The discovery and early validation of novel plasma biomarkers in mild-to-moderate Alzheimer's disease patients responding to treatment with rosiglitazone

Recent advances in clinical, pathological and neuroscience studies have identified disease-modifying therapeutic approaches for Alzheimer's disease that are now in clinical trials. This has highlighted the need for reliable and convenient biomarkers for both early disease diagnosis and a rapid signal of drug efficacy. We describe the identification and assessment of a number of candidate biomarkers in patients with Alzheimer's disease and the correlation of those biomarkers with rosiglitazone therapeutic efficacy, as represented by a change in the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog). Plasma from 41 patients with Alzheimer's disease were analysed by open platform proteomics at baseline and after receiving 8 mg rosiglitazone for 24 weeks. From a comparison of protein expression following treatment with rosiglitazone, 97 proteins were observed to be differentially ediffer xpressed with a p-value<0.01. From this analysis and comparison to recently published data from our laboratory, a prioritized list of 10 proteins were analysed by immunoassay and/or functional assay in a wider set of samples from the same clinical study, representing a rosiglitazone dose response, in order to verify the changes observed. A number of these proteins appeared to show a correlation with change in ADAS-Cog at the higher treatment doses compared with the placebo. Alpha-2-macroglobulin, complement C1 inhibitor, complement factor H and apolipoprotein E expression showed a correlation with ADAS-Cog score at the higher doses (4 mg and 8 mg). These results are discussed in light of the pathology and other recently published data.     

Identification and validation of novel cerebrospinal fluid biomarkers for staging early Alzheimer's disease

Background

Ideally, disease modifying therapies for Alzheimer disease (AD) will be applied during the ‘preclinical’ stage (pathology present with cognition intact) before severe neuronal damage occurs, or upon recognizing very mild cognitive impairment. Developing and judiciously administering such therapies will require biomarker panels to identify early AD pathology, classify disease stage, monitor pathological progression, and predict cognitive decline. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid (CSF) proteome.

Methods and Findings

CSF samples from individuals with mild AD (Clinical Dementia Rating [CDR] 1) (n = 24) and cognitively normal controls (CDR 0) (n = 24) were subjected to two-dimensional difference-in-gel electrophoresis. Within 119 differentially-abundant gel features, mass spectrometry (LC-MS/MS) identified 47 proteins. For validation, eleven proteins were re-evaluated by enzyme-linked immunosorbent assays (ELISA). Six of these assays (NrCAM, YKL-40, chromogranin A, carnosinase I, transthyretin, cystatin C) distinguished CDR 1 and CDR 0 groups and were subsequently applied (with tau, p-tau181 and Aβ42 ELISAs) to a larger independent cohort (n = 292) that included individuals with very mild dementia (CDR 0.5). Receiver-operating characteristic curve analyses using stepwise logistic regression yielded optimal biomarker combinations to distinguish CDR 0 from CDR>0 (tau, YKL-40, NrCAM) and CDR 1 from CDR<1 (tau, chromogranin A, carnosinase I) with areas under the curve of 0.90 (0.85–0.94 95% confidence interval [CI]) and 0.88 (0.81–0.94 CI), respectively.

Conclusions

Four novel CSF biomarkers for AD (NrCAM, YKL-40, chromogranin A, carnosinase I) can improve the diagnostic accuracy of Aβ42 and tau. Together, these six markers describe six clinicopathological stages from cognitive normalcy to mild dementia, including stages defined by increased risk of cognitive decline. Such a panel might improve clinical trial efficiency by guiding subject enrollment and monitoring disease progression. Further studies will be required to validate this panel and evaluate its potential for distinguishing AD from other dementing conditions.     

Role of biomarkers in the early diagnosis of Alzheimer's disease

Alheimer's disease is the most frequent cause of cognitive decline and behavioral abnormalities in adults. Diagnosis is currently made in the advanced phases. An an early diagnosis in the prodromal phase (or earlier if possible) is required for the prevention of this disease, its early management and the development of potential therapies that could alter its natural course. The syndromic concept of mild cognitive impairment (the presence of detectable and quantifiable deterioration in one of the cognitive domains but without affecting -or without substantially affecting- autonomic performance of instrumental function) and its variants has aided understanding of the predementia stages of Alheimer's disease, even though its etiology may involve multiple factors. The use of biomarkers such as determination of the proteins involved in the disease in cerebrospinal fluid (Aβ??-amyloid, total and phosphorylated tau) and measurement of the hippocampus and entorhinal cortex with magnetic resonance imaging and positron emission tomography (both glucose and amyloid measurements), alone or combined, could allow early and etiologic diagnosis. Patients with Alzheimer's disease show reduced Aβ??-amyloid levels and increased total and phosphorylated tau levels in cerebrospinal fluid.     

Differences in abundances of cell-signalling proteins in blood reveal novel biomarkers for early detection of clinical Alzheimer's disease

Background

In November 2007 a study published in Nature Medicine proposed a simple test based on the abundance of 18 proteins in blood to predict the onset of clinical symptoms of Alzheimer''s Disease (AD) two to six years before these symptoms manifest. Later, another study, published in PLoS ONE, showed that only five proteins (IL-1, IL-3, EGF, TNF- and G-CSF) have overall better prediction accuracy. These classifiers are based on the abundance of 120 proteins. Such values were standardised by a Z-score transformation, which means that their values are relative to the average of all others.

Methodology

The original datasets from the Nature Medicine paper are further studied using methods from combinatorial optimisation and Information Theory. We expand the original dataset by also including all pair-wise differences of z-score values of the original dataset (“metafeatures”). Using an exact algorithm to solve the resulting Feature Set problem, used to tackle the feature selection problem, we found signatures that contain either only features, metafeatures or both, and evaluated their predictive performance on the independent test set.

Conclusions

It was possible to show that a specific pattern of cell signalling imbalance in blood plasma has valuable information to distinguish between NDC and AD samples. The obtained signatures were able to predict AD in patients that already had a Mild Cognitive Impairment (MCI) with up to 84% of sensitivity, while maintaining also a strong prediction accuracy of 90% on a independent dataset with Non Demented Controls (NDC) and AD samples. The novel biomarkers uncovered with this method now confirms ANG-2, IL-11, PDGF-BB, CCL15/MIP-1; and supports the joint measurement of other signalling proteins not previously discussed: GM-CSF, NT-3, IGFBP-2 and VEGF-B.     

The "Alzheimer's disease signature": potential perspectives for novel biomarkers

Alzheimer's disease is a progressive and neurodegenerative disorder which involves multiple molecular mechanisms. Intense research during the last years has accumulated a large body of data and the search for sensitive and specific biomarkers has undergone a rapid evolution. However, the diagnosis remains problematic and the current tests do not accurately detect the process leading to neurodegeneration. Biomarkers discovery and validation are considered the key aspects to support clinical diagnosis and provide discriminatory power between different stages of the disorder. A considerable challenge is to integrate different types of data from new potent approach to reach a common interpretation and replicate the findings across studies and populations. Furthermore, long-term clinical follow-up and combined analysis of several biomarkers are among the most promising perspectives to diagnose and manage the disease. The present review will focus on the recent published data providing an updated overview of the main achievements in the genetic and biochemical research of the Alzheimer's disease. We also discuss the latest and most significant results that will help to define a specific disease signature whose validity might be clinically relevant for future AD diagnosis.     

Construction and validation of a scale to assess attitudes toward patients with Alzheimer's disease and related disorders

Introduction

Patients suffering from dementia are often subjected to discriminatory attitudes, but there are few instruments able to measure these.

Objective

the construction and validation of a scale to detect negative attitudes toward patients with dementia and determine its psychometric properties, reliability criteria, and convergent and divergent validity.

Participants and methods

Fifty subjects were recruited for item selection. The scale was validated using 296 subjects of both genders, and included psychology, medical and nursing students.

Material and methods

A Likert-type attitudes scale of 30 items with 7 answers options was developed. After the pilot analysis of the scale and total item-item correlation a definitive tool of 20 items was developed, with a Cronbach‘s alpha index of 0.89. After the main component analysis, a 3 factors solution was preferred (rejection, effect and experience), which alone could explain the 65.7% of the total variance. Confirmatory factorial analysis with structural equations showed good fitting indexes (CFI=0.92; GFI=0.94; AGFI=0.90; PGFI=0.91; RMSEA=0.05). Convergent validation showed a positive correlation with scales measuring attitudes toward dementia, handicapped people, aging, and a questionnaire on negative stereotypes toward aging.

Results

Higher education and previous contact with the elderly were associated with more positive attitudes (F=29.16 and z=12.45, respectively).

Conclusions

The attitudes toward dementia scale is a valid and reliable instrument to detect these in the general population.     

Multiplexed immunoassay panel identifies novel CSF biomarkers for Alzheimer's disease diagnosis and prognosis

Background

Clinicopathological studies suggest that Alzheimer''s disease (AD) pathology begins ∼10–15 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset would, therefore, be invaluable for patient care and efficient clinical trial design. We utilized a targeted proteomics approach to discover novel cerebrospinal fluid (CSF) biomarkers that can augment the diagnostic and prognostic accuracy of current leading CSF biomarkers (Aβ42, tau, p-tau181).

Methods and Findings

Using a multiplexed Luminex platform, 190 analytes were measured in 333 CSF samples from cognitively normal (Clinical Dementia Rating [CDR] 0), very mildly demented (CDR 0.5), and mildly demented (CDR 1) individuals. Mean levels of 37 analytes (12 after Bonferroni correction) were found to differ between CDR 0 and CDR>0 groups. Receiver-operating characteristic curve analyses revealed that small combinations of a subset of these markers (cystatin C, VEGF, TRAIL-R3, PAI-1, PP, NT-proBNP, MMP-10, MIF, GRO-α, fibrinogen, FAS, eotaxin-3) enhanced the ability of the best-performing established CSF biomarker, the tau/Aβ42 ratio, to discriminate CDR>0 from CDR 0 individuals. Multiple machine learning algorithms likewise showed that the novel biomarker panels improved the diagnostic performance of the current leading biomarkers. Importantly, most of the markers that best discriminated CDR 0 from CDR>0 individuals in the more targeted ROC analyses were also identified as top predictors in the machine learning models, reconfirming their potential as biomarkers for early-stage AD. Cox proportional hazards models demonstrated that an optimal panel of markers for predicting risk of developing cognitive impairment (CDR 0 to CDR>0 conversion) consisted of calbindin, Aβ42, and age.

Conclusions/Significance

Using a targeted proteomic screen, we identified novel candidate biomarkers that complement the best current CSF biomarkers for distinguishing very mildly/mildly demented from cognitively normal individuals. Additionally, we identified a novel biomarker (calbindin) with significant prognostic potential.     

Increased plasma levels of matrix metalloproteinase-9 in patients with Alzheimer's disease

Matrix metalloproteinases (MMPs) may play a role in the pathophysiology of Alzheimer's disease (AD). MMP-9 and tissue inhibitors of metalloproteinases (TIMPs) are elevated in postmortem brain tissue of AD patients. MMPs and TIMPs are found in neurons, microglia, vascular endothelial cells and leukocytes. The aim of this study was to determine whether circulating levels of MMP-2, MMP-9, TIMP-1 and TIMP-2 are elevated in the plasma of AD patients. We compared AD patients to age- and gender-matched controls as well as to Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) patients. There was constitutive expression of gelatinase A (MMP-2), and gelatinase B (MMP-9), in all the samples as shown by zymographic analysis. Levels of MMP-9 were significantly (P=0.003) elevated in the plasma of AD patients as compared to controls. Plasma levels of MMP-2, TIMP-1 and TIMP-2 were unchanged. There were no significant changes of MMP-2, MMP-9, TIMP-1 and TIMP-2 levels in PD and ALS samples. TIMP-1 and TIMP-2 were significantly correlated with MMP-9 in the AD patients. ApoE genotyping of plasma samples showed that levels of MMP-2, TIMP-1 and TIMP-2 and MMP-9 were not significantly different between the ApoE subgroups. These findings indicate that circulating levels of MMP-9 are increased in AD and may contribute to disease pathology.     

Plasma biomarkers of brain atrophy in Alzheimer's disease

Peripheral biomarkers of Alzheimer''s disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N = 27) and MCI (N = 17). In the current report, we validated this finding in a large independent cohort of AD (N = 79), MCI (N = 88) and control (N = 95) subjects using alternative complementary methods—quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, γ-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis.     

Dkk-3 is elevated in CSF and plasma of Alzheimer's disease patients

Biomarkers in CSF can offer improved diagnostic accuracy for Alzheimer's disease (AD). The present study investigated whether the glycoprotein and putative tumor suppressor Dickkopf homolog 3 (Dkk-3) is secreted into CSF and evaluated its applicability as a diagnostic marker for AD. Using our highly specific immunoenzymometric assay, Dkk-3 levels were measured in plasma and/or CSF of patients suffering from depression, mild cognitive impairment (MCI), or AD and compared with healthy subjects. Dkk-3 identity was verified by western blot and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS)/MS. High concentrations of Dkk-3 were detected in CSF compared with plasma (28.2 ± 1.3 vs. 1.22 ± 0.04 nmol/L, respectively). Consistently Dkk-3 expression was demonstrated in neurons of the cortex and epithelial cells of the choroid plexus, the major source of CSF. Significantly increased Dkk-3 levels in plasma and CSF were observed for AD patients compared with healthy subjects but not patients suffering from MCI or depression. In summary, our data indicate that elevated Dkk-3 levels are specifically associated with AD and might serve as a potential non-invasive AD biomarker in plasma.     

Autoantibodies to redox-modified oligomeric Abeta are attenuated in the plasma of Alzheimer's disease patients

Accumulation of Abeta protein in beta-amyloid deposits is a hallmark event in Alzheimer's disease (AD). Recent findings suggest anti-Abeta autoantibodies may have a role in AD pathology. However, a consensus has yet to emerge as to whether endogenous anti-Abeta autoantibodies are elevated, depressed, or unchanged in AD patients. Whereas experiments to date have used synthetic unmodified monomeric Abeta (Abetamon) to test autoimmunity, up to 40% of the Abeta pool inB AD brain consists of low molecular weight oligomeric cross-linked beta-amyloid protein species (CAPS). Recent studies also suggest that CAPS may be the primary neurotoxic agent in AD. In the present study, AD and nondemented control plasma were analyzed for immunoreactivity to CAPS and Abetamon. Plasma of both nondemented and AD patients were found to contain autoantibodies specific for soluble CAPS. Nondemented control and AD plasmas demonstrated similar immunoreactivity to Abetamon. In contrast, anti-CAPS antibodies in AD plasma were found to be significantly reduced compared with nondemented controls (p=0.018). Furthermore, age at onset for AD correlated significantly (p=0.041) with plasma immunoreactivity to CAPS. These data suggest that autoantibodies to CAPS are depleted in AD patients and raise the prospect that immunization with anti-CAPS antibodies might provide therapeutic benefit for AD.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Metabolomics in early Alzheimer's disease: identification of altered plasma sphingolipidome using shotgun lipidomics

Background

The development of plasma biomarkers could facilitate early detection, risk assessment and therapeutic monitoring in Alzheimer''s disease (AD). Alterations in ceramides and sphingomyelins have been postulated to play a role in amyloidogensis and inflammatory stress related neuronal apoptosis; however few studies have conducted a comprehensive analysis of the sphingolipidome in AD plasma using analytical platforms with accuracy, sensitivity and reproducibility.

Methods and Findings

We prospectively analyzed plasma from 26 AD patients (mean MMSE 21) and 26 cognitively normal controls in a non-targeted approach using multi-dimensional mass spectrometry-based shotgun lipidomics [1], [2] to determine the levels of over 800 molecular species of lipids. These data were then correlated with diagnosis, apolipoprotein E4 genotype and cognitive performance. Plasma levels of species of sphingolipids were significantly altered in AD. Of the 33 sphingomyelin species tested, 8 molecular species, particularly those containing long aliphatic chains such as 22 and 24 carbon atoms, were significantly lower (p<0.05) in AD compared to controls. Levels of 2 ceramide species (N16:0 and N21:0) were significantly higher in AD (p<0.05) with a similar, but weaker, trend for 5 other species. Ratios of ceramide to sphingomyelin species containing identical fatty acyl chains differed significantly between AD patients and controls. MMSE scores were correlated with altered mass levels of both N20:2 SM and OH-N25:0 ceramides (p<0.004) though lipid abnormalities were observed in mild and moderate AD. Within AD subjects, there were also genotype specific differences.

Conclusions

In this prospective study, we used a sensitive multimodality platform to identify and characterize an essentially uniform but opposite pattern of disruption in sphingomyelin and ceramide mass levels in AD plasma. Given the role of brain sphingolipids in neuronal function, our findings provide new insights into the AD sphingolipidome and the potential use of metabolomic signatures as peripheral biomarkers.     

Cerebrospinal fluid acetylcholinesterase changes after treatment with donepezil in patients with Alzheimer's disease

We analyzed whether donepezil differently influences acetylcholinesterase (AChE) variants from cerebrospinal fluid (CSF) in patients with Alzheimer's disease (AD) after long-term treatment. Overall CSF-AChE activity in AD patients before treatment was not different from controls, but the ratio between the major tetrameric form, G(4), and the smaller G(1) and G(2) species was significantly lower. AChE levels at study outset were found to correlate positively with beta-amyloid (1-42) (Abeta42). When patients were re-examined after 12 months treatment with donepezil, there was a remarkable increase in both the G(4) and the lighter species of CSF AChE. As compared with placebo, donepezil caused decreases in the percentage of AChE that failed to bind to the lectin concanavalin A and the antibody AE1. These non-binding species comprised primarily a small subset of G(1) and G(2) forms. In treated patients, these light variants were the only subset of CSF AChE that correlated with CSF-Abeta42 levels. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis showed that a 77-kDa band, attributed in part to inactive AChE, was lower in AD patients than in controls. Unlike enzyme activity, the intensity of this band did not increase after donepezil treatment. The varying responses of different AChE species to ChE-I treatment suggest different modes of regulation, which may have therapeutic implications.     

Metabolic profiling in validation of plasma biomarkers for green tea polyphenols

Green tea polyphenols (GTP) effectively protect against chronic diseases in various animal models but human studies have been inconclusive. GTP components and metabolites in body fluids have been suggested as potential biomarkers, but validation of these biomarkers has rarely been done in human populations. A randomized, double-blinded, and placebo-controlled phase IIa chemoprevention study with GTP was conducted in 120 human subjects for 3 months. To validate GTP biomarker profiles, plasma samples were collected at baseline, 1-month, and 3-month and were analyzed by HPLC-Coularray electrochemical detection (ECD) for specific GTP components as well as for non-targeted metabolites. The levels of 2 GTP components, epigallocatechin-3-gallate (EGCG) and epicatechin-3-gallate (ECG), were homogenous at baseline (p > 0.45) but were significantly elevated (p < 0.01) by GTP treatment. Metabolic profiling identified 106 metabolites, and 56 of them were chosen to construct discriminant functions (DFs) based on the data at 3 months. The DFs clearly separated the placebo, 500 mg GTP, and 1000 mg GTP groups with an accuracy rate of 97.3%. When the DFs were applied to the combined baseline and 1-month data, the accuracy rate was 62.9% in classifying subjects into the 3 intervention groups. DFs derived from 1-month data showed similar results. Overall, this study validated plasma EGCG and ECG as reliable biomarkers for GTP consumption, and found metabolic profiles effective in discriminating different GTP dosages.     

The effects of simvastatin treatment on plasma lipid-related biomarkers in men with dyslipidaemia

Background: Lipidomic biomarkers will facilitate the development of novel anti-atherosclerotic therapies.

Objective: To evaluate the responses of circulating biomarkers to simvastatin treatment.

Methods: A randomized, cross-over study in men with mixed dyslipidaemia was used to compare effects of simvastatin 40?mg/day with placebo.

Results: Plasma concentrations of nine fatty acids (FA; of 33 evaluated) were reduced significantly by simvastatin. No changes in the rates of FA synthesis or in hepatic lipase or lipoprotein lipase activities were apparent. Circulating proprotein convertase subtilisin/kexin type 9 (PCSK9) levels increased.

Conclusion: We identified lipidomic biomarkers of simvastatin treatment effect that are consistent with statin inhibition of 3-hydroxy-3-methyl-glutaryl coenzyme A (HMG-CoA) reductase.

Trial registration: ClinicalTrials.gov identifier: NCT00935259.     

Application of quantitative proteomic analysis using tandem mass tags for discovery and identification of novel biomarkers in periodontal disease

Periodontal disease is a bacterial infection that destroys the gingiva and surrounding tissues of the oral cavity. Gingival crevicular fluid (GCF) is extracted from the gingival sulcus and pocket. Analysis of biochemical markers in GCF, which predict the progression of periodontal disease, may facilitate disease diagnosis. However, no useful GCF biochemical markers with high sensitivity for detecting periodontal disease have been identified. Thus, the search for biochemical markers of periodontal disease is of continued interest in experimental and clinical periodontal disease research. Using tandem mass tag (TMT) labeling, we analyzed GCF samples from healthy subjects and patients with periodontal disease, and identified a total of 619 GCF proteins based on proteomic analysis. Of these, we focused on two proteins, matrix metalloproteinase (MMP)‐9 and neutrophil gelatinase‐associated lipocalin (LCN2), which are involved in the progression of periodontal disease. Western blot analysis revealed that the levels of MMP‐9 and LCN2 were significantly higher in patients with periodontal disease than in healthy subjects. In addition, ELISA also detected significantly higher levels of LCN2 in patients with periodontal disease than in healthy subjects. Thus, LC‐MS/MS analyses of GCF using TMT labeling led to the identification of LCN2, which may be a promising GCF biomarker for the detection of periodontal disease.     

The impact of cerebrospinal fluid biomarkers on the diagnosis of Alzheimer's disease

The cerebrospinal fluid (CSF) biomarkers β-amyloid(1-42) (Aβ(1-42)), total tau protein (T-tau), and tau phosphorylated at threonine 181 (P-tau(181P)) are gradually finding their way into routine clinical practice as an affirmative diagnostic tool for Alzheimer's disease (AD). These biomarkers have also been implemented in the revised diagnostic criteria for AD. The combination of the CSF biomarkers Aβ(1-42), T-tau, and P-tau(181P) leads to high (around 80%) levels of sensitivity, specificity, and diagnostic accuracy for discrimination between AD and controls (including psychiatric disorders like depression) and can be applied for diagnosing AD in the predementia phases of the disease (mild cognitive impairment). The added value of CSF biomarkers could lie within those cases in which the clinical diagnostic work-up is not able to discriminate between AD and non-AD dementias. However, their discriminatory power for the differential diagnosis of dementia is suboptimal. Other CSF biomarkers, especially those that are reflective of the pathology of non-AD dementia etiologies, could improve the accuracy of differential dementia diagnosis. CSF biomarkers will be of help to establish a correct and early AD diagnosis, even in the preclinical stages of the disease, which will be of importance once disease-modifying drugs for AD become available. Variation in biomarker measurements still jeopardize the introduction of CSF biomarkers into routine clinical practice and clinical trials, but several national and international standardization initiatives are ongoing.