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Research Article

Relationship between Plasma Analytes and SPARE-AD Defined Brain Atrophy Patterns in ADNI

  • Jon B. Toledo,

    Affiliation: Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America

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  • Xiao Da,

    Affiliation: Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

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  • Priyanka Bhatt,

    Affiliation: Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

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  • David A. Wolk,

    Affiliation: Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

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  • Steven E. Arnold,

    Affiliation: Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

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  • Leslie M. Shaw,

    Affiliation: Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America

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  • John Q. Trojanowski,

    Affiliation: Department of Pathology & Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America

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  • Christos Davatzikos, for the Alzheimer’s Disease Neuroimaging Initiative mail

    christos@rad.upenn.edu

    Affiliation: Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

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  • Published: February 08, 2013
  • DOI: 10.1371/journal.pone.0055531

Abstract

Different inflammatory and metabolic pathways have been associated with Alzheimeŕs disease (AD). However, only recently multi-analyte panels to study a large number of molecules in well characterized cohorts have been made available. These panels could help identify molecules that point to the affected pathways. We studied the relationship between a panel of plasma biomarkers (Human DiscoveryMAP®) and presence of AD-like brain atrophy patterns defined by a previously published index (SPARE-AD) at baseline in subjects of the ADNI cohort. 818 subjects had MRI-derived SPARE-AD scores, of these subjects 69% had plasma biomarkers and 51% had CSF tau and Aβ measurements. Significant analyte-SPARE-AD and analytes correlations were studied in adjusted models. Plasma cortisol and chromogranin A showed a significant association that did not remain significant in the CSF signature adjusted model. Plasma macrophage inhibitory protein-1α and insulin-like growth factor binding protein 2 showed a significant association with brain atrophy in the adjusted model. Cortisol levels showed an inverse association with tests measuring processing speed. Our results indicate that stress and insulin responses and cytokines associated with recruitment of inflammatory cells in MCI-AD are associated with its characteristic AD-like brain atrophy pattern and correlate with clinical changes or CSF biomarkers.

Introduction

Alzheimeŕs disease (AD) is defined by extracellular deposits of Aβ in senile plaques and intracellular aggregates of tau protein in neurofibrillary tangles accompanied by neuronal loss [1], [2], [3], [4], [5] in association with other abnormalities such synaptic and dendritic loss [6], [7], [8], [9], inflammation [10], [11], [12] and gliosis [13]. However, it is increasingly evident that these pathologies slowly emerge over a decade or more before AD is diagnosed clinically [14] and progresses through different pathophysiological stages that ultimately culminate in death [15].

Genetic heritability accounts for 60–80% of the risk for AD [16], with the APOE ε4 allele being the major genetic risk factor for AD in a dose dependent manner. Environmental factors and vascular risk factors such as head trauma, metabolic syndrome, education, hypertension, diabetes, stress, etc. [17] also increase the risk for AD, and it is postulated that changes in life style practices could reduce the risk for AD [18]. For example, vascular risk factors may cause cognitive changes via different but inter-related pathways, which converge to induce cerebrovascular pathology and Aβ deposition in brain vasculature [19], [20], [21].

The availability of neuroimaging biomarkers to monitor and track morphological brain changes and multi-panel molecular biomarkers that reflect different inflammatory and other biochemical pathways enable dissection and investigation of pathways that may be related to brain atrophy and pathology in patients with neurodegenerative disease.

To investigate how blood-based biochemical biomarkers may relate to AD specific brain atrophy, we chose to use an index known as SPARE-AD (Spatial Pattern of Abnormality for Recognition of Early Alzheimer’s disease) that maximally captures spatial patterns of brain atrophy related to AD, and which may be more sensitive than a single region of interest, such as hippocampal volume [22], [23]. Positive SPARE-AD values at baseline have also been associated with subsequent cognitive decline and conversion from mild cognitive impairment (MCI) to AD [24], [25], whereas SPARE-AD values have been found to increase with age and to correlate with cognitive performance in cognitively normal older adults [26].

We tested the association of 130 plasma analytes measured simultaneously using a large-scale commercial multiplex panel (Rules Based Medicine (RBM) Inc. (Austin, TX)) with the SPARE-AD to identify analytes related to disease pathways or specific patterns of structural changes in AD patients.

Methods

Subjects

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). The ADNI was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration, private pharmaceutical companies and non-profit organizations. Its primary goal has been to test whether serial magnetic resonance imaging (MRI) [27], positron emission tomography (PET) [28], other biological markers [29], and clinical and neuropsychological assessment [30] can be combined to measure the progression of MCI and early AD. The Principal Investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California – San Francisco. ADNI is the result of efforts of many co- investigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the U.S. and Canada. At baseline, all subjects scored 6 or less in the short version of the geriatric depression scale (GDS-15) [31], which excludes subjects with depression. Exclusion criteria included any serious neurological disease other than possible AD, any history of brain lesions or head trauma, any recent history of substance abuse, any significant systemic illness or unstable medical condition and a previous history of major depression, bipolar disorder or schizophrenia. At baseline patients were not taking psychoactive medication (including antidepressants, neuroleptics, chronic anxiolytics or sedative hypnotics). For more details, see http://www.adni-info.org. Data was downloaded March 2012.

We included 818 adult subjects who had at least an MRI, 55 to 90 years old, who meet criteria for a clinical diagnosis of mild cognitive impairment (MCI, n = 396), probable AD (n = 193) or cognitively normal (CN, n = 229). Of these subjects, 58 CN, 395 MCI and 112 AD subjects had plasma RBM Luminex results. Subjects were also classified based on the presence of a normal or pathological/AD cerebrospinal fluid (CSF) signature using the LRTAA model described by Shaw et al [29], that uses CSF total tau and Aβ1−42 and number APOε4 alleles in a logistic regression model to classify subjects into AD-type CSF and control-like CSF. None of the included subjects was receiving oral, i.v. or i.m. corticosteroids.

Ethics Statement

Ethics approval was obtained for each institution involved. This study was conducted according to Good Clinical Practice guidelines, the Declaration of Helsinki, US 21CFR Part 50– Protection of Human Subjects, and Part 56– Institutional Review Boards, and pursuant to state and federal HIPAA regulations. Written informed consent for the study was obtained from all subjects and/or authorized representatives and study partners before protocol-specific procedures are carried out. Institutional Review Boards were constituted according to applicable State and Federal requirements for each participating location. The protocols were submitted to appropriate Boards and their written unconditional approval obtained and submitted to Regulatory Affairs at the Alzheimer’s Disease Neuroimaging Initiative Coordinating Center (ADNI-CC) prior to commencement of the study.

Cognitive Testing

Neuropsychological evaluation and criteria for clinical diagnosis have been described previously [32]. Based on the scores of cognitively normal participants who did not convert to MCI and had a follow-up of at least three years we developed z-scores for processing speed and short term memory as previously described [33].

Biomarker Collection and Analysis

Details of the CSF collection, processing, storage and measurements as well as the algorithm for classifying subjects as having an AD-like CSF signature were described by Shaw et al [29]. Plasma was prepared from blood samples collected from each study subject, following an overnight fast, at each visit scheduled in the ADNI protocol. At each scheduled visit blood samples were collected, centrifugation, within one hour, placed in dry ice and shipped to the UPenn Biomarker Core laboratory on dry ice. Aliquots (0.5 mL), prepared from plasma samples following thawing at room temperature, were stored in polypropylene aliquot tubes at −80°C until the day of testing. Samples were then interrogated by Rules-Based Medicine, Inc. (RBM, Austin, TX) for levels of 190 analytes using the multiplex Human DiscoveryMAP™ panel and a Luminex 100 platform. Of these, we selected analytes that had less than 10% of the data missing, less than 10% of the data labeled as “LOW” (“LOW” is used by the assay for detected values that are below the least detectable dose) and a coefficient of variation below 20% as outlined in the ADNI statistical analysis plan, which left us with 130 analytes. Assay and procedures are further detailed in the data primer (http://www.adni-info.org/).

Selected analytes had the non-numeric values imputed as follows:

  • Values recorded as “LOW” were imputed to least detectable dose/2.
  • Values recorded as “>value” were imputed to 2 times the maximum non-missing value for that analyte.
  • Missing values were imputed to be the mean of the non-missing values for that analyte.

For each analyte, the distribution of measured values within each diagnostic group was examined. If the distributions were not normal appropriate transformations was applied so the transformed markers approximate normality.

MRI Processing and Analysis

Acquisition of 1.5-T MRI data at each performance site followed a previously described standardized protocol that was rigorously validated across sites that included a sagittal volumetric 3D MPRAGE with 1.25×1.25 mm in-plane spatial resolution and 1.2-mm thickness for Siemens scanner, 1.09×1.09 mm in-plane spatial resolution and 1.2-mm thickness in General Electric scanner and 1.20×0.94 mm in-plane spatial resolution and 1.2-mm thickness in Philips scanner. Targeted TR and TE values of the ADNI protocol values were TR ~8.9 ms and ~3.9 ms, respectively [27].

Image Analysis

The images were pipeline processed as previously described [34]. The first step was rigid alignment to the ac-pc plane, followed by semi-automated removal of skull and cerebellum tissues. The images were then segmented into four tissue types: grey matter (GM), white matter (WM) sulcal CSF and ventricles (VN). These segmented images were registered to the common brain atlas [35] using high dimensional image warping in order to create RAVENS [34] tissue density maps for GM, WM and VN. The RAVENS maps are the results of elastic registration of original brain regions to the standard template while preserving the original tissue volumes. Therefore, regional volumetric measurements and comparisons are performed via measurements and comparisons of the respective RAVENS maps [36].

Quantification of Patterns of Atrophy via SPARE-AD

The normalized RAVENS maps were smoothed using 8 mm full-width at half-maximum Gaussian smoothing kernel. With the aim to provide abnormality scores for individual MCI subjects, we utilized a high dimensional pattern classification method [37]. Briefly, this method looks for the combination of brain regions which form a unique pattern that maximally differentiates between two groups. This classifier was trained on the AD and CN subjects in the ADNI [36]; it provides an output that tends to be positive for AD patients and negative for CN subjects.

MRI Regression Analysis and Group Comparison

For measuring regional correlations between RAVENS and RBM analytes, “beta” maps were created by applying voxel-wise linear regression between baseline RAVENS maps and analytes which were significantly associated with the SPARE-AD score. A voxel-wise t-test was performed to display the different regions on “beta” maps. A threshold corrected for false discovery rate (FDR) was utilized to determine significance of a voxel using AFNI software (http://afni.nimh.nih.gov/afni). Results were tested in a univariate model (analyte concentration and SPARE-AD score) and in multivariate models adjusting for age and clinical diagnosis.

For comparing clinical groups (MCI and AD) adjusting for CSF signature, voxel-based comparisons were done on baseline RAVENS map. Group comparisons involved voxel-by-voxel t-tests applied by the AFNI software. Comparison for multiple corrections utilized the FDR method as implemented in the AFNI software.

Statistics

SPARE-AD score showed a non-normal distribution; therefore a Yeo-Johnson power transformation was applied [38]. For one-way comparisons of the three clinical groups one way ANOVA test was applied followed by a Tukey HSD for the post-Hoc comparison. For the comparison of categorical variables χ2 test was applied. For the comparison of SPARE-AD across clinical and CSF groups a two way ANOVA was applied. Analytes that were significantly associated with SPARE-AD in the model adjusted for age and gender after multiple comparisons were selected for further analyses in two more models [39]. The first model was further adjusted for clinical diagnosis and APOE genotype and the second model included in addition CSF AD-like signature. The errors in the American National Adult Reading test (e-AMNART) [40] were used to assess premorbid performance and used as an estimate of cognitive reserve after a correction using the MMSE score [33], [41]. Normality and homoscedasticity assumptions were tested in all the linear regression models and all of them fulfilled the criteria.

Results

As expected the CN, MCI and AD groups differed in MMSE and modified ADAS-Cog scores, the percentage of patients with one or more copies of APO ε4 allele and CSF measured analytes. Values are summarized in Table 1.

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Table 1. Characteristics of subjects included in the study.

doi:10.1371/journal.pone.0055531.t001

SPARE-AD across Demographic and Clinical Variables, Cognitive Categories and CSF Defined Groups

First, we compared the SPARE-AD values across the three clinically defined groups and we found significant differences between these three groups (F (2, 815) = 553.6, p<0.0001). The post-hoc Tukey HSD analysis showed differences between the three groups; CN group showed lower SPARE-AD than MCI (difference (diff) = 1.96, 95% Confidence interval (CI) = 1.79−2.13) and AD groups (diff = 2.65, 95% CI = 2.45−2.85). MCI group showed lower SPARE-AD than the AD group (diff = 0.69, 95% CI = 0.51−0.88). In addition, we added to the model the presence of pathological CSF signature which improved the model (F = 4.48, p = 0.0041). Patients with pathological CSF signature showed lower SPARE AD values (F(1, 408) = 9.17, p = 0.003) (Figure 1).

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Figure 1. SPARE AD values in the different groups according to clinical diagnosis (CN, MCI, AD) and CSF signature (N-CSF: non AD-like CSF signature; P-CSF: pathological AD CSF signature).

doi:10.1371/journal.pone.0055531.g001

We also found a correlation between the log-scaled t-tau/AB42 ratio and SPARE-AD scores among the whole sample in the analysis adjusted for age, gender and clinical diagnosis (r = 0.66, p<0.0001) (Figure 2A). In a multivariate linear regression model, adjusted for gender, age and re-AMNART, SPARE-AD showed a significant association with the modified ADAS-Cog score (t value (1,799) = 25.1, p<0.0001) (Figure 2B).

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Figure 2. Scatterplot showing log-transformed total-tau/Aβ42 ratio and power-transformed SPARE-AD (A) and power transformed SPARE-AD and ADAS-Cog score (B).

The marginal box plots on x-axis represent the distribution of the power transformed SPARE-AD and the marginal box-plots on the y-axis represent the t-tau/Aβ42 ratio (A) and ADAS-Cog (B) values.

doi:10.1371/journal.pone.0055531.g002

We also compared demographic characteristics that have been associated with dementia or changes in MRI measures. Gender showed no association with SPARE-AD (F (1, 815) = 0.57, p = 0.57). Age showed a mild direct correlation with SPARE-AD (r = 0.11, n = 816, p = 0.001). Subjects with one or more APOE4 copies showed lower SPARE-AD values (F (1, 816) = 70.78, p = <0.0001). A small correlation was found between e-AMNART and SPARE-AD (r = 0.14, p<0.001), whereas no correlation was found between SPARE-AD and education (Education: r = −0.035, p = 0.31). We developed a baseline linear regression model to assess the relationship between biomarkers and SPARE-AD. This baseline model, which included clinical diagnosis, CSF signature, APOE, e-AMNART and age, explained 79.4% of the SPARE-AD variability (adjusted-R2).

RBM Plasma Biomarkers and SPARE-AD

After correcting for multiple comparisons, a significant association with SPARE-AD was found for apolipoprotein E (ApoE protein, q<0.0001), brain natriuretic peptide (BNP, q = 0.0007), chromogranin A (CgA, q = 0.033), cortisol (q = 0.033), eotaxin 3(ET-3, q = 0.001), insulin-like growth factor binding protein 2 (IGFBP-2, q = 0.022) and macrophage inflammatory protein1 alpha (MIP-1α, q = 0.031). Adjusted p-values for all the analytes are summarized in Table S1.

We studied these analytes in two more models; in the first one we adjusted for APOE genotype and clinical diagnosis (to avoid findings based on diagnostic group effects) and in the second one we also adjusted for CSF AD-like signature (to avoid findings based on CSF predicted group effects). When we adjusted the previous analytes by APOE genotype the associations between SPARE-AD and CgA, cortisol and IGFBP-2 remained significant (Table 2). In the model adjusted for APOE genotype and CSF AD-like signature only IGFBP-2 and MIP-1α showed an association with SPARE-AD score (Table 3).

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Table 2. Unstandardized coefficients derived from multiple regression models studying the association between RBM plasma analytes and SPARE AD adjusted for clinical diagnosis, age, and APOE ε4 presence.

doi:10.1371/journal.pone.0055531.t002
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Table 3. Unstandardized coefficients derived from multiple regression models studying the association between RBM plasma analytes and SPARE AD adjusted for clinical category, APOE ε4 presence, age and CSF signature.

doi:10.1371/journal.pone.0055531.t003

Neuropsychological Measures and Cortisol and MIP-1 α Measurements

We tested the association of CgA, cortisol, IGFBP-2 and MIP-1α with the z-scored cognitive measures (short term memory, delayed memory and processing speed) in a model adjusted for age, clinical diagnostic group and cognitive reserve. Only cortisol showed a significant inverse association with the processing speed (q = 0.045) and a trend with the short term memory domain (q = 0.084). All the other associations were not significant (data not shown).

Correlation of Plasma Analyte Concentration with MRI Regional Cortical Volumes and CSF Total and p-tau Levels

In the analysis adjusting for age, subjects with higher cortisol levels showed atrophy in left fusiform gyrus, right dorsolateral prefrontal and ventromedial prefrontal (vmPFC) cortex, biparietal cortex, affecting precuneus and paracentral lobule, and both hipocampi. Changes in the splenium of the corpus callosum associated with white matter changes were also observed. Results were similar although more circumscribed when the analysis was also adjusted for cognitive status (Figure 3).

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Figure 3. Regression of cortisol without adjustment (A), with adjustment for age (B) and with adjustment for age and clinical diagnosis (C), against regional grey matter (GM) volumes.

The color scale represents in blue a decrease in GM volumes associated to a increase levels of the biomarker in plasma. White matter changes (in red) indicate abnormal brain tissue, commonly associated with leukoaraiosis.

doi:10.1371/journal.pone.0055531.g003

As shown in Figure 4 subjects with higher MIP-1α after adjustment for age showed bilateral atrophy in the anterior hippocampus, amygdala, insula, as well as in posterior parieto-occipital and occipito-temporal cortex. After adjusting for clinical diagnosis results most of the areas remained significantly associated with MIP-1α (areas not associated with MIP-1α in the last model were the precuneus and occipital cortex).

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Figure 4. Regression of MIP-1α without (A), with adjustment for age (B) and with adjustment for age and clinical diagnosis (C), against grey matter (GM) volumes.

The color scale represents in blue a decrease in GM volumes associated to increased levels of the biomarker in plasma. White matter changes (in red) indicate abnormal periventricular brain tissue, commonly associated with leukoaraiosis.

doi:10.1371/journal.pone.0055531.g004

No association between brain atrophy and the other two plasma biomarkers (CgA and IGFBP-2) were found. However, CgA and IGFBP-2 were associated with CSF total tau levels and CgA was associated in addition with p-tau levels (Table S2).

Discussion

In this study, we found an association between AD-like patterns of brain atrophy, quantified by the SPARE-AD index, and plasma cortisol, CgA, IGFBP-2 and MIP-1α levels. Increased cortisol levels showed a significant association with worse processing speed and short term memory scores. In addition, we found that SPARE-AD correlates with t-tau/Aβ42 ratio and that MCI and AD patients with a negative AD CSF signature have lower (more normal) SPARE-AD values.

We have previously described an association between plasma cortisol levels and PIB PET scores in human subjects [21]. This previous result is in agreement with the results from several animal model studies that have found that chronic stress and chronic increased hypothalamic-pituitary-adrenal axis activity increase Aβ deposition and tau hyperphosphorylation not only in transgenic animal models [42], [43], but also in wild type mice [44] and in-vitro models [45]. Although, some of the studies have described that the effect is only mediated by corticotropin-releasing factor and not by corticosterone [46], [47] other studies have described an effect of glucocorticoids [42], [44], [48], [49]. Some studies indicate that cortisol might independently cause brain atrophy by other pathways that are independent of the amyloid pathway; glucocorticoids have widespread effects on cells through rapid and delayed effects, which are results of no-genomic, indirect genomic and direct genomics effects [50], [51] and increased cortisol levels affect dendritic spine development and reduce neurogenesis [52], [53], [54], [55]. In addition, glucocorticoids and chronic stress modify glutamate transmission in hippocampus and prefrontal cortex [56] and impair long term potentiation [57], [58]. This has been linked to impairment in prefrontal cortex and hippocampus dependent cognitive processes in cognitively normal subjects [59], [60].

One previous study has reported the association between cortisol and MRI changes, describing temporal atrophy with increasing cortisol levels [61]. We found a more widespread pattern of cortisol correlation with brain atrophy that includes dorsolateral prefrontalcortex, vmPFC and insular cortex, as well as cuneus and precuneus in addition to the medial temporal lobe. The dorsolateral prefrontal atrophy and the processing impairment we describe are in line with previous results in animal models that show changes in prefrontal areas and impairments in behavioral tests associated with these areas [53], [62], [63]. Previous studies in aged controls and hypertensive cognitively normal controls had described impairment in prefrontal cortex dependent tasks, but did not include MRI analyses describing the presence of prefrontal cortex atrophy [60], [64]. Lesion in vmPFC impair the retention of extinction learning in animal models [65] and studies in human subjects have also shown an association with fear extinction and vmPFC volume [66]. Chronic exposure to glucocorticoids in animal models leads to an impaired extinction in conditional fear conditioning and has been implicated in altered glutamatergic signaling in vmPFC [67]. We did not have any measures of fear conditioning in our cohort, but our results of atrophy in vmPFC associated to high cortisol levels link cortisol to atrophy in regions related to fear conditioning in human subjects and confirm results described in animal models. As detailed before glucocorticoids regulate dendritic spine development and modulate gene expression resulting in changes that are independent of Aβ deposition and glucocorticoid levels are modulated by chronic stress, therefore part of the associations we described might be related to these factors. Chronic stress and post-traumatic stress disorder studies have described atrophy affecting medial prefrontal cortex, anterior cingulate, right insula and hippocampus [68], [69]. These areas overlap partially with our findings. Therefore, it might be possible that chronic stress has an additional deleterious effect and can be a coincident disease, which is a common finding in aged population of demented subjects [70], [71] or act as an additional risk factor for dementia.

Recent GWAS studies have found susceptibility-linked SNP expressed in immune cells and related to immune response [72], [73], [74], [75], [76] and microglial activation is a common finding in AD neuropathological studies and is involved in the pathological changes [77]. MIP-1α is a ligand for chemokine-CC-motif-receptor 5 (CCR5), a transmembrane-domain, G protein-coupled receptor belonging to the β-chemokine receptor family, which is implicated in the migration of in the migration of monocytes, NK, dendritic and Th1 cells. A previous study has shown that T lymphocytes of AD patients overexpress MIP-1α and the expression of this cytokine is associated with increased transendothelial migration in the endothelial permeability assay and decreased integrity of the human brain microvascular endothelial cell monolayer [78]. The injection of Aβ into ratś hippocampus increased the expression of CCR5 and MIP-1α in endothelial cells and peripheral T cells, respectively [78]. The same researcher described how RAGE was associated to the increased expression of endothelial CCR5, which is mediated by the PI3K and JNK signaling cascade [79]. There are also results that indicate that MIP-1 might be also implicated in the dendritic cell transendothelial migration [80]. Our study further is the first to describe an association with MRI atrophy in human subjects and adds evidence to the involvement of this molecule in Alzheimeŕs disease.

Higher IGFBP-2 levels were associated with increased brain atrophy. This protein has been shown to modulate the actions of IGF-1; high levels result in an inhibition of IGF-1 dependent signaling while low levels increase the signaling [81], [82], [83]. IGF-1signals downstream through insulin receptor substrate 2 (IRS-2) and this pathway is associated with the prenatal and postnatal brain growth [84]. In addition, reduced IRS-2 signaling has been associated with accumulation of phosphorylated tau [85] and non-diabetic AD patients have shown IGF-1 resistance associated with IRS-2 dysfunction [86]. Plasma IGFBP-2 levels correlated with CSF t-tau levels (r = 0.11, p = 0.040) and showed a trend with p-tau levels (r = 0.09, p = 0.075). Therefore, higher IGFBP-2 levels could further impair the already affected IGF-1 signaling in AD cases and results in increased atrophy.

Chromogranins are prohormones, which are the major constituents of secretory large dense-core vesicles. CgA inhibits nicotinic acid transmission and is involved in the regulation of secretory granules. In AD CgA can be found in neuritic plaques and has been implicated in microglial activation [87]. One previous study found low CgA in CSF of patients with early AD [88].

IGFBP-2 and CgA levels were associated with t-tau levels this indicates that their levels might be related to neuronal injury. However, none of these analytes showed a correlation with atrophy in specific brain regions. This might be to the fact that SPARE-AD summarizes the whole pattern of atrophy and increase the power to detect associations due to the avoidance of the multivariate comparisons and adjustments that that are needed for whole brain comparisons.

Only cortisol levels were associated with cognitive measures. There are several factors that could account for the absence of an association between the other plasma biomarkers and the cognitive scores. It is possible the cognitive deficits and the biomarkers take place at different time points, that they show different floor and ceiling effects or that SPARE-AD captures nonlinear relations and measurements from different regions.

Previous studies measuring plasma or serum analytes using similar multi-analyte profiling platforms have been mainly focused on classifying AD patients vs. cognitively normal controls [89], [90], [91], [92], [93]. They have shown a lower [89], [91] or similar classification accuracy [93], [94]. than CSF Aβ42, t- and p-tau measurements, indicating that plasma analyte panels might be useful for screening patients and as measure of disease progression [90]. Only one previous study has studied the association between plasma analytes and the classical CSF biomarkers (CSF Aβ42, t- and p-tau) finding a different set of analytes [95]. These differences are not unexpected because CSF biomarkers and MRI atrophy behave differently during disease progression [15] and MRI captures changes that might be independent of amyloid and tau deposition as we have proposed.

SPARE-AD also correlated with the CSF T-tau/Aβ42 ratio, which discriminates between AD patients and CN individuals [29], distinguishes patients with AD from those with FTLD [96], and predicts future cognitive impairment [97], [98]. While repeated measurements of CSF Aβ as well as PIB PET Aβ plaque burden in AD patients have not been shown to change further with the progression of AD [99], [100], [101], [102], t-tau CSF levels have been reported to increase as AD progresses over time [99], [102] thereby suggesting that the CSF T-tau/Aβ42 ratio could increase with the progression of AD and serve as a biomarker of increasing disease severity. The relationship of this ratio with SPARE-AD, which is a measure of brain atrophy that likely reflects the degree of AD neurodegeneration, supports this notion. Furthermore, the moderate correlation with the ADAS-Cog is in agreement with previous findings [26] and consistent with the sensitivity of SPARE-AD to disease progression. The difference in SPARE-AD values in subjects who are positive or negative for the AD CSF signature reflects the ability of the SPARE-AD to identify specific atrophy patterns that are associated with AD pathology. However, the high values in MCI and AD subjects with a negative AD CSF signature indicate that the SPARE-AD may be less specific for other neurodegenerative disorders that cause brain atrophy patterns similar to those in AD. More specific biomarkers can be built by looking specifically at patient populations with different pathologies, as shown before [103], where subtle differences in brain atrophy patterns differentiated between AD and FTD patients.

In summary, our MRI analysis describes for the first time in humans the effect of MIP-1α on cortical areas and the implication in the pathogenesis of AD in agreement with previous results in experimental animals and offers new insight of the association between cortisol and brain atrophy and cognitive changes. The association of brain atrophy with MIP-1α adds further evidence for the importance of the immune response in AD pathogenesis. Last, we also describe an association between our recognition algorithm and CgA and IGFBP-2, which need further validation. These findings shed more light to the association between inflammation markers and AD-like neurodegeneration, a topic of high importance in the aging and AD literatures.

Supporting Information

Table S1.

Association between plasma analytes and SPARE-AD in the multivariable linear regression analysis adjusted for age and gender.

doi:10.1371/journal.pone.0055531.s001

(DOCX)

Table S2.

Association between significant plasma analytes and CSF total and p-tau levels in the multivariable linear regression analysis adjusted for age and gender.

doi:10.1371/journal.pone.0055531.s002

(DOCX)

Acknowledgments

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/upl​oads/how_to_apply/ADNI_Acknowledgement_L​ist.pdf.

Author Contributions

Performed the experiments: XD PB CD. Conceived and designed the experiments: JBT XD PB DAW SEA LMS JQT CD. Analyzed the data: JBT XD. Contributed reagents/materials/analysis tools: JBT XD CD. Wrote the paper: JBT XD PB DAW SEA LMS JQT CD.

References

  1. 1. Hyman BT, Trojanowski JQ (1997) Consensus recommendations for the postmortem diagnosis of Alzheimer disease from the National Institute on Aging and the Reagan Institute Working Group on diagnostic criteria for the neuropathological assessment of Alzheimer disease. J Neuropathol Exp Neurol 56: 1095–1097.
  2. 2. Khachaturian ZS (1985) Diagnosis of Alzheimer’s disease. Arch Neurol 42: 1097–1105.
  3. 3. Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82: 239–259.
  4. 4. Mirra SS, Heyman A, McKeel D, Sumi SM, Crain BJ, et al. (1991) The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology 41: 479–486.
  5. 5. Gomez-Isla T, Price JL, McKeel DW Jr, Morris JC, Growdon JH, et al. (1996) Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s disease. J Neurosci 16: 4491–4500.
  6. 6. Scheff SW, Price DA (1993) Synapse loss in the temporal lobe in Alzheimer’s disease. Ann Neurol 33: 190–199.
  7. 7. Scheff SW, Price DA, Schmitt FA, DeKosky ST, Mufson EJ (2007) Synaptic alterations in CA1 in mild Alzheimer disease and mild cognitive impairment. Neurology 68: 1501–1508.
  8. 8. Selkoe DJ (2002) Alzheimer’s disease is a synaptic failure. Science 298: 789–791.
  9. 9. Knobloch M, Mansuy IM (2008) Dendritic spine loss and synaptic alterations in Alzheimer’s disease. Mol Neurobiol 37: 73–82.
  10. 10. Akiyama H, Barger S, Barnum S, Bradt B, Bauer J, et al. (2000) Inflammation and Alzheimer’s disease. Neurobiol Aging 21: 383–421.
  11. 11. Glass CK, Saijo K, Winner B, Marchetto MC, Gage FH (2010) Mechanisms underlying inflammation in neurodegeneration. Cell 140: 918–934.
  12. 12. McGeer PL, Kawamata T, Walker DG, Akiyama H, Tooyama I, et al. (1993) Microglia in degenerative neurological disease. Glia 7: 84–92.
  13. 13. Mancardi GL, Liwnicz BH, Mandybur TI (1983) Fibrous astrocytes in Alzheimer’s disease and senile dementia of Alzheimer’s type. Acta Neuropathol 61: 76–80.
  14. 14. Acosta-Baena N, Sepulveda-Falla D, Lopera-Gomez CM, Jaramillo-Elorza MC, Moreno S, et al. (2011) Pre-dementia clinical stages in presenilin 1 E280A familial early-onset Alzheimer’s disease: a retrospective cohort study. Lancet Neurol 10: 213–220.
  15. 15. Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, et al. (2010) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9: 119–128.
  16. 16. Gatz M, Reynolds CA, Fratiglioni L, Johansson B, Mortimer JA, et al. (2006) Role of Genes and Environments for Explaining Alzheimer Disease. Arch Gen Psychiatry 63: 168–174.
  17. 17. Reitz C, Brayne C, Mayeux R (2011) Epidemiology of Alzheimer disease. Nat Rev Neurol 7: 137–152.
  18. 18. Barnes DE, Yaffe K (2011) The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol 10: 819–828.
  19. 19. Kalaria RN, Akinyemi R, Ihara M (2012) Does vascular pathology contribute to Alzheimer changes? J Neurol Sci.
  20. 20. Kling MA, Trojanowski JQ, Wolk DA, Lee VM, Arnold SE (2012) Vascular disease and dementias: Paradigm shifts to drive research in new directions. Alzheimers Dement.
  21. 21. Toledo JB, Toledo E, Weiner MW, Jack CR Jr, Jagust W, et al. (2012) Cardiovascular risk factors, cortisol, and amyloid-beta deposition in Alzheimer’s Disease Neuroimaging Initiative. Alzheimers Dement 8: 483–489.
  22. 22. Wang Y, Fan Y, Bhatt P, Davatzikos C (2010) High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. Neuroimage 50: 1519–1535.
  23. 23. Filipovych R, Davatzikos C (2011) Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). NeuroImage 55: 1109–1119.
  24. 24. Misra C, Fan Y, Davatzikos C (2009) Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. NeuroImage 44: 1415–1422.
  25. 25. Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ (2011) Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging 32: 2322 e2319–2327.
  26. 26. Davatzikos C, Xu F, An Y, Fan Y, Resnick SM (2009) Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain 132: 2026–2035.
  27. 27. Jack CR Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, et al. (2008) The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27: 685–691.
  28. 28. Jagust WJ, Bandy D, Chen K, Foster NL, Landau SM, et al. (2010) The Alzheimer’s Disease Neuroimaging Initiative positron emission tomography core. Alzheimer’s and Dementia 6: 221–229.
  29. 29. Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark C, Aisen P, et al. (2009) Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Annals of Neurology 65: 403–413.
  30. 30. Petersen RCPMD, Aisen PSM, Beckett LAP, Donohue MCP, Gamst ACP, et al. (2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology 74: 201–209.
  31. 31. Friedman B, Heisel MJ, Delavan RL (2005) Psychometric properties of the 15-item geriatric depression scale in functionally impaired, cognitively intact, community-dwelling elderly primary care patients. J Am Geriatr Soc 53: 1570–1576.
  32. 32. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, et al. (2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology 74: 201–209.
  33. 33. Toledo JB, Vanderstichele H, Figurski M, Aisen PS, Petersen RC, et al. (2011) Factors affecting Abeta plasma levels and their utility as biomarkers in ADNI. Acta Neuropathol 122: 401–413.
  34. 34. Goldszal AF, Davatzikos C, Pham DL, Yan MX, Bryan RN, et al. (1998) An image-processing system for qualitative and quantitative volumetric analysis of brain images. J Comput Assist Tomogr 22: 827–837.
  35. 35. Kabani N, MacDonald D, Holmes CJ, Evans A (1998) A 3D atlas of the human brain. NeuroImage 7: S717.
  36. 36. Fan Y, Batmanghelich N, Clark CM, Davatzikos C (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage 39: 1731–1743.
  37. 37. Fan Y, Shen D, Gur RC, Gur RE, Davatzikos C (2007) COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans Med Imaging 26: 93–105.
  38. 38. Weisberg S (2005) Applied Linear Regression 3rd edition: Wiley.
  39. 39. Strimmer K (2008) A unified approach to false discovery rate estimation. BMC Bioinformatics 9: 303.
  40. 40. Ryan JR, Paolo AM (1992) A screening procedure for estimating premorbid intelligence in the elderly. The Clinical Neuropsychologist 6: 53–62.
  41. 41. Alexander G, Furey M, Grady C, Pietrini P, Brady D, et al. (1997) Association of premorbid intellectual function with cerebral metabolism in Alzheimer’s disease: implications for the cognitive reserve hypothesis. Am J Psychiatry 154: 165–172.
  42. 42. Green KN, Billings LM, Roozendaal B, McGaugh JL, LaFerla FM (2006) Glucocorticoids Increase Amyloid-β and Tau Pathology in a Mouse Model of Alzheimer’s Disease. The Journal of Neuroscience 26: 9047–9056.
  43. 43. Kang JE, Cirrito JR, Dong H, Csernansky JG, Holtzman DM (2007) Acute stress increases interstitial fluid amyloid-beta via corticotropin-releasing factor and neuronal activity. Proc Natl Acad Sci U S A 104: 10673–10678.
  44. 44. Sotiropoulos I, Catania C, Pinto LG, Silva R, Pollerberg GE, et al. (2011) Stress acts cumulatively to precipitate Alzheimer’s disease-like tau pathology and cognitive deficits. J Neurosci 31: 7840–7847.
  45. 45. Sotiropoulos I, Catania C, Riedemann T, Fry JP, Breen KC, et al. (2008) Glucocorticoids trigger Alzheimer disease-like pathobiochemistry in rat neuronal cells expressing human tau. J Neurochem 107: 385–397.
  46. 46. Rissman RA, Lee KF, Vale W, Sawchenko PE (2007) Corticotropin-releasing factor receptors differentially regulate stress-induced tau phosphorylation. J Neurosci 27: 6552–6562.
  47. 47. Carroll JC, Iba M, Bangasser DA, Valentino RJ, James MJ, et al. (2011) Chronic stress exacerbates tau pathology, neurodegeneration, and cognitive performance through a corticotropin-releasing factor receptor-dependent mechanism in a transgenic mouse model of tauopathy. J Neurosci 31: 14436–14449.
  48. 48. Yau JL, Noble J, Seckl JR (2011) 11beta-hydroxysteroid dehydrogenase type 1 deficiency prevents memory deficits with aging by switching from glucocorticoid receptor to mineralocorticoid receptor-mediated cognitive control. J Neurosci 31: 4188–4193.
  49. 49. Budas G, Coughlan CM, Seckl JR, Breen KC (1999) The effect of corticosteroids on amyloid beta precursor protein/amyloid precursor-like protein expression and processing in vivo. Neurosci Lett 276: 61–64.
  50. 50. Haller J, Mikics E, Makara GB (2008) The effects of non-genomic glucocorticoid mechanisms on bodily functions and the central neural system. A critical evaluation of findings. Front Neuroendocrinol 29: 273–291.
  51. 51. Yamamoto KR (1985) Steroid receptor regulated transcription of specific genes and gene networks. Annu Rev Genet 19: 209–252.
  52. 52. Liston C, Gan W-B (2011) Glucocorticoids are critical regulators of dendritic spine development and plasticity in vivo. Proceedings of the National Academy of Sciences 108: 16074–16079.
  53. 53. Wellman CL (2001) Dendritic reorganization in pyramidal neurons in medial prefrontal cortex after chronic corticosterone administration. J Neurobiol 49: 245–253.
  54. 54. Stranahan AM, Arumugam TV, Cutler RG, Lee K, Egan JM, et al. (2008) Diabetes impairs hippocampal function through glucocorticoid-mediated effects on new and mature neurons. Nat Neurosci 11: 309–317.
  55. 55. Joels M, Karst H, Krugers HJ, Lucassen PJ (2007) Chronic stress: implications for neuronal morphology, function and neurogenesis. Front Neuroendocrinol 28: 72–96.
  56. 56. Popoli M, Yan Z, McEwen BS, Sanacora G (2012) The stressed synapse: the impact of stress and glucocorticoids on glutamate transmission. Nat Rev Neurosci 13: 22–37.
  57. 57. Cerqueira JJ, Mailliet F, Almeida OF, Jay TM, Sousa N (2007) The prefrontal cortex as a key target of the maladaptive response to stress. J Neurosci 27: 2781–2787.
  58. 58. Goldwater DS, Pavlides C, Hunter RG, Bloss EB, Hof PR, et al. (2009) Structural and functional alterations to rat medial prefrontal cortex following chronic restraint stress and recovery. Neuroscience 164: 798–808.
  59. 59. Young AH, Sahakian BJ, Robbins TW, Cowen PJ (1999) The effects of chronic administration of hydrocortisone on cognitive function in normal male volunteers. Psychopharmacology (Berl) 145: 260–266.
  60. 60. Lupien S, Lecours AR, Lussier I, Schwartz G, Nair NP, et al. (1994) Basal cortisol levels and cognitive deficits in human aging. J Neurosci 14: 2893–2903.
  61. 61. Huang C-W, Lui C-C, Chang W-N, Lu C-H, Wang Y-L, et al. (2009) Elevated basal cortisol level predicts lower hippocampal volume and cognitive decline in Alzheimer’s disease. Journal of Clinical Neuroscience 16: 1283–1286.
  62. 62. Cerqueira JJ, Pego JM, Taipa R, Bessa JM, Almeida OF, et al. (2005) Morphological correlates of corticosteroid-induced changes in prefrontal cortex-dependent behaviors. J Neurosci 25: 7792–7800.
  63. 63. Brown SM, Henning S, Wellman CL (2005) Mild, short-term stress alters dendritic morphology in rat medial prefrontal cortex. Cereb Cortex 15: 1714–1722.
  64. 64. Gold SM, Dziobek I, Rogers K, Bayoumy A, McHugh PF, et al. (2005) Hypertension and hypothalamo-pituitary-adrenal axis hyperactivity affect frontal lobe integrity. J Clin Endocrinol Metab 90: 3262–3267.
  65. 65. Quirk GJ, Russo GK, Barron JL, Lebron K (2000) The role of ventromedial prefrontal cortex in the recovery of extinguished fear. J Neurosci 20: 6225–6231.
  66. 66. Hoefer M, Allison SC, Schauer GF, Neuhaus JM, Hall J, et al. (2008) Fear conditioning in frontotemporal lobar degeneration and Alzheimer’s disease. Brain 131: 1646–1657.
  67. 67. Gourley SL, Kedves AT, Olausson P, Taylor JR (2009) A history of corticosterone exposure regulates fear extinction and cortical NR2B, GluR2/3, and BDNF. Neuropsychopharmacology 34: 707–716.
  68. 68. Ansell EB, Rando K, Tuit K, Guarnaccia J, Sinha R (2012) Cumulative adversity and smaller gray matter volume in medial prefrontal, anterior cingulate, and insula regions. Biol Psychiatry 72: 57–64.
  69. 69. Lindauer RJ, Vlieger EJ, Jalink M, Olff M, Carlier IV, et al. (2004) Smaller hippocampal volume in Dutch police officers with posttraumatic stress disorder. Biol Psychiatry 56: 356–363.
  70. 70. Toledo JB, Brettschneider J, Grossman M, Arnold SE, Hu WT, et al.. (2012) CSF biomarkers cutoffs: the importance of coincident neuropathological diseases. Acta Neuropathol.
  71. 71. Jellinger KA, Attems J (2010) Prevalence of dementia disorders in the oldest-old: an autopsy study. Acta Neuropathol 119: 421–433.
  72. 72. Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN, et al. (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet 43: 436–441.
  73. 73. Reiman EM, Webster JA, Myers AJ, Hardy J, Dunckley T, et al. (2007) GAB2 alleles modify Alzheimer’s risk in APOE epsilon4 carriers. Neuron 54: 713–720.
  74. 74. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, et al. (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet 43: 429–435.
  75. 75. Kauwe JSK, Cruchaga C, Karch CM, Sadler B, Lee M, et al. (2011) Fine Mapping of Genetic Variants in BIN1, CLU, CR1 and PICALM for Association with Cerebrospinal Fluid Biomarkers for Alzheimer’s Disease. PLoS ONE 6: e15918.
  76. 76. Thambisetty M, An Y, Kinsey A, Koka D, Saleem M, et al. (2012) Plasma clusterin concentration is associated with longitudinal brain atrophy in mild cognitive impairment. NeuroImage 59: 212–217.
  77. 77. Prinz M, Priller J, Sisodia SS, Ransohoff RM (2011) Heterogeneity of CNS myeloid cells and their roles in neurodegeneration. Nat Neurosci 14: 1227–1235.
  78. 78. Man SM, Ma YR, Shang DS, Zhao WD, Li B, et al. (2007) Peripheral T cells overexpress MIP-1alpha to enhance its transendothelial migration in Alzheimer’s disease. Neurobiol Aging 28: 485–496.
  79. 79. Li M, Shang DS, Zhao WD, Tian L, Li B, et al. (2009) Amyloid beta interaction with receptor for advanced glycation end products up-regulates brain endothelial CCR5 expression and promotes T cells crossing the blood-brain barrier. J Immunol 182: 5778–5788.
  80. 80. Zozulya AL, Reinke E, Baiu DC, Karman J, Sandor M, et al. (2007) Dendritic cell transmigration through brain microvessel endothelium is regulated by MIP-1alpha chemokine and matrix metalloproteinases. J Immunol 178: 520–529.
  81. 81. Hoeflich A, Nedbal S, Blum WF, Erhard M, Lahm H, et al. (2001) Growth inhibition in giant growth hormone transgenic mice by overexpression of insulin-like growth factor-binding protein-2. Endocrinology 142: 1889–1898.
  82. 82. Kiepe D, Ulinski T, Powell DR, Durham SK, Mehls O, et al. (2002) Differential effects of insulin-like growth factor binding proteins-1, -2, -3, and -6 on cultured growth plate chondrocytes. Kidney Int 62: 1591–1600.
  83. 83. Shen X, Xi G, Maile LA, Wai C, Rosen CJ, et al.. (2012) Insulin-like growth factor binding protein-2 functions coordinately with receptor protein tyrosine phosphatase beta and the IGF-I receptor to regulate IGF-I-stimulated signaling. Mol Cell Biol.
  84. 84. de la Monte SM, Wands JR (2005) Review of insulin and insulin-like growth factor expression, signaling, and malfunction in the central nervous system: relevance to Alzheimer’s disease. J Alzheimers Dis 7: 45–61.
  85. 85. Schubert M, Brazil DP, Burks DJ, Kushner JA, Ye J, et al. (2003) Insulin receptor substrate-2 deficiency impairs brain growth and promotes tau phosphorylation. J Neurosci 23: 7084–7092.
  86. 86. Talbot K, Wang HY, Kazi H, Han LY, Bakshi KP, et al. (2012) Demonstrated brain insulin resistance in Alzheimer’s disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. J Clin Invest 122: 1316–1338.
  87. 87. Hooper C, Fry VA, Sevastou IG, Pocock JM (2009) Scavenger receptor control of chromogranin A-induced microglial stress and neurotoxic cascades. FEBS Lett 583: 3461–3466.
  88. 88. Perrin RJ, Craig-Schapiro R, Malone JP, Shah AR, Gilmore P, et al. (2011) Identification and validation of novel cerebrospinal fluid biomarkers for staging early Alzheimer’s disease. PLoS ONE 6: e16032.
  89. 89. Soares HD, Chen Y, Sabbagh M, Roher A, Schrijvers E, et al. (2009) Identifying early markers of Alzheimer’s disease using quantitative multiplex proteomic immunoassay panels. Ann N Y Acad Sci 1180: 56–67.
  90. 90. Schrijvers EMC, Koudstaal PJ, Hofman A, Breteler MMB (2011) Plasma Clusterin and the Risk of Alzheimer Disease. JAMA: The Journal of the American Medical Association 305: 1322–1326.
  91. 91. O’Bryant SE, Xiao G, Barber R, Huebinger R, Wilhelmsen K, et al. (2011) A blood-based screening tool for Alzheimer’s disease that spans serum and plasma: findings from TARC and ADNI. PLoS ONE 6: e28092.
  92. 92. Hu WT, Holtzman DM, Fagan AM, Shaw LM, Perrin R, et al. (2012) Plasma multianalyte profiling in mild cognitive impairment and Alzheimer disease. Neurology 79: 897–905.
  93. 93. Soares HD, Potter WZ, Pickering E, Kuhn M, Immermann FW, et al.. (2012) Plasma Biomarkers Associated With the Apolipoprotein E Genotype and Alzheimer Disease. Arch Neurol: 1–8.
  94. 94. O’Bryant SE, Xiao G, Barber R, Reisch J, Doody R, et al. (2010) A serum protein-based algorithm for the detection of Alzheimer disease. Arch Neurol 67: 1077–1081.
  95. 95. Britschgi M, Rufibach K, Huang SL, Clark CM, Kaye JA, et al.. (2011) Modeling of pathological traits in Alzheimer’s disease based on systemic extracellular signaling proteome. Mol Cell Proteomics 10: M111 008862.
  96. 96. Bian H, Van Swieten JC, Leight S, Massimo L, Wood E, et al. (2008) CSF biomarkers in frontotemporal lobar degeneration with known pathology. Neurology 70: 1827–1835.
  97. 97. Fagan AM, Roe CM, Xiong C, Mintun MA, Morris JC, et al. (2007) Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults. Arch Neurol 64: 343–349.
  98. 98. Li G, Sokal I, Quinn JF, Leverenz JB, Brodey M, et al. (2007) CSF tau/Abeta42 ratio for increased risk of mild cognitive impairment: a follow-up study. Neurology 69: 631–639.
  99. 99. Kester MI, Scheffer PG, Koel-Simmelink MJ, Twaalfhoven H, Verwey NA, et al.. (2011) Serial CSF sampling in Alzheimer’s disease: specific versus non-specific markers. Neurobiol Aging.
  100. 100. Ikonomovic MD, Klunk WE, Abrahamson EE, Mathis CA, Price JC, et al. (2008) Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer’s disease. Brain 131: 1630–1645.
  101. 101. Kadir A, Marutle A, Gonzalez D, Schöll M, Almkvist O, et al. (2011) Positron emission tomography imaging and clinical progression in relation to molecular pathology in the first Pittsburgh Compound B positron emission tomography patient with Alzheimer’s disease. Brain 134: 301–317.
  102. 102. Buchhave P, Blennow K, Zetterberg H, Stomrud E, Londos E, et al. (2009) Longitudinal Study of CSF Biomarkers in Patients with Alzheimer’s Disease. PLoS ONE 4: e6294.
  103. 103. Davatzikos C, Resnick SM, Wu X, Parmpi P, Clark CM (2008) Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. NeuroImage 41: 1220–1227.