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Sleep disturbances and dementia are two common and significant health problems in older adults. Investigations suggest that sleep disturbances might increase the risk of dementia. The aim of the present study was to systematically review and meta-analyze the predictive roles of overall sleep disturbances, their subtypes (e.g., insomnia, sleep disordered breathing [SDB]), and other sleep problems (e.g., excessive daytime sleepiness, sleep-related movement disorder, circadian rhythm sleep disorder, and nonspecific sleep problems) in incident all-cause dementia and Alzheimer's disease (AD) and vascular dementia subtypes. We performed a systematic search of the PubMed, EMBase, ISI Web of Science, and PsycINFO databases for longitudinal studies that were published up to October 28, 2016. A total of 12,926 papers were retrieved. Eighteen longitudinal studies that included 246,786 subjects at baseline and 25,847 dementia cases after an average 9.49 y of follow-up were eligible for inclusion. Compared with individuals without sleep disturbances, subjects who reported sleep disturbances had a higher risk of incident all-cause dementia, AD, and vascular dementia. The subgroup analysis showed that insomnia increased the risk of AD but not vascular or all-cause dementia. In contrast, SDB was associated with a higher incidence of all-cause dementia, AD, and vascular dementia. This meta-analysis suggests that sleep disturbances may predict the risk of incident dementia. Moreover, insomnia was associated only with incident AD, and SDB was a risk factor of all-cause dementia, AD, and vascular dementia. However, sleep disturbances were evaluated mainly based on self-reports, and some confounders may mediate the relationship between sleep disturbances and dementia. Therefore, the results should be further validated. In summary, these findings may help identify individuals who are at risk for dementia and optimize early prevention strategies. [ABSTRACT FROM AUTHOR]

• Surrogate data generation method IAAFT solves the issue of EEG data shortage. • BiLSTM obtains better classification result than other traditional methods. • The SampEn extraction method retaining the high temporal resolution of EEG. The early diagnosis of mild cognitive impairment (MCI) is a essential prevention of further development of MCI into Alzheimer's disease (AD). Electroencephalogram (EEG) has many advantages compared to other methods in the analysis of AD in an early stage, but there are some limitations of EEG such as small size of datasets caused by difficulty in clinical data collection and too many other interfering signals are contained. Recent years, deep learning (DL) have overcome these limitations relatively. In this study, a novel model which aims to classify MCI and healthy control (HC) was constructed based on iterative amplitude adjusted Fourier transform (IAAFT) and bidirectional long short-term memory (BiLSTM). IAAFT is used to overcome the problems caused by small datasets; sample entropy (SampEn) feature extraction is used to further reduce computational time and obtain better classification results; BiLSTM for better capture of EEG temporal connections. The performance of the model was evaluated on a clinical dataset containing 10 MCI and 10 HC. Compared with the traditional EEG classification method, the result shows that BiLSTM is more suitable for the EEG classification task, and the classification accuracy is significantly improved by data augmentation. After performing 10-fold cross-validation and 10-fold data augmentation, the model achieved a maximum classification accuracy of 97.20 ± 1.74 %. The results indicate that the model can be used to diagnose MCI patients with the EEG small datasets. Meanwhile, The data augmentation used in this study has a high reference value for other resting-state EEG classification tasks. [ABSTRACT FROM AUTHOR]

Increased levels of Alzheimer-associated neuronal thread protein (AD7c-NTP) are often detected in urine in the early course of Alzheimer’s disease (AD), which makes it a promising biomarker for AD. However, whether the concentration of urinary AD7c-NTP is increased in patients with mild cognitive impairment (MCI) remains unclear. The aim of this study was to explore the value of urinary AD7c-NTP to assist in the diagnosis of cognitive impairment by comparing differences in urinary AD7c-NTP among normal controls, MCI patients and AD patients. One hundred and seventy patients from the Xuan wu Hospital, Capital Medical University were divided into three groups according to their clinical diagnosis: an AD group (n = 45), an MCI group (n = 60) and a normal group (n = 65). The Mini Mental State Examination and the Montreal Cognitive Assessment scale were used to screen for the diagnosis of AD and MCI, and patients met the diagnostic criteria of the National Institute of Neurological Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association. The level of urinary AD7c-NTP was determined using the enzyme-linked immunosorbent assay method. The urinary levels of AD7c-NTP in the AD group (median 2.14 [range 0.49–6.39] ng/ml) and the MCI group (median 1.57 [range 0.4–4.15] ng/ml) were significantly higher than those of the normal group (median 0.53 [range 0.04–2.07] ng/ml). To our knowledge our study is the first to show that the level of urinary AD7c-NTP in MCI patients is higher than in healthy people, which suggests that the level of urinary AD7c-NTP may be an important biomarker for early diagnosis of MCI. [ABSTRACT FROM AUTHOR]

Apolipoprotein E-ε4 (APOE-ε4) carriers had elevated cerebrospinal fluid (CSF) presynaptic protein growth-associated protein-43 (GAP-43), but the underlying mechanism is not fully understood. We investigated how the APOE-ε4 genotype affects the baseline and longitudinal changes in CSF GAP-43 and their associations with β-amyloid positron emission tomography (Aβ PET), CSF phosphorylated tau 181 (p-Tau 181), neurodegeneration, and cognitive decline. Compared to APOE-ε4 non-carriers, APOE-ε4 carriers had higher baseline levels and faster rates of increases in Aβ PET, CSF p-Tau 181 , and CSF GAP-43. Both higher baseline levels and faster rates of increase in CSF GAP-43 were associated with greater baseline Aβ PET and CSF p-Tau 181 , which fully mediated the APOE-ε4 effect on CSF GAP-43 elevations. Independent of Aβ PET and CSF p-Tau 181 , APOE-ε4 carriage was associated with exacerbated GAP-43-related longitudinal hippocampal atrophy and cognitive decline, especially in Aβ+ participants (GAP-43 × time × APOE-ε4). These findings suggest that the APOE-ε4 effect on GAP-43-related presynaptic dysfunction is mediated by primary Alzheimer's pathologies and independently correlates to hippocampal atrophy and cognitive decline in the future. [ABSTRACT FROM AUTHOR]

Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease (AD). However, whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia and whether these features provide any neurobiological foundation remains unclear. The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging (MRI) markers for AD. Multivariate classifier-based support vector machine (SVM) analysis provided individual-level predictions for distinguishing AD patients (n = 261) from normal controls (NCs; n = 231) with an accuracy of 88.21% and intersite cross-validation. Further analyses of a large, independent the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1228) reinforced these findings. In MCI groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aβ, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up. These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI, and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus. The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific brain network changes. Uncovering disease heterogeneity by using functional networks could provide insights into precise diagnoses. We investigated the subtypes of AD using nonnegative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal control subjects. We conducted the analysis using a discovery dataset (n = 809) and a validated dataset (n = 291). Next, we grouped individuals with mild cognitive impairment according to the model obtained in the AD groups. Finally, the clinical measures and brain structural characteristics were compared among the subtypes to assess their relationship with differences in the functional network. Individuals with AD were clustered into 4 subtypes reproducibly, which included those with 1) diffuse and mild functional connectivity disruption (subtype 1), 2) predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), 3) predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in prefrontal cortex connectivity (subtype 3), and 4) predominantly decreased connectivity in the basal ganglia accompanied by an increase in prefrontal cortex connectivity (subtype 4). In addition to these differences in functional connectivity, differences between the AD subtypes were found in cognition, structural measures, and cognitive decline patterns. These comprehensive results offer new insights that may advance precision medicine for AD and facilitate strategies for future clinical trials. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disease that influences elderly populations, with no effective method for its treatment so far. To improve its diagnosis and treatment, changes of small molecule metabolite during AD should be elucidated. Kai-Xin-San (KXS) is an herbal formulae that has been widely used to treat mental disorders, especially amnesia and depression in China. Experimental AD was induced in rats by an intraperitoneal injection of d -galactose ( d -gal) and administered intragastrically with aluminum chloride (AlCl 3 ) simultaneously for 105 days. Morris water maze task as a behavior test was used for testing the effects of KXS on AD model and pathological changes to the brain were assessed by hematoxylin-eosin staining and immunohistochemistry. The levels of Bcl-2 and ChAT in hippocampus were evaluated by western-blot. Furthermore, metabolite profiling of AD was performed through ultra-performance liquid chromatography/electrospray ionization quadruple time-of- flight-high-definition mass spectrometry (UPLC/ESI-Q-TOF/HDMS) combined with pattern recognition approaches and pathway analysis. d -gal and AlCl 3 -treated caused a decline in spatial learning and memory, hippocampal histopathological abnormalities and increased Aβ1-40 levels in the brain cortex and hippocampus along with decreased Bcl-2 and ChAT expression in the hippocampus. KXS significantly improved the cognitive impairment induced by d -gal and AlCl 3 , attenuated hippocampal histopathological abnormalities, reduced Aβ1-40 levels and increased Bcl-2 and ChAT expression in the hippocampus. A total of 48 metabolites were considered as potential biomarkers of AD, and 36 metabolites may correlate with the regulation of KXS treatment on AD. Changes in AD metabolic profiling were close to normal states through regulating multiple perturbed pathways after KXS treatment. This study has revealed the potential biomarkers and metabolic networks of AD, illuminated the biochemistry mechanism of AD and the metabolic pathways influenced by KXS. [ABSTRACT FROM AUTHOR]

• Anticholinergic drug use is an independent risk factor for all-cause dementia and Alzheimer's disease. • Heavier anticholinergic drug exposure is associated with a higher risk of dementia. • Different categories of anticholinergic drugs exert distinct effects on incident dementia. • It is important to manage and prescribe anticholinergic drugs to help optimize early prevention strategies for dementia. Dementia is one of the greatest global challenges for public health; however, the relationship between anticholinergic drugs and dementia remains unclear. The aim of the present study was to perform a systematic review and meta-analysis of the predictive roles of anticholinergic drugs in dementia risk. After pooling fourteen longitudinal and case-control studies with a total of 1,564,181 subjects, anticholinergic drug use was associated with an increased risk of all-cause dementia and Alzheimer's disease. Both low and high anticholinergic drug burdens were associated with dementia. Moreover, there was a dose-dependent relationship between anticholinergic drugs and risk of dementia. With respect to the categories of anticholinergic drugs, antiparkinson, urological drugs, and antidepressants increased the risk for dementia; however, cardiovascular and gastrointestinal drugs played potentially protective roles. These findings underscore the importance of anticholinergic drugs as a potential modifiable risk factor for dementia and provide treatment priorities to optimize dementia prevention. [ABSTRACT FROM AUTHOR]

A growing awareness about brain health and Alzheimer's disease in the general population is leading to an increasing number of cognitively unimpaired individuals, who are concerned that they have reduced cognitive function, to approach the medical system for help. The term subjective cognitive decline (SCD) was conceived in 2014 to describe this condition. Epidemiological data provide evidence that the risk for mild cognitive impairment and dementia is increased in individuals with SCD. However, the majority of individuals with SCD will not show progressive cognitive decline. An individually tailored diagnostic process might be reasonable to identify or exclude underlying medical conditions in an individual with SCD who actively seeks medical help. An increasing number of studies are investigating the link between SCD and the very early stages of Alzheimer's disease and other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

[Display omitted] In our continuing efforts to explore structure–activity relationships around the novel class of potent, isonicotinamide-based GSK3 inhibitors described in our previous report, we extensively explored structural variations around both 4/5-pyridine substitutions and the amide group. Some analogs were found to have greatly improved pTau lowering potency while retaining high kinase selectivity. In contrast to previous active compounds 1a-c , a close analog 3h did not show in vivo efficacy in a triple-transgenic mouse Alzheimer's disease model. In general, these 2‑pyridinyl amide derivatives were prone to amidase mediated hydrolysis in mouse plasma. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer’s disease (AD). Non-invasive diagnostic methods are desirable to identify MCI for early therapeutic interventions. In this study, we proposed a support vector machine (SVM)-based method to discriminate between MCI patients and normal controls (NCs) using multi-level characteristics of magnetic resonance imaging (MRI). This method adopted a radial basis function (RBF) as the kernel function, and a grid search method to optimize the two parameters of SVM. The calculated characteristics, i.e., the Hurst exponent (HE), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and gray matter density (GMD), were adopted as the classification features. A leave-one-out cross-validation (LOOCV) was used to evaluate the classification performance of the method. Applying the proposed method to the experimental data from 29 MCI patients and 33 healthy subjects, we achieved a classification accuracy of up to 96.77%, with a sensitivity of 93.10% and a specificity of 100%, and the area under the curve (AUC) yielded up to 0.97. Furthermore, the most discriminative features for classification were found to predominantly involve default-mode regions, such as hippocampus (HIP), parahippocampal gyrus (PHG), posterior cingulate gyrus (PCG) and middle frontal gyrus (MFG), and subcortical regions such as lentiform nucleus (LN) and amygdala (AMYG). Therefore, our method is promising in distinguishing MCI patients from NCs and may be useful for the diagnosis of MCI. [ABSTRACT FROM AUTHOR]