For a successful ELISA development, it is important to know the different possible conformational states of the target protein (monomers, dimers, aggregates) in the corresponding matrix (i.e., CSF) and thus samples should be analyzed under different denaturing and reducing conditions (**Figure 4B**). In addition to Western blotting, it is recommended to analyze samples via direct ELISA, in order to further test which antibodies are able to recognize both the recombinant protein/peptide and CSF in native conditions. At this stage, the type of ELISA plate should also be defined. The most common ELISA plate is the flat-bottomed 96-well polystyrene microplate, which allows the adsorption of the antibodies to the well plate by hydrophobic interactions (low and medium binding). Nevertheless, high binding microplates are also available, in which the surface is modified by radiation to increase the binding strength between the antibodies and the plate (38).

Antibodies with proven specificity and ability to recognize the native protein in the human samples are the optimal ones for further immunoassay development. If no optimal antibodies are found, new antibodies detecting different epitopes should be produced. Usually, when good antibodies are produced, this phase will last approximately 3 months of one full-time equivalent, though it will also depend on the number of antibodies as well as the availability of all the reagents, samples, and expertise needed.

### ELISA Development

### Antibody Pair Selection

A prerequisite for a good sandwich ELISA is that the two different antibodies (capture and detection antibodies) optimally match. Thus, the best antibody pairs able to detect the target CSF biomarker are identified by screening every possible combination. In order to avoid false positive measurements due to, e.g., direct reactivity between the antibodies, the optimal concentration of capture and detection antibodies for each combination has to be established. This can be done performing a checkerboard titration using the recombinant protein fragments/peptides (or full protein if available) at one fixed concentration (i.e., 0.5 μg/ mL) as a standard sample/calibrator (**Figure 4C**). As a starting point, a concentration up to 2 and 10 μg/mL for capture and detection antibodies, respectively, can be tested. During the subsequent steps, it is recommended to always include the standard calibrators and the pooled positive control CSF samples previously prepared (27).

Once the optimal antibody concentrations are established for each combination, both the standard sample and the CSF pools should be measured in serial dilutions to define which antibody pairs are able to detect the target CSF protein and the corresponding standard in a dose–response manner. Dose–response reactivity gives a good indication that the antibody pairs are detecting the corresponding protein. Sample dilution experiments will unravel the standard curve range as well as the optimal dilution factor of the CSF. If dose–response reactivity is not acquired, this may indicate that the reactivity observed is non-specific. The source of the non-specific signal should be identified, which may arise among other possibilities from the detection system used (i.e., secondary antibodies) or an inadequate blocking buffer (see below). If the source of the non-specific signal is not identified and mitigated, the corresponding antibody pairs should not be used for further assay development.

Once results are successful, i.e., at least one or two positive antibody pairs are present, production of monoclonal antibodies recognizing the same epitopes can be considered. It is expected that the produced monoclonal antibodies will behave similar to the corresponding polyclonal due to the limited epitopes that were used for immunization of the latter. Depending on the number of antibodies to be tested and whether non-specific signal is detected, this phase may last from 3 to 10 months approximately.

### Assay Set-Up and Fine-Tuning

The different conditions and reagents used (i.e., incubation times, blocking buffers, assay diluent, secondary antibodies) can also play a critical role in the development of an ELISA (38). Blocking buffers are used to cover the unoccupied hydrophobic spaces of the ELISA plate wells once capture antibodies have been coated, reducing subsequent non-specific binding of the sample/ reagents to the well. Different types of proteins are commonly used as a blocking agents, such as bovine serum albumin, nonfat dry milk, casein, normal serum, or fish gelatin, which can be diluted at different concentrations (ranging from 1 to 5%) in either phosphate- or tris-buffer saline (PBS or TBS). The different types of buffers and different protein concentrations should be tested since very low amount of blocking agent can lead to high background while excessive concentration may mask the binding epitope of the antibody. PBS can reduce signal of antiphospho-epitope-specific antibodies, and in that case, TBS will be the first choice and should likewise be used for sample diluent. Non-ionic detergents can be added to the sample dilution buffer, such as Tween20 that disrupts low affinity protein–protein interactions and increases contact of the H2O-component of the buffer to the surface. However, when background is high, it is recommended to add the protein used for blocking to the sample diluent, though at a lower concentration. This detergent buffer is also used during the washing steps between the different incubations of an ELISA procedure, but usually without added proteins. Selecting the optimal diluent helps to keep the background low, this will lead to an increase of sensitivity of the assay and can reduce matrix effects.

In addition to the buffer requirements, one should select the detection system used to create a quantitative signal. Enzymes (i.e., Horseradish Peroxidase, alkaline phosphatase) are commonly used, which are attached to either the detection antibody, to a secondary antibody or streptavidin when biotinylated detection antibodies are used. The enzyme reaction will produce a specific color once the corresponding chromogenic substrate or fluorochrome has been added (i.e., 3,5,3′,5′-tetramethylbenzidine, p-nitrophenyl phosphate). The amount of signal generated within the linear range of the assay is proportional to the activity of enzyme present and thus, to the concentration of the target protein.

Once the different buffers and reagents have been established, it is recommended to re-test the optimal concentration of the coating and detection antibodies, since the improvements achieved with the different conditions may allow one to reduce the antibody concentration. Taking into account that only optimal antibody pairs are tested in this phase, it may take a maximum of 3 months to establish the best conditions leading to the highest signal/noise ratio for each of the antibody pairs. At this stage, a small number of individual patient samples should be tested. If available, it is recommended to use the same samples that were used during the discovery phase in order to replicate the proteomics findings (qualification).

### ELISA Validation

### Initial ELISA Validation

Once an optimal assay has been developed or when it is commercially available, it is essential to test its analytical performance in the appropriate matrix (i.e., CSF) before assessing the clinical utility of the corresponding ELISA (28, 37, 39, 40). Several parameters need to be established such as precision, limits of detection, recovery, or parallelism among others. Validation of the assay will unravel whether the developed ELISA is accurate and robust in measuring the real levels of the candidate biomarker or if, on the contrary, the obtained values are influenced by other independent factors (i.e., pipetting errors, matrix effects, pre-analytical confounding factors). For example, the developed ELISA should have an optimal recovery, which is the ability of the assay to measure the specific candidate within the (complex) matrix (i.e., CSF) (41). During spike-recovery analysis, CSF samples with known concentration of the candidate biomarker are spiked with high, medium, low, and none amount of the calibrator. A bad recovery indicates that the different components of the matrix (i.e., CSF) affect the ability of the assay to measure the real concentration of the target molecule, which will affect the trueness of the results. Bad recoveries may be optimized by either using a different assay buffer mimicking better the matrix of interest or by further diluting the matrix of interest. ELISA validation will help to identify the different factors that compromise the reliability of the assay, which should be solved in order to draw accurate conclusions regarding the diagnostic performance of the biomarker candidate.

Previous guidelines have been published highlighting the parameters that should be stablished for the general validation of assays with different purposes (27, 41–45). This special issue in *Frontiers Neurology* contains a step-by-step and consensus standardized operating procedure (SOP) for a thorough ELISA validation for biomarkers for neurodegeneration (Andreasson et al.), developed by the members of the Joint Programming Neurodegenerative Disease (JPND) BIOMARKAPD (JPND-BIOMARKAPD), a consortium aiming to standardize the biomarker analysis for Alzheimer's and Parkinson's disease across Europe (46).

The fulfillment of the different parameters established by the JPND-BIOMARKAPD consortium as described by Andreasson and colleagues suggests that the assay is accurately measuring the candidate biomarker in CSF and thus a proof-of-concept analysis (verification) can be performed with a small cohort of individual samples (approximately 20 samples per clinical group). In case that some of the parameters are not fulfilled, it is recommended to re-analyze and test some of the incubation times, reagents, and concentrations established during assay development. Even if no changes in the concentration of the biomarker candidate are detected between the different clinical groups, it is worth to continue with a full-assay validation, since the assay might also be useful for other research purposes besides biomarker validation. However, full validation can only be performed on the final version of the assay. Noteworthy, when other matrices are used (i.e., post-mortem tissue, cell culture supernatants, cell lysates), an additional validation should always be performed to confirm the suitability of the assay for the corresponding matrix. The time frame for the completion of this phase typically lies between 2 and 8 months.

### Full ELISA Validation

Once the new ELISA is fully developed, the novel assay should undergo an extensive validation for the targeted matrix in which other important parameters, including the reproducibility or the robustness of the assay, are tested as also indicated by Andreasson and colleagues in the current issue. The stability of the candidate biomarker under certain conditions should be also analyzed. Although the effect of pre-analytical variables have been likely minimized if the general guidelines for sample handling have been followed (31), some of the pre-analytical confounding factors should be specifically measured for the biomarker candidate to detect possible effects induced by different pre-analytical issues. Pre-analytical confounding factors include not only patient variables such as diurnal variation and fasting, but also processing factors such as the effect of freeze/thaw cycles and length of storage at different temperatures (28, 32, 47).

Since samples need to be prepared for pre-analytical variability testing (including storage over long time), this phase can take between 4 months and even a couple of years for long-term storage. Although the fulfillment of a complete ELISA validation ensures that the assay is suitable to measure the targeted molecule in the validated matrix, it is important to note that assay validation is a continuous process since reagents are continuously being renewed (i.e., quality control samples, standards, primary, and secondary antibodies). Thus, batch-to-batch variations should be always analyzed, tracked, and reported and, if needed, validation should be re-tested and an internal quality control program should be initiated. Nevertheless, since biomarker validation is a continuous process, current guidelines and workflows will have to be revised and updated regularly.

### Clinical Assessment of the Biomarker

The different processes followed until this point allow to successfully develop and to analytically validate an assay that can specifically and accurately measure the novel discovered CSF biomarker candidate. In this latest stage, the assay can be used for clinical validation of the biomarker for the intended purpose (i.e., diagnosis, prognosis, treatment efficiency). A considerably larger number of samples must be analyzed compared to the discovery phase and thus a power analysis should be performed in order to define the optimal group size. Specificity and sensitivity of the corresponding biomarker should be calculated and the ability of the biomarker to discriminate between control and disease can be assessed using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), alone and/ or in combination with currently used CSF biomarker tests (i.e., Aβ42, t-Tau, and p-Tau in AD) (48). According to international dementia biomarker criteria, a sensitivity and specificity of at least 85% is needed for a clinically useful biomarker (49). If a longitudinal study is performed, it might also be useful to assess the predictive value of the biomarkers that reflect the conversion from non-demented or mild cognitive impairment cases to the specific dementia with Cox proportional hazards models and Kaplan–Meier curves (50). When positive results are obtained, data should be independently replicated using larger cohorts, different populations and multi-center studies before its future possible implementation in routine analysis (51, 52).

### Concluding Remarks and Perspectives for Future Assay Implementation in Routine Analysis

There is a great need to develop specific, sensitive, and practical tools to differentially diagnose AD and related dementias in its earliest possible phase. The gold standard format for biomarker analysis is ELISA, which usually needs to be developed when a novel biomarker candidate is identified. In order to facilitate the development of a novel ELISA and ease the validation of the potential candidate CSF biomarkers, here we suggest a straightforward workflow for ELISA development, which we divided into four different steps (**Figure 2**). In each step, different key issues need to be tested (**Table 1**). When a commercial ELISA is available, the corresponding assay should be validated for its use in the corresponding matrix (i.e., CSF) (**Figure 2**, step 3). In the last steps, a clinical assessment of the biomarker candidate should be performed using the validated ELISA.

Once the optimal assay has been fully developed and validated, and the diagnostic utility of the corresponding biomarker has been solidly established, it will be necessary to initiate the phase that will ultimately lead to the implementation of the diagnostic assay in routine diagnosis, i.e., to establish an *in vitro* diagnostic (IVD) test. Such tests are preferably developed on an automated platform (53, 54), which will strongly reduce variation between centers allowing the establishment of cut-off values. In order to implement in clinical practice, several governmental requirements need to be fulfilled, which can range from the reproducibility and stability of the analytical platform to proof of added diagnostic value of the discovered biomarker. The exact set of rules that need to be complied to implement an IVD test depends on the regulatory institution of each region, which is, for example, the 510(k) premarketing clearance oversight by the food and drug administration in United States (55) or the IVD Directive 98/97/EC established by the European Commission (56).

Developing a successful ELISA for the validation of novel protein biomarker candidates starts by taking the right decisions during early stages of the development, for which we believe the workflow described in this paper will be a very useful aid.

### References

### Acknowledgments

This work is part of the BIOMARKAPD project under the aegis of EU Joint Programme – Neurodegenerative Disease Research (JPND) – www.jpnd.eu, and the Memorabel program PRODIA, which are both supported through ZonMw (The Netherlands). We also acknowledge the support provided by the International Stichting Alzheimer Onderzoerk (ISAO), the association for frontotemporal degeneration (AFTD), and the Alzheimer's Drug Discovery Foundation (ADDF). We would like to also acknowledge all the members of Neurochemistry Laboratory of the Clinical Chemistry department at the VUmc (Amsterdam) for their expertise in assay development.

the National Center for Advancing Translational Sciences. Available from: http://www.ncbi.nlm.nih.gov/books/NBK92434/

**Conflict of Interest Statement:** Dr. Teunissen serves on the advisory board of Fujirebio and Roche, received research consumables from Euroimmun, IBL, Fujirebio, Invitrogen, and Mesoscale Discovery. None of the other authors have any competing interest.

*Copyright © 2015 del Campo, Jongbloed, Twaalfhoven, Veerhuis, Blankenstein and Teunissen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Expanding the Repertoire of Biomarkers for Alzheimer's Disease: Targeted and Non-targeted Approaches

#### *Douglas Galasko\**

*Department of Neurosciences, Shiley-Marcos Alzheimer's Disease Research Center, University of California, San Diego, La Jolla, CA, USA*

The first biofluid markers developed for Alzheimer's disease (AD) used targeted approaches for discovery. These initial biomarkers were directed at key protein constituents of the hallmark brain lesions in AD. Biomarkers for plaques targeted the amyloid beta protein (Aβ) and for tangles, the microtubule-associated protein tau. Cerebrospinal fluid levels of Aβ and tau have excellent diagnostic utility and can be used to monitor aspects of therapeutic development. Recent research has extended our current concepts of AD, which now include a slow buildup of pathology during a long pre-symptomatic period, a complex cascade of pathological pathways in the brain that may accelerate once symptoms develop, the potential of aggregated proteins to spread across brain pathways, and interactions with vascular and other age-associated brain pathologies. There are many potential roles for biomarkers within this landscape. A more diverse set of biomarkers would provide a better picture of the staging and state of pathological events in the brain across the stages of AD. The aim of this review is to focus on methods of biomarker discovery that may help to expand the currently accepted biomarkers. Opportunities and approaches for targeted and non-targeted (or −omic) biomarker discovery are highlighted, with examples from recent studies. How biomarker discoveries can be developed and integrated to become useful tools in diagnostic and therapeutic efforts is discussed.

#### *Edited by:*

*Charlotte Elisabeth Teunissen, VU University Medical Center Amsterdam, Netherlands*

#### *Reviewed by:*

*Davide Chiasserini, University of Perugia, Italy Hugo Marcel Vanderstichele, ADx NeuroSciences, Belgium Claire Bridel, VU University Medical Center Amsterdam, Netherlands*

> *\*Correspondence: Douglas Galasko<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neurology*

*Received: 01 October 2015 Accepted: 23 November 2015 Published: 16 December 2015*

#### *Citation:*

*Galasko D (2015) Expanding the Repertoire of Biomarkers for Alzheimer's Disease: Targeted and Non-targeted Approaches. Front. Neurol. 6:256. doi: 10.3389/fneur.2015.00256*

Keywords: Alzheimer's disease, biomarker, biofluid, amyloid, tau, synapse, proteomics

## INTRODUCTION

Biomarkers have many potential uses in Alzheimer's disease (AD), related neurodegenerative disorders and brain aging. Initial efforts to develop diagnostic biomarkers for AD were focused on the hallmark pathological lesions of senile plaques and neurofibrillary tangles. Amyloid betaprotein (Aβ), an integral component of plaques, and the microtubule-associated protein tau, the major protein found in tangles, were detected in cerebrospinal fluid (CSF). Sensitive enzymelinked immunosorbent assays (ELISAs) were developed to selectively detect pathogenic forms of Aβ (Aβ42) and tau, with the later advent of assays for phosphorylated forms of tau (P-tau) (1–3). Different phosphor-epitopes of tau have been identified in CSF and are increased in AD, including tau phosphorylated at threonine181 (the form most commonly measured), serine 199, and serine 231 (4). Increased levels of P-tau are more specific for AD than other dementias and may add value in differential diagnosis (4, 5). The profile of decreased Aβ42 and increased total tau and P-tau in CSF has high diagnostic value for AD (6) and has been a mainstay of AD biomarker research. Changes in CSF biomarkers are apparent in early symptomatic stages of AD, such as mild cognitive impairment (MCI) (7), and also occur pre-symptomatically (8). In these settings, the core biomarkers can provide prognostic information, for example, which patients with MCI may progress to AD dementia (7, 9–11). Also, studies have shown that patients with MCI or AD with higher baseline levels of CSF tau or P-tau (12, 13), and more recently higher baseline levels of the postsynaptic protein neurogranin (14) may show more rapid progression. This indicates the value of CSF biomarkers for predicting progression, e.g., for prognosis in preclinical stages of AD. Many forms of A-beta exist in CSF, and profiling N-terminal truncated forms was shown to increase prognostic value in MCI in one study (15).

Several themes that have emerged from AD research highlight the increased need for biomarkers, and also set the stage for how they may be used. First, AD is now viewed as a chronic and slowly progressive disorder, with a long buildup of pathology that precedes symptoms by a decade or longer (16). Also, among people with late-onset AD, autopsy studies highlight the frequent cooccurrence of other brain pathologies, such as vascular changes (macro-infarcts, lacunes and micro-infarcts, amyloid angiopathy, arteriosclerosis, and microbleeds) and other protein aggregates (e.g., alpha-Synuclein and TDP43) (17, 18). These may contribute to dementia and can be difficult to detect during life. In patients with atypical presentations, such as younger onset of dementia, the clinical picture may not be clear, and biomarkers can provide pointers to underlying pathology. Finally, treatment interventions for AD are shifting to earlier intervention, including stages of prodromal AD, where symptoms are mild, and most recently to prevention studies, where cognition falls within normal limits. Biomarkers have valuable roles to play in this pre-symptomatic stage to provide measures that may guide therapeutics. By measuring several biomarkers in CSF through individual or multiplex assays, it may be possible to index a number of biochemical processes in the brain that are informative about AD and related neurodegenerative disorders simultaneously. This enhances the value of CSF sampling. This review will summarize the potential roles for biomarkers and how approaches to biomarker discovery can help to build a pipeline that will address these needs and inform risk assessment, diagnosis, and treatment (**Figure 1**).

### SOURCES OF FLUID BIOMARKERS

The most obvious source of biomarkers relevant to the brain is CSF, which bathes the brain and spinal cord. CSF biomarkers reflect overall brain biochemistry, and processes such as neuronal damage, synapse loss, and inflammation may result in detectable biomarker changes in CSF if they are extensive enough. CSF is sampled through the lumbar space and may have different concentrations of analytes compared to the ventricular CSF. Typically, analytes are more concentrated in lumbar CSF, as noted for Aβ40, Aβ42, and tau (19). The question of concentration gradients within the lumbar CSF arises for many analytes and needs to be studied – this is not a major problem for Tau, P-tau, and Aβ42. Blood derivatives, such as plasma and serum, are easier to access than CSF, but typically reflect the body as a whole. If a brain-specific protein crosses into the blood, it may be subject to dilution, the action of proteases, and clearance by the liver and kidney, rendering it difficult to detect. As a further complication, systemic features of AD, such as weight loss or lower physical activity may result in subtle changes in blood biomarker levels.

These are many of the reasons why it has been extremely difficult to identify a blood biomarker that directly reflects the state of neurodegeneration (20, 21).

There are other questions or areas where blood biomarkers may have utility. Some plasma or serum analytes may relate to traits that predispose to neurodegeneration, for example, biomarkers that may be influenced by susceptibility genes. If age or environmental risk factors related to dementia have systemic effects, then these may be evident through the analysis of blood biomarkers. Blood biomarkers are particularly helpful as measures of drug levels and can provide peripheral indices of target engagement. Blood cells, e.g., lymphocytes or leukocytes, may be used to derive immune signatures or measures of RNA expression that may be indices of susceptibility for AD. Plasma and blood biomarkers are influenced by genetic factors and a wide spectrum of environmental factors, for example, diet, systemic illness, and physical activity. A recent paper studied over 300 plasma analytes longitudinally in twins, and identified variability that could be attributed to all of these factors. These findings suggest that a search for peripheral markers for AD may be extremely complicated, because in addition to these variables, aging is yet another factor that may impact on levels of peripheral markers. Plasma levels of Aβ, including ratios between different forms of Aβ (such as the ratio of Aβ42/Aβ40) have been inconsistent across studies, are only weakly correlated with CSF levels of Aβ or with markers of amyloid brain imaging, and although they may have some predictive value for the development of AD, this is relatively low [reviewed in Ref. (22)]. Peripheral issue may be a source of pathological proteins if there are systemic features of a neurodegenerative disease. This has been identified in Parkinson's disease (PD), where nerve endings can be stained for abnormal forms of alpha-synuclein in skin and salivary gland biopsy (23).

Regardless of whether a biomarker is measured in blood, CSF, or in biopsy material, data that shed light on how the biomarker is produced, released, cleared, and metabolized should be sought. To understand the biomarker comprehensively, it may require data from cell, model organism, and animal studies, as well as human biofluids and postmortem tissue. A recent development is the ability to study kinetics of CSF and plasma analytes by administering stable isotopes intravenously or orally to human subjects (24, 25). Examining the relationships between different types of biomarkers can also inform about pathogenetic processes, for example, by correlating biofluid biomarker changes with neuroimaging markers. This also allows modeling of when the biomarker becomes abnormal and how it changes during the early course of AD (26).

### EXPANDED ROLES FOR BIOMARKERS IN AD

There are many potential roles of biomarkers for AD and neurodegenerative disorders (**Table 1**). New biomarker discovery efforts need to take into consideration the current landscape of AD diagnosis and treatment efforts. The clinical diagnosis of typical AD by experts is often highly accurate; therefore, diagnostic biomarkers should be sensitive enough to help in early diagnosis, e.g., at stages of MCI or prodromal AD (27, 28). Because the sensitivity of CSF Aβ42, tau, and P-tau to discriminate prodromal AD from cognitively normal individuals is high, it may be challenging for additional biomarkers to improve on this. The differential diagnosis of unusual or atypical cases is a situation where biomarkers may clearly augment clinical judgment. Evaluating whether non-AD pathology may be present is an important question, particularly in elderly individuals with cognitive problems, and additional biomarkers could be helpful if they inform about processes, such as alpha-Synuclein, TDP-43, or vascular brain pathology. Mixed pathology is often present in the brains of elderly individuals with dementia, and a biomarker panel that allowed clear prediction of the types of underlying pathology would be useful.

Therapeutic efforts for AD are shifting to earlier intervention, including studies of secondary prevention, and even primary prevention in people with genetic predisposition. Potential uses of CSF biomarkers in clinical trials for AD and PD were recently reviewed in detail (46). Neuropathology and clinical research have shown that there are preclinical stages of AD during which amyloid and tau pathology accumulates, before the onset of memory decline (47). This provides an opportunity to start treatment interventions with the goal of delaying the onset of AD. Changes in biomarkers may provide a clearer early readout from prevention studies than changes in cognitive measures. Biomarkers are critical to identifying the presence of amyloid or tau brain pathology in this situation. For studies of early intervention, screening biomarkers, e.g., blood tests, that can improve the likelihood of detecting pathological brain changes through a more definitive test, such as molecular brain imaging and lumbar puncture, would be a great asset.

Biomarkers may help to improve the understanding of risk factors and mechanisms of disease. One would expect that causative or susceptibility genetic factors should be easy to link to biomarkers in biofluids. This has only been demonstrated in a few instances. For example, in AD, the APOE e4 allele has not yet been associated with a unique biomarker profile but modulates levels of the ApoE protein (48, 49). Inflammation plays a role in AD and other neurodegenerative disorders, and genetic variants related to the TREM2 gene increase the risk of AD and other dementias (50). In CSF, levels of a secreted soluble form of TREM were recently found to be decreased in AD (51). Other inflammatory biomarkers, such as secreted cytokines and chemokines, are unchanged or slightly increased in CSF in AD (52, 53). CSF biomarkers have been used as endophenotypes to discover genetic variants related to their levels, for example, CSF tau in AD (39), and CSF biomarkers related to inflammation (54). Genetic forms of non-AD dementia have provided clues for novel biomarkers. For example, inherited forms of frontotemporal dementia (FTD) due to mutations in the progranulin gene result in haplo-insufficiency with decreased production of granulin. Correspondingly, levels of granulin in plasma and CSF are markedly (and diagnostically) decreased (55). Burgeoning research on AD pathology has identified abnormalities in many biological processes, and it is likely that many pathogenic steps and events are occurring in a cascade (56). It may be feasible to develop biomarkers that can help to track many of these events,

#### Table 1 | Roles for fluid biomarkers in Alzheimer's disease.

*This table is not intended as a comprehensive listing, but shows representative biomarkers that can aid in diagnostic or therapeutic efforts. The biomarkers in this table are discussed in the text. The majority of markers are proteins discovered through candidate approaches, but there is room for an expanded suite of markers using diverse discovery approaches to improve our understanding of AD.*

*t-Tau, total tau levels;* α*-Syn, alpha-Synuclein; sAPP, secreted amyloid protein precursor; NFL, neurofilament light; TREM2, triggering receptor expressed on myeloid cells 2; MBP, myelin basic protein; MMP, matrix metalloprotease; BACE, beta-site APP cleaving enzyme 1; SILK, stable isotope kinetic labeling; CJD, Creutzfeld–Jacob disease; PSP, progressive supranuclear palsy; FTLD, fronto-temporal lobar degeneration.*

for example, microglial activation, inflammation, synaptic damage, and dysfunction (discussed later). This approach, together with neuroimaging methods, offers an opportunity to build a more complete picture of neurodegeneration in living patients at different stages of disease.

There are many potential therapeutic applications of biomarkers in AD. These typically have involved targeted biomarkers. During preclinical development, screening for gamma-secretase inhibitors and modulators and BACE inhibitors in cell and animal models have obtained their readout by using assays for the same secreted forms of Aβ that are used in AD diagnosis (57–59). These assays can be further applied to animal models and in human studies to identify target engagement and pharmacodynamic effects. A more detailed application is through CSF catheter placement to sample CSF during 24–36 h. This has been extended using stable isotope labeling kinetics (SILK) to estimate the fractional production and clearance rates of Aβ from CSF (24). In clinical trials, CSF biomarkers may be used to select patients or to stratify treatment. For example, in trials that aim to enroll patients with MCI due to AD, requiring a baseline CSF biomarker profile can increase confidence that the study population has symptoms due to AD rather than other causes. Target engagement may be demonstrated for certain types of amyloid-related interventions, in particular, secretase inhibitors. For example, gamma-secretase inhibitors that were studied in human clinical trials (43) and Beta-secretase inhibitors (60) showed robust effects in decreasing secreted forms of APP as well as Aβ in early phase studies, and the gamma-secretase inhibitor semagacestat showed plasma biomarker evidence of target activation in a phase 3 trial (61). Mass spectrometry (MS) characterization has identified a specific Aβ peptide signature after BACE inhibitor treatment (60). However, it is more challenging to show target engagement by antibodies directed against Aβ, because these bind Aβ and alter its levels in CSF and plasma. As novel drug targets are identified, efforts to identify companion biomarkers that help to identify immediate and downstream effects of drug action should be pursued.

Changes in levels of tau and P-tau in CSF have been examined as prototypic AD biomarkers of neurodegeneration or neuronal damage, with the hypothesis that neuroprotective or diseasemodifying drug effects may result in a decrease of these markers. It is likely that profiling biomarkers more broadly could be more informative. For example, biomarkers that index aspects of preand postsynaptic change, microglial activation, and astrocytic responses combined with neuroimaging could provide greater insights into the dynamics and interactions of neurons and glial cells in response to interventions. In efforts to make a claim to support drug efficacy, biofluid biomarkers are expected to play a supporting rather than a primary role. For example, if one of the effects of a drug treatment is to slow neurodegeneration enough to produce a meaningful cognitive readout, biomarker changes could be used to identify which disease-related pathways have been affected. To better understand events during neurodegeneration or disease progression, further exploration using non-targeted −omic approaches is worth pursuing. A complicated situation arises if biomarker changes are present in the absence of an appropriate clinical readout; this could indicate that the drug hit its target and influenced biomarkers but this is ineffective clinically, or that the changes in the biomarker are ambiguous. For example, CSF P-tau levels have been shown to decrease significantly in patients who received bapineuzumab, with a trend for total tau to decrease, but this did not correlate with clinical efficacy (62).

## APPROACHES TO DISCOVER BIOMARKERS IN BIOFLUIDS

Protein and peptide biomarkers in biofluids have formed the mainstay of clinical diagnostic tests in AD and other neurodegenerative disorders. As discussed above, despite over two decades of research, we have identified only a small number of fluid biomarkers for AD. The currently available biomarkers of CSF Aβ, tau, and P-tau have problems with measurement and standardization issues (63) that have hindered their routine and widespread use. The development of quality standards, a MS assay, and secondgeneration assays for these analytes are likely to improve this situation. As yet there are no established biomarkers for other neurodegenerative disorders and for vascular cognitive impairment. In view of the complexity of AD, the coexistence of mixed pathology in late-onset dementia, and the increasing emphasis for early diagnosis of AD and other neurodegenerative disorders, the search for additional biomarkers is highly warranted. One challenge is that CSF and plasma both contain proteins whose concentration spans several orders of magnitude, and almost all other proteins are overshadowed in concentration by albumin. Methods to identify novel biomarkers, in particular, proteomics, have improved, allowing post-translational modifications to be sought, and low abundance proteins (members of the "deep proteome") to be detected. Two main strategies for biomarker discovery have emerged, namely, targeted or candidate biomarker discovery, and multiplex or −omic approaches.

### Targeted Approaches to Identify and Develop Protein and Peptide Biomarkers

The search for targeted or candidate biomarkers for AD met with significant early successes. Based on the expectation that abnormal forms of Aβ and tau could be found in CSF, methods to detect forms of these proteins in CSF and plasma were developed. Many important and complex steps have been involved in understanding and translating these hallmark AD biomarkers. To start, assays that selectively detected the longer and more aggregationprone form of Aβ, Aβ42, were required. Total levels of Aβ in CSF were unchanged in AD, and the paradox that levels of Aβ42 were selectively decreased in CSF in AD (1) has been "explained" by aggregation of this peptide within the brain, leaving less to diffuse into the CSF. CSF levels of Aβ42 were later found to correlate inversely with the extent of fibrillar brain amyloid deposition as measured by amyloid PET imaging (64, 65). Although increased levels of CSF tau were present in AD relative to controls, why this occurred was not clear – CSF tau is not a marker of tangle formation, but is increased in situations of significant neuronal damage, for example, after acute stroke (66) or in Creutzfeld–Jacob disease (36). Assays for specifically P-tau also showed increases in AD, and CSF P-tau had higher specificity for AD than did increases of total tau. Only a few studies have tried to identify the forms of tau that are released into CSF. These were found to be N-terminal fragments of tau, with little if any of the full-length protein present (67, 68). The mechanisms of the release of tau into CSF remain unclear. Although converging data across many laboratories and studies have confirmed the profile of decreased Aβ42 and increased total and P-tau in CSF, cutoffs vary across laboratories (63, 69). Extensive quality control efforts have helped to decrease the variability. There are new efforts under way to develop fully automated assays for these key analytes, which will dramatically improve standardization.

Selecting a candidate biomarker has several advantages. Defined biochemical pathways and pathological mechanisms can help to relate the candidate to AD or to another neurodegenerative disorder, which may help to "make sense" of findings regarding the biomarker. Tools for detecting candidate biomarkers may be available, and sensitive detection methods can be developed. As a recent example, tau is released into CSF after neuronal injury. Increased levels of tau can be detected in plasma using ultrasensitive assay methods and were found to be transiently increased in boxers after bouts (70). Post-translational modifications of candidate biomarkers may also be sought and may provide markers related to mechanisms of disease. For example, phosphorylation is important in regulatory and signaling pathways and has been implicated in altering the solubility and promoting aggregation of proteins. P-tau (4) and alpha-synuclein (71) are detectable in CSF and may provide insights into processes relevant to AD and PD, respectively.

Although CSF Aβ42 and tau reflect certain steps of pathology in the brain, much attention has focused on small oligomeric aggregates of these proteins. Evidence suggests that oligomeric forms of Aβ may be the culprits responsible for toxicity (72–74) and also suggests that oligomers and aggregates of tau are species that contribute to neurodegeneration and correlate with cognitive loss in postmortem studies (75, 76). Also, aggregated or oligomeric forms of Aβ and tau may contribute to propagation of pathology (77). Despite the development of sensitive assays that can detect extremely low levels of Aβ oligomers, these have not been consistently or reliably identified in CSF in relation to AD (78, 79).

Several further examples of recent candidate biomarker discovery highlight the continued value of candidate approaches. A candidate approach led to the identification of the neuronal calcium sensor protein visinin-like protein-1 in CSF, and levels were found to be increased in AD relative to controls and predicted progression from non-demented to mild dementia (35) Similarly, a candidate approach was recently used to identify the dendritic protein neurogranin, which is involved in long-term potentiation and calcium regulation, and is decreased and mislocalized in brain tissue in AD. After initial characterization in CSF by HPLC and MS methods an ELISA was developed. Levels of neurogranin were reported to be increased in CSF in AD, even at the stage of MCI (80), and predicted progression from prodromal AD to dementia, as well as rate of progression of MRI change in AD (14). As a second example, genetic studies have implicated variation in the gene that encodes TREM2 as a risk factor in some patients with late-onset AD and later for other neurodegenerative disorders [reviewed in Ref. (81)]. Studies into the biology of cells derived from people homozygous for TREM2 mutations revealed impaired secretion of a cleaved fragment of TREM2. Decreased levels of this fragment were detected using an ELISA in CSF samples from patients with AD (51). Another example is the measurement of levels of granulin to identify people with mutations in the progranulin gene that predisposes to FTD. Progranulin mutations result in haplo-insufficiency and therefore people who carry mutations have a marked decrease in levels of secreted granulin in plasma and CSF (82).

One further example of an important application of CSF biomarkers relates to blood–brain barrier (BBB) integrity. An increased CSF:serum ratio of albumin is an established index used for many years as an indicator of loss of BBB integrity, and together with the IgG index and measurement of myelin basic protein levels, has been used as a diagnostic aid in multiple sclerosis. More recently, other markers of BBB integrity have emerged, particularly in relation to vascular cognitive impairment, and analysis of matrix metalloproteases and neurofilament-light levels have been proposed to supplement the albumin ratio and increase the diagnostic utility for subcortical small vessel disease (40).

A broader targeted approach to discovery is to multiplex known assays in combination [e.g., Luminex panels of assays of secreted proteins; multiple reagent monitoring (MRM) methods to examine selected panels of analytes with spiked in calibrator peptides for quantitation]. Several studies in AD have used arrays or multiplex ELISA-type assays for known secreted proteins to identify biomarkers in plasma and CSF (83, 84). Findings have been inconsistent, and different panels of plasma biomarkers have emerged from different studies, depending on analytical as well as biostatistical methods. Some of the analytes measured in these panels of secreted proteins in CSF showed correlations with cognitive test scores (85), or neuroimaging changes (86) although a validated panel of markers capable of tracking progression in AD has not yet emerged. Data from these studies were used to examine genetic variation associated with CSF levels of 59 proteins, and there were associations for proteins involved in inflammatory signaling (54). There are no validated CSF biomarkers for most non-AD dementias, although patterns of biomarkers, such as CSF P-tau181/total tau ratio, may be helpful in discriminating tauopathies from TDP43-associated FTLD disorders (37).

Targeted biomarker approaches have some disadvantages. Their detection and analysis need specific reagents, e.g., antibodies with high affinity, and antibodies against different regions are typically required to enable quantitative assays to be established and post-translational modifications to be analyzed. Finally, carrying out serial studies of candidate biomarkers and running individual assays to obtain multi-analyte data can be time consuming.

#### Highly Sensitive Assays

Many analytes detectable in plasma or CSF occur at low levels. This can pose a challenge to routine methods of analysis, such as ELISA. Recent technological refinements have resulted in ultrasensitive assay methods, capable of quantitation over low picomolar or femtomolar levels of analytes (87). For example, immuno-PCR, in which an oligonucleotide is conjugated to a detector antibody in a sandwich format, then amplified, has been developed and refined to allowed multiplex assays (88). Another refinement, single molecule arrays (SIMOA), which divides samples into microwells and allows higher detection of signal to background, has been used to identify changes in plasma Aβ in patients who had experienced cardiac arrest (89) and increases in serum or plasma levels of tau in professional athletes after concussion (90), in combat-related traumatic brain injury (TBI) (91), and in patients with major brain trauma (92). Plasma levels of tau are slightly increased in AD compared to controls but are not diagnostically useful (31).The general theme that measuring multiple analytes may paint a more detailed and clearer picture applies to the setting of TBI: recent studies have shown that biomarkers of neuronal, axonal, and astroglial injury appear acutely after the injury, and that axonal markers such as neurofilament protein persist longer in plasma and CSF than markers such as tau (93).

### Non-Targeted Approaches to Protein and Peptide Biomarker Discovery

Non-targeted approaches to biomarker discovery typically involve multiplex and −omic methods, which range from analyzing 10 to 100 analytes to performing large-scale unbiased proteomic or metabolomic screens. These approaches have the advantages of providing coverage of a wide range of potential biomarkers, and of identifying novel markers and mechanisms that may not have been obvious from pathogenic mechanisms or pathology. Also, analyses of interactions between markers, and of how markers relate to biological pathways, can be undertaken. There are several challenges to conducting, analyzing and interpreting large-scale −omic studies. For single analyte assays, a great deal of effort typically goes into development, standardization, and quantitation. By contrast, the analytes in large-scale −omic or similar methods may not be accurately quantified across their dynamic range. Both plasma and CSF have a few dominant proteins, in particular albumin, which are orders of magnitude higher in concentration than the vast majority of proteins and peptides. Methods to deplete the most dominant proteins are often used in −omic studies, but these preparation steps may alter the proteome. It is encouraging that test–retest proteomic analyses after immunodepletion of major proteins in CSF from subjects who underwent repeated lumbar punctures about 1 week apart provided evidence for a reasonably stable proteome (94). Detecting truncated forms of proteins or post-translational modifications may be more difficult in −omic studies using biofluids. Study design and data analysis need to be carefully considered to take proteomic studies from the stage of description or annotation to searching for group differences and the complex series of downstream steps that may lead to identification of candidate peptides and potential markers (95, 96). It is easy to identify false positive biomarker hits when hundreds of potential markers are analyzed and multiple comparisons are made, therefore separate cohorts for discovery and validation are essential. When interpreting findings, it is important to consider what factors may have contributed to the significant group of analytes. For example, vascular disease often coexists with AD, and vascular risk factors may be over-represented in AD patients compared to controls. Especially for proteomic studies of plasma, it is important to take factors such as hypertension, diabetes, weight loss, and decreased physical activity into account during data analyses. As an example of the promise of proteomic studies, recent promising results were reported in a large-scale effort to identify potential biomarkers related to aging through proteomic analysis of plasma, and strategies used in this project are summarized in Ref. (96).

Many non-targeted large-scale proteomic studies of CSF have been conducted in AD. It is interesting to note that Aβ42 and tau have not been detected as AD biomarkers in proteomic analyses of CSF. Early methods of separation, such as 2-dimensional gel electrophoresis (2DGE), resulted in detection and annotation of members of the CSF proteome, but few consistent markers specific for AD appeared. An extension of 2DGE called DIGE uses different fluorescent labels for biosamples from different groups of subjects (e.g., controls and those with disease) and allows for subtle differences to be identified. This has resulted in the discovery of a few novel biomarkers for AD, notably YKL40, a molecule secreted by astrocytes whose levels are increased in CSF in AD (34). MS methods remain the workhorse of proteomics and have been refined and improved in recent years. Analyses of CSF have continued to expand the catalog of proteins detectable in CSF, and a recent study identified and annotated over 2,500 proteins, each identified by at least 2 unique peptides [Ref. (97); database available at http://<IP_ADDRESS>/csf-pr/].

Technical improvements in MS have greatly improved the reproducibility of sample runs. Isobaric labeling of peptides, followed by a MS pipeline, can be used to compare samples from different groups of subjects. An approach that resembles the methods used in DIGE yielded several candidate peptide biomarkers for AD (98). Other approaches have allowed targeted quantitative analysis of selected peptides, as well as multiplexing (99, 100). By spiking in samples with heavily labeled known peptides as calibrators, a series of analytes may be analyzed quantitatively, termed MRM or selective reaction monitoring (SRM). For example, an exploratory proteomic study using CSF from patients with familial AD and controls yielded a set of novel candidate biomarkers (101), but these have not been replicated. Another study examined a panel of 39 candidate CSF biomarkers using MRM, and identified 4 that changed over 12 months with progression of AD (102). Recent studies of PD have explored whether a panel of analytes monitored using MRM may have value in diagnosis or relate to cognitive impairment (103). A pipeline for incorporating SRM methods into novel proteomic biomarker discovery has been proposed and its feasibility was demonstrated in a mouse cancer model (104). The sensitivity of MRM is much higher than that of untargeted proteomics, but it still is easier to quantify more abundant proteins, and antibody methods for highly sensitive assays have advantages for lower abundance analytes. Another analytical approach, immunoprecipitation followed by MS, allows differently processed forms of the same protein to be measured in biofluid samples. This targeted approach of MS has been used for the analysis of different forms of Aβ peptides with a variety of different N- and C-terminal amino acids and has provided signatures of the effects of BACE inhibitors on APP processing (60).

Novel approaches to multiplex detection, such as the use of aptamer-based assays or antibody arrays, have allowed the profiling of hundreds to over one thousand analytes simultaneously from small starting volumes of biofluid sample, although the data generated are not truly quantitative (105). Aptamer approaches to screen for plasma biomarkers for AD are under way and have shown some initial promise. For example, in one study, a panel of 13 proteins predicted AD with an area under the ROC curve of 0.7 (30). Other studies that used this technology have found differences between patients with MCI and AD compared to controls, but the specific analytes that were most highly predictive have differed across studies (106, 107). Aptamer technology has also been applied to identify members of the plasma proteome that are changed with aging. In an aging twin study that was followed by replication in several other cohorts, 13 plasma proteins were identified that showed robust changes with aging, some of which are growth factors (108). About 26% of the variability of the markers measured in twins could be explained by a heritable component. Understanding more about the biology of analytes that are detected by aptamer-based tests, and conducting replication studies will be helpful to advance this novel approach to protein biomarker identification.

### Non-Protein and "Unconventional" Biomarkers

Antibodies directed against novel antigens have been sought in serum or plasma as diagnostic markers for AD. Results have not always been consistent, and biomarkers have not yet been established using this method. One approach is to look for antibodies against pathogenic proteins, such as different forms of Aβ, e.g., by screening plasma or serum using micro-arrays. In recent examples, studies that screened for novel conformational forms of pathogenic proteins or unknown antigens that may be diagnostically altered in AD, PD, or other disorders have used auto-antibody and peptoid approaches [e.g., Ref. (32, 33, 109)]. Although initial hits emerged from these studies have not been replicated and the approaches have not yet matured into readily usable assays.

Metabolomic approaches measure small molecules that are substrates or products of metabolic processes. Two analytical methods are typically used, namely MS, which can identify large numbers of metabolites but has slow throughput, and magnetic resonance spectroscopy (MRS), which has higher throughput but lower sensitivity. Several recent small-scale studies have been able to distinguish patterns in CSF samples from AD patients and controls (110, 111). These studies will require extension and replication. Methods to standardize acquisition of metabolomics data are needed in order for these to be able to be readily used by reference laboratories. Increased statistical rigor and the need for extensive replication strongly need to be applied to metabolomic studies (112). Lipidomic analyses have also been applied to AD, with inconsistent findings. One recent study identified a panel of lipid-related biomarkers in plasma that predicted conversion to AD (29). Although clinical assessment, sample handling, and biomarker analysis were carefully standardized in this study, the number of subjects who progressed from normal cognition to impairment was small. This panel of biomarkers has not yet been replicated. Another lipidomic study identified changes in long chain cholesteryl esters in plasma that discriminated patients with AD and controls, but lacked replication cohorts (113). Careful study design with large enough numbers and replication cohorts are essential to make progress in this area. Also, robust assay platforms will need to be developed that will allow a set of lipidomic assays to be routinely run as a mature assay.

Exosomes are a subset of microvesicles and are released from cells under physiological and pathological conditions and circulate in body fluids. Exosomes are smaller than microparticles, and are usually defined as <100 nM in diameter. This small size poses a challenge to current methods of detection using flow cytometry. Exosomes arise from intracellular microvesicular bodies, whereas microparticles originate from the plasma membranes of cells or from apoptotic bodies. Exosomes may be implicated in neurodegenerative disorders in altered intercellular communication, for example, by transporting microRNA (miRNA), or by contributing to the spread of misfolded proteins (114). Methods to isolate exosomes have not been well standardized, and commercial kits yield mixed populations of exosomes and other particles. Extracellular vesicles, including exosomes, are found in CSF and their proteome has been characterized (115, 116). To date, there are no clear diagnostic markers that distinguish AD based on CSF exosomes, but much work is ongoing. Recent reports have isolated and analyzed exosomes in plasma, after using an immunopurification step to isolate a subset that have surface markers suggesting their neuronal origin, such as L1 cellular adhesion molecule (L1CAM) (117, 118). Subsequent protein analyses using ELISA identified differences in levels of AD protein biomarkers of Aβ42 and tau (118) between AD and controls. These are promising initial findings, but much further work is needed to replicate and extend the findings. For example, it is unclear how exosomes might traffic from the CNS to the bloodstream, and therefore whether these truly reflect neuronal pathophysiology. Also, the multiple steps necessary to isolate exosomes and then assay their contents poses challenges to assay standardization.

MicroRNAs are small RNA species that control gene expression by binding to sets of target mRNAs and may play roles in intracellular communication. They can be isolated from exosomes or directly from biofluids. There are technical problems in quantifying levels of miRNAs, and the development of methods and standards are still in their early stages. Studies in AD have identified profiles of miRNAs in CSF that may distinguish patients from controls but have been inconsistent across studies (119–121). Levels of miRNA levels are affected by the presence of cells, so that careful standardization will be necessary for studies using CSF (121). Studies of miRNA are reviewed in more detail in this collection of reviews (122).

Peripheral cells, such as mononuclear cells and lymphocytes, as well as platelets have been the subjects of many types of biomarker studies in AD. The nature of these studies and the types of biomarkers that have been sought are too diverse to be easily summarized here. Although an enormous number of markers and biological processes can be interrogated using cells, to date, no consistent biomarker profiles have emerged that were subsequently widely replicated.

### VALIDATING AND UNDERSTANDING BIOMARKERS

The initial validation of biomarkers requires the development of quantitative, sensitive, and reliable assays, and identifying preanalytical and analytical factors that may influence the levels that are measured (123). As examples of pre-analytical factors, for Aβ, polypropylene collection tubes are required, whereas for alpha-synuclein, measuring the extent of contamination by hemoglobin is important (124). Effects of storage, freeze–thaw cycles, and sample handling need to be carefully determined. Assay performance metrics, the type of analytical platform to be used, preparation and use of analytical standards and biological replicates also need to be standardized. Appropriately scaled clinical studies aimed at determining cutoff points, sensitivity, and specificity need to be conducted. Depending on the proposed use of the biomarker, longitudinal studies and postmortem confirmation of pathological features of brain pathology may add credence to claims for sensitivity and specificity. Meta-analyses or pooled analyses of multi-center data can provide information about effects of age and APOE genotype on CSF biomarkers (65). Assays typically progress through different stages of qualification. Much effort has gone into comparisons of A-beta and tau assays, including round robin efforts, which also were recently applied to MS assays for A-beta (125), and international quality control efforts. Next-generation assays for A-beta42, tau, and P-tau may help to decrease variability and to develop rigorous and standardized cutoff points that are readily applicable across laboratories. Understanding the phenomena that the biomarkers are measuring goes beyond these validation steps that have been outlined, and it is a critical step in determining the use of biomarkers, particularly regarding therapeutic studies. As a sobering observation, although increased CSF levels of tau and P-tau are routinely detected in AD, the mechanisms whereby these biomarkers are released into the CSF are not well understood.

There are many opportunities to study genetics in relation to biomarkers, some of which have been discussed earlier. Largescale studies of patients with inherited forms of early onset AD are helping to expand the map and timeline of biomarkers (126). Because age is the strongest risk factor for sporadic AD, it is important to continue to study how biomarkers and related brain processes change during aging. As an example, studies of Aβ metabolism using SILK have shown that there are marked changes in parameters related to production and clearance of Aβ from the CSF in association with aging (127).

### TOWARD AN EXPANDED SUITE OF BIOMARKERS

Biomarkers in biofluids have provided several important insights into AD, and currently have a role both in diagnosis and in the development of therapy. An attainable future goal is to improve and standardize current assays for Aβ, tau, and P-tau to permit routine and widespread clinical use. Progress will continue to be made in the development of assays to allow early and pre-symptomatic detection of AD to facilitate therapeutic studies (128). An ambitious goal will be to identify biomarkers that predict who is at risk for beginning to developing amyloid deposition in the brain before these deposits arise. In the area of diagnostics, the development of multi-analyte panels that are able to provide indices of non-AD degenerative disorders and important biological processes will remain an important area of research. As an illustrative example, a recent study of a nine analyte panel of CSF biomarkers had good differential diagnostic ability to distinguish between atypical movement disorders, PD and AD (129).

For clinical trials, a suite of biomarkers to evaluate amyloid processing exists, but markers related to oligomers remain elusive. Biomarkers that inform about target engagement for other therapeutic areas, for example, tau therapeutics, require further development. Prognostic, predictive, and companion biomarkers have not yet been identified and can be sought in the context of longitudinal studies. Relationships between biofluid markers, brain imaging, and cognitive testing will help to refine the roadmap of progression along the way to dementia in AD, especially during preclinical and prodromal stages. The potential for plasma biomarkers to provide screening, diagnostic, or prognostic tools merits continued study, but the design and validation of a plasma biomarker may be more complex than for a CSF biomarker.

The growth of research and development of new technologies gives hope that we may be able to develop a more comprehensive suite of biomarkers to build a detailed picture of the brain, that may integrate markers related to different cell types, important cellular structures such as synapses, biological processes such as transport, lipid metabolism, and exosome release, and effects of damage, oxidative stress, and inflammation. Progress in these areas holds the promise of greatly extending the reach of biofluid biomarkers for AD and related disorders.

### FUNDING

Dr. DG is funded by the National Institute on Aging (grant AGO5131). He also receives funding from the Michael J. Fox Foundation, the California Institute for Regenerative Medicine, and clinical trial funding from Eli Lilly, Inc. and Roche, Inc. He is an Editor of Alzheimer's Research and Therapy, and has received consulting fees from Prothena Neurosciences, Inc, Eli Lilly, Inc, Astra-Zeneca, Inc, and Lance Pharmaceuticals, Inc.

Alzheimer's disease biomarkers in patients with normal pressure hydrocephalus and posttraumatic hydrocephalus. *J Alzheimers Dis* (2014) **41**:1057–62. doi:10.3233/JAD-132708

hydro-5H-dibenzo[b,d]azepin-7-yl]-L-alaninamide (*LY-411575*). *J Pharmacol Exp Ther* (2004) **309**:49–55.

**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Galasko. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# **Autosomal dominant Alzheimer disease: a unique resource to study CSF biomarker changes in preclinical AD**

### *Suzanne Elizabeth Schindler and Anne M. Fagan\**

*Department of Neurology, Knight Alzheimer's Disease Research Center, Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA*

#### *Edited by:*

*Sylvain Lehmann, Montpellier University Hospital, France*

#### *Reviewed by:*

*Zhihui Yang, University of Florida, USA David Wallon, CHU de Rouen, France*

#### *\*Correspondence:*

*Anne M. Fagan, Department of Neurology, Washington University School of Medicine, Campus Box 8111, 660 South Euclid Avenue, St. Louis, MO 63110, USA<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neurology*

> *Received: 24 April 2015 Accepted: 12 June 2015 Published: 29 June 2015*

#### *Citation:*

*Schindler SE and Fagan AM (2015) Autosomal dominant Alzheimer disease: a unique resource to study CSF biomarker changes in preclinical AD. Front. Neurol. 6:142. doi: 10.3389/fneur.2015.00142* Our understanding of the pathogenesis of Alzheimer disease (AD) has been greatly influenced by investigation of rare families with autosomal dominant mutations that cause early onset AD. Mutations in the genes coding for amyloid precursor protein (*APP*), presenilin 1 (*PSEN-1*), and presenilin 2 (*PSEN-2*) cause over-production of the amyloid-β peptide (Aβ) leading to early deposition of Aβ in the brain, which in turn is hypothesized to initiate a cascade of processes, resulting in neuronal death, cognitive decline, and eventual dementia. Studies of cerebrospinal fluid (CSF) from individuals with the common form of AD, late-onset AD (LOAD), have revealed that low CSF Aβ42 and high CSF tau are associated with AD brain pathology. Herein, we review the literature on CSF biomarkers in autosomal dominant AD (ADAD), which has contributed to a detailed road map of AD pathogenesis, especially during the preclinical period, prior to the appearance of any cognitive symptoms. Current drug trials are also taking advantage of the unique characteristics of ADAD and utilizing CSF biomarkers to accelerate development of effective therapies for AD.

#### **Keywords: cerebrospinal fluid, biomarkers, Alzheimer disease, autosomal dominant, familial**

### **Introduction**

In 1901, Dr. Alois Alzheimer began treating Auguste D., a 51-year-old woman with memory loss and hallucinations. Ms. D's dementia progressed and she died at the age of 56. Upon histopathological examination, Alzheimer found two types of abnormalities in the brain that were later termed amyloid plaques and neurofibrillary tangles (1). Over a century later, when patients die with a characteristic history of progressive cognitive decline and upon autopsy are found to have significant quantities of amyloid plaques and neurofibrillary tangles, they are assigned the neuropathological diagnosis of Alzheimer disease (AD). The vast majority of patients with AD develop dementia at age 65 or older. Genetic studies of patients like Ms. D, who develop cognitive decline before age 65, have revealed rare autosomal dominant mutations that cause AD (2). Recently, surviving samples from Ms. D were subjected to genetic analysis and found to have a genetic mutation in presenilin 1 (*PSEN-1*) (3), although there has been some controversy about this finding (4).

There is some concern that the pathogenesis of autosomal dominant AD (ADAD) may vary from the common late-onset AD (LOAD). However, while there are certainly some differences between ADAD and LOAD in terms of disease etiology, clinical features, and neuropathology, they share many characteristics including an abnormal pattern of cerebrospinal fluid (CSF) biomarkers (Table S1 in Supplementary Material). Although we cannot completely dismiss the notions that the pathogenesis of ADAD and LOAD are distinct and that findings from ADAD do not apply to LOAD, investigation of families with ADAD have contributed enormously to our understanding of AD. Finding mutations that cause ADAD identified key molecules in the disease process (5–9). Transgenic mice expressing human ADAD mutations revolutionized the field and have been used to examine almost every aspect of the disease (10). Recently, studies of CSF and brain imaging biomarkers have helped establish the time course of AD-related brain changes in individuals affected by ADAD, especially during the preclinical stage, prior to the appearance of cognitive symptoms (11, 12). Furthermore, after the failure of numerous drug trials to halt, slow, or reverse cognitive decline in symptomatic individuals with LOAD, clinical trials are now utilizing the unique nature of ADAD and the data derived from these families to design prevention trials for AD dementia in both ADAD mutation carriers (MCs) and individuals at risk for LOAD, while they are still asymptomatic (13, 14). Just as Ms. D's genetic misfortune benefited the entire field of AD research, it is likely that ADAD patients will lead us to better treatments for all people afflicted by this disease.

### **Epidemiology**

Alzheimer disease is the most common cause of dementia and, in the United States, affects ~4.7 million individuals aged 65 and older (15). Less than five percent of AD patients develop symptoms before age 65 and are classified as having early onset Alzheimer disease (EOAD) (16). Even rarer are the <1% of AD patients who carry mutations that cause ADAD with 100% penetrance who are distributed world-wide. Carriers of ADAD mutations typically develop symptoms of dementia in their 30s to 60s, depending on their specific gene mutation and the age of onset within their family (17, 18). Much of our current knowledge about ADAD and biomarkers of ADAD comes from two large studies: the multi-center, international Dominantly Inherited Alzheimer Network (DIAN) cohort, and the Alzheimer's Prevention Initiative (API) cohort that studies a large pedigree living in the state of Antioquia in Colombia, South America. The DIAN cohort includes carriers and non-carrier (NC) family members with many different ADAD mutations, while the Colombian kindred is likely descended from a single individual (19) and carries the E280A mutation in the *PSEN-1* gene.

### **Clinical Features**

Regardless of whether patients develop symptoms of AD before age 65 (EOAD) or after age 65 (LOAD), the typical first symptom of brain dysfunction is progressive episodic memory loss that slowly worsens over years (20). However, about 30–40% of patients with early symptom onset either from non-familial EOAD or ADAD have an increased frequency of atypical presentations, such as impairments in non-memory domains, including executive, behavioral, language, and visuospatial (21–23). *PSEN-1* MCs have been reported to be more likely to have headaches, myoclonus, gait abnormalities, pseudobulbar affect, and spastic paraparesis (24–26). Some mutations in the gene for amyloid precursor protein (*APP*) cause severe cerebral amyloid angiopathy (CAA), with resultant strokes and brain hemorrhages (27). These clinical features are rarely observed in LOAD.

### **Neuropathology**

The hallmarks of AD, regardless of the age at dementia onset and its underlying cause (ADAD versus LOAD), are aggregation of the amyloid-β (Aβ) peptide into amyloid plaques and region-specific development of intraneuronal neurofibrillary tangles composed of hyperphosphorylated forms of the microtubuleassociated protein, tau (28). AD-affected brains also demonstrate significant neuronal loss and associated neuroinflammation (29– 31), although these features are not specific to AD.

In addition to these classic pathologies, some ADAD mutations have been associated with neuropathological abnormalities not typically seen in LOAD. For example, amyloid deposition has been observed in the cerebellum of *PSEN-1* E280A carriers (32), an area not typically affected in LOAD. "Cotton-wool" type plaques that are larger than typical plaques, lack congophilic cores and have few associated dystrophic neurites (33) are often seen in individuals carrying certain *PSEN-1* mutations (34). Some ADAD mutations (notably in *APP*) result in severe CAA, which appears histologically as deposition of Aβ40 in the blood vessel wall. The specific pattern of CAA distribution in the brain depends on the mutation (e.g., Dutch, Flemish, Arctic, Iowa, and Italian) (34).

## **Genetics and Pathogenesis**

The genetics of ADAD have provided key insights into the molecular pathogenesis of AD. The observation in 1984 that older adults with Trisomy 21, also known as Down syndrome, develop the brain changes of AD suggested that a genetic locus on chromosome 21 might be involved in AD (35). Indeed, the first ADAD mutations were identified in the *APP* gene that resides on chromosome 21, thus implicating amyloid as a key player in AD pathogenesis (5–7, 36). We also now know that duplication of the *APP* locus results in ADAD (37, 38), likely because of amyloid over-production. Following the discovery of *APP* mutations, mutations in *PSEN-1* (8) and the gene for presenilin 2 (*PSEN-2*) (9) were identified and found to increase the amount of the more aggregation-prone Aβ42 compared to Aβ40 (39). Later, it was discovered that presenilin 1 is a critical component of the γ-secretase enzyme complex that cleaves APP to form Aβ (40). To date, 40 mutations in *APP*, 197 mutations in *PSEN-1*, and 25 mutations in *PSEN-2* have been identified that cause ADAD (2).

Since ADAD mutations either increase total Aβ or increase the ratio of Aβ42:Aβ42, amyloid has been hypothesized to be the initiator of AD, an idea described as the "Amyloid Hypothesis" (41). In further support of this hypothesis, a mutation was recently discovered in *APP* that decreases Aβ production and lowers the risk for AD (42). According to this hypothesis, initial deposition of Aβ into amyloid plaques leads to downstream tau-related neuronal pathology (tangles), neuronal injury, and subsequent neuronal death, which is then manifested as cognitive impairment, ultimately culminating in dementia at the end stage of the disease. Data from neuropathological, brain imaging, and CSF biomarker studies in LOAD are consistent with this hypothesis (43–49), but it has only been through study of ADAD that we have a more precise knowledge of the timing of these changes during the early, preclinical (presymptomatic) stage.

### **CSF Biomarkers in ADAD**

Due to its high prevalence, the majority of AD biomarker studies to date have evaluated individuals with LOAD. CSF levels of Aβ42, tau, and phosphotau181 (ptau) (markers of amyloid, neuronal injury, and tangles, respectively) have stood the test of time in exhibiting both diagnostic and prognostic utility (50). Individuals diagnosed with very mild or mild AD dementia have low levels of CSF Aβ42 (51–54) that inversely correlate with the presence of amyloid as visualized by positron emission tomography (PET) (55–59). Concentrations of CSF tau and ptau are increased in AD and have been shown to positively correlate (albeit to differing degrees) with tangle load at autopsy (52, 53, 60) and regional brain atrophy as defined by magnetic resonance imaging (MRI) (61–64). When paired, the combination of low CSF Aβ42 and high tau/ptau has been shown to be a strong predictor of future cognitive decline in both early symptomatic (very mild dementia or mild cognitive impairment, MCI) and asymptomatic individuals (55, 65–68). However, while such analyses in individuals at risk for LOAD can estimate the risk for decline, they cannot provide the information that is most useful for clinical care – where an individual falls along the pathologic disease cascade or when an individual can expect to develop symptoms of dementia.

In contrast, ADAD provides a unique resource for characterizing changes in CSF biomarkers, especially those that occur long before the onset of dementia. With ADAD families, investigators know if and when an individual will develop dementia. Mutations have 100% penetrance, allowing investigators to know with certainty that an individual will develop AD. Furthermore, within a given family, the age of dementia onset remains fairly consistent, allowing researchers to calculate an estimated number of years until symptom onset (EYO). The EYO construct permits evaluation of biomarker concentrations as a function of where along the disease trajectory an individual falls, independent of the actual age of dementia onset of their parent (17). Using ADAD families, studies can examine biomarker levels in MCs and NCs at distinct time points throughout the course of the disease, including the preclinical AD interval many years prior to dementia onset. However, the low prevalence of ADAD has historically created difficulties in evaluating CSF biomarkers in these families. Most early studies analyzed CSF from fewer than 10 MCs (69–71) (**Table 1**), and with the exception of those evaluating the large Columbia kindred (*PSEN-1* E280A) (12, 72), most have pooled together carriers of

*API, Alzheimer's Prevention Initiative; APP, amyloid precursor protein; DIAN, Dominantly Inherited Alzheimer Network; EAO, estimated age of symptom onset; EYO, estimated years to symptom onset; MC, mutation carrier; NC, mutation non-carrier (typically first-degree relative of MC); N.S., not significant; PSEN-1; presenilin 1. Numbers in parentheses refer to associated reference.*

different mutations. Despite the relatively small sample sizes and potential heterogeneity caused by pooling together individuals with different mutations, the pattern of CSF biomarker changes seen in ADAD MCs is remarkably similar to that observed in LOAD, namely, reduced levels of CSF Aβ42 and elevated levels of tau and ptau (**Table 1**; Table S1 in Supplementary Material). The one exception is very young MCs (in their 20s, about 25 years prior to AD symptom onset), who have elevated CSF Aβ42 (72). This was hypothesized to reflect over-production of CSF Aβ42 in ADAD MCs, which has more recently been confirmed directly in kinetic studies (73).

The larger DIAN and API studies have permitted analysis of CSF and imaging biomarkers in greater numbers of both asymptomatic and symptomatic individuals that span a wide range of EYOs, thus allowing conclusions to be drawn regarding the timing of such biomarker changes during the preclinical period (**Figure 1**). Results from cross-sectional analyses demonstrate higher levels of CSF Aβ42 in MCs compared to NCs very early in the disease process (~20–30 years prior to estimated symptom onset, EYO *−*20 to *−*30), which then drop with disease progression, becoming significantly lower than NCs ~10–20 years prior to symptom onset (~EYO *−*10 to *−*20) (11, 12, 72, 77). These low levels then begin to plateau with the development of cognitive symptoms. After Aβ42 levels begin to drop, levels of tau and ptau in MCs become significantly higher than NCs (~EYO *−*15), and then continue to increase with disease progression. However, a recent study of within-person change in biomarkers in a small sub-cohort of DIAN participants with longitudinal biomarker data has shown that although levels of CSF tau and ptau increase in MCs during the preclinical (asymptomatic) phase, levels stabilize or decline over time in individuals who are symptomatic (77). Similar patterns were observed in levels of

**FIGURE 1 | A time course of changes in ADAD mutation carriers versus non-carriers**. Cross-sectional data obtained in the DIAN cohort demonstrates that CSF Aβ42 (yellow) declines as Aβ deposition increases as shown by amyloid PET imaging (orange). CSF tau (green) increases as hippocampal volume (blue) and glucose metabolism as shown by FDG PET (purple) decreases. CDR-SOB (Clinical Dementia Rating-Sum of Boxes) (black), which quantifies clinical symptoms of dementia, increases (indicating worse performance) relatively late in the disease course. Reproduced with permission from Bateman et al. (11).

visinin-like protein 1 (VILIP-1) (77), a neuronal calcium sensor protein that is a marker of neuronal injury/death (78). Consistent with this pattern, a previous report of a single asymptomatic ADAD (*APP* V717I) MC showed substantial increases in tau and ptau over a 4- to 5-year period very early in the disease process (~EYO *−*19 to *−*14) (79), whereas a longitudinal decrease (or a lack of increase) in ptau was reported in a small Japanese cohort (*n* = 4) of symptomatic *PSEN1* MCs (80). Although not often discussed, results consistent with these changes in the trajectories of neuronal injury-related markers have been reported in LOAD (81–83).

Although this general model is consistent with data obtained from cross-sectional studies in LOAD (49, 84–86) and suggests a common pathophysiology for AD due to mutations and the much more common "sporadic" form, the longitudinal data from DIAN supports a model that incorporates an eventual slowing down of the rate of neuronal injury and death as may be indicated by reductions in these markers. It is also possible that the later decreases during the symptomatic phase may reflect fewer neurons left to contribute to the pool of CSF tau/ptau/VILIP-1. If corroborated in additional cohorts, this reversing pattern of marker change will likely have an impact on the definition of a positive neurodegenerative biomarker outcome in clinical trials, especially during the symptomatic phase. For example, an effective therapy may only slow the rate of increase in injury markers in individuals who are in the preclinical phase, but stabilize or decrease the rate of change in injury markers later in the disease. Confirmation of such patterns awaits evaluation of biomarker trajectories in clinical trials.

### **Use of ADAD in Clinical Trial Design**

Many clinical trials in symptomatic individuals with LOAD have failed to meet their clinical endpoints of delaying, halting, or reversing cognitive decline. One possibility proposed to explain this failure is that therapies must be delivered earlier, in individuals known to have underlying AD pathology, but before significant symptoms are manifest (87). However, there are several challenges associated with the design and implementation of such "prevention trials," including identifying asymptomatic participants with known underlying AD pathology and who are at a point in their disease trajectory when they are close to becoming symptomatic. Although CSF and imaging biomarkers are currently being used in clinical trials to confirm underlying amyloid pathology in individuals at risk for developing LOAD (http://www.nia.nih.gov/ alzheimers/clinical-trials/), the onset of dementia in LOAD is characteristically difficult to predict, even in individuals who are biomarker-positive. As a result, large numbers of participants are required in order to provide adequate statistical power to show a potential drug effect. In contrast, since ADAD is fully penetrant and the time until onset of dementia symptoms in MCs can be predicted with relatively high precision, fewer trial participants are required to demonstrate treatment efficacy within a suitable timeframe. Two such prevention trials in ADAD are currently underway; the DIAN-Trials Unit (DIAN-TU) and API, both of which are testing monoclonal antibodies directed against various forms of Aβ (13, 14).

Another possibility to explain the failure of previous clinical trials in LOAD is that the drug did not engage its purported target. Given the compelling data from observational biomarker studies of ADAD (**Table 1**), biomarkers can serve as meaningful endpoints to verify target engagement even before the possible appearance of significant cognitive effects. To this end, the DIAN-TU has defined biomarkers as the primary endpoint [amyloid PET or CSF Aβ, with CSF tau(s) as downstream targets], with the trial design transitioning to a cognitive endpoint only for those drugs shown to have properly engaged their pathologic targets (14, 88, 89). CSF biomarkers are also being used as exploratory measures in the API trial (13) and the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) prevention trial in LOAD (90).

### **Conclusion**

Although there are some differences in the pathology and clinical expression in ADAD compared to LOAD (Table S1 in Supplementary Material), studies of ADAD have provided critical insight that has propelled our knowledge and investigation of all forms of AD. Investigators have proposed the relative timing of biomarker changes in LOAD (48, 49), but these hypotheses cannot yet be empirically verified because we do not know *a priori* when individuals with LOAD will develop symptoms. Because the EYO is known in ADAD cases, data-based models of AD can be generated (**Figure 1**) (11, 12). Curves representing changes in

### **References**

CSF and imaging biomarkers over the disease course in ADAD can be superimposed on curves of cognitive function, resulting in a detailed road map of AD pathologic processes. These analyses confirm that AD brain changes begin to develop over two decades before the onset of dementia. Now, as researchers work to develop drugs that prevent dementia associated with AD pathology, they are using ADAD to accelerate clinical trials (13, 14). It would be appropriate if ADAD, which represents <1% of all AD but has provided so much insight into the disease, leads to a drug that ultimately prevents all forms of AD.

### **Author Contributions**

SS and AF were involved in all aspects of preparing, writing, and editing the manuscript.

### **Acknowledgments**

SS is supported by a NIH K career development award (5K12 HD001459-15). AF is supported by grants from the NIH (P01AG026276, PO1AG003991, 2UF1AG032438).

### **Supplementary Material**

The Supplementary Material for this article can be found online at http://journal.frontiersin.org/article/10.3389/fneur.2015.00142

imaging biomarkers in Swedish familial Alzheimer's disease. *J Alzheimer Dis* (2015) **43**(4):1393–402. doi:10.3233/JAD-140339

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Schindler and Fagan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Biochemical markers of physical exercise on mild cognitive impairment and dementia: systematic review and perspectives

*Camilla Steen Jensen1 \*, Steen Gregers Hasselbalch1,2, Gunhild Waldemar 1,2 and Anja Hviid Simonsen1*

*1Department of Neurology, Danish Dementia Research Centre, Rigshospitalet – Copenhagen University Hospital, Copenhagen, Denmark, 2Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark*

#### *Edited by:*

*Charlotte Elisabeth Teunissen, VU University Medical Center, Netherlands*

#### *Reviewed by:*

*Jason Eriksen, University of Houston, USA Wiesje M. Van Der Flier, VU University Medical Center, Netherlands*

#### *\*Correspondence:*

*Camilla Steen Jensen, Department of Neurology, Danish Dementia Research Center, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, Copenhagen DK-2100, Denmark<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neurology*

> *Received: 30 April 2015 Accepted: 12 August 2015 Published: 26 August 2015*

#### *Citation:*

*Jensen CS, Hasselbalch SG, Waldemar G and Simonsen AH (2015) Biochemical markers of physical exercise on mild cognitive impairment and dementia: systematic review and perspectives. Front. Neurol. 6:187. doi: 10.3389/fneur.2015.00187*

Background: The cognitive effects of physical exercise in patients with dementia disorders or mild cognitive impairment have been examined in various studies; however the biochemical effects of exercise from intervention studies are largely unknown. The objective of this systematic review is to investigate the published results on biomarkers in physical exercise intervention studies in patients with MCI or dementia.

Methods: The PubMed database was searched for studies from 1976 to February 2015. We included intervention studies investigating the effect of physical exercise activity on biomarkers in patients with MCI or dementia.

Results: A total of eight studies were identified (*n* = 447 patients) evaluating exercise regimes with variable duration (single session–three sessions/week for 26 weeks) and intensity (light-resistance training–high-intensity aerobic exercise). Various biomarkers were measured before and after intervention. Seven of the eight studies found a significant effect on their selected biomarkers with a positive effect of exercise on brain-derived neurotrophic factor, cholesterol, testosterone, estradiol, dehydroepiadrosterone, and insulin in the intervention groups compared with controls.

Conclusion: Although few studies suggest a beneficial effect on selected biomarkers, we need more knowledge of the biochemical effect of physical exercise in dementia or MCI.

Keywords: dementia, MCI, exercise intervention, biomarkers, physical activity

### Introduction

The prevalence of dementia is increasing, currently affecting more than 44 million people, and estimated affect 75 million people worldwide by 2030. Alzheimer's disease (AD) accounts for the majority of dementia cases (1–3). Currently there is no cure for these disorders, and there are currently no effective pharmacological interventions (4, 5). Attention has therefore turned toward non-pharmacological approaches, including exercise, to slow the cognitive decline associated with dementia (2, 6). Linking evidence from population-based cohorts or RCT studies with biochemical evidence will be crucial in order to understand how non-pharmacological interventions may potentially alter the course of the disease.

In epidemiological studies, retrospective cohort studies, and case–control studies, there is consensus that an active lifestyle in midlife decreases the risk of dementia in late adulthood (7, 8). The cognitive effects of physical exercise and an active lifestyle in healthy elderly subjects, and in those with MCI and dementia, have also been examined in various cross-sectional studies, intervention studies, and prospective studies, with conflicting results (9–21). Almost all studies in patients with mild cognitive impairment (MCI) show some effect on cognition, but recent systematic reviews call for caution when interpreting results in dementia due to limited evidence (22, 23). Lack of consensus could be due to differences in the study methodologies used, type of physical activity, or in the cognitive measures used.

Because some studies have identified a clinical effect of physical exercise, it is imperative to understand if and how exercise alters the pathophysiology of dementia. Such an understanding is necessary for the successful promotion and implementation of physical exercise as a part of the treatment for dementia. Our current knowledge comes largely from animal studies. Beta-Amyloid (Aβ) pathology can be altered in response to exercise in a mouse and rat model for AD (24, 25), and brain plasticity proteins, like brain-derived neurotrophic factor (BDNF), can be up-regulated in response to physical exercise (26). Also, long-term exercise treatment reduces oxidative stress (OX) in the hippocampus of aging rats (27). In a large study of healthy elderly subjects, lower plasma and brain Aβ was observed in those reporting higher levels of physical activity (21), and similar findings has been found in preclinical AD subjects (28), consistent with animal studies suggesting that physical activity may modulate specific AD pathology in humans as well. However, because observational and cross-sectional designs cannot establish causality, we need randomized controlled intervention trials to understand the biochemical effects of exercise.

Exercise-based interventions studies in various diseases have clarified some of the biochemical effects of physical activity, such as improved metabolic homeostasis in diabetes mellitus (29), reduced OX in obese subjects (30), and reduced low-grade inflammation in coronary artery disease (31). Thus, physical exercise may exert its effect through modulation of specific AD pathology and/or through pathological processes common to other diseases.

Therefore, the object of this study was to systematically review and evaluate the scientific literature regarding the biochemical effect of exercise in MCI and dementia disorders in intervention trials and furthermore to provide recommendations for future biochemical studies in this field. Based on the studies cited above, we hypothesized that physical exercise interventions would improve not only specific Aβ pathology, but also pathological processes downstream of Aβ accumulation.

### Methods

This systematic review was performed according to the recommendation of the Cochrane collaboration (32) and the Preferred Reporting Items for Systematic Review and Meta-Analysis: the PRISMA statement (33).

### Eligibility Criteria

Randomized controlled trials or clinical trials investigating the effect of physical exercise or activity on patients with MCI or dementia were selected to review. Studies must have obtained bio-fluid markers, regardless of whether the biomarkers were included as primary or secondary outcome.

#### Search Strategy

The following electronic database was searched: MEDLINE (accessed via PubMed). The database PubMed was selected because it contains more that 23 million citations from biomedical literature from MEDLINE, life science journals and online books.

The search conducted in February 2015 searched databases for the following MeSH terms and their English synonyms. Studies published from 1976 to 2015 were included.

Medline (Via Pubmed.org) was searched with the keywords and Boolean operators with the filter English and Human:

("Dementia"[Majr]) AND ("Exercise"[Majr]) ("Mild Cognitive impairment"[Majr]) AND ("Exercise"[Majr]) ("Dementia"[Majr]) AND ("physical fitness"[Majr]) ("Mild Cognitive impairment"[Majr]) AND ("Physical fitness"[Majr])

The search was done by two authors separately (first and second) author. The search results are described in **Figure 1**.

Inclusion criteria included: original work (no review or metaanalysis), physical activity/exercise as intervention, only full-text publication, and English language.

### Study Selection and Data Extraction

Studies were selected on the basis of the inclusion criteria listed above. The selected studies are listed in **Table 1** and Table S1 in Supplementary Material. Data extraction was done by the first author according to the data extraction form seen in Table S2 in Supplementary Material, in regards to author, endpoints measured, subjects, intervention, and results found.

### Results

The initial search gave 228 publications, from which 187 were collected for further reading and 111 of which were excluded due to irrelevance or because they did not meet the inclusion criteria on the basis of their title or abstract. From the remaining 76 publications, 54 met inclusion and exclusion criteria and were selected for analysis. After a detailed analysis, publications were excluded if they did not include analysis of biomarkers. The excluded publications are listed in Table S1 in Supplementary Material. In total, eight publications remained for inclusion in the review. Publications included are listed in **Table 1**. **Figure 1** shows the flowchart of the data gathering process.

#### Sample Subjects

Although our MeSH term search covered all dementia diagnoses, the majority of identified publications studied patients

with AD. Subjects were either from a nursing home-residing population (15, 35, 37) or a home-living population (34, 41, 42). Two studies did not describe living status (38, 41).

In **Table 1**, mean age and mean MMSE have been listed, giving a general indication of the sample subjects studied. The age range was from 66.4 to 85.4 years. The MMSE range was from 13.9 to 28.7. Only Baker et al. have a population with a MMSE above 21, which indicated that the majority of the studies have been on patients with moderate-to-severe dementia.

### Sample Size

The numbers of subjects used in the selected studies range from 13 to 110 subjects. In general, the small sample sizes have generated little power for calculation of effect.

### Exercise Protocol

Four studies implemented an aerobic training program with low-to-high intensity (34, 35, 37, 39), three studies investigated the effect of a single bout of high-intensity aerobic or resistance training exercise (38, 41, 42) and one study investigated the effect of light resistance training and stretching (15). Thus, most studies investigated aerobic training to investigate the effect on biochemical biomarkers. The studies reviewed applied very different training regimes, with regards to intensity, duration and frequency. Eggermont et al. (37) applied the lowest intensity with a walking program at a self-selected speed, and they did not report any significant results on any of their selected biomarkers. Cheng et al. applied a light exercise program and found that the exercise groups had a slower decline in their cognitive measures compared to controls. The three remaining aerobic exercise studies have applied a moderate-tohigh-intensity exercise program, and they found a significant increase in levels of their selected biomarkers, and in the cognitive measures.

### Exercise Supervision

Five of the eight selected studies had non-supervised training or supervision by caregivers. Three studies had supervision by trainers. Two studies reported use of heart rate monitors to ensure that the intended exercise intensity of the exercise was reached. In Baker et al. (34), only some of the training sessions were supervised.

### Cognition

Six out of these eight studies also investigated cognitive performance (15, 34, 35, 37, 39, 41). Of these six studies, four reported

#### Table 1 | Studies chosen for review.

*(Continued)*

#### table 1 | Continued

*ADL, activities of daily living; DHEA, dehydroepiadrosterone; AD, Alzheimer's disease; aMCI, amnestic mild cognitive impairment; MCI, mild cognitive impairment; IGF-1, insulin-like growth factor 1; BDNF, brain-derived neurotrophic factor; A*β*<sup>40</sup>*+*42, beta-amyloid isoform 40 and 42; VO2 peak, peak oxygen uptake; HRR, heart rate reserve;* ♀*, women;* ♂*, men; MMSE, mini-mental state examination; HDL, high-density lipoprotein; COX, cytochrome c oxidase; MoCA, Montreal Cognitive Assessment; TNF-a, tumor necrosis factor alpha; IL-6, interleukin-6; IPAS; International Affective Picture Set.*

*a Diagnosed according to international guidelines.*

*bSupervised by caregiver, healthcare worker, or not mentioned.*

*c Supervised by trainer or physiotherapist.*

any significant effect on the cognitive measures. Baker et al. (34) reported an improvement in several tests of executive function, but only in women, Cheng et al. (35) reported a reduced decline in MMSE, Nascimento et al. (39) found an improved cognition measured by the Montreal Cognitive Assessment (MoCA), and Segal et al. (41) found a significant improved picture recall after exercise.

#### Effect on Biomarkers

In total, eight studies that focused on biochemical markers were identified. Seven out of eight studies investigated protein biomarkers (15, 34, 35, 38–42), and two studies investigated the difference in the effect of exercise depending on the patients ApoE genotype (35, 37). One study also investigated markers of cardiovascular health (34). **Table 1** summarized the biochemical and cognitive findings.

In seven out of eight studies, a positive relationship was found between their selected biomarkers and the exercise intervention. Only one study did not find any significant results on any of their selected biomarkers (37).

Coelho et al. (36) and Segal et al. (41), found that exercise resulted in a significant up-regulation in the neuroplasticity protein BDNF. Baker et al. (34) also measured BDNF and found higher levels after exercise, however only in women. Nascimento et al. reported BDNF as one of their end points, however they did not report any findings. They did, however, report decreased Tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) levels after exercise.

Besides blood levels of BDNF, other biochemical compounds in blood including cholesterol and insulin were analyzed. Baker et al. (34) and Cheng et al. (35), measured plasma levels of insulin and cholesterol. Cheng et al. (35) also measured HDL, tri-glycerides, and glucose. Only Baker et al. (34) reported an effect of the intervention on these biomarkers, namely a significant decrease in cholesterol and an increase in insulin sensitivity.

On a different note besides neurological markers and metabolic markers, Akishita et al. (15) found that exercise had a significant up-regulating effect on selected sex hormones (testosterone, estradiol, and DEHA) in women. Segal et al. (41) found an increase in salivary alpha-amylase (sAA), an indirect measure of endogenous norepinephrine (NE), after a single session of highintensity exercise (70% HRR). Mancuso et al. (38) found that lactate increased after exercise, and that platelet mitochondria COX activity was decreased.

We did not find any intervention studies that investigated the effect of exercise on established diagnostic markers for dementia disease, such as Aβ, tau, p-tau, and α-synuclein.

### Discussion

Physical exercise as a non-pharmacological treatment for medical disease has proven beneficial for reducing the risk for many diseases including stroke, high blood pressure, and mental disorders like chronic stress and depression (43). However, compared to our understanding or physical exercise's impact on cardiovascular health and general fitness, our understanding of physical exercise's impact on cognitive health is still very much in its infancy. An impact of physical exercise on quality of life and activity of daily living in patients with dementia has been established; however evidence of the molecular effects is not clear.

The objective of this study was to conduct a systematic review to identify and evaluate the scientific literature published on the effect of an exercise intervention in dementia, in regards to relevant biomarkers.

### Biochemical Evidence of the Effects of Exercise

Insulin and diabetes have been connected to an increased risk of developing AD and cognitive impairment (44, 45). Two of the reviewed studies measured insulin sensitivity or glucose control. Baker et al. (34) found increased insulin sensitivity and increased insulin in the exercise group, however only in women. Cheng et al. (35) measured blood glucose levels, but they did not find any significant result.

Mancuso et al. (38) investigated the reactive oxygen species (ROS) generation and OX, measured via COX activity and lactate production in platelets. ROS and OX are thought to be involved in AD through the neurotoxicity of amyloid build up, metabolic impairments and free-radical production in mitochondria (46, 47). To investigate the metabolic contribution of mitochondrial impairment, COX activity and lactate production was measured before and after an exercise intervention. At baseline, the AD groups displayed higher levels of lactate and significantly lower activity of COX, compared to aged match cognitive normal individuals. This increased level of lactate in AD patients was unchanged throughout the exercise intervention, indicating mitochondrial impairment in AD. The exercise intervention did not alter COX activity, indicating that exercise might not be able to influence the mitochondrial electron transport chain (ETC). However, since Mancuso et al. (38) did not find any correlation with cognitive elements, such as MMSE, mitochondrial impairment might be an angle to study the pathology of AD, and not so much a way to improve cognitive decline.

Nascimento et al. (39, 41) investigated the influence of exercise on inflammation markers in MCI and cognitively normal subjects and found decreased levels of the pro-inflammatory cytokines, TNF-α and IL-6. Inflammation is a known factor in neurodegenerative diseases (48–50). Both pro-inflammatory cytokines (e.g., IL-6) and anti-inflammatory cytokines (e.g., IL-10 and IL-18) have been found to be increased in AD (51), and wherein it is speculated that an increased inflammatory response negatively contributes to neurodegeneration in AD (50). Several studies have shown that inflammation is directly influenced by physical activity, which down-regulates pro-inflammatory reactions in the brain (52). For further insight into the pathology of neuroinflammation, it might be beneficial to measure a variety of factors, both pro-inflammatory and anti-inflammatory.

Three of eight identified studies have focused on BDNF, all of which found an increase in BDNF after exercise. Lower levels of brain tissue BDNF have been seen in patients with AD compared to healthy controls (26, 53). The exercise-induced BDNF increase seen in the studies in this review has also been reported in animal studies, where brain levels of BDNF were increased after exercise (26), and in an intervention study in young healthy men, where plasma BDNF was increased with exercise. In order to achieve a more precise measurement of neuronal BDNF without the systemic component, BDNF levels in CSF could be assessed.

Alongside BDNF, Akishita et al. (15) measured increased levels of female sex hormones after exercise. Lower levels of sex hormones have previously been shown to increase the risk of AD (54).  One could therefore speculate that an increase in sex hormones is beneficial to the cognitive performance in patients already diagnosed with AD. In the study by Akishita et al. (15), there was no effect on ADL or cognition, and the up-regulating effect of exercise on sex hormones was lost after 3 months post-exercise.

Pharmacological evidence established that NE is involved in memory modulation, and can be regulated by exercise (58–60). This makes NE modulation by exercise an ideal target for memory modulation in patients with cognitive impairments. Segal et al. (41) studied this relationship with a single bout of high-intensity exercise in patients with aMCI. The cognitive performance was investigated with picture recall before and after exercise, and NE was measured indirectly via sAA. They found that performance in picture recall was significantly improved in the exercise cognitive normal control group as well in the exercise aMCI groups, and not in the corresponding non-exercise groups. Furthermore, sAA levels were equally increased in both exercise groups (cognitive normal and aMCI). When it comes to dementia diseases, like AD with more advanced neurodegeneration, it is unknown if exercise is able to upregulate NE, so further studies are needed. In addition, the potential harms of recurring acute increases in NE need to be investigated.

None of the review studies in this review focused their attention on already previously established markers of neurodegenerative disease. Baker et al. (61) has studied the effect of a diet intervention with or without high-intensity physical activity, and its effect on CSF levels of the amyloidogenic peptide Aβ42. The main outcome was that patients with MCI subjected to a modulated diet, and who had a high-intensity physically active lifestyle, had higher levels of CSF Aβ42, than those without an active lifestyle. Furthermore, brain levels of Aβ42 have been shown in animal studies to be reduced in response to physical exercise (62). Aβ42 therefore appears to be a physiologically relevant biomarker that was not measured in any of the included studies likely due to difficulty of including CSF measures in study design, attributable to the discomfort of lumbar puncture.

### Normal Aging

In previous studies on the effect of exercise in a population of healthy elderly individuals, a decrease in the metabolic biomarkers of cholesterol, HDL, and leptin (63) was described. Furthermore, the exercise group showed increased glucose sensitivity after intervention, compared to controls (63). Aging is connected with chronic low-grade inflammation, increased risk for disease, poor physical function and mortality (64). Exercise has been shown to decrease the levels of circulating inflammatory cytokines (65). The expected effects of exercise on biomarkers of metabolism and inflammation are similar between normal aging individuals and patients with dementia. In the study by Nascimento et al. (39, 41), where aged matched cognitive normal controls were studied, the effect of exercise on the inflammation biomarkers were not specific to either the dementia group or the control group. However, only the MCI group showed improved cognition. This could indicate a link between cognitive measures and alterations in the inflammation profile. One could speculate that the lack of effect on cognition seen in the control group could be due to the scale chosen for measuring cognition (MoCA). MoCA may not be sensitive enough to quantify cognition in a group that already performs well cognitively, as this group had high MoCA scores even before intervention, and thereby improvement in the controls group will not be detected.

BDNF has previously been investigated not only for its brain plasticity modulating effect in dementia patients, but also in subjects with depression (66) and in animal studies, high-intensity exercise has a modulating effect on BDNF (26). Studies have found that BDNF levels decline with age, and it has been shown to be associated with memory deficits (67). An up-regulation of BDNF would therefore be beneficial, and maybe act as a protecting factor against dementia and other memory deficiencies.

### Genetic Risk Factors

The effect of the known risk factor for AD, ApoE (68), was found not to have an effect for the outcome on biomarkers after an exercise intervention. Previous studies have indicated that that outcome of an exercise intervention could be ApoE genotype dependent (69). However, neither Eggermont et al. (37) nor Cheng et al. (35), found any significant difference in effect according to ApoE genotype.

A possibility to further explore the ApoE effect on AD could be to investigate the gene product of ApoE, the protein apoE. A recent study has indicated that low levels of apoE increases risk of AD (70). Currently there are no plasma markers for AD, and perhaps apoE may have the potential to be a ground-breaking new risk factor for AD.

### Exercise Intervention

In regards to the exercise protocol, most studies applied an aerobic training, like walking, to investigate the effect on biochemical markers. The studies reviewed have applied varying training regimes, durations, and frequencies. This makes a direct comparison difficult. However, most of the studies applying a moderate-to-high-intensity aerobic exercise protocol, have found a significant effect on biomarkers, while low-intensity protocols did not show significant effects. This could indicate that the level of intensity of the aerobic exercise is important for achieving an effect of an exercise intervention.

The level of supervision for protocol adherence and intensity varied greatly among the reviewed studies. Overall supervision is necessary to ensure general adherence to the program and that exercise intensity is maintained, especially when it comes to moderate-to-high-intensity exercise, where the physical demands on the patients are far greater. For example, supervised training sessions with professional trainers and equipment, such as a pulse watch, can be used.

### Cognition

Another caveat worth considering is whether the effect of physical exercise on cognition is caused by measurable changes in biomarkers that reflect the pathophysiology of the disorder or whether exercise improves cognition through general improvement of brain function through other mechanisms. These could include up-regulation of neurotransmitters relevant for cognition, such as NE or increase in vascular endothelial growth factor (71). This remains to be determined.

Biomarkers are the main outcome assessed in this review article, but when studying dementias, cognitive measures have to be taken into account. It is unclear whether lack of cognitive assessment as a measured outcome is due to negative findings or that cognition went untested. Although a change in a biochemical markers with physical exercise does not imply a therapeutic effect on symptomatology, an objectively measurable effect on a relevant pathophysiological biochemical parameter will support the importance of an implementation of physical exercise as part of the treatment for dementia.

### Recommendations for Sampling and Analysis

Of all the studies that have been conducted on exercise in patients with dementia disease or cognitive impairment, only eight studies have included biomarkers as a part of their assessment. However, the effect on functional activity and quality of life are relevant to the patient and caregiver, useful biochemical measures of these effects are still lacking.

Our recommendation for investigating the effect of physical exercise effect on dementia at a biochemical level is to (1) investigate whether any metabolic pathways that can be altered by increased physical activity could also be involved in dementia, (2) measure molecules from these pathways that have a neuronal contribution, and distinguishing their neuronal-specific contribution from systemic contribution, (3) investigate proteins and pathways that are involved not only in the generation and maintenance of neurons, but also relevant for cognitive function in general, and (4) measure an broad cytokine effect on neuroinflammation, since exercise has a putative anti-inflammatory effect (72).

Due to the fact that many established diagnostic markers for dementia disease are measured in CSF, including Aβ42 (73), tau (74), p-tau (74), α-synuclein (75), and huntingtin (76), we would further recommend to include CSF assessment in future studies, as this better reflects cerebral biomarker levels.

In order to achieve valid biomarker measurements, the highest quality of samples for analysis are required, and we recommend that strict sampling processing and storage procedures are observed (77).

### Conclusion

Eight out of fifty-four exercise studies in dementia or MCI have investigated biochemical markers of the effect of exercise on dementia and MCI. There is an overall trend of beneficial effect

### References

of exercise on the selected biomarkers. However, there were no studies that investigated specific Aβ pathology, or pathological processes downstream of Aβ accumulation.

Future studies with greater samples size, more thorough exercise supervision, educated trainers and well-defined intensity measures, as well as various sampling protocols (blood, CSF, etc.) are required. Such studies are in progress (78) and will hopefully help to understand the beneficial effect of physical exercise on dementia.

### Author Contributions

Study conception and design: CJ, AS, and SH. Acquisition of data: CJ and SH. Analysis and interpretation of data: CJ, SH, GW, and AS. Drafting of manuscript: CJ, SH, GW, and AS. Critical revision: SH, GW, and AS.

### Acknowledgments

The project was supported by the Innovation Fund Denmark. This study was performed under Neurodegenerative Disease Research (JPND) – Innovation fund Denmark (grant no. 0603-00470B).

### Supplementary Material

The Supplementary Material for this article can be found online at http://journal.frontiersin.org/article/10.3389/fneur.2015.00187

amyloid-β levels in normal aging and mild cognitive impairment. *J Alzheimers Dis* (2012) **28**:137–46. doi:10.3233/JAD-2011-111076

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Jensen, Hasselbalch, Waldemar and Simonsen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# **Pro-apoptotic kinase levels in cerebrospinal fluid as potential future biomarkers in Alzheimer's disease**

#### *Claire Paquet\*, Julien Dumurgier and Jacques Hugon*

*INSERM UMR-S942, Centre Mémoire de Ressources et de Recherche (CMRR) Paris Nord Ile de France, Groupe Hospitalier Lariboisière Fernand-Widal Saint-Louis, AP-HP, Université Paris Diderot, Paris, France*

Alzheimer's disease (AD) is characterized by the accumulation of Aβ peptides, hyperphosphorylated tau proteins, and neuronal loss in the brain of affected patients. The causes of neurodegeneration in AD are not clear, but apoptosis could be one of the cell death mechanisms. According to the amyloid hypothesis, abnormal aggregation of Aβ leads to altered kinase activities inducing tau phosphorylation and neuronal degeneration. Several studies have shown that pro-apoptotic kinases could be a link between Aβ and tau anomalies. Here, we present recent evidences from AD experimental models and human studies that three pro-apoptotic kinases (double-stranded RNA kinase (PKR), glycogen synthase kinase-3β, and C-Jun terminal kinase (JNK) could be implicated in AD physiopathology. These kinases are detectable in human fluids and the analysis of their levels could be used as potential surrogate markers to evaluate cell death and clinical prognosis. In addition to current biomarkers (Aβ1–42, tau, and phosphorylated tau), these new evaluations could bring about valuable information on potential innovative therapeutic targets to alter the clinical evolution.

**Keywords: cerebrospinal fluid, pro-apoptotic kinase, PKR, GSK-3, JNK, Alzheimer's disease**

### **Introduction**

Neuropathological lesions in Alzheimer's disease (AD) include senile plaques, neurofibrillary tangles, and amyloid angiopathy leading to synaptic and neuronal degradations. Aβ is formed after the cleavage of amyloid precursor protein (APP) by β secretase (BACE1) and γ secretase (1). According to the amyloid cascade hypothesis (2), biochemical and genetic findings have suggested that Aβ accumulation can induce tau phosphorylation and aggregation, synaptic dysfunction, and neuronal alteration, responsible for clinical signs of dementia (3). The precise mechanisms of neuronal demise have not been fully elucidated; however, apoptosis has been one of the most analyzed mechanisms in previous reports. Apoptosis is a sequence of events leading to the activation of caspases and cell disintegration. It has been proposed as the predominant form of cell death in AD due to unbalanced actions between pro and anti-apoptotic proteins (4, 5). Increased expression of several pro-apoptotic kinases has been observed in AD brains and their cellular pathways could be linked to AD physiopathology (6–13).

Protein kinases represent one of the largest super families, and they are molecular switches activating and inhibiting many biological processes, such as memory, differentiation, cell division, and cell death. They belong to complex metabolisms interacting with other kinases and their dysfunctions can be associated with various diseases (14). According to Hardy's hypothesis (3), Aβ peptides can trigger protein kinases in AD participating in neuronal signaling pathways between Aβ

#### *Edited by:*

*Charlotte Elisabeth Teunissen, VU University Medical Center Amsterdam, Netherlands*

#### *Reviewed by:*

*Jesus Avila, Centro de Biología Molecular Severo Ochoa, Spain Andrea F. N. Rosenberger, VU University Medical Center Amsterdam, Netherlands*

#### *\*Correspondence:*

*Claire Paquet, INSERM UMR-S942, Centre Mémoire de Ressources et de Recherche (CMRR) Paris Nord Ile de France, Groupe Hospitalier Lariboisière Fernand-Widal Saint Louis, 200 Rue du Faubourg Saint Denis, Paris 75010, France<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neurology*

> *Received: 12 May 2015 Accepted: 20 July 2015 Published: 04 August 2015*

#### *Citation:*

*Paquet C, Dumurgier J and Hugon J (2015) Pro-apoptotic kinase levels in cerebrospinal fluid as potential future biomarkers in Alzheimer's disease. Front. Neurol. 6:168. doi: 10.3389/fneur.2015.00168*

and tau phosphorylation. Many of these kinases are also proapoptotic and could induce synaptic and neuronal sheddings.

Recently, reports have shown that cerebrospinal fluid (CSF) levels of Aβ1–42, total tau (T-tau), and phosphorylated tau (ptau) were altered in AD patients and in patients with mild cognitive impairment (MCI) with higher risks to convert to AD (15). The analysis of CSF biomarkers has brought about new insights into the managing procedures of AD patients leading to new diagnostic criteria (16). The levels of these CSF biomarkers correlate with the severity of neuropathological lesions (17, 18). The use of CSF biomarkers has improved the confidence of clinicians for AD diagnosis (19) and now serves for the screening of patients in clinical trials (20). Because lumbar puncture (LP) is better known, more practiced, and well tolerated (21–24), physicians resort in CSF biomarkers more and more in clinical practice reflecting the impact on AD diagnosis (25, 26).

However, these classic CSF biomarkers are not directly predictive of the AD evolution. The need for new biomarkers remains to avoid the classification of patients by quintiles or clusters (27, 28). Furthermore, they have several pre-analytical requirements limiting the analysis to expert centers while AD patients are located everywhere in the world (29–31). Consequently, additional biomarkers are needed to predict the clinical evolution and cognitive decline, and to assess the efficiency of treatment targeting pathways of neuronal death.

This brief report will provide an overview of three proapoptotic protein kinases that are involved in AD physiopathology with detectable levels in biological fluids. We aim to address their place as new biomarkers reflecting the rate of neuronal death, predicting possibly the clinical evolution and requiring less preanalytical preparations.

### **Pro-Apoptotic Kinases in Alzheimer's Disease**

### **Double-Stranded RNA-Dependent Protein Kinase** Involvement of PKR in AD Pathophysiology