Edited by: Charlotte Elisabeth Teunissen, VU University Medical Center Amsterdam, Netherlands
Reviewed by: Jesus Avila, Centro de Biología Molecular Severo Ochoa, Spain; H. Bea Kuiperij, Radboud University Medical Center, Netherlands
Specialty section: This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neurology
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The complexity of Alzheimer’s disease (AD) and its long prodromal phase poses challenges for early diagnosis and yet allows for the possibility of the development of disease modifying treatments for secondary prevention. It is, therefore, of importance to develop biomarkers, in particular, in the preclinical or early phases that reflect the pathological characteristics of the disease and, moreover, could be of utility in triaging subjects for preventative therapeutic clinical trials. Much research has sought biomarkers for diagnostic purposes by comparing affected people to unaffected controls. However, given that AD pathology precedes disease onset, a pathology endophenotype design for biomarker discovery creates the opportunity for detection of much earlier markers of disease. Blood-based biomarkers potentially provide a minimally invasive option for this purpose and research in the field has adopted various “omics” approaches in order to achieve this. This review will, therefore, examine the current literature regarding blood-based proteomic biomarkers of AD and its associated pathology.
Dementia is now a huge public health priority, with 115.4 million people worldwide estimated to be living with dementia by 2050 (
The most common form of dementia is Alzheimer’s disease (AD), comprising approximately 50–70% of the elderly dementia population. AD is characterized by multiple cognitive deficits, which cause significant impairment to social or occupational functioning. The disease typically has a gradual onset followed by continuing cognitive decline, with a mean duration of approximately 8.5 years from the onset of clinical symptoms to the death of the patient (
Most clinical trials of potential therapeutic disease-modifying agents have involved individuals with clinically manifest dementia and have been relatively unsuccessful to date. Earlier stages of the disease are now being targeted, posing a challenge as it is difficult to detect individuals at this stage of AD; brain pathology is developing silently and cognitive symptoms if detectable are subtle. The underlying neuropathology characteristic of AD precedes symptom onset by many years, with the accumulation of amyloid-beta (Aβ) plaques believed to occur 15–20 years in advance of clinical manifestation of the disease (
In this review, we will discuss various studies that have utilized proteomic-based approaches to discover blood-based biomarkers for early and ideally preclinical detection of AD pathological processes and their use in clinical trials. We performed literature searches on PubMed
Search | Search terms |
---|---|
Plasma Aβ and Tau as biomarkers of AD | [alzheimer*(Title/Abstract) OR dementia(Title/Abstract) AND AD(Title/Abstract)] AND [blood(Title/Abstract) OR plasma(Title/Abstract) OR serum(Title/Abstract)] AND [proteomic*(Title/Abstract) OR proteome(Title/Abstract) OR protein(Title/Abstract) OR proteins(Title/Abstract)] AND [biomarker*(Title/Abstract) OR marker*(Title/Abstract)] AND [beta-amyloid(Title/Abstract) OR amyloid beta(Title/Abstract) OR abeta(Title/Abstract) OR tau(Title/Abstract)] |
Plasma biomarkers of AD (case–control studies) | [alzheimer*(Title/Abstract) OR dementia(Title/Abstract) AND AD(Title/Abstract)] AND [blood(Title/Abstract) OR plasma(Title/Abstract) OR serum(Title/Abstract)] AND [proteomic*(Title/Abstract) OR proteome(Title/Abstract) OR protein(Title/Abstract) OR proteins(Title/Abstract)] AND [biomarker*(Title/Abstract) OR marker*(Title/Abstract)] AND [diagnos*(Title/Abstract) OR prognos*(Title/Abstract) OR progression(Title/Abstract)] |
Plasma biomarkers of brain atrophy | [alzheimer*(Title/Abstract) OR dementia(Title/Abstract) AND AD(Title/Abstract)] AND [blood(Title/Abstract) OR plasma(Title/Abstract) OR serum(Title/Abstract)] AND [proteomic*(Title/Abstract) OR proteome(Title/Abstract) OR protein(Title/Abstract) OR proteins(Title/Abstract)] AND [biomarker*(Title/Abstract) OR marker*(Title/Abstract)] AND [atrophy(Title/Abstract) OR brain volume(Title/Abstract) OR sMRI(Title/Abstract) OR structural magnetic resonance imaging(Title/Abstract) OR structural MRI(Title/Abstract)] |
Plasma biomarkers of cognitive decline | [alzheimer*(Title/Abstract) OR dementia(Title/Abstract) AND AD(Title/Abstract)] AND [blood(Title/Abstract) OR plasma(Title/Abstract) OR serum(Title/Abstract)] AND [proteomic*(Title/Abstract) OR proteome(Title/Abstract) OR protein(Title/Abstract) OR proteins(Title/Abstract)] AND [biomarker*(Title/Abstract) OR marker*(Title/Abstract)] AND [cognitive decline(Title/Abstract) OR cognition(Title/Abstract) OR MMSE(Title/Abstract) OR ADAS(Title/Abstract) OR CDR(Title/Abstract)] |
Plasma biomarkers of PET amyloid | [alzheimer*(Title/Abstract) OR dementia(Title/Abstract) AND AD(Title/Abstract)] AND [blood(Title/Abstract) OR plasma(Title/Abstract) OR serum(Title/Abstract)] AND [proteomic*(Title/Abstract) OR proteome(Title/Abstract) OR protein(Title/Abstract) OR proteins(Title/Abstract)] AND [biomarker*(Title/Abstract) OR marker*(Title/Abstract)] AND [pib(Title/Abstract) OR Pittsburgh compound b(Title/Abstract) OR florbetapir(Title/Abstract) OR flutemetamol(Title/Abstract) OR florbetaben(Title/Abstract) OR amyloid PET(Title/Abstract) OR brain amyloid(Title/Abstract)] |
Today, the biomarkers used most extensively in clinical trials for dementia and to some extent in clinical practice are structural magnetic resonance imaging (MRI), molecular imaging of amyloid deposition using positron emission tomography (PET), imaging of metabolism using fluoro-deoxy-
The most well-characterized and validated tissue fluid molecular-based biomarker for AD is the decrease in Aβ and increase in tau and phospho-tau (pTau) observed in the CSF of people with AD, with a number of studies documenting discrimination of AD patients from healthy controls with good sensitivity and specificity, as reviewed by others (
In a revised model of the temporal relationship between key biomarkers of AD pathology, Jack et al. (
The minimally invasive and potentially inexpensive nature of tests using blood-based proteomic biomarkers make these approaches practical to implement, allowing for repeated sampling in large cohorts, and, therefore, might have significant advantages over other biomarker modalities. However, the use of blood as a matrix for measurement of biomarkers has the inherent disadvantage of its complex composition and subsequently poses technical difficulties for biomarker detection.
The most challenging of many obstacles to developing blood-based biomarkers is the massive dynamic range of proteins in blood, spanning up to 12 orders of magnitude (
The complexity of blood as a source of biomarkers is reflected in the limitations of various proteomic techniques that have been employed to investigate blood-based biomarkers for AD. In the following section, we will provide a brief overview of some of the tools available for proteomic biomarker discovery in blood, including mass spectrometry (MS), immunocapture, and aptamer-based techniques. Each of these approaches has their advantages and disadvantages and to date studies have combined a number of these approaches in the discovery pipeline for identifying protein biomarkers related to AD.
For discovery-level proteomics, a key attribute required of the technique used is the ability to measure multiple targets simultaneously in a multiplexing manner. MS-based approaches have been widely used in this way and possess the inherent advantage of there being no requirement for prior knowledge of the proteins being identified, hence, allowing for unbiased hypothesis-free biomarker discovery. Moreover, to facilitate multiplexing capabilities of MS-based protein quantification, approaches for labeling peptides or intact proteins have been developed, for example, the use of isobaric tags (
However, the huge abundance of a select few proteins in plasma and serum limits the detection of lower molecular weight proteins by MS. In plasma and serum, albumin and the immunoglubulins (IgG, IgA, IgM, and IgD) represent 75% of the total protein weight, and 99% of these samples are constituted by only 22 different protein species (
The gold standard for soluble protein quantification is ELISA. However, with the increasing need to measure multiple protein targets, with limited sample availability, multiplexing approaches for targeted and hypothesis-driven biomarker discovery are now increasingly being used. Two of the most widely used immunocapture-based multiplexing systems for this purpose are mesoscale discovery (MSD) and the Luminex xMAP technology.
Both MSD and Luminex xMAP technologies are similar to the “sandwich” ELISA in priniciple. However, in an MSD assay, the capture antibodies are coated on specific spot regions at the base of the wells of a microtiter plate. Capture antibodies for different targets can be coated on each of the different spots, thus, allowing for multiple protein targets to be captured simultaneously in a single sample. Electrochemiluminescence (SULFO-TAG) labels are then bound to the detection antibodies and upon electrical stimulation the SULFO-TAG labels emit light, which is used to quantify the amount of target protein present. Luminex xMAP assays, in contrast, use microsphere-based technology, which involves coating of the capture antibody to microspheres “beads” in suspension, and fluorescently labeled detection antibodies for detection and quantification. In this way, multiple beads may be coated with multiple capture antibodies for multiplexing protein measurements in a single sample. Whichever approach to multiplexed affinity capture is used, the method is dependent on the quality, binding characteristics, and batch stability of the primary (and indeed secondary) antibodies used.
Given the targeted nature of immunocapture-based assays, these approaches are not necessarily suitable for unbiased hypothesis-free approaches for biomarker discovery. Furthermore, protein quantification by immunocapture methods will be epitope specific, and the quantitative values obtained will relate to the region of the protein recognized by the antibodies used within the assay. This is an important property to note when using immunocapture-based methods for replication of findings that may have been discovered on a different methodological platform, such as MS. Where failure to technically replicate data between platforms is observed, it could be due to differences in the region of the protein being recognized by the different assays. Platform and assay differences in protein quantification are, therefore, important points to consider when designing the pipeline for biomarker discovery and development.
Aptamer-based approaches also provide another approach for relative quantification of multiple proteins in a multiplexing manner. Aptamers are single-stranded oligonucleotides, which recognize and bind target proteins with high affinity and specificity. Using this technology, the protein signal is effectively transformed to a nucleotide signal for subsequent microarray-based quantification of the relative fluorescence levels. An example of this approach is the panel that Somalogic has developed, which measures over 1300 analytes in a single sample
Each of the proteomic techniques described here have inherent advantages and disadvantages for both hypothesis-generating and hypothesis-driven biomarker discovery. Furthermore, as described earlier, platform and assay differences may impact upon the ability to technically replicate findings at the discovery level, and should, therefore, be considered carefully when designing biomarker studies.
In the CSF, Aβ42 (along with tau and pTau) shows good sensitivity and specificity for classifying AD patients from healthy controls (
Amyloid-beta fragments are produced by β and γ-secretase metabolism of the protein APP. β-secretase cleavage of APP produces sAPPβ and a 99 amino acid membrane bound fragment, which upon subsequent γ-secretase cleavage produces various Aβ species (
To date, Aβ42 and Aβ40 are the predominant species that have been investigated in blood, however, as reviewed extensively by others (
In terms of disease progression, the results are equally contradictory. An association of decreased plasma Aβ42 with more rapid cognitive decline in AD (
The results of blood Aβ40 as an AD biomarker have also been conflicting and are perhaps not as promising as that of Aβ42. For example, both increased serum Aβ40 (
Given the differing results of plasma Aβ42 and Aβ40 in relation to AD, it is not surprising that studies examining the potential of Aβ42/Aβ40 in blood as an AD marker have also been conflicting in their results. A number of studies have documented reduced plasma Aβ42/Aβ40 in association with AD-related parameters, including in MCI and AD subjects compared to healthy controls (
Very recently, however, a much larger, prospective, community-based study examined the levels of plasma Aβ42 and Aβ40 in over 2000 dementia-free individuals, and followed these individuals for dementia/AD over an 8-year period (
The conflicting findings of different Aβ studies may perhaps suggest that the utility of plasma Aβ as a marker is quite disease-stage specific, as postulated by Blasko et al. Their findings of a relationship of plasma Aβ with conversion from cognitively healthy to MCI, but not later in the disease course when participants convert from MCI to AD, would indicate that plasma Aβ may be more successful as a marker of pathology at the preclinical stages of disease. This theory would also be in line with why plasma Aβ42 appears to perform as a marker of risk for developing dementia over an 8-year period, as documented by Chouraki et al.
Given that CSF Aβ is normally cleared in blood (
The relationship between plasma Aβ and brain pathology is also not yet resolved. Levels of Aβ40 and Aβ42 1 year prior to post-mortem brain tissue collection were not associated with frontal and temporal necortex Aβ40 and Aβ42 burden at post mortem (
These findings indicate a potential relationship between plasma Aβ species and the neuropathology of AD, however, given the contradictory results of plasma Aβ as a marker of AD diagnosis and clinical progression, it is clear that further work is required in order to consolidate the findings. As mentioned earlier, potential theories for the variability in the blood Aβ study results have been suggested and include disease-heterogeneity effects upon Aβ levels, and a disease-stage-specific nature of Aβ as a marker, with perhaps Aβ acting as an effective marker of preclinical rather than established disease. While these are valid theories that likely are having an impact, they are not able to explain the full extent of variability between the different Aβ study findings.
Important additional issues that likely contribute to the variability observed between studies are the technical challenges encountered with measuring Aβ. First, Abdullah et al. reported high intra-subject differences in plasma Aβ measures, as assessed by ELISA in two to three separate blood samples retrieved within a 4-week period from each individual (
Second, it should be noted that many of the studies documented here have assessed plasma Aβ by immunocapture-based approaches, including commercially available and in-house optimized ELISAs (
Another factor to be considered is the technical difficulties of measuring Aβ, which is present at low concentrations in blood and will readily bind other circulating proteins, such as albumin, lipoproteins, and complement factors (
To date the investigation of plasma tau-based measures and their utility as biomarkers for AD have also been limited, primarily due to tau being an axonal protein and, therefore, of low abundance in blood. Efforts have, therefore, been made to develop more sensitive assays for detection and reliable quantification.
First, Henriksen et al. have reported measurement of specific tau fragments using an ELISA method. These assays quantified specific tau fragments in serum [ADAM10-generated fragment (Tau-A) and caspase-3-generated fragment (Tau-C)] (
Since the blood–brain barrier damage that occurs in AD would facilitate movement of proteins between brain and blood (
However, a panel of proteins rather than single protein candidates may have greater sensitivity and specificity as a biomarker and may collectively better describe and characterize the disease and its pathology. A number of studies, including from our group, have, therefore, taken an approach of analyzing multivariate signatures for prediction of AD and/or MCI status, and have identified and evaluated different proteins that collectively demonstrate sensitivity and specificity for classifying AD and/or MCI to varying degrees (
Alzheimer’s disease biomarker studies premised upon a case–control study design have been extensively reviewed by others (
These studies comparing established disease to non-disease or prediction of rate of progression in established disease are promising but of more value would be marker sets that detected preclinical or prodromal disease. One design enabling such discovery is the prediction of conversion from MCI by using historical samples from research cohort participants with MCI comparing those who subsequently converted to dementia in a given time-frame to those who did not. One of the first such studies identified an 18 plasma protein signature that not only classified AD from control subjects with 90% accuracy but was also able to predict MCI patients who would convert to AD within 5 years (
Although a number of plasma protein signatures of AD diagnosis, disease severity, and progression have been identified in discovery-based studies, a key concern for the field has been the lack of reproducibility of these results. As yet there has been no single blood-based proteomic signature that can successfully distinguish between AD and MCI and cognitively healthy elderly in a reproducible manner. The reason for such non-reproducibility is unknown. It might be the inherent heterogeneity of the disease and the differences, therefore, between cohort studies. It might also be technical variability, including assay variation and sample collection and curation variation, or it might be that the findings are in fact artifactual and there is no consistent proteomic signature to be found in blood. However, another reason for the failure to replicate might be the intrinsic limitation of case–control studies in a condition with such a long prodrome.
First, it is important to consider the heterogeneity of dementia and the extensive comorbidity and differential environmental exposure in the elderly. As well as multiple dementia conditions being hard to distinguish from each other, the AD group itself can be clinically heterogeneous as can MCI. Moreover, comorbid conditions are common in AD, and might not only alter the blood proteome directly but the associated polypharmacy prevalent in the elderly could also have an impact.
Second, case–control-based studies have inherent limitations when the target of discovery is in prodromal, or, worse, preclinical disease. In the context of AD research, the goal of biomarker discovery is primarily to detect individuals harboring early pathological change but without manifest dementia, as these individuals might be the most likely to respond to disease modifying agents. And yet in case–control studies such individuals will be included in studies not in the “case” group but in the “control” group. Clearly, this study design is at best non-optimal and at worse, destined for failure.
The recent failure of phase III clinical trials of antibody therapies targeting amyloid pathology, in part probably due to the absence of brain amyloid pathology in a considerable proportion of the participants (
Endophenotype-based approaches for blood-based biomarker discovery have begun to be implemented and have utilized various AD-related measures to identify blood-based biomarkers reflective of disease activity and pathology, including at the preclinical stages. These studies have included endophenotypes defined by measures such as brain atrophy (structural MRI), rate of cognitive decline, and brain amyloid β burden (Pittsburgh B (PiB) PET brain imaging), with change in PiB PET amyloid burden being the earliest event of these in the disease course. These studies have identified a number of different potential proteomic biomarkers (Tables
Proteins | Outcome variables (subjects) | Analytical platform | Study |
---|---|---|---|
C3, FGG, albumin, CFI, clusterin, A1M and SAP | Hippocampal atrophy (AD and MCI) | 2DGE LC-MS/MS | ( |
C3, C3a, CFI, FGG, and A1M | Whole brain volume (AD) | ELISA and western blots | ( |
ApoB/ApoA1a, ApoC3b, ApoEb, and Clusterinb,c | Hippocampal volumea, gray matter volumeb, and white matter volumec (MCI and non-demented elderly) | Luminex xMAP (Myriad RBM) | ( |
Clusterin | Rate of brain atrophy (multiple brain regions in MCI) | ELISA | ( |
IL-1rad IL-6d, IL-10d, IL-13e, and TNF-αf | Ventricular volumed, entorhinal cortex volumee, and whole brain volumef (AD) | Luminex xMAP | ( |
MIP1α, IGFBP2, CgA, and cortisol | SPARE-AD measures of brain atrophy (AD, MCI, and non-demented elderly) | Luminex xMAP (Myriad RBM) | ( |
RANTESg, NSEg,h, TTRg,h, clustering, A1ATh, ApoC3h, ApoA1h, ApoEh, BDNF?h and Aβ40h | Atrophy in multiple brain regions (MCIg and ADh) | Luminex xMAP | ( |
PPY, fetuin B, PSA-ACT, and ChkT | Entorhinal cortex and hippocampal volume (AD, MCI, and non-demented elderly) | SOMAscan | ( |
ApoE | Hippocampal volume (MCI, non demented elderly) | Luminex xMAP (Myriad RBM) | ( |
C4a, C8, clusterin, ApoA1, and TTR | Rate of cognitive decline (AD) | 2DGE LC-MS/MS | ( |
ApoA1, ApoA2, ApoH, and ApoB/ApoA1 ratio | Risk of cognitive decline (non-demented elderly) | Luminex xMAP (Myriad RBM | ( |
TTR | Rate of cognitive decline (AD) | ELISA | ( |
IL-4, IL-10, G-CSF, IL-2, IFN-γ, and PDGF | Rate of cognitive decline (AD) | Luminex xMAP | ( |
NCAM, sRAGE, and ICAM | Rate of cognitive decline (AD) | Luminex xMAP | ( |
Clusterin and NAP2 | Rate of cognitive decline (AD) | SOMAscan | ( |
Protein(s) | Outcome variable (subjects) | Analytical platform | Study |
---|---|---|---|
Clusterin | PiB PET amyloid (non-demented elderly) | ELISA | ( |
ApoE, C3, albumin, plasminogen, haptoglobin and IgG C chain region | PiB PET amyloid (non-demented elderly) | 2DGE LC-MS/MS | ( |
C-peptide, fibrinogen, A1AT, PPY, C3, vitronectin, cortisol, AXL receptor kinase, IL-3, IL-13, MMP9, ApoE, and IgE (this panel of proteins combined with covariates predicts amyloid positive subjects with 92 and 55% sensitivity and specificity, respectively) | PiB PET amyloid (AD, MCI, and non-demented elderly) | Luminex xMAP (Myriad RBM) | ( |
Aβ1–42, CXCL-13, IL-17, IgM-1, PPY, and VCAM-1 (this panel of proteins with age, |
PiB PET amyloid (AD, MCI, and non-demented elderly) | Luminex xMAP (Myriad RBM) | ( |
A2M, CFHR1, and FGG. (FGG in combination with age predicts NAB with 59 and 78% sensitivity and specificity, respectively) | PiB PET amyloid (AD, MCI, and non-demented elderly) | TMT LC-MS/MS | ( |
IL-6R, ApoE, and clusterin (in combination with clinical measures: trails B, AVLT, MMSE, education, |
CSF Aβ and PiB PET amyloid (MCI) | Luminex xMAP (Myriad RBM) | ( |
BDNF | PiB PET amyloid (AD, MCI and non-demented elderly) | Luminex xMAP (Myriad RBM) | ( |
PPY and IgM |
PiB PET amyloid (AD, MCI, and non-demented elderly, |
SOMAscan | ( |
We began by focusing on endophenotype approaches using mostly the AddNeuroMed, a European multicentre study (
In 2010, we published a study that utilized a 2DGE-MS/MS-based approach to discover plasma protein markers of both of these outcome variables in AD (
However, the most promising candidate marker identified in this discovery study was the protein clusterin, which associated with both hippocampal atrophy and clinical progression (
Adding weight to our hypothesis that changes in plasma clusterin were an early event, increased levels of plasma clusterin in association with slower rates of brain atrophy in MCI were demonstrated (
To date, clusterin is likely to be the most promising potential biomarker of AD-related phenotypes that we have identified in our studies, as supported by an association on the proteomic level with both clinical and neuroimaging measures of AD pathology, on the genetic level with AD risk and on a mechanistic level with amyloid function and processing.
Following the identification of clusterin using a dual endophenotype-based approach founded upon both brain atrophy and cognitive decline measures, we sought to extend this approach further to find biomarkers of these endophenotypes using different proteomic methods, which may be more sensitive for detection of alternative groups of proteins. One such study was reported by Sattlecker et al. who utilized the SOMAscan technology for plasma protein biomarker discovery in a cohort of AD, MCI, and controls. The strongest findings of this study included an association of clusterin with cognitive decline, replicating the findings of Thambisetty et al. (
In addition to hypothesis generating discovery approaches, targeted hypothesis-driven approaches have also been successful in identifying potential biomarkers of brain atrophy and cognitive decline. For example, the apolipoprotein family is widely implicated in neurodegeneration (
We have also taken a targeted approach to examine the biomarker potential of inflammatory proteins (
Following the identification of various plasma proteins related to AD and proxy measures of disease activity (neuroimaging measured of brain atrophy and clinical measures of cognitive decline), we next sought to validate the most promising and disease-relevant protein markers. To do this, we used multiplex bead assays to measure candidate proteins in a larger (
Blood-based biomarkers of neocortical Aβ (extracellular β-amyloid) burden (NAB) as measured by PET brain imaging have also been sought (Table
The first study we carried out used the BLSA study to discover plasma proteins that were associated with NAB in non-demented elderly individuals (
Following this, we carried out a separate study to examine the association of plasma proteins with NAB in AD, MCI, and control subjects included in the ADNI
Shortly after this, Burnham et al. published a study that again utilized the RBM panel for identifying plasma proteins predictive of NAB in an AD, MCI, and control-based population; however, this study utilized the AIBL study for discovery, followed by validation of potential biomarkers of NAB in the ADNI (
More recently, a study carried out in an ADNI-based MCI cohort revealed that plasma IL-6 receptor, clusterin, and ApoE levels coupled with a number of clinical and demographic measures,
We also recently reported the results of an LC-MS/MS-based approach for the discovery of plasma protein biomarkers of NAB in AD, MCI, and healthy controls enrolled in the AIBL study (
Although the exact protein biomarker panels identified by these studies for prediction of NAB differs between the studies, it is of note that there are some commonalities in the proteins included in these biomarker panels, including ApoE (
These various studies utilizing an AD pathology endophenotype-based approach for biomarker discovery show promise in identifying biomarkers reflective of core AD pathology and disease activity. However, it is important to note that there are some issues surrounding the approach of predicating blood-based biomarker discovery on PET amyloid measures. First, PiB PET detects insoluble fibrillary but not insoluble oligomeric Aβ, which are known to possess neurotoxic and synaptotoxic properties (
Therefore, in order to assess the reproducibility and robustness of plasma proteins biomarkers of amyloid (as indicated by PiB PET), it will be essential to perform replication and validation studies examining their association with brain amyloid burden (1) in larger independent cohorts, (2) using orthogonal technical platforms for biomarker quantification, and (3) using alternative measures indicative of amyloid (for example, alternative PET amyloid radiotracers and CSF Aβ).
While endophenotype-based designs founded upon rates of cognitive decline, brain atrophy, and brain amyloid burden show promise, there are further measures of AD and other aspects of core AD neuropathology that warrant investigation as potential endophenotypes for biomarker discovery. FDG-PET measures cerebral metabolic glucose utilization rate and serves as an indicator of synaptic activity, neuronal function, and neuronal metabolic activity (
With the development of tau imaging comes the opportunity to investigate blood-based biomarkers related specifically to brain tau pathology, which could obviously be of potential utility beyond AD and for tauopathies such as fronto-temporal dementia. The development of tau imaging has been challenging due to the deposition of tau protein being intracellular, which impacts upon radiotracer binding and image contrast (
Moreover, other types of biomarkers detectable in the blood show promise as potential markers of AD, including, for example, metabolomic (
Much research has sought blood-based proteomic biomarkers that may have diagnostic utility in discriminating AD cases from control, with limited success in identifying a reproducible signature of diagnostic or trials utility.
An alternative approach, which we have increasingly employed is using surrogates for disease activity – endophenotypes – such as cerebral atrophy imaging or molecular markers of amyloid pathology and rate of decline. Such an approach yields different but overlapping panels of markers. It is, therefore, possible that such markers predicated on pathological processes might be more reproducible and ultimately of more utility in diagnostic, prognostic, predictive, and other utilities especially in the context of clinical trials.
However, it seems intrinsically unlikely to us that a blood-based biomarker would replace relatively specific and reliable markers such as molecular markers in CSF or PET imaging markers that are more proximal to the disease state. Rather, we predict that blood-based biomarkers might be less specific but possibly more sensitive and certainly more readily conducted repeatedly in the context of large-scale, community-based studies and where repeated measures to track change is required. This then raises the prospect of what might be termed the biomarker funnel, a series of tests and investigations starting with the minimally invasive, highly sensitive, poorly specific marker set leading toward a technologically demanding or invasive test that is highly specific. This would be a blood test triage or selection process for CSF or PET tests in effect. Such a funnel is commonplace in medicine – fasting glucose before a glucose tolerance test, mammography before biopsy are examples, but there are many others. A biomarker funnel with blood-based markers as an early step toward a pathological diagnosis in life would be a very substantial step forward and maybe an essential step before clinical trials can be both effective and achievable.
AB, SW, and SL all contributed to the design of the review and interpretation of the studies included within it. AB, SW, and SL all contributed to the drafting and revision of the content and provided final approval of this version to be published.
Simon Lovestone is named as an inventor on biomarker intellectual property patent protected by Proteome Sciences and Kings College London. Alison L. Baird and Sarah Westwood have no conflict of interest to declare.
Research in the authors’ laboratories is supported by Alzheimer’s Research UK, Alzheimer’s Society, MRC, NIHR, Parkinson’s UK, Wellcome trust, and the EU.
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