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Meng Q, Lin MS, Tzeng IS. Relationship Between Exercise and Alzheimer's Disease: A Narrative Literature Review. Front Neurosci 2020; 14:131. [PMID: 32273835 PMCID: PMC7113559 DOI: 10.3389/fnins.2020.00131] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 01/31/2020] [Indexed: 02/02/2023] Open
Abstract
This narrative review aimed to summarize evidence regarding the responses to exercise among patients with preclinical Alzheimer’s disease (AD) and the effectiveness of long-term exercise interventions in improving cognitive function and neuropsychiatric symptoms. We performed a narrative review of existing literature on the effectiveness of long-term exercise interventions in improving cognitive function and neuropsychiatric symptoms in patients with AD. Patients with AD who presented with long-term exercise interventions appeared to have improved blood flow, increased hippocampal volume, and improved neurogenesis. Most prospective studies have proven that physical inactivity is one of the most common preventable risk factors for developing AD and that higher physical activity levels are associated with a reduced risk of AD development. Physical exercise seems to be effective in improving several neuropsychiatric symptoms of AD, notably cognitive function. Compared with medications, exercise has been shown to have fewer side effects and better adherence.
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Affiliation(s)
- Qing Meng
- School of Physical Education, Huaqiao University, Quanzhou, China.,Sport and Health Research Center, Huaqiao University, Quanzhou, China
| | - Muh-Shi Lin
- Department of Biotechnology and Animal Science, College of Bioresources, National Ilan University, Yilan, Taiwan.,Division of Neurosurgery, Department of Surgery, Kuang Tien General Hospital, Taichung, Taiwan.,Department of Biotechnology, College of Medical and Health Care, HungKuang University, Taichung, Taiwan.,Department of Health Business Administration, College of Medical and Health Care, HungKuang University, Taichung, Taiwan
| | - I-Shiang Tzeng
- Department of Exercise and Health Promotion, College of Education, Chinese Culture University, Taipei, Taiwan
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Goodkin O, Pemberton H, Vos SB, Prados F, Sudre CH, Moggridge J, Cardoso MJ, Ourselin S, Bisdas S, White M, Yousry T, Thornton J, Barkhof F. The quantitative neuroradiology initiative framework: application to dementia. Br J Radiol 2019; 92:20190365. [PMID: 31368776 DOI: 10.1259/bjr.20190365] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
There are numerous challenges to identifying, developing and implementing quantitative techniques for use in clinical radiology, suggesting the need for a common translational pathway. We developed the quantitative neuroradiology initiative (QNI), as a model framework for the technical and clinical validation necessary to embed automated segmentation and other image quantification software into the clinical neuroradiology workflow. We hypothesize that quantification will support reporters with clinically relevant measures contextualized with normative data, increase the precision of longitudinal comparisons, and generate more consistent reporting across levels of radiologists' experience. The QNI framework comprises the following steps: (1) establishing an area of clinical need and identifying the appropriate proven imaging biomarker(s) for the disease in question; (2) developing a method for automated analysis of these biomarkers, by designing an algorithm and compiling reference data; (3) communicating the results via an intuitive and accessible quantitative report; (4) technically and clinically validating the proposed tool pre-use; (5) integrating the developed analysis pipeline into the clinical reporting workflow; and (6) performing in-use evaluation. We will use current radiology practice in dementia as an example, where radiologists have established visual rating scales to describe the degree and pattern of atrophy they detect. These can be helpful, but are somewhat subjective and coarse classifiers, suffering from floor and ceiling limitations. Meanwhile, several imaging biomarkers relevant to dementia diagnosis and management have been proposed in the literature; some clinically approved radiology software tools exist but in general, these have not undergone rigorous clinical validation in high volume or in tertiary dementia centres. The QNI framework aims to address this need. Quantitative image analysis is developing apace within the research domain. Translating quantitative techniques into the clinical setting presents significant challenges, which must be addressed to meet the increasing demand for accurate, timely and impactful clinical imaging services.
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Affiliation(s)
- Olivia Goodkin
- 1Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, United Kingdom.,2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Hugh Pemberton
- 1Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, United Kingdom.,2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom.,3Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B Vos
- 1Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, United Kingdom.,2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom.,4Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom.,5Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom
| | - Ferran Prados
- 1Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, United Kingdom.,6Queen Square MS Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,7Universitat Oberta de Catalunya, Barcelona, Spain
| | - Carole H Sudre
- 8School of Biomedical Engineering and Imaging Sciences, King's College London.,9Department of Medical Physics and Biomedical Engineering, University College London
| | - James Moggridge
- 2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom.,4Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom
| | - M Jorge Cardoso
- 8School of Biomedical Engineering and Imaging Sciences, King's College London
| | - Sebastien Ourselin
- 8School of Biomedical Engineering and Imaging Sciences, King's College London
| | - Sotirios Bisdas
- 2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom.,4Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom
| | - Mark White
- 10Digital Services, University College London Hospital, London United Kingdom
| | - Tarek Yousry
- 2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom.,4Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom
| | - John Thornton
- 2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom.,4Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom
| | - Frederik Barkhof
- 1Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, United Kingdom.,2Neuroradiological Academic Unit, Queen Square Institute of Neurology, University College London, London, United Kingdom.,4Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, United Kingdom.,6Queen Square MS Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,11Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam, The Netherlands
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Gao CM, Yam AY, Wang X, Magdangal E, Salisbury C, Peretz D, Zuckermann RN, Connolly MD, Hansson O, Minthon L, Zetterberg H, Blennow K, Fedynyshyn JP, Allauzen S. Aβ40 oligomers identified as a potential biomarker for the diagnosis of Alzheimer's disease. PLoS One 2010; 5:e15725. [PMID: 21209907 PMCID: PMC3012719 DOI: 10.1371/journal.pone.0015725] [Citation(s) in RCA: 86] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2010] [Accepted: 11/21/2010] [Indexed: 12/02/2022] Open
Abstract
Alzheimer's Disease (AD) is the most prevalent form of dementia worldwide, yet the development of therapeutics has been hampered by the absence of suitable biomarkers to diagnose the disease in its early stages prior to the formation of amyloid plaques and the occurrence of irreversible neuronal damage. Since oligomeric Aβ species have been implicated in the pathophysiology of AD, we reasoned that they may correlate with the onset of disease. As such, we have developed a novel misfolded protein assay for the detection of soluble oligomers composed of Aβ x-40 and x-42 peptide (hereafter Aβ40 and Aβ42) from cerebrospinal fluid (CSF). Preliminary validation of this assay with 36 clinical samples demonstrated the presence of aggregated Aβ40 in the CSF of AD patients. Together with measurements of total Aβ42, diagnostic sensitivity and specificity greater than 95% and 90%, respectively, were achieved. Although larger sample populations will be needed to confirm this diagnostic sensitivity, our studies demonstrate a sensitive method of detecting circulating Aβ40 oligomers from AD CSF and suggest that these oligomers could be a powerful new biomarker for the early detection of AD.
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Affiliation(s)
- Carol Man Gao
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
| | - Alice Y. Yam
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
| | - Xuemei Wang
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
| | - Erika Magdangal
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
| | - Cleo Salisbury
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
| | - David Peretz
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
| | - Ronald N. Zuckermann
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
| | - Michael D. Connolly
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Lennart Minthon
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Department of Neuroscience and Physiology, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Department of Neuroscience and Physiology, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Joseph P. Fedynyshyn
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
- * E-mail:
| | - Sophie Allauzen
- Research and Development, Novartis Vaccines and Diagnostics, Emeryville, California, United States of America
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