100 research outputs found

    Table_1_Insulin-Like Growth Factor Binding Protein 2 Is Associated With Biomarkers of Alzheimer’s Disease Pathology and Shows Differential Expression in Transgenic Mice.DOCX

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    <p>There is increasing evidence that metabolic dysfunction plays an important role in Alzheimer’s disease (AD). Brain insulin resistance and subsequent impairment of insulin and insulin-like growth factor (IGF) signaling are associated with the neurodegenerative and clinical features of AD. Nevertheless, how the brain insulin/IGF signaling system is altered in AD and the effects of these changes on AD pathobiology are not well understood. IGF binding protein 2 (IGFBP-2) is an abundant cerebral IGF signaling protein and there is early evidence suggesting it associates with AD biomarkers. We evaluated the relationship between protein levels of IGFBP-2 with cerebrospinal fluid (CSF) biomarkers and neuroimaging markers of AD progression in 300 individuals from across the AD spectrum. CSF IGFBP-2 levels were correlated with CSF tau levels and brain atrophy in non-hippocampal regions. To further explore the role of IGFBP2 in tau pathobiology, we evaluated the expression of IGFBP2 in different human and mouse brain cell types and brain tissue from two transgenic mouse models: the P301L-tau model of tauopathy and TASTPM model of AD. We observed significant differential expression of IGFBP2 in both transgenic mouse models relative to wild-type mice in cortex but not in hippocampus. In both humans and mice, IGFBP2 is most highly expressed in astrocytes. Taken together, our findings suggest that IGFBP-2 may be linked to tau pathology and provides further evidence for a relationship between metabolic dysregulation and neurodegeneration. Our results also raise the possibility that this relationship may extend beyond neurons.</p

    Probing the Association between Early Evolutionary Markers and Schizophrenia

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    <div><p>Schizophrenia is suggested to be a by-product of the evolution in humans, a compromise for our language, creative thinking and cognitive abilities, and thus, essentially, a human disorder. The time of its origin during the course of human evolution remains unclear. Here we investigate several markers of early human evolution and their relationship to the genetic risk of schizophrenia. We tested the schizophrenia evolutionary hypothesis by analyzing genome-wide association studies of schizophrenia and other human phenotypes in a statistical framework suited for polygenic architectures. We analyzed evolutionary proxy measures: human accelerated regions, segmental duplications, and ohnologs, representing various time periods of human evolution for overlap with the human genomic loci associated with schizophrenia. Polygenic enrichment plots suggest a higher prevalence of schizophrenia associations in human accelerated regions, segmental duplications and ohnologs. However, the enrichment is mostly accounted for by linkage disequilibrium, especially with functional elements like introns and untranslated regions. Our results did not provide clear evidence that markers of early human evolution are more likely associated with schizophrenia. While SNPs associated with schizophrenia are enriched in HAR, Ohno and SD regions, the enrichment seems to be mediated by affiliation to known genomic enrichment categories. Taken together with previous results, these findings suggest that schizophrenia risk may have mainly developed more recently in human evolution.</p></div

    Rates of Decline in Alzheimer Disease Decrease with Age

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    <div><p>Age is the strongest risk factor for sporadic Alzheimer disease (AD), yet the effects of age on rates of clinical decline and brain atrophy in AD have been largely unexplored. Here, we examined longitudinal rates of change as a function of baseline age for measures of clinical decline and structural MRI-based regional brain atrophy, in cohorts of AD, mild cognitive impairment (MCI), and cognitively healthy (HC) individuals aged 65 to 90 years (total n = 723). The effect of age was modeled using mixed effects linear regression. There was pronounced reduction in rates of clinical decline and atrophy with age for AD and MCI individuals, whereas HCs showed increased rates of clinical decline and atrophy with age. This resulted in convergence in rates of change for HCs and patients with advancing age for several measures. Baseline cerebrospinal fluid densities of AD-relevant proteins, Aβ<sub>1–42</sub>, tau, and phospho-tau<sub>181p</sub> (ptau), showed a similar pattern of convergence with advanced age across cohorts, particularly for ptau. In contrast, baseline clinical measures did not differ by age, indicating uniformity of clinical severity at baseline. These results imply that the phenotypic expression of AD is relatively mild in individuals older than approximately 85 years, and this may affect the ability to distinguish AD from normal aging in the very old. Our findings show that inclusion of older individuals in clinical trials will substantially reduce the power to detect disease-modifying therapeutic effects, leading to dramatic increases in required clinical trial sample sizes with age of study sample.</p> </div

    Estimated sample sizes with respect to age, per arm, to detect a 25% reduction in rate of change in MCI participants relative to age-matched change in HCs, at the p<0.05 level with 80% power assuming a 24 month trial with scans every six months.

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    <p>Sample sizes are estimated using a linear mixed effects model with fixed intercepts (no relative change at baseline) and random slopes and linear dependence on age applied to all data available up through 36 months. Dashed lines show the 95% confidence intervals.</p

    Fits of recurrent thalamocortical model.

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    <p>Illustration of fits of recurrent thalamocortical model in Eq. (2) to data from experiments 1–6. Each black dot corresponds to the experimentally measured layer-4 firing rate at a specific time point plotted against the model value of . The red dots are corresponding experimental data points taken from the first 5 ms after stimulus onset (for all 27 stimuli). These data points show the activity prior to any stimulus-evoked thalamic or cortical firing and correspond to background activity. The solid green curve corresponds to the fitted model activation function .</p

    Comparison of thalamocortical model fits.

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    <p>Comparison of fits of the recurrent and feedforward thalamocortical models with experimental data set 1 for the same 9 (of 27) stimulus conditions considered in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000328#pcbi-1000328-g002" target="_blank">Fig. 2</a>. The green line corresponds to the thalamic input firing rate , the black line to the experimentally extracted layer-4 firing rate , while the red and blue lines correspond to the best fits of the recurrent (Eq. 2) and feedforward (Eq. 8) models for the layer-4 firing rate , respectively. Time zero corresponds to stimulus onset.</p

    Stratified Q-Q plots and true discovery rates show consistency of enrichment.

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    <p><i>Upper panel:</i> Stratified Q-Q plots illustrating consistent enrichment of genic annotation categories across diverse phenotypes: (A) Height, (B) Schizophrenia (SCZ), and (C) Cigarettes per Day (CPD). All figures are corrected for inflation using intergenic inflation control. Only nominal p-values below the standard genome-wide significance threshold (p<5×10<sup>−8</sup>) are shown. <i>Lower panel:</i> Stratified True Discovery Rate (TDR) plots illustrating the increase in TDR associated with increased enrichment in (D) Height, (E) SCZ and (F) CPD. Genic annotation categories were: 5′ untranslated region (5′UTR), Exon, Intron, 3′ untranslated region (3′UTR), All SNPs, in addition to Intergenic.</p

    Estimated sample sizes, per arm, to detect a 25% reduction in annual rate of change in MCI participants under several enrichment strategies, relative to the annual rate of change in amyloid-negative stable HCs, at the p<0.05 level with 80% power assuming a 24 month trial with scans every six months.

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    <p>Sample sizes are estimated using a linear mixed effects model with fixed intercepts (no relative change at baseline) and random slopes applied to all data available up through 36 months. Error bars show the 95% confidence intervals. N is the number of participants. All numerical values are shown <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047739#pone-0047739-t003" target="_blank">Table 3</a>; p-values for comparisons are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047739#pone-0047739-t004" target="_blank">Tables 4</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047739#pone-0047739-t005" target="_blank">5</a>.</p