Multiplexed Immunoassay Panel Identifies Novel CSF Biomarkers for Alzheimer's Disease Diagnosis and Prognosis

Craig‐Schapiro, Rebecca; Kühn, Max; Xiong, Chengjie; Pickering, Eve H.; Liu, Jingxia; Misko, Thomas P.; Perrin, Richard J.; Bales, Kelly R.; Soares, Holly; Fagan, Anne M.; Holtzman, David M.

doi:10.1371/journal.pone.0018850

Cited by 210 publications

(220 citation statements)

References 93 publications

Supporting

Mentioning

199

Contrasting

Unclassified

Order By: Relevance

“…The analysis of a complete set of mediators can therefore be considered to be of more value than the single analysis of only one single analyte (Gardy et al 2009;de Jager et al 2003). Although protein microarray technology has been successfully applied for the analysis of multiple analytes in complex biological mixtures -such as plasma, serum, urine and cerebrospinal fluid-publications using protein arrays for the analysis of wound status are rare (Hsu et al 2008;Weigelt et al 2009;Craig-Schapiro et al 2011;Swain et al 2011). For instance, Grimstad et al measured the concentration of 27 different cytokines in wound fluid derived from acute wounds using a bead-based system (Grimstad et al 2011).…”

Section: Protein Arrays For the Analysis Of Minute Sample Amountsmentioning

confidence: 99%

Wound Fluid Diagnostics in Diabetic Foot Ulcers

Löffler¹,

Schmohl²,

Schneiderhan‐Marra³

et al. 2011

Global Perspective on Diabetic Foot Ulcerations

View full text Add to dashboard Cite

Section: Protein Arrays For the Analysis Of Minute Sample Amountsmentioning

confidence: 99%

Wound Fluid Diagnostics in Diabetic Foot Ulcers

Löffler¹,

Schmohl²,

Schneiderhan‐Marra³

et al. 2011

Global Perspective on Diabetic Foot Ulcerations

View full text Add to dashboard Cite

“…[91] . that augment the core CSF biomarkers (Aβ42, t-tau, and p-tau) for distinguishing very mildly/mildly demented from cognitively normal individuals [92] . In addition, calbindin may be a novel biomarker.…”

Section: Pigment Epithelium-derived Factor (Pedf) Haptoglobinmentioning

confidence: 99%

“…precursor, Aβ, apolipoprotein E precursor, serum albumin precursor, keratin type I cytoskeletal 9, and tetranectin) was able to discriminate AD patients from controls with a median accuracy of 95% (sensitivity 85% and specificity 97%) [90] . that augment the core CSF biomarkers (Aβ42, t-tau, and p-tau) for distinguishing very mildly/mildly demented from cognitively normal individuals [92] . In addition, calbindin may be a novel biomarker.…”

mentioning

confidence: 99%

Cerebrospinal fluid biomarkers of Alzheimer’s disease

Sui

Yang

2014

Neurosci. Bull.

View full text Add to dashboard Cite

Alzheimer's disease (AD) is a fatal neurodegenerative disorder that takes about a decade to develop, making early diagnosis possible. Clinically, the diagnosis of AD is complicated, costly, and inaccurate, so it is urgent to fi nd specifi c biomarkers. Due to its multifactorial nature, a panel of biomarkers for the multiple pathologies of AD, such as cerebral amyloidogenesis, neuronal dysfunction, synapse loss, oxidative stress, and infl ammation, are most promising for accurate diagnosis. Highly sensitive and high-throughput proteomic techniques can be applied to develop a panel of novel biomarkers for AD. In this review, we discuss the metabolism and diagnostic performance of the well-established core candidate cerebrospinal fl uid (CSF) biomarkers (β-amyloid, total tau, and hyperphosphorylated tau). Meanwhile, novel promising CSF biomarkers, especially those identifi ed by proteomics, updated in the last fi ve years are also extensively discussed. Furthermore, we provide perspectives on how biomarker discovery for AD is evolving.Keywords: Alzheimer's disease; biomarker; cerebrospinal fl uid; β-amyloid; tau; proteomics ·Review· Introduction Alzheimer's disease (AD) is becoming more prevalent due to the increasing population of people aged over 65 [1] .It is estimated that it will affect 66 million by 2030 and 115 million by 2050 worldwide if no effective therapeutic strategies are found [2] . Clinically, AD is characterized by cognitive impairment, progressive disturbance of daily activities, many neuropsychiatric symptoms, and behavioral deterioration [3][4][5][6][7][8] . Pathologically, it is characterized by deposition of extracellular neuritic plaques composed of β-amyloid (Aβ) [9] and intracellular neurofibrillary tangles (NFTs) consisting of a hyperphosphorylated form of the microtubule-associated protein tau [10][11][12] . Besides, loss of neurons and synapses is also a pathological hallmark of AD [13][14][15] . The worldwide cost of dementia is huge and expected to skyrocket in the next few years. AD may become one of the most marked social, health, and economic challenges in the twenty-fi rst century [16] .Clinically, the procedure for the diagnosis of AD is diffi cult and complex. Neuropsychological tests, such as the mini-mental state examination, magnetic resonance imaging of hippocampal volume, and clinical assessment, are used to aid in diagnosis. However, a defi nitive diagnosis of AD requires confirmation at autopsy. It is estimated to take 8-10 years or longer before mild cognitive impairment (MCI) develops into AD [17,18] . Meanwhile, intervention therapies work most effectively in the early stage of AD. Thus, it is urgent and feasible to seek novel specifi c biomarkers to aid in diagnosis and the evaluation of treatments [19] . Surrounding the brain and spinal cord, cerebrospinal fluid (CSF) is an ideal source refl ecting biochemical changes in the brain of the AD patient [20] . Extensive studies have focused on seeking AD biomarkers in CSF.A comprehensive search of the Web of Science (Janua...

show abstract

“…The biochemical change in the brain due to progressive nature of AD provides a reasonable pool of diagnostic CSF fluid biomarkers. In this regard, a clinical study on the subject with MCI and healthy control was conducted to screen appropriate CSF biomarker required to classify subjects under study as impaired or healthy control [10]. Therefore, the goal of the present study is to generate a classification model using the dataset comprising of early stage CSF biomarker and demographic data generated by CraigSchapiro et al 2011 to predict subject with early stages of the AD.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers

Hassan¹,

Khan²

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Clinical studies in the past have shown that the pathology of Alzheimer's disease (AD) initiates, 10 to 15 years before the visible clinical symptoms of cognitive impairment starts to appear in AD diagnosed patients. Therefore, early diagnosis of the AD using potential early stage cerebrospinal fluid (CSF) biomarkers will be valuable in designing a clinical trial and proper care of AD patients. Therefore, the goal of our study was to generate a classification model to predict earlier stages of the AD using specific early-stage CSF biomarkers obtained from a clinical Alzheimer dataset. The dataset was segmented into variable sizes and classification models based on three machine learning (ML) algorithms, such as Sequential Minimal Optimization (SMO), Naïve Bayes (NB), and J48 were generated. The efficacy of the models to accurately predict the cognitive impairment status was evaluated and compared using various model performance parameters available in Weka software tool. The current findings show that J48 based classification model can be effectively employed for classifying cognitive impaired Alzheimer patient from normal healthy individuals with an accuracy of 98.82%, area under curve (AUC) value of 0.992 and sensitivity & specificity of 99.19% and 97.87%, respectively. The sample size (60% training and 40% independent test data) showed significant improvement in T-test with J48 algorithm when compared with other classifiers tested on Alzheimer dataset.

show abstract

Multiplexed Immunoassay Panel Identifies Novel CSF Biomarkers for Alzheimer's Disease Diagnosis and Prognosis

Cited by 210 publications

References 93 publications

Wound Fluid Diagnostics in Diabetic Foot Ulcers

Wound Fluid Diagnostics in Diabetic Foot Ulcers

Cerebrospinal fluid biomarkers of Alzheimer’s disease

A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers

Contact Info

Product

Resources

About