysis of a precision medicine approach to treating Parkinson's disease: Analysis of the DATATOP study

INTRODUCTION

The aim of this study was to examine the potential application of a targeted proteomic predictive biomarker comprised predominantly of inflammatory proteins in distinguishing those who responded to a previously conducted clinical trial for Parkinson's disease (PD).

METHODS

Plasma samples obtained from a biorepository were assayed from a total of n = 520 DATATOP (Deprenyl And Tocopherol Antioxidative Therapy Of Parkinsonism) clinical trial participants across treatment arms. Support vector machine analyses were conducted to distinguish responder status on primary (need for Levodopa) and secondary trial endpoints (UPDRS Motor and Total Scores).

RESULTS

For the α-tocopherol and deprenyl placebo treatment arm (TOC), the targeted proteomic biomarker was able to distinguish responder status with an accuracy (area under the curve [AUC]) of 91% for the primary endpoint while it was 100% across secondary endpoints. For the deprenyl and α-tocopherol placebo treatment arm (DEP), the AUC was 93% for the primary endpoint and 99-100% for the secondary endpoints. For the combined treatment arm, AUC was 87% for the primary and 94-96% for the secondary endpoints.

DISCUSSION

The targeted proteomic predictive biomarker was highly accurate in distinguishing responder status across treatment arms thereby supporting the application of a precision medicine approach to treating PD.

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.

Plasma Total-Tau and Neurofilament Light Chain as Diagnostic Biomarkers of Alzheimer's Disease Dementia and Mild Cognitive Impairment in Adults with Down Syndrome

BACKGROUND

The need for diagnostic biomarkers of cognitive decline is particularly important among aging adults with Down syndrome (DS). Growing empirical support has identified the utility of plasma derived biomarkers among neurotypical adults with mild cognitive impairment (MCI) and Alzheimer's disease (AD); however, the application of such biomarkers has been limited among the DS population.

OBJECTIVE

This study aimed to investigate the cross-sectional diagnostic performance of plasma neurofilament light chain (Nf-L) and total-tau, individually and in combination among a cohort of DS adults.

METHODS

Plasma samples were analyzed from n = 305 (n = 225 cognitively stable (CS); n = 44 MCI-DS; n = 36 DS-AD) participants enrolled in the Alzheimer's Biomarker Consortium -Down Syndrome.

RESULTS

In distinguishing DS-AD participants from CS, Nf-L alone produced an AUC of 90%, total-tau alone reached 74%, and combined reached an AUC of 86%. When age and gender were included, AUC increased to 93%. Higher values of Nf-L, total-tau, and age were all shown to be associated with increased risk for DS-AD. When distinguishing MCI-DS participants from CS, Nf-L alone produced an AUC of 65%, while total-tau alone reached 56%. A combined model with Nf-L, total-tau, age, and gender produced an AUC of 87%. Both higher values in age and total-tau were found to increase risk for MCI-DS; Nf-L levels were not associated with increased risk for MCI-DS.

CONCLUSION

Advanced assay techniques make total-tau and particularly Nf-L useful biomarkers of both AD pathology and clinical status in DS and have the potential to serve as outcome measures in clinical trials for future disease-modifying drugs.

A Precision Medicine Approach to Treating Alzheimer's Disease Using Rosiglitazone Therapy: A Biomarker Analysis of the REFLECT Trials

BACKGROUND

The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist.

OBJECTIVE

To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials.

METHODS

Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile.

RESULTS

A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNFα, FABP-3, and PPY) correctly classified treatment response with 100%accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders.

CONCLUSION

A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.

An epigenome-wide association study of Alzheimer's disease blood highlights robust DNA hypermethylation in the HOXB6 gene

A growing number of epigenome-wide association studies have demonstrated a role for DNA methylation in the brain in Alzheimer's disease. With the aim of exploring peripheral biomarker potential, we have examined DNA methylation patterns in whole blood collected from 284 individuals in the AddNeuroMed study, which included 89 nondemented controls, 86 patients with Alzheimer's disease, and 109 individuals with mild cognitive impairment, including 38 individuals who progressed to Alzheimer's disease within 1 year. We identified significant differentially methylated regions, including 12 adjacent hypermethylated probes in the HOXB6 gene in Alzheimer's disease, which we validated using pyrosequencing. Using weighted gene correlation network analysis, we identified comethylated modules of genes that were associated with key variables such as APOE genotype and diagnosis. In summary, this study represents the first large-scale epigenome-wide association study of Alzheimer's disease and mild cognitive impairment using blood. We highlight the differences in various loci and pathways in early disease, suggesting that these patterns relate to cognitive decline at an early stage.

Human plasma biomarker responses to inhalational general anaesthesia without surgery

BACKGROUND

Postoperative neurocognitive disorders may arise in part from adverse effects of general anaesthetics on the CNS, especially in older patients or individuals otherwise vulnerable to neurotoxicity because of systemic disease or the presence of pre-existing neuropathology. Previous studies have documented cytokine and injury biomarker responses to surgical procedures that included general anaesthesia, but it is not clear to what degree anaesthetics contribute to these responses.

METHODS

We performed a prospective cohort study of 59 healthy volunteers aged 40-80 yr who did not undergo surgery. Plasma markers of neurological injury and inflammation were measured immediately before and 5 h after induction of general anaesthesia with 1 minimum alveolar concentration of sevoflurane. Biomarkers included interleukin-6 (IL-6), tumour necrosis factor alpha (TNF-α), C-reactive protein (CRP), and neural injury (tau, neurofilament light [NF-L], and glial fibrillary acidic protein [GFAP]).

RESULTS

Baseline biomarkers were in the normal range, although NF-L and GFAP were elevated as a function of age. At 5 h after induction of anaesthesia, plasma tau, NF-L, and GFAP were significantly decreased relative to baseline. Plasma IL-6 was significantly increased after anaesthesia, but by a biologically insignificant degree (<1 pg ml^-1); plasma TNF-α and CRP were unchanged.

CONCLUSIONS

Sevoflurane general anaesthesia without surgery, even in older adults, did not provoke an inflammatory state or neuronal injury at a concentration that is detectable by an acute elevation of measured plasma biomarkers in the early hours after exposure.

CLINICAL TRIAL REGISTRATION

NCT02275026.

Proteomic profiles of prevalent mild cognitive impairment and Alzheimer's disease among adults with Down syndrome

INTRODUCTION

We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease (AD) in the general population would apply to adults with Down syndrome (DS).

METHODS

Plasma samples were obtained from 398 members of a community-based cohort of adults with DS. A total of n = 186 participants were determined to be non-demented and without mild cognitive impairment (MCI) at baseline and throughout follow-up; n = 50 had prevalent MCI; n = 42 had prevalent AD.

RESULTS

The proteomic profile yielded an area under the curve (AUC) of 0.92, sensitivity (SN) = 0.80, and specificity (SP) = 0.98 detecting prevalent MCI. For detecting prevalent AD, the proteomic profile yielded an AUC of 0.89, SN = 0.81, and SP = 0.97. The overall profile closely resembled our previously published profile of AD in the general population.

DISCUSSION

These data provide evidence of the applicability of our blood-based algorithm for detecting MCI/AD among adults with DS.

Alzheimer's Disease Diagnosis Using Misfolding Proteins in Blood

Alzheimer's disease (AD) is pathologically characterized by a long progressive phase of neuronal changes, including accumulation of extracellular amyloid-β (Aβ) and intracellular neurofibrillary tangles, before the onset of observable symptoms. Many efforts have been made to develop a blood-based diagnostic method for AD by incorporating Aβ and tau as plasma biomarkers. As blood tests have the advantages of being highly accessible and low cost, clinical implementation of AD blood tests would provide preventative screening to presymptomatic individuals, facilitating early identification of AD patients and, thus, treatment development in clinical research. However, the low concentration of AD biomarkers in the plasma has posed difficulties for accurate detection, hindering the development of a reliable blood test. In this review, we introduce three AD blood test technologies emerging in South Korea, which have distinctive methods of heightening detection sensitivity of specific plasma biomarkers. We discuss in detail the multimer detection system, the self-standard analysis of Aβ biomarkers quantified by interdigitated microelectrodes, and a biomarker ratio analysis comprising Aβ and tau.

The impact of comorbid depression-diabetes on proteomic outcomes among community-dwelling Mexican Americans with mild cognitive impairment

BACKGROUND

Mexican Americans suffer from a disproportionate burden of modifiable risk factors, which may contribute to the health disparities in mild cognitive impairment (MCI) and Alzheimer's disease (AD).

OBJECTIVE

The purpose of this study was to elucidate the impact of comorbid depression and diabetes on proteomic outcomes among community-dwelling Mexican American adults and elders.

METHODS

Data from participants enrolled in the Health and Aging Brain among Latino Elders study was utilized. Participants were 50 or older and identified as Mexican American (N = 514). Cognition was assessed via neuropsychological test battery and diagnoses of MCI and AD adjudicated by consensus review. The sample was stratified into four groups: Depression only, Neither depression nor diabetes, Diabetes only, and Comorbid depression and diabetes. Proteomic profiles were created via support vector machine analyses.

RESULTS

In Mexican Americans, the proteomic profile of MCI may change based upon the presence of diabetes. The profile has a strong inflammatory component and diabetes increases metabolic markers in the profile.

CONCLUSION

Medical comorbidities may impact the proteomics of MCI and AD, which lend support for a precision medicine approach to treating this disease.

A Precision Medicine Model for Targeted NSAID Therapy in Alzheimer's Disease

BACKGROUND

To date, the therapeutic paradigm for Alzheimer's disease (AD) has focused on a single intervention for all patients. However, a large literature in oncology supports the therapeutic benefits of a precision medicine approach to therapy. Here we test a precision-medicine approach to AD therapy.

OBJECTIVE

To determine if a baseline, blood-based proteomic companion diagnostic predicts response to NSAID therapy.

METHODS

Proteomic assays of plasma from a multicenter, randomized, double-blind, placebo-controlled, parallel group trial, with 1-year exposure to rofecoxib (25 mg once daily), naproxen (220 mg twice-daily) or placebo.

RESULTS

474 participants with mild-to-moderate AD were screened with 351 enrolled into the trial. Using support vector machine (SVM) analyses, 89% of the subjects randomized to either NSAID treatment arms were correctly classified using a general NSAID companion diagnostic. Drug-specific companion diagnostics yielded 98% theragnostic accuracy in the rofecoxib arm and 97% accuracy in the naproxen arm.

CONCLUSION

Inflammatory-based companion diagnostics have significant potential to identify select patients with AD who have a high likelihood of responding to NSAID therapy. This work provides empirical support for a precision medicine model approach to treating AD.

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.

Blood-based biomarkers for Down syndrome and Alzheimer's disease: A systematic review

Down syndrome (DS) occurs due to triplication of chromosome 21. Individuals with DS face an elevated risk for development of Alzheimer's disease (AD) due to increased amyloid beta (Aβ) resulting from the over-expression of the amyloid precursor protein found on chromosome 21. Diagnosis of AD among individuals with DS poses particular challenges resulting in an increased focus on alternative diagnostic methods such as blood-based biomarkers. The aim of this review was to evaluate the current state of the literature of blood-based biomarkers found in individuals with DS and particularly among those also diagnosed with AD or in prodromal stages (mild cognitive impairment [MCI]). A systematic review was conducted utilizing a comprehensive search strategy. Twenty-four references were identified, of those, 22 fulfilled inclusion criteria were selected for further analysis with restriction to only plasma-based biomarkers. Studies found Aβ to be consistently higher among individuals with DS; however, the link between Aβ peptides (Aβ1-42 and Aβ1-40) and AD among DS was inconsistent. Inflammatory-based proteins were more reliably found to be elevated leading to preliminary work focused on an algorithmic approach with predominantly inflammatory-based proteins to detect AD and MCI as well as predict risk of incidence among DS. Separate work has also shown remarkable diagnostic accuracy with the use of a single protein (NfL) as compared to combined proteomic profiles. This review serves to outline the current state of the literature and highlights the potential plasma-based biomarkers for use in detecting AD and MCI among this at-risk population.

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.

Sleep and circadian rhythm disruption and stress intersect in Alzheimer's disease

Alzheimer's disease (AD) was discovered and the pathological hallmarks were revealed more than a century ago. Subsequently, many remarkable discoveries and breakthroughs provided us with mechanistic insights into the pathogenesis of AD. The identification of the molecular underpinning of the disease not only provided the framework of AD pathogenesis but also targets for therapeutic inventions. Despite all the initial successes, no effective treatment for AD has emerged yet as all the late stages of clinical trials have failed. Many factors ranging from genetic to environmental factors have been critically appraised as the potential causes of AD. In particular, the role of stress on AD has been intensively studied while the relationship between sleep and circadian rhythm disruption (SCRD) and AD have recently emerged. SCRD has always been thought to be a corollary of AD pathologies until recently, multiple lines of evidence converge on the notion that SCRD might be a contributing factor in AD pathogenesis. More importantly, how stress and SCRD intersect and make their concerted contributions to AD phenotypes has not been reviewed. The goal of this literature review is to examine at multiple levels - molecular, cellular (e.g. microglia, gut microbiota) and holistic - how the interaction between stress and SCRD bi-directionally and synergistically exacerbate AD pathologies and cognitive impairment. AD, in turn, worsens stress and SCRD and forms the vicious cycle that perpetuates and amplifies AD.

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.

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.

Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry

In the 12 years since the publication of the first Consensus Paper of the WFSBP on biomarkers of neurodegenerative dementias, enormous advancement has taken place in the field, and the Task Force takes now the opportunity to extend and update the original paper. New concepts of Alzheimer's disease (AD) and the conceptual interactions between AD and dementia due to AD were developed, resulting in two sets for diagnostic/research criteria. Procedures for pre-analytical sample handling, biobanking, analyses and post-analytical interpretation of the results were intensively studied and optimised. A global quality control project was introduced to evaluate and monitor the inter-centre variability in measurements with the goal of harmonisation of results. Contexts of use and how to approach candidate biomarkers in biological specimens other than cerebrospinal fluid (CSF), e.g. blood, were precisely defined. Important development was achieved in neuroimaging techniques, including studies comparing amyloid-β positron emission tomography results to fluid-based modalities. Similarly, development in research laboratory technologies, such as ultra-sensitive methods, raises our hopes to further improve analytical and diagnostic accuracy of classic and novel candidate biomarkers. Synergistically, advancement in clinical trials of anti-dementia therapies energises and motivates the efforts to find and optimise the most reliable early diagnostic modalities. Finally, the first studies were published addressing the potential of cost-effectiveness of the biomarkers-based diagnosis of neurodegenerative disorders.

Blood Inflammatory Mediators and Cognitive Decline in Alzheimer's Disease: A Two Years Longitudinal Study

Peripheral inflammatory processes are involved in Alzheimer's disease (AD). We aimed to determine whether plasma inflammatory mediator levels at diagnosis are associated with cognitive decline through a 2-year follow-up in AD patients. Patients (n = 109, mean age 79.44 (6.82) years) were included at diagnosis with MMSE scores between 16 and 25, with C-reactive protein <10 mg/L, and without any acute or chronic inflammation status. Plasma IL-1β, IL-6, TNF-α, and CCL5 were measured using Luminex X-MAP technology at baseline, and after one year and two years of follow-up. The mean values of IL-1β, IL-6, TNF-α, and CCL5 at diagnosis were 0.3, 1.94, 6.57, and 69,615.81 pg/mL, respectively. Mean cognitive decline in MMSE was 3.35 points. No correlation between plasmatic value of IL-1β, IL-6, TNF-α, or CCL5 at diagnosis and cognitive decline during the two years of follow-up was found. Cognitive decline in AD does not appear to be predictable by the tested inflammatory mediators.

Blood-Based Biomarker Screening with Agnostic Biological Definitions for an Accurate Diagnosis Within the Dimensional Spectrum of Neurodegenerative Diseases

The discovery, development, and validation of novel candidate biomarkers in Alzheimer's disease (AD) and other neurodegenerative diseases (NDs) are increasingly gaining momentum. As a result, evolving diagnostic research criteria of NDs are beginning to integrate biofluid and neuroimaging indicators of pathophysiological mechanisms. More than 10% of people aged over 65 suffer from NDs. There is an urgent need for a refined two-stage diagnostic model to first initiate an early, sensitive, and noninvasive process in primary care settings. Individuals that meet detection criteria will then be channeled to more specific, costly (positron-emission tomography), and invasive (cerebrospinal fluid) assessment methods for confirmatory biological characterization and diagnosis.A reliable and sensitive blood test for AD and other NDs is not yet established; however, it would provide the golden screening gate for an efficient primary care management. A limitation to the development of a large-scale blood-screening biomarker-based test is the traditional application of clinically descriptive criteria for the categorization of single late-stage ND constructs. These are genetically and biologically heterogeneous, reflected in multiple pathophysiological mechanisms and subsequent pathologies throughout a dimensional continuum. Evidence suggests that a shared, "open-source" integrated multilevel categorization of NDs that clusters individuals based on descriptive clinical phenotypes and pathophysiological biomarker signatures will provide the next incremental step toward an improved diagnostic process of NDs. This intermediate objective toward unbiased biomarker-guided early detection of individuals at risk for NDs is currently carried out by the international pilot Alzheimer Precision Medicine Initiative Cohort Program (APMI-CP).

Altered levels of blood proteins in Alzheimer's disease longitudinal study: Results from Australian Imaging Biomarkers Lifestyle Study of Ageing cohort

INTRODUCTION

A blood-based biomarker panel to identify individuals with preclinical Alzheimer's disease (AD) would be an inexpensive and accessible first step for routine testing.

METHODS

We analyzed 14 biomarkers that have previously been linked to AD in the Australian Imaging Biomarkers lifestyle longitudinal study of aging cohort.

RESULTS

Levels of apolipoprotein J (apoJ) were higher in AD individuals compared with healthy controls at baseline and 18 months (P = .0003) and chemokine-309 (I-309) were increased in AD patients compared to mild cognitive impaired individuals over 36 months (P = .0008).

DISCUSSION

These data suggest that apoJ may have potential in the context of use (COU) of AD diagnostics, I-309 may be specifically useful in the COU of identifying individuals at greatest risk for progressing toward AD. This work takes an initial step toward identifying blood biomarkers with potential use in the diagnosis and prognosis of AD and should be validated across other prospective cohorts.

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.