Amyloid-Beta in Braak Stages and Their Associations with Cognitive Impairment Using Machine Learning
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Amyloid-beta (Aß) and tau tangles are hallmarks of Alzheimer’s disease. Aß distributions in the tau-defined Braak staging regions and their multivariate predictive relationships with mild cognitive impairment (MCI) are not known. In this study, we used PiB PET data from 60 participants (33 with MCI and 27 controls), quantified Aß as distribution volume ratio (DVR) in Braak regions and compared between MCI and controls to test the hypothesis that DVR alters with declining cognition. We found elevated DVR in participants with MCI, especially in the spatial distribution of Braak stages III-IV and V-VII, while an alteration in Braak stage I-II was near the statistical significance. DVR markers correlated with cognitive status, especially in Braak stages III-IV and VI-V. To evaluate whether these markers are predictive of cognitive impairment, we designed support vector machine and artificial neural network models. These methods showed predictive multivariate relationships between Aß makers of Braak regions and cognitive impairment. Overall, these results highlight the importance of computer-aided research efforts for understanding AD pathophysiology.Keywords:
Amyloid (mycology)
Amyloid beta
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Although recent advances in neuroimaging techniques have enabled the detection of in vivo Alzheimer's disease (AD) pathology in human, only a limited number of memory clinics are able to utilize it in clinical practice due to high cost and low accessibility. This study, therefore, aimed to develop prediction models for the beta-amyloid (Aβ) positivity on amyloid positron emission tomography (PET) in mild cognitive impairment (MCI) individuals with data that are routinely obtained in memory clinic setting. Sixty seven MCI patients were included in this study. All subjects received clinical and neuropsychological assessments, laboratory evaluations for blood sample, magnetic resonance imaging, and 11C-labelled Pittsburgh Compound B (PiB) PET, For the development of Aβ positivity on PiB PET prediction models, all the variables were first categorized into four groups: clinical (C), neuropsychological (N), laboratory (L), and imaging (I) groups. In each group, the variables that showed significant or trend level association with Aβ positivity in univariate analyses were selected for further analyses. The selected variables of each group were combined sequentially in the prediction models, and logit values were calculated. Finally, with the logit values, the receiver operating characteristic (ROC) analyses were performed for each model to calculate the area under the curve (AUC). For each group, following variables were selected: Total scores of geriatric depression scale, subjective memory complaint questionnaire, trait anxiety, blessed dementia scale-activities of daily living and history of hypertension for group C; raw scores of word list recall and recognition, and constructional recall for group N; triiodothyronine, high-density lipoprotein cholesterol, erythrocyte sedimentation rate, and APOE 4 positivity for group L; adjusted hippocampal volume for group I. In the ROC analyses, AUC for the prediction models were as follows: 0.793 for group C, 0.894 for combined group C+N, 0.956 for combined group C+N+L, and 0.944 for combined group C+N+L+I. The findings suggest that the systematic combinations of data obtained from routine clinical practice may be successfully used to predict Aβ positivity in MCI individuals. The receiver operating characteristic (ROC) curves of each prediction model for amyloid positivity.
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Culprit
Amyloid (mycology)
BETA (programming language)
Amyloid beta
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We have recently demonstrated that a diagnostic framework with a Aβ deposition by [11C]-PIB PET allows for an earlier and more specific AD diagnosis. This study is to define "mild cognitive impairment (MCI) due to Alzheimer's disease (AD)" using beta-amyloid protein (Aβ) and neuronal injury as biomarkers. Furthermore, we sought to determine whether episodic memory impairment, age and apolipoprotein-E (APOE) genotype have an effect on progression from MCI to AD dementia. Fifty-six MCI patients underwent cognitive testing, 60-min dynamic [11C]-PIB PET and 20-min static [18F]-FDG PET at baseline, 12 months and 24 months, and APOE genotype assessment at baseline. Regions of interest were defined on co-registered MRI. PIB distribution volume ratios (DVR) were calculated using Logan graphical analysis, and quantitative analysis for [18F]-FDG used the standardized uptake value ratio (SUVR) on the same regions. Twenty-eight (50%) of all 56 MCI patients (MMSE: 27.0 ± 1.6, CDR: 0.5, CDR SB: 0.8 ± 0.3) converted to AD (MMSE: 22.7 ± 2.3, CDR: 0.6 ± 0.2, CDR SB: 2.7 ± 1.0) over 17.5 ± 8.0 months, whereas 28 (63.6%) of 44 MCI patients with positive Aβ biomarker (DVR≥1.49) converted. In addition, for 43 MCI patients with both positive biomarkers of Aβ and neuronal injury (SUVR≤0.99), the rate of conversion was 65.1%. In contrast, of 39 MCI patients with impaired paragraph delayed recall (WMS-R Logical Memory II) and both positive biomarkers, 27 (69.2%) converted to AD. In 14 MCI patients aged 75–89 years with impaired paragraph recall and both positive biomarkers, the rate of converters increased to 84.6% compared to 61.9% of 21 MCI patients aged 65–74 years. All (100%) of 4 APOE E4/4 carriers with positive Aβ biomarker converted to AD while 52.6% of the 19 APOE E4/3 carriers converted. All of 7 MCI patients with negative biomarkers of both Aβ and neuronal injury did not convert to AD. A biomarker of Aβ, in addition to neuronal injury, is most important to accurately diagnose "MCI due to AD." Furthermore, MCI in these individuals who have APOE e4/4 allele or are older than 75 years old is more likely to convert to AD dementia.
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Amyloid beta
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The biguanide metformin has been in safe use for decades for the treatment of type 2 diabetes. Previous reports showed that metformin induces protein phosphatase 2A (PP2A) activity, which is capable of dephosphorylating Tau protein (Kickstein et al., PNAS 2010), targeting one of the major neuropathological hallmarks of Alzheimer's disease (AD). In the current in vivo study, we wanted to investigate whether metformin treatment can also target the second main pathological hallmark of AD, amyloid plaques, which consist of Aβ peptides generated from amyloid precursor protein (APP). APP/PS1ΔE9 mice (age 12-13 months) were treated for 8 months with 5 g/l metformin in the drinking water. After behavioral studies, brains were processed for western blots, qPCR, SDD-AGE, and thioflavin staining. In vitro-transcribed RNA and extracts from primary cortical neurons were used for RNA pull-down experiments. Long-term metformin treatment decreased APP expression and consistently reduced Aβ levels in the brains of APP/PS1 mice, leading to improvements in hippocampus-dependent learning. We show that metformin reduces APP levels by disassembling the MID1-PP2A complex, which regulates the translation of APP. Additionally, thioflavin staining and SDD-AGE indicated altered Aβ plaque morphology. This could be attributed to a down-regulation of IRS1 and IRS2, two proteins involved in insulin/insulin-like growth factor 1 signaling. Our data demonstrate that translation of IRS1 and IRS2 is, similarly to APP, regulated by the MID1-PP2A complex. In the current preclinical study, we show that long-term treatment with the anti-diabetic drug metformin decreases protein levels of APP and its cleavage products, including Aβ, in a mouse model of AD. We propose a mechanism of metformin's action involving disassembly of the MID1-PP2A complex. The ability to reduce APP production together with the previously demonstrated capability of decreasing Tau phosphorylation makes metformin a particularly interesting candidate for further AD treatment studies.
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