Hierarchical Interactions Model for Predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) Conversion

Identifying patients with Mild Cognitive Impairment (MCI) who are likely to convert to dementia has recently attracted increasing attention in Alzheimer''s disease (AD) research. An accurate prediction of conversion from MCI to AD can aid clinicians to initiate treatments at early stage and monitor their effectiveness. However, existing prediction systems based on the original biosignatures are not satisfactory. In this paper, we propose to fit the prediction models using pairwise biosignature interactions, thus capturing higher-order relationship among biosignatures. Specifically, we employ hierarchical constraints and sparsity regularization to prune the high-dimensional input features. Based on the significant biosignatures and underlying interactions identified, we build classifiers to predict the conversion probability based on the selected features. We further analyze the underlying interaction effects of different biosignatures based on the so-called stable expectation scores. We have used 293 MCI subjects from Alzheimer''s Disease Neuroimaging Initiative (ADNI) database that have MRI measurements at the baseline to evaluate the effectiveness of the proposed method. Our proposed method achieves better classification performance than state-of-the-art methods. Moreover, we discover several significant interactions predictive of MCI-to-AD conversion. These results shed light on improving the prediction performance using interaction features.     

Intra-individual variability in Alzheimer's disease and cognitive aging: definitions, context, and effect sizes

Background/Aims

To explore different definitions of intra-individual variability (IIV) to summarize performance on commonly utilized cognitive tests (Mini Mental State Exam; Clock Drawing Test); compare them and their potential to differentiate clinically-defined populations; and to examine their utility in predicting clinical change in individuals from the Alzheimer''s Disease Neuroimaging Initiative (ADNI).

Methods

Sample statistics were computed from ADNI cohorts with no cognitive diagnosis, a diagnosis of mild cognitive impairment (MCI), and a diagnosis of possible or probable Alzheimer''s disease (AD). Nine different definitions of IIV were computed for each sample, and standardized effect sizes (Cohen''s d) were computed for each of these definitions in 500 simulated replicates using scores on the Mini Mental State Exam and Clock Drawing Test. IIV was computed based on test items separately (‘within test’ IIV) and the two tests together (‘across test’ IIV). The best performing definition was then used to compute IIV for a third test, the Alzheimer''s Disease Assessment Scale-Cognitive, and the simulations and effect sizes were again computed. All effect size estimates based on simulated data were compared to those computed based on the total scores in the observed data. Association between total score and IIV summaries of the tests and the Clinician''s Dementia Rating were estimated to test the utility of IIV in predicting clinically meaningful changes in the cohorts over 12- and 24-month intervals.

Results

ES estimates differed substantially depending on the definition of IIV and the test(s) on which IIV was based. IIV (coefficient of variation) summaries of MMSE and Clock-Drawing performed similarly to their total scores, the ADAS total performed better than its IIV summary.

Conclusion

IIV can be computed within (items) or across (totals) items on commonly-utilized cognitive tests, and may provide a useful additional summary measure of neuropsychological test performance.     

Bayesian Network Analysis Reveals Alterations to Default Mode Network Connectivity in Individuals at Risk for Alzheimer's Disease

Alzheimer''s disease (AD) is associated with abnormal functioning of the default mode network (DMN). Functional connectivity (FC) changes to the DMN have been found in patients with amnestic mild cognitive impairment (aMCI), which is the prodromal stage of AD. However, whether or not aMCI also alters the effective connectivity (EC) of the DMN remains unknown. We employed a combined group independent component analysis (ICA) and Bayesian network (BN) learning approach to resting-state functional MRI (fMRI) data from 17 aMCI patients and 17 controls, in order to establish the EC pattern of DMN, and to evaluate changes occurring in aMCI. BN analysis demonstrated heterogeneous regional convergence degree across DMN regions, which were organized into two closely interacting subsystems. Compared to controls, the aMCI group showed altered directed connectivity weights between DMN regions in the fronto-parietal, temporo-frontal, and temporo-parietal pathways. The aMCI group also exhibited altered regional convergence degree in the right inferior parietal lobule. Moreover, we found EC changes in DMN regions in aMCI were correlated with regional FC levels, and the connectivity metrics were associated with patients'' cognitive performance. This study provides novel sights into our understanding of the functional architecture of the DMN and adds to a growing body of work demonstrating the importance of the DMN as a mechanism of aMCI.     

基于体素的形态测量学在阿尔茨海默病及轻度认知功能障碍研究中的应用

阿尔茨海默病(Alzheimer's disease,AD)是发生于老年和老年前期、以进行性认知功能障碍和行为异常为特征的中枢神经系统退行性疾病,是老年痴呆中最常见类型。轻度认知功能障碍(mild cognitive impairment,MCI)是介于正常衰老和痴呆之间的一种中间状态,指有轻度的记忆或认知损伤,但尚未达到痴呆程度的一种状态,日常生活和社会功能不受影响,其中很大一部分患者最终进展为AD。临床诊断AD患者多已达中晚期,为了能早期诊断AD及预测MCI的转归,有关AD的生物学标注物的研究成为近年来的科研热点。AD患者颅脑的大体病理特征为脑萎缩,其萎缩有别于正常老龄化所致的退行性改变,有其自身特点,这种特定形式的萎缩有可能成为AD早期诊断的生物学标志物。基于体素的形态测量学(voxel-based morphometry,VBM)是一种基于像素水平对脑核磁图像进行自动、全面、客观分析的技术,可以定量分析全脑结构、刻画出局部脑区结构特征,是一种较好的脑形态分析工具,广泛用于阿尔茨海默病及轻度认知功能障碍的研究中,本文综述了近年来其研究进展,期望为临床及科研提供参考。     

Identification of a 5-protein biomarker molecular signature for predicting Alzheimer's disease

Background

Alzheimer''s disease (AD) is a progressive brain disease with a huge cost to human lives. The impact of the disease is also a growing concern for the governments of developing countries, in particular due to the increasingly high number of elderly citizens at risk. Alzheimer''s is the most common form of dementia, a common term for memory loss and other cognitive impairments. There is no current cure for AD, but there are drug and non-drug based approaches for its treatment. In general the drug-treatments are directed at slowing the progression of symptoms. They have proved to be effective in a large group of patients but success is directly correlated with identifying the disease carriers at its early stages. This justifies the need for timely and accurate forms of diagnosis via molecular means. We report here a 5-protein biomarker molecular signature that achieves, on average, a 96% total accuracy in predicting clinical AD. The signature is composed of the abundances of IL-1α, IL-3, EGF, TNF-α and G-CSF.

Methodology/Principal Findings

Our results are based on a recent molecular dataset that has attracted worldwide attention. Our paper illustrates that improved results can be obtained with the abundance of only five proteins. Our methodology consisted of the application of an integrative data analysis method. This four step process included: a) abundance quantization, b) feature selection, c) literature analysis, d) selection of a classifier algorithm which is independent of the feature selection process. These steps were performed without using any sample of the test datasets. For the first two steps, we used the application of Fayyad and Irani''s discretization algorithm for selection and quantization, which in turn creates an instance of the (alpha-beta)-k-Feature Set problem; a numerical solution of this problem led to the selection of only 10 proteins.

Conclusions/Significance

the previous study has provided an extremely useful dataset for the identification of AD biomarkers. However, our subsequent analysis also revealed several important facts worth reporting:1. A 5-protein signature (which is a subset of the 18-protein signature of Ray et al.) has the same overall performance (when using the same classifier).2. Using more than 20 different classifiers available in the widely-used Weka software package, our 5-protein signature has, on average, a smaller prediction error indicating the independence of the classifier and the robustness of this set of biomarkers (i.e. 96% accuracy when predicting AD against non-demented control).3. Using very simple classifiers, like Simple Logistic or Logistic Model Trees, we have achieved the following results on 92 samples: 100 percent success to predict Alzheimer''s Disease and 92 percent to predict Non Demented Control on the AD dataset.     

Mid-life leukocyte telomere length and dementia risk: An observational and mendelian randomization study of 435,046 UK Biobank participants

Telomere attrition is one of biological aging hallmarks and may be intervened to target multiple aging-related diseases, including Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD). The objective of this study was to assess associations of leukocyte telomere length (TL) with AD/ADRD and early markers of AD/ADRD, including cognitive performance and brain magnetic resonance imaging (MRI) phenotypes. Data from European-ancestry participants in the UK Biobank (n = 435,046) were used to evaluate whether mid-life leukocyte TL is associated with incident AD/ADRD over a mean follow-up of 12.2 years. In a subsample without AD/ADRD and with brain imaging data (n = 43,390), we associated TL with brain MRI phenotypes related to AD or vascular dementia pathology. Longer TL was associated with a lower risk of incident AD/ADRD (adjusted Hazard Ratio [aHR] per SD = 0.93, 95% CI 0.90–0.96, p = 3.37 × 10⁻⁷). Longer TL also was associated with better cognitive performance in specific cognitive domains, larger hippocampus volume, lower total volume of white matter hyperintensities, and higher fractional anisotropy and lower mean diffusivity in the fornix. In conclusion, longer TL is inversely associated with AD/ADRD, cognitive impairment, and brain structural lesions toward the development of AD/ADRD. However, the relationships between genetically determined TL and the outcomes above were not statistically significant based on the results from Mendelian randomization analysis results. Our findings add to the literature of prioritizing risk for AD/ADRD. The causality needs to be ascertained in mechanistic studies.     

A Clinical Index to Predict Progression from Mild Cognitive Impairment to Dementia Due to Alzheimer's Disease

Background

Mild cognitive impairment is often a precursor to dementia due to Alzheimer''s disease, but many patients with mild cognitive impairment never develop dementia. New diagnostic criteria may lead to more patients receiving a diagnosis of mild cognitive impairment.

Objective

To develop a prediction index for the 3-year risk of progression from mild cognitive impairment to dementia relying only on information that can be readily obtained in most clinical settings.

Design and Participants

382 participants diagnosed with amnestic mild cognitive impairment enrolled in the Alzheimer''s Disease Neuroimaging Initiative (ADNI), a multi-site, longitudinal, observational study.

Main Predictors Measures

Demographics, comorbid conditions, caregiver report of participant symptoms and function, and participant performance on individual items from basic neuropsychological scales.

Main Outcome Measure

Progression to probable Alzheimer''s disease.

Key Results

Subjects had a mean (SD) age of 75 (7) years and 43% progressed to probable Alzheimer''s disease within 3 years. Important independent predictors of progression included being female, resisting help, becoming upset when separated from caregiver, difficulty shopping alone, forgetting appointments, number of words recalled from a 10-word list, orientation and difficulty drawing a clock. The final point score could range from 0 to 16 (mean [SD]: 4.2 [2.9]). The optimism-corrected Harrell''s c-statistic was 0.71(95% CI: 0.68–0.75). Fourteen percent of subjects with low risk scores (0–2 points, n = 124) converted to probable Alzheimer''s disease over 3 years, compared to 51% of those with moderate risk scores (3–8 points, n = 223) and 91% of those with high risk scores (9–16 points, n = 35).

Conclusions

An index using factors that can be obtained in most clinical settings can predict progression from amnestic mild cognitive impairment to probable Alzheimer''s disease and may help clinicians differentiate between mild cognitive impairment patients at low vs. high risk of progression.     

Genetic background modifies CNS‐mediated sensorimotor decline in the AD‐BXD mouse model of genetic diversity in Alzheimer's disease

Many patients with Alzheimer's dementia (AD) also exhibit noncognitive symptoms such as sensorimotor deficits, which can precede the hallmark cognitive deficits and significantly impact daily activities and an individual's ability to live independently. However, the mechanisms underlying sensorimotor dysfunction in AD and their relationship with cognitive decline remains poorly understood, due in part to a lack of translationally relevant animal models. To address this, we recently developed a novel model of genetic diversity in Alzheimer's disease, the AD‐BXD genetic reference panel. In this study, we investigated sensorimotor deficits in the AD‐BXDs and the relationship to cognitive decline in these mice. We found that age‐ and AD‐related declines in coordination, balance and vestibular function vary significantly across the panel, indicating genetic background strongly influences the expressivity of the familial AD mutations used in the AD‐BXD panel and their impact on motor function. Although young males and females perform comparably regardless of genotype on narrow beam and inclined screen tasks, there were significant sex differences in aging‐ and AD‐related decline, with females exhibiting worse decline than males of the same age and transgene status. Finally, we found that AD motor decline is not correlated with cognitive decline, suggesting that sensorimotor deficits in AD may occur through distinct mechanisms. Overall, our results suggest that AD‐related sensorimotor decline is strongly dependent on background genetics and is independent of dementia and cognitive deficits, suggesting that effective therapeutics for the entire spectrum of AD symptoms will likely require interventions targeting each distinct domain involved in the disease.     

Classification of Structural MRI Images in Alzheimer's Disease from the Perspective of Ill-Posed Problems

Background

Machine learning neuroimaging researchers have often relied on regularization techniques when classifying MRI images. Although these were originally introduced to deal with “ill-posed” problems it is rare to find studies that evaluate the ill-posedness of MRI image classification problems. In addition, to avoid the effects of the “curse of dimensionality” very often dimension reduction is applied to the data.

Methodology

Baseline structural MRI data from cognitively normal and Alzheimer''s disease (AD) patients from the AD Neuroimaging Initiative database were used in this study. We evaluated here the ill-posedness of this classification problem across different dimensions and sample sizes and its relationship to the performance of regularized logistic regression (RLR), linear support vector machine (SVM) and linear regression classifier (LRC). In addition, these methods were compared with their principal components space counterparts.

Principal Findings

In voxel space the prediction performance of all methods increased as sample sizes increased. They were not only relatively robust to the increase of dimension, but they often showed improvements in accuracy. We linked this behavior to improvements in conditioning of the linear kernels matrices. In general the RLR and SVM performed similarly. Surprisingly, the LRC was often very competitive when the linear kernel matrices were best conditioned. Finally, when comparing these methods in voxel and principal component spaces, we did not find large differences in prediction performance.

Conclusions and Significance

We analyzed the problem of classifying AD MRI images from the perspective of linear ill-posed problems. We demonstrate empirically the impact of the linear kernel matrix conditioning on different classifiers'' performance. This dependence is characterized across sample sizes and dimensions. In this context we also show that increased dimensionality does not necessarily degrade performance of machine learning methods. In general, this depends on the nature of the problem and the type of machine learning method.     

Association between NME8 Locus Polymorphism and Cognitive Decline,Cerebrospinal Fluid and Neuroimaging Biomarkers in Alzheimer's Disease

Recently, a large meta-analysis of five genome wide association studies (GWAS) identified a novel locus (rs2718058) adjacent to NME8 that played a preventive role in Alzheimer''s disease (AD). However, this link between the single nucleotide polymorphism (SNP) rs2718058 and the pathology of AD have not been mentioned yet. Therefore, this study assessed the strength of association between the NME8 rs2718058 genotypes and AD-related measures including the cerebrospinal fluid (CSF) amyloid beta, tau, P-tau concentrations, neuroimaging biomarkers and cognitive performance, in a large cohort from Alzheimer''s Disease Neuroimaging Initiative (ADNI) database. We used information of a total of 719 individuals, including 211 normal cognition (NC), 346 mild cognitive impairment (MCI) and 162 AD. Although we didn''t observe a positive relationship between rs2718058 and AD, it was significantly associated with several AD related endophenotypes. Among the normal cognitively normal participants, the minor allele G carriers showed significantly associated with higher CDRSB score than A allele carriers (P = 0.021). Occipital gyrus atrophy were significantly associated with NME8 genotype status (P = 0.002), with A allele carriers has more atrophy than the minor allele G carriers in AD patients; lateral ventricle (both right and left) cerebral metabolic rate for glucose (CMRgl) were significantly associated with NME8 genotype (P<0.05), with GA genotype had higher metabolism than GG and AA genotypes in MCI group; the atrophic right hippocampus in 18 months is significantly different between the three group, with GG and AA genotypes had more hippocampus atrophy than GA genotypes in the whole group. Together, our results are consistent with the direction of previous research, suggesting that NME8 rs2718058 appears to play a role in lowering the brain neurodegeneration.     

Alzheimer's Therapeutics Targeting Amyloid Beta 1–42 Oligomers II: Sigma-2/PGRMC1 Receptors Mediate Abeta 42 Oligomer Binding and Synaptotoxicity

Amyloid beta (Abeta) 1–42 oligomers accumulate in brains of patients with Mild Cognitive Impairment (MCI) and disrupt synaptic plasticity processes that underlie memory formation. Synaptic binding of Abeta oligomers to several putative receptor proteins is reported to inhibit long-term potentiation, affect membrane trafficking and induce reversible spine loss in neurons, leading to impaired cognitive performance and ultimately to anterograde amnesia in the early stages of Alzheimer''s disease (AD). We have identified a receptor not previously associated with AD that mediates the binding of Abeta oligomers to neurons, and describe novel therapeutic antagonists of this receptor capable of blocking Abeta toxic effects on synapses in vitro and cognitive deficits in vivo. Knockdown of sigma-2/PGRMC1 (progesterone receptor membrane component 1) protein expression in vitro using siRNA results in a highly correlated reduction in binding of exogenous Abeta oligomers to neurons of more than 90%. Expression of sigma-2/PGRMC1 is upregulated in vitro by treatment with Abeta oligomers, and is dysregulated in Alzheimer''s disease patients'' brain compared to age-matched, normal individuals. Specific, high affinity small molecule receptor antagonists and antibodies raised against specific regions on this receptor can displace synthetic Abeta oligomer binding to synaptic puncta in vitro and displace endogenous human AD patient oligomers from brain tissue sections in a dose-dependent manner. These receptor antagonists prevent and reverse the effects of Abeta oligomers on membrane trafficking and synapse loss in vitro and cognitive deficits in AD mouse models. These findings suggest sigma-2/PGRMC1 receptors mediate saturable oligomer binding to synaptic puncta on neurons and that brain penetrant, small molecules can displace endogenous and synthetic oligomers and improve cognitive deficits in AD models. We propose that sigma-2/PGRMC1 is a key mediator of the pathological effects of Abeta oligomers in AD and is a tractable target for small molecule disease-modifying therapeutics.     

A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI

Context

There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer''s disease (AD) at the population level.

Objective

To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma.

Design, Setting, Participants

Analysis of serum biomarker proteins were conducted on 197 Alzheimer''s disease (AD) participants and 199 control participants from the Texas Alzheimer''s Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer''s Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data.

Major Outcome Measures

Alzheimer''s disease.

Results

11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49–14.47), the likelihood ratio of not having AD based on the algorithm (LR−) = 3.55 (SE = 1.15; 2.22–5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86–69.47).

Conclusions

It is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.     

Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.     

Systemic vaccination with anti‐oligomeric monoclonal antibodies improves cognitive function by reducing Aβ deposition and tau pathology in 3xTg‐AD mice

Alzheimer's disease (AD) is a devastating disorder that is clinically characterized by a comprehensive cognitive decline. Accumulation of the amyloid‐beta (Aβ) peptide plays a pivotal role in the pathogenesis of AD. In AD, the conversion of Aβ from a physiological soluble monomeric form into insoluble fibrillar conformation is an important event. The most toxic form of Aβ is oligomers, which is the intermediate step during the conversion of monomeric form to fibrillar form. There are at least two types of oligomers: oligomers that are immunologically related to fibrils and those that are not. In transgenic AD animal models, both active and passive anti‐Aβ immunotherapies improve cognitive function and clear the parenchymal accumulation of amyloid plaques in the brain. In this report we studied effect of immunotherapy of two sequence‐independent non‐fibrillar oligomer specific monoclonal antibodies on the cognitive function, amyloid load and tau pathology in 3xTg‐AD mice. Anti‐oligomeric monoclonal antibodies significantly reduce the amyloid load and improve the cognition. The clearance of amyloid load was significantly correlated with reduced tau hyperphosphorylation and improvement in cognition. These results demonstrate that systemic immunotherapy using oligomer‐specific monoclonal antibodies effectively attenuates behavioral and pathological impairments in 3xTg‐AD mice. These findings demonstrate the potential of using oligomer specific monoclonal antibodies as a therapeutic approach to prevent and treat Alzheimer's disease.     

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical,MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

Background

Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer''s disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level.

Methods

Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework.

Results

Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer''s Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions.

Conclusions

We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.     

The Effect of S-Adenosylmethionine on Cognitive Performance in Mice: An Animal Model Meta-Analysis

Background

Alzheimer''s disease (AD) is the most frequently diagnosed form of dementia resulting in cognitive impairment. Many AD mouse studies, using the methyl donor S-adenosylmethionine (SAM), report improved cognitive ability, but conflicting results between and within studies currently exist. To address this, we conducted a meta-analysis to evaluate the effect of SAM on cognitive ability as measured by Y maze performance. As supporting evidence, we include further discussion of improvements in cognitive ability, by SAM, as measured by the Morris water maze (MWM).

Methods

We conducted a comprehensive literature review up to April 2014 based on searches querying MEDLINE, EMBASE, Web of Science, the Cochrane Library and Proquest Theses and Dissertation databases. We identified three studies containing a total of 12 experiments that met our inclusion criteria and one study for qualitative review. The data from these studies were used to evaluate the effect of SAM on cognitive performance according to two scenarios: 1. SAM supplemented folate deficient (SFD) diet compared to a folate deficient (FD) diet and 2. SFD diet compared to a nutrient complete (NC) diet. Hedge''s g was used to calculate effect sizes and mixed effects model meta-regression was used to evaluate moderating factors.

Results

Our findings showed that the SFD diet was associated with improvements in cognitive performance. SFD diet mice also had superior cognitive performance compared to mice on an NC diet. Further to this, meta-regression analyses indicated a significant positive effect of study quality score and treatment duration on the effect size estimate for both the FD vs SFD analysis and the SFD vs NC analysis.

Conclusion

The findings of this meta-analysis demonstrate efficacy of SAM in acting as a cognitive performance-enhancing agent. As a corollary, SAM may be useful in improving spatial memory in patients suffering from many dementia forms including AD.     

The Clinical Utility of Informants' Appraisals on Prospective and Retrospective Memory in Patients with Early Alzheimer's Disease

Increasing studies suggest the importance of including prospective memory measures in clinical evaluation of dementia due to its sensitivity and functional relevance. The Prospective and Retrospective Memory Questionnaire (PRQM) is originally a self-rated memory inventory that offers a direct comparison between prospective and episodic memory. However, the informant''s report has been recognized as a more valid source of cognitive complaints. We thus aimed to examine the validity of the informant-rated form of the PRMQ in assessing memory function of the patients and in detecting individuals with early dementia. The informants of 140 neurological outpatients with memory complaints completed the Taiwan version of the PRMQ. Tests of prospective memory, short-term memory, and general cognitive ability were also administered to non-demented participants and patients with early stages of Alzheimer''s disease (AD). Results showed significant relationships between the PRMQ ratings and objective cognitive measures, and showed that higher ratings on the PRMQ were associated with increasing odds of greater dementia severity. Receiver operative characteristic (ROC) curves showed an adequate ability of the PRMQ to identify patients with dementia (93% sensitivity and 84% specificity). Hierarchical regression revealed that the PRMQ has additional explanatory power for dementia status after controlling for age, education and objective memory test results, and that the prospective memory subscale owns predictive value for dementia beyond the retrospective memory subscale. The present study demonstrated the external validity and diagnostic value of informants'' evaluation of their respective patients'' prospective and retrospective memory functioning, and highlighted the important role of prospective memory in early dementia detection. The proxy-version of the PRMQ is a useful tool that captures prospective and episodic memory problems in patients with early AD, in combination with standardized cognitive testing.     

轻度认知障碍的研究进展

老年性痴呆(阿尔茨海默病,Alzheimer disease,AD)是目前严重影响老年人生存质量的疾病,且疗效不佳。据推测到2050年阿尔茨海默病的患病率将是现今的三倍。轻度认知障碍(mild cognitive impairment,MCI)是介于阿尔茨海默病和正常衰老之间的一种认知功能损害状态,是发生阿尔茨海默病的高危因素。文献报道轻度认知障碍每年以8%-25%的比例进展为阿尔茨海默病,较正常人群阿尔茨海默病发病率高10倍。与阿尔茨海默病病理损害不可逆相比,轻度认知障碍患者通过早期干预治疗,可延缓或阻止病情发展为阿尔茨海默病。因此,对阿尔茨海默病早期出现的轻度认知功能障碍诊断及干预尤为重要。本文就认知功能早期阶段,轻度认知功能障碍的历年(2000年到2014年3月)研究进展从概念及分型、临床表现、诊断标准、病理生理及其影像学研究、危险因素及其预防、干预措施(药物和非药物)等方面的最新进展进行论述。     

Differentiating the Cognitive Profile of Schizophrenia from That of Alzheimer Disease and Depression in Late Life

Background

To compare the cognitive profile of older patients with schizophrenia to those with other neuropsychiatric disorders assessed in a hospital-based memory clinic.

Methods

Demographic, clinical, and cognitive data of all patients referred to the memory clinic at the Centre for Addiction and Mental Health between April 1, 2006 and August 15, 2008 were reviewed. We then identified four groups of older patients with: (1) late-life schizophrenia (LLS) and no dementia or depression (DEP); (2) Alzheimer''s disease (AD); (3) DEP and no dementia or LLS; (4) normal cognition (NC) and no DEP or LLS.

Results

The four groups did not differ in demographic data except that patients with AD were about 12 years older than those with LLS. However, they differed on cognitive tests even after controlling for age. Patients with LLS were impaired on most cognitive tests in comparison with patients with NC but not on recalling newly learned verbal information at a short delay. They experienced equivalent performance on learning new verbal information in comparison with patients with AD, but better performance on all other tests of memory, including the ability to recall newly learned verbal information. Finally, they were more impaired than patients with DEP in overall memory.

Conclusions

Patients with LLS have a different cognitive profile than patients with AD or DEP. Particularly, memory impairment in LLS seems to be more pronounced in learning than recall. These findings suggest that cognitive and psychosocial interventions designed to compensate for learning deficits may be beneficial in LLS.