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We recently reported that expression levels of tumor necrosis factor (TNF) receptors, TNFR1 and TNFR2, are significantly changed in the brains and cerebral spinal cerebral fluid (CSF) with Alzheimer's disease (AD). Moreover, we also found that, in an Alzheimer's mouse model, genetic deletion of TNF receptor (TNFR1) reduces amyloid plaques and amyloid beta peptides (Abeta) production through beta-secretase (BACE1) regulation. TNF-alpha converting enzyme (TACE/ADAM-17) does not only cleave pro- TNF-alpha but also TNF receptors, however, whether the TACE activity was changed in the CSF was not clear. In this study, we examined TACE in the CSF in 32 AD patients and 27 age-matched healthy controls (HCs). Interestingly, we found that TACE activity was significantly elevated in the CSF from AD patients compared with HCs. Furthermore, we also assayed the CSF levels of TACE cleaved soluble forms of TNFR1 and TNFR2 in the same patients. We found that AD patients had higher levels of both TACE cleaved soluble TNFR1 (sTNFR1) and TNFR2 (sTNFR2) in the CSF compared with aged- and gender-matched healthy controls. Levels of sTNFR1 correlated strongly with the levels of sTNFR2 (rs = 0.567-0.663, p < 0.01). The levels of both sTNFR1 and sTNFR2 significantly correlated with the TACE activity (rs = 0.491-0.557, p < 0.05). To examine if changes in TACE activity and in levels of cleaved soluble TNFRs are an early event in the course of Alzheimer's disease, we measured these molecules in the CSF from 47 patients with mild cognitive impairment (MCI) which is considered as a preclinical stage of AD. Unexpectedly, we found significantly higher levels of TACE activity and soluble TNFR in the MCI group. These results suggest that TACE activity and soluble TNF receptors may be potential diagnostic candidate biomarkers in AD and MCI.
Family members provide most of the patient care and administer most of the treatments to patients with Alzheimer’s disease (AD). Family caregivers have an important impact on clinical outcomes, such as quality of life (QoL). As a consequence of this service, family caregivers suffer high rates of psychological and physical illness as well as social and financial burdens. Hence, it is important to involve family caregivers in multimodal treatment settings and provide interventions that are both suitable and specifically tailored to their needs. In recent years, several clinical guidelines have been presented worldwide for evidence-based treatment of AD and other forms of dementia. Most of these guidelines have considered family advice as integral to the optimal clinical management of AD. This article reviews current and internationally relevant guidelines with emphasis on recommendations concerning family advice.
Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.
Background: Alzheimer's disease is a common debilitating dementia with known heritability, for which 20 late onset susceptibility loci have been identified, but more remain to be discovered. This study sought to identify new susceptibility genes, using an alternative gene-wide analytical approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 m genotypes from 25,580 Alzheimer's cases and 48,466 controls.
Principal findings: In addition to earlier reported genes, we detected genome-wide significant loci on chromosomes 8 (TP53INP1, p = 1.4×10−6) and 14 (IGHV1-67 p = 7.9×10−8) which indexed novel susceptibility loci.
Significance: The additional genes identified in this study, have an array of functions previously implicated in Alzheimer's disease, including aspects of energy metabolism, protein degradation and the immune system and add further weight to these pathways as potential therapeutic targets in Alzheimer's disease.
Memory Concerns, Memory Performance and Risk of Dementia in Patients with Mild Cognitive Impairment
(2014)
Background: Concerns about worsening memory (“memory concerns”; MC) and impairment in memory performance are both predictors of Alzheimer's dementia (AD). The relationship of both in dementia prediction at the pre-dementia disease stage, however, is not well explored. Refined understanding of the contribution of both MC and memory performance in dementia prediction is crucial for defining at-risk populations. We examined the risk of incident AD by MC and memory performance in patients with mild cognitive impairment (MCI).
Methods: We analyzed data of 417 MCI patients from a longitudinal multicenter observational study. Patients were classified based on presence (n = 305) vs. absence (n = 112) of MC. Risk of incident AD was estimated with Cox Proportional-Hazards regression models.
Results: Risk of incident AD was increased by MC (HR = 2.55, 95%CI: 1.33–4.89), lower memory performance (HR = 0.63, 95%CI: 0.56–0.71) and ApoE4-genotype (HR = 1.89, 95%CI: 1.18–3.02). An interaction effect between MC and memory performance was observed. The predictive power of MC was greatest for patients with very mild memory impairment and decreased with increasing memory impairment.
Conclusions: Our data suggest that the power of MC as a predictor of future dementia at the MCI stage varies with the patients' level of cognitive impairment. While MC are predictive at early stage MCI, their predictive value at more advanced stages of MCI is reduced. This suggests that loss of insight related to AD may occur at the late stage of MCI.
Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer’s disease where the early identification of subjects with MCI is critical.
Introduction Complex psychopathological and behavioral symptoms, such as delusions and aggression against care providers, are often the primary cause of acute hospital admissions of elderly patients to emergency units and psychiatric departments. This issue resembles an interdisciplinary clinically highly relevant diagnostic and therapeutic challenge across many medical subjects and general practice. At least 50% of the dramatically growing number of patients with dementia exerts aggressive and agitated symptoms during the course of clinical progression, particularly at moderate clinical severity. Methods Commonly used rating scales for agitation and aggression are reviewed and discussed. Furthermore, we focus in this article on benefits and limitations of all available data of anticonvulsants published in this specific indication, such as valproate, carbamazepine, oxcarbazepine, lamotrigine, gabapentin and topiramate. Results To date, most positive and robust data are available for carbamazepine, however, pharmacokinetic interactions with secondary enzyme induction limit its use. Controlled data of valproate do not seem to support the use in this population. For oxcarbazepine only one controlled but negative trial is available. Positive small series and case reports have been reported for lamotrigine, gabapentin and topiramate. Conclusions So far, data of anticonvulsants in demented patients with behavioral disturbances are not convincing. Controlled clinical trials using specific, valid and psychometrically sound instruments of newer anticonvulsants with a better tolerability profile are mandatory to verify whether they can contribute as treatment option in this indication.
Previous PET and MRI studies have indicated that the degree to which pathology translates into clinical symptoms is strongly dependent on sex with women more likely to express pathology as a diagnosis of AD, whereas men are more resistant to clinical symptoms in the face of the same degree of pathology. Here we use DTI to investigate the difference between male and female white matter tracts in healthy older participants (24 women, 16 men) and participants with mild cognitive impairment (21 women, 12 men). Differences between control and MCI participants were found in fractional anisotropy (FA), radial diffusion (DR), axial diffusion (DA) and mean diffusion (MD). A significant main effect of sex was also reported for FA, MD and DR indices, with male control and male MCI participants having significantly more microstructural damage than their female counterparts. There was no sex by diagnosis interaction. Male MCIs also had significantly less normalised grey matter (GM) volume than female MCIs. However, in terms of absolute brain volume, male controls had significantly more brain volume than female controls. Normalised GM and WM volumes were found to decrease significantly with age with no age by sex interaction. Overall, these data suggest that the same degree of cognitive impairment is associated with greater structural damage in men compared with women.
The E4 allele of the ApoE gene has consistently been shown to be related to an increased risk of Alzheimer's disease (AD). The E4 allele is also associated with functional and structural grey matter (GM) changes in healthy young, middle-aged and older subjects. Here, we assess volumes of deep grey matter structures of 22 healthy younger ApoE4 carriers and 22 non-carriers (20–38 years). Volumes of the nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen, thalamus and brain stem were calculated by FMRIB's Integrated Registration and Segmentation Tool (FIRST) algorithm. A significant drop in volume was found in the right hippocampus of ApoE4 carriers (ApoE4+) relative to non-carriers (ApoE4−), while there was a borderline significant decrease in the volume of the left hippocampus of ApoE4 carriers. The volumes of no other structures were found to be significantly affected by genotype. Atrophy has been found to be a sensitive marker of neurodegenerative changes, and our results show that within a healthy young population, the presence of the ApoE4+ carrier gene leads to volume reduction in a structure that is vitally important for memory formation. Our results suggest that the hippocampus may be particularly vulnerable to further degeneration in ApoE4 carriers as they enter middle and old age. Although volume reductions were noted bilaterally in the hippocampus, atrophy was more pronounced in the right hippocampus. This finding relates to previous work which has noted a compensatory increase in right hemisphere activity in ApoE4 carriers in response to preclinical declines in memory function. Possession of the ApoE4 allele may lead to greater predilection for right hemisphere atrophy even in healthy young subjects in their twenties.