A blood-based algorithm for the detection of Alzheimer's disease

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

BACKGROUND

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

RESULTS

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

SIGNIFICANCE

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

Predicting early Alzheimer's with blood biomarkers and clinical features

Alzheimer's disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.

Early Detection of Alzheimer's Disease-Related Pathology Using a Multi-Disease Diagnostic Platform Employing Autoantibodies as Blood-Based Biomarkers

BACKGROUND

Evidence for the universal presence of IgG autoantibodies in blood and their potential utility for the diagnosis of Alzheimer's disease (AD) and other neurodegenerative diseases has been extensively demonstrated by our laboratory. The fact that AD-related neuropathological changes in the brain can begin more than a decade before tell-tale symptoms emerge has made it difficult to develop diagnostic tests useful for detecting the earliest stages of AD pathogenesis.

OBJECTIVE

To determine the utility of a panel of autoantibodies for detecting the presence of AD-related pathology along the early AD continuum, including at pre-symptomatic [an average of 4 years before the transition to mild cognitive impairment (MCI)/AD)], prodromal AD (MCI), and mild-moderate AD stages.

METHODS

A total of 328 serum samples from multiple cohorts, including ADNI subjects with confirmed pre-symptomatic, prodromal, and mild-moderate AD, were screened using Luminex xMAP ® technology to predict the probability of the presence of AD-related pathology. A panel of eight autoantibodies with age as a covariate was evaluated using randomForest and receiver operating characteristic (ROC) curves.

RESULTS

Autoantibody biomarkers alone predicted the probability of the presence of AD-related pathology with 81.0% accuracy and an area under the curve (AUC) of 0.84 (95% CI = 0.78-0.91). Inclusion of age as a parameter to the model improved the AUC (0.96; 95% CI = 0.93-0.99) and overall accuracy (93.0%).

CONCLUSION

Blood-based autoantibodies can be used as an accurate, non-invasive, inexpensive, and widely accessible diagnostic screener for detecting AD-related pathology at pre-symptomatic and prodromal AD stages that could aid clinicians in diagnosing AD.

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.

Early Diagnosis of Alzheimer's Disease with Blood Test; Tempting but Challenging

The increasing prevalence of Alzheimer's disease (AD) has led to a health crisis. According to official statistics, more than 55 million people globally have AD or other types of dementia, making it the sixth leading cause of death. It is still difficult to diagnose AD and there is no definitive diagnosis yet; post-mortem autopsy is still the only definite method. Moreover, clinical manifestations occur very late in the course of disease progression; therefore, profound irreversible changes have already occurred when the disease manifests. Studies have shown that in the preclinical stage of AD, changes in some biomarkers are measurable prior to any neurological damage or other symptoms. Hence, creating a reliable, fast, and affordable method capable of detecting AD in early stage has attracted the most attention. Seeking clinically applicable, inexpensive, less invasive, and much more easily accessible biomarkers for early diagnosis of AD, blood-based biomarkers (BBBs) seem to be an ideal option. This review is an inclusive report of BBBs that have been shown to be altered in the course of AD progression. The aim of this report is to provide comprehensive insight into the research status of early detection of AD based on BBBs.

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.

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.

Culture, Ethnicity, and Level of Education in Alzheimer's Disease

Alzheimer's disease (AD) is the most frequent cause of dementia, where the abnormal accumulation of beta-amyloid (Aβ) and tau lead to neurodegeneration as well as loss of cognitive, behavioral, and functional abilities. The present review analyzes AD from a cross-cultural neuropsychological perspective, looking at differences in culture-associated variables, neuropsychological test performance and biomarkers across ethnic and racial groups. Studies have found significant effects of culture, preferred language, country of origin, race, and ethnicity on cognitive test performance, although the definition of those grouping terms varies across studies. Together, with the substantial underrepresentation of minority groups in research, the inconsistent classification might conduce to an inaccuratte diagnosis that often results from biases in testing procedures that favor the group to which test developers belong. These biases persist even after adjusting for variables related to disadvantageous societal conditions, such as low level of education, unfavorable socioeconomic status, health care access, or psychological stressors. All too frequently, educational level is confounded with culture. Minorities often have lower educational attainment and lower quality of education, causing differences in test results that are then attributed to culture. Higher levels of education are also associated with increased cognitive reserve, a protective factor against cognitive decline in the presence of neurodegeneration. Biomarker research suggests there might be significant differences in specific biomarker profiles for each ethnicity/race in need of accurate cultural definitions to adequately predict risk and disease progression across ethnic/racial groups. Overall, this review highlights the need for diversity in all domains of AD research that lack inclusion and the collection of relevant information from these groups.

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.

Microfluidic Platforms to Unravel Mysteries of Alzheimer's Disease: How Far Have We Come?

Alzheimer’s disease (AD) is a significant health concern with enormous social and economic impact globally. The gradual deterioration of cognitive functions and irreversible neuronal losses are primary features of the disease. Even after decades of research, most therapeutic options are merely symptomatic, and drugs in clinical practice present numerous side effects. Lack of effective diagnostic techniques prevents the early prognosis of disease, resulting in a gradual deterioration in the quality of life. Furthermore, the mechanism of cognitive impairment and AD pathophysiology is poorly understood. Microfluidics exploits different microscale properties of fluids to mimic environments on microfluidic chip-like devices. These miniature multichambered devices can be used to grow cells and 3D tissues in vitro, analyze cell-to-cell communication, decipher the roles of neural cells such as microglia, and gain insights into AD pathophysiology. This review focuses on the applications and impact of microfluidics on AD research. We discuss the technical challenges and possible solutions provided by this new cutting-edge technique to understand disease-associated pathways and mechanisms.

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.

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.

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.

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.

The Interplay between Diabetes and Alzheimer's Disease-In the Hunt for Biomarkers

The brain is an organ in which energy metabolism occurs most intensively and glucose is an essential and dominant energy substrate. There have been many studies in recent years suggesting a close relationship between type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (AD) as they have many pathophysiological features in common. The condition of hyperglycemia exposes brain cells to the detrimental effects of glucose, increasing protein glycation and is the cause of different non-psychiatric complications. Numerous observational studies show that not only hyperglycemia but also blood glucose levels near lower fasting limits (72 to 99 mg/dL) increase the incidence of AD, regardless of whether T2DM will develop in the future. As the comorbidity of these diseases and earlier development of AD in T2DM sufferers exist, new AD biomarkers are being sought for etiopathogenetic changes associated with early neurodegenerative processes as a result of carbohydrate disorders. The S100B protein seem to be interesting in this respect as it may be a potential candidate, especially important in early diagnostics of these diseases, given that it plays a role in both carbohydrate metabolism disorders and neurodegenerative processes. It is therefore necessary to clarify the relationship between the concentration of the S100B protein and glucose and insulin levels. This paper draws attention to a valuable research objective that may in the future contribute to a better diagnosis of early neurodegenerative changes, in particular in subjects with T2DM and may be a good basis for planning experiments related to this issue as well as a more detailed explanation of the relationship between the neuropathological disturbances and changes of glucose and insulin concentrations in the brain.

A proteomic signature for dementia with Lewy bodies

INTRODUCTION

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

METHODS

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

RESULTS

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

DISCUSSION

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

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.

Early Detection and Treatment of Patients with Alzheimer's Disease: Future Perspectives

Alzheimer's disease affects approximately 6% of people over the age of 65 years. It is characterized as chronic degeneration of cortical neurons, with loss of memory, cognition and executive functions. As the disease progresses, it is accompanied by accumulation of amyloid plaques and neurofibrillary tangles in key areas of the brain, leading to a loss of neurogenesis and synaptic plasticity in the hippocampus, along with changes in the levels of essential neurotransmitters such as acetylcholine and glutamate. Individuals with concomitant diseases such as depression, diabetes and cardiovascular disorders have a higher risk of developing Alzheimer's disease, and those who have a healthier diet and partake in regular exercise and intellectual stimulation have a lower risk of developing the disorder. This chapter describes the advances made in early diagnosis of Alzheimer's disease as this could help to improve outcomes for the patients by facilitating earlier treatment.

Diagnosis of Alzheimer's disease utilizing amyloid and tau as fluid biomarkers

Current technological advancements in clinical and research settings have permitted a more intensive and comprehensive understanding of Alzheimer’s disease (AD). This development in knowledge regarding AD pathogenesis has been implemented to produce disease-modifying drugs. The potential for accessible and effective therapeutic methods has generated a need for detecting this neurodegenerative disorder during early stages of progression because such remedial effects are more profound when implemented during the initial, prolonged prodromal stages of pathogenesis. The aggregation of amyloid-β (Aβ) and tau isoforms are characteristic of AD; thus, they are considered core candidate biomarkers. However, research attempting to establish the reliability of Aβ and tau as biomarkers has culminated in an amalgamation of contradictory results and theories regarding the biomarker concentrations necessary for an accurate diagnosis. In this review, we consider the capabilities and limitations of fluid biomarkers collected from cerebrospinal fluid, blood, and oral, ocular, and olfactory secretions as diagnostic tools for AD, along with the impact of the integration of these biomarkers in clinical settings. Furthermore, the evolution of diagnostic criteria and novel research findings are discussed. This review is a summary and reflection of the ongoing concerted efforts to establish fluid biomarkers as a diagnostic tool and implement them in diagnostic procedures.

Markers from body fluids could help clinicians diagnose Alzheimer’s disease before cognitive decline appears. After numerous setbacks in treating advanced Alzheimer’s, researchers are eager to identify biological indicators that facilitate earlier disease detection and interception. A review by YoungSoo Kim and colleagues at Yonsei University in South Korea, explores the promise of ‘fluid biomarkers,’ which enables diagnosis using cerebrospinal fluid (CSF), blood, oral, ocular, and olfactory fluid samples. Shifts in CSF levels of amyloid beta and tau, two proteins central to Alzheimer’s pathology, can reliably monitor at-risk individuals. Although CSF collection is unpleasant for patients, it remains more promising than blood, where current data for candidate fluid biomarkers are relatively inconclusive. In this review, investigations to discover safer, cheaper, and more reliable diagnostic tools to shift treatment from alleviation to prevention are introduced.

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.

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.

Characteristics of Cognitively Normal Mexican-Americans with Cognitive Complaints

BACKGROUND

Subjective cognitive complaints in cognitively normal adults have been linked to later cognitive decline and dementia. Research on the characteristics of this group has been conducted on a variety of clinical and community-based populations. The current study focuses on the rapidly expanding population of Mexican-American elders.

OBJECTIVE

The objective of the study is the determination of characteristics of cognitively normal Mexican-Americans with cognitive complaints.

METHODS

Data on 319 cognitively normal participants in a large-scale community-based study of elderly Mexican-Americans (HABLE) were analyzed comparing those with cognitive complaints with those without on clinical characteristics, affective status, neuropsychological functioning, and proteomic markers.

RESULTS

Those expressing concern about cognitive decline scored lower on the MMSE, were more likely to have significantly more affective symptoms, higher levels of diabetic markers, poorer performance on attention and executive functioning, and a different pattern of inflammatory markers.

CONCLUSION

Although longitudinal research is needed to determine the impact of these differences on later cognition, possible targets for early intervention with Mexican-Americans were identified.

Reduction of aluminum ion neurotoxicity through a small peptide application - NAP treatment of Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia in late life. It is difficult to precisely diagnose AD at early stages, making biomarker search essential for further developments. The objective of this study was to identify protein biomarkers associated with aluminum ions toxicity (AD-like toxicity) in a human neuroblastoma cell model, SH-SY5Y and assess potential prevention by NAP (NAPVSIPQ). Complete proteomic techniques were implemented. Four proteins were identified as up-regulated with aluminum ion treatment, CBP80/20-dependent translation initiation factor (CTIF), Early endosome antigen 1 (EEA1), Leucine-rich repeat neuronal protein 4 (LRRN4) and Phosphatidylinositol 3-kinase regulatory subunit beta (PI3KR2). Of these four proteins, EEA1 and PI3KR2 were down-regulated after NAP-induced neuroprotective activity in neuroblastoma cells. Thus, aluminum ions may increase the risk for neurotoxicity in AD, and the use of NAP is suggested as a treatment to provide additional protection against the effects of aluminum ions, via EEA1 and PI3KR2, associated with sorting and processing of the AD amyloid precursor protein (APP) through the endosomal system.

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.

Blood-Derived Plasma Protein Biomarkers for Alzheimer's Disease in Han Chinese

It is well known that Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases; it begins gradually, and therefore no effective medicine is administered in the beginning. Thus, early diagnosis and prevention of AD are crucial. The present study focused on comparing the plasma protein changes between patients with AD and their healthy counterparts, aiming to explore a specific protein panel as a potential biomarker for AD patients in Han Chinese. Hence, we recruited and collected plasma samples from 98 AD patients and 101 elderly healthy controls from Wuxi and Shanghai Mental Health Centers. Using a Luminex assay, we investigated the expression levels of fifty plasma proteins in these samples. Thirty-two out of 50 proteins were found to be significantly different between AD patients and healthy controls (P < 0.05). Furthermore, an eight-protein panel that included brain-derived neurotrophic factor (BDNF), angiotensinogen (AGT), insulin-like growth factor binding protein 2 (IGFBP-2), osteopontin (OPN), cathepsin D, serum amyloid P component (SAP), complement C4, and prealbumin (transthyretin, TTR) showed the highest determinative score for AD and healthy controls (all P = 0.00). In conclusion, these findings suggest that a combination of eight plasma proteins can serve as a promising diagnostic biomarker for AD with high sensitivity and specificity in Han Chinese populations; the eight plasma proteins were proven important for AD diagnosis by further cross-validation studies within the AD cohort.

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.

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.

Evolving Relevance of Neuroproteomics in Alzheimer's Disease

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

A blood-based biomarker panel indicates IL-10 and IL-12/23p40 are jointly associated as predictors of β-amyloid load in an AD cohort

Alzheimer’s Disease (AD) is the most common form of dementia, characterised by extracellular amyloid deposition as plaques and intracellular neurofibrillary tangles of tau protein. As no current clinical test can diagnose individuals at risk of developing AD, the aim of this project is to evaluate a blood-based biomarker panel to identify individuals who carry this risk. We analysed the levels of 22 biomarkers in clinically classified healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer’s participants from the well characterised Australian Imaging, Biomarker and Lifestyle (AIBL) study of aging. High levels of IL-10 and IL-12/23p40 were significantly associated with amyloid deposition in HC, suggesting that these two biomarkers might be used to detect at risk individuals. Additionally, other biomarkers (Eotaxin-3, Leptin, PYY) exhibited altered levels in AD participants possessing the APOE ε4 allele. This suggests that the physiology of some potential biomarkers may be altered in AD due to the APOE ε4 allele, a major risk factor for AD. Taken together, these data highlight several potential biomarkers that can be used in a blood-based panel to allow earlier identification of individuals at risk of developing AD and/or early stage AD for which current therapies may be more beneficial.

Blood-Based Biomarker Candidates of Cerebral Amyloid Using PiB PET in Non-Demented Elderly

Increasingly, clinical trials for Alzheimer's disease (AD) are being conducted earlier in the disease phase and with biomarker confirmation using in vivo amyloid PET imaging or CSF tau and Aβ measures to quantify pathology. However, making such a pre-clinical AD diagnosis is relatively costly and the screening failure rate is likely to be high. Having a blood-based marker that would reduce such costs and accelerate clinical trials through identifying potential participants with likely pre-clinical AD would be a substantial advance. In order to seek such a candidate biomarker, discovery phase proteomic analyses using 2DGE and gel-free LC-MS/MS for high and low molecular weight analytes were conducted on longitudinal plasma samples collected over a 12-year period from non-demented older individuals who exhibited a range of 11C-PiB PET measures of amyloid load. We then sought to extend our discovery findings by investigating whether our candidate biomarkers were also associated with brain amyloid burden in disease, in an independent cohort. Seven plasma proteins, including A2M, Apo-A1, and multiple complement proteins, were identified as pre-clinical biomarkers of amyloid burden and were consistent across three time points (p <  0.05). Five of these proteins also correlated with brain amyloid measures at different stages of the disease (q <  0.1). Here we show that it is possible to detect a plasma based biomarker signature indicative of AD pathology at a stage long before the onset of clinical disease manifestation. As in previous studies, acute phase reactants and inflammatory markers dominate this signature.

Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer's database

The concept of combinatorial biomarkers was conceived when it was noticed that simple biomarkers are often inadequate for recognizing and characterizing complex diseases. Here we present an algorithmic search method for complex biomarkers which may predict or indicate Alzheimer's disease (AD) and other kinds of dementia. We show that our method is universal since it can describe any Boolean function for biomarker discovery. We applied data mining techniques that are capable to uncover implication-like logical schemes with detailed quality scoring. The new SCARF program was applied for the Tucson, Arizona based Critical Path Institute's CAMD database, containing laboratory and cognitive test data for 5821 patients from the placebo arm of clinical trials of large pharmaceutical companies, and consequently, the data is much more reliable than numerous other databases for dementia. The results of our study on this larger than 5800-patient cohort suggest beneficial effects of high B12 vitamin level, negative effects of high sodium levels or high AST (aspartate aminotransferase) liver enzyme levels to cognition. As an example for a more complex and quite surprising rule: Low or normal blood glucose level with either low cholesterol or high serum sodium would also increase the probability of bad cognition with a 3.7 multiplier. The source code of the new SCARF program is publicly available at http://pitgroup.org/static/scarf.zip.

Autoantibodies as diagnostic biomarkers for the detection and subtyping of multiple sclerosis

The goal of this preliminary proof-of-concept study was to use human protein microarrays to identify blood-based autoantibody biomarkers capable of diagnosing multiple sclerosis (MS). Using sera from 112 subjects, including 51 MS subjects, autoantibody biomarkers effectively differentiated MS subjects from age- and gender-matched normal and breast cancer controls with 95.0% and 100% overall accuracy, but not from subjects with Parkinson's disease. Autoantibody biomarkers were also useful in distinguishing subjects with the relapsing-remitting form of MS from those with the secondary progressive subtype. These results demonstrate that autoantibodies can be used as noninvasive blood-based biomarkers for the detection and subtyping of MS.

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.

Molecular markers of amnestic mild cognitive impairment among Mexican Americans

BACKGROUND

Mexican Americans face a significant health disparity when it comes to Alzheimer's disease (AD) as they present with higher rates of the disease and develop AD at an earlier age compared to other ethnic groups. Recent work identified a proteomic profile of AD among this population; however, no work to date has sought to examine the biological profile of pre-AD among Mexican Americans.

OBJECTIVE

This study aims to identify an amnestic mild cognitive impairment (aMCI) proteomic profile among Mexican Americans.

METHODS

Data were analyzed from 284 Mexican American participants (aMCI, n = 73; normal controls, n = 211) from the Health & Aging Brain among Latino Elders study. Fasting serum samples were analyzed using a multi-plex biomarker assay platform. A biomarker profile was generated using random forest analyses.

RESULTS

Among aMCI cases, the biomarker profile was found to be largely inflammatory with the top three markers shown to include TNFα, IL10, and TARC. The overall diagnostic accuracy of the biomarkers in detecting aMCI was 96% (sensitivity = 0.82; specificity = 0.97). Inclusion of clinical variables with the selected biomarkers did not impact the overall detection accuracy (area under the curve = 0.96) but led to a slight improvement in specificity (specificity = 0.99) and decrease in sensitivity (sensitivity = 0.74).

CONCLUSION

The biomarker profile of aMCI was shown to be different from our previously generated AD profile among Mexican Americans, which was largely metabolic in nature. The findings implicate a possible interplay between inflammatory and metabolic processes and additional work is needed to further examine this.

The Role of Alzheimer's and Cerebrovascular Pathology in Mediating the Effects of Age, Race, and Apolipoprotein E Genotype on Dementia Severity in Pathologically-Confirmed Alzheimer's Disease

BACKGROUND

Dementia severity can be modeled as the construct δ, representing the "cognitive correlates of functional status."

OBJECTIVE

We recently validated a model for estimating δ in the National Alzheimer's Coordinating Center's Uniform Data Set; however, the association of δ with neuropathology remains untested.

METHODS

We used data from 727 decedents evaluated at Alzheimer's Disease (AD) Centers nationwide. Participants spoke English, had no genetic abnormalities, and were pathologically diagnosed with AD as a primary or contributing etiology. Clinical data from participants' last visit prior to death were used to estimate dementia severity (δ).

RESULTS

A structural equation model using age, education, race, and apolipoprotein E (APOE) genotype (number of ɛ2 and ɛ4 alleles) as predictors and latent AD pathology and cerebrovascular disease (CVD) pathology as mediators fit the data well (RMSEA = 0.031; CFI = 0.957). AD pathology mediated the effects of age and APOE genotype on dementia severity. An older age at death and more ɛ2 alleles were associated with less AD pathology and, in turn, with less severe dementia. In contrast, more ɛ4 alleles were associated with more pathology and more severe dementia. Although age and race contributed to differences in CVD pathology, CVD pathology was not related to dementia severity in this sample of decedents with pathologically-confirmed AD.

CONCLUSIONS

Using δ as an estimate of dementia severity fits well within a structural model in which AD pathology directly affects dementia severity and mediates the relationship between age and APOE genotype on dementia severity.

Biomarkers in Sporadic and Familial Alzheimer's Disease

Most forms of Alzheimer's disease (AD) are sporadic (sAD) or inherited in a non-Mendelian fashion, and less than 1% of cases are autosomal-dominant. Forms of sAD do not exhibit familial aggregation and are characterized by complex genetic and environmental interactions. Recently, the expansion of genomic methodologies, in association with substantially larger combined cohorts, has resulted in various genome-wide association studies that have identified several novel genetic associations of AD. Currently, the most effective methods for establishing the diagnosis of AD are defined by multi-modal pathways, starting with clinical and neuropsychological assessment, cerebrospinal fluid (CSF) analysis, and brain-imaging procedures, all of which have significant cost- and access-to-care barriers. Consequently, research efforts have focused on the development and validation of non-invasive and generalizable blood-based biomarkers. Among the modalities conceptualized by the systems biology paradigm and utilized in the "exploratory biomarker discovery arena", proteome analysis has received the most attention. However, metabolomics, lipidomics, transcriptomics, and epigenomics have recently become key modalities in the search for AD biomarkers. Interestingly, biomarker changes for familial AD (fAD), in many but not all cases, seem similar to those for sAD. The integration of neurogenetics with systems biology/physiology-based strategies and high-throughput technologies for molecular profiling is expected to help identify the causes, mechanisms, and biomarkers associated with the various forms of AD. Moreover, in order to hypothesize the dynamic trajectories of biomarkers through disease stages and elucidate the mechanisms of biomarker alterations, updated and more sophisticated theoretical models have been proposed for both sAD and fAD.

Comorbid Depression and Diabetes as a Risk for Mild Cognitive Impairment and Alzheimer's Disease in Elderly Mexican Americans

BACKGROUND

The links between diabetes, depression, and Alzheimer's disease (AD) has been established, but they are still poorly understood. However, little research has examined the effect that comorbidity of depression and diabetes has on cognitive impairment in an ethnically diverse sample.

OBJECTIVE

The purpose of this study was to investigate the relationship between comorbid diabetes and depression on cognitive dysfunction; and examine the relationship in an ethnically diverse population.

METHODS AND RESULTS

Analyses of data from 2,436 participants (914 men and 1,522 women) of three separate cohorts: HABLE, FRONTIER, and TARCC. In the HABLE cohort, comorbidity (odds ratio [OR] = 3.008; 95% CI = 1.358-6.667), age (OR = 1.138; 95% CI = 1.093-1.185), and education (OR = 0.915; 95% CI = 0.852-0.982) increased the risk of mild cognitive impairment (MCI) diagnosis among elderly Mexican American. In the TARCC cohort, results showed an increase risk of MCI in both non-Hispanic whites (OR = 18.795; 95% CI = 2.229-158.485) and Mexican Americans (OR = 8.417; 95% CI = 2.967-23.878). Finally, results in the FRONTIER cohort showed that in elderly Mexican Americans, comorbidity (OR = 2.754; 95% CI = 1.084-6.995) and age (OR = 1.069; 95% CI = 1.023-1.118) significantly increased risk of MCI. In non-Hispanic whites, comorbidity did not significantly increase risk of MCI.

CONCLUSIONS

Among elderly Mexican Americans, comorbid depression and diabetes significantly increased risk for MCI and AD across cohorts. Effects of comorbid diabetes and depression on MCI were inconclusive. Our results support the link between comorbid diabetes and depression and risk for cognitive decline among Mexican Americans. This finding is of critical importance as the Hispanic population is at higher risk of developing AD.

A blood screening test for Alzheimer's disease

INTRODUCTION

This study combined data across four independent cohorts to examine the positive and negative predictive values of an Alzheimer's disease (AD) blood test if implemented in primary care.

METHODS

Blood samples from 1329 subjects from multiple independent, multiethnic, community-based, and clinic-based cohorts were analyzed. A "locked-down" referent group of 1128 samples was generated with 201 samples randomly selected for validation purposes. Random forest analyses were used to create the AD blood screen. Positive (PPV) and negative (NPV) predictive values were calculated.

RESULTS

In detecting AD, PPV was 0.81, and NPV was 0.95 while using the full AD blood test. When detecting mild cognitive impairment, PPV and NPV were 0.74 and 0.93, respectively. Preliminary analyses were conducted to detect any "neurodegenerative disease". The full 21-protein AD blood test yielded a PPV of 0.85 and NPV of 0.94.

DISCUSSION

The present study creates the first-ever multiethnic referent sample that spans community-based and clinic-based populations for implementation of an AD blood screen.

Serum-based protein profiles of Alzheimer's disease and mild cognitive impairment in elderly Hispanics

AIM

To describe the biomarker profiles in elderly Panamanians diagnosed with Alzheimer's disease (AD), mild cognitive impairment (MCI) or no impairment using serum-based biomarkers.

METHODS

Twenty-four proteins were analyzed using an electrochemiluminescence-based multiplex biomarker assay platform. A biomarker profile was generated using random forest analyses.

RESULTS

Two proteins differed among groups: IL-18 and T-lymphocyte-secreted protein I-309. The AD profile was highly accurate and independent of age, gender, education and Apolipoprotein E ε4 status. AD and MCI profiles had substantial overlap among the top markers, suggesting common functions in AD and MCI but differences in their relative importance.

CONCLUSION

Our results underscore the potential influence of genetic and environmental differences within Hispanic populations on the proteomic profile of AD.

Detection of Alzheimer's disease at mild cognitive impairment and disease progression using autoantibodies as blood-based biomarkers

Introduction

There is an urgent need to identify biomarkers that can accurately detect and diagnose Alzheimer's disease (AD). Autoantibodies are abundant and ubiquitous in human sera and have been previously demonstrated as disease-specific biomarkers capable of accurately diagnosing mild-moderate stages of AD and Parkinson's disease.

Methods

Sera from 236 subjects, including 50 mild cognitive impairment (MCI) subjects with confirmed low CSF Aβ42 levels, were screened with human protein microarrays to identify potential biomarkers for MCI. Autoantibody biomarker performance was evaluated using Random Forest and Receiver Operating Characteristic curves.

Results

Autoantibody biomarkers can differentiate MCI patients from age-matched and gender-matched controls with an overall accuracy, sensitivity, and specificity of 100.0%. They were also capable of differentiating MCI patients from those with mild-moderate AD and other neurologic and non-neurologic controls with high accuracy.

Discussion

Autoantibodies can be used as noninvasive and effective blood-based biomarkers for early diagnosis and staging of AD.

Application of Systems Theory in Longitudinal Studies on the Origin and Progression of Alzheimer's Disease

This chapter questions the prevailing "implicit" assumption that molecular mechanisms and the biological phenotype of dominantly inherited early-onset alzheimer's disease (EOAD) could serve as a linear model to study the pathogenesis of sporadic late-onset alzheimer's disease (LOAD). Now there is growing evidence to suggest that such reductionism may not be warranted; these suppositions are not adequate to explain the molecular complexities of LOAD. For example, the failure of some recent amyloid-centric clinical trials, which were largely based on the extrapolations from EOAD biological phenotypes to the molecular mechanisms in the pathogenesis of LOAD, might be due to such false assumptions. The distinct difference in the biology of LOAD and EOAD is underscored by the presence of EOAD cases without evidence of familial clustering or Mendelian transmission and, conversely, the discovery and frequent reports of such clustering and transmission patterns in LOAD cases. The primary thesis of this chapter is that a radically different way of thinking is required for comprehensive explanations regarding the distinct complexities in the molecular pathogenesis of inherited and sporadic forms of Alzheimer's disease (AD). We propose using longitudinal analytical methods and the paradigm of systems biology (using transcriptomics, proteomics, metabolomics, and lipidomics) to provide us a more comprehensive insight into the lifelong origin and progression of different molecular mechanisms and neurodegeneration. Such studies should aim to clarify the role of specific pathophysiological and signaling pathways such as neuroinflammation, altered lipid metabolism, apoptosis, oxidative stress, tau hyperphosphorylation, protein misfolding, tangle formation, and amyloidogenic cascade leading to overproduction and reduced clearance of aggregating amyloid-beta (Aβ) species. A more complete understanding of the distinct difference in molecular mechanisms, signaling pathways, as well as comparability of the various forms of AD is of paramount importance. The development of knowledge and technologies for early detection and characterization of the disease across all stages will improve the predictions regarding the course of the disease, prognosis, and response to treatment. No doubt such advances will have a significant impact on the clinical management of both EOAD and LOAD patients. The approach propped here, combining longitudinal studies with the systems biology paradigm, will create a more effective and comprehensive framework for development of prevention therapies in AD.

Longitudinal Protein Changes in Blood Plasma Associated with the Rate of Cognitive Decline in Alzheimer's Disease

Biomarkers of Alzheimer's disease (AD) progression are needed to support the development of urgently needed disease modifying drugs. We employed a SOMAscan assay for quantifying 1,001 proteins in blood samples from 90 AD subjects, 37 stable mild cognitive impaired (MCI) subjects, 39 MCI subjects converting to AD within a year and 69 controls at baseline and one year follow up. We used linear mixed effects models to identify proteins changing significantly over one year with the rate of cognitive decline, which was quantified as the reduction in Mini Mental State Examination (MMSE) scores. Additionally, we investigated proteins changing differently across disease groups and during the conversion from MCI to AD. We found that levels of proteins belonging to the complement cascade increase significantly in fast declining AD patients. Longitudinal changes in the complement cascade might be a surrogate biomarker for disease progression. We also found that members of the cytokine-cytokine receptor interaction pathway change during AD when compared to healthy aging subjects.

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

Introduction

This study investigated the comparability of potential Alzheimer's disease (AD) biomarkers across blood fractions and assay platforms.

Methods

Nonfasting serum and plasma samples from 300 participants (150 AD patients and 150 controls) were analyzed. Proteomic markers were obtained via electrochemiluminescence or Luminex technology. Comparisons were conducted via Pearson correlations. The relative importance of proteins within an AD diagnostic profile was examined using random forest importance plots.

Results

On the Meso Scale Discovery multiplex platform, 10 of the 21 markers shared >50% of the variance across blood fractions (serum amyloid A R² = 0.99, interleukin (IL)10 R² = 0.95, fatty acid-binding protein (FABP) R² = 0.94, I309 R² = 0.94, IL-5 R² = 0.94, IL-6 R² = 0.94, eotaxin3 R² = 0.91, IL-18 R² = 0.87, soluble tumor necrosis factor receptor 1 R² = 0.85, and pancreatic polypeptide R² = 0.81). When examining protein concentrations across platforms, only five markers shared >50% of the variance (beta 2 microglobulin R² = 0.92, IL-18 R² = 0.80, factor VII R² = 0.78, CRP R² = 0.74, and FABP R² = 0.70).

Discussion

The current findings highlight the importance of considering blood fractions and assay platforms when searching for AD relevant biomarkers.

Lipoprotein-associated phospholipase A2, homocysteine, and Alzheimer's disease

INTRODUCTION

Lipoprotein-associated phospholipase A2 (Lp-PLA2) and homocysteine (Hcy) have been linked to inflammation and Alzheimer's disease (AD). Using a case-control design, we examined their independent effects and interactions with cardiovascular disease equivalent (CVDE), on AD risk.

METHODS

AD cases and controls were from the Texas Alzheimer's Research and Care Consortium study. Lp-PLA2 was determined using the PLAC test (diaDexus, Inc), and Hcy by recombinant cycling assay (Roche Hitachi 911). Logistic regression was used to predict AD case status. We assayed for Lp-PLA2 in the brain tissue of cases and controls.

RESULTS

AD case status was independently associated with Lp-PLA2 and Hcy above the median (odds ratio [OR] = 1.91; 95% confidence interval [CI] = 1.22-2.97; P < .001 and OR = 1.81; 95% CI = 1.16-2.82; P = .009, respectively). Lp-PLA2, but not Hcy, interacted with CVDE to increase risk. Lp-PLA2 was absent from the brain tissue in both groups.

DISCUSSION

Higher Lp-PLA2 and Hcy are independently associated with AD. The association of Lp-PLA2 with AD may be mediated through vascular damage.

Can Genetic Analysis of Putative Blood Alzheimer's Disease Biomarkers Lead to Identification of Susceptibility Loci?

Although 24 Alzheimer’s disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in Case-Control sample size is prohibitive. The current study tests whether exploring the genetic basis of endophenotypes, in this case based on putative blood biomarkers for AD, can accelerate the identification of susceptibility loci using modest sample sizes. Each endophenotype was used as the outcome variable in an independent GWAS. Endophenotypes were based on circulating concentrations of proteins that contributed significantly to a published blood-based predictive algorithm for AD. Endophenotypes included Monocyte Chemoattractant Protein 1 (MCP1), Vascular Cell Adhesion Molecule 1 (VCAM1), Pancreatic Polypeptide (PP), Beta2 Microglobulin (B2M), Factor VII (F7), Adiponectin (ADN) and Tenascin C (TN-C). Across the seven endophenotypes, 47 SNPs were associated with outcome with a p-value ≤1x10^-7. Each signal was further characterized with respect to known genetic loci associated with AD. Signals for several endophenotypes were observed in the vicinity of CR1, MS4A6A/MS4A4E, PICALM, CLU, and PTK2B. The strongest signal was observed in association with Factor VII levels and was located within the F7 gene. Additional signals were observed in MAP3K13, ZNF320, ATP9B and TREM1. Conditional regression analyses suggested that the SNPs contributed to variation in protein concentration independent of AD status. The identification of two putatively novel AD loci (in the Factor VII and ATP9B genes), which have not been located in previous studies despite massive sample sizes, highlights the benefits of an endophenotypic approach for resolving the genetic basis for complex diseases. The coincidence of several of the endophenotypic signals with known AD loci may point to novel genetic interactions and should be further investigated.

Blood-Based Proteomic Biomarkers of Alzheimer's Disease Pathology

The complexity of Alzheimer’s disease (AD) and its long prodromal phase poses challenges for early diagnosis and yet allows for the possibility of the development of disease modifying treatments for secondary prevention. It is, therefore, of importance to develop biomarkers, in particular, in the preclinical or early phases that reflect the pathological characteristics of the disease and, moreover, could be of utility in triaging subjects for preventative therapeutic clinical trials. Much research has sought biomarkers for diagnostic purposes by comparing affected people to unaffected controls. However, given that AD pathology precedes disease onset, a pathology endophenotype design for biomarker discovery creates the opportunity for detection of much earlier markers of disease. Blood-based biomarkers potentially provide a minimally invasive option for this purpose and research in the field has adopted various “omics” approaches in order to achieve this. This review will, therefore, examine the current literature regarding blood-based proteomic biomarkers of AD and its associated pathology.

Association Between Cognitive Impairment and Chronic Kidney Disease in Mexican Americans

Objectives

To analyze the association between chronic kidney disease (CKD) and mild cognitive impairment (MCI) in Mexican Americans and to determine whether there is a blood‐based proteomic profile linking CKD to MCI.

Design

Retrospective analysis of cohort study.

Setting

Health and Aging Brain among Latino Elders study.

Participants

Mexican Americans (N = 437, 105 men, 332 women).

Measurements

Data were analyzed to examine the link between estimated glomerular filtration rate (eGFR) and detailed neuropsychological functioning. Serum proteomic markers were also examined.

Results

Lower eGFR levels were associated with significantly poorer neuropsychological functioning across multiple domains. After adjusting for age, sex, education, and diabetes mellitus, participants with an eGFR less than 45 mL/min per 1.73 m² performed significantly worse than those with an eGFR from 45 to 59 mL/min per 1.73 m² or 60 mL/min per 1.73 m² and higher in processing speed (F = 14.1, P < .001), executive functioning (F = 4.5, P = .01), visuospatial skills (F = 4.8, P = .009), and global cognitive functioning (F = 6.2, P = .002). Participants with an eGFR less than 45 mL/min per 1.73 m² also performed significantly worse than those with an eGFR of 60 mL/min per 1.73 m² or greater on delayed memory (F = 3.8, P = .02). There was a trend toward lower eGFR levels being associated with greater risk of MCI (odds ratio (OR) = 2.4, 95% confidence interval (CI) = 0.91–6.1, P = .07), which was stronger for men (OR = 9.6, 95% CI = 1.3–74.3, P = .03). A serum proteomic profile consisting of Factor VII, interleukin‐10, C‐reactive protein, and fatty acid binding protein was 93% accurate in detecting CKD‐related MCI.

Conclusion

Lower eGFR was associated with significantly poorer neuropsychological functioning in Mexican Americans. A blood‐based profile was generated that was highly accurate in detecting CKD‐related MCI. A blood profile capable of predicting CKD‐related cognitive impairment would be of benefit for the design of clinical interventions.