Alzheimer's Disease Subgroups Research Articles

MMANet: A Multi-Task Residual Network for Alzheimer's Disease Classification and Brain Age Prediction

Objective: Alzheimer's disease (AD) is an irreversible neurodegenerative disease, while mild cognitive impairment (MCI) is a clinical precursor of AD, thus differentiation of AD, MCI and normal control (NC) from noninvasive magnetic resonance imaging (MRI) has positive clinical implications. Material and method: We utilize a 3D residual network to classify AD, MCI, and NC, and add a multiscale module to the original network to enhance the feature representation capability of the network, as well as a cross-dimensional attentional mechanism to enhance the network's attention to important brain regions. We experimentally verified that the network is more inclined to overestimate the brain age of patients in AD and MCI subgroups, thus proving that there is a high correlation between the brain age prediction task and the AD classification task. Therefore, we adopted a multi-task learning approach, using brain age prediction as a supplementary task for AD classification to reduce the risk of overfitting of the network during the training process. Results: Our method achieved 96.02% accuracy, 93.40% precision, 91.48% recall, and 92.24% F1 value in AD/MCI/NC classification. Conclusions: Ablation experiments confirmed that our proposed cross-dimensional attention and multiscale modules can improve the diagnostic performance of AD and MCI, and that multi-task learning in conjunction with brain age prediction can further improve the performance.

Relationship between Plasma Lipid Profile and Cognitive Status in Early Alzheimer Disease.

Alzheimer disease (AD) is a heterogeneous and complex disease in which different pathophysiological mechanisms are involved. This heterogenicity can be reflected in different atrophy patterns or clinical manifestations. Regarding biochemical pathways involved in early AD, lipid metabolism plays an important role; therefore, lipid levels have been evaluated as potential AD diagnosis biomarkers, and their levels could be related to different AD clinical manifestations. Therefore, the aim of this work is to study AD lipid profiles from early AD patients and evaluate their clinical significance. For this purpose, untargeted plasma lipidomic analysis was carried out in early AD patients (n = 31) diagnosed with cerebrospinal fluid (CSF) biomarkers. Cluster analysis was carried out to define early AD subgroups according to the lipid levels. Then, the clinical significance of each lipid profile subgroup was studied, analyzing differences for other variables (cognitive status, CSF biomarkers, medication, comorbidities, age, and gender). The cluster analysis revealed two different groups of AD patients. Cluster 1 showed higher levels of plasma lipids and better cognitive status than Cluster 2. However, no differences were found for the other variables (age, gender, medication, comorbidities, cholesterol, and triglycerides levels) between both groups. Plasma lipid levels could differentiate two early AD subgroups, which showed different cognitive statuses. However, further research with a large cohort and longitudinal study evaluating the clinical evolution of these patients is required. In general, it would involve a relevant advance in the knowledge of AD pathological mechanisms, potential treatments, and precision medicine.

Genetic-based patient stratification in Alzheimer’s disease

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein–protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

Unraveling the therapeutic potential of ginsenoside compound Mc1 in Alzheimer's disease: Exploring the role of AMPK/SIRT1/NF-κB signaling pathway and mitochondrial function.

Alzheimer's disease (AD) is a disabling neurodegenerating disorder characterized by chronic neuroinflammation, cognitive impairment and memory loss. Current treatment options for AD offer limited benefits, underscoring the urgent need for alternative therapeutics. Despite the promising effects of ginsenosides in neurodegenerative diseases, the therapeutic potential of ginsenoside compound Mc1 (GCMc1) in AD remains to be thoroughly investigated. This study aimed to investigate the therapeutic potential of GCMc1 in rats with AD and to elucidate the molecular mechanisms responsible for its effects. Alzheimer's disease was induced in Sprague Dawley rats through a single intra-cerebro-ventricular injection of amyloid-beta (Aβ)1-42 peptide. The animals were divided into 5 groups: a control group and 4 AD subgroups, with or without receiving 10 mg/kg of GCMc1 and/or 100 μg/kg of compound C intraperitoneally (ip.). Behavioral tests, mitochondrial function, inflammatory cytokines, and proteins expression were evaluated using the Morris water maze (MWM) test, fluorometry, enzyme-linked immunosorbent assay (ELISA), and immunoblotting techniques, respectively. Treatment with GCMc1 improved cognitive function, reduced hippocampal Aβ accumulation, and suppressed interleukin (IL)-1β, IL-10 and tumor necrosis factor alpha (TNF-α) levels. Ginsenoside compound Mc1 reduced mitochondrial reactive oxygen species (ROS) levels and membrane depolarization, increased adenosine triphosphate (ATP) levels, upregulated the expression of AMPK, PGC-1α and SIRT1 proteins, and downregulated the nuclear factor-kappa-B (NF-κB) expression. Importantly, co-administration of compound C, an AMPK inhibitor, attenuated the beneficial effects of GCMc1, suggesting the involvement of AMPK pathway in mediating GCMc1's neuroprotective effects. We showed that GCMc1 confers substantial neuroprotection in rats with AD by modulating the AMPK/SIRT1/NF-κB signaling pathway. These findings highlight the potential of GCMc1 as a promising therapeutic agent for AD treatment.

Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns.

We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups. We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory. There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p=1.15×10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group. MRI findings differ across cognitively defined AD subgroups.

Subclinical epileptiform activity and sleep disturbances in Alzheimer's disease.

Subclinical epileptiform activity (SEA) and sleep disturbances are frequent in Alzheimer's disease (AD). Both have an important relation to cognition and potential therapeutic implications. We aimed to study a possible relationship between SEA and sleep disturbances in AD. In this cross-sectional study, we performed a 24-h ambulatory EEG and polysomnography in 48 AD patients without diagnosis of epilepsy and 34 control subjects. SEA, mainly detected in frontotemporal brain regions during N2 with a median of three spikes/night [IQR1-17], was three times more prevalent in AD. AD patients had lower sleep efficacy, longer wake after sleep onset, more awakenings, more N1%, less REM sleep and a higher apnea-hypopnea index (AHI) and oxygen desaturation index (ODI). Sleep was not different between AD subgroup with SEA (AD-Epi+) and without SEA (AD-Epi-); however, compared to controls, REM% was decreased and AHI and ODI were increased in the AD-Epi+ subgroup. Decreased REM sleep and more severe sleep-disordered breathing might be related to SEA in AD. These results could have diagnostic and therapeutic implications and warrant further study at the intersection between sleep and epileptiform activity in AD.

Hemoglobin A1c Serum Level Predicts 5-year Mortality in Patients with Cognitive Impairment

BackgroundSubjective cognitive decline (SCD) and mild cognitive impairment (MCI) may occur as preclinical stages of Alzheimer's disease (AD), ultimately leading to dementia. Glycated hemoglobin A1c (HbA1c) is a diagnostic marker for diabetes mellitus and indicates mortality risk.ObjectivesThis university-based, exploratory retrospective study examined the impact of HbA1c serum level on 5-year mortality among individuals with cognitive impairment.MethodsIncluded were 1076 subjects aged at least 50 years who visited the Memory Outpatient Clinic of the Medical University of Vienna due to memory problems. Participants were diagnosed with SCD, MCI, or AD subsequent to neurological examination, standard laboratory blood tests, and neuropsychological testing. Survival was compared between diagnostic subgroups and with respect to HbA1c categories using log-rank tests based on Kaplan–Meier functions. The Neuropsychological Test Battery Vienna (NTBV) was dimensionally reduced, and a principal component analysis (PCA) was performed to further analyze results. Corresponding factor scores, HbA1c values, and baseline characteristics were included in Cox proportional hazards models to assess 5-year mortality risk.ResultsDuring the observation period, 323 patients (30%) died at a mean age comparable between diagnostic subgroups (SCD 84.2 ± 10.1, MCI 81.2 ± 8.3, AD 82.2 ± 7.4 years). Individuals with normal serum HbA1c levels had significant advantages in survival within the MCI (12.9 ± .3 vs. 10.0 ± .8 years) and the AD subgroups (8.2 ± .4 vs. 5.5 ± .6 years), and metric HbA1c predicted 5-year mortality (HR 1.24).ConclusionThis study demonstrates an association between abnormal HbA1c serum levels and increased mortality.

Immunological characterization and diagnostic models of RNA N6-methyladenosine regulators in Alzheimer's disease

Alzheimer's disease (AD) is the most prevalent form of dementia, and it displays both clinical and molecular variability. RNA N6-methyladenosine (m6A) regulators are involved in a wide range of essential cellular processes. In this study, we aimed to identify molecular signatures associated with m6A in Alzheimer's disease and use those signatures to develop a predictive model. We examined the expression patterns of m6A regulators and immune features in Alzheimer’s disease using the GSE33000 dataset. We examined the immune cell infiltration and molecular groups based on m6A-related genes in 310 Alzheimer's disease samples. The WGCNA algorithm was utilized to determine differently expressed genes within each cluster. After evaluating the strengths and weaknesses of the random forest model, the support vector machine model, the generalized linear model, and eXtreme Gradient Boosting, the best machine model was selected. Methods such as nomograms, calibration curves, judgment curve analysis, and the use of independent data sets were used to verify the accuracy of the predictions made. Alzheimer's disease and non-disease Alzheimer's groups were compared to identify dysregulated m6A-related genes and activated immune responses. In Alzheimer's disease, two molecular clusters linked to m6A were identified. Immune infiltration analysis indicated substantial variation in protection between groups. Cluster 1 included processes like the Toll-like receptor signaling cascade, positive regulation of chromatin binding, and numerous malignancies; cluster 2 included processes like the cell cycle, mRNA transport, and ubiquitin-mediated proteolysis. With a lower residual and root mean square error and a larger area under the curve (AUC = 0.951), the Random forest machine model showed the greatest discriminative performance. The resulting random forest model was based on five genes, and it performed well (AUC = 0.894) on external validation datasets. Accuracy in predicting Alzheimer's disease subgroups was also shown by analyses of nomograms, calibration curves, and decision curves. In this research, we methodically outlined the tangled web of connections between m6A and AD and created a promising prediction model for gauging the correlation between m6A subtype risk and AD pathology.

A trichotomy method for defining homogeneous subgroups in a dementia population.

Diagnosis of dementia in the aging brain is confounded by the presence of multiple pathologies. Mixed dementia (MX), a combination of Alzheimer's disease (AD) proteins with vascular disease (VD), is frequently found at autopsy, and has been difficult to diagnose during life. This report develops a method for separating the MX group and defining preclinical AD (presence of AD factors with normal cognition) and preclinical VD subgroups (presence of white matter damage with normal cognition). Clustering was based on three diagnostic axes: (1) AD factor (ADF) derived from cerebrospinal fluid proteins (Aβ42 and pTau), (2) VD factor (VDF) calculated from mean free water and peak width of skeletonized mean diffusivity in the white matter, and (3) Cognition (Cog) based on memory and executive function. The trichotomy method was applied to an Alzheimer's Disease Neuroimaging Initiative cohort (N = 538). Eight biologically defined subgroups were identified which included the MX group with both high ADF and VDF (9.3%) and a preclinical VD group (3.9%), and a preclinical AD group (13.6%). Cog is significantly associated with both ADF and VDF, and the partial-correlation remains significant even when the effect of the other variable is removed (r(Cog, ADF/VDF removed) = 0.46, p < 10-28 and r(Cog, VDF/ADF removed) = 0.24, p < 10-7 ). The trichotomy method creates eight biologically characterized patient groups, which includes MX, preclinical AD, and preclinical VD subgroups. Further longitudinal studies are needed to determine the utility of the 3-way clustering method with multimodal biological biomarkers.

Cholinesterase Inhibitors for Treatment of Psychotic Symptoms in Alzheimer Disease and Parkinson Disease

Psychotic symptoms greatly increase the burden of disease for people with neurodegenerative disorders and their caregivers. Cholinesterase inhibitors (ChEIs) may be effective treatment for psychotic symptoms in these disorders. Previous trials only evaluated neuropsychiatric symptoms as a secondary and an overall outcome, potentially blurring the outcomes noted with ChEI use specifically for psychotic symptoms. To quantitatively assess the use of ChEIs for treatment of individual neuropsychiatric symptoms, specifically hallucinations and delusions, in patients with Alzheimer disease (AD), Parkinson disease (PD), and dementia with Lewy bodies (DLB). A systematic search was performed in PubMed (MEDLINE), Embase, and PsychInfo, without year restrictions. Additional eligible studies were retrieved from reference lists. The final search cutoff date was April 21, 2022. Studies were selected if they presented the results of placebo-controlled randomized clinical trials, including at least 1 donepezil, rivastigmine, or galantamine treatment arm in patients with AD, PD, or DLB; if they applied at least 1 neuropsychiatric measure including hallucinations and/or delusions; and if a full-text version of the study was available in the English language. Study selection was performed and checked by multiple reviewers. Original research data were requested on eligible studies. A 2-stage meta-analysis was then performed, using random-effects models. Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were followed for extracting data and assessing the data quality and validity. Data extraction was checked by a second reviewer. Primary outcomes were hallucinations and delusions; secondary outcomes included all other individual neuropsychiatric subdomains as well as the total neuropsychiatric score. In total, 34 eligible randomized clinical trials were selected. Individual participant data on 6649 individuals (3830 [62.6%] women; mean [SD] age, 75.0 [8.2] years) were obtained from 17 trials (AD: n = 12; PD: n = 5; individual participant data were not available for DLB). An association with ChEI treatment was shown in the AD subgroup for delusions (-0.08; 95% CI, -0.14 to -0.03; P = .006) and hallucinations (-0.09; 95% CI, -0.14 to -0.04; P = .003) and in the PD subgroup for delusions (-0.14; 95% CI, -0.26 to -0.01; P = .04) and hallucinations (-0.08, 95% CI -0.13 to -0.03; P = .01). The results of this individual participant data meta-analysis suggest that ChEI treatment improves psychotic symptoms in patients with AD and PD with small effect sizes.

Alzheimer's disease heterogeneity explained by polygenic risk scores derived from brain transcriptomic profiles.

Alzheimer's disease (AD) is heterogeneous, both clinically and neuropathologically. We investigated whether polygenic risk scores (PRSs) integrated with transcriptome profiles from AD brains can explain AD clinical heterogeneity. We conducted co-expression network analysis and identified gene sets (modules) that were preserved in three AD transcriptome datasets and associated with AD-related neuropathological traits including neuritic plaques (NPs) and neurofibrillary tangles (NFTs). We computed the module-based PRSs (mbPRSs) for each module and tested associations with mbPRSs for cognitive test scores, cognitively defined AD subgroups, and brain imaging data. Of the modules significantly associated with NPs and/or NFTs, the mbPRSs from two modules (M6 and M9) showed distinct associations with language and visuospatial functioning, respectively. They matched clinical subtypes and brain atrophy at specific regions. Our findings demonstrate that polygenic profiling based on co-expressed gene sets can explain heterogeneity in AD patients, enabling genetically informed patient stratification and precision medicine in AD. Co-expression gene-network analysis in Alzheimer's disease (AD) brains identified gene sets (modules) associated with AD heterogeneity. AD-associated modules were selected when genes in each module were enriched for neuritic plaques and neurofibrillary tangles. Polygenic risk scores from two selected modules were linked to the matching cognitively defined AD subgroups (language and visuospatial subgroups). Polygenic risk scores from the two modules were associated with cognitive performance in language and visuospatial domains and the associations were confirmed in regional-specific brain atrophy data.

Multimodal investigation of melanopsin retinal ganglion cells in Alzheimer's disease.

In Alzheimer's disease (AD), the presence of circadian dysfunction is well-known and may occur early in the disease course. The melanopsin retinal ganglion cell (mRGC) system may play a relevant role in contributing to circadian dysfunction. In this study, we aimed at evaluating, through a multimodal approach, the mRGC system in AD at an early stage of disease. We included 29 mild-moderate AD (70.9 ± 11 years) and 26 (70.5 ± 8 years) control subjects. We performed an extensive neurophtalmological evaluation including optical coherence tomography with ganglion cell layer segmentation, actigraphic evaluation of the rest-activity rhythm, chromatic pupillometry analyzed with a new data-fitting approach, and brain functional MRI combined with light stimuli assessing the mRGC system. We demonstrated a significant thinning of the infero-temporal sector of the ganglion cell layer in AD compared to controls. Moreover, we documented by actigraphy the presence of a circadian-impaired AD subgroup. Overall, circadian measurements worsened by age. Chromatic pupillometry evaluation highlighted the presence of a pupil-light response reduction in the rod condition pointing to mRGC dendropathy. Finally, brain fMRI showed a reduced occipital cortex activation with blue light particularly for the sustained responses. Overall, the results of this multimodal innovative approach clearly document a dysfunctional mRGC system at early stages of disease as a relevant contributing factor for circadian impairment in AD providing also support to the use of light therapy in AD.

Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment.

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 ± 0.003), peak signal-to-noise ratio (31.04 ± 0.09), and mean squared error (0.0014 ± 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t-test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837-0.897) and 0.752 (95%: 0.71-0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection.

Clinical Manifestations of Early-Onset Dementia With Lewy Bodies Compared With Late-Onset Dementia With Lewy Bodies and Early-Onset Alzheimer Disease

Early-onset dementia, presenting in individuals younger than 65 years, is a diagnosis with significant social and financial implications. The early-onset form of dementia with Lewy bodies (DLB) is poorly understood. To investigate clinical features that distinguish early-onset DLB (onset and diagnosis at age <65 years) from late-onset DLB (onset at age ≥65 years) and from early-onset Alzheimer disease (AD) dementia. This is a retrospective case-control study on patients with pathologically confirmed DLB or AD enrolled in the National Alzheimer's Coordinating Center database from January 2005 to July 2017. The National Alzheimer's Coordinating Center Uniform Data Set comprised deidentified data collected by Alzheimer disease centers in the United States. Of patients fulfilling criteria for all-cause dementia at enrollment (n = 1152), those who at post mortem received a pathological diagnosis of either AD (n = 848) or Lewy body disease (n = 218) were selected. Excluding 52 patients owing to missing data and 12 diagnosed with Parkinson disease dementia, remaining patients were classified by age of symptom onset into early-onset AD, early-onset DLB, and late-onset DLB subgroups. Data were analyzed from June to December 2018 and from November to December 2021. Demographics, cognitive, behavioral, and motor features recorded at first clinic visit and neuropathological characteristics at autopsy were analyzed by disease subgroup. Concordance between initial etiologic diagnosis of dementia and final pathological diagnosis was assessed, as was time to death. A total of 542 individuals were categorized as having early-onset AD (n = 363; mean [SD] age, 53.0 [5.8] years; 208 [57.3%] male), early-onset DLB (n = 32; mean [SD] age, 57.9 [3.2] years; 23 [71.9%] male), and late-onset DLB (n = 147; mean [SD] age, 73.5 [5.5] years; 103 [70.1%] male). Early-onset DLB was clinically misdiagnosed in 16 individuals (50%). Features that predicted a diagnosis of early-onset DLB over early-onset AD included visual hallucinations (15 [46.9%] vs 42 [11.6%]), slowness (23 [71.9%] vs 95 [26.2%]), apathy (23 [71.9%] vs 189 [52.1%]), and motor deterioration that preceded cognitive and behavioral symptoms (7 [21.9%] vs 6 [1.7%]). Late-onset DLB had more amnestic features, but this was accounted for by a higher proportion of neocortical neuritic plaques and diffuse plaques (frequent in 79 [53.7%] vs 8 [25%]) than seen in early-onset DLB. This study found that early-onset DLB has clinical features that distinguish it from early-onset AD, whereas features of late-onset DLB are associated with a higher burden of AD copathology.

Estimating the X chromosome-mediated risk for developing Alzheimer's disease.

Parental lineage has been shown to increase the risk of Alzheimer's disease (AD) in the offspring, with greater risk attributed to maternal lineage. While 40 genes/loci have been linked to the risk of developing AD, none has been found on the X chromosome. We propose a new method to estimate the risk for developing AD mediated by the X chromosome in a subgroup of late-onset AD (LOAD) patients with amnestic mild cognitive impairment (aMCI) or early AD and unilateral ancestral history of AD or dementia, and pilot-test it on our clinic data. Records of patients aged 55-80years presenting to our Memory Disorders Clinic with aMCI or early AD between May 2015 and September 2020, were reviewed, counting patients with a family history of AD or dementia and unilateral ancestral lineage. The X chromosome-attributable relative risk was estimated by calculating the following odds ratio (OR): (women with paternal lineage:women with maternal lineage)/(men with paternal lineage:men with maternal lineage). The proportion of genetic risk borne by the X chromosome is equal to (OR-1)/OR. 40 women aged 66.1 ± 5.1years (mean ± standard deviation) and 31 men aged 68.1 ± 6.5 were identified. The OR was (18:22)/(6:25) = 3.4 (95% confidence interval 1.1-10.1; p = 0.027). The estimated proportion of genetic risk borne by the X chromosome in this population is 70% (95% CI 12-90%). This paper presents the first application of a new method. The numbers are small, the confidence intervals wide. The findings need to be replicated. The method may be generalizable to other diseases.

Can Social Cognition Measurements Differentiate Behavioral Variant Frontotemporal Dementia from Alzheimer's Disease Regardless of Apathy?

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) share cognitive and behavioral symptoms, such as apathy. Social cognition measurements are useful in distinguishing bvFTD from AD, but their accuracies may be affected by apathy. To investigate whether social cognition measurements can distinguish bvFTD from either apathetic or non-apathetic AD patients. Three groups of participants were enrolled in the present study: bvFTD (n = 22), AD (n = 20), and healthy controls (HC, n = 23). The AD group was divided into apathetic (n = 10) and non-apathetic (n = 10). All subjects underwent comprehensive neuropsychological examination, including the short version of the Social and Emotional Assessment (Mini-SEA), which comprises the facial emotion recognition test and the faux-pas recognition test (Faux-Pas Test). Apathy was assessed according to the Starkstein's Apathy (SA) Scale. The bvFTD and AD groups did not differ on global cognitive efficiency and on executive functions. In comparison to the whole AD group, bvFTD displayed lower Faux-Pas Test and Mini-SEA scores. Both AD subgroups, apathetic or non-apathetic, exhibited similar performance on all social cognition measurements. In comparison to either apathetic AD or non-apathetic AD, bvFTD patients underperformed on the Faux-Pas Test and on the Mini-SEA. The area under the curve values for the Mini-SEA total score were 0.87 (bvFTD versus AD), 0.90 (bvFTD versus apathetic AD), and 0.83 (bvFTD versus non-apathetic AD). Social cognition tests provide accurate distinction between bvFTD against either apathetic AD or non-apathetic AD. Social cognition measurements did not correlate with apathy severity.

Uric Acid and High-Density Lipoprotein Cholesterol Are Differently Associated with Alzheimer's Disease and Vascular Dementia.

Uric acid (UA) is a major contributor to naturally-occurring antioxidant activity and is thought to have protective effects against neurodegenerative processes. However, UA is also implicated as a risk factor in vascular, including cerebrovascular, disease. Its association with, and role in, dementia and its component diseases including Alzheimer's disease (AD) and vascular dementia (VaD) remains unclear and inconsistently studied. Changes in blood lipids, particularly cholesterol measures, have also been implicated in dementias although the relationship or interactions with UA have been little studied.We have measured plasma UA and lipids taken from 187 subjects first attending a general hospital neurology department for symptoms associated with dementia, and from a series of 79 healthy control subjects. Diagnoses of AD and VaD were made following neuroimaging; laboratory measures were compared between dementia and control groups and between AD and VaD subgroups. No overall change in UA was seen in dementia, although a substantial and highly significant reduction was found in the AD patients. Reduced values in total cholesterol, HDL, and LDL were found in dementia, independent of statin treatment. Further investigation found a significant reduction of HDL only in the VaD group, while total cholesterol was significantly reduced in both AD and VaD subjects.These findings indicate that in our Chinese sample, UA deficits are specifically associated with AD, while deficits in HDL cholesterol found in dementia tend to be greater in VaD.

A Qualitative Analysis Based on Relative Expression Orderings Identifies Transcriptional Subgroups for Alzheimer’s Disease

Alzheimer's disease (AD) is a heterogeneous neurodegenerative disease. However, few studies have investigated the heterogeneous gene expression patterns in AD. We examined the gene expression patterns in four brain regions of AD based on the within-sample relative expression orderings (REOs). Gene pairs with significantly reversed REOs in AD samples compared to non-AD controls were identified for each brain region using Fisher's exact test, and filtered according to their transcriptional differences between AD samples. Subgroups of AD were classified by cluster analysis. REO-based gene expression profiling analyses revealed that transcriptional differences, as well as distinct disease subsets, existed within AD patients. For each brain region, two main subgroups were classified: one subgroup reported differentially expressed genes overlapped with the age-related genes, and the other was related to neuroinflammation. AD transcriptional subgroups might help understand the underlying pathogenesis of AD, and lend support to a personalized approach to AD management.

Effects of cardiovascular pharmacotherapies on incident dementia in patients with atrial fibrillation: A cohort study of all patients above 45 years diagnosed with AF in hospitals in Sweden

BackgroundPatients with atrial fibrillation (AF) have an increased dementia risk dementia. We aimed to study the effect of antihypertensive drugs on dementia in AF patients. MethodsIncluded patients were ≥45 years diagnosed with AF in Swedish National Patient Register (n=160,251; 89,723 men and 70,528 women) and alive on January 1, 2007. We excluded patients with dementia before onset of AF. Cox regression was used (hazard ratios, HRs, and 99% confidence interval, CI) with adjustments for sex, age, socioeconomic factors and co-morbidities, using incident dementia diagnosis until December 31, 2015 as outcome. Cardiovascular pharmacotherapies were obtained from the Swedish Prescribed Drug Register. ResultsIncident dementia occurred in 9532 patients (5.9%), 4669 men (5.2%) and 4863 women (6.9%). ARBs were associated with lower risk for all patients (HR 0.87, 99% CI 0.78–0.98), especially in the ages 65–84 years of age (HR 0.87, 99% CI 0.76–0.99). Loop-diuretics were associated with higher risk for all dementia among patients 65–84 years of age (HR 1.16, 99% CI 1.00–1.35), and in the sub-group of other causes of dementia than Alzheimer Disease (AD) and vascular dementia (VaD) (HR 1.14, 99% CI 1.00–1.30), but with a lower risk in the sub-group of AD and VaD (HR 0.81, 99% CI 0.68–0.95). ConclusionARBs were associated with a decreased incidence of dementia, and loop diuretics with a higher risk in general but lower risk in the AD and VaD sub-group. ARBs could have specific advantages in prevention of dementia, but the results need confirmation in further studies.

Long Non-coding RNA BACE1-AS May Serve as an Alzheimer’s Disease Blood-Based Biomarker

Circulating long noncoding RNAs (lncRNAs) might serve as biomarkers for different pathological conditions. BACE1-AS lncRNA upregulates in the brain of people with Alzheimer's disease (AD) and might be detected in the bloodstream. To reveal if lncRNA BACE1-AS may serve as a blood-based biomarker for AD, we compared its levels in plasma and plasma-derived exosomes between AD (n = 45) and healthy people (n = 36). Exosomes were purified from plasma by Invitrogen™ Total Exosome Isolation Kit and characterized by scanning electron microscopy (SEM) and dynamic light scattering (DLS). Total RNA was extracted from whole plasma, and plasma-derived exosomes using TRIzol® LS or TRIzol® Reagents respectively were then reverse transcribed to the cDNA using PrimeScript II cDNA synthesis kit. The BACE1-AS levels were quantified by real-time PCR, and their biomarker potencies were evaluated using ROC curve analysis. Results obtained verified the presence of BACE1-AS in the plasma samples of both AD and healthy controls. We did not observe any significant differences between the levels of BACE1-AS in the plasma or plasma-derived exosomes of AD and control people. However, there were significant differences between AD subgroups and control in the whole plasma samples. The BACE1-AS level was low in pre-AD subgroup but it was high in full-AD people compared to the healthy controls. Moreover, ROC curve analysis revealed that lncRNA BACE1-AS may discriminate pre-AD and healthy control (75% sensitivity and 100% specificity), full-AD and healthy control (68% sensitivity and 100% specificity), and pre-AD and full-AD subgroups (78% sensitivity and 100% specificity), highlighting its potential as a biomarker for AD development. In conclusion, plasma BACE1-AS level may serve as a potent blood-based biomarker for Alzheimer's disease.