You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

The Past and the Future of Alzheimer’s Disease Fluid Biomarkers


Following the development of the first methods to measure the core Alzheimer’s disease (AD) cerebrospinal fluid (CSF) biomarkers total-tau (T-tau), phosphorylated tau (P-tau) and the 42 amino acid form of amyloid-β (Aβ42), there has been an enormous expansion of this scientific research area. Today, it is generally acknowledged that these biochemical tests reflect several central pathophysiological features of AD and contribute diagnostically relevant information, also for prodromal AD. In this article in the 20th anniversary issue of the Journal of Alzheimers Disease, we review the AD biomarkers, from early assay development to their entrance into diagnostic criteria. We also summarize the long journey of standardization and the development of assays on fully automated instruments, where we now have high precision and stable assays that will serve as the basis for common cut-off levels and a more general introduction of these diagnostic tests in clinical routine practice. We also discuss the latest expansion of the AD CSF biomarker toolbox that now also contains synaptic proteins such as neurogranin, which seemingly is specific for AD and predicts rate of future cognitive deterioration. Last, we are at the brink of having blood biomarkers that may be implemented as screening tools in the early clinical management of patients with cognitive problems and suspected AD. Whether this will become true, and whether it will be plasma Aβ42, the Aβ42/40 ratio, or neurofilament light, or a combination of these, remains to be established in future clinical neurochemical studies.

This paper in the 20th anniversary issue of the Journal of Alzheimers Disease is a review on the development of cerebrospinal fluid (CSF) biomarkers, from early assay development to the current status with fully automated assays and the highest level of standardization, with focus on the most important, but also most troublesome, Alzheimer’s disease (AD) biomarker; Aβ42. We also review the path from early clinical biomarker studies to the very extensive and consistent clinical validation of the diagnostic performance of the core AD CSF biomarkers we have today. Last, we give an update on recent developments, including biomarkers for synaptic proteins in CSF and the promise of blood biomarkers with potential application as screening tools.


Twenty years ago, there was not much discussion on if, or why, there is a need of biomarkers for AD. At that time, “probable AD” was diagnosed using the exclusion criteria published in 1984 by the Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) [1]. Further, the diagnosis could not be set until the patient had reached the relatively advanced stage of clinically overt dementia. At that time, no biomarkers (e.g., amyloid PET scans or CSF tests amyloid-β (Aβ) or tau) for positive identification of AD pathology were available, so this was the only possible way to make the diagnosis.

The identification of Aβ and phosphorylated tau aggregates as the main components of plaques and tangles, respectively [2, 3], opened the possibility to find AD biomarkers by developing assays for proteins related to the core pathology of the disease (plaques, tangles, and neuronal degeneration), and evaluate their performance as diagnostic tests in CSF samples from AD patients and controls. To compare with diagnostic tools in other areas, laboratory medicine tests influence up to 70% of clinical decisions and thus have a central position in clinical medicine [4]. For brain disorders such as AD, the advantage of CSF over blood is its proximity to the brain parenchyma, and that proteins are secreted from the brain extracellular space to the CSF. CSF can be collected by lumbar puncture, a diagnostic procedure in which a needle introduced into the subarachnoid space in the lumbar region (L3/L4 or L4/L5), i.e., at a level that is safely below the end of the spinal cord. CSF collection is a routine procedure in the clinical practice setting in the diagnostic work-up of brain disorders such as infectious CNS diseases, multiple sclerosis, and Guillain-Barré syndrome [5]. However, still today, in the routine diagnostic evaluation of patients with cognitive symptoms, the use of CSF biomarkers varies between countries and medical specialties, being higher in some European countries than in the US and Japan.

It is well known that not only the clinical symptoms in AD often are diffuse and overlap with other disorders, but also that the clinical progression is slow and variable. Further, it is known that the severity of neuropathological changes varies considerably between AD patients and overlaps with pathology found in cognitively unimpaired elderly [6–10], and that the majority of patients with clinical Alzheimer-type dementia have mixed (multiple) pathologies; in addition to plaques and tangles, varying degrees of Lewy body, TDP-43, and other pathologies are often seen [11]. Thus, it was not surprising that an increasing number of papers showed that these purely clinical criteria for AD have poor diagnostic accuracy, also in expert academic centers, with sensitivity and specificity figures of around 70% [12]. The pathological heterogeneity of late-onset AD-type dementia also highlights a need for biomarkers reflecting different types of pathophysiology, such as α-synuclein deposition, as reviewed under the “Biomarker candidates for other aspects of AD pathophysiology” section below.

In general, AD biomarkers have a potential to be used to support a clinical diagnosis, especially in the early stages of the disease, to predict disease progression, to monitor effects of novel drug candidates in clinical trials, and last also in clinical research to deepen our understanding on the pathogenesis of the disease [13].


Early assay developments and clinical neurochemical studies

It is now around two decades since the most commonly used ELISA, the so-called INNOTEST assays, to measure CSF levels of total tau (T-tau), phosphorylated tau (P-tau), and the 42 amino acid isoform of amyloid-β (Aβ42) were published [14–16]. These articles showed a marked increase in both CSF T-tau and P-tau in AD, together with a marked decrease in Aβ42, a CSF biomarker change that today is known as the “Alzheimer profile”. The core AD CSF biomarkers also aid in the differentiation of AD from many differential diagnoses such as depression and Parkinson’s disease, with P-tau levels giving substantial aid also in the differentiation from other dementias, such as frontotemporal dementia and Lewy body dementia [15, 17]. These findings were subsequently validated in numerous papers, with a recent meta-analysis showing very consistent changes of these biomarkers in AD patients, with a mean fold change to elderly control groups of 2.54 for T-tau, 1.88 for P-tau, and 0.56 for Aβ42 [18]. Studies on the core AD CSF biomarkers (T-tau, P-tau, and Aβ42) are also, together with other top candidate CSF and blood AD biomarkers such as VLP-1 and sTREM2, continuously (last update April 2017) curated and presented both individually and in the meta-analysis format at the online Alzbiomarker database, see

Amyloid PET set the pace in the clinical validation of CSF biomarkers

A problem with studies evaluating the diagnostic accuracy for the CSF biomarkers in clinically diagnosed AD patients as compared with cognitively normal elderly was that a percentage of normal elderly, as well as patients with other dementias, had biomarker concentrations similar to those found in AD patients. This was often interpreted as being due to suboptimal performance of the AD biomarkers.

Although high diagnostic accuracy of the core AD CSF biomarkers also was validated in studies with diagnosis confirmed by autopsy, with even better performance than studies based on pure clinical diagnoses [19, 20], these studies were largely based on cases with near end stage disease.

The availability of amyloid PET made it possible to identify amyloid pathology in vivo, and thereby also offered the possibility to compare the AD CSF biomarkers, especially Aβ42, with amyloid load directly in patients and cognitively unimpaired elderly. Importantly, amyloid PET marked a major change in AD biomarker research, since it became clear that 20–30% of apparently healthy elderly showed positive on scans [21]. This knowledge rather quickly changed the view on how to interpret low CSF Aβ42 levels in cognitively intact elderly, from poor assay quality or suboptimal biomarker performance, to an indicator of cerebral amyloidosis, and thus possibly of preclinical AD.

After the first paper showing that individuals (regardless of whether they had clinical AD symptoms or were cognitively unimpaired) with low CSF Aβ42 had positive amyloid PET scans, and vice versa [22], a large number of papers have consistently found a very high concordance between CSF Aβ42 and amyloid PET outcomes, with almost identical diagnostic accuracy to identify AD [23]. Importantly, high concordance between CSF Aβ42 and amyloid PET has also been validated in a large prospective and longitudinal clinical study enrolling consecutive patients with early cognitive disturbances at a memory clinic [24]. Further, a large clinical study showed that regional PET measures did not outperform assessment of global cortical amyloid deposition as both measures were highly concordant with CSF Aβ42 [25].

Results from some studies suggest that cases showing discordance between CSF Aβ42 and amyloid PET most often have low CSF Aβ42 but a negative amyloid PET scan [23]. This type of discordancy is much more common in cognitively normal elderly and early mild cognitive impairment (MCI) cases than in late MCI or in AD dementia cases [26], i.e., it is preferentially found in the earlier disease stages. A recent study also found that non-demented elderly who are CSF Aβ42 positive but amyloid PET negative continue to build up brain amyloid deposits to the same degree as those who are positive for both CSF Aβ42 and amyloid PET [25]. These results suggest that CSF Aβ42 may be an earlier biomarker for cortical amyloid deposition than amyloid PET. If this can be verified in independent cohorts, it may govern the clinical decision whether to perform CSF analysis or amyloid PET in patients with mild memory complaints and suspected early AD pathology, e.g., the initiate treatment with anti-amyloid disease modifying drugs if such will be available. Additional factors of relevance for this may include availability, costs, and risks (radiation exposure versus post CSF tap headache), as well as both physician and patient preferences.

Interestingly, discordancy (low CSF Aβ42 but negative amyloid PET) is preferentially found in cognitively unimpaired elderly and early AD, while it is rare in AD dementia cases [26]. One study examined a large cohort with non-demented ADNI subjects and found that those with low CSF Aβ42, but negative amyloid PET, at baseline accumulated brain amyloid deposits at a rate similar to cases positive for both biomarkers, and at a rate three times higher than those with both biomarkers being normal [27]. These findings suggest that CSF Aβ42 may be the earliest AD biomarker, becoming positive before amyloid PET and neurodegeneration.

Evaluating CSF biomarkers for early diagnosis

It may be logical to assume that novel anti-Aβ and anti-tau disease-modifying drug candidates likely will be more effective if treatment can be initiated early in the course of disease, before neurodegeneration is too severe [28], in the MCI stage of AD, or even pre-clinically. Given the diagnostic challenges to accurately diagnose AD in the MCI stage, there was a need to evaluate the CSF biomarkers for early diagnosis, and also for studies in which the core AD CSF biomarkers show a change earliest during the course of the disease.

In 1999, the first paper evaluating the AD CSF biomarkers in MCI patients showed that those who progressed to AD dementia (previously called “converted”) during clinical follow-up had the typical AD CSF profile, with biomarker levels being equally abnormal as in the dementia stage of the disease [29]. In this first study, no cognitively stable MCI group was included (to ascertain absence of progressive neurodegenerative disease, follow-up over several years is needed). In 2006, the first study with an extended clinical follow-up period (4–7 years) was published, showing a very high sensitivity (95%) for the core AD CSF biomarkers to identify prodromal AD, and also a high specificity to differentiate prodromal AD from stable MCI cases and those developing other dementias [30]. A high diagnostic accuracy of the CSF biomarkers for prodromal AD was soon thereafter verified in several large multi-center studies [20, 31, 32].

The Aβ42/40 ratio compensates for factors complicating Aβ42 measurements

In addition to Aβ42, CSF contains several other Aβ species, the most abundant one being Aβ40 [33]. Even if CSF Aβ40 shows no clear change in AD [18], the CSF Aβ42/40 ratio has been suggested to have stronger diagnostic accuracy for AD compared to CSF Aβ42 alone [34–36]. Recent studies also suggest that the Aβ42/40 ratio has better concordance with amyloid PET than Aβ42 [37–39], and that the CSF Aβ42/40 ratio is also valuable in the clinical setting [40].

The explanation has been hypothesized to be due to the ratio normalizing the “total” Aβ production level between individuals, so that lowering of CSF Aβ42 in high Aβ producers can more accurately be identified, and vice versa [41]. Alternative explanations may involve the ratio normalizing for differences in CSF dynamics (affecting both Aβ42 and Aβ40 similarly), such as variable CSF production or clearance rate, or in the proportion of CSF diffusing along the spinal cord to the lumbar sac as compared with proportion flowing over the cortex to the venous sinuses. Such differences between individuals may affect Aβ42 levels, but may be compensated for by the Aβ42/40 ratio. Last, the CSF Aβ42/40 ratio may compensate for pre-analytical confounders that affects both Aβ42 and Aβ40 in the same way [42]. As an alternative to the Aβ42/40 ratio, the ratios between T-tau/Aβ42 and P-tau/Aβ42 have also been evaluated in some studies. Indeed, all three ratios show very high concordance figures [receiver operating characteristic (ROC) area under curves (AUCs) around 0.95] with amyloid PET [24, 37], and another study also found very high concordance figures for all of CSF Aβ42 (ROC AUC 0.94) and the T-tau/Aβ42 and P-tau/Aβ42 ratios (ROC AUCs of 0.98 and 0.97) [43]. Further studies are warranted to evaluate, for example, whether the Aβ42/40 ratio, that is based only on amyloid markers, may signal earlier during the course of AD than the T-tau/Aβ42 and P-tau/Aβ42 ratios, which combine amyloid with neurodegeneration and tau pathology biomarkers.

The core AD CSF biomarkers enter novel diagnostic criteria

In 2007, the International Working Group (IWG) published the first research criteria for the diagnosis of prodromal AD [44], which provided a new conceptual framework based on that AD could be diagnosed before the dementia stage, by the combination of a clinical phenotype of episodic memory disturbances and one or more abnormal AD biomarker (CSF biomarkers, volumetric MRI, and amyloid PET). Similar, but not identical, criteria for MCI due to AD [45] and dementia due to AD [46] have been published by the National Institute on Aging-Alzheimer’s Association (NIA-AA) working groups on diagnostic guidelines for AD. Recently, in the updated IWG-2 criteria [47], the CSF biomarkers got a more significant role, together with amyloid PET, while the downstream topographical biomarkers (volumetric MRI and FDG-PET), were assigned as tools to monitoring disease course in AD.


As mentioned above, the most commonly used methods for measurement of the core AD CSF biomarkers Aβ42, T-tau, and P-tau in clinical studies and routine diagnostics are ELISAs [9–11], while the multiplex method used in the ADNI study [20] is based on the Luminex technology [48]. Although an increasing number of clinical studies showed very promising results for these biomarkers, it was easy to see a marked difference in the reported absolute levels between the studies, i.e., between clinical cohorts and laboratories [49]. It was also evident that this between-laboratory variability was more pronounced for Aβ42 than for T-tau or P-tau [50]. This type of differences in absolute levels may stem from differences in pre-analytical procedures between clinical centers [51], but also from inconsistencies in analytical procedures between laboratories, and not the least from variability in manufacturing procedures for the assays that results in batch-to-batch variations. To monitor the latter, the Alzheimer’s Association quality control (QC) program for CSF biomarkers was started in 2009 [50].

Monitoring assay performance in the quality control program

Although strict quality control procedures within a clinical laboratory can assure accurate measurements over time [24] and facilitate implementation in clinical routine diagnostics [52], the between-laboratory differences in absolute levels precludes the introduction of uniform global cut-off levels and thus a more widespread implementation of CSF biomarkers in clinical routine. This is not unique for the AD CSF biomarkers, but a problem for any novel, either fluid or imaging (PET or MRI), biomarker, which simply calls for standardization efforts to solve the problems.

From the start, the QC program had several goals. The main goal was to establish a working program to monitor the performance of the CSF biomarker measurements between laboratories and between batches of reagents, just like any proficiency program that is running for common routine assays such as blood cholesterol, glucose, and tumor markers. Important goals were also to stimulate spin-off projects focused on standardization, as well as Biotech company efforts on development of novel high-quality assay versions, or building CSF biomarker assays on fully automated laboratory instruments.

Disappointingly, between-laboratory CVs have been around 15–25% for the ELISA and Luminex methods since the beginning, without any trend for improvement, despite training efforts and attempts to introduce common standard operating procedures for the methods [53], indicating the need of more automated analytical techniques.

Standardization efforts

In clinical chemistry, the highest level of standardization is through a Certified Reference Material (CRM). For the AD CSF biomarkers, this means a “Gold Standard” large CSF pool with known exact biomarker levels, from which aliquots can be distributed to kit vendors and large laboratories for harmonization of levels between assay formats, and to secure long-term (batch-to-batch) stability of assays. From the Alzheimer’s Association QC program, it was known that the largest problem with between-assay and between-batch variability was for CSF Aβ42 [50]. Thus, the International Federation of Clinical Chemistry and Laboratory Medicine Working Group for CSF proteins (IFCC WG-CSF,, was initiated to start working on standardization of this biomarker [54], in close conjunction with the Alzheimer’s Association Global Biomarker Standardization Consortium [55].

To set the absolute concentration of Aβ42 in the CRM, methods capable of absolute quantification without matrix effects is needed. With the aim to develop such a “Gold Standard” method, called a Reference Measurement Procedure (RMP), a first paper on a selected reaction monitoring mass spectrometry method for CSF Aβ42, where isotope-labeled Aβ42 was added to the CSF sample prior to sample workup for use as an internal standard, was published [56]. The fully validated CSF Aβ42 candidate RMP was published in 2014 [57], and was approved and listed as an RMP (No. C11RMP9) by the Joint Committee for Traceability in Laboratory Medicine [58], which is the regulatory body for reference methods, in 2015.

In the development process of a CRM, the type of matrix for this material needs to be evaluated in so called commutability studies, i.e., studies examining whether the measured values in different candidate CRMs align with individual CSF samples when measured using different assay formats. For Aβ42, such a commutability study showed that only native human CSF works as a CRM, and that so-called artificial CSF, or Aβ42 spiked into buffer, did not commute, meaning that such materials did not behave like the native peptide present in human CSF when quantified using different assays [59]. For this reason, together with the Joint Research Centre European Commission Science Hub (see, the IFCC-WG CSF decided to develop three CRMs, with low, medium, and high Aβ42 levels, with exact concentrations assigned using the RMP and equivalent methods that have been tested in Round Robin studies for concordance [39]. These three CRMs (or aliquot sets) have also been tested for homogeneity, long-term stability, and other quality measures, and will serve at the top of the calibration hierarchy, for calibration of commercial assays to these reference aliquots, which will make the different assays directly comparable to each other. We see the above as important steps in the standardization of Aβ42, the trickiest AD CSF biomarker.

Fully automated instruments for the AD CSF biomarkers

In clinical chemistry, for the majority of protein biomarkers, e.g., PSA for prostate cancer and troponin-T for myocardial infarction, analyses are performed on fully automated laboratory instruments. These instruments are very exact, and there are no manual steps, meaning that the assays have superior performance as compared with, for example, ELISA methods. In 2016, a paper was published on the full validation and analytical performance a novel β-amyloid (1–42) assay on such a fully automated instrument [60]. Results showed excellent performance (repeatability CVs for human CSF pools of 1.0%–1.6% and intermediate CVs of 1.9%–4.0%) and very low between-batch variability (correlation coefficients for 100 individual samples analyzed using three different batches of reagents of 0.996–0.998). This assay has also been running in the QC program since 2014, with coefficients of variations (CVs) dropping to a mean of 4% (for in total 22 individual QC samples analyzed from 2014–2017) as compared with a mean of 15-16% for the ELISA methods during the same time period. Assays for T-tau and P-tau on fully automated instruments have also been developed and have done one round in the QC program, showing excellent CVs of 3.7% (T-tau) and 1.9% (P-tau). A number of companies are building assays on fully automated laboratory, meaning that clinical laboratories can choose between different platforms for their clinical routine measurements.

These new types of assays will serve as the basis for highly stable and precise results for CSF biomarkers, which, together with certified reference materials, will allow for the establishment of uniform worldwide cut-off levels. We believe that this will lead to a more general use of CSF biomarkers in the routine diagnostic evaluation of patients with suspected AD, and also in clinical trials in novel disease-modifying drugs. Last, highly exact CSF biomarker levels with stable results between batches will allow for both longitudinal studies and merging data from clinical research centers worldwide, in clinical studies on disease pathogenesis.

As mentioned above, the proportion of cognitively unimpaired elderly harboring AD pathology increases with age, particularly after age 65 [7–9, 61, 62], and that the amounts of plaques and tangles varies between late onset AD patients, and overlaps with those found in cognitively unimpaired elderly [8, 61, 63]. Therefore, it will probably not be possible to identify any AD biomarker reflecting pathology that will show a complete discrimination; instead, studies show a clear overlap in both CSF Aβ42 and SUVr values between AD patients and controls [64]. It is our belief that reporting absolute biomarker values (instead of reporting back results as being “positive” or “negative”) will allow clinicians to manage patients with clearly abnormal biomarker values and those with values close to the cut-off (sometimes called “grey zone” values) differently. The improved performance of the novel automated assays may help in this respect.


Despite that the core CSF AD biomarkers reflect central pathogenic mechanisms of the disease, novel biomarkers to monitor additional important molecular mechanisms in AD are constantly sought. One important component of AD pathologic change and pathophysiology is synaptic dysfunction and degeneration. Synapses are the central communication units in the neuronal networks the brain. Synapses consist of a pre-synaptic domain, where synaptic vesicles that contain the neurotransmitters that are released upon activation are located. Neurotransmitter release is a process regulated by a delicate machinery of specific pre-synaptic proteins [65]. After release to the synaptic cleft, neurotransmitters bind to post-synaptic receptors at the dendritic spines and activate a cascade of molecular events to advance the neuronal signal [66]. Synaptic dysfunction and degeneration are likely the direct cause of the cognitive deterioration in AD.

A large body of literature supports a marked degeneration and loss of synapses in grey matter regions in AD, also in the early disease stages [67, 68]. Importantly, severity of synaptic loss is more tightly correlated with degree of cognitive impairment than either plaque or tangle counts [69–71], and synaptic degeneration has been suggested as the best anatomical correlate of cognitive deficits in AD [69, 72]. Further, experimental animal studies suggest that both Aβ fibrils [73] and diffusible Aβ oligomers [74] may disturb dendritic spines by distinct mechanisms. In addition, tau hyperphosphorylation and microglia activation may also contribute to spine loss [75, 76]. Thus, synaptic biomarkers in CSF may serve as tools to explore this important aspect of AD pathophysiology in man, and to examine the link between effects on AD molecular pathology and cognitive symptoms by novel drug candidates with disease-modifying potential. Synapses are plastic structures in the brain and, potentially, synaptic markers would change rapidly in response to successful treatment.

Early search for synaptic proteins in CSF

Based on semi-preparative scale chromatographic and gel electrophoretic protein separation combined with western blotting and mass spectrometric identification, in the late 1990s we were able to identify synaptic proteins in CSF from the key synaptic compartments, including the presynaptic vesicle proteins synaptotagmin and rab3a, the presynaptic membrane protein SNAP-25, and the dendritic protein neurogranin [77, 78]. These discoveries served as the motivation to initiate a project on production on novel antibodies and detailed mass spectrometric characterization of synaptic proteins in human CSF aiming at developing quantitative immunoassays for reliable quantification in individual samples. A first pilot study in 2010, based on semi-quantitative immunoprecipitation combined with western blotting showed promising results with a marked increase in CSF neurogranin in AD [79].

Dendritic proteins: Neurogranin

Dendritic spines are specialized protrusions on the dendrites, the point where neurons receive and integrate information. Neurogranin is a dendritic protein, expressed in the cortex and hippocampus by excitatory neurons [80, 81], and is known to play an important role in long-term potentiation [82, 83]. Neurogranin expression is highest in associative cortical areas, but levels are markedly reduced in the hippocampus and the frontal cortex in AD, indicating loss of post-synaptic elements [84, 85]. Thus, measurement of neurogranin in CSF may serve as a biomarker for dendritic instability and synaptic degeneration.

After developing novel monoclonal antibodies to measure neurogranin by ELISA, high CSF levels were found to predict prodromal AD in MCI [86]. High CSF neurogranin in AD dementia and prodromal AD has been confirmed in several subsequent papers [87, 88], including in the ADNI study [89]. High CSF neurogranin also correlates with future rate of hippocampal trophy measured by MRI and rate of metabolic reductions on FDG-PET [89]. Interestingly, a recent study suggests that high CSF neurogranin may be specific for AD, and not found in other neurodegenerative disorders such as frontotemporal dementia, Lewy body dementia, Parkinson’s disease, progressive supranuclear palsy, or multiple system atrophy [90]. A recent large study confirms that increased CSF neurogranin levels is found in AD dementia and prodromal AD, but not in other neurodegenerative disorders such as frontotemporal dementia, Lewy body dementia, Parkinson’s disease, progressive supranuclear palsy, corticobasal degeneration, or amyotrophic lateral sclerosis (Portelius et al., unpublished).

Mass spectrometry characterization of neurogranin in CSF suggests that it is present in CSF as a series of C-terminal peptides [86], while other studies using a sandwich immunoassay combining N- and C-terminal antibodies, which thus measures full-length neurogranin [91], as well as an assay specific for neurogranin peptides ending at position 75 [92], also found high CSF levels in AD and MCI as compared with controls Thus, we need further studies on how neurogranin is processed and released from neurons into the CSF, including studies comparing the diagnostic potential of full-length versus C-terminal neurogranin peptides.

Presynaptic biomarkers

In the presynaptic terminal, the SNARE complex proteins, including synaptosomal-associated protein 25 (SNAP-25), syntaxin-1, and vesicle-associated membrane protein (VAMP)/synaptobrevin, are key components of the molecular machinery that drives fusion of membranes in neurotransmitter exocytosis [93]. While SNAP-25 is located at the synaptic vesicles, synaptotagmin-1 (SYT1) is found in the presynaptic plasma membrane, and is essential for synaptic vesicle exocytosis, and thus neurotransmitter release [94].

The levels of both SNAP-25 and SYT1 are reduced in cortical areas in the AD brain [84, 95], reflecting the synaptic degeneration and loss in AD. Interestingly, using immunoprecipitation mass spectrometry methods, a marked increase in the CSF levels of both SNAP-25 and SYT1 was found in AD dementia and prodromal AD cases [95, 96]. These promising results need validation in future studies, but suggest that a set of synaptic proteins covering different components of the synaptic unit (dendrites – neurogranin, presynaptic plasma membrane – SNAP-25, synaptic vesicles – SYT1) may be valuable tools in clinical studies on the relevance of synaptic dysfunction and degeneration in AD pathogenesis, and maybe also in the clinical evaluation of patients.


Some candidate biomarkers for other pathologies have been developed, for example measurement of α-synuclein in CSF, that in theory may reflect degree of Lewy bodies and Lewy neurite pathology. Several [97–99], but not all [100–102], studies have found a slight decrease in CSF α-synuclein in disorders with Lewy body pathology, e.g., in Parkinson’s disease and Lewy body dementia. However, other studies have shown a marked increase in CSF α-synuclein in AD, which is even more pronounced in Creutzfeldt-Jakob disease [103, 104]. Further, CSF α-synuclein levels correlate with the neuronal injury biomarker tau [101, 104]. Taken together, these findings suggest that CSF α-synuclein is biomarker for neurodegeneration or synapse loss [101, 102, 104]. Novel assays with antibodies specific for other variants of α-synuclein, such as phosphorylated or oligomeric forms [105, 106], may improve performance detect Lewy body pathology.

The studies reporting a genetic association between the triggering receptor expressed on myeloid cells 2 (TREM2) gene and AD [107, 108], and the finding that the ectodomain of TREM2 (sTREM2) is secreted into CSF, and that increased CSF levels are found in patients with relapsing-remitting and primary progressive multiple sclerosis [109], initiated an interest for biomarker tools to monitor microglial activation in AD pathogenesis. Several studies have found an increase in CSF sTREM2 in AD that also correlates with CSF T-tau and P-tau levels [110–112]. However, as reviewed in a recent meta-analysis [18], the degree of increase modest, which does not support the use of this biomarker for diagnostic purposes, but it may serve to study microglial activation in clinical studies on AD pathogenesis.


Since blood is more accessible than CSF, there is little doubt that blood sampling would be preferable to CSF when it comes to taking fluid samples to measure AD biomarkers, both for clinical diagnosis or screening and for repeated sampling in clinical trials. However, developing blood biomarkers for AD has proven difficult; while the CSF is continuous with the brain extracellular fluid, with a free exchange of molecules from the brain to the CSF, only a fraction of brain proteins enters the bloodstream. Further, blood is a more challenging matrix than CSF for brain biomarkers, for several reasons. First, the minute amounts of brain proteins entering the blood have to be measured in a matrix containing very high levels of plasma proteins, such as albumin and IgG, introducing a high risk of interference in analytical methods [113]. Second, in addition to dilution, brain proteins released into blood may be degraded by proteases, metabolized in the liver or cleared by the kidneys, which will introduce a variance that is unrelated to brain changes and difficult to control for. This limits the potential of finding blood biomarkers for AD [114]. Nevertheless, technical developments in the field of ultrasensitive immunoassays and mass spectrometry have given new hopes [115].

Aβ in plasma

While numerous papers on CSF Aβ42 consistently have found a high concordance with amyloid PET measures of plaque burden [23], and a marked decrease in AD, studies on plasma Aβ42 as a biomarker reflecting brain amyloid pathology (and thus AD) have been disappointing, with contradictory results, with no or minor changes and large overlaps in both Aβ42 and Aβ40 levels between patients and controls [18]. This lack of association with disease pathology may be due to the contribution from peripheral tissues to plasma Aβ, as also evidenced by the lack of correlation between plasma and CSF Aβ concentrations [116]. The poor disease association might also be related to analytical shortcomings using ELISA methods or other standard immunoassays, e.g., epitope masking by hydrophobic Aβ peptides binding to plasma proteins [117], or other interferences that might be mitigated by analytical improvements.

In 2011, we published a novel method based on the Single Molecule Array (Simoa) technique for measurement of Aβ42 in plasma [118]. This technique is based on immunocapture of the protein biomarker on magnetic beads, which are trapped in femto-liter volume wells, followed by addition of enzyme-labelled detection antibody and digital quantification that allows for exact quantification of Aβ42 down to sub-picogram per mL levels (limit of quantification of 0.04 pg/mL). The high analytical sensitivity allows for pre-dilution of samples that may reduce matrix interferences. When evaluating this assay in the large Swedish BioFINDER study cohort, weak but significant correlations were found between both plasma Aβ42 and the Aβ42/40 ratio and the corresponding CSF measures, as well as to cortical [18F]flutemetamol PET retention [119]. Significantly lower plasma Aβ42/40 ratio (p < 0.002) was found in both MCI and AD cases as compared with controls.

In an attempt to evaluate if mass spectrometric analysis may give a more accurate quantification of Aβ peptides in plasma, we developed an immunoprecipitation (IP) mass spectrometry (MS) selected reaction monitoring method for quantification of Aβ42 and Aβ40, where stable isotope-labeled Aβ peptides are added to the sample before analysis (and thus processed and analyzed simultaneously with endogenous Aβ peptides) and using the detergent octyl-glucopyranoside to disrupt complexes between Aβ and plasma proteins such as albumin [120]. In a small pilot clinical study based on clinically diagnosed cases, we were not able to find any significant change, even if there was an apparent trend for a reduction on both plasma Aβ42 and the Aβ42/40 ratio in AD [120]. Interestingly, using a similar IP-MS method, also involving LysN proteolytic digestion of Aβ peptides before analysis, significantly lower Aβ42 concentration and Aβ42/40 ratio was found in amyloid PET-positive compared with negative cases [121]. The Aβ42/40 ratio was 14% lower in the amyloid PET positive group, which gave an impressive ROC value of 0.89 [121]. These very promising results call for further studies to evaluate plasma Aβ as a screening tool for brain amyloidosis and AD, also including larger clinical cohorts and comparisons of different analytical platforms for measurement.

Tau protein in plasma

Ultrasensitive immunoassay techniques also allow for measurement of tau protein in blood samples [115], with increased tau levels in plasma in AD found using both the immuno-magnetic reduction [122] and Simoa [123], methods. A large study on both the ADNI and BIOFINDER cohorts could confirm an increase in plasma tau concentrations in AD dementia, although with a substantial overlap in levels with controls [124]. Interestingly, longitudinal data showed significant correlations between plasma tau levels and future cognitive decline, as well as increases in atrophy measured by MRI and in hypometabolism measured by FDG PET during follow-up [124]. Thus, current data suggest a minor increase in plasma tau in AD, although with too large overlap with controls to be diagnostically useful. Tau protein in CSF has been found to be present as truncated fragments [125], and it is possible that development of assays based on antibodies for specific tau fragments will improve performance. Alternatively, measurement of T-tau or P-tau in neuron-enriched exosome preparations may improve performance for tau as a blood biomarker [126], but further studies are needed to validate this finding.

Neurofilament light in plasma

We have also developed a highly sensitive Simoa method for the axonal protein neurofilament light (NFL) protein [127]. This assay has many-fold higher analytical sensitivity than assays using the same anti-NFL antibodies based on the electrochemiluminescence (ECL) Meso Scale Diagnostics (MSD) technique or standard ELISA [128], meaning that NFL can be measured also in blood samples from normal individuals who have plasma NFL concentrations that are below the level for accurate quantification when using ECL-MSD or ELISA. In contrast to tau protein, the correlation between plasma and CSF levels of NFL protein is tight [127].

A recent study on the ADNI cohort showed a marked increase in plasma NFL in AD cases (149% of control levels), with a ROC AUC value of 0.87, which is comparable to the core AD CSF biomarkers [129]. While the change in the MCI group was less pronounced, plasma NFL was highest MCI cases with positive amyloid PET scans, and predicted faster cognitive deterioration, higher rate of future both brain atrophy (measured by MRI) and hypometabolism as measured by FDG-PET [129]. Importantly, in a study on 48 familial AD mutation carriers and non-carriers, blood NFL was increased in symptomatic familial FAD cases, but also in pre-symptomatic mutation carriers, with levels correlating with expected estimated year of symptom onset as well as both cognitive and MRI measures of disease stage [130]. These results indicate that blood NFL detects neurodegeneration also in the preclinical stage of AD.

In this context, an important piece of knowledge is that high plasma (or CSF) NFL is not a feature that is specific for AD. Instead, increased levels are found in many neurodegenerative disorders, such as frontotemporal dementia, progressive supranuclear palsy and corticobasal syndrome [131, 132]. Thus, a possible future application for plasma NFL is as a screening test at the first clinical evaluation of patients with cognitive disturbances, e.g., at the primary care unit. Here, plasma NFL might serve as simple, non-invasive, and cheap screening tool, primarily to rule out neurodegeneration.


The last 20 years have seen an enormous expansion in research on fluid biomarkers for AD. The core AD CSF biomarkers T-tau, P-tau, and Aβ42 (and Aβ42/40 ratio) have been evaluated in hundreds of clinical neurochemical studies with extraordinary consistent results, showing high diagnostic accuracy both for AD dementia, but importantly also for prodromal AD. These biomarkers have undergone a phase of standardization and new assay versions on fully automated instruments show excellent analytical performance. The core AD biomarkers are today part of research diagnostic criteria, and we foresee an increased use of these diagnostic tests in clinical routine practice. The AD CSF biomarker toolbox has been expanded with novel biomarker reflecting additional aspects of AD pathology, such as synaptic dysfunction.

We envision further validated assays reflecting other pathologies common in age-related neurodegenerative disorders, e.g., Lewy body and TDP-43 pathology, reaching the stage of clinical applications in the coming years, so that CSF biomarkers can be part in a personalized medicine approach to the clinical evaluation of patients with cognitive disturbances. Last, we hope that blood biomarkers may be implemented as screening tools in the first-in-line clinical evaluation of this group of patients.


Dr. Blennow holds the Torsten Söderberg Professorship at the Royal Swedish Academy of Sciences. Dr. Zetterberg is a Wallenberg Academy Fellow.

Authors’ disclosures available online (



McKhann G , Drachman D , Folstein M , Katzman R , Price D , Stadlan EM ((1984) ) Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34: , 939–944.


Grundke-Iqbal I , Iqbal K , Tung YC , Quinlan M , Wisniewski HM , Binder LI ((1986) ) Abnormal phosphorylation of the microtubule-associated protein tau (tau) in Alzheimer cytoskeletal pathology. Proc Natl Acad Sci U S A 83: , 4913–4917.


Masters CL , Simms G , Weinman NA , Multhaup G , McDonald BL , Beyreuther K ((1985) ) Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc Natl Acad Sci U S A 82: , 4245–4249.


Beastall GH , Watson ID ((2013) ) Clinical chemistry and laboratory medicine: An appreciation. Clin Chem Lab Med 51: , 3–4.


Engelborghs S , Niemantsverdriet E , Struyfs H , Blennow K , Brouns R , Comabella M , Dujmovic I , van der Flier W , Frolich L , Galimberti D , Gnanapavan S , Hemmer B , Hoff E , Hort J , Iacobaeus E , Ingelsson M , Jan de Jong F , Jonsson M , Khalil M , Kuhle J , Lleo A , de Mendonca A , Molinuevo JL , Nagels G , Paquet C , Parnetti L , Roks G , Rosa-Neto P , Scheltens P , Skarsgard C , Stomrud E , Tumani H , Visser PJ , Wallin A , Winblad B , Zetterberg H , Duits F , Teunissen CE ((2017) ) Consensus guidelines for lumbar puncture in patients with neurological diseases. Alzheimers Dement (Amst) 8: , 111–126.


Dayan AD ((1970) ) Quantitative histological studies on the aged human brain. II. Senile plaques and neurofibrillary tangles in senile dementia (with an appendix on their occurrence in cases of carcinoma). Acta Neuropathol 16: , 95–102.


Dayan AD ((1970) ) Quantitative histological studies on the aged human brain. I. Senile plaques and neurofibrillary tangles in “normal” patients. Acta Neuropathol 16: , 85–94.


Mann DM , Yates PO , Marcyniuk B ((1984) ) Alzheimer’s presenile dementia, senile dementia of Alzheimer type and Down’s syndrome in middle age form an age related continuum of pathological changes. Neuropathol Appl Neurobiol 10: , 185–207.


Price JL , McKeel DW Jr , Buckles VD , Roe CM , Xiong C , Grundman M , Hansen LA , Petersen RC , Parisi JE , Dickson DW , Smith CD , Davis DG , Schmitt FA , Markesbery WR , Kaye J , Kurlan R , Hulette C , Kurland BF , Higdon R , Kukull W , Morris JC ((2009) ) Neuropathology of nondemented aging: Presumptive evidence for preclinical Alzheimer disease. Neurobiol Aging 30: , 1026–1036.


Price JL , Morris JC ((1999) ) Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol 45: , 358–368.


James BD , Wilson RS , Boyle PA , Trojanowski JQ , Bennett DA , Schneider JA ((2016) ) TDP-43 stage, mixed pathologies, and clinical Alzheimer’s-type dementia. Brain 139: , 2983–2993.


Beach TG , Monsell SE , Phillips LE , Kukull W ((2012) ) Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010. J Neuropathol Exp Neurol 71: , 266–273.


Blennow K , Hampel H , Weiner M , Zetterberg H ((2010) ) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol 6: , 131–144.


Andreasen N , Hesse C , Davidsson P , Minthon L , Wallin A , Winblad B , Vanderstichele H , Vanmechelen E , Blennow K ((1999) ) Cerebrospinal fluid beta-amyloid(1-42) in Alzheimer disease: Differences between early- and late-onset Alzheimer disease and stability during the course of disease. Arch Neurol 56: , 673–680.


Blennow K , Wallin A , Agren H , Spenger C , Siegfried J , Vanmechelen E ((1995) ) Tau protein in cerebrospinal fluid: A biochemical marker for axonal degeneration in Alzheimer disease? Mol Chem Neuropathol 26: , 231–245.


Vanmechelen E , Vanderstichele H , Davidsson P , Van Kerschaver E , Van Der Perre B , Sjogren M , Andreasen N , Blennow K ((2000) ) Quantification of tau phosphorylated at threonine 181 in human cerebrospinal fluid: A sandwich ELISA with a synthetic phosphopeptide for standardization. Neurosci Lett 285: , 49–52.


Hampel H , Buerger K , Zinkowski R , Teipel SJ , Goernitz A , Andreasen N , Sjoegren M , DeBernardis J , Kerkman D , Ishiguro K , Ohno H , Vanmechelen E , Vanderstichele H , McCulloch C , Moller HJ , Davies P , Blennow K ((2004) ) Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: A comparative cerebrospinal fluid study. Arch Gen Psychiatry 61: , 95–102.


Olsson B , Lautner R , Andreasson U , Ohrfelt A , Portelius E , Bjerke M , Holtta M , Rosen C , Olsson C , Strobel G , Wu E , Dakin K , Petzold M , Blennow K , Zetterberg H ((2016) ) CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: A systematic review and meta-analysis. Lancet Neurol 15: , 673–684.


Koopman K , Le Bastard N , Martin JJ , Nagels G , De Deyn PP , Engelborghs S ((2009) ) Improved discrimination of autopsy-confirmed Alzheimer’s disease (AD) from non-AD dementias using CSF P-tau(181P). Neurochem Int 55: , 214–218.


Shaw LM , Vanderstichele H , Knapik-Czajka M , Clark CM , Aisen PS , Petersen RC , Blennow K , Soares H , Simon A , Lewczuk P , Dean R , Siemers E , Potter W , Lee VM , Trojanowski JQ , Alzheimer’s Disease Neuroimaging Initiative ((2009) ) Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 65: , 403–413.


Klunk WE ((2011) ) Amyloid imaging as a biomarker for cerebral beta-amyloidosis and risk prediction for Alzheimer dementia. Neurobiol Aging 32: (Suppl 1), S20–S36.


Fagan AM , Mintun MA , Mach RH , Lee SY , Dence CS , Shah AR , LaRossa GN , Spinner ML , Klunk WE , Mathis CA , DeKosky ST , Morris JC , Holtzman DM ((2006) ) Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol 59: , 512–519.


Blennow K , Mattsson N , Scholl M , Hansson O , Zetterberg H ((2015) ) Amyloid biomarkers in Alzheimer’s disease. Trends Pharmacol Sci 36: , 297–309.


Palmqvist S , Zetterberg H , Blennow K , Vestberg S , Andreasson U , Brooks DJ , Owenius R , Hagerstrom D , Wollmer P , Minthon L , Hansson O ((2014) ) Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid beta-amyloid 42: A cross-validation study against amyloid positron emission tomography. JAMA Neurol 71: , 1282–1289.


Palmqvist S , Zetterberg H , Mattsson N , Johansson P , Alzheimer’s Disease Neuroimaging Initiative, Minthon L , Blennow K , Olsson M , Hansson O , Swedish Bio FSG ((2015) ) Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology 85: , 1240–1249.


Mattsson N , Insel PS , Donohue M , Landau S , Jagust WJ , Shaw LM , Trojanowski JQ , Zetterberg H , Blennow K , Weiner MW , Alzheimer’s Disease Neuroimaging Initiative ((2015) ) Independent information from cerebrospinal fluid amyloid-beta and florbetapir imaging in Alzheimer’s disease. Brain 138: , 772–783.


Palmqvist S , Mattsson N , Hansson O , Alzheimer’s Disease Neuroimaging Initiative ((2016) ) Cerebrospinal fluid analysis detects cerebral amyloid-beta accumulation earlier than positron emission tomography. Brain 139: , 1226–1236.


Blennow K ((2010) ) Biomarkers in Alzheimer’s disease drug development. Nat Med 16: , 1218–1222.


Andreasen N , Minthon L , Vanmechelen E , Vanderstichele H , Davidsson P , Winblad B , Blennow K ((1999) ) Cerebrospinal fluid tau and Abeta42 as predictors of development of Alzheimer’s disease in patients with mild cognitive impairment. Neurosci Lett 273: , 5–8.


Hansson O , Zetterberg H , Buchhave P , Londos E , Blennow K , Minthon L ((2006) ) Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: A follow-up study. Lancet Neurol 5: , 228–234.


Mattsson N , Zetterberg H , Hansson O , Andreasen N , Parnetti L , Jonsson M , Herukka SK , van der Flier WM , Blankenstein MA , Ewers M , Rich K , Kaiser E , Verbeek M , Tsolaki M , Mulugeta E , Rosen E , Aarsland D , Visser PJ , Schroder J , Marcusson J , de Leon M , Hampel H , Scheltens P , Pirttila T , Wallin A , Jonhagen ME , Minthon L , Winblad B , Blennow K ((2009) ) CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302: , 385–393.


Visser PJ , Verhey F , Knol DL , Scheltens P , Wahlund LO , Freund-Levi Y , Tsolaki M , Minthon L , Wallin AK , Hampel H , Burger K , Pirttila T , Soininen H , Rikkert MO , Verbeek MM , Spiru L , Blennow K ((2009) ) Prevalence and prognostic value of CSF markers of Alzheimer’s disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: A prospective cohort study. Lancet Neurol 8: , 619–627.


Portelius E , Westman-Brinkmalm A , Zetterberg H , Blennow K ((2006) ) Determination of beta-amyloid peptide signatures in cerebrospinal fluid using immunoprecipitation- mass spectrometry. J Proteome Res 5: , 1010–1016.


Hansson O , Zetterberg H , Buchhave P , Andreasson U , Londos E , Minthon L , Blennow K ((2007) ) Prediction of Alzheimer’s disease using the CSF Abeta42/Abeta40 ratio in patients with mild cognitive impairment. Dement Geriatr Cogn Disord 23: , 316–320.


Lewczuk P , Esselmann H , Otto M , Maler JM , Henkel AW , Henkel MK , Eikenberg O , Antz C , Krause WR , Reulbach U , Kornhuber J , Wiltfang J ((2004) ) Neurochemical diagnosis of Alzheimer’s dementia by CSF Abeta Abeta42/Abeta40 ratio and total tau. Neurobiol Aging 25: , 273–281.


Wiltfang J , Esselmann H , Bibl M , Hull M , Hampel H , Kessler H , Frolich L , Schroder J , Peters O , Jessen F , Luckhaus C , Perneczky R , Jahn H , Fiszer M , Maler JM , Zimmermann R , Bruckmoser R , Kornhuber J , Lewczuk P ((2007) ) Amyloid beta peptide ratio 42/40 but not A beta 42 correlates with phospho-Tau in patients with low- and high-CSF A beta 40 load. J Neurochem 101: , 1053–1059.


Janelidze S , Zetterberg H , Mattsson N , Palmqvist S , Vanderstichele H , Lindberg O , van Westen D , Stomrud E , Minthon L , Blennow K , Swedish BioFINDER study group, Hansson O ((2016) ) CSF Abeta42/Abeta40 and Abeta42/Abeta38 ratios: Better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol 3: , 154–165.


Lewczuk P , Matzen A , Blennow K , Parnetti L , Molinuevo JL , Eusebi P , Kornhuber J , Morris JC , Fagan AM ((2017) ) Cerebrospinal fluid Abeta42/40 corresponds better than Abeta42 to amyloid PET in Alzheimer’s disease. J Alzheimers Dis 55: , 813–822.


Pannee J , Gobom J , Shaw LM , Korecka M , Chambers EE , Lame M , Jenkins R , Mylott W , Carrillo MC , Zegers I , Zetterberg H , Blennow K , Portelius E ((2016) ) Round robin test on quantification of amyloid-beta 1-42 in cerebrospinal fluid by mass spectrometry. Alzheimers Dement 12: , 55–59.


Dumurgier J , Schraen S , Gabelle A , Vercruysse O , Bombois S , Laplanche JL , Peoc’h K , Sablonniere B , Kastanenka KV , Delaby C , Pasquier F , Touchon J , Hugon J , Paquet C , Lehmann S ((2015) ) Cerebrospinal fluid amyloid-beta 42/40 ratio in clinical setting of memory centers: A multicentric study. Alzheimers Res Ther 7: , 30.


Lewczuk P , Lelental N , Spitzer P , Maler JM , Kornhuber J ((2015) ) Amyloid-beta 42/40 cerebrospinal fluid concentration ratio in the diagnostics of Alzheimer’s disease: Validation of two novel assays. J Alzheimers Dis 43: , 183–191.


Lewczuk P , Beck G , Esselmann H , Bruckmoser R , Zimmermann R , Fiszer M , Bibl M , Maler JM , Kornhuber J , Wiltfang J ((2006) ) Effect of sample collection tubes on cerebrospinal fluid concentrations of tau proteins and amyloid beta peptides. Clin Chem 52: , 332–334.


Li QX , Villemagne VL , Doecke JD , Rembach A , Sarros S , Varghese S , McGlade A , Laughton KM , Pertile KK , Fowler CJ , Rumble RL , Trounson BO , Taddei K , Rainey-Smith SR , Laws SM , Robertson JS , Evered LA , Silbert B , Ellis KA , Rowe CC , Macaulay SL , Darby D , Martins RN , Ames D , Masters CL , Collins S , AIBL Research Group ((2015) ) Alzheimer’s disease normative cerebrospinal fluid biomarkers validated in PET amyloid-beta characterized subjects from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. J Alzheimers Dis 48: , 175–187.


Dubois B , Feldman HH , Jacova C , Dekosky ST , Barberger-Gateau P , Cummings J , Delacourte A , Galasko D , Gauthier S , Jicha G , Meguro K , O’Brien J , Pasquier F , Robert P , Rossor M , Salloway S , Stern Y , Visser PJ , Scheltens P ((2007) ) Research criteria for the diagnosis of Alzheimer’s disease: Revising the NINCDS-ADRDA criteria. Lancet Neurol 6: , 734–746.


Albert MS , DeKosky ST , Dickson D , Dubois B , Feldman HH , Fox NC , Gamst A , Holtzman DM , Jagust WJ , Petersen RC , Snyder PJ , Carrillo MC , Thies B , Phelps CH ((2011) ) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7: , 270–279.


McKhann GM , Knopman DS , Chertkow H , Hyman BT , Jack CR Jr , Kawas CH , Klunk WE , Koroshetz WJ , Manly JJ , Mayeux R , Mohs RC , Morris JC , Rossor MN , Scheltens P , Carrillo MC , Thies B , Weintraub S , Phelps CH ((2011) ) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7: , 263–269.


Dubois B , Feldman HH , Jacova C , Hampel H , Molinuevo JL , Blennow K , DeKosky ST , Gauthier S , Selkoe D , Bateman R , Cappa S , Crutch S , Engelborghs S , Frisoni GB , Fox NC , Galasko D , Habert MO , Jicha GA , Nordberg A , Pasquier F , Rabinovici G , Robert P , Rowe C , Salloway S , Sarazin M , Epelbaum S , de Souza LC , Vellas B , Visser PJ , Schneider L , Stern Y , Scheltens P , Cummings JL ((2014) ) Advancing research diagnostic criteria for Alzheimer’s disease: The IWG-2 criteria. Lancet Neurol 13: , 614–629.


Olsson A , Vanderstichele H , Andreasen N , De Meyer G , Wallin A , Holmberg B , Rosengren L , Vanmechelen E , Blennow K ((2005) ) Simultaneous measurement of beta-amyloid(1-42), total tau, and phosphorylated tau (Thr181) in cerebrospinal fluid by the xMAP technology. Clin Chem 51: , 336–345.


Mattsson N , Zetterberg H , Hansson O ,et al. ((2009) ) CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302: , 385–393.


Mattsson N , Andreasson U , Persson S , Arai H , Batish SD , Bernardini S , Bocchio-Chiavetto L , Blankenstein MA , Carrillo MC , Chalbot S , Coart E , Chiasserini D , Cutler N , Dahlfors G , Duller S , Fagan AM , Forlenza O , Frisoni GB , Galasko D , Galimberti D , Hampel H , Handberg A , Heneka MT , Herskovits AZ , Herukka SK , Holtzman DM , Humpel C , Hyman BT , Iqbal K , Jucker M , Kaeser SA , Kaiser E , Kapaki E , Kidd D , Klivenyi P , Knudsen CS , Kummer MP , Lui J , Llado A , Lewczuk P , Li QX , Martins R , Masters C , McAuliffe J , Mercken M , Moghekar A , Molinuevo JL , Montine TJ , Nowatzke W , O’Brien R , Otto M , Paraskevas GP , Parnetti L , Petersen RC , Prvulovic D , de Reus HP , Rissman RA , Scarpini E , Stefani A , Soininen H , Schroder J , Shaw LM , Skinningsrud A , Skrogstad B , Spreer A , Talib L , Teunissen C , Trojanowski JQ , Tumani H , Umek RM , Van Broeck B , Vanderstichele H , Vecsei L , Verbeek MM , Windisch M , Zhang J , Zetterberg H , Blennow K ((2011) ) The Alzheimer’s Association external quality control program for cerebrospinal fluid biomarkers. Alzheimers Dement 7: , 386 e386–395 e386.


Bjerke M , Portelius E , Minthon L , Wallin A , Anckarsater H , Anckarsater R , Andreasen N , Zetterberg H , Andreasson U , Blennow K ((2010) ) Confounding factors influencing amyloid beta concentration in cerebrospinal fluid. Int J Alzheimers Dis 2010: , 986310.


Andreasen N , Minthon L , Davidsson P , Vanmechelen E , Vanderstichele H , Winblad B , Blennow K ((2001) ) Evaluation of CSF-tau and CSF-Abeta42 as diagnostic markers for Alzheimer disease in clinical practice. Arch Neurol 58: , 373–379.


Mattsson N , Andreasson U , Persson S , Carrillo MC , Collins S , Chalbot S , Cutler N , Dufour-Rainfray D , Fagan AM , Heegaard NH , Robin Hsiung GY , Hyman B , Iqbal K , Lachno DR , Lleo A , Lewczuk P , Molinuevo JL , Parchi P , Regeniter A , Rissman R , Rosenmann H , Sancesario G , Schroder J , Shaw LM , Teunissen CE , Trojanowski JQ , Vanderstichele H , Vandijck M , Verbeek MM , Zetterberg H , Blennow K , Kaser SA , Alzheimer’s Association QC Program Work Group ((2013) ) CSF biomarker variability in the Alzheimer’s Association quality control program. Alzheimers Dement 9: , 251–261.


Kuhlmann J , Andreasson U , Pannee J , Bjerke M , Portelius E , Leinenbach A , Bittner T , Korecka M , Jenkins RG , Vanderstichele H , Stoops E , Lewczuk P , Shaw LM , Zegers I , Schimmel H , Zetterberg H , Blennow K , IFCC Working Group on Standardization of CSF proteins (WG-CSF) ((2016) ) CSF Abeta1-42 – an excellent but complicated Alzheimer’s biomarker – a route to standardisation. Clin Chim Acta.


Carrillo MC , Blennow K , Soares H , Lewczuk P , Mattsson N , Oberoi P , Umek R , Vandijck M , Salamone S , Bittner T , Shaw LM , Stephenson D , Bain L , Zetterberg H ((2013) ) Global standardization measurement of cerebral spinal fluid for Alzheimer’s disease: An update from the Alzheimer’s Association Global Biomarkers Consortium. Alzheimers Dement 9: , 137–140.


Pannee J , Portelius E , Oppermann M , Atkins A , Hornshaw M , Zegers I , Hojrup P , Minthon L , Hansson O , Zetterberg H , Blennow K , Gobom J ((2013) ) A selected reaction monitoring (SRM)-based method for absolute quantification of Abeta38, Abeta40, and Abeta42 in cerebrospinal fluid of Alzheimer’s disease patients and healthy controls. J Alzheimers Dis 33: , 1021–1032.


Leinenbach A , Pannee J , Dulffer T , Huber A , Bittner T , Andreasson U , Gobom J , Zetterberg H , Kobold U , Portelius E , Blennow K , IFCC Scientific Division Working Group on CSF proteins ((2014) ) Mass spectrometry-based candidate reference measurement procedure for quantification of amyloid-beta in cerebrospinal fluid. Clin Chem 60: , 987–994.


Armbruster D , Miller RR ((2007) ) The Joint Committee for Traceability in Laboratory Medicine (JCTLM): A global approach to promote the standardisation of clinical laboratory test results. Clin Biochem Rev 28: , 105–113.


Bjerke M , Andreasson U , Kuhlmann J , Portelius E , Pannee J , Lewczuk P , Umek RM , Vanmechelen E , Vanderstichele H , Stoops E , Lewis J , Vandijck M , Kostanjevecki V , Jeromin A , Salamone SJ , Schmidt O , Matzen A , Madin K , Eichenlaub U , Bittner T , Shaw LM , Zegers I , Zetterberg H , Blennow K ((2016) ) Assessing the commutability of reference material formats for the harmonization of amyloid-beta measurements. Clin Chem Lab Med 54: , 1177–1191.


Bittner T , Zetterberg H , Teunissen CE , Ostlund RE Jr , Militello M , Andreasson U , Hubeek I , Gibson D , Chu DC , Eichenlaub U , Heiss P , Kobold U , Leinenbach A , Madin K , Manuilova E , Rabe C , Blennow K ((2016) ) Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of beta-amyloid (1-42) in human cerebrospinal fluid. Alzheimers Dement 12: , 517–526.


Mann DM , Yates PO , Marcyniuk B ((1985) ) Some morphometric observations on the cerebral cortex and hippocampus in presenile Alzheimer’s disease, senile dementia of Alzheimer type and Down’s syndrome in middle age. J Neurol Sci 69: , 139–159.


Tomlinson BE ((1989) ) Second Dorothy S. Russell memorial lecture. The neuropathology of Alzheimer’s disease–issues in need of resolution. Neuropathol Appl Neurobiol 15: , 491–512.


Hansen LA , Deteresa R , Tobias H , Alford M , Terry RD ((1988) ) Neocortical morphometry and cholinergic neurochemistry in Pick’s disease. Am J Pathol 131: , 507–518.


Mattsson N , Insel PS , Landau S , Jagust W , Donohue M , Shaw LM , Trojanowski JQ , Zetterberg H , Blennow K , Weiner M , Alzheimer’s Disease Neuroimaging Initiative ((2014) ) Diagnostic accuracy of CSF Aβ42 and florbetapir PET for Alzheimer’s disease. Ann Clin Transl Neurol 1: , 534–543.


Jahn R , Fasshauer D ((2012) ) Molecular machines governing exocytosis of synaptic vesicles. Nature 490: , 201–207.


Sudhof TC ((2013) ) Neurotransmitter release: The last millisecond in the life of a synaptic vesicle. Neuron 80: , 675–690.


Masliah E , Mallory M , Alford M , DeTeresa R , Hansen LA , McKeel DW Jr , Morris JC ((2001) ) Altered expression of synaptic proteins occurs early during progression of Alzheimer’s disease. Neurology 56: , 127–129.


Scheff SW , Price DA , Schmitt FA , DeKosky ST , Mufson EJ ((2007) ) Synaptic alterations in CA1 in mild Alzheimer disease and mild cognitive impairment. Neurology 68: , 1501–1508.


DeKosky ST , Scheff SW ((1990) ) Synapse loss in frontal cortex biopsies in Alzheimer’s disease: Correlation with cognitive severity. Ann Neurol 27: , 457–464.


Blennow K , Bogdanovic N , Alafuzoff I , Ekman R , Davidsson P ((1996) ) Synaptic pathology in Alzheimer’s disease: Relation to severity of dementia, but not to senile plaques, neurofibrillary tangles, or the ApoE4 allele. J Neural Transm 103: , 603–618.


Sze CI , Troncoso JC , Kawas C , Mouton P , Price DL , Martin LJ ((1997) ) Loss of the presynaptic vesicle protein synaptophysin in hippocampus correlates with cognitive decline in Alzheimer disease. J Neuropathol Exp Neurol 56: , 933–944.


Terry RD , Masliah E , Salmon DP , Butters N , DeTeresa R , Hill R , Hansen LA , Katzman R ((1991) ) Physical basis of cognitive alterations in Alzheimer’s disease: Synapse loss is the major correlate of cognitive impairment. Ann Neurol 30: , 572–580.


Bittner T , Burgold S , Dorostkar MM , Fuhrmann M , Wegenast-Braun BM , Schmidt B , Kretzschmar H , Herms J ((2012) ) Amyloid plaque formation precedes dendritic spine loss. Acta Neuropathol 124: , 797–807.


Shankar GM , Bloodgood BL , Townsend M , Walsh DM , Selkoe DJ , Sabatini BL ((2007) ) Natural oligomers of the Alzheimer amyloid-beta protein induce reversible synapse loss by modulating an NMDA-type glutamate receptor-dependent signaling pathway. J Neurosci 27: , 2866–2875.


Dickstein DL , Brautigam H , Stockton SD Jr , Schmeidler J , Hof PR ((2010) ) Changes in dendritic complexity and spine morphology in transgenic mice expressing human wild-type tau. Brain Struct Funct 214: , 161–179.


Bellucci A , Westwood AJ , Ingram E , Casamenti F , Goedert M , Spillantini MG ((2004) ) Induction of inflammatory mediators and microglial activation in mice transgenic for mutant human P301S tau protein. Am J Pathol 165: , 1643–1652.


Davidsson P , Jahn R , Bergquist J , Ekman R , Blennow K ((1996) ) Synaptotagmin, a synaptic vesicle protein, is present in human cerebrospinal fluid: A new biochemical marker for synaptic pathology in Alzheimer disease? Mol Chem Neuropathol 27: , 195–210.


Davidsson P , Puchades M , Blennow K ((1999) ) Identification of synaptic vesicle, pre- and postsynaptic proteins in human cerebrospinal fluid using liquid-phase isoelectric focusing. Electrophoresis 20: , 431–437.


Thorsell A , Bjerke M , Gobom J , Brunhage E , Vanmechelen E , Andreasen N , Hansson O , Minthon L , Zetterberg H , Blennow K ((2010) ) Neurogranin in cerebrospinal fluid as a marker of synaptic degeneration in Alzheimer’s disease. Brain Res 1362: , 13–22.


Represa A , Deloulme JC , Sensenbrenner M , Ben-Ari Y , Baudier J ((1990) ) Neurogran: Immunocytochemical localization of a brain-secific protein kinase C substrate. J Neurosci 10: , 3782–3792.


Guadano-Ferraz A , Vinuela A , Oeding G , Bernal J , Rausell E ((2005) ) RC3/neurogranin is expressed in pyramidal neurons of motor and somatosensory cortex in normal and denervated monkeys. J Comp Neurol 493: , 554–570.


Huang KP , Huang FL , Jager T , Li J , Reymann KG , Balschun D ((2004) ) Neurogranin/RC3 enhances long-term potentiation and learning by promoting calcium-mediated signaling. J Neurosci 24: , 10660–10669.


Wu J , Li J , Huang KP , Huang FL ((2002) ) Attenuation of protein kinase C and cAMP-dependent protein kinase signal transduction in the neurogranin knockout mouse. J Biol Chem 277: , 19498–19505.


Davidsson P , Blennow K ((1998) ) Neurochemical dissection of synaptic pathology in Alzheimer’s disease. Int Psychogeriatr 10: , 11–23.


Reddy PH , Mani G , Park BS , Jacques J , Murdoch G , Whetsell W Jr , Kaye J , Manczak M ((2005) ) Differential loss of synaptic proteins in Alzheimer’s disease: Implications for synaptic dysfunction. J Alzheimers Dis 7: , 103–117; discussion 173–180.


Kvartsberg H , Duits FH , Ingelsson M , Andreasen N , Ohrfelt A , Andersson K , Brinkmalm G , Lannfelt L , Minthon L , Hansson O , Andreasson U , Teunissen CE , Scheltens P , Van der Flier WM , Zetterberg H , Portelius E , Blennow K ((2015) ) Cerebrospinal fluid levels of the synaptic protein neurogranin correlates with cognitive decline in prodromal Alzheimer’s disease. Alzheimers Dement 11: , 1180–1190.


Hellwig K , Kvartsberg H , Portelius E , Andreasson U , Oberstein TJ , Lewczuk P , Blennow K , Kornhuber J , Maler JM , Zetterberg H , Spitzer P ((2015) ) Neurogranin and YKL-40: Independent markers of synaptic degeneration and neuroinflammation in Alzheimer’s disease. Alzheimers Res Ther 7: , 74.


Kvartsberg H , Portelius E , Andreasson U , Brinkmalm G , Hellwig K , Lelental N , Kornhuber J , Hansson O , Minthon L , Spitzer P , Maler JM , Zetterberg H , Blennow K , Lewczuk P ((2015) ) Characterization of the postsynaptic protein neurogranin in paired cerebrospinal fluid and plasma samples from Alzheimer’s disease patients and healthy controls. Alzheimers Res Ther 7: , 40.


Portelius E , Zetterberg H , Skillback T , Tornqvist U , Andreasson U , Trojanowski JQ , Weiner MW , Shaw LM , Mattsson N , Blennow K , Alzheimer’s Disease Neuroimaging Initiative ((2015) ) Cerebrospinal fluid neurogran: Relation to cognition and neurodegeneration in Alzheimer’s disease. Brain 138: , 3373–3385.


Wellington H , Paterson RW , Portelius E , Tornqvist U , Magdalinou N , Fox NC , Blennow K , Schott JM , Zetterberg H ((2016) ) Increased CSF neurogranin concentration is specific to Alzheimer disease. Neurology 86: , 829–835.


Kester MI , Teunissen CE , Crimmins DL , Herries EM , Ladenson JH , Scheltens P , van der Flier WM , Morris JC , Holtzman DM , Fagan AM ((2015) ) Neurogranin, a CSF biomarker for synaptic loss, predicts decline to symptomatic Alzheimer disease. JAMA Neurol 72: , 1275–1280.


De Vos A , Jacobs D , Struyfs H , Fransen E , Andersson K , Portelius E , Andreasson U , De Surgeloose D , Hernalsteen D , Sleegers K , Robberecht C , Van Broeckhoven C , Zetterberg H , Blennow K , Engelborghs S , Vanmechelen E ((2015) ) C-terminal neurogranin is increased in cerebrospinal fluid but unchanged in plasma in Alzheimer’s disease. Alzheimers Dement 11: , 1461–1469.


Sutton RB , Fasshauer D , Jahn R , Brunger AT ((1998) ) Crystal structure of a SNARE complex involved in synaptic exocytosis at 2.4 A resolution. Nature 395: , 347–353.


Geppert M , Goda Y , Hammer RE , Li C , Rosahl TW , Stevens CF , Sudhof TC ((1994) ) Synaptotagmin I: A major Ca2+ sensor for transmitter release at a central synapse. Cell 79: , 717–727.


Brinkmalm A , Brinkmalm G , Honer WG , Frolich L , Hausner L , Minthon L , Hansson O , Wallin A , Zetterberg H , Blennow K , Ohrfelt A ((2014) ) SNAP-25 is a promising novel cerebrospinal fluid biomarker for synapse degeneration in Alzheimer’s disease. Mol Neurodegener 9: , 53.


Ohrfelt A , Brinkmalm A , Dumurgier J , Brinkmalm G , Hansson O , Zetterberg H , Bouaziz-Amar E , Hugon J , Paquet C , Blennow K ((2016) ) The pre-synaptic vesicle protein synaptotagmin is a novel biomarker for Alzheimer’s disease. Alzheimers Res Ther 8: , 41.


Hong Z , Shi M , Chung KA , Quinn JF , Peskind ER , Galasko D , Jankovic J , Zabetian CP , Leverenz JB , Baird G , Montine TJ , Hancock AM , Hwang H , Pan C , Bradner J , Kang UJ , Jensen PH , Zhang J ((2010) ) DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson’s disease. Brain 133: , 713–726.


Parnetti L , Chiasserini D , Bellomo G , Giannandrea D , De Carlo C , Qureshi MM , Ardah MT , Varghese S , Bonanni L , Borroni B , Tambasco N , Eusebi P , Rossi A , Onofrj M , Padovani A , Calabresi P , El-Agnaf O ((2011) ) Cerebrospinal fluid Tau/alpha-synuclein ratio in Parkinson’s disease and degenerative dementias. Mov Disord 26: , 1428–1435.


Tokuda T , Salem SA , Allsop D , Mizuno T , Nakagawa M , Qureshi MM , Locascio JJ , Schlossmacher MG , El-Agnaf OM ((2006) ) Decreased alpha-synuclein in cerebrospinal fluid of aged individuals and subjects with Parkinson’s disease. Biochem Biophys Res Commun 349: , 162–166.


Borghi R , Marchese R , Negro A , Marinelli L , Forloni G , Zaccheo D , Abbruzzese G , Tabaton M ((2000) ) Full length alpha-synuclein is present in cerebrospinal fluid from Parkinson’s disease and normal subjects. Neurosci Lett 287: , 65–67.


Magdalinou NK , Paterson RW , Schott JM , Fox NC , Mummery C , Blennow K , Bhatia K , Morris HR , Giunti P , Warner TT , de Silva R , Lees AJ , Zetterberg H ((2015) ) A panel of nine cerebrospinal fluid biomarkers may identify patients with atypical parkinsonian syndromes. J Neurol Neurosurg Psychiatry 86: , 1240–1247.


Ohrfelt A , Grognet P , Andreasen N , Wallin A , Vanmechelen E , Blennow K , Zetterberg H ((2009) ) Cerebrospinal fluid alpha-synuclein in neurodegenerative disorders-A marker of synapse loss? Neurosci Lett 450: , 332–335.


Hall S , Ohrfelt A , Constantinescu R , Andreasson U , Surova Y , Bostrom F , Nilsson C , Hakan W , Decraemer H , Nagga K , Minthon L , Londos E , Vanmechelen E , Holmberg B , Zetterberg H , Blennow K , Hansson O ((2012) ) Accuracy of a panel of 5 cerebrospinal fluid biomarkers in the differential diagnosis of patients with dementia and/or parkinsonian disorders. Arch Neurol 69: , 1445–1452.


Oeckl P , Metzger F , Nagl M , von Arnim CA , Halbgebauer S , Steinacker P , Ludolph AC , Otto M ((2016) ) Alpha-, beta-, and gamma-synuclein quantification in cerebrospinal fluid by multiple reaction monitoring reveals increased concentrations in Alzheimer’s and Creutzfeldt-Jakob disease but no alteration in synucleinopathies. Mol Cell Proteomics 15: , 3126–3138.


Hansson O , Hall S , Ohrfelt A , Zetterberg H , Blennow K , Minthon L , Nagga K , Londos E , Varghese S , Majbour NK , Al-Hayani A , El-Agnaf OM ((2014) ) Levels of cerebrospinal fluid alpha-synuclein oligomers are increased in Parkinson’s disease with dementia and dementia with Lewy bodies compared to Alzheimer’s disease. Alzheimers Res Ther 6: , 25.


Majbour NK , Vaikath NN , van Dijk KD , Ardah MT , Varghese S , Vesterager LB , Montezinho LP , Poole S , Safieh-Garabedian B , Tokuda T , Teunissen CE , Berendse HW , van de Berg WD , El-Agnaf OM ((2016) ) Oligomeric and phosphorylated alpha-synuclein as potential CSF biomarkers for Parkinson’s disease. Mol Neurodegener 11: , 7.


Guerreiro R , Wojtas A , Bras J , Carrasquillo M , Rogaeva E , Majounie E , Cruchaga C , Sassi C , Kauwe JS , Younkin S , Hazrati L , Collinge J , Pocock J , Lashley T , Williams J , Lambert JC , Amouyel P , Goate A , Rademakers R , Morgan K , Powell J , St George-Hyslop P , Singleton A , Hardy J , Alzheimer Genetic Analysis Group ((2013) ) TREM2 variants in Alzheimer’s disease. N Engl J Med 368: , 117–127.


Jonsson T , Stefansson K ((2013) ) TREM2 and neurodegenerative disease. N Engl J Med 369: , 1568–1569.


Piccio L , Buonsanti C , Cella M , Tassi I , Schmidt RE , Fenoglio C , Rinker 2nd J , Naismith RT , Panina-Bordignon P , Passini N , Galimberti D , Scarpini E , Colonna M , Cross AH ((2008) ) Identification of soluble TREM-2 in the cerebrospinal fluid and its association with multiple sclerosis and CNS inflammation. Brain 131: , 3081–3091.


Heslegrave A , Heywood W , Paterson R , Magdalinou N , Svensson J , Johansson P , Ohrfelt A , Blennow K , Hardy J , Schott J , Mills K , Zetterberg H ((2016) ) Increased cerebrospinal fluid soluble TREM2 concentration in Alzheimer’s disease. Mol Neurodegener 11: , 3.


Piccio L , Deming Y , Del-Aguila JL , Ghezzi L , Holtzman DM , Fagan AM , Fenoglio C , Galimberti D , Borroni B , Cruchaga C ((2016) ) Cerebrospinal fluid soluble TREM2 is higher in Alzheimer disease and associated with mutation status. Acta Neuropathol 131: , 925–933.


Suarez-Calvet M , Kleinberger G , Araque Caballero MA , Brendel M , Rominger A , Alcolea D , Fortea J , Lleo A , Blesa R , Gispert JD , Sanchez-Valle R , Antonell A , Rami L , Molinuevo JL , Brosseron F , Traschutz A , Heneka MT , Struyfs H , Engelborghs S , Sleegers K , Van Broeckhoven C , Zetterberg H , Nellgard B , Blennow K , Crispin A , Ewers M , Haass C ((2016) ) sTREM2 cerebrospinal fluid levels are a potential biomarker for microglia activity in early-stage Alzheimer’s disease and associate with neuronal injury markers. EMBO Mol Med 8: , 466–476.


Blennow K , Zetterberg H ((2015) ) Understanding biomarkers of neurodegeneration: Ultrasensitive detection techniques pave the way for mechanistic understanding. Nat Med 21: , 217–219.


O’Bryant SE , Gupta V , Henriksen K , Edwards M , Jeromin A , Lista S , Bazenet C , Soares H , Lovestone S , Hampel H , Montine T , Blennow K , Foroud T , Carrillo M , Graff-Radford N , Laske C , Breteler M , Shaw L , Trojanowski JQ , Schupf N , Rissman RA , Fagan AM , Oberoi P , Umek R , Weiner MW , Grammas P , Posner H , Martins R , STAR-B and BBBIG working groups ((2015) ) Guidelines for the standardization of preanalytic variables for blood-based biomarker studies in Alzheimer’s disease research. Alzheimers Dement 11: , 549–560.


Andreasson U , Blennow K , Zetterberg H ((2016) ) Update on ultrasensitive technologies to facilitate research on blood biomarkers for central nervous system disorders. Alzheimers Dement (Amst) 3: , 98–102.


Hansson O , Zetterberg H , Vanmechelen E , Vanderstichele H , Andreasson U , Londos E , Wallin A , Minthon L , Blennow K ((2010) ) Evaluation of plasma Abeta(40) and Abeta(42) as predictors of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neurobiol Aging 31: , 357–367.


Kuo YM , Emmerling MR , Lampert HC , Hempelman SR , Kokjohn TA , Woods AS , Cotter RJ , Roher AE ((1999) ) High levels of circulating Abeta42 are sequestered by plasma proteins in Alzheimer’s disease. Biochem Biophys Res Commun 257: , 787–791.


Zetterberg H , Mortberg E , Song L , Chang L , Provuncher GK , Patel PP , Ferrell E , Fournier DR , Kan CW , Campbell TG , Meyer R , Rivnak AJ , Pink BA , Minnehan KA , Piech T , Rissin DM , Duffy DC , Rubertsson S , Wilson DH , Blennow K ((2011) ) Hypoxia due to cardiac arrest induces a time-dependent increase in serum amyloid beta levels in humans. PLoS One 6: , e28263.


Janelidze S , Stomrud E , Palmqvist S , Zetterberg H , van Westen D , Jeromin A , Song L , Hanlon D , Tan Hehir CA , Baker D , Blennow K , Hansson O ((2016) ) Plasma beta-amyloid in Alzheimer’s disease and vascular disease. Sci Rep 6: , 26801.


Pannee J , Tornqvist U , Westerlund A , Ingelsson M , Lannfelt L , Brinkmalm G , Persson R , Gobom J , Svensson J , Johansson P , Zetterberg H , Blennow K , Portelius E ((2014) ) The amyloid-beta degradation pattern in plasma–a possible tool for clinical trials in Alzheimer’s disease. Neurosci Lett 573: , 7–12.


Ovod V , Ramsey KN , Mawuenyega KG , Bollinger JG , Hicks T , Schneider T , Sullivan M , Paumier K , Holtzman DM , Morris JC , Benzinger T , Fagan AM , Patterson BW , Bateman RJ ((2017) ) Amyloid beta concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement 13: , 841–849.


Tzen KY , Yang SY , Chen TF , Cheng TW , Horng HE , Wen HP , Huang YY , Shiue CY , Chiu MJ ((2014) ) Plasma Abeta but not tau is related to brain PiB retention in early Alzheimer’s disease. ACS Chem Neurosci 5: , 830–836.


Zetterberg H , Wilson D , Andreasson U , Minthon L , Blennow K , Randall J , Hansson O ((2013) ) Plasma tau levels in Alzheimer’s disease. Alzheimers Res Ther 5: , 9.


Mattsson N , Zetterberg H , Janelidze S , Insel PS , Andreasson U , Stomrud E , Palmqvist S , Baker D , Tan Hehir CA , Jeromin A , Hanlon D , Song L , Shaw LM , Trojanowski JQ , Weiner MW , Hansson O , Blennow K , ADNI Investigators ((2016) ) Plasma tau in Alzheimer disease. Neurology 87: , 1827–1835.


Meredith JE Jr , Sankaranarayanan S , Guss V , Lanzetti AJ , Berisha F , Neely RJ , Slemmon JR , Portelius E , Zetterberg H , Blennow K , Soares H , Ahlijanian M , Albright CF ((2013) ) Characterization of novel CSF Tau and ptau biomarkers for Alzheimer’s disease. PLoS One 8: , e76523.


Fiandaca MS , Kapogiannis D , Mapstone M , Boxer A , Eitan E , Schwartz JB , Abner EL , Petersen RC , Federoff HJ , Miller BL , Goetzl EJ ((2015) ) Identification of preclinical Alzheimer’s disease by a profile of pathogenic proteins in neurally derived blood exosomes: A case-control study. Alzheimers Dement 11: , 600–607 e601.


Gisslen M , Price RW , Andreasson U , Norgren N , Nilsson S , Hagberg L , Fuchs D , Spudich S , Blennow K , Zetterberg H ((2016) ) Plasma concentration of the neurofilament light protein (NFL) is a biomarker of CNS injury in HIV infection: A cross-sectional study. EBioMedicine 3: , 135–140.


Kuhle J , Barro C , Andreasson U , Derfuss T , Lindberg R , Sandelius A , Liman V , Norgren N , Blennow K , Zetterberg H ((2016) ) Comparison of three analytical platforms for quantification of the neurofilament light chain in blood samples: ELISA, electrochemiluminescence immunoassay and Simoa. Clin Chem Lab Med 54: , 1655–1661.


Mattsson N , Andreasson U , Zetterberg H , Blennow K , Alzheimer’s Disease Neuroimaging Initiative ((2017) ) Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol 74: , 557–566.


Weston PSJ , Poole T , Ryan NS , Nair A , Liang Y , Macpherson K , Druyeh R , Malone IB , Ahsan RL , Pemberton H , Klimova J , Mead S , Blennow K , Rossor MN , Schott JM , Zetterberg H , Fox NC ((2017) ) Serum neurofilament light in familial Alzheimer disease: A marker of early neurodegeneration. Neurology 89: , 2167–2175.


Rohrer JD , Woollacott IO , Dick KM , Brotherhood E , Gordon E , Fellows A , Toombs J , Druyeh R , Cardoso MJ , Ourselin S , Nicholas JM , Norgren N , Mead S , Andreasson U , Blennow K , Schott JM , Fox NC , Warren JD , Zetterberg H ((2016) ) Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia. Neurology 87: , 1329–1336.


Rojas JC , Karydas A , Bang J , Tsai RM , Blennow K , Liman V , Kramer JH , Rosen H , Miller BL , Zetterberg H , Boxer AL ((2016) ) Plasma neurofilament light chain predicts progression in progressive supranuclear palsy. Ann Clin Transl Neurol 3: , 216–225.