579 research outputs found

    ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease

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    Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity. © 2013 The Authors

    Airway epithelial cell isolation techniques affect DNA methylation profiles with consequences for analysis of asthma related perturbations to DNA methylation

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    The airway epithelium forms the interface between the inhaled environment and the lung. The airway epithelium is dysfunctional in asthma and epigenetic mechanisms are considered a contributory factor. We hypothesised that the DNA methylation profiles of cultured primary airway epithelial cells (AECs) would differ between cells isolated from individuals with asthma (n = 17) versus those without asthma (n = 16). AECs were isolated from patients by two different isolation techniques; pronase digestion (9 non-asthmatic, 8 asthmatic) and bronchial brushings (7 non-asthmatic and 9 asthmatic). DNA methylation was assessed using an Illumina Infinium HumanMethylation450 BeadChip array. DNA methylation of AECs clustered by isolation technique and linear regression identified 111 CpG sites differentially methylated between isolation techniques in healthy individuals. As a consequence, the effect of asthmatic status on DNA methylation was assessed within AEC samples isolated using the same technique. In pronase isolated AECs, 15 DNA regions were differentially methylated between asthmatics and non-asthmatics. In bronchial brush isolated AECs, 849 differentially methylated DNA regions were identified with no overlap to pronase regions. In conclusion, regardless of cell isolation technique, differential DNA methylation was associated with asthmatic status in AECs, providing further evidence for aberrant DNA methylation as a signature of epithelial dysfunction in asthma

    The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement

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    INTRODUCTION: The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI-3, which began on August 1, 2016, is a 5-year renewal of the current ADNI-2 study. METHODS: ADNI-3 will follow current and additional subjects with normal cognition, mild cognitive impairment, and AD using innovative technologies such as tau imaging, magnetic resonance imaging sequences for connectivity analyses, and a highly automated immunoassay platform and mass spectroscopy approach for cerebrospinal fluid biomarker analysis. A Systems Biology/pathway approach will be used to identify genetic factors for subject selection/enrichment. Amyloid positron emission tomography scanning will be standardized using the Centiloid method. The Brain Health Registry will help recruit subjects and monitor subject cognition. RESULTS: Multimodal analyses will provide insight into AD pathophysiology and disease progression. DISCUSSION: ADNI-3 will aim to inform AD treatment trials and facilitate development of AD disease-modifying treatments

    Geographically touring the eastern bloc: British geography, travel cultures and the Cold War

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    This paper considers the role of travel in the generation of geographical knowledge of the eastern bloc by British geographers. Based on oral history and surveys of published work, the paper examines the roles of three kinds of travel experience: individual private travels, tours via state tourist agencies, and tours by academic delegations. Examples are drawn from across the eastern bloc, including the USSR, Poland, Romania, East Germany and Albania. The relationship between travel and publication is addressed, notably within textbooks, and in the Geographical Magazine. The study argues for the extension of accounts of cultures of geographical travel, and seeks to supplement the existing historiography of Cold War geography

    Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014

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    INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. METHODS: We searched for ADNI publications using established methods. RESULTS: ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. DISCUSSION: ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials

    Dissociation of tau pathology and neuronal hypometabolism within the ATN framework of Alzheimer’s disease

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    Alzheimer’s disease (AD) is defined by amyloid (A) and tau (T) pathologies, with T better correlated to neurodegeneration (N). However, T and N have complex regional relationships in part related to non-AD factors that influence N. With machine learning, we assessed heterogeneity in 18F-flortaucipir vs. 18F-fluorodeoxyglucose positron emission tomography as markers of T and neuronal hypometabolism (NM) in 289 symptomatic patients from the Alzheimer’s Disease Neuroimaging Initiative. We identified six T/NM clusters with differing limbic and cortical patterns. The canonical group was defined as the T/NM pattern with lowest regression residuals. Groups resilient to T had less hypometabolism than expected relative to T and displayed better cognition than the canonical group. Groups susceptible to T had more hypometabolism than expected given T and exhibited worse cognitive decline, with imaging and clinical measures concordant with non-AD copathologies. Together, T/NM mismatch reveals distinct imaging signatures with pathobiological and prognostic implications for AD
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