Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler Divergence
Dementia, including Alzheimer’s disease, is a neurodegenerative illness characterized by the progressive impairment of cognitive functions, posing a significant global health threat. Physical exercise is widely recognized for its preventive role, providing benefits for both the body composition and...
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MDPI AG
2025-01-01
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| author | Agnieszka Wosiak Małgorzata Krzywicka Katarzyna Żykwińska |
| author_facet | Agnieszka Wosiak Małgorzata Krzywicka Katarzyna Żykwińska |
| author_sort | Agnieszka Wosiak |
| collection | DOAJ |
| description | Dementia, including Alzheimer’s disease, is a neurodegenerative illness characterized by the progressive impairment of cognitive functions, posing a significant global health threat. Physical exercise is widely recognized for its preventive role, providing benefits for both the body composition and brain health. This study aimed to explore the relationship between physical exercise, the body composition, and the progression of dementia. The analysis used clinical and neuroradiology data from 42 patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our study mainly focused on crucial parameters such as the body mass index (BMI), skeletal muscle index (SMI), and MRI biomarkers, including the hippocampal volume and white matter integrity. We grouped the participants according to the similarities of their body compositions through clustering techniques. Then, atrophy-related changes in the brain structures were computed using the Kullback–Leibler divergence. Our findings suggest that a higher BMI and greater muscle mass may slow down brain atrophy, suggesting a protective effect on the brain. Based on these results, preserving muscle mass and metabolic health through resistance and aerobic exercise appears crucial in reducing the risk of dementia. Body composition interventions may slow neurodegenerative changes and promote brain health. This is an essential piece of information about prevention strategies, especially for individuals at risk of dementia who may benefit from following structured physical activity strategies. |
| format | Article |
| id | doaj-art-47a5b22d3aa54d71904ca0cc45de15f4 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-47a5b22d3aa54d71904ca0cc45de15f42025-01-24T13:20:18ZengMDPI AGApplied Sciences2076-34172025-01-0115265210.3390/app15020652Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler DivergenceAgnieszka Wosiak0Małgorzata Krzywicka1Katarzyna Żykwińska2Institute of Information Technology, Lodz University of Technology, Wólczańska 215, 90-924 Łódź, PolandInstitute of Information Technology, Lodz University of Technology, Wólczańska 215, 90-924 Łódź, PolandInstitute of Information Technology, Lodz University of Technology, Wólczańska 215, 90-924 Łódź, PolandDementia, including Alzheimer’s disease, is a neurodegenerative illness characterized by the progressive impairment of cognitive functions, posing a significant global health threat. Physical exercise is widely recognized for its preventive role, providing benefits for both the body composition and brain health. This study aimed to explore the relationship between physical exercise, the body composition, and the progression of dementia. The analysis used clinical and neuroradiology data from 42 patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our study mainly focused on crucial parameters such as the body mass index (BMI), skeletal muscle index (SMI), and MRI biomarkers, including the hippocampal volume and white matter integrity. We grouped the participants according to the similarities of their body compositions through clustering techniques. Then, atrophy-related changes in the brain structures were computed using the Kullback–Leibler divergence. Our findings suggest that a higher BMI and greater muscle mass may slow down brain atrophy, suggesting a protective effect on the brain. Based on these results, preserving muscle mass and metabolic health through resistance and aerobic exercise appears crucial in reducing the risk of dementia. Body composition interventions may slow neurodegenerative changes and promote brain health. This is an essential piece of information about prevention strategies, especially for individuals at risk of dementia who may benefit from following structured physical activity strategies.https://www.mdpi.com/2076-3417/15/2/652dementia progressionphysical activityKullback–Leibler divergenceclustering analysisneuroimaging biomarkers |
| spellingShingle | Agnieszka Wosiak Małgorzata Krzywicka Katarzyna Żykwińska Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler Divergence Applied Sciences dementia progression physical activity Kullback–Leibler divergence clustering analysis neuroimaging biomarkers |
| title | Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler Divergence |
| title_full | Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler Divergence |
| title_fullStr | Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler Divergence |
| title_full_unstemmed | Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler Divergence |
| title_short | Assessing the Impact of Physical Activity on Dementia Progression Using Clustering and the MRI-Based Kullback–Leibler Divergence |
| title_sort | assessing the impact of physical activity on dementia progression using clustering and the mri based kullback leibler divergence |
| topic | dementia progression physical activity Kullback–Leibler divergence clustering analysis neuroimaging biomarkers |
| url | https://www.mdpi.com/2076-3417/15/2/652 |
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