Eric Westman

Association of Plasma Clusterin Concentration With Severity, Pathology, and Progression in Alzheimer Disease

CONTEXT Blood-based analytes may be indicators of pathological processes in Alzheimer disease (AD). OBJECTIVE To identify plasma proteins associated with AD pathology using a combined proteomic and neuroimaging approach. DESIGN Discovery-phase proteomics to identify plasma proteins associated with correlates of AD pathology. Confirmation and validation using immunodetection in a replication set and an animal model. SETTING A multicenter European study (AddNeuroMed) and the Baltimore Longitudinal Study of Aging. PARTICIPANTS Patients with AD, subjects with mild cognitive impairment, and healthy controls with standardized clinical assessments and structural neuroimaging. MAIN OUTCOME MEASURES Association of plasma proteins with brain atrophy, disease severity, and rate of clinical progression. Extension studies in humans and transgenic mice tested the association between plasma proteins and brain amyloid. RESULTS Clusterin/apolipoprotein J was associated with atrophy of the entorhinal cortex, baseline disease severity, and rapid clinical progression in AD. Increased plasma concentration of clusterin was predictive of greater fibrillar amyloid-beta burden in the medial temporal lobe. Subjects with AD had increased clusterin messenger RNA in blood, but there was no effect of single-nucleotide polymorphisms in the gene encoding clusterin with gene or protein expression. APP/PS1 transgenic mice showed increased plasma clusterin, age-dependent increase in brain clusterin, as well as amyloid and clusterin colocalization in plaques. CONCLUSIONS These results demonstrate an important role of clusterin in the pathogenesis of AD and suggest that alterations in amyloid chaperone proteins may be a biologically relevant peripheral signature of AD.

Cognitive impairment in patients with Parkinson's disease: diagnosis, biomarkers, and treatment.

Dementia is one of the most common and important aspects of Parkinsons disease and has consequences for patients and caregivers, and has health-related costs. Mild cognitive impairment is also common and frequently progresses to dementia. The underlying mechanisms of dementia associated with Parkinsons disease are only partly known and no mechanism-based treatments are available. Both dysmetabolism of α-synuclein and amyloid-protein and cholinergic deficits contribute to cognitive impairment in Parkinsons disease, and preliminary findings show that imaging and neurophysiological and peripheral biomarkers could be useful in diagnosis and prognosis. Rivastigmine is the only licensed treatment for dementia in Parkinsons disease, but emerging evidence suggests that memantine might also be useful. Whether these or other treatments can delay the progression from mild cognitive impairment to dementia in Parkinsons disease is a key research question.

Plasma proteins predict conversion to dementia from prodromal disease

The study aimed to validate previously discovered plasma biomarkers associated with AD, using a design based on imaging measures as surrogate for disease severity and assess their prognostic value in predicting conversion to dementia.

MRI Measures of Alzheimer's Disease and the AddNeuroMed Study

Here we describe the AddNeuroMed multicenter magnetic resonance imaging (MRI) study for longitudinal assessment in Alzheimers disease (AD). The study is similar to a faux clinical trial and has been established to assess longitudinal MRI changes in AD, mild cognitive impairment (MCI), and healthy control subjects using an image acquisition protocol compatible with the Alzheimers Disease Neuroimaging Initiative (ADNI). The approach consists of a harmonized MRI acquisition protocol across centers, rigorous quality control, a central data analysis hub, and an automated image analysis pipeline. Comprehensive quality control measures have been established throughout the study. An intelligent web‐accessible database holds details on both the raw images and data processed using a sophisticated image analysis pipeline. A total of 378 subjects were recruited (130 AD, 131 MCI, 117 healthy controls) of which a high percentage (97.3%) of the T1‐weighted volumes passed the quality control criteria. Measurements of normalized whole brain volume, whole brain cortical thickness, and point‐by‐point group‐based cortical thickness measurements, demonstrating the power of the automated image analysis techniques employed, are reported.

The AddNeuroMed framework for multi-centre MRI assessment of Alzheimer's disease : experience from the first 24 months

To describe the AddNeuroMed imaging framework for multi‐centre magnetic resonance imaging (MRI) assessment of longitudinal changes in Alzheimers disease and report on early results from the first 24 months of the project.

Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls.

We have used multivariate data analysis, more specifically orthogonal partial least squares to latent structures (OPLS) analysis, to discriminate between Alzheimers disease (AD), mild cognitive impairment (MCI) and elderly control subjects combining both regional and global magnetic resonance imaging (MRI) volumetric measures. In this study, 117 AD patients, 122 MCI patients and 112 control subjects (from the AddNeuroMed study) were included. High-resolution sagittal 3D MP-RAGE datasets were acquired from each subject. Automated regional segmentation and manual outlining of the hippocampus were performed for each image. Altogether this yielded volumes of 24 different anatomically defined structures which were used for OPLS analysis. 17 randomly selected AD patients, 12 randomly selected control subjects and the 22 MCI subjects who converted to AD at 1-year follow up were excluded from the initial OPLS analysis to provide a small external test set for model validation. Comparing AD with controls we found a sensitivity of 87% and a specificity of 90% using hippocampal measures alone. Combining both global and regional measures resulted in a sensitivity of 90% and a specificity of 94%. This increase in sensitivity and specificity resulted in an increase of the positive likelihood ratio from 9 to 15. From the external test set, the model predicted 82% of the AD patients and 83% of the control subjects correctly. Finally, 73% of the MCI subjects which converted to AD at 1 year follow-up were shown to resemble AD patients more closely than controls. This method shows potential for distinguishing between different patient groups. Combining the different MRI measures together resulted in a significantly better classification than using them separately. OPLS also shows potential for predicting conversion from MCI to AD.

AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America.

The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimers disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.

Candidate Blood Proteome Markers of Alzheimer's Disease Onset and Progression: A Systematic Review and Replication Study

A blood-based protein biomarker, or set of protein biomarkers, that could predict onset and progression of Alzheimers disease (AD) would have great utility; potentially clinically, but also for clinical trials and especially in the selection of subjects for preventative trials. We reviewed a comprehensive list of 21 published discovery or panel-based (> 100 proteins) blood proteomics studies of AD, which had identified a total of 163 candidate biomarkers. Few putative blood-based protein biomarkers replicate in independent studies but we found that some proteins do appear in multiple studies; for example, four candidate biomarkers are found to associate with AD-related phenotypes in five independent research cohorts in these 21 studies: α-1-antitrypsin, α-2-macroglobulin, apolipoprotein E, and complement C3. Using SomaLogics SOMAscan proteomics technology, we were able to conduct a large-scale replication study for 94 of the 163 candidate biomarkers from these 21 published studies in plasma samples from 677 subjects from the AddNeuroMed (ANM) and the Alzheimers Research UK/Maudsley BRC Dementia Case Registry at Kings Health Partners (ARUK/DCR) research cohorts. Nine of the 94 previously reported candidates were found to associate with AD-related phenotypes (False Discovery Rate (FDR) q-value < 0.1). These proteins show sufficient replication to be considered for further investigation as a biomarker set. Overall, we show that there are some signs of a replicable signal in the range of proteins identified in previous studies and we are able to further replicate some of these. This suggests that AD pathology does affect the blood proteome with some consistency.

Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology

Blood proteins and their complexes have become the focus of a great deal of interest in the context of their potential as biomarkers of Alzheimers disease (AD). We used a SOMAscan assay for quantifying 1001 proteins in blood samples from 331 AD, 211 controls, and 149 mild cognitive impaired (MCI) subjects. The strongest associations of protein levels with AD outcomes were prostate‐specific antigen complexed to α1‐antichymotrypsin (AD diagnosis), pancreatic prohormone (AD diagnosis, left entorhinal cortex atrophy, and left hippocampus atrophy), clusterin (rate of cognitive decline), and fetuin B (left entorhinal atrophy). Multivariate analysis found that a subset of 13 proteins predicted AD with an accuracy of area under the curve of 0.70. Our replication of previous findings provides further evidence that levels of these proteins in plasma are truly associated with AD. The newly identified proteins could be potential biomarkers and are worthy of further investigation.

Blood Oxygenation Level-Dependent Visualization of Synaptic Relay Stations of Sensory Pathways along the Neuroaxis in Response to Graded Sensory Stimulation of a Limb

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) was used to test at which levels of the neuroaxis signals are elicited when different modalities of sensory information from the limbs ascend to cortex cerebri. We applied graded electric stimuli to the rat hindlimbs and used echo-planar imaging to monitor activity changes in the lumbar spinal cord and medulla oblongata, where primary afferents of painful and nonpainful sensation synapse, respectively. BOLD signals were detected in ipsilateral lumbar spinal cord gray matter using sufficiently strong stimuli. Using stimuli well below the threshold needed for signals to be elicited in the spinal cord, we found BOLD responses in dorsal medulla oblongata. The distribution of these signals is compatible with the neuroanatomy of the respective synaptic relay stations of the corresponding sensory pathways. Hence, the sensory pathways conducting painful and nonpainful information were successfully distinguished. The fMRI signals in the spinal cord were markedly decreased by morphine, and these effects were counteracted by naloxone. We conclude that fMRI can be used as a reliable and valid method to monitor neuronal activity in the rat spinal cord and medulla oblongata in response to sensory stimuli. Previously, we also documented BOLD signals from thalamus and cortex. Thus, BOLD responses can be elicited at all principal synaptic relay stations along the neuroaxis from lumbar spinal cord to sensory cortex. Rat spinal cord fMRI should become a useful tool in experimental spinal cord injury and pain research.