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- ten Kate, M., et al.
(författare)
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MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study
- 2018
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Ingår i: Alzheimers Research & Therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Background: With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) epsilon 4 genotype, can be used to predict amyloid pathology using machine-learning classification. Methods: We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 +/- 72, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69. 1 +/- 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 +/- 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE epsilon 4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. Results: In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 +/- O. 07 in MCI and an AUC of 0.74 +/- 0.08 in CN. In CN, selected features for the classifier included APOE epsilon 4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE epsilon 4 information did not improve after additionally adding imaging measures. Conclusions: Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE epsilon 4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.
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- Vendelbo Lind, Mads, 1988, et al.
(författare)
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Plasma Alkylresorcinols Reflect Gluten Intake and Distinguish between Gluten-Rich and Gluten-Poor Diets in a Population at Risk of Metabolic Syndrome
- 2016
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Ingår i: Journal of Nutrition. - : Elsevier BV. - 1541-6100 .- 0022-3166. ; 146:10, s. 1991-1998
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Tidskriftsartikel (refereegranskat)abstract
- Background: Many patients with celiac disease experience difficulties in adherence to a gluten-free diet. Methods for testing compliance to a gluten-free diet are costly and cumbersome. Thus, a simple biomarker of gluten intake is needed in a clinical setting and will be useful for epidemiologic studies investigating wider effects of gluten intake. Objective: The aim was to evaluate plasma total alkylresorcinol concentrations as a measure of gluten intake. Methods: In this randomized, controlled, crossover intervention study in 52 Danish adults with features of the metabolic syndrome, we compared 8 wk of a gluten-rich and gluten-poor diet separated by a washout period of wk. We measured fasting plasma concentrations of alkylresorcinols to determine if they reflected differences in gluten intake as a secondary outcome of the original study. In addition, we investigated in 118 Danish adults the cross-sectional association between self reported gluten intake and plasma alkylresorcinols in the same and a similar study at baseline. We used mixed-model ANCOVA for examining treatment effects, a classification tree to determine compliance to the gluten-poor diet, and linear regression models for examining baseline correlation between plasma alkylresorcinol concentrations and gluten intake. Results: Plasma total alkylresorcinols decreased more during the gluten-poor period (geometric mean: -124.8 nmol/L; 95% CI: -156.5, -93.0 nmol/L) than in the gluten-rich period (geometric mean: -31.8 nmol/L; 95% CI: -63.1, -0.4 nmol/L) (P
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