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Träfflista för sökning "WFRF:(Legido Quigley C) ;pers:(Tsolaki M)"

Sökning: WFRF:(Legido Quigley C) > Tsolaki M

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  • Bos, I., et al. (författare)
  • Cerebrospinal fluid biomarkers of neurodegeneration, synaptic integrity, and astroglial activation across the clinical Alzheimer's disease spectrum
  • 2019
  • Ingår i: Alzheimers & Dementia. - : Wiley. - 1552-5260 .- 1552-5279. ; 15:5, s. 644-654
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: We investigated relations between amyloid-beta (A beta) status, apolipoprotein E (APOE) e4, and cognition, with cerebrospinal fluid markers of neurogranin (Ng), neurofilament light (NFL), YKL-40, and total tau (T-tau). Methods: We included 770 individuals with normal cognition, mild cognitive impairment, and Alzheimer's disease (AD)-type dementia from the EMIF-AD Multimodal Biomarker Discovery study. We tested the association of Ng, NFL, YKL-40, and T-tau with A beta status (Ab beta- vs. A beta+), clinical diagnosis APOE epsilon 4 carriership, baseline cognition, and change in cognition. Results: Ng and T-tau distinguished between A beta+ from A beta- individuals in each clinical group, whereas NFL and YKL-40 were associated with A beta+ in nondemented individuals only. APOE epsilon 4 carriership did not influence NFL, Ng, and YKL-40 in A beta+ individuals. NFL was the best predictor of cognitive decline in A beta+ individuals across the cognitive spectrum. Discussion: Axonal degeneration, synaptic dysfunction, astroglial activation, and altered tau metabolism are involved already in preclinical AD. NFL may be a useful prognostic marker. (C) 2019 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
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3.
  • 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. - : Springer Science and Business Media LLC. - 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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4.
  • Neumann, A., et al. (författare)
  • Rare variants in IFFO1, DTNB, NLRC3 and SLC22A10 associate with Alzheimer's disease CSF profile of neuronal injury and inflammation
  • 2022
  • Ingår i: Molecular Psychiatry. - : Springer Science and Business Media LLC. - 1359-4184 .- 1476-5578. ; 27
  • Tidskriftsartikel (refereegranskat)abstract
    • Alzheimer's disease (AD) biomarkers represent several neurodegenerative processes, such as synaptic dysfunction, neuronal inflammation and injury, as well as amyloid pathology. We performed an exome-wide rare variant analysis of six AD biomarkers (beta-amyloid, total/phosphorylated tau, NfL, YKL-40, and Neurogranin) to discover genes associated with these markers. Genetic and biomarker information was available for 480 participants from two studies: EMIF-AD and ADNI. We applied a principal component (PC) analysis to derive biomarkers combinations, which represent statistically independent biological processes. We then tested whether rare variants in 9576 protein-coding genes associate with these PCs using a Meta-SKAT test. We also tested whether the PCs are intermediary to gene effects on AD symptoms with a SMUT test. One PC loaded on NfL and YKL-40, indicators of neuronal injury and inflammation. Four genes were associated with this PC: IFFO1, DTNB, NLRC3, and SLC22A10. Mediation tests suggest, that these genes also affect dementia symptoms via inflammation/injury. We also observed an association between a PC loading on Neurogranin, a marker for synaptic functioning, with GABBR2 and CASZ1, but no mediation effects. The results suggest that rare variants in IFFO1, DTNB, NLRC3, and SLC22A10 heighten susceptibility to neuronal injury and inflammation, potentially by altering cytoskeleton structure and immune activity disinhibition, resulting in an elevated dementia risk. GABBR2 and CASZ1 were associated with synaptic functioning, but mediation analyses suggest that the effect of these two genes on synaptic functioning is not consequential for AD development.
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5.
  • Kurbatova, N., et al. (författare)
  • Urinary metabolic phenotyping for Alzheimer's disease
  • 2020
  • Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer's Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer's Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer's Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer's Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer's pathology in previous studies.
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