Dataset Information

Mutational analysis of PRNP in Alzheimer's disease and frontotemporal dementia in China.

ABSTRACT: The prion protein (PRNP) gene is associated with prion diseases, whereas variants of the PRNP gene may also explain some cases of Alzheimer disease (AD) and frontotemporal dementia (FTD) in Caucasian populations. To determine the prevalence of the PRNP gene in patients with AD and FTD in China, we screened all exons of the PRNP gene in a cohort of 683 cases (606 AD and 77 FTD) in the Chinese Han population and we detected a novel missense mutation p.S17G in a late-onset AD (LOAD) patient. Furthermore, we analyzed the PRNP M/V polymorphism at codon 129, which was previously reported as a risk factor. However, there were no significant differences in genotype and allele frequency either in AD (OR?=?0.75[0.378-1.49], P?=?0.492), or FTD patients (OR?=?2.046[0.265-15.783], P?=?0.707). To our knowledge, this is the first study to reveal a correlation between the PRNP gene and Chinese AD and FTD patients in a large cohort. This study reports a novel p.S17G mutation in a clinically diagnosed LOAD patient, suggesting that the PRNP mutation is present in Chinese AD patients, whereas, M129V polymorphism is not a risk factor for AD or FTD in the Chinese Han population.

SUBMITTER: Zhang W

PROVIDER: S-EPMC5133586 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Mutational analysis of PRNP in Alzheimer's disease and frontotemporal dementia in China.

Zhang Weiwei W Jiao Bin B Xiao Tingting T Pan Chuzheng C Liu Xixi X Zhou Lin L Tang Beisha B Shen Lu L

Scientific reports 20161202

The prion protein (PRNP) gene is associated with prion diseases, whereas variants of the PRNP gene may also explain some cases of Alzheimer disease (AD) and frontotemporal dementia (FTD) in Caucasian populations. To determine the prevalence of the PRNP gene in patients with AD and FTD in China, we screened all exons of the PRNP gene in a cohort of 683 cases (606 AD and 77 FTD) in the Chinese Han population and we detected a novel missense mutation p.S17G in a late-onset AD (LOAD) patient. Furthe ...[more]

PMID: 27910931

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Project description:Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

Project description:Cortical and subcortical nuclei degenerate in the dementias, but less is known about changes in the white matter tracts that connect them. To better understand white matter changes in behavioral variant frontotemporal dementia (bvFTD) and early-onset Alzheimer's disease (EOAD), we used a novel approach to extract full 3D profiles of fiber bundles from diffusion-weighted MRI (DWI) and map white matter abnormalities onto detailed models of each pathway. The result is a spatially complex picture of tract-by-tract microstructural changes. Our atlas of tracts for each disease consists of 21 anatomically clustered and recognizable white matter tracts generated from whole-brain tractography in 20 patients with bvFTD, 23 with age-matched EOAD, and 33 healthy elderly controls. To analyze the landscape of white matter abnormalities, we used a point-wise tract correspondence method along the 3D profiles of the tracts and quantified the pathway disruptions using common diffusion metrics - fractional anisotropy, mean, radial, and axial diffusivity. We tested the hypothesis that bvFTD and EOAD are associated with preferential degeneration in specific neural networks. We mapped axonal tract damage that was best detected with mean and radial diffusivity metrics, supporting our network hypothesis, highly statistically significant and more sensitive than widely studied fractional anisotropy reductions. From white matter diffusivity, we identified abnormalities in bvFTD in all 21 tracts of interest but especially in the bilateral uncinate fasciculus, frontal callosum, anterior thalamic radiations, cingulum bundles and left superior longitudinal fasciculus. This network of white matter alterations extends beyond the most commonly studied tracts, showing greater white matter abnormalities in bvFTD versus controls and EOAD patients. In EOAD, network alterations involved more posterior white matter - the parietal sector of the corpus callosum and parahipoccampal cingulum bilaterally. Widespread but distinctive white matter alterations are a key feature of the pathophysiology of these two forms of dementia.

Project description:BackgroundIn a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method.MethodsWe recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability.ResultsThe classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1-4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA.ConclusionsIn the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.

Dataset Information

Mutational analysis of PRNP in Alzheimer's disease and frontotemporal dementia in China.

Publications

Mutational analysis of PRNP in Alzheimer's disease and frontotemporal dementia in China.

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