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Background And Hypothesis: Haemodialysis (HD) patients often develop cognitive impairment, negatively impacting health-related quality of life. We use brain magnetic resonance imaging (MRI) measures to study the acute changes in cerebral water content during HD, alongside chronic changes in HD patients compared to healthy volunteers (HV) to assess whether the brain changes associated with ageing develop more rapidly in HD patients ('accelerated brain ageing'). We also study associated cardiac MRI measures.
Methods: 3T MRI scans were performed during HD in 12 patients to characterise the acute effect of HD on cerebral water content (T1 mapping), alongside previously reported results from the HD-REMODEL trial. MRI changes in brain structure (volumes and T1 of white (WM) and grey matter (GM), WM diffusion fractional anisotropy (FA) and mean diffusivity (MD)), perfusion, blood flow, and cardiac measures were compared between HD patients pre-dialysis and HVs (age and gender matched).
Results: WM T1 increased during HD (3.8 ± 1.7%, p = 0.0005). GM and WM volume (total intracranial volume (TIV)-corrected) were lower in HD compared to HVs (GMV/TIV: 0.37[0.34-0.41] vs. 0.42[0.42-0.44], WMV/TIV: 0.34 ± 0.03 vs. 0.37 ± 0.01, p = 0.009). In HD, FA was lower and MD higher than HV (FA: 0.32 ± 0.02 vs. 0.35 ± 0.01, MD: 0.59 ± 0.03 vs. 0.53 ± 0.01, p < 0.0001).. Higher MD and lower FA was seen in older participants, with steeper slopes in HD (MD: 0.003 vs. 0.0006 × 10-3 mm2/s/y p = 0.003, FA: -0.001 vs -0.0003 units/y p < 0.0001), suggestive of accelerated ageing. There were no differences between groups in age-related heart changes.
Conclusions: An acute increase in WM T1 during HD has been shown for the first time, reflecting a rise in brain water content. This is potentially caused by the development of an osmotic gradient across the blood-brain barrier due to slower diffusion of urea, and may contribute to acute symptoms and chronic pathological changes contributing to accelerated brain ageing in HD patients.
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http://dx.doi.org/10.1093/ndt/gfaf117 | DOI Listing |
ACS Appl Bio Mater
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Biomedical Engineering Faculty, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran.
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Programa de Pós-Graduação em Neurociências, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brasil; Departamento de Ciências Morfológicas, Instituto de Ciências Básicas da Saúde, Universidade Federal do Rio Grande do Sul, Porto Alegre,
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Department of Integration of Chinese and Western Medicine, School of Basic Medical Sciences, Peking University, Beijing, China; Academy of Integration of Chinese and Western Medicine, Peking University Health Science Center, Beijing, China; The Key Discipline for Integration of Chinese and Western B
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Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. Electronic address:
Modelling the diffusion-relaxation magnetic resonance (MR) signal obtained from multi-parametric sequences has recently gained immense interest in the community due to new techniques significantly reducing data acquisition time. A preferred approach for examining the diffusion-relaxation MR data is to follow the continuum modelling principle that employs kernels to represent the tissue features, such as the relaxations or diffusion properties. However, constructing reasonable dictionaries with predefined signal components depends on the sampling density of model parameter space, thus leading to a geometrical increase in the number of atoms per extra tissue parameter considered in the model.
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