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Sökning: WFRF:(Schaeverbeke J)

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1.
  • ten Kate, M., et al. (författare)
  • MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study
  • 2018
  • Ingår i: Alzheimers Research & Therapy. - 1758-9193. ; 10
  • 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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2.
  • Hong, Shengjun, et al. (författare)
  • Genome-wide association study of Alzheimer's disease CSF biomarkers in the EMIF-AD Multimodal Biomarker Discovery dataset.
  • 2020
  • Ingår i: Translational psychiatry. - 2158-3188. ; 10:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Susceptibility to AD is considerably determined by genetic factors which hitherto were primarily identified using case-control designs. Elucidating the genetic architecture of additional AD-related phenotypic traits, ideally those linked to the underlying disease process, holds great promise in gaining deeper insights into the genetic basis of AD and in developing better clinical prediction models. To this end, we generated genome-wide single-nucleotide polymorphism (SNP) genotyping data in 931 participants of the European Medical Information Framework Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample to search for novel genetic determinants of AD biomarker variability. Specifically, we performed genome-wide association study (GWAS) analyses on 16 traits, including 14 measures derived from quantifications of five separate amyloid-beta (Aβ) and tau-protein species in the cerebrospinal fluid (CSF). In addition to confirming the well-established effects of apolipoprotein E (APOE) on diagnostic outcome and phenotypes related to Aβ42, we detected novel potential signals in the zinc finger homeobox 3 (ZFHX3) for CSF-Aβ38 and CSF-Aβ40 levels, and confirmed the previously described sex-specific association between SNPs in geminin coiled-coil domain containing (GMNC) and CSF-tau. Utilizing the results from independent case-control AD GWAS to construct polygenic risk scores (PRS) revealed that AD risk variants only explain a small fraction of CSF biomarker variability. In conclusion, our study represents a detailed first account of GWAS analyses on CSF-Aβ and -tau-related traits in the EMIF-AD MBD dataset. In subsequent work, we will utilize the genomics data generated here in GWAS of other AD-relevant clinical outcomes ascertained in this unique dataset.
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4.
  • Adamczuk, Katarzyna, et al. (författare)
  • Diagnostic value of cerebrospinal fluid A beta ratios in preclinical Alzheimer's disease
  • 2015
  • Ingår i: Alzheimer's Research & Therapy. - 1758-9193. ; 7
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: In this study of preclinical Alzheimer's disease (AD) we assessed the added diagnostic value of using cerebrospinal fluid (CSF) A beta ratios rather than A beta 42 in isolation for detecting individuals who are positive on amyloid positron emission tomography (PET). Methods: Thirty-eight community-recruited cognitively intact older adults (mean age 73, range 65-80 years) underwent F-18-flutemetamol PET and CSF measurement of A beta 1-42, A beta 1-40, A beta 1-38, and total tau (ttau). F-18-flutemetamol retention was quantified using standardized uptake value ratios in a composite cortical region (SUVRcomp) with reference to cerebellar grey matter. Based on a prior autopsy validation study, the SUVRcomp cut-off was 1.57. Sensitivities, specificities and cut-offs were defined based on receiver operating characteristic analysis with CSF analytes as variables of interest and F-18-flutemetamol positivity as the classifier. We also determined sensitivities and CSF cut-off values at fixed specificities of 90 % and 95 %. Results: Seven out of 38 subjects (18 %) were positive on amyloid PET. A beta 42/ttau, A beta 42/A beta 40, A beta 42/A beta 38, and A beta 42 had the highest accuracy to identify amyloid-positive subjects (area under the curve (AUC) >= 0.908). A beta 40 and A beta 38 had significantly lower discriminative power (AUC = 0.571). When specificity was fixed at 90 % and 95 %, A beta 42/ttau had the highest sensitivity among the different CSF markers (85.71 % and 71.43 %, respectively). Sensitivity of A beta 42 alone was significantly lower under these conditions (57.14 % and 42.86 %, respectively). Conclusion: For the CSF-based definition of preclinical AD, if a high specificity is required, our data support the use of A beta 42/ttau rather than using A beta 42 in isolation.
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