In this study, fibrillar amyloid beta levels, in ADNI subjects, have been compared and related to the level of analytes on the RBM panel in plasma. Brain amyloid burden appears to be distributed bimodally in the RBM-PiB PET cohort, as has been previously reported for a larger ADNI subcohort by Ewers et al. 
. Associations of C-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, von willebrand factor, cortisol, serum amyloid p-component, AXL receptor tyrosine kinase, interleukin-3, interleukin-13, matrix metallproteinase-9, APOE, leptin and immunoglobulin E with brain amyloid burden have been found in this study. Some of these markers of brain amyloid burden were also found to associate with other AD related phenotypes, such as CSF Aβ1−42
, MRI features, cognitive tests and diagnostic groups. In regression models it was found that models including both RBM analytes and co-variates performed better than those using only co-variate information, suggesting that the RBM panel of analytes can be used as markers of brain amyloid burden. Combining highly correlated variables was found to reduce overfitting and led to a set of 13 RBM analytes that together with co-variates could explain >30% of the variance of brain amyloid burden and predict PiB positive individuals with a high sensitivity. This result, and the increased predictive accuracy of the 13 RBM model in comparison to using only co-variates to predict fibrillar amyloid alone, indicate that these analytes reflect levels of fibrillar amyloid in the brain.
The potential of APOE level in plasma to be used as a biomarker for brain amyloid burden, previously shown in Thambisetty et al. (2010) 
, is given support by this study. However it should be noted that the association between plasma APOE level and brain amyloid burden was seen to be positive in that study and negative in this. This inconsistency may relate to differences between the cohorts, for example the BLSA cohort studied in Thambisetty et al. (2010) were selected to be cognitively normal. This fits with the recent finding that plasma level of APOE correlates negatively with brain amyloid burden in the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing, which includes subjects suffering from AD 
. Similarly, the effect of the presence of APOE
ϵ 4 alleles on plasma APOE level in the study presented here was found to be the opposite of that found in both Thambisetty et al. (2010) and Evans et al. 
, however it is the same as that seen in Slooter et al., Siest et al. and Gupta et al. 
. Additionally, this relation was replicated in an independent cohort (ANM + KHPDCR) and it's control subcohort. This latter finding suggests that the differences between the findings of these studies is not due to the subjects cognitive status. The factor/s responsible for these inconsistencies are not known, but the fact that strong associations are seen in all studies is encouraging.
While no association between the level of RBM analytes in plasma and brain amyloid burden was found to be significant at the 5% level after multiple testing corrections, it should be noted the cohort used (RBM-PiB PET) contained relatively few subjects and that many analytes that associated significantly at the uncorrected 0.05 level have known relations to AD pathology, for example levels of APOE and complement C3 in plasma have previously been found to associate with fibrillar amyloid levels in the Baltimore Longitudinal Study of Aging (BLSA) cohort 
. In addition, some of these analytes were subsequently found to associate with surrogate phenotypes of AD pathology in the larger ADNI-RBM cohort, such as: diagnostic groups, MMSE score, ADAS-cog 13 score, CSF Aβ1−42
level, and the thickness and/or volume of the entorhinal cortices. However, 7/16 of the markers of brain amyloid burden – c-peptide, von Willebrand factor, serum amyloid p-component, AXL receptor tyrosine kinase, interleukin-13, matrix metalloproteinase-9 total and IgE – were not found to associate with any of the surrogate phenotypes of AD pathology that were tested. It should be noted that 5 of the 7 are linked to AD in the literature (Table S3
), only IgE and interleukin-13 have no prior reported association. Most of these surrogate phenotypes, except CSF Aβ1−42
, are believed to change at a later disease stage than amyloid pathology, and so it is possible that the lack of association of some of the markers with, for example, diagnostic groups is due to the mixture of high and low brain amyloid burden control subjects used. However, the lack of association of the majority of the markers with CSF Aβ1−42
levels is of greater concern because CSF Aβ1−42
levels are strongly associated with brain amyloid burden 
; this may indicate that we are over-fitting the available data and further highlights the need for datasets with larger sample sizes for future studies of markers of brain amyloid burden.
While half of the markers associated with diagnostic groups, 3/8 of these markers – alpha-1-antitrypsin, pancreatic polypeptide and interleukin-3 – had a median difference between AD (or MCI) and control subjects that was of the opposite sign to the partial SRC coefficient measuring their association with brain amyloid burden. This result is surprising as brain amyloid burden is positively associated with AD and MCI diagnosis groups. This discrepancy could relate to the delay between these disease stages, and may mean that the level of some of the markers in plasma changes during Aβ
deposition and then changes again, but in the opposite direction, before the onset of clinical symptoms. Similar u-shaped profiles, but between subjects in different diagnostic groups, have been observed (cross-sectionally) in the level of many leukocyte transcripts during AD progression 
Partial correlation showed that the number of APOE ϵ 4 alleles partly confounded the association between APOE level in plasma and brain amyloid burden; however, plasma APOE levels did help a regression model predict brain amyloid burden and so further study is required to get a clearer idea of the APOE ϵ 4 independent information conveyed by plasma APOE levels. This study has revealed many novel potential markers of brain amyloid burden, chosen to give APOE ϵ 4 independent information, as well as replicating findings from other studies. This will allow further validation work that can test the replicability and clinical utility of these markers.
In a previous study that used discovery proteomics to identify proteins associated with brain amyloid levels, Thambisetty et al. (2010) showed that levels of APOE and Complement C3 precursor in plasma were different between subjects with high and low brain amyloid burdens 
. It was encouraging that both were seen to be associated with brain amyloid burden in this study as well. Complement C3 precursor has also been found to be associated with atrophy of hippocampal volume, another imaging marker of AD 
, and to have a role in plaque clearance in a mouse model 
. It has also been found along with vitronectin to be at different levels in serum between control and AD subjects 
. The level of fibrinogen gamma was also found to be associated with atrophy of hippocampal volume in Thambisetty et al. (2011) 
. Fibrinogen alpha, beta and gamma are targeted by the same RBM analyte and were found to associate with brain amyloid burden in this study.
Of the 16 RBM analytes whose level in plasma associated with brain amyloid burden, many have known relationships with Alzheimer's disease. The levels of the following have previously been found to be different between control and AD subjects: alpha-1-antitrypsin 
, APOE 
, cortisol 
, interleukin-3 
, matrix metalloproteinase-9 
, pancreatic polypeptide 
, serum amyloid p-component 
and von Willebrand factor 
. Serum amyloid p-component 
and insulin 
have been shown to affect ‘AD’-like pathology in vitro
. Interleukin-3 
and leptin 
have been found to affect the interaction of neurons and Aβ
. Additionally, interleukin-13 has been found to be produced in microglia in response to Aβ
. More recently, APOE and matrix metalloproteinase-9 have been shown to be involved together in the breakdown of the blood brain barrier, which can initiate neurodegeneration 
Given the relatively small number of subjects in this study it was not practical to separate the subjects into training and test sets, to assess the predictive accuracy of the regression model. Instead, k-fold cross-validation was used, allowing more of the subjects to be used for fitting the regression model. Generally it is advisable to use 10-fold cross-validation because it has been found to have a lower variance 
. However, given the limited number of samples available, a leave one out cross-validation approach was used in this study to allow the maximal use of the subjects available. Given the limited number of subjects on which the model is based, it will be important in the future to study the ability of these biomarkers to predict brain amyloid burden in an independent cohort. Validation studies would benefit from greater numbers of subjects and better sampling strategies. For example, the distribution of brain amyloid burden in the RBM-PiB PET subcohort is affected by the sampling strategy applied to select control subjects for RBM measurements; only plasma of control subjects with high CSF Aβ1−42
were selected, for reasons unrelated to the current study. This means that the resulting model may not extrapolate to cognitively normal subjects with high brain amyloid burden, this could be tested in a validation study. Additionally, the use of only three control subjects may make the regression model less likely to generalise to prediction of brain amyloid burden in early Alzheimer's disease.
In conclusion, analytes associating with brain amyloid burden have the potential to act as biomarkers of early AD-related pathology. In this study sixteen analytes were found to associate with brain amyloid burden, including two (APOE and complement C3) that had had already been shown to associate with brain amyloid burden in an independent cohort. Some of these analytes were also found to associate with other AD related phenotypes in a larger ADNI subcohort, such as: CSF Aβ1−42, MRI features, cognitive scores and diagnostic groups. Some of these analytes were found to correlate highly with each other, and so a representative set of thirteen analytes – c-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, cortisol, AXL receptor kinase, interleukin-3, interleukin-13, matrix metalloproteinase-9 total, APOE and IgE – were used along with subject age, gender, years of education, the number of APOE ϵ 4 alleles and sampling dates to predict brain amyloid burden. The 13 analyte and co-variate model was found by cross-validation to account for >30% of the variance of brain amyloid burden, as opposed to ~ 4–13% using just co-variates alone, showing the potential of plasma analytes as markers of brain amyloid burden. The model was also able to predict PiB positive individuals with a high sensitivity. The two variables with the largest contribution to the model were found to be the number of APOE ϵ 4 alleles and plasma APOE level. The association of plasma APOE level with brain amyloid burden was shown to be partly confounded by the number of APOE ϵ 4 alleles, highlighting the importance of novel biomarkers that are less confounded by the APOE genotype revealed by this study.