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- Comley, Robert A., et al.
(author)
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A Comparison of Gray Matter Density in Restless Legs Syndrome Patients and Matched Controls Using Voxel-Based Morphometry
- 2012
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In: Journal of Neuroimaging. - : WILEY-BLACKWELL. - 1051-2284 .- 1552-6569. ; 22:1, s. 28-32
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Journal article (peer-reviewed)abstract
- BACKGROUND Restless legs syndrome (RLS) is a common neurological disorder the pathophysiology of which is incompletely understood. Four studies have examined structural differences between the brains of RLS patients and healthy controls, using voxel-based morphometry (VBM). All 4 studies have provided different results. METHODS Optimized VBM was used to search for structural differences in gray matter density. Sixteen RLS patients naive to dopaminergic drugs and 16 age-and sex-matched controls received structural T1-weighted MR scans. Structural data were analyzed using FSL-VBM. RESULTS No difference in gray matter density was detected between the two groups (voxel-wise significance: no significant voxels at P = .89 (whole brain Family Wise Error (FWE) corrected); no significant voxels at P < .05 (whole brain False Discovery Rate (FDR) corrected; smallest achievable FDR threshold .99). CONCLUSION/DISCUSSION The present study did not replicate (confirm) previous findings of structural brain changes in RLS, but instead supported the findings of a recent study showing a lack of gray matter alteration in an elderly RLS population. More specifically, the results do not support neuronal loss as an underlying disease mechanism in RLS. Potential limitations in the application of VBM are also discussed.
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| 2. |
- Linge, Jennifer, et al.
(author)
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Sub-phenotyping Metabolic Disorders Using Body Composition : An Individualized, Nonparametric Approach Utilizing Large Data Sets
- 2019
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In: Obesity. - : John Wiley & Sons. - 1930-7381 .- 1930-739X. ; 27:7, s. 1190-1199
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Journal article (peer-reviewed)abstract
- Objective: This study performed individual-centric, data-driven calculations of propensity for coronary heart disease (CHD) and type 2 diabetes (T2D), utilizing magnetic resonance imaging-acquired body composition measurements, for sub-phenotyping of obesity and nonalcoholic fatty liver disease (NAFLD).Methods: A total of 10,019 participants from the UK Biobank imaging substudy were included and analyzed for visceral and abdominal subcutaneous adipose tissue, muscle fat infiltration, and liver fat. An adaption of the k-nearest neighbors algorithm was applied to the imaging variable space to calculate individualized CHD and T2D propensity and explore metabolic sub-phenotyping within obesity and NAFLD.Results: The ranges of CHD and T2D propensity for the whole cohort were 1.3% to 58.0% and 0.6% to 42.0%, respectively. The diagnostic performance, area under the receiver operating characteristic curve (95% CI), using disease propensities for CHD and T2D detection was 0.75 (0.73-0.77) and 0.79 (0.77-0.81). Exploring individualized disease propensity, CHD phenotypes, T2D phenotypes, comorbid phenotypes, and metabolically healthy phenotypes were found within obesity and NAFLD.Conclusions: The adaptive k-nearest neighbors algorithm allowed an individual-centric assessment of each individual’s metabolic phenotype moving beyond discrete categorizations of body composition. Within obesity and NAFLD, this may help in identifying which comorbidities a patient may develop and conse- quently enable optimization of treatment.
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