Plasma clusterin concentration is associated with longitudinal brain atrophy in mild cognitive impairment

Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA.
NeuroImage (Impact Factor: 6.36). 07/2011; 59(1):212-7. DOI: 10.1016/j.neuroimage.2011.07.056
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Recent genetic and proteomic studies demonstrate that clusterin/apolipoprotein-J is associated with risk, pathology, and progression of Alzheimer's disease (AD). Our main aim was to examine associations between plasma clusterin concentration and longitudinal changes in brain volume in normal aging and mild cognitive impairment (MCI). A secondary objective was to examine associations between peripheral concentration of clusterin and its concentration in the brain within regions that undergo neuropathological changes in AD. Non-demented individuals (N=139; mean baseline age 70.5 years) received annual volumetric MRI (912 MRI scans in total) over a mean six-year interval. Sixteen participants (92 MRI scans in total) were diagnosed during the course of the study with amnestic MCI. Clusterin concentration was assayed by ELISA in plasma samples collected within a year of the baseline MRI. Mixed effects regression models investigated whether plasma clusterin concentration was associated with rates of brain atrophy for control and MCI groups and whether these associations differed between groups. In a separate autopsy sample of individuals with AD (N=17) and healthy controls (N=4), we examined the association between antemortem clusterin concentration in plasma and postmortem levels in the superior temporal gyrus, hippocampus and cerebellum. The associations of plasma clusterin concentration with rates of change in brain volume were significantly different between MCI and control groups in several volumes including whole brain, ventricular CSF, temporal gray matter as well as parahippocampal, superior temporal and cingulate gyri. Within the MCI but not control group, higher baseline concentration of plasma clusterin was associated with slower rates of brain atrophy in these regions. In the combined autopsy sample of AD and control cases, representing a range of severity in AD pathology, we observed a significant association between clusterin concentration in the plasma and that in the superior temporal gyrus. Our findings suggest that clusterin, a plasma protein with roles in amyloid clearance, complement inhibition and apoptosis, is associated with rate of brain atrophy in MCI. Furthermore, peripheral concentration of clusterin also appears to reflect its concentration within brain regions vulnerable to AD pathology. These findings in combination suggest an influence of this multi-functional protein on early stages of progression in AD pathology.

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Available from: Muzamil Saleem, Jan 15, 2014
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    • "This allows researchers to identify adverse as well as protective factors that may influence healthy and pathological changes in brain anatomy and function over time (see e.g. Taki et al., 2013; Thambisetty et al., 2012; Smith et al., 2010; Debette et al., 2011; den Heijer et al., 2012). Moreover, individual subjects' trajectories are promising biomarkers for early stage diagnosis (Chetelat and Baron, 2003), tracking of disease progression (Fonteijn et al., 2012; Jedynak et al., 2012; Sabuncu et al., 2014; Donohue et al., 2014; Young et al., 2014) and monitoring of potential treatments (Douaud et al., 2013). "
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    ABSTRACT: We introduce a mass-univariate framework for the analysis of whole-brain structural trajectories using longitudinal Voxel-Based Morphometry data and Bayesian inference. Our approach to developmental and aging longitudinal studies characterizes heterogeneous structural growth/decline between and within groups. In particular, we propose a probabilistic generative model that parameterizes individual and ensemble average changes in brain structure using linear mixed-effects models of age and subject-specific covariates. Model inversion uses Expectation Maximization (EM), while voxelwise (empirical) priors on the size of individual differences are estimated from the data. Bayesian inference on individual and group trajectories is realized using Posterior Probability Maps (PPM). In addition to parameter inference, the framework affords comparisons of models with varying combinations of model order for fixed and random effects using model evidence. We validate the model in simulations and real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We further demonstrate how subject specific characteristics contribute to individual differences in longitudinal volume changes in healthy subjects, Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 07/2015; 121. DOI:10.1016/j.neuroimage.2015.06.094 · 6.36 Impact Factor
    • "The relation between CSF levels of A 42 and clusterin in the MCI group (Table 2), suggests some type of relation between A metabolism and clusterin expression in early AD stages. The correlation between Tau and CSF clusterin levels in addition suggests an involvement of clusterin in neuronal cell-death, as Tau, which is released upon cell death, is considered a general marker for neuronal degeneration [28], especially since clusterin levels were found to be associated with cortical atrophy in AD patients [17]. Clusterin levels may, however, also reflect a neuroprotective and neuroregenerative response to neuroinflammation and neurodegenerative changes [7] [29] [30], or even with normal neuronal plasticity , since we found CSF clusterin to be associated with increased Tau and pTau levels also in healthy subjects. "
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    ABSTRACT: Background: Increased clusterin levels have been reported in brain, cerebrospinal fluid (CSF), and plasma of Alzheimer's disease (AD) patients. Because changes are also observed in mild cognitive impairment (MCI), a possible relationship between clusterin levels and early neurodegenerative changes in AD was suggested. Objectives: To determine whether clusterin concentrations could 1) serve as a diagnostic marker for AD, 2) predict disease progression in MCI, and 3) correlate with AD-biomarkers. Methods: Clusterin levels in CSF and plasma, as well as AD biomarker levels of Aβ42, Tau, and pTau in CSF and Mini-Mental State Examination scores (MMSE) were determined in 67 controls, 50 MCI, and 107ADpatients. Repeated MMSE was obtained for 44 MCI and 72 AD patients after, on average, 2.7 years. Results: Elevated clusterin concentrations in plasma, but not in CSF, were a risk factor for AD (HR 18.6; 95%CI 2.8-122), and related to cognitive decline in MCI (r =.0.38; p < 0.01). An inverse relation between plasma clusterin levels and cognitive decline was observed in AD patients (r = 0.23; p≤0.05). In CSF, but not in plasma, clusterin levels correlated with Tau and pTau in all groups. Conclusion: Elevated plasma clusterin levels in MCI confer an increased risk for progression to AD, and more rapid cognitive decline. We speculate that clusterin levels in CSF may reflect its involvement in the earliest neurodegenerative processes associated with AD pathology. Whereas neither clusterin levels in CSF nor in plasma had diagnostic value, plasma clusterin levels may serve as a prognostic marker for AD.
    Journal of Alzheimer's disease: JAD 06/2015; 46(4):1103-1110. DOI:10.3233/JAD-150036 · 4.15 Impact Factor
    • "ICV, along with age and gender are reported as covariates to adjust for regression analyses in investigating progressive neurodegenerative brain disorders, such as Alzheimer's disease (Dukart et al. 2013; Fennema-Notestine et al. 2009; Lampert et al. 2013; Piguet et al. 2011; Westman et al. 2013), aging and cognitive impairment (Trivedi et al. 2011). ICV has also been utilized as an independent voxel based morphometric feature to evaluate age-related changes in the structure of premorbid brain (Cardenas et al. 2005; Peper et al. 2009; Roussotte et al. 2012; Szentkuti et al. 2004; Taki et al. 2013), determine characterizing atrophy patterns in subjects with mild cognitive impairment (MCI) and Alzheimer's disease (AD) (Pa et al. 2009; Thambisetty et al. 2012), delineate structural abnormalities in the white matter (WM) in schizophrenia (Cullen et al. 2012), epilepsy (Gong et al. 2012), and gauge cognitive efficacy (Chee et al. 2011). Of the existing protocols for calculating ICV, despite their methodological differences, they can be classified mainly into two broad categories, manual and automated. "
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    ABSTRACT: Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure. Four groups of 53 subjects are considered, including adult controls (AC, adults with Alzheimer's disease (AD), pediatric controls (PC) and group of pediatric epilepsy subjects (PE). Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (FreeSurfer Ver. 5.3.0, FSL Ver. 5.0, SPM8 and SPM12) were examined in their ability to automatically estimate ICV across the groups. Results on sub-sampling studies with a 95 % confidence showed that in order to keep the accuracy of the inter-leaved slice sampling protocol above 99 %, sampling period cannot exceed 20 mm for AC, 25 mm for PC, 15 mm for AD and 17 mm for the PE groups. The study assumes a priori knowledge about the population under study into the automated ICV estimation. Tuning of the parameters in FSL and the use of proper atlas in SPM showed significant reduction in the systematic bias and the error in ICV estimation via these automated tools. SPM12 with the use of pediatric template is found to be a more suitable candidate for PE group. SPM12 and FSL subjected to tuning are the more appropriate tools for the PC group. The random error is minimized for FS in AD group and SPM8 showed less systematic bias. Across the AC group, both SPM12 and FS performed well but SPM12 reported lesser amount of systematic bias.
    Neuroinformatics 03/2015; DOI:10.1007/s12021-015-9266-5 · 2.83 Impact Factor
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