Iwona Kloszewska

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.

Automated hippocampal shape analysis predicts the onset of dementia in Mild Cognitive Impairment

The hippocampus is involved at the onset of the neuropathological pathways leading to Alzheimers disease (AD). Individuals with mild cognitive impairment (MCI) are at increased risk of AD. Hippocampal volume has been shown to predict which MCI subjects will convert to AD. Our aim in the present study was to produce a fully automated prognostic procedure, scalable to high throughput clinical and research applications, for the prediction of MCI conversion to AD using 3D hippocampal morphology. We used an automated analysis for the extraction and mapping of the hippocampus from structural magnetic resonance scans to extract 3D hippocampal shape morphology, and we then applied machine learning classification to predict conversion from MCI to AD. We investigated the accuracy of prediction in 103 MCI subjects (mean age 74.1 years) from the longitudinal AddNeuroMed study. Our model correctly predicted MCI conversion to dementia within a year at an accuracy of 80% (sensitivity 77%, specificity 80%), a performance which is competitive with previous predictive models dependent on manual measurements. Categorization of MCI subjects based on hippocampal morphology revealed more rapid cognitive deterioration in MMSE scores (p<0.01) and CERAD verbal memory (p<0.01) in those subjects who were predicted to develop dementia relative to those predicted to remain stable. The pattern of atrophy associated with increased risk of conversion demonstrated initial degeneration in the anterior part of the cornus ammonis 1 (CA1) hippocampal subregion. We conclude that automated shape analysis generates sensitive measurements of early neurodegeneration which predates the onset of dementia and thus provides a prognostic biomarker for conversion of MCI to AD.

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—The European Collaboration for the Discovery of Novel Biomarkers for Alzheimer's Disease

There is an urgent need for Alzheimers disease (AD) biomarkers—especially in the context of clinical trials. Biomarkers for early diagnosis, disease progression, and prediction are most critical, and disease‐modification therapy development may depend on the discovery and validation of such markers. AddNeuroMed is a cross European, public/private consortium developed for AD biomarker discovery. We report here the development and design of AddNeuroMed and the progress toward the development of plasma markers. Despite the obstacles to such markers, we have identified a range of markers including CFH and A2M, both of which have been independently replicated. The experience of AddNeuroMed leads us to three overall conclusions. First, collaboration is essential. Second, design is paramount and combining modalities, such as imaging and proteomics, may be informative. Third, animal models are valuable in biomarker research. Most importantly, we have learned that plasma markers are feasible.

Prevalence and prognosis of Alzheimer's disease at the mild cognitive impairment stage.

Three sets of research criteria are available for diagnosis of Alzheimers disease in subjects with mild cognitive impairment: the International Working Group-1, International Working Group-2, and National Institute of Aging-Alzheimer Association criteria. We compared the prevalence and prognosis of Alzheimers disease at the mild cognitive impairment stage according to these criteria. Subjects with mild cognitive impairment (n = 1607), 766 of whom had both amyloid and neuronal injury markers, were recruited from 13 cohorts. We used cognitive test performance and available biomarkers to classify subjects as prodromal Alzheimers disease according to International Working Group-1 and International Working Group-2 criteria and in the high Alzheimers disease likelihood group, conflicting biomarker groups (isolated amyloid pathology or suspected non-Alzheimer pathophysiology), and low Alzheimers disease likelihood group according to the National Institute of Ageing-Alzheimer Association criteria. Outcome measures were the proportion of subjects with Alzheimers disease at the mild cognitive impairment stage and progression to Alzheimers disease-type dementia. We performed survival analyses using Cox proportional hazards models. According to the International Working Group-1 criteria, 850 (53%) subjects had prodromal Alzheimers disease. Their 3-year progression rate to Alzheimers disease-type dementia was 50% compared to 21% for subjects without prodromal Alzheimers disease. According to the International Working Group-2 criteria, 308 (40%) subjects had prodromal Alzheimers disease. Their 3-year progression rate to Alzheimers disease-type dementia was 61% compared to 22% for subjects without prodromal Alzheimers disease. According to the National Institute of Ageing-Alzheimer Association criteria, 353 (46%) subjects were in the high Alzheimers disease likelihood group, 49 (6%) in the isolated amyloid pathology group, 220 (29%) in the suspected non-Alzheimer pathophysiology group, and 144 (19%) in the low Alzheimers disease likelihood group. The 3-year progression rate to Alzheimers disease-type dementia was 59% in the high Alzheimers disease likelihood group, 22% in the isolated amyloid pathology group, 24% in the suspected non-Alzheimer pathophysiology group, and 5% in the low Alzheimers disease likelihood group. Our findings support the use of the proposed research criteria to identify Alzheimers disease at the mild cognitive impairment stage. In clinical settings, the use of both amyloid and neuronal injury markers as proposed by the National Institute of Ageing-Alzheimer Association criteria offers the most accurate prognosis. For clinical trials, selection of subjects in the National Institute of Ageing-Alzheimer Association high Alzheimers disease likelihood group or the International Working Group-2 prodromal Alzheimers disease group could be considered.

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.