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. Author manuscript; available in PMC: 2015 Jan 25.
Published in final edited form as: J Neural Transm (Vienna). 2012 Feb 22;119(7):843–850. doi: 10.1007/s00702-012-0772-4

Longitudinal plasma amyloid beta as a biomarker of Alzheimer’s disease

Robert A Rissman 1,✉, John Q Trojanowski 2, Leslie M Shaw 3, Paul S Aisen 4
PMCID: PMC4305447  NIHMSID: NIHMS444691  PMID: 22354745

Abstract

Alzheimer’s disease (AD) affects more than twenty-five million people worldwide and is the most common form of dementia. Symptomatic treatments have been developed, but effective intervention to alter disease progression is needed. Targets have been identified for disease-modifying drugs, but the results of clinical trials have been disappointing. Peripheral biomarkers of disease state may improve clinical trial design and analysis, increasing the likelihood of successful drug development. Amyloid-related measures, presumably reflecting principal pathology of AD, are among the leading cerebrospinal fluid and neuroimaging biomarkers, and measurement of plasma levels of amyloid peptides has been the focus of much investigation. In this review, we discuss recent data on plasma β-amyloid (Aβ) and examine the issues that have arisen in establishing it as a reliable biomarker of AD.

Keywords: Alzheimer’s disease, Protein biomarker, Plasma amyloid

Plasma Aβ as a biomarker: overview

β-Amyloid (Aβ) is a 38- to 43-amino-acid peptide produced by proteolytic cleavage of amyloid precursor protein (APP). In the AD brain, Aβ accumulation is considered to play a pivotal role in neuronal loss and cognitive impairment (Selkoe 1994). Although APP exists throughout the body (Arai et al. 1991), Aβ is primarily produced in brain (Laird et al. 2005), making cerebrospinal fluid (CSF) an obvious candidate for Aβ detection. Levels of CSF Aβ42 decline with AD progression, which may suggest neuronal loss and increasing Aβ plaque development (Jensen et al. 1999; Andreasen and Blennow 2002).

Given the somewhat invasive nature of CSF sampling, changes in Aβ in plasma would be an attractive biomarker. As reviewed in detail recently (Hampel et al. 2011), changes in CSF and plasma Aβ may reflect a shift from soluble Aβ species in the periphery with increased insoluble Aβ plaque formation in the brain. In subjects with familial AD or Down syndrome (DS), plasma Aβ rises sharply before dementia onset, perhaps reflecting increased Aβ production (Scheuner et al. 1996; Tokuda et al. 1997; Younkin 1998; Ertekin-Taner et al. 2000, 2008; Coppus et al. 2011). In the case of DS, recent studies suggest that plasma Aβ is less of a predictor of dementia, per se, but can predict cognitive function in adults with DS independently (Head et al. 2011). Data from our group demonstrate that ratio of Aβ42:40 rather than absolute levels of the peptides is important to the pathophysiology of AD in genetically susceptible populations (Matsuoka et al. 2009). More recently, in an Alzheimer’s disease neuroimaging initiative (ADNI) study (Toledo et al. 2011b), we analyzed data from 715 ADNI subjects with baseline Aβ40 and Aβ42 plasma measurements (50% with 4 serial annual measurements) including: 205 cognitively normal controls (CN), 348 patients mild cognitive impairment (MCI) and 162 with AD (n = 162). We also integrated these analyses with all available ADNI data on PIB PET, MRI and tau, and Aβ42 measures in cerebrospinal fluid (CSF) on these subjects. We noted that the effect of age differed according to AD stage and that plasma Aβ42 showed a mild correlation with other biomarkers of Aβ pathology, but they also were associated with infarctions in MRI. However, longitudinal measurements of Aβ40 and Aβ42 plasma levels showed only modest value as a prognostic factor for clinical progression. Thus, these findings show that plasma Aβ measurements have limited value for disease classification and modest value as prognostic factors over the 3-year follow-up period in this ADNI study. However, with longer follow-up, plasma Aβ measurements within subjects could be developed into a minimally invasive screen to identify those at increased risk for AD. However, there is clearly a need for further research into the biology and dynamics of Aβ, as discussed below. There is also a need for longer term studies to determine the clinical utility of measuring plasma Aβ. Data collected thus far on plasma Aβ in sporadic, familial AD and Down Syndrome are summarized in Table 1.

Table 1.

Reported changes in plasma amyloid as a function of age, disease and Down Syndrome

References Setting Cohort and outcome Main finding: cross sectional Main finding: longitudinal change
Scheuner et al. (1996) Cohort study Familial or sporadic AD and age matched controls Increase in Aβ42 in familial AD Not examined
Tokuda et al. (1997) Cohort study Down syndrome (age 19–61) and aged matched normal controls Increase in Aβ40 and Aβ42 in DS compared to controls Not examined
Younkin (1998) Cohort study Elderly patients with sporadic AD and control matched Aβ42 increase in plasma of subjects with mutated gene causing early onset familial AD Elevated Aβ42 in subjects with FAD-linked mutations in not a secondary effects of AD state
Mayeux et al. (1999) Population-based study Normal elderly development of AD High baseline Aβ42 and Aβ40 in incident AD No change in incipient AD. Only baseline Aβ42 significant after the statistical adjustments
Ertekin-Taner et al. (2000) Cohort study First-degree relatives of patients with typical late-onset AD (LOAD) and AD patients Aβ42 increase in first-degree relatives of patients with typical late-onset AD (LOAD) and early AD Identification of a novel LOAD locus on chromosome 10 that acts to increase Aβ
Fukumoto et al. (2003) Cohort study AD, MCI, and Parkinson patients, and control Plasma Aβ measures increase with age Plasma Aβ measures are unspecific for the clinical diagnosis of MCI or sporadic AD
Mayeux et al. (2003) Population-based study Normal elderly and MCI—development of AD High baseline Aβ42 in incident AD Longitudinal decline in Aβ42 in incipient AD
Gurol et al. (2006) Cohort study Subjects with AD or AD/MCI and an subjects with cerebral amyloid angiopathy (CAA) Plasma Aβ40 concentration is independently associated with extent of white matter hyperintensity in subjects with AD, MCI, or CCA Circulating Aβ40 peptide is a novel biomarker in AD, MCI, and CCA
Van Oijen et al. (2006) Population-based study Normal elderly development of AD High baseline Aβ40, but not Aβ42, in incident AD Not examined
Graff-Radford et al. (2007) Cohort study, primary care Normal elderly development of MCI or AD Low baseline Aβ42/Aβ40 ratio in incident MCI and AD Not examined
Giedraitis et al. (2007) Cohort study Control, MCI, and AD Relationship between CSF and plasma Aβ in control but not between MCI and AD. No different in plasma Aβ between diagnostic groups Not examined
Blasko et al. (2008) Community-based cohort study Normal elderly development of MCI or AD No change in baseline Aβ42 in incident AD Longitudinal increase in Aβ42 in incipient MCI and AD
Lopez et al. (2008) Population-based study Healthy elderly and MCI development of AD High baseline Aβ42 and Aβ40 in incident AD Longitudinal increase in Aβ42 and Aβ40 in independently of cognitive change
Schupf et al. (2008) Cohort study of Medicare recipients, Normal elderly development of AD High baseline Aβ42 (but not Aβ40) in incident AD Longitudinal decrease in Aβ42 (but not Aβ40) in incident AD
Ertekin-Taner et al. (2008) Cohort study First-degree relatives of AD patients and controls Plasma Aβ is significantly elevated in the LOAD first-degree relatives in comparison to unrelated controls Changes are not due to ApoE4 or pathogenic coding mutations
Sundelof et al. (2008) Population-based study Normal elderly development of AD Low baseline Aβ40 (but not Aβ42) in incident AD at age 77 No longitudinal change in Aβ42 or Aβ40 in incident AD. No change in Aβ40 or Aβ42 in prodromal AD at age 70
Okereke et al. (2009) Population-based study Female registered nurses age 30–55 followed up into late life Increase in Aβ 40:42 ratios since midlife predicted greater decline in the global score. No association of cognitive decline with midlife plasma Aβ42 levels alone or with change in Aβ42 levels since midlife Higher Aβ40/Aβ42 ratio in midlife and increase of Aβ40/Aβ42 ratio in late life predict cognitive decline
Roher et al. (2009) Longitudinal case–control study AD and non-demented controls No change in Aβ42 or Aβ40 No disease-associated change over time
Matsuoka et al. (2009) Cohort DS patients with dementia Ratio of Aβ42:Aβ40 was associated with the presence of dementia which persisted after adjustment for age, sex level of mental retardation, and apolipoprotein E genotype Not examined
Hansson et al. (2009) Cohort study, memory clinic MCI development of AD No change in Aβ42 or Aβ40 Not examined
Hampel et al. (2011) Review Normal elderly and MCI development of AD Contradictory finding are due to analytic difficulties Not examined
Head et al. (2011) Cohort study Non-demented adults with DS, young and old individuals without DS and patients with AD In demented DS adults, ApoE4 was associated with higher Aβ40 but not Aβ42. Improved prediction of neuropsychological scores by plasma Aβ. After controlling for level of intellectual disability (mild, moderate, severe) and the presence or absence of dementia Not examined
Coppus et al. (2011) Population-based study Down Syndrome have a significantly higher plasma Aβ1–40 concentration than the non-demented Increase in plasma Aβ at dementia onset in Down’s Syndrome. The risk to develop dementia increased to 2.56 for Aβ1–42 Not examined
Toledo et al. (2011a, b) Longitudinal case, Cohort study Normal elderly and MCI development of AD Plasma Aβ42 showed mild correlation with other biomarkers of Aβ pathology and were associated with infarctions in MRI Longitudinal increase in Aβ42 and Aβ40
Toledo et al. (2011a, b) Population-based study Normal elderly and MCI development of AD Association between Aβ brain burden and diastolic blood pressure and cortisol Possible link between these cardiovascular risk factors and Aβ burden
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Sources of plasma Aβ

APP has been localized in several peripheral tissues and organs that express β-secretase (Arai et al. 1991; Sinha et al. 1999; Vassar et al. 1999). Although these tissues are viable sources to plasma Aβ, their contribution is likely to be nominal given the vastly greater amount of Aβ produced in the brain (DeMattos et al. 2002; Cirrito and Holtzman 2003). Furthermore, data suggest that Aβ in brain can be cleared into the periphery through rapid receptor-mediated transport across the blood–brain barrier (Shibata et al. 2000; DeMattos et al. 2002; Deane et al. 2003, 2004a, 2004b). Increased levels of plasma Aβ have been reported in AD mouse models (Kawarabayashi et al. 2001) or after treatment with binding agents (DeMattos et al. 2002; Deane et al. 2003; Matsuoka et al. 2003). In AD patients, plasma Aβ levels are elevated and mainly incorporated into lipo-proteins and different plasma proteins (Biere et al. 1996).

Variables that affect plasma Aβ

In comparison to CSF, validation of plasma Aβ as a predictor of dementia in sporadic or late-onset forms of AD has been more difficult to determine. Relationships have been found between plasma Aβ40:42 to Aβ42:40 and dementia, but the direction of these associations vary among studies (Fukumoto et al. 2003; Mayeux et al. 2003; Blasko et al. 2008; Lopez et al. 2008; Schupf et al. 2008; Toledo et al. 2011b). More consistency has been found in the ratio of plasma Aβ42:40, with non-demented patients usually having higher risk of AD with lower Aβ42:40 ratios (Graff-Radford et al. 2007; Okereke et al. 2009). In terms of predicting whether patients with MCI will convert to AD, no consistent change in plasma Aβ or ratio has been found (Fukumoto et al. 2003; Lopez et al. 2008; Toledo et al. 2011b). However, studies demonstrate that age-related changes (increases) in plasma Aβ and reduced Aβ40:42 ratio are primarily restricted to MCI patients or individuals with worsening cognitive status (Toledo et al. 2011b).

Sources of variability in findings from existing studies are potentially due to variability in subject age and/or with disease severity (Fukumoto et al. 2003; Giedraitis et al. 2007), but may also relate to study size; very few large-scale studies have been attempted. A recently published study in a large cohort of elderly patients identified an association between low cognitive reserve and plasma Aβ42:40, which accentuated the relationship between low plasma Aβ42:40 and greater cognitive decline in non-demented participants (Yaffe et al. 2011). Below we discuss potential factors that may contribute to the variability seen in studies and what steps are still needed to identify the parameters for clinical utility of plasma Aβ.

Impact of age on plasma Aβ

Several studies suggest that the levels of Aβ in plasma increase as a function of age in non-demented individuals (Matsubara et al. 1999; Fukumoto et al. 2003; Mayeux et al. 2003). Although the mechanisms underlying this change are not confirmed, data from rodent studies implicate age-related reduction hepatic clearance of Aβ, due to alterations in low-density lipoprotein receptor-related protein 1 (LRP-1) (Tamaki et al. 2006). Studies in humans implicate the renal system and have found strong relationships between plasma Aβ and creatinine (Cr) levels (Arvanitakis et al. 2002).

Concurrent disease states

To different degrees, aged populations are characterized by multiple health concerns. Although the etiology of AD has not been demonstrated to be vascular origin, plasma Aβ level changes have been linked to neurovascular disease. A growing body of evidence suggests an association between vascular risk factors and AD, with neurovascular disease hypothesized to be an exacerbating factor of AD neuropathology (Girouard and Iadecola 2006). Indeed, in another recent ADNI study (Toledo et al. 2011a), we examined the relationship between vascular risk factors (VRF) and Aβ brain burden measured by Pittsburgh Compound B (PIB) PET studies. Briefly, 99 subjects had a PIB PET study and had information available on VRF (body mass index, systolic and diastolic blood pressure, cholesterol and fasting glucose) in the ADNI cohort, while 81 subjects also had plasma cortisol, brain natriuretic peptide, leptin, c-reactive protein and superoxide dismutase-1 measurements. All subjects also had APOE genotyping. We used these data in a regression stepwise model to assess the relation between the VRF and the composite PIB PET score. The final model included baseline age, clinical diagnosis, number of APO ε4 alleles, body mass index (BMI) and diastolic blood pressure (DBP) as variables. BMI showed an inverse relation with PIB PET score, and DBP had a positive relation with PIB PET score. In a second adjusted model, plasma cortisol levels also were associated with PIB PET score, but there was no association of systolic blood pressure, cholesterol, or impaired fasting glucose with PIB PET values. Thus, based on these data there appears to be an association between PIB PET measures and DBP as well as cortisol, thereby suggesting a possible link between these two VRF and deposits of Aβ in brain. In terms of plasma Aβ, although longitudinal studies are needed to confirm, current data suggest that subjects with AD, MCI, cerebral amyloid angiopathy, or ischemia, levels of plasma Aβ40 may be a viable biomarker of cerebrovascular damage (Lee et al. 2005; Gurol et al. 2006).

In addition to vascular conditions, studies have focused on how plasma Aβ levels can be influenced by known covariates of AD, such as clearance of Cr. As both Cr and Aβ are cleared through the kidney, elevated levels of Cr are considered indicative of renal insufficiency. Indeed, data suggest direct relationships between renal function and plasma Aβ (Arvanitakis et al. 2002). Another important covariate of AD that may influence plasma Aβ levels is homocysteine, whose levels have been correlated with cognitive decline in AD and conversion of MCI to AD (Tucker et al. 2005; Blasko et al. 2008).

Methodological concerns

Compared to CSF Aβ levels, the levels of plasma Aβ42 are very low, which has complicated studies due to the propensity for subjects to fall below the linear range of detection on many platforms. Levels of Aβ40 in plasma are much higher, but data are still mixed regarding the direction of change with advancing disease. A potential factor in this variability may lie with the variety of ELISA and flourometric bioassay platforms available. Sensitivity and specificity of the platforms used are highly reliant on the antibodies used, which can vary. There are also several ELISA systems and protocols that have been used in clinical studies, which make data comparison difficult. Interestingly, recent studies investigating multiple assay protocols for plasma Aβ in different laboratories found very good reproducibility of values (Okereke et al. 2009). While this has not necessarily been the case for CSF measures (Mattsson et al. 2011), a recent ADNI round-robin study of CSF tau and Aβ showed very good inter-lab reproducibility (Shaw et al. 2011).

In addition to issues arising as a result of assays, Aβ in both CSF and plasma is typically complexed to a variety of binding proteins, which may complicate detection (Biere et al. 1996; Koudinov et al. 1998). For example, Aβ is hydrophobic and binds to certain test tube walls as well as to several plasma proteins, including albumin and low-density lipoprotein receptor-related protein-1 (Matsubara et al. 1999; Hampel et al. 2011). Further, since measurement of soluble Aβ is done with assays that are independent of aggregation state, both plasma protein binding and oligomerisation could mask the detection of Aβ in plasma or CSF measured by these assays, which may explain contradictory results.

Variability in samples is also an issue, with reports suggesting that fluctuations of Aβ levels in CSF are dependent on the time of day and subject activity level (Bateman et al. 2007). Whether this is the case for plasma Aβ has yet to be conclusively determined. Data from AD rodent models are in concert with that seen in humans, with Aβ in brain being acutely impacted by environmental stimuli could significantly affect brain Aβ level (Kang et al. 2007). Other data from animal models suggest that time after eating can significantly affect plasma Aβ level. Recent data suggest that plasma Aβ in a fed state was significantly higher than that in a fasted state (Takeda et al. 2009).

Conclusions

Though controversy remains, there is a consensus that a pivotal and presumably initiating process in sporadic and genetically determined AD is the accumulation of toxic Aβ species in brain. Biomarkers reflecting this accumulation are thus likely to be important in the selection of individuals for AD prevention and treatment trials and monitoring the effects of anti-amyloid interventions. Indeed, among the most exciting developments in AD therapeutic research is the development of amyloid PET imaging and CSF Aβ42 measurement as indicators of brain amyloid. If anti-Aβ therapies become available, it is likely that treatment will be guided by biomarkers of brain Aβ.

Unfortunately, amyloid PET scanning is expensive and not yet generally available, and lumbar puncture is somewhat invasive. A peripheral blood biomarker would offer substantial advantages over these approaches, allowing readily acceptable screening and monitoring. While it is known that there is communication between the peripheral and central Aβ pools (via receptor mediated as well as passive mechanisms), the utility of plasma Aβ measurements has remained limited. While some studies have shown correlations between plasma Aβ and dementia risk and/or progression, such findings have been inconsistent. Biological and methodological issues likely contribute to these limitations, thereby underlining the need for a better understanding of the biology and dynamics of plasma Aβ as well as the need for longer term studies to determine the clinical utility of measuring plasma Aβ.

As discussed, plasma Aβ increases sharply before dementia onset, perhaps reflecting increased Aβ production in subjects with familial AD or Down syndrome (Scheuner et al. 1996; Tokuda et al. 1997; Ertekin-Taner et al. 2008). The same has not been found in sporadic or late-onset forms of AD. Although relationships have been found with plasma Aβ40 or 42 and dementia, the direction of these associations is variable. A recent study of plasma Aβ42 in normal, mildly impaired, and mildly demented cohorts is discouraging and yet provides some reason for optimism (Toledo et al. 2011b). Overall, plasma Aβ measurements were not useful in distinguishing among the cohorts, and showed minimal association with disease progression. At present, this study and that reported previously support the idea that plasma Aβ assays are not useful for diagnosis or prediction of decline. However, a significant association between plasma Aβ42 and brain amyloid was found, as indicated by CSF Aβ42, while the ADNI study reported mild correlation of plasma Aβ42 and other biomarkers of Aβ pathology, but they also were associated with infarcts detected by MRI (Toledo et al. 2011b).

As we discuss, sources of variability in plasma Aβ studies may be due to difficulty in consistency of subject age, disease severity. Recent data also suggest that technical precision may also be involved. Using a robotized method for specific steps allowed for a large improvement in consistency over results reported in the literature, and several significant relationships between plasma and CSF biomarkers were found (Figurski et al. 2012). Although these correlations are still not strong enough to support use of plasma Aβ as a diagnostic screening test, the data suggest that plasma Aβ42 is well suited for use as a pharmacodynamic marker.

As opposed to studies examining levels of peptide alone, reports on ratio of plasma plasma Aβ42:40, have had more consistent results, with lower Aβ42:40 ratios predicting higher risk of AD (Graff-Radford et al. 2007; Okereke et al. 2009). Furthermore, a large cohort study of elderly patients found that low cognitive reserve and plasma Aβ42:40, which accentuated the relationship between low plasma Aβ42:40 and greater cognitive decline in non-demented participants (Yaffe et al. 2011).

With improvements of assay conditions (e.g., with increasing sensitivity and reproducibility, and standardization of specimen handling to minimize interactions with other blood constituents and collection materials), and if the confounding effects of factors such as age and renal function are fully considered, it may be reasonable to expect that such measurements may become useful biomarkers of brain amyloidosis. This, in turn, could greatly facilitate the development and clinical application of disease-modifying therapies for AD. Improvements in the reproducibility of plasma Aβ42 and Aβ40 measurement support the utility of this biomarker test as a pharmacodynamic marker that reflects drug effect on Aβ processing.

Acknowledgments

The authors thank Seti Moghadam and Kath-leen Lao (Rissman lab, ADCS, UCSD) for assistance with manuscript preparation. Supported by NIH/NIA AG010483 (PSA), AG032755 (RAR), the Alzheimer’s Art Quilt Initiative (RAR), the Alzheimer’s Association (RAR), a pilot grant to RAR from the Shiley-Marcos Alzheimer’s Disease Research Center at the UCSD (AG005131), and the Penn Alzheimer Core Center (AG10124) (JQT, LMS).

Contributor Information

Robert A. Rissman, Email: rrissman@ucsd.edu, Alzheimer’s Disease Cooperative Study, Department of Neurosciences, UCSD School of Medicine, 9500 Gilman Drive, MTF 314 M/C 0624, La Jolla, CA 92037-0624, USA

John Q. Trojanowski, Department of Pathology and Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

Leslie M. Shaw, Department of Pathology and Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

Paul S. Aisen, Alzheimer’s Disease Cooperative Study, Department of Neurosciences, UCSD School of Medicine, 9500 Gilman Drive, MTF 314 M/C 0624, La Jolla, CA 92037-0624, USA

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