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Abstract
The increasing global prevalence of dementia demands concrete actions that are aimed strategically at optimizing processes that drive clinical innovation. The first step in this direction requires outlining hurdles in the transition from research to practice. The different parties needed to support translational processes have communication mismatches; methodological gaps hamper evidence‐based decision‐making; and data are insufficient to provide reliable estimates of long‐term health benefits and costs in decisional models. Pilot projects are tackling some of these gaps, but appropriate methods often still need to be devised or adapted to the dementia field. A consistent implementation perspective along the whole translational continuum, explicitly defined and shared among the relevant stakeholders, should overcome the “research‐versus‐adoption” dichotomy, and tackle the implementation cliff early on. Concrete next steps may consist of providing tools that support the effective participation of heterogeneous stakeholders and agreeing on a definition of clinical significance that facilitates the selection of proper outcome measures.
Reduction and oxidation reactions are essential for biochemical processes. They are part of metabolic pathways and signal transduction. Reactive oxygen species (ROS) as second messengers and oxidative modifications of cysteinyl (Cys) residues are key to transduce and translate intracellular and intercellular signals. Dysregulation of cellular redox signaling is known as oxidative distress, which has been linked to various pathologies, including neurodegeneration. Alzheimer's disease (AD) is a neurodegenerative pathology linked to both, abnormal amyloid precursor protein (APP) processing, generating Aβ peptide, and Tau hyperphosphorylation and aggregation. Signs of oxidative distress in AD include: increase of ROS (H2O2, O2•−), decrease of the levels or activities of antioxidant enzymes, abnormal oxidation of macromolecules related to elevated Aβ production, and changes in mitochondrial homeostasis linked to Tau phosphorylation. Interestingly, Cys residues present in APP form disulfide bonds that are important for intermolecular interactions and might be involved in the aggregation of Aβ. Moreover, two Cys residues in some Tau isoforms have been shown to be essential for Tau stabilization and its interaction with microtubules. Future research will show the complexities of Tau, its interactome, and the role that Cys residues play in the progression of AD. The specific modification of cysteinyl residues in redox signaling is also tightly connected to the regulation of various metabolic pathways. Many of these pathways have been found to be altered in AD, even at very early stages. In order to analyze the complex changes and underlying mechanisms, several AD models have been developed, including animal models, 2D and 3D cell culture, and ex-vivo studies of patient samples. The use of these models along with innovative, new redox analysis techniques are key to further understand the importance of the redox component in Alzheimer's disease and the identification of new therapeutic targets in the future.
Introduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual’s pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD.
Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend).
Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen’s f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen’s f2 = 0.009). This association was mainly driven by the immediate recall performance.
Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too.