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R.J. Bateman1, K. Blennow2, R. Doody3, S. Hendrix4, S. Lovestone5, S. Salloway6, R. Schindler7, M. Weiner8, H. Zetterberg2,9,10, P. Aisen11, B. Vellas12 and the EU/US CTAD Task Force*


* EU/US/CTAD TASK FORCE: Bjorn Aaris Gronning (Valby); Paul Aisen (San Diego); John Alam (Cambridge); Sandrine Andrieu (Toulouse), Randall Bateman (St. Louis); Monika Baudler (Basel);  Joanne Bell (Wilmington); Kaj Blennow (Mölndal); Claudine Brisard (Blue Bell); Samantha Budd-Haeberlein (USA); Szofia Bullain (Basel); Marc Cantillon (Princeton) ; Maria Carrillo (Chicago);  Gemma Clark (Princeton); Jeffrey Cummings (Las Vegas); Daniel Di Giusto (Basel); Rachelle Doody (Basel); Sanjay Dubé (Aliso Viejo); Michael Egan (North Wales); Howard Fillit (New York); Adam Fleisher (Philadelphia); Mark Forman (North Wales); Cecilia Gabriel-Gracia (Suresnes); Serge Gauthier (Verdun); Jeffrey Harris (South San Francisco); Suzanne Hendrix (Salt Lake City); Dave Henley (Titusville); David Hewitt (Blue Bell); Mads Hvenekilde (Basel); Takeshi Iwatsubo (Tokyo); Keith Johnson (Boston); Michael Keeley (South San Francisco); Gene Kinney (South San Francisco); Ricky Kurzman (Woodcliffe Lake); Valérie Legrand (Nanterre); Stefan Lind (Valby); Hong Liu-Seifert (Indianapolis); Simon Lovestone (Oxford); Johan Luthman (Woodcliffe); Annette Merdes (Munich); David Michelson (Cambridge); Mark Mintun (Philadelphia); José Luis Molinuevo (Barcelona); Susanne Ostrowitzki (South San Francisco); Anton Porsteinsson (Rochester);  Martin Rabe (Woodcliffe Lake); Rema Raman (San Diego); Elena Ratti (Cambridge);  Larisa Reyderman (Woodcliffe Lake); Gary Romano (Titusville); Ivana Rubino (Cambridge); Marwan Noel Sabbagh (Las Vegas);  Stephen Salloway (Providence); Cristina Sampaio (Princeton); Rachel Schindler (New York); Peter Schüler (Langen); Dennis Selkoe (Boston); Eric Siemers (New York);  John Sims (Indianapolis); Heather Snyder (Chicago); Georgina Spence (Galashiels); Bjorn Sperling (Valby); Reisa Sperling (Boston); Andrew Stephens (Berlin); Joyce Suhy (Newark); Gilles Tamagnan (New Haven); Edmond Teng (South San Francisco); Gary Tong (Valby); Jan Torleif Pedersen (Valby); Jacques Touchon (Montpellier); Bruno Vellas (Toulouse ); Vissia Viglietta (Cambridge) ; Christian Von Hehn (Cambridge); Philipp Von Rosenstiel (Cambridge) ; Michael Weiner (San Francisco); Kathleen Welsh-Bohmer (Durham);  Iris Wiesel (Basel); Haichen Yang (North Wales);  Wagner Zago (South San Francisco); Beyhan Zaim (Woodcliffe Lake); Henrik Zetterberg (Mölndal)

1. Washington University School of Medicine, St. Louis, MO, USA; 2. Clinical Neurochemistry Laboratory, University of Gothenburg, Sahlgrenska University Hospital, MöIndal, Sweden; 3. Genentech/Roche, Basel, Switzerland; 4. Pentara Corporation, Salt Lake City, UT, USA; 5. Janssen Pharmaceuticals, Oxford, UK; 6. The Warren Alpert Medical School of Brown University, Providence RI, USA; 7. Schindler Neuroscience Consulting Group, New York NY, USA; 8. University of California, San Francisco, USA; 9. Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, United Kingdom; 10. UK Dementia Research Institute at UCL, London, United Kingdom; 11. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA; 12. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France

Corresponding Author: Randall J. Bateman, Washington University School of Medicine, St. Louis, MO, USA,

J Prev Alz Dis 2019;
Published online April 18, 2019,



There is an urgent need to develop reliable and sensitive blood-based biomarkers of Alzheimer’s disease (AD) that can be used for screening and to increase the efficiency of clinical trials. The European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU/US CTAD Task Force) discussed the current status of blood-based AD biomarker development at its 2018 annual meeting in Barcelona, Spain. Recent improvements in technologies to assess plasma levels of amyloid beta indicate that a single sample of blood could provide an accurate estimate of brain amyloid positivity. Plasma neurofilament light protein appears to provide a good marker of neurodegeneration, although not specific for AD. Plasma tau shows some promising results but weak or no correlation with CSF tau levels, which may reflect rapid clearance of tau in the bloodstream. Blood samples analyzed using -omics and other approaches are also in development and may provide important insight into disease mechanisms as well as biomarker profiles for disease prediction. To advance these technologies, international multidisciplinary, multi-stakeholder collaboration is essential.

Key words: Blood test, biomarker, Alzheimer’s disease, plasma.



Biomarkers of Alzheimer’s disease (AD) are essential tools in drug development to assess and monitor the pharmacodynamic effects of compounds, demonstrate target engagement, aid in the selection of participants for drug trials, help in dose selection, and assess the efficacy of therapies (1, 2). Clinically, they can provide crucial diagnostic information and, when an effective treatment becomes available, they may also be useful as tools to personalize interventions according to stage and patient characteristics (3).
In the recently published National Institutes on Aging and Alzheimer’s Association (NIA-AA) Research Framework, which defines AD biologically through the use of biomarkers, recognized AD biomarkers included cerebrospinal fluid (CSF) measures of amyloid-beta (Aβ) and tau, positron emission tomography (PET) assessment of amyloid and tau, and two other imaging measures: anatomic magnetic resonance imaging (MRI) and fluorodeoxyglucose PET (FDG-PET, a measure of brain metabolism) (4).
Despite their enormous promise in advancing the development of early and preventive treatments, there are  challenges to using CSF and imaging biomarkers in diverse geographies globally, including rural areas within developed countries, and lower- and middle-income countries which have limited resources to fund these procedures (5). Many efforts worldwide are attempting to meet this challenge by developing blood-based biomarkers that could limit the number of people who require more expensive testing and would enable screening, aid clinical diagnosis, and allow for repeated sampling as possible pharmacodynamic markers in clinical trials (6). Recognizing the urgency of advancing the development of blood-based biomarkers for AD, the European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU/US CTAD Task Force) addressed this issue at its 2018 meeting in Barcelona, Spain. The Task Force provided a forum for investigators from the pharmaceutical and diagnostics industries to join researchers from academia and regulatory agencies in efforts to build consensus on the path forward in developing and bringing to market blood-based biomarker tests.
Many challenges have been encountered in efforts to identify reliable, sensitive, and specific biomarkers of AD in plasma or serum. The close and continuous contact of the brain with the CSF results in relatively high levels of specific molecules associated with brain disease, while much lower amounts exist in the bloodstream (6). Further complicating the measurement of plasma-based AD biomarkers are high levels of other proteins from peripheral organs in the blood and the presence of proteases that may degrade brain proteins.
Nonetheless, in recent years there have been dramatic improvements in highly sensitive and specific immunoassays and mass spectrometry-based assays used to assess plasma levels of molecules that could serve as biomarkers of AD and other types of neurodegeneration (7). These advances have increased optimism in the field regarding the use of blood-based biomarker “profiles” for diagnosis, prognosis, and disease progression monitoring (8). Blood-based biomarkers are also seen as an essential part of efforts to develop precision medicine approaches for AD (9, 10).


Plasma amyloid beta

Longitudinal studies in individuals with autosomal dominant forms of AD have shown that CSF levels of Aβ42 decline 25 years before expected symptom onset; and that amyloid plaques are detectable by PET imaging 15 years before expected symptom onset (11-13). Early attempts to measure Aβ peptides in plasma indicated that these tests had limited value as tools for diagnosis or prognosis (5), but these studies were based on comparing plasma Aβ in clinically diagnosed AD patients and cognitively unimpaired elderly, which, given the uncertainty of AD diagnosis and overlap in pathology, limits the chance to identify minor changes in biomarker levels, as compared with using brain amyloid positivity as the reference standard. High variability was attributed, in part, to a lack of standardized protocols and methods. In addition, plasma Aβ originates not only in the brain but also in other organs and tissues (14).
Recent improvements in the technologies used to assess plasma levels of Aβ have shown more promising results. For example, investigators at Washington University have demonstrated that the ratio of plasma Aβ42/40 provides a sensitive and reliable measure of amyloid status that predicts future conversion to positive amyloid PET independent of the time of day and correlates with CSF Aβ42/40 (15). Other studies in European memory clinics (16), the Swedish BioFINDER cross-sectional and ESTHER longitudinal cohorts (17, 18), the Australian Imaging, Biomarker and Lifestyle Flagship Study (AIBL) cohort (13), and the National Center for Geriatrics and Gerontology (NCGG) Hospital in Japan (19, 20) have also shown good correlations with amyloid PET.
While further studies are needed to validate plasma Aβ42/40 in comparison to CSF or PET, these encouraging results suggest that plasma Aβ42/40 can be used with a high degree of sensitivity and specificity to detect AD amyloid plaques in individuals before symptom onset, as well as in symptomatic individuals with unclear clinical diagnoses. For clinical use, a single sample of blood could provide a highly accurate estimate of who is amyloid positive and thus support the diagnosis of AD (15 20); while in clinical trials, a blood Aβ42/40 test could be used as a prescreening tool to identify who has or is at risk for AD and facilitate efficient and cost-efficient recruitment of participants, thus accelerating trials, lowering costs, and speeding drug discovery (15, 20). For example, it is estimated that more than 50% of amyloid PET scans could be avoided with blood-based screening for Aβ pathology in the brain.


Plasma tau

In CSF, total tau (T-tau) and phosphorylated tau (P-tau) have been well validated as biomarkers reflecting AD pathology (12). In the A/T/N classification system, P-tau is taken to represent the presence of tau pathology, including neurofibrillary tangles, while  CSF T-tau more likely represents neuronal injury or neurodegeneration (21), although recent data on the kinetics of tau suggests that in AD, CSF tau may reflect increased neuronal secretion of tau in response to Aβ pathology, rather than neurodegeneration (22).
Several studies have reported that T-tau levels are also elevated in the plasma of people with AD, although there is substantial overlap between diagnostic groups (cognitively normal, MCI, AD) (23, 24). T-tau in CSF and plasma is elevated in other disorders involving substantial brain injury, such as Creutzfeldt-Jacob disease (CJD) (25, 26), stroke (27), cardiac arrest (28), and traumatic brain injury (29). P-tau181 levels are also elevated in AD dementia and show better associations with both Aβ and tau PET, suggesting greater specificity for AD pathology (4).
In regards to P-tau, a semi-sensitive assay for tau phosphorylated at threonine 181 (similar to the most- employed CSF test) with electrochemiluminescence detection has been developed (4). Using this assay, plasma P-tau concentration was higher in AD dementia patients than controls. Plasma P-tau concentration was associated with both Aβ and tau PET, which is a promising result in need of replication.
The expression of tau is brain-enriched, but tau is also detectable at both mRNA and protein level in salivary glands and kidney ( This is an important potential confounder that may help explain the weak correlation of plasma with CSF tau. The weak correlation may also reflect rapid clearance of tau in the bloodstream (30, 31).


Neurofilament light (NFL)

Neurofilament light chain (NFL) is an intraneuronal protein and a component of the axonal cytoskeleton; thus, its presence in the CSF indicates neuronal damage or degeneration (32). In AD, CSF NFL concentrations increase in early stages of disease and increase over time as cognition declines and atrophy and white matter changes in the brain increase (33).
In a recent study comparing three analytical platforms for assessing NFL in serum, the single-molecule array (Simoa) method is emerging as more sensitive than conventional enzyme-linked immunosorbent assay (ELISA) or electroluminescence (ECL) (34). A large study in the ADNI population using the Simoa assay showed that plasma NFL correlates with CSF NFL as an indicator of neurodegeneration across the AD continuum, has diagnostic accuracy for AD dementia similar to that of CSF biomarkers, and is associated with cognitive decline and neuroimaging biomarkers of AD (35, 36). Similarly, in a study conducted in Germany using the Simoa method, plasma NFL concentrations were significantly higher in people with MCI and AD dementia compared to normal controls even after correcting for age (37). Plasma NFL concentrations were also inversely correlated with Mini Mental Status Examination (MMSE) scores, which suggests that unlike other CSF biomarkers of AD, increased NFL may indicate ongoing neurodegeneration and functional decline (37). These studies suggest that NFL may have potential for prognosis and monitoring of disease progression. A small study in patients with familial AD suggested that plasma NFL increases about 5 years prior to estimate onset, suggesting its utility as a screening tool (38), and a larger study in the Dominantly Inherited Alzheimer Network (DIAN) demonstrated serum NFL correlates with neurodegeneration and clinical decline and longitudinal change identifies mutations carriers 16 years before symptom onset (39, 40).
However, NFL is not specific for AD, but a general neuronal injury biomarker [for review, see (40)]. Knowledge about the usefulness of NFL as a biomarker for neurodegeneration emerged in large part from studies in multiple sclerosis, and it has also been used to assess CNS injury in HIV infection, frontotemporal dementia, amyotrophic lateral sclerosis (ALS), CJD, Parkinson’s disease (PD) and other CNS disorders (35, 38, 41). One study in people with HIV infection suggested that plasma NFL may be useful to monitor downstream drug effects on the intensity of neurodegeneration (42). In patients with CJD, elevations of both tau and NFL in serum at baseline predict steeper increases over time (26). Studies in patients with multiple sclerosis also suggest a role for NFL as an indicator of treatment effectiveness (43, 44).
Differences in the preanalytic handling of serum samples was shown to significantly affect the measurement of NFL, pointing to the importance of standardized protocols for sample collection, storage, and transport (37).


Omics and other approaches

Blood samples are also useful for obtaining high-dimensional biomarker profiles using a combination of omics approaches, including genomics, transcriptomics, metabolomics, lipidomics, and proteomics. Advances in mass spectrometry have even enabled the molecular characterization of biological processes from single cells (45). These approaches enable the discovery of unknown unknowns and may also provide insight into molecular mechanisms that underlie diseases such as AD.
Different approaches may be used to harness the power of these technologies for omics studies (7). However, the choice of method may have substantial implications on what is found, and thus interpretation of omics studies must take into account the approach used. For example, Hye and colleagues used mass spectrometry and 2-D gel electrophoresis in a case-control approach comparing the plasma proteomes from elderly people with AD and normal elders (46). They found an elevation in complement factor H, and this finding was subsequently replicated in multiple studies. Using the same technology with an endophenotype approach in people with AD, where discovery was predicated on either hippocampal atrophy or speed of progression, these same investigators showed that elevations of plasma clusterin – an amyloid chaperone – was associated with both endophenotypes (47). This finding has also been widely replicated.
Now, the European Medical Information Framework – Multimodal Biomarker Discovery (EMIF-MBD) project is using an endophenotype approach to identify biomarkers (including plasma biomarkers) of pre-dementia AD. The endophenotypes selected for this multicenter study include amyloid positivity assessed by PET or CSF, MCI conversion to AD, and the rate of cognitive decline. First, they analyzed results from 10 years of studies using multiple omics approaches, which allowed them to identify 7 proteins predictive of amyloid positivity. Next, they used an aptamer capture array provided by SomaLogic to measure 4,600 plasma proteins simultaneously. A machine learning approach revealed 46 features (44 proteins plus ApoE status and age) that predicted amyloid positivity with an area under the curve (AUC) 0f .78, which indicates fair accuracy. Even in people with no signs of AD, the 46 features predicted preclinical AD with an AUC of .68, which is statistically significant. Although still in the exploratory phase and with nowhere near the accuracy of a well-targeted protein study such as CSF Aβ, tau, or plasma NFL, this approach may with further refinement enable screening of large populations to identify potential candidates for clinical studies targeting preclinical AD.



Studies completed in the last few years have produced substantial data supporting the further development and potential uses of blood-based biomarkers. Multiple groups have shown that plasma Aβ studies may be useful to predict brain amyloid status. If these results are confirmed, it is possible that a blood-based test of Aβ may ultimately enable screening of large populations to identify who is at risk for AD and start intervention before memory loss and brain damage. Yet while plasma Aβ assays using both mass spectrometry and immunoassay methods have shown promise in predicting brain amyloid levels measured by PET scanning, these studies need to be replicated in different populations to ensure that plasma assay methods are truly generalizable. Most studies have been conducted in patients without comorbidities, which might affect the ability of plasma Aβ to predict brain amyloid levels. In addition, large scale, longitudinal validation studies will be needed, and the usefulness of plasma Aβ markers to monitor disease progression in clinical trials will need to be determined (20). To help facilitate such studies, ADNI has huge numbers of coded and blinded plasma and CSF samples available upon request.
Plasma NFL has also been shown to be indicative of neurodegeneration in many populations, including in the DIAN population to measure progression, onset, and decline; as well as in sporadic AD. Current data are less supportive of the use of plasma tau as a useful biomarker for AD, at least using the current assay formats, which are based on N-terminal and mid-domain tau antibodies, although this remains a very active area of investigation. Proteomics appear to be useful primarily to search and find targets but may not be useful as inclusion criteria or outcome measures. Many other biomarkers are also being investigated, but thus far none has risen to the standards set by PET scans and CSF measures.
To advance development of plasma-based biomarkers for drug development and clinical use, much more work is also needed to develop the best methods for plasma collection, shipping and storage, and to determine the optimum approach to use all data – including genetic factors such as APOE, demographics such as age, and other analyses – to identify individuals at risk for development of AD. The Task Force concluded that global standardization and harmonization of preanalytical and analytical protocols will be necessary, which will require international multi-stakeholder collaboration (8, 48). Multiple public and private groups are now undertaking the important task of standardization and commercialization of plasma Aβ biomarkers. Round robins are planned in 2019 for plasma Aβ and the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) has recently initiated a project to create reference materials and a reference method for plasma and serum NfL.


Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.

Conflict of interest: The Task Force was partially funded by registration fees from industrial participants. These corporations placed no restrictions on this work. Dr. Bateman reports grants from BrightFocus Foundation, Pharma Consortium (Abbvie, AstraZeneca, Biogen, Eisai, Eli Lilly and Co., Hoffman La-Roche Inc., Janssen, Pfizer, Sanofi-Aventi),  the Tau SILK/PET Consortium (Biogen/Abbvie/Lilly), Association for Frontotemporal Degeneration FTD Biomarkers Initiative, Anonymous Foundation, GHR Foundation, NIH, Alzheimer’s Association, Lilly, Rainwater Foundation Tau Consortium, and Cure Alzheimer’s Fund, grants, personal fees and non-financial support from Roche and Janssen, personal fees and non-financial support from Pfizer, Eisai, and Merck, and non-financial support from Avid Radiopharmaceuticals outside the submitted work. Washington University, Dr. Bateman, and David Holtzman have equity ownership interest in C2N Diagnostics and receive royalty income based on technology (stable isotope labeling kinetics and blood plasma assay) licensed by Washington University to C2N Diagnostics. RJB receives income from C2N Diagnostics for serving on the scientific advisory board. Washington University, with RJB as co-inventor, has submitted the US nonprovisional patent application “Methods for Measuring the Metabolism of CNS Derived Biomolecules In Vivo” and provisional patent application “Plasma Based Methods for Detecting CNS Amyloid Deposition”. Rachelle Doody is full time employee of Genetic/Roche. Dr Schindler travels fees received as member of CTAD organizing committee. Dr. Weiner reports grants and other from NIH, grants from DOD, grants from Johnson & Johnson, grants from Kevin & Connie Shanahan, grants from General Electric, grants from PCORI, grants from CA Dept of Public Health, grants from Veterans Administration, grants from U. of M, grants from Australian Catholic U., grants from Biogen, grants from Hillblom Foundation, grants and other from Alzheimer’s Association, grants from Stroke Foundation, grants from Siemens, other from Bioclinica, other from Accera, Inc./Cerecin, other from Genentech, other from Indiana U., other from CHU Toulouse, other from St. George Hospital U, other from Eli Lilly, other from Roche, other from Lynch Group, LLC, other from Dolby Family Ventures, other from Nestec, other from Health & Wellness Partners, other from AC Immune, other from Alzheon, Inc., other from Japanese Government Alliance, other from ATRI/ACTC, other from U. of Melbourne, other from U. Tokyo, other from National Cntr for Geriatrics & Gerontology (Japan),  outside the submitted work. Dr. Aisen reports grants from Lilly, personal fees from Proclara, other from Lilly, other from Janssen, other from Eisai, grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, personal fees from Merck, personal fees from Roche, personal fees from Lundbeck, personal fees from Biogen, personal fees from ImmunoBrain Checkpoint,  outside the submitted work. Dr. Vellas reports grants from Lilly, Merck, Roche, Lundbeck, Biogen, grants from Alzheimer’s Association, European Commission, personal fees from Lilly, Merck, Roche, Biogen, outside the submitted work.



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V. Pérez-Grijalba1, J. Romero1, P. Pesini1, L. Sarasa1, I. Monleón1, I. San-José1, J. Arbizu2, P. Martínez-Lage3, J. Munuera4, A. Ruiz5, L. Tárraga5, M. Boada5, M. Sarasa1 on behalf of The AB255 Study Group6


1. Araclon Biotech S.L., Zaragoza, Spain; 2. Servicio de Medicina Nuclear, Clínica Universidad de Navarra, Pamplona, Spain; 3. Center for Research and Advanced Therapies and Memory Clinic, Fundación CITA-Alzheimer, San Sebastián, Spain; 4. Institut de recerca Sant Joan de Déu, Hospital Infantil Sant Joan de Déu. Barcelona, Spain; 5. Alzheimer Research Center and Memory Clinic. Fundació ACE. Institut Català de Neurociències Aplicades. Barcelona, Spain; 6.

Corresponding Author: Pedro Pesini, PhD. Vía Hispanidad 21, 50009 Zaragoza, Spain; Telephone number: +34 976 796 562; Fax: (+34) 976 217 802; Email:

J Prev Alz Dis 2018 inpress
Published online October 19, 2018,



Background: Easily accessible biomarkers are needed for the early identification of individuals at risk of developing Alzheimer’s disease (AD) in large population screening strategies.
Objectives: This study evaluated the potential of plasma β-amyloid (Aβ) biomarkers in identifying early stages of AD and predicting cognitive decline over the following two years.
Design: Total plasma Aβ42/40 ratio (TP42/40) was determined in 83 cognitively normal individuals (CN) and 145 subjects with amnestic mild cognitive impairment (a-MCI) stratified by an FDG-PET AD-risk pattern.
Results: Significant lower TP42/40 ratio was found in a-MCI patients compared to CN. Moreover, a-MCIs with a high-risk FDG-PET pattern for AD showed even lower plasma ratio levels. Low TP42/40 at baseline increased the risk of progression to dementia by 70%. Furthermore, TP42/40 was inversely associated with neocortical amyloid deposition (measured with PiB-PET) and was concordant with the AD biomarker profile in cerebrospinal fluid (CSF).
Conclusions: TP42/40 demonstrated value in the identification of individuals suffering a-MCI, in the prediction of progression to dementia, and in the detection of underlying AD pathology revealed by FDG-PET, Amyloid-PET and CSF biomarkers, being, thus, consistently associated with all the well-established indicators of AD.

Key words: β-amyloid (Aβ), Alzheimer’s disease, plasma, biomarker, Aβ ratio.



Alzheimer’s disease (AD) is a progressive condition characterized by a loss of synaptic integrity and consequent neurodegeneration caused by a series of pathological events, including deposition of amyloid-β (Aβ) peptides in cerebral plaques and tau protein aggregation in neurofibrillary tangles (1). AD represents the most common form of dementia, currently affecting over 46 million people worlwide, and is associated with large economical and personal costs. Being able to delay the onset of AD by just a year could significantly reduce the AD cases expected in the coming decades (2). However, the complexity of AD makes it difficult to evaluate therapies and even to detect preclinical and prodromal stages of the disease.
Early application of a potential therapy for AD, specially those targeting Aβ, may significantly increase its efficacy. In this line, success of secondary prevention clinical trials may depend on the inclusion of a well-characterised population at pre-dementia stages of the disease (3, 4). Thus, objective identification of individuals suffering (or at risk of developing) the disease early on in the pathological process has become a key aspect in AD research. In this scenario, there is growing need for biomarkers of AD pathology to improve not only drug development and efficient disease progression monitoring, but also to help in population selection and enrichment strategies in clinical trials (5). An objective and accesible biomarker able to reflect the pathological process of AD leading to clinical symptoms is necessary.
Current diagnostic criteria consider the concept of mild cognitive impairment (MCI) as a prodromal state of Alzheimer’s dementia (6). This concept is derived from neuropsychological findings and describes subclasses of MCI: the amnestic type (a-MCI) with memory deficits only, and non-amnestic type (na-MCI) with deficits in at least one cognitive domain other than memory. Additionally, neuroimaging techniques have been developed to identify even earlier stages in the prodromal subclassification of AD based on [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) neuroimaging (7).
Cerebrospinal fluid (CSF) biomarkers, including Aβ peptides and tau, reflect key processes of AD pathophysiology (8). Amyloid burden in brain is also measured by amyloid-beta positron emission tomography (Aβ-PET) using radioligands specific for Aβ fibrils (9). Both CSF and Aβ-PET biomarkers have demonstrated value in the early detection of AD (10, 11). Nevertheless, they have several limitations, such as economic costs, invasiveness and limited availability in primary healthcare, that highlight the relevance of a more accesible peripheric biomarker to be easily applied in large population screening.
However, despite the general agreement of the association of CSF Aβ levels with AD, studies evaluating the corresponding peripheric Aβ biomarkers in plasma Aβ have shown discrepant results. Some studies found a lack of association between plasma Aβ levels and AD (12-14), while others showed associations in opposite directions (15, 16). Nevertheless, recent works including large populations of well-characterised participants found consistent lower levels of plasma Aβ42/40 ratio with increased risk of incident AD (17-19). Furthermore, we recently showed an association between low total plasma Aβ42/40 (TP42/40) ratio and evidence of a pathological event of AD measured by Aβ-PET in a preclinical cohort from the AIBL study (20). These findings are also in line with recently published studies using mass-spectrometry based methods which showed reduced plasma Aβ42/40 ratio associated with neocortical Aβ burden (21, 22).
This work was aimed to determine: (i) the potential of TP42/40 to detect a-MCI in a cohort from the AB255 study; (ii) whether TP42/40 demonstrates the ability to distinguish between a-MCI with an FDG-PET pattern of AD and a-MCI without this pattern; (iii) the value of TP42/40 as a marker of risk of conversion from a-MCI to AD in the 2 years of follow-up of the study; (iv) the degree of concordance of TP42/40 with CSF and Aβ-PET biomarkers.



The AB255 study

The AB255 study is a multicenter longitudinal study with evaluations of the cognitive state of individuals at 0, 12 and 24 months (see Appendix, Figure S1). Participants were recruited and assessed at 19 clinical memory research sites in Spain, Italy, Sweden and France. The study was comprised of 228 participants over 65 years of age, including 83 cognitively normal (CN) individuals and 145 age-paired subjects with probable a-MCI (6). All subjects were subjected to structural Magnetic Resonance Imaging (MRI), and had the following inclusion criteria: no personal history of significant neurological or psychiatric illnesses; independence in important and basic daily activities; normal performance (according to age and academic level) in the Mini-Mental State Examination (MMSE); a Hachinski ischemic scale score ≤4; score on the Geriatric Depression scale ≤11; at least minimal elementary school; capacity to undertake cognitive tests; and good visual and audio acuity. Those subjects with significant vascular pathology on MRI, which could relate to memory deficits, and/or contraindications for neuroimaging administration were excluded from the study.

Clinical diagnosis

Diagnosis of each participant was performed using a battery of neuropsychological tests (23) and neurological examination. Specific inclusion criteria and neuropsychological battery description is provided in the Appendix. Briefly, CN individuals showed no memory complaints or other cognitive deficiencies and no close family history of dementia. They scored 0 on the Clinical Dementia Rating (CDR) scale, and showed normal performance in the item recognition of a list of words from the test of Learning and Deferred Recall of the Wechsler Memory Scale (WMS) as well as in the Free and Cued Selective Reminding Test (FCSRT).
The a-MCI patients fulfilled Petersen’s diagnostic criteria (24). a-MCIs showed a score on the CDR scale of 0.5, with a 0.5 or 1 score for memory, low performance in WMS and Buschke FCSRT, and a score ≤39 in the Interview for Deterioration in Daily Activities (IDDD). All the participants of the study were monitored for 2 years to evaluate their changes in cognitive performance. DSM-IV criteria (25) for dementia were applied to evaluate progression to dementia through the follow-up. As a whole, 62 a-MCI patients progressed to AD dementia during the two year study period (incident AD subgroup, a-MCIprog), which implied a conversion rate of 42.8%, whereas 81 remained stable (a-MCIsta).
The a-MCI participants underwent further subclassification based on an FDG-PET pattern of neurodegeneration, either suggestive or not, of a mild cognitive impairment due to AD using a visual analysis of images as proposed by Jagust et al. (26). Thus, the a-MCI group was subdivided into patients with a high level of likelihood of suffering AD according to a positive FDG-PET pattern (a-MCIFDG(+)), and those with low risk of AD according to a negative FDG-PET pattern (a-MCIFDG(-)). a-MCIFDG(+) subjects had to fulfill evidence of uni- or bilateral hypometabolism in at least two out of the three following cerebral regions: temporal, parietal or posterior cingulate, or show uni- or bilateral cingulate or parietal hypometabolism.

Sample collection

Blood samples were collected after overnight fasting at 0, 12 and 24 months in 10ml EDTA tubes containing one pill of a protease inhibitor cocktail (CompleteMini, Roche). Samples were immediately cooled to 2-8°C until processing by centrifugation at 2500xg for 15 min at 4°C within 30h of collection. Plasma was transferred to polypropylene tubes, conveniently aliquoted to avoid any extra freeze/thaw cycle, and stored at -80°C until analysis. Before blood centrifugation, an aliquot of complete blood was reserved and store at -80°C to carry out APOE genotyping.
CSF was obtained at baseline in a subcohort of 43 individuals from two different centers following standardized procedures previously described by the Alzheimer Disease Neuroimaging Initiative ( Briefly, 10ml of CSF were collected by standard lumbar puncture using atraumatic needles (25 gauge), immediately frozen after extraction and stored at -80°. CSF was shipped to the laboratory of analysis in dry ice, thawed, aliquoted into polypropylene tubes and conserved at -80°C until analysis.

Neuroimaging analysis

FDG-PET was performed following standard procedures detailed in Appendix, methods. Cortical Aβ burden was assessed at baseline in a separate subcohort of 59 individuals from Fundació ACE using 11C-Pittsburg compound B (PiB-PET). Detailed procedures of neuroimaging acquisition and analysis are described by Espinosa et al. (27). Imaging data were analyzed using the Fundació ACE Pipeline for Neuroimaging Analysis, available at Participants with PiB-PET measures were classified as β-amyloid positive (PET-Aβ(+)) or β-amyloid negative (PET-Aβ(-)) with relation to a cut-off of 1.4 SUVR (28).

Analysis of samples

Plasma Aβ40 and Aβ42 levels were quantified using ELISA kits ABtest40 and ABtest42, respectively (Araclon Biotech Ltd, Spain). Total Aβ in plasma was obtained by proprietary treatment of the plasma samples before analysis. The specific analytical procedures followed and performance characteristics of these tests are described elsewhere (29). In this work, total in plasma Aβ42/Aβ40 ratio (TP42/40) was evaluated as a biomarker for AD.
Aβ levels were also determined in CSF using ABtest40 and ABtest42 after adaptation of the quantification range of the assays to the peptide levels existing in this fluid. Aβ40 and Aβ42 were measured in 100-fold and 20-fold diluted CSF, respectively, following the standard procedure and reactants of ABtest (29, 30). Total-tau (tTau) and 181phospo-tau (pTau) were determined in an external laboratory using INNOTEST (Innogenetics, now Fujirebio, Ghent, Belgium).
Each analysis was carried out using fresh aliquots of samples and being blinded to any characteristic of the individuals. Samples were randomized and encoded by an external CRO to guarantee the validity of results.

APOE genotyping

APOE genotyping was performed by DNA extraction from blood cells and restriction analysis of the pattern of fragments obtained after digestion with HhaI (31).

Statistical analysis

Statistical analyses were carried out using SPSS Version 22 for Windows (SPSS Inc., Chicago, IL) and R Statistical Environment 3.4. Two-tailed Comparisons of demographic variables and biomarkers among groups were performed using Pearson’s χ2 test for categorical variables and Mann-Whitney U test for continuous variables (not normally distributed). Generalized linear regression models (GLM) adjusted for significant demographic covariates (age, APOE genotype and education level; gender was not significantly associated either with TP42/40 or clinical diagnosis) were performed to evaluate the association of TP42/40 levels and the cognitive status of the individuals determined by clinical assessment, FDG-PET neurodegeneration pattern and Aβ-PET status.
A multivariate Cox proportional hazard model was performed to estimate the relationship of baseline TP42/40 levels with the risk of progression to AD during the follow-up period. The ability of TP42/40 to predict those individuals who would progress to dementia was evaluated using logistic regression models and receiver operating characteristic (ROC) analysis. In these regression analyses, TP42/40 was dichotomized with regard to the median of the whole population (0.116). All models were adjusted for age, APOE genotype and education.
Further analyses of the association of plasma TP42/40 with CSF and Aβ-PET biomarkers was carried out using Spearman’s Rank correlation and linear regression models (LRM) that allowed adjusting for significant demographic covariates (age, APOE genotype and education).



Quantification of Aβ40 and Aβ42 levels in plasma was performed using ABtest40 and ABtest42, which had been previously validated in an independent work (29). In the AB255 study, variability was further evaluated within the study by repeating the same plasma control samples (n=3) in every microplate. Mean intra-assay and inter-assay coefficients of variation were 7.0% and 3.4% for ABtest40, and 9.35% and 10.60% for ABtest42, respectively. Descriptive statistics of the study population is presented in Table 1.

Table 1. Characteristics of the population from the AB255 study: demographic variables and descriptive biomarker statistics

Table 1. Characteristics of the population from the AB255 study: demographic variables and descriptive biomarker statistics

P-value refers to U Mann-Whitney test for continuous variables and Pearson’s χ2 test for categorical variables. CN, cognitively normal subjects; Total a-MCI, complete population of amnestic mild cognitive impairment individuals; a-MCIFDG(-), a-MCI subjects with low probability of AD according to a FDG-PET neurodegeneration pattern; a-MCIFDG(+), a-MCI subjects with high probability of AD according to a FDG-PET neurodegeneration pattern; SD, standard deviation; SUVR, standardized uptake value ratio: CSF: cerebrospinal fluid; tTau: total Tau protein; pTau: phosphorylated Tau protein; Aβ: beta-amyloid peptide.


Cross-sectional analysis

At baseline, plasma TP42/40 ratio was significantly lower (Mann-Whitney test, p=0.001) in a-MCI compared to cognitively normal individuals (Table 1 and Figure 1A), with a reduction of 8.1% in TP42/40 levels. Generalized linear models confirmed the significance of the inverse association (p=0.046) between TP42/40 and clinical diagnosis after adjusting for covariates (Table 2). This association was repeated in a consistent pattern throughout the different time-points of the study, in which even lower levels of TP42/40 were found in the incident AD group (Figure 1B and 1C).


Table 2. Association of plasma TP42/40 with prodromal stages of AD

Table 2. Association of plasma TP42/40 with prodromal stages of AD

Comparison of TP42/40 levels between groups was carried out using generalized linear models adjusted for age, APOE genotype and education level. TP42/40 levels were compared between CN and a-MCI individuals, and between the subclassification of a-MCI subjects based on FDG-PET at baseline. β, coefficient of GLM regression; 95% CI, 95% confidence interval; CN, cognitively normal individuals; a-MCI, amnestic mild cognitive impairment; a-MCIFDG(-), a-MCI subjects with low probability of AD according to a FDG-PET neurodegeneration pattern; a-MCIFDG(+), a-MCI subjects with high probability of AD according to a FDG-PET neurodegeneration pattern. a-MCIsta, a-MCI stables at the end of the follow-up; a-MCIprog, a-MCI at baseline who progressed to AD dementia during the study period.


Patients were separated at baseline into those with either a positive or negative FDG-PET pattern of cortical hypometabolism suggestive of AD (a-MCIFDG(+) and a-MCIFDG(-), respectively). a-MCIFDG(+) individuals showed a lower TP42/40 ratio than those classified as a-MCIFDG(-) (Figure 1D). After adjusting for demographic covariates in GLM, TP42/40 levels in the a-MCIFDG(+) group were significantly reduced compared both to CN (β=-4.57, p=0.002) and to a-MCIFDG(-) (β=-3.29, p=0.013) (Table 2). Patients classified as a-MCIFDG(+) showed a 15.4% reduction of TP42/40 with regard to CN.

Figure 1. TP42/40 levels in the different groups of the study population and TP42/40 utility in detecting progression to AD

Figure 1. TP42/40 levels in the different groups of the study population and TP42/40 utility in detecting progression to AD


A TP42/40 levels and the risk of progression to AD

During the two years of follow-up, 62 a-MCI individuals (42.8%) progressed to AD. In terms of progression to AD dementia, 52.4% of a-MCI subjects with low TP42/40 ratio (below the median of the study population) at baseline progressed to AD within 24 months, whereas only 28.8% of those with high TP42/40 did. The a-MCI subjects who progressed to AD (a-MCIprog) showed significantly lower baseline levels (p=0.017) of TP42/40 than a-MCI participants who remained stable (a-MCIsta) after follow-up (Figure 1E). Low TP42/40 ratio at baseline implied an increase of ≈70% in the risk of progression from a-MCI to AD (Hazard Ratio, HR=1.687, CI 95% 1.058-2.691, p=0.028). ROC analysis evaluating the discrimination ability of baseline TP42/40 to detect a-MCIprog, with regard to cognitively normal stability, gave a significant AUC of 0.857, with sensitivity and specificity above 70% at Youden’s optimized cut-off (Figure 1F).

Correlation of plasma TP42/40 with Aβ-PET and CSF biomarkers

A significant inverse correlation between plasma TP42/40 and the PiB-PET SUVR (Spearman’s Rank coefficient rs=-0.464, p<0.001) was found (Figure 2), being also statistically significant in a LRM adjusted for age, education and APOE genotype (linear regression coefficient β=-0.39, p<0.0021). At baseline, plasma TP42/40 showed a direct association with CSF Aβ42 levels (rs=0.549, p<0.001), which was also statistically significant in adjusted LRM (β=0.382, p=0.021). This direct association was also found for the CSF Aβ42/40 ratio (rs=0.407, p=0.008), although it lost statistical significance after adjusting for covariates in LRM (β=0.245, p=0.138). A significant inverse correlation (Figure 2) of TP42/40 with tau levels in CSF (tTau rs=-0.314, p=0.031; pTau rs=0.-329, p=0.040) was also found (adjusted LRM: tTau β=-0.348, p=0.043; pTau=-0.342, p=0.055). Thus, low plasma TP42/40 ratio was concordant with the CSF biomarker profile characteristic of AD, as well as with Aβ load in brain.
Additionally, TP42/40 levels were compared between amyloid positive and negative individuals with regard to their brain Aβ levels measured with PiB-PET at baseline. Significantly lower TP42/40 was found in PET-Aβ(+) participants (β=-15.7, CI 95% -25.2 to -6.2, p=0.001) in a GLM adjusted for age, education and APOE genotype, compared to PET-Aβ(-) individuals.

Figure 2. Correlation of plasma TP42/40 with cortical Aβ deposition and CSF biomarkers at baseline

Figure 2. Correlation of plasma TP42/40 with cortical Aβ deposition and CSF biomarkers at baseline

A) PiB-PET measures were performed in a different subcohort of 59 individuals at visits 0, 12 and 24 months; baseline data are presented in the dot plot. (B-E) Subcohort of 43 individuals in which tTau, pTau, Aβ40 and Aβ42 were quantified at baseline. Solid line represents the regression line and dashed lines are depicted as the confidence interval based on the mean predicted values obtained from the regression line. Spearman rank correlation results were summarized for each evaluation of association. rs Spearman rank coefficient, CSF: cerebrospinal fluid.



In the present work, significantly lower levels of plasma TP42/40 ratio were consistently found in prodromal stages of AD. Participants classified as a-MCI showed lower TP42/40 than CN at the three time-points analyzed (baseline, 12 and 24 months of follow-up). Moreover, TP42/40 levels were even lower in those patients with incident AD. The consistency of this association throughout the entire follow-up gives reliability to the results, and is in accordance with other studies in which low plasma Aβ42/40 ratios are associated with increased risk of dementia (17-19, 32, 33), and greater cognitive decline (34, 35). Nevertheless, studies evaluating the association of plasma Aβ levels with AD severity have also provided controversial results, showing either no association (12-14) or contradictory results (15, 16).
In this study, a-MCI participants were subclassified based on visual interpretation of FDG-PET scans (26) in order to separate the a-MCI subjects showing a hypometabolism pattern suggestive of high risk of early progression to AD dementia (a-MCIFDG(+)) from those subjects with a negative FDG-PET pattern of neurodegeneration (a-MCIFDG(-)). In fact, 72.4% of the a-MCIFDG(+) subjects progressed to dementia during follow-up, whereas only 31.4% of those a-MCIFDG(-) did, validating also the applicability of this approach to detect disease progression in the short term among individuals at prodromal stages of AD. The association of low TP42/40 with prodromal stages of AD was further confirmed in this study as lower levels of TP42/40 were found in those a-MCIFDG(+) compared to the a-MCIFDG(-).
Aβ plasma levels were also evaluated in this study as predictors of disease progression, and we found that a low TP42/40 ratio at baseline related to a 70% increase in the risk of progression from a-MCI to AD dementia in two years. This finding is highly consistent with the results from a meta-analysis including more than 10,000 subjects, in which similar overall risk ratios were obtained with low Aβ42/40 plasma levels (36). Those a-MCI subjects progressing to AD were detected based on the baseline levels of TP42/40 with over 70% specificity and sensitivity. Although this diagnostic performance may not be sufficient to set plasma Aβ42/40 ratio as a standalone diagnostic referent for probability of AD, these results support its potential as a screening tool to select individuals more prone to develop dementia in a stepped diagnosis process, as is common in most branches of diagnostic medicine.
A significant inverse association was also found between the TP42/40 ratio and the neocortical Aβ deposition evaluated by PiB-PET in a subcohort of 59 individuals. Previous studies from different groups also found this inverse association of plasma Aβ42/40 ratio and cortical Aβ burden (21, 22, 37, 38). Furthermore, results from a work from our group, specifically evaluating plasma TP42/40 association with Aβ-PET, showed also reduced levels of TP42/40 ratio with brain Aβ load in an independent cohort from the AIBL study (20). The results from the AB255 study validate our prior observation of TP42/40 as a biomarker mirroring the Aβ accumulation process in brain. In fact, despite coming from completely independent populations, the significantly lower TP42/40 ratio found in AB255 PET-Aβ(+) subjects are remarkably consistent with our previous results from the AIBL study (20).
The AB255 study also included a subcohort of participants from whom CSF was extracted. Despite the small sample size of this cohort (n=43), significant correlations of TP42/40 with not only CSF Aβ species, but also with markers of neurodegeneration such as pTau and tTau, were obtained (Figure 2). Low TP42/40 was associated with the biomarker signature of AD in CSF (39), characterized by low Aβ42 and high tau levels. This finding was in agreement with previous studies showing significant correlations between Aβ levels in plasma and CSF (38, 40), but not with other studies that did not find a plasma-CSF correlation (16, 41). Limited sample size and follow-up times of this substudy limit hypothesis making in this regard. Thus, although plasma TP42/40 may be reflecting pathological changes occurring in CSF, further investigation needs to be undertaken to establish the possible dynamics of the association and whether it is maintained over time and disease stage.
Moreover, a deep understanding of Aβ biology in plasma is needed. ABtest allows determination of the total Aβ peptide quantifiable in plasma from a more comprehensive quantification approach (29), which could be relevant for consistency of results. Differences among studies could be partly due to the different nature of the Aβ quantified (14). Furthermore, the diverse sources of peripheral Aβ (42) and their modulation by other factors unrelated to amyloid brain pathology (38) may be blurring its potential as an AD biomarker. Yet, a considerable percentage of plasma Aβ was confirmed to come from central nervous system (21), enabling the reflection of brain Aβ pathology with blood Aβ measurements. In particular, recent results from different groups are in agreement with our findings despite using a different quantification method, confirming an inverse association between plasma Aβ42/40 ratio and Aβ-PET imaging load (21, 22).
The small differences (8.1% reduction in the a-MCI group, and 15.4% in a-MCIFDG(+) subgroup compared to the CN group), together with the significant overlap of plasma Aβ42/40 levels between groups found by us and others (20, 21, 43), could limit the potential use of a plasma biomarker as a “one shot” diagnostic tool. Plasma Aβ quantification requires an extra level of sensitivity, precision and accuracy to overcome the small TP42/40 difference (effect size) existing among diagnostic groups. General performance of ABtest has been previously assessed in a complete validation study (29). Additionally, the specific ABtest reproducibility throughout this study was also evaluated, showing an overall variability of 7.6%, which is in accordance with previous performance testing and the precision found in the quantification of Aβ in plasma with automated methods(44). Although it may be still insufficient to establish plasma Aβ as a diagnostic tool, TP42/40 ratio emerges as a valuable screening biomarker to be used before considering CSF or PET testing.
Further studies with increased sample size are needed to validate these results and to establish practical cut-offs. Due to the multifactorial and complex nature of AD, a combination of several markers and risk factors, and not a singular measurement, will probably be needed to identify those individuals at risk. Association of the TP42/40 ratio with the severity of AD may not be sufficient to establish a diagnosis based on it. Nevertheless, large population screening in primary clinical practice could be performed by the use of a sequential diagnostic procedure. An accessible plasma measurement showing a high negative predictive value could be used as a first step to exclude most of those without the condition and, then, apply a confirmatory second step based on, for example, neuroimaging or CSF. It would imply a decrease in the number of invasive and costly techniques, such as CSF and PET, that could be used in a confirmative second step, reducing overall costs and logistic difficulties of clinical trials (45).
In conclusion, there is an increasing body of evidence that TP42/40, as measured by ABtest40 and ABtest42, is a useful first step screening tool and predictor of AD progression and severity. These results are congruent with a considerable number of studies reporting an inverse association of plasma Aβ42/40 ratios with AD. The fact that TP42/40 ratio was associated with various other correlates of the presence of the disease, such as clinical assessment, FDG-PET and PiB-PET neuroimaging, and CSF biomarkers, adds value to the results presented here. Moreover, our findings validate previous results with ABtest in an independent population, conferring reliability to the measure. Although larger studies should be completed to establish definite cut-offs and practical application, plasma TP42/40 ratio appears progressively consolidated as a cost-effective tool, which could be useful in the screening process for secondary prevention clinical trials in AD and, eventually, for population management in primary care settings.

Funding: This work was funded by Araclon Biotech. Fundació ACE Memory Clinic, as well as the Spanish Ministry of Health through Instituto de Salud Carlos III (Madrid) (FISS PI10/00954) and by Agència d’Avaluació de Tecnologia i Recerca Mèdiques, Departament de Salut de la Generalitat de Catalunya (RECERCALIA grant 390/06/2009). Araclon Biotech was the main funder of the study, participating in conceptualization, design, data collection and manuscript preparation.

Conflict of interest: VPG, PP, JR, ISJ, LS, IM and MS are employees of Araclon Biotech. All other co-authors do not have conflicts of interest related to this study.

Ethical standards: The study was approved by the CEIC ethic committee 2009/5455, and all participants provided written informed consent prior to inclusion. Patient recruitment and collection protocols were in accordance with ethical standards according to WMA Declaration of Helsinki-Ethical Principles for Medical Research Involving Human Subjects.

Acknowledgments: The authors would like to thank the participants and collaborators of this study, and the entire AB255 Study Group.

Other AB255 Study Group collaborators: Miguel Goñi7; Francesc Pujadas8; Alberto Villarejo9; Ana Frank10; Jordi Peña-Casanova11; Manuel Fernández12; Gerard Piñol13; Rafael Blesa14; Pedro Gil15; Luis F. Pascual16; Miquel Aguilar17; Giovanni B Frisoni18; Jorge Matias-Guiu15; Niels Andreasen9; Carmen Antúnez20; Bruno Vellas21; Jacques Touchon22 (7. Hospital Divino Vallés, Burgos, Spain; 8. Hospital Universitari Vall d’Hebrón, Barcelona, Spain; 9. Hospital Doce de Octubre, Madrid, Spain; 10 .Hospital La Paz, Madrid, Spain; 11. Hospital del Mar, Barcelona, Spain; 12. CAE Oroitu Algorta, Vizcaya, Spain; 13. Hospital Santa María de Lleida, Lleida, Spain; 14. Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; 15. Hospital Clínico San Carlos, Madrid, Spain; 16. Hospital Lozano Blesa, Zaragoza, Spain; 17. Hospital Universitari Mútua Terrassa, Terrassa, Spain; 18. IRCCS Centro San Giovanni di Dio FBF, Brescia, Italy; 19. Karolinska Institutet, Stockholm, Sweden; 20. Hospital Virgen de la Arrixaca, Fundación Alzheimur, Murcia, Spain; 21. Hôpital CHU La Grave, Casselardit, Toulouse, France; 22. Hôpital Gui de Chauliac, CHU, Montpellier, France)



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K. Blennow1, H. Zetterberg1,2

1. Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, the Mölndal Campus at the Sahlgrenska Academy at University of Gothenburg, SE-43180 Mölndal, Sweden; 2. UCL Institute of Neurology, Queen Square, London, UK

Corresponding Author: Kaj Blennow, MD, Ph.D. Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal Campus, Sahlgrenska University Hospital, SE-431 80 Mölndal, Sweden, Tel:  + 46 31 3431791, Fax: + 43 31 3432426, E-mail:


J Prev Alz Dis 2015;2(1):46-50
Published online Januay 20, 2015,


The number of failed Alzheimer’s disease (AD) clinical trials on Aβ-targeting drugs is increasing. The explanation for this is most likely multi-factorial. An optimistic standpoint is that trials have to be on patients in an earlier stage of the disease, before neurodegeneration is too severe, to show efficacy, and probably also of longer duration. Further, there is a general agreement that enrolled patients have to be diagnosed based on combined clinical and biomarker criteria, to avoid noise from the large proportion (20%) of cases that are misdiagnosed if only clinical criteria are used. Last, the poor predictive power of translating an “anti-Aβ” or “anti-plaque” effect from AD transgenic animal models to AD patients also calls for biomarkers to verify target engagement in man, and to show downstream effects of Aβ-targeting drug candidates in AD patients. The focus of this review is on the possible role of cerebrospinal fluid (CSF) biomarkers in AD clinical trials for diagnostics, and thus patient enrichment, and for theragnostics, to provide evidence of target engagement of the drug on Aβ metabolism or aggregation, and of effects on the molecular pathology of the disease.


Key words: Alzheimer’s disease, Biomarker, β-amyloid (Aβ), Cerebrospinal fluid, Clinical trial, Diagnosis, Mild cognitive impairment (MCI), Phosphorylated tau, Plasma, tau protein, Theragnostic. 



Recent failures of several late phase clinical trials on drug candidates targeting β-amyloid (Aβ) is causing concern among Alzheimer’s disease (AD) researchers as well as pharmaceutical companies, and other stakeholders. A positive and realistic standpoint is that we need to initiate treatment before the dementia stage of the disease, before neurodegeneration is too severe and plaque and tangle load to large. As an example, in the solanezumab clinical phase III trials, there was a trend for a treatment effect in patients with mild AD (1). Further, it has been known since long that the accuracy of the clinical diagnosis of AD based on purely clinical grounds is poor. In line with this, it was reported that approximately 25% of clinically diagnosed AD patients in the bapineuzumab phase III clinical trial had negative amyloid PET scans, i.e., were misdiagnosed as having AD (2). It is logical that inclusion of a large percentage of cases with other diseases than AD in an anti-Aβ trial will increase variation and noise and markedly affect the possibility to identify a beneficial clinical effect of a drug candidate..

The failures of the anti-Aβ trials have also caused distress that the amyloid cascade hypothesis will be falsified, i.e., that Aβ aggregation and deposition is not the primary cause, but merely a by-stander of the disease, or that the hypothesis may only be valid for the familial form of AD. An alternative explanation for at least some of the trial failures may be that poor drug candidates have been taken directly into phase II and III clinical trials, based only on promising, but misleading, results from preclinical studies performed in AD transgenic mice (3).

In this paper, we review the position of biomarkers in AD drug development and clinical trials as fundamental tools to enable selection of the optimal clinical cohorts, i.e., for diagnostics, and as theragnostic tools, to examine the pharmacodynamic properties of drug candidates, including identification of target engagement and monitoring downstream effects on neurodegeneration; an application we call theragnostics (4).

CSF Biomarkers in clinical trials

As reviewed elsewhere (5), there is a consensus among the academy, pharmaceutical industry and regulatory authorities that biomarkers may have several uses in clinical trials. Given the diagnostic challenges not only to identify prodromal AD cases in MCI cohorts, but also to make a diagnosis cases with mild AD dementia, an attractive application of CSF biomarkers is as diagnostic tools to enrich the trial cohort with pure AD cases. This procedure will increase the proportion of patients with Alzheimer pathology, thereby increasing the chance to identify a clinical effect of the drug. Regulatory authorities such as the European Medicines Agency (EMA) also recommend the use of CSF for enrichment of clinical trial populations with prodromal AD cases (6).

While pharmacokinetics, i.e., measurement of drug absorption, distribution, metabolism, and excretion is not the theme of this paper, some aspects of pharmacodynamics, intended biochemical drug effects in particular, will be reviewed. Theragnostic biomarker Phase I studies to verify appropriate target engagement in man may be important in for the decision whether to take the drug candidate into large and expensive Phase II or III clinical trials (Table 1). Further, Phase II biomarker studies providing evidence of downstream effects on the neurodegenerative process may in a similar way be important in the decision making on launching large multi-center Phase III trials (Table 1). Last, this type of biomarker-based evidence of an effect of the drug on neurodegeneration may also be important in Phase III registration trials to provide objective evidence of disease modification (5).

Enrichment of true AD cases 

For anti-Aβ trials, identification of cases with biomarker evidence of cortical amyloid pathology is a logical strategy. The reduction in CSF Aβ42 correlates inversely with cerebral plaque load at autopsy (7) and global cortical amyloid ligand binding as measured by positron emission tomography (PET) (8). Importantly, recent large studies have shown an agreement between CSF Aβ42, also measured in clinical routine, and amyloid PET measurements of cortical Aβ load with more than 90% concordance (9), and almost identical diagnostic performance (10). Further, regional assessments of amyloid load on PET scans provide no improvement in diagnostic performance as compared with mean global cortical binding or CSF Aβ42 (10). Thus, provided that harmonized cut-offs for both measurements can be identified, amyloid PET and CSF Aβ42 can be used interchangeably for enrichment in clinical trials.

Except for Aβ42, there is a wide range of other Aβ species in CSF, of which Aβ40 is the most abundant (11). Although there is no major change in CSF Aβ40 in AD, there is a marked decrease in the ratio of CSF Aβ42/Aβ40 in both AD dementia and MCI, which is more pronounced than the reduction in CSF Aβ42 alone (12, 13). The use of the CSF Aβ42/Aβ40 ratio may improve diagnostic performance as compared with Aβ42 by itself (14, 15). The hypothesis behind this is that the ratio compensates for high or low production of total Aβ (all isoforms), while single analysis of Aβ42 may give false positive cases among “low producers” and false negative cases among “high producers”.

It is likely that combination of CSF T-tau and P-tau adds to the diagnostic performance of Aβ42 to identify prodromal AD among MCI cases (16, 17). However, the major contribution of CSF T-tau and P-tau may be in predicting progression (Table 1). While amyloid biomarkers become positive 5-10 years before symptoms in the preclinical phase and before dementia in the MCI stage, addition of CSF T-tau and P-tau improves predictive power of progression during a clinically relevant time period (18, 19, 20). Indeed, the updated International Working Group (IWG) criteria for AD also recommend the algorithm low CSF Aβ42 together with high T-tau and/or P-tau (21). 

Biomarkers to verify target engagement

Testing new anti-Aβ drug candidates in AD transgenic mouse, using a reduction in plaque load as the indication to launch large Phase II trials on AD patients has been mainstream in AD drug development. As reviewed elsewhere (22), the very large number of compounds found to reduce Aβ pathology in these models but not in AD patients makes them poor predictors of treatment success in sporadic AD. Increasing attention is drawn to the low predictivity of transgenic animal models for success in later clinical trials, a problem that by no means is unique to AD drugs development (23). For this reason, it may be wise not to rely only on preclinical findings of target engagement and a plaque-lowering effect in AD transgenic mice for the decision to move into large and expensive phase II or III clinical trials, without any data in man speaking for appropriate target engagement.

Target engagement on Aβ metabolism or clearance may be identified in short-term proof-of-principle studies on a small number of healthy volunteers in phase I, or proof-of-concept studies on AD patients in phase IIa, the design of these depends on the type of anti-Aβ drug. As a reassuring example, solid target engagement data for a BACE1 inhibitor was obtained in a single-dose study on 30 healthy volunteers (24). The CSF biomarker response to Aβ immunotherapy is complex, and differ depending on whether active immunization or passive immunotherapy with anti-Aβ antibodies were used, for review see (25). Analysis of a change in CSF levels of Aβ oligomers have a potential for use as a pharmacodynamic biomarker to identify target engagement in immunotherapy trials, but due to the very low levels of Aβ oligomers in CSF, assays have been hampered by low analytical sensitivity. A recent paper reported on a novel immunoassay based on the ultrasensitive Erenna platform, that was highly selective for Aβ oligomers over monomers, and had a limit of detection far below standard ELISA methods (26). This type of technical developments brings hope that CSF Aβ oligomers levels can be applied in early trials to identify target engagement and monitor treatment effects on Aβ aggregation and plaques (Table 1).

This type of early clinical biomarker study would aid to select drug candidates with a proven effect on Aβ metabolism or clearance also in man, which would be of value in the decision making whether to embark on expensive phase II and III trials. Hopefully, such an approach will improve the success rate of future clinical trials. 

Table 1. Position of cerebrospinal fluid biomarkers in clinical trials – anti-Aβ trials as example

Abbreviations: Aβ, amyloid-β; AD, Alzheimer disease; BACE1, β-site APP cleaving enzyme 1; CSF, cerebrospinal fluid; H-FABP, Heart fatty acid-binding protein; P-tau, phosphorylated tau; sAPP, soluble amyloid precursor protein extracellular domain; T-tau, total tau; VLP-1, visinin-like protein-1.

Biomarkers to identify downstream effects on pathology

The term downstream biomarkers can be applied to biomarkers used to identify and monitor effects downstream of the primary target of the drug (3). For example, evidence from biomarkers reflecting the intensity of the neuronal degeneration (such as T-tau) or phosphorylation state of tau (P-tau) may provide valuable indications that an anti-Aβ drug candidate has effects downstream of Aβ production or aggregation (Table 1). An example was found in the bapineuzumab Phase II and III trials, in which a lowering of P-tau (2) or P-tau and t-tau (27) was found.

Since tau is involved in the pathogenic processes in AD, complementary data on downstream effects on neurodegeneration from other biomarkers may increase the validity of results (Table 1). Such CSF biomarkers may include the neuronal biomarkers heart-type fatty acid-binding protein (HFABP or FABP3) (28, 29) and visinin-like protein 1 (VLP-1 or VILIP-1) (30, 31). Both HFABP and VLP-1 show a clear increase in AD, also in the early stages of the disease, and correlate with CSF tau levels (28-31) and may thus provide valuable information on disease-modifying effects on novel drug candidates.

Synapses are the primary functional unit for neuronal communication, composed on a pre-synaptic unit containing synaptic vesicles filled with neurotransmitters that upon release regulated by a delicate machinery of presynaptic proteins and a post-synaptic unit with receptors and proteins involved in advancing the signal [32, 33]. The degree of synaptic degeneration show the best correlation with severity of cognitive symptoms in AD, for review see (34). Measurement of synaptic proteins in CSF may thus serve as valuable biomarkers for synaptic function and degeneration, which may correlate well with a change in cognitive function due to treatment. Although all of presynaptic vesicle proteins (such as synaptotagmin and rab3a), presynaptic membrane proteins such as SNAP-25, and dendritic proteins such as neurogranin have been shown to be present in human CSF (35, 36), assay development for synaptic proteins has proven difficult due to their low abundance. Nevertheless, a pilot study based on immunoprecipitation and Western blot showed a marked increase in CSF neurogranin in AD (37), a finding that recently has been confirmed using a novel ELISA method (38). Importantly, high CSF neurogranin levels predicted progression to AD dementia in mild cognitive impairment (MCI) patients, and within (amyloid positive) prodromal AD cases, high CSF neurogranin levels correlated with a more rapid deterioration in cognitive symptoms during clinical follow-up [38]. These findings suggest that synaptic biomarkers may be useful in the earlier phases of AD as biomarkers linked to cognition.

Importantly, biomarker-based evidence of both target engagement in man and an effect of the drug candidate on downstream AD-related molecular mechanisms and a general effect on neuronal and synaptic degeneration is likely to be important in Phase III registration trials, to provide objective measures of disease modification (5). CSF biomarkers may also qualify as surrogate markers if a change in CSF biomarkers with treatment, e.g. for synaptic proteins, can be shown to predict clinical outcome. This would be highly useful in future clinical trails on AD disease-modifying drugs, allowing shorter trial duration and a lower number of subjects.


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P. Pesini, M. Sarasa

Araclon Biotech Ltd. Zaragoza 50009, Spain.

Corresponding Author: Pedro Pesini, Araclon Biotech Ltd. Zaragoza 50009, Spain, P: +34976796562, Fax: +34 976 217 802, Mail:


J Prev Alz Dis 2015;2(1):51-55
Published online Januay 15, 2015,


Central biomarkers of Alzheimer’s disease (AD) have been proven to have diagnostic and prognostic capacity.  However, both amyloid positron emission tomography and cerebrospinal fluid collection studies present problems that limit their widespread acceptability in global clinical trials. Thus, development of other measures as potential surrogates of amyloid positivity should be pursued. Results from numerous experimental studies strongly suggest that the association between Aβ plasma levels, particularly the Aβ42/Aβ40 ratio, and AD diagnosis goes beyond what could be attributable to pure chance, although this association is still controversial. The aim of this review is to consider selected works that may help to improve the design of blood based biomarkers studies by controlling a number of confounding sources related to the clinical gold standard, the time-course of central and peripheral biomarkers, and the metabolism of Aβ in blood that may be blurring the presumptive association between Aβ blood levels and AD. Based on these data and to get pass the controversy, we tentatively postulate that at early stages of preclinical AD, blood Aβ levels and central Aβ biomarkers would follow parallel but temporally displaced trajectories. This association would become eventually lost as the disease progresses and the clearance mechanisms in the blood brain barrier are increasingly impaired.

Key words: Alzheimer’s disease, plasma, amyloid beta.



Studies on AD biomarkers have provided clear evidence of the existence of a preclinical phase of the disease. The analysis of a large autopsy series has shown that the onset of cortical amyloid pathology leading to AD may precede the apparition of overt clinical syndrome by one to two decades, which opens a window for the anticipated diagnosis and implementing early, eventually preventive, treatments (1). The results of large multicenter initiatives like the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imagine, Biomarkers and Lifestyle Flagship Study of Ageing (AIBL), the Dominantly Inherited Alzheimer Network (DIAN), and many others for the discovery of biomarkers have led to the proposal of a conceptual model of the pathophysiology of AD in which the biomarkers become abnormal in an ordered manner (2,3). The model considered cerebrocortical Aβ accumulation as the first observable change leading to AD and has since received strong experimental support (4). Based on this model, a panel commissioned by the National Institute of Aging and the Alzheimer’s Association (NIA-AA) proposed the guidelines to subdivided preclinical AD into three stages for research purposes. The first stage was described as asymptomatic cerebral amyloidosis. The second stage was amyloid positivity plus evidence of early neurodegeneration, and the third stage was amyloid positivity plus evidence of neurodegeneration and subtle cognitive decline (5).    

The operational capacity of these guidelines remains to be fully explored, but two different reports with enlightening results have already been published (6, 7). The first of these population based studies shows that from 450 cognitively normal elderly persons, 31% fit into any of the three hypothesized stages of preclinical AD (6). In addition, 43% were negative for all biomarkers and were considered at “stage 0” with no signs of amyloid pathology, and 23% displayed negative amyloid scans but positive neurodegeneration and were classified as suspected non-Alzheimer’s pathology (SNAP). The remaining 3% did not fit into any of these groups. An almost identical distribution of the sample population was reported by Vos, et al. in a study involving 311 cognitively normal community-dwelling volunteers aged over 65 years (7). Interestingly, these authors found that the progression rate to symptomatic AD (Clinical Dementia Rating ≥ 0.5) after 5 years of follow up increased across preclinical stages (2% for stage 0; 11% for stage 1; 26% for stage 2; 56% for stage 3; 5% for SNAPs). In addition, individuals with preclinical AD had an increased risk of death compared with the individuals classified as normal (Clinical Dementia Rating = 0.0). These results show that preclinical AD is a common condition among cognitively normal elderly individuals. Moreover, it is associated with cognitive decline and mortality, which indicate that this condition could be an important target for therapeutic intervention (6, 7).

It has been recently reported that ~25% of participants in 2 anti-Aβ passive immunotherapy trials for mild-to moderate AD had a negative baseline amyloid-positron emission tomography (PET) scan (8-10). Similar results have been confirmed in a large series of autopsies from clinically diagnosed subjects with mild-to-moderate probable AD (11). Approximately 14% (22 out of 161) of the autopsied subjects clinically diagnosed with mild-to-moderate probable AD have none or sparse neuritic plaques, which would expectedly yield a negative amyloid-PET scan. More than half of these «Aβ-negative» subjects have low neurofibrillary tangle Braak stages. Whether these Aβ-negative individuals have AD or are AD clinical phenocopies remains unknown, but it is highly probable that these patients would not benefit from amyloid targeted treatments and their exclusion from any study of this type of treatments would allow for a sounder and potentially positive assessment of treatment efficacy.

Once disease-modifying therapies become available, the use of biomarkers will be indispensable for the screening of the general population to detect persons at increased risk of developing AD. These ideas have augmented the research on blood-based biomarkers for AD, particularly those based on the quantification of Aβ peptides which would be a desirable non-invasive, first-step screening tool. As recently pointed out by the EU/US/CTAD task force (12), “amyloid positivity is a useful marker for identifying persons with amyloid pathology; however, both PET and lumbar puncture for CSF [cerebrospinal fluid] studies present problems that limit their widespread acceptability in global clinical trials. Thus, development of other measures as potential surrogates of amyloid positivity should be pursued.”

Beyond the controversy

Results from numerous experimental studies strongly suggest that the association between Aβ plasma levels, particularly the Aβ42/Aβ40 ratio, and AD diagnosis goes beyond what could be attributable to pure chance, although this association is still controversial. Comprehensive reviews of this issue have been recently published (13-15). The aim of this article is not so much to be exhaustive as to consider selected works that may help to improve the design of blood based biomarkers studies and get past that controversy. In this line, the most informative of these are those few studies which include longitudinal repeated plasma sampling throughout the follow-up at various time points (16-20). Of these, one study carried out by Schupf and collaborators included a cohort of 1,125 elderly persons without dementia at baseline; 104 (9.2%) patients developed AD over 4.6 years of follow-up (18). These authors found that higher plasma Aβ42 levels at baseline were associated with a three-fold increased risk of AD, while conversion to AD was associated with decreasing levels of Aβ42 or a decline in the Aβ42/Aβ40 ratio. A similar prospective, population-based study, including 730 participants in which 7% developed dementia (5% classified as AD) during a five year follow-up period, reported no difference in baseline plasma Aβ42, Aβ40, or Aβ42/Aβ40 ratio levels between converters to dementia or AD compared with the cognitively stable individuals (16). However, individuals with plasma Aβ40 levels above the median level for the group at baseline had an increased risk of developing dementia and AD during the follow-up, even after adjustment for age, gender, APOE genotype, and educational level (odds ratio [OR] = 2.2). Accordingly, a study by Okereke and collaborators reported an association of higher midlife Aβ40/Aβ42 plasma ratio with worse late-life cognitive decline in 481 participants from the Nurses’ Health Study (20). Moreover, these authors found that an increase in Aβ40/Aβ42 ratios after midlife predicted greater decline in the global score. It should be noted that these authors used the inverse to the Aβ42/Aβ40 ratio of previous studies.

In the ADNI cohort, a more complex and limited association was found for plasma Aβ40 and Aβ42 levels, which precluded the use of plasma Aβ levels as a diagnostic classifier (19). However, the authors sustained the possibility that with longer follow-up, within-subject plasma Aβ measurements could be used as a simple and minimally invasive screen to identify those at increased risk for AD.

Furthermore, the largest meta-analysis on this issue, including thirteen studies encompassing 10,303 subjects, concluded that plasma Aβ42/Aβ40 ratios predict development of AD and dementia (21). However, significant heterogeneity in the meta-analysis underlines the need for substantial further investigation of plasma Aβ levels as a preclinical biomarker. It is clear that for the advance and translation of the quantification of blood Aβ into a sensitive first-step screening tool, these results should be replicated and improved upon by controlling a number of confounding sources that may be blurring the presumptive association between Aβ blood levels and AD.

The clinical gold standard as a confounding source

Part of the controversy could be accounted for by the mixed distribution of individuals with and without cerebral Aβ deposition (as visualized by amyloid-PET and/or indirectly by CSF analysis) among the usual diagnostic groups (healthy control [HC], mild cognitive [MCI], and AD), which seriously hampers the chances to validate an Aβ blood test against the clinical gold standard (1, 4, 22-32).

In keeping with this, we propose that Aβ blood biomarkers should be validated against central biomarkers such as amyloid-PET imaging and/or CSF amyloid analysis. In this regard, it should be mentioned that previous studies failed to show a correlation between plasma and brain Aβ levels (27, 33-35). However other recent studies, enriched with a higher proportion of cognitively healthy controls, have reported a statistically significant inverse correlation, although weak, between Aβ plasma levels and cortical amyloid burden measured by amyloid-PET (19, 36, 37). This controversy on the correlation between central and peripheral biomarkers cannot be properly addressed in cross-sectional studies; accurate time dependent models of those biomarkers are necessary. Thus, to ascertain whether the trajectories of Aβ plasma levels are associated with the trajectories of the Aβ central biomarkers (amyloid-PET and CSF) parallel longitudinal studies on the kinetics of plasma and CSF biomarkers during the very early stages of preclinical AD are required.

Further, it should be considered that the mechanisms in the blood-brain barrier and in the blood-CSF barrier for the clearance of brain Aβ could be compromised and eventually lost with aging and/or disease progression (38-41). Indeed, it has been shown that the clearance of brain Aβ is reduced ~30% in AD patients compared to healthy controls (42). This is consistent with a report by Giedraitis, et al. that found a correlation between CSF and plasma levels of both Aβ40 and Aβ42 in healthy individuals (n = 18), whereas no correlations were seen for AD (n = 39) or MCI (n = 29) cases (43). Thus in our opinion, the search for an association between the levels of Aβ in blood and levels in the brain should be oriented toward the earliest stages of preclinical AD.

The time-course of central and peripheral biomarkers as another confounding source

The signal in amyloid-PET scans come from aggregates of β-pleated Aβ fibrils deposited in the brain cortex, and increased cortical deposition of Aβ fibrils would explain the decline of CSF Aβ42 levels detected in AD patients. In contrast, it is thought that blood Aβ levels are most probably related to the clearance of soluble Aβ species from the brain (44-48).

Studies in some transgenic mice have shown that soluble brain Aβ levels are already detectable from five months of age, whereas detection of insoluble brain Aβ levels are delayed until the animals are seven months old (49). In congruence with this, Bateman, et al. have shown that in humans carrying PS1 and/or PS2 mutations, plasma Aβ42 levels are significantly higher than in non-carriers five years before such differences reach statistical significance in Aβ42 CSF levels (50).

It remains unknown whether there is an identifiable pre-clinical stage of AD in which soluble Aβ, but not detectable amyloid-PET fibrillar plaques, are increased in the brains; although some experimental results point to this possibility (51, 52). Thus, changes in Aβ blood levels, related to early changes in brain Aβ soluble monomers or oligomers, might occur before the deposited Aβ fibrils became detectable by amyloid-PET or indirectly by decreased CSF levels of Aβ42. This again may blur the actual strength of the relationship between two biomarkers that display different kinetics when they are analyzed at a single time point, as is observed in many cross-sectional studies (19, 27, 33-37). Thus in our opinion, the search for an association between the levels of Aβ in brain and blood should be addressed by longitudinal studies describing accurate time-dependent models for these two biomarkers.

The metabolism of Aß in blood as a third confounding source.

The third important source of uncertainty is the incomplete knowledge of blood Aβ metabolism. There is a clear necessity for a better understanding of the biology and dynamic interactions between Aβ peptides and the complex plasma proteome matrix. These interactions together with the variable patient history of the elderly, presenting various comorbidities, may affect the metabolism of Aβ peptides and interfere with their quantification. For example in a study with 51 AD patients, 53 HC and 36 MCI, we found that diastolic blood pressure correlated with the free, directly accessible Aβ40 in plasma and with a number of hematological and serum biochemical analytes, including hematocrit and creatinine, independently of the diagnostic group (53). These identified correlates and others reported in the literature such as blood cell counts (erythrocytes, platelets, neutrophils, lymphocytes, monocytes), hemoglobin, homocysteine, urea, uric acid, glomerular filtration rate, triglycerides, and serum total proteins, should be considered as potential confounding factors in studies investigating blood Aβ levels as AD biomarkers (19 ,54, 55).

To get a more comprehensive assessment of Aβ in plasma, we separately assessed the Aβ (Aβ42, Aβ40 and Aβ17) peptide level: i) directly accessible (DA) for ELISA in an undiluted plasma sample and, ii) the total plasma levels measured in a sample diluted with a formulated buffer that suppress the matrix effect. The difference between these two measurements, the total Aβ in plasma minus the DA, gives the peptide “recovered” from plasma (RP) that was initially inaccessible for ELISA due to its interactions with other plasma components. We consider this RP value as an indirect estimate of the matrix effect in each individual that could be interpreted as a “plasma biochemical composite measurement” for each subject (56, 57). Following this approach, in a study including 19 HC and 27 MCI patients, we have found that the ratio DA42/40, dichotomized with the median of the pooled population sample, was significantly associated with the diagnosis [OR (95% Confidence Interval [CI]): 9.54 (1.77–51.38)] which is consistent with previous studies on Aβ plasma markers (21). Furthermore, we found that the likelihood of being MCI for patients with RP42 and DA42/RP42 ratio below the corresponding population median (“positive test”) was 11.48 (1.87–70.50)-fold and 22.09 (3.19–152.61)-fold higher, respectively, than in those with a “negative test” after adjusting for the effect of APOE genotype (54). These results indicate that a comprehensive assessment of the Aβ peptide levels directly accessible in plasma and recovered from plasma after suppressing the matrix effect may improve the presumptive diagnostic ability of Aβ plasma tests.

Additionally in this same population, we have found that subjects with the ratio between RP Aβ17 and RP Aβ42 (RP17/RP42) below the median of the population had an increased likelihood of being MCI (OR 20.00; CI 1.17–333.33) than those with RP17/RP42 above the median. Although the CIs are wide, most probably due to the relatively low number of participants in this pilot study, these findings suggest that assessment of other isoforms as Aβ17 may also increase the diagnostic performance of blood-based Aβ tests (58-60).

Nevertheless, the relatively wide range of individual plasma Aβ measurements reported within any diagnostic group (HC, MCI, and AD) across the literature and the considerable overlapping values between groups indicate that the highest diagnostic value of any given Aβ blood marker could be more directly related to their trajectories over time than to their levels at any given moment. In this regard, it has been recently reported that whereas plasma Aβ42 levels showed an elevation (p = 0.002) in an stable HC group over an 18 months follow-up period, the HC transitioning to MCI group showed an overall decrease of Aβ1–42 levels (p = 0.003) (55). Furthermore, comparisons of plasma Aβ levels between the group of patients that transition to AD in a 36 month follow-up and the non-transitional group showed that Aβ42 and the Aβ42/Aβ40 ratio were significantly lower at baseline (p = 0.008 and p = 0.002, respectively) and at 18 months (p = 0.003 and p = 0.004, respectively) in the transition group (61). These findings suggest that longitudinal plasma Aβ measurements could significantly contribute to a panel of biomarkers for preclinical AD.

In summary, our hypothesis is that Aβ levels in blood may drop prior to brain amyloid deposits becoming detectable by amyloid-PET or indirectly by CSF analysis. We tentatively postulate that at early stages of preclinical AD, blood Aβ levels and central Aβ biomarkers would follow parallel but temporally displaced trajectories. This association would become blurred and eventually lost as the disease progresses and the clearance mechanisms in the blood brain barrier are increasingly impaired. Thus, parallel longitudinal studies on the kinetics of central and peripheral Aβ biomarkers are required to ascertain whether plasma Aβ levels could predict the levels of central Aβ biomarkers at the very early stages of preclinical AD.

Founding: This work has been financed by Araclon Biotech Ltd. The authors are employees at Araclon Biotech. Dr. Sarasa is a stakeholder of the company.

Acknowledgements: The authors acknowledge Latoya M. Mitchell, PhD CMPP of Grifols, Inc for English language editing and journal formatting.

Disclosures: Dr. Pesini has nothing to disclose. Dr. Sarasa has a patent High sensitivity immunoassays and kits for the determination of peptides and proteins of biological interest issued, a patent methods and reagents for improved detection of amyloid beta petides pending, and a patent antibody, kit and method for determination of amyloid peptides pending.

Conflicts of Interest: The authors declare no conflicts of interest but are employed by Araclon Biotech Ltd.

Ethical standards: No incentives were given to participants, informed consent was obtained, data were stored securely and kept confidential, and ethical standards were strictly adhered to.


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