Mild Cognitive Impairment Alzheimer Research Articles

Psychometric Properties of the Persian Montreal Cognitive Assessment in Mild Cognitive Impairment and Alzheimer Disease

Introduction: The Montreal Cognitive Assessment (MoCA) is a cognitive screening test widely used in clinical practice and suited for the detection of Mild Cognitive Impairment (MCI). The aims were to evaluate the psychometric properties of the Persian MoCA as a screening test for mild cognitive dysfunction in Iranian older adults and to assess its accuracy as a screening test for MCI and mild Alzheimer disease (AD). Method: One hundred twenty elderly with a mean age of 73.52 ± 7.46 years participated in this study. Twenty-one subjects had mild AD (MMSE score ≤21), 40 had MCI, and 59 were cognitively healthy controls. All the participants were administered the Mini-Mental State Examination (MMSE) to evaluate their general cognitive status. Also, a battery of comprehensive neuropsychological assessments was administered. Results: The mean score on the Persian version of the MoCA and the MMSE were 19.32 and 25.62 for MCI and 13.71 and 22.14 for AD patients, respectively. Using an optimal cutoff score of 22 the MoCA test detected 86% of MCI subjects, whereas the MMSE with a cutoff score of 26 detected 72% of MCI subjects. In AD patients with a cutoff score of 20, the MoCA had a sensitivity of 94% whereas the MMSE detected 61%. The specificity of the MoCA was 70% and 90% for MCI and AD, respectively. Discussion: The results of this study show that the Persian version of the MoCA is a reliable screening tool for detection of MCI and early stage AD. The MoCA is more sensitive than the MMSE in screening for cognitive impairment, proving it to be superior to MMSE in detecting MCI and mild AD.

Correlation between cerebral blood flow and olfactory function in mild cognitive impairment and Alzheimer's disease.

Olfactory dysfunction is common in patients with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We sought to elucidate brain regions associated with olfactory dysfunction in patients with MCI and early AD by using 123I-IMP-SPECT to detect regional cerebral blood flow (CBF). We included 218 patients diagnosed with AD or MCI, who underwent a comprehensive battery of neuropsychiatric and neuropsychological tests, Alzheimer's Disease Assessment Scale-Cognitive Part (ADAS-Cog), and forward- and backward-digit span. Olfactory function was assessed using T&T olfactometry of five odors; patients stated whether they experienced any smell (detection test) and identified the odor (identification test). The association between single-photon emission computerized tomography based regional CBF and olfactory function was examined by voxel-by-voxel multiple regression analysis, considering sex, age, and education as covariate parameters. Of the 218 patients, 78 had mildly impaired olfactory detection and 15 had olfactory detection loss; additionally, 213 had mild olfactory identification impairment. The odor detection score correlated significantly with the ADAS-Cog word recall score (r=0.193, p=0.004). The odor identification score correlated significantly with the ADAS memory (r=0.408, p<0.001) and ADAS orientation (r=0.292, p<0.001) scores. The odor identification score correlated negatively with CBF in the left temporal pole, entorhinal area, and bilateral frontal poles (p<0.001). Olfactory identification dysfunction in patients with MCI and AD is attributable to reduced CBF of the left temporal pole, entorhinal area, and bilateral frontal pole.

Diagnostic accuracy of the Cogstate Brief Battery for prevalent MCI and prodromal AD (MCI A+ T+ ) in a population-based sample.

This study evaluated the diagnostic accuracy of the Cogstate Brief Battery (CBB) for mild cognitive impairment (MCI) and prodromal Alzheimer's disease (AD) in a population-based sample. Participants included adults ages 50+ classified as cognitively unimpaired (CU, n=2866) or MCI (n=226), and a subset with amyloid (A) and tau (T) positron emission tomography who were AD biomarker negative (A-T-) or had prodromal AD (A+T+). Diagnostic accuracy of the Learning/Working Memory Composite (Lrn/WM) for discriminating all CU and MCI was moderate (area under the curve [AUC]=0.75), but improved when discriminating CU A-T- and MCI A+T+ (AUC=0.93) and when differentiating MCI participants without AD biomarkers from those with prodromal AD (AUC=0.86). Conventional cut-offs yielded lower than expected sensitivity for both MCI (38%) and prodromal AD (73%). Clinical utility of the CBB for detecting MCI in a population-based sample is lower than expected. Caution is needed when using currently available CBB normative data for clinical interpretation.

Assessment of the DTI-ALPS Parameter Along the Perivascular Space in Older Adults at Risk of Dementia.

Recently, there has been growing interest in the glymphatic system (the functional waste clearance pathway for the central nervous system and its role in flushing solutes (such as amyloid ß and tau), metabolic, and other cellular waste products in the brain. Herein, we investigate a recent potential biomarker for glymphatic activity (the diffusion tensor imaging along the perivascular space [DTI-ALPS] parameter) using diffusion MRI imaging in an elderly cohort comprising 10 cognitively normal, 10 mild cognitive impairment (MCI), and 16 Alzheimer's disease (AD). All 36 participants imaged on a Siemens 3.0T Tim Trio. Single-SE diffusion weighted Echo-planar imaging scans were acquired as well as T1 magnetization prepared rapid gradient echo, T2 axial, and susceptibility weighted imaging. Three millimeter regions of interest were drawn in the projection and association fibers adjacent to the medullary veins at the level of the lateral ventricle. The DTI-ALPS parameter was calculated in these regions and correlated with cognitive status, Mini-Mental State Examination (MMSE), and ADASCog11 measures. Significant correlations were found between DTI-ALPS and MMSE and ADASCog11 in the right hemisphere adjusting for age, sex, and APoE ε4 status. Significant differences were also found in the right DTI-ALPS indices between cognitively normal and AD groups (P < .026) and MCI groups (P < .025) in a univariate general linear model corrected for age, sex, and APoE ε4. Significant differences in apparent diffusion coefficient between cognitively normal and AD groups were found in the right projection fibers (P = .028). Further work is needed to determine the utility of DTI-ALPS index in larger elderly cohorts and whether it measures glymphatic activity.

A Machine-Learning-Based Assessment Method for Early-Stage Neurocognitive Impairment by an Immersive Virtual Supermarket.

Alzheimer's disease (AD) is a neurodegenerative disorder. Though it is not yet curable or reversible, research has shown that clinical intervention or intensive cognitive training at an early stage may effectively delay the progress of the disease. As a result, screening populations with mild cognitive impairment (MCI) or early AD via efficient, effective and low-cost cognitive assessments is important. Currently, a cognitive assessment relies mostly on cognitive tests, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), which must be performed by therapists. Also, cognitive functions can be divided into a variety of dimensions, such as memory, attention, executive function, visual spatial and so on. Executive functions (EF), also known as executive control or cognitive control, refer to a set of skills necessary to perform higher-order cognitive processes, including working memory, planning, attention, cognitive flexibility, and inhibitory control. Along with the fast progress of virtual reality (VR) and artificial intelligence (AI), this study proposes an intelligent assessment method aimed at assessing executive functions. Utilizing machine learning to develop an automatic evidence-based assessment model, behavioral information is acquired through performing executive-function tasks in a VR supermarket. Clinical trials were performed individuals with MCI or early AD and six healthy participants. Statistical analysis showed that 45 out of 46 indices derived from behavioral information were found to differ significantly between individuals with neurocognitive disorder and healthy participants. This analysis indicates these indices may be potential bio-markers. Further, machine-learning methods were applied to build classifiers that differentiate between individuals with MCI or early AD and healthy participants. The accuracy of the classifier is up to 100%, demonstrating the derived features from the VR system were highly related to diagnosis of individuals with MCI or early AD.

Changes in the use of psychotropic drugs during the course of Alzheimer's disease: A large-scale longitudinal study of French medical records.

IntroductionWe aim to understand how patients with Alzheimer's disease (AD) are treated by identifying in a longitudinal fashion the late‐life changes in patients’ medical history that precede and follow AD diagnosis.MethodsWe use prescription history of 34,782 patients followed between 1996 and 2019 by French general practitioners. We compare patients with an AD diagnosis, patients with mild cognitive impairment (MCI), and patients free of mental disorders. We use a generalized mixed‐effects model to study the longitudinal changes in the prescription of eight drug types for a period 15 years before diagnosis and 10 years after.ResultsIn the decades preceding diagnosis, we find that future AD patients are treated significantly more than MCI patients with most psychotropic drugs and that most studied drugs are increasingly prescribed with age. At the time of diagnosis, all psychotropic drugs except benzodiazepines show a significant increase in prescription, while other drugs are significantly less prescribed. In the 10 years after diagnosis, nearly all categories of drugs are less and less prescribed including antidementia drugs.DiscussionPre‐diagnosis differences between future AD patients and MCI patients may indicate that subtle cognitive changes are recognized and treated as psychiatric symptoms. The disclosure of AD diagnosis drastically changes patients’ care, priority being given to the management of psychiatric symptoms. The decrease of all prescriptions in the late stages may reflect treatment discontinuation and simplification of therapeutic procedures. This study therefore provides new insights into the medical practices for management of AD.

Parametric estimation of reference signal intensity in the quantification of amyloid-beta deposition: an 18F-AV-45 study.

Positron emission tomography (PET) with the radiotracer florbetapir (18F-AV-45) allows the pathophysiology of Alzheimer's disease (AD) to be tracked in vivo. The semi-quantification of amyloid-beta (Aβ) has been extensively evaluated with the standardized uptake value ratio (SUVR) but is susceptible to disturbance from the candidate reference region and the partial volume effect (PVE). In the present study, we applied the parametric estimation of reference signal intensity (PERSI) method to 18F-AV-45 PET images for intensity normalization. We enrolled 479 people with 18F-AV-45 images from the Alzheimer's Disease Neuroimaging Initiative database: 261 healthy controls (HCs), 102 patients with mild cognitive impairment (MCI), and 116 AD patients. We used white matter post-processed by PERSI (PERSI-WM) as the reference region and compared our proposed method with the traditional method for semi-quantification. SUVRs were calculated for eight regions of interest: the frontal lobe, the parietal lobe, the temporal lobe, the occipital lobe, the anterior cingulate cortex, the posterior cingulate cortex, the precuneus, and the global cortex. The SUVRs derived from PERSI-WM and other reference regions were evaluated by effect size and receiver-operator characteristic curve analyses. The SUVRs derived from PERSI-WM showed significantly higher trace retention in the frontal, parietal, temporal, and occipital lobes, as well as in the anterior cingulate, posterior cingulate, precuneus, and global cortex in the AD Aβ-positive (+) group (mean: +43.3%±5.4%, P<0.01) and MCI Aβ+ group (mean: +29.6%±5.3%, P<0.01). For the global cortex, PERSI-WM had the greatest Cohen's d effect size compared with the HC Aβ-negative (-) group (AD Aβ+ and MCI Aβ+: 3.02, AD Aβ+: 3.56, MCI Aβ+: 2.34), and the highest area under the curve (AUC) between the HC Aβ- and AD Aβ+ groups (AUC: 0.983, 95% confidence interval: 0.978-0.998). PERSI-WM could mitigate the influence of PVE and improve the semi-quantification of 18F-AV-45 images; therefore, it could be used for large-scale clinical application in the nuclear medicine domain.

Cerebral amyloid accumulation is associated with distinct structural and functional alterations in the brain of depressed elders with mild cognitive impairment

BackgroundElderly patients with late-life depression (LLD) often report mild cognitive impairment (MCI), so Alzheimer's disease (AD) is hard to identify in these patients. We aimed to identify the structural and functional differences between prodromal AD and LLD-related MCI. MethodsWe performed voxel-based morphometry and functional connectivity (FC) analyses in elderly patients with both LLD and MCI to compare alterations between those with cerebral amyloidopathy and those without. We subdivided patients into subthreshold depression (STD) and major depressive disorder (MDD) groups. Using florbetaben positron emission tomography (PET), we compared volume and connectivity between healthy controls and four STD and MDD groups with or without amyloid deposition(A): STD-MCI-A(+), MDD-MCI-A(+), STD-MCI-A(-), and MDD-MCI-A(-). ResultsSubjects with MDD or amyloid deposition showed greater volume reduction in the left middle temporal gyrus. MDD groups had lower FC than STD groups in the frontal, cortical, and limbic areas. The STD-MCI-A(+) group showed greater FC reduction than the MDD-MCI-A(-) and STD-MCI-A(-) groups, particularly in the hippocampus, parahippocampus, and frontal and temporal cortices. The functional differences associated with amyloid plaques were more evident in the STD group than in the MDD group. LimitationsLimitations include disproportional sex ratios, inability to determine the longitudinal effects of amyloidopathy in large populations. ConclusionsRegional gray matter loss and alterations in brain networks may reflect impairments caused by amyloid deposition and depression. Such changes may facilitate the detection of prodromal AD in elderly patients with both depression and cognitive dysfunction, allowing earlier intervention and more appropriate treatment.

Outcomes and interventions in the elderly with and without cognitive impairment: a longitudinal study.

ABSTRACT.Background:Clinical follow-up studies are necessary for a better understanding of the evolution of cognitive impairment as well as the development of better assessment and intervention tools.Objective:To investigate whether older people with preserved cognition (PC), mild cognitive impairment (MCI) and mild Alzheimer's disease (AD) show differences in clinical outcomes and interventions after a 32-month period.Methods:One hundred twenty-four community-dwelling older people were included and classified in one of three groups (PC, MCI and mild AD). Information on clinical outcomes (deaths, new diagnoses, falls, need for assistance or changes in routine and hospitalizations) and interventions (increased use of medication, physiotherapeutic intervention, practice of physical exercise, etc.) in the 32-month period were collected by telephone or during a home visit on a single day.Results:Ninety-five participants (35 with PC, 33 with MCI and 27 with AD) were reevaluated after 32 months. The need for assistance/changes in routine was significantly higher in the AD group, especially with regard to basic activities of daily living. Unlike the other groups, the PC group did not show “other diagnoses” (urinary incontinence, prolapse, change in vision or autoimmune disease). No significant differences were found regarding other variables.Conclusions:Older people with and without cognitive impairment exhibited differences in some clinical outcomes after 32 months, such as need for assistance or changes in their routine and new diagnoses of specific diseases. Therefore, the multidimensionality of geriatric patients should be considered when planning assessments and interventions.

Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images.

Deep-learning methods (especially convolutional neural networks) using structural magnetic resonance imaging (sMRI) data have been successfully applied to computer-aided diagnosis (CAD) of Alzheimer's disease (AD) and its prodromal stage [i.e., mild cognitive impairment (MCI)]. As it is practically challenging to capture local and subtle disease-associated abnormalities directly from the whole-brain sMRI, most of those deep-learning approaches empirically preselect disease-associated sMRI brain regions for model construction. Considering that such isolated selection of potentially informative brain locations might be suboptimal, very few methods have been proposed to perform disease-associated discriminative region localization and disease diagnosis in a unified deep-learning framework. However, those methods based on task-oriented discriminative localization still suffer from two common limitations, that is: 1) identified brain locations are strictly consistent across all subjects, which ignores the unique anatomical characteristics of each brain and 2) only limited local regions/patches are used for model training, which does not fully utilize the global structural information provided by the whole-brain sMRI. In this article, we propose an attention-guided deep-learning framework to extract multilevel discriminative sMRI features for dementia diagnosis. Specifically, we first design a backbone fully convolutional network to automatically localize the discriminative brain regions in a weakly supervised manner. Using the identified disease-related regions as spatial attention guidance, we further develop a hybrid network to jointly learn and fuse multilevel sMRI features for CAD model construction. Our proposed method was evaluated on three public datasets (i.e., ADNI-1, ADNI-2, and AIBL), showing superior performance compared with several state-of-the-art methods in both tasks of AD diagnosis and MCI conversion prediction.

Computer-Assisted Speech Analysis in Mild Cognitive Impairment and Alzheimer's Disease: A Pilot Study from Shanghai, China.

Language dysfunction is a frequently reported symptom in Alzheimer's disease (AD). However, computer-assisted analysis of spontaneous speech in AD and mild cognitive impairment (MCI) is rarely used to date. To characterize the language impairment in AD and amnestic MCI (aMCI) with computer-based automatic analysis via the "Automatic Speech Recognition (ASR) software for cognitive impairment V1.3". A total of 64 subjects, including 20 AD patients, 20 aMCI patients, and 24 healthy controls were recruited. All subjects underwent neuropsychological tests, and spontaneous speech samples were recorded through the description of the "Cookie-Theft Picture" and then analyzed by the computerized software. Subsequently, we compared the speech parameters between the subjects and the controls. We identified seven spontaneous speech parameters (percentage of silence duration, average duration of phrasal segments, average duration of silence segments, number of speech segments, number of long pauses, ratio of hesitation/speech counts and ratio of short pause/speech counts) demonstrating significant differences between the three groups (p < 0.05). All seven speech parameters significantly correlated with cognitive performance, with average duration of silence segments demonstrating the best correlation to cognitive performance on stepwise multiple linear regression analysis. Computer-assisted automated analysis of speech/silence segments demonstrated the potential to reflect the intrinsic linguistic impairment associated with MCI and AD. It has a promising prospect in the early detection of AD and assessment of disease severity.

EVALUATING THE COGNITIVE EFFECTS OF EXERCISE PRIMING AND TRANSCRANIAL DIRECT CURRENT STIMULATION IN MILD COGNITIVE IMPAIRMENT AND MILD ALZHEIMER'S DISEASE: THE EXPRESS STUDY

Introduction Current pharmacotherapies for Alzheimer's disease (AD) are only modestly effective. There remains a critical need for treatments focused on early stages of the disease, when quality of life is still potentially excellent. While transcranial direct current stimulation (tDCS) has been shown to improve memory in some people with Mild Cognitive Impairment (MCI) and mild AD with minimal side-effects, recent evidence suggests that tDCS works better when brain circuits are active. As exercise increases brain activity using mechanisms that may support tDCS response, it may be an effective primer given before tDCS. This study assesses whether exercise priming with tDCS can help improve memory in MCI and mild AD, and whether this combination improves memory better than exercise or tDCS alone. Methods In this 5-week, randomized, blinded, sham-controlled clinical trial, eligible patients receive either 1) combined exercise and tDCS, 2) exercise and sham tDCS, or 3) exercise education only and tDCS. Repeated measures analyses of covariance will be conducted to evaluate between-group differences on cognitive outcomes over time. Results Blinded preliminary findings are presented (N=10, mean age=74±6?years, 50% female, mean years of education=16.2±2.5?years, mean Montreal Cognitive Assessment (MoCA) score= 21.5±2.9). There was a significant effect of treatment over time on Word Recall scores (F(2,7)=5.5, p=0.036). There were no statistically significant differences between groups over times on MoCA (F(2,7)=3.6, p=0.085) or Word Recognition scores (F(2,7)=4.5, p=0.054). Participants randomized to exercise education only with tDCS did not increase their exercise frequency or intensity according to the Leisure Time Exercise Questionnaire. Participants randomized to exercise priming were able to exercise at a moderate-intensity level for the last two weeks of the study when receiving combined tDCS or sham tDCS treatment. Overall adherence to study interventions was 98%. Conclusions Recruitment is ongoing (N=10/30). Updated results will be presented. This study may identify a new, feasible, non-invasive combination therapy that improves cognitive function in MCI and early stages of AD, therefore having major health implications. This research was funded by: Canadian Institutes of Health Research

Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease by the Plasma and Serum Amyloid-beta 42 Assay through Highly Sensitive Peptoid Nanosheet Sensor.

Alzheimer's disease (AD) is the most common neurodegenerative disorder with a continuous pathophysiological process starting from the preclinical and mild cognitive impairment (MCI) phases to the dementia phase. Early diagnosis is prerequisite for the early intervention of AD but meanwhile challenging. Amyloid-beta 1-42 (Aβ42) plays a crucial part in AD pathology. Positron-emission tomography (PET) imaging of Aβ42 in the brain and the measurement of Aβ42 in the cerebrospinal fluid (CSF) have been adopted for the auxiliary diagnosis of AD, but their widespread clinical application has been limited due to the radiation and the high-cost of PET and the invasive lumbar puncture for collecting CSF. Noninvasive and cost-effective blood-based assay is desirable for the early diagnosis of AD. Here, a label-free assay for the quantification of blood Aβ42 was developed using the high-throughput surface plasmon resonance imaging method with the aid of an antibody-mimetic peptoid nanosheet equipping Aβ42-recognizing loops. We demonstrated that this nanosheet-based sensor system could distinguish the plasma and sera from normal individuals and patients suffering AD and amnestic MCI with high sensitivity and specificity, preceding the diagnostic performance of the Aβ42-recognizing molecule and the antibody specific to Aβ42. This work provides a label-free, cost-effective, highly sensitive, and high-throughput blood-based assay for early detection of AD.

Personalized Computer-Aided Diagnosis for Mild Cognitive Impairment in Alzheimer’s Disease Based on sMRI and ¹¹C PiB-PET Analysis

Alzheimer’s disease (AD) is a neurodegenerative condition that affects the central nervous system and represents 60% to 70% of all dementia cases. Due to an increased aging population, the number of patients diagnosed with AD is expected to exceed 131 million worldwide by 2050. The disease is characterized by various clinical symptoms and pathological features that define three main sequential decline stages, namely, early/mild, intermediate/moderate and late/severe stages. Although it is considered irreversible, early diagnosis of AD is highly desirable to help preserve cognitive function. However, early diagnosis is difficult due to different factors, including the patient-specific development of AD. The main contribution of the proposed work is to present a personalized (i.e., local/brain regional) computer-aided diagnosis (CAD) system for early diagnosis of AD from two perspectives, functional and structural to assist diagnosis. In other words, the proposed system uniquely yields local/regional diagnosis by combining 11C PiB positron emission tomography (11C PiB PET), which provides functional diagnosis, with structural magnetic resonance imaging (sMRI), which provides structural diagnosis. To the best of our knowledge, this is the first work to combine sMRI and the 11C PiB PET for local/regional early diagnosis of AD. The system processes the two modalities through a number of steps: pre-processing, brain labeling (parcellation), feature extraction, and diagnosis. A local/regional diagnosis is presented for each modality separately, followed by the final global diagnosis obtained by integrating the results from the two modalities. Evaluation of the proposed system shows average results of 97.5%, 100%, and 96.77% for accuracy, specificity, and sensitivity, respectively. With further development, it is envisioned that this system could contribute to the early diagnosis of AD in the clinical setting.

Predicting Prodromal Dementia Using Linguistic Patterns and Deficits

Language deficiency is evident in the onset of several neurodegenerative disorders yet has barely been investigated when first occurs on the continuum of cognitive impairment for the purpose of early diagnoses. Alzheimer's disease (AD) is a neurodegenerative pathology that develops years prior to clinical manifestations and typically preceded by prodromal stages such as Mild Cognitive Impairment (MCI). Currently, the manual diagnostic procedures of both types are time consuming, following certain clinical criteria and neuropsychological examinations. Our study aims to establish state-of-the-art performance in the automatic identification of different dementia etiologies, including AD, MCI, and Possible AD (PoAD), and to determine whether patients with initial cognitive declines exhibit language deficits through the analysis of language samples deduced with the cookie theft picture description task. Data was derived from the cookie theft picture corpus of DementiaBank, from which all language samples of the identified etiologies were used, with a random subsampling technique that handles the skewness of the classes. Several original lexical and syntactic (i.e., lexicosyntactic) features were introduced and used alongside previously established lexicosyntactics to train machine learning (ML) classifiers against these etiologies. Further, a statistical analysis was conducted to uncover the deficiency across these etiologies. Our models resulted in benchmarks for differentiating all the identified classes with accuracies ranging between 95 to 98% and corresponding F1 values falling between 94 and 98%. The statistical analysis of our lexicosyntactic biomarkers shows that linguistic deviations are associated with prodromal as well as advanced neurodegenerative pathologies, being greatly impacted as cognitive decline increases and suggesting that language biomarkers may aid the early diagnosis of these pathologies.

Proteomic profiles of incident mild cognitive impairment and Alzheimer's disease among adults with Down syndrome.

IntroductionWe sought to determine if proteomic profiles could predict risk for incident mild cognitive impairment (MCI) and Alzheimer's disease (AD) among adults with Down syndrome (DS).MethodsIn a cohort of 398 adults with DS, a total of n = 186 participants were determined to be non‐demented and without MCI or AD at baseline and throughout follow‐up; n = 103 had incident MCI and n = 81 had incident AD. Proteomics were conducted on banked plasma samples from a previously generated algorithm.ResultsThe proteomic profile was highly accurate in predicting incident MCI (area under the curve [AUC] = 0.92) and incident AD (AUC = 0.88). For MCI risk, the support vector machine (SVM)‐based high/low cut‐point yielded an adjusted hazard ratio (HR) = 6.46 (P < .001). For AD risk, the SVM‐based high/low cut‐point score yielded an adjusted HR = 8.4 (P < .001).DiscussionThe current results provide support for our blood‐based proteomic profile for predicting risk for MCI and AD among adults with DS.

Abnormalities of DYRK1A-Cytoskeleton Complexes in the Blood Cells as Potential Biomarkers of Alzheimer's Disease.

Background:DYRK1A is implicated in mental retardation and Alzheimer’s disease (AD) dementia of Down syndrome (DS) individuals. The protein is associated with cytoskeleton and altered expression has been shown to impair the cytoskeletal network via dosage effect.Objective:Our original observations of marked reduction of cytoskeletal proteins associated with DYRK1A in brains and lymphoblastoid cell lines from DS and AD prompted an investigation whether cytoskeleton abnormalities could potentially be used as biomarkers of AD.Methods:Our assay relied on quantification of co-immunoprecipitated cytoskeletal proteins with DYRK1A (co-IP assay) and analysis of the profile of G- and F-actin fractions obtained by high-speed centrifugations (spin-down assay).Results:In co-IP assay, both DS and AD samples displayed reduced abundance of associated cytoskeletal proteins. In spin-down assay, G-actin fractions of controls displayed two closely spaced bands of actin in SDS-PAGE; while in AD and DS, only the upper band of the doublet was present. In both assays, alterations of actin cytoskeleton were present in DS, sporadic and familial AD cases, and in asymptomatic persons who later progressed to confirmed AD, but not in non-AD donors. In blind testing involving six AD and six controls, the above tests positively identified ten cases. Analysis of blood samples revealed the diversity of mild cognitive impairment (MCI) cases regarding the presence of the AD biomarker allowing distinction between likely prodromal AD and non-AD MCI cases.Conclusions:Both brain tissue and lymphocytes from DS and AD displayed similar semi-quantitative and qualitative alterations of actin cytoskeleton. Their specificity for AD-type dementia and the presence before clinical onset of the disease make them suitable biomarker candidates for early and definite diagnosis of AD. The presence of alterations in peripheral tissue points to systemic underlying mechanisms and suggests that early dysfunction of cytoskeleton may be a predisposing factor in the development of AD.

The Differences of Serum Metabolites Between Patients With Early-Stage Alzheimer's Disease and Mild Cognitive Impairment.

Background: Mild cognitive impairment (MCI) is regarded as a transition phase between normal aging and Alzheimer's disease (AD). Identification of novel and non-invasive biomarkers that can distinguish AD at an early stage from MCI is warranted for therapeutic and support planning. The goal of this study was to identify the differences of serum metabolomic profiles between MCI and early-stage AD, which could be potential non-invasive biomarkers for early diagnosis of AD.Methods: The subjects enrolled in the study were classified into two diagnostic groups: MCI (n = 40) and early-stage AD (n = 40). Targeted metabolomics analysis of serum samples was performed using the Biocrates Absolute-IDQ P180 kit. Targeted metabolic data were analyzed by TargetLynx, and MetIDQ software was applied to integrate the metabolites by automated calculation of metabolite concentrations.Results: The datasets of targeted metabolite analysis were analyzed by the orthogonal-projection-to-latent-structure–discriminant-analysis (OPLS-DA) model. The OPLS-DA score plots demonstrated considerable separation between the MCI and early-stage AD patients. The levels of pimelylcarnitine, putrescine, SM (OH) C24:1, and SM C24:0 were significantly lower, whereas the levels of acetylornithine, methionine sulfoxide, and PC ae C44:3 were significantly higher in early-stage AD patients as compared with MCI patients. Receiver operating characteristic curve analysis of a combination of three lipid metabolites [SM (OH) C24:1, SM C24:0, and PC ae C44:3] showed an acceptable discrimination between the early-stage AD and MCI patients (area under the curve = 0.788).Conclusions: Our results characterized the differences of serum metabolic profiles between MCI and early-stage AD patients. The positive findings from this study indicate that the minimally invasive method of blood sampling may help to identify patients with AD at an early stage from those with MCI.

Malnutrition is Associated with Behavioral and Psychiatric Symptoms of Dementia in Older Women with Mild Cognitive Impairment and Early-Stage Alzheimer's Disease.

We examined the nutritional status and its association with behavioral psychiatric symptoms of dementia (BPSD) among 741 memory clinic patients (normal cognition (NC), 152; mild cognitive impairment (MCI), 271; early-stage Alzheimer disease (AD), 318). Nutritional status and BPSD were assessed using the Mini Nutritional Assessment Short-Form (MNA-SF) and the Dementia Behavior Disturbance Scale (DBD), respectively. Compared to subjects with NC, more subjects with MCI and early-stage AD were at risk of malnutrition (MNA-SF, 8–11: NC, 34.2%; MCI, 47.5%; early-stage AD, 53.8%) and were malnourished (MNA-SF, 0–7: NC, 4.6%; MCI, 5.9%; early-stage AD, 8.2%). Among patients with MCI or early-stage AD, those at risk of/with malnutrition showed higher DBD scores than those well-nourished (12.7 ± 9.0 vs. 9.5 ± 7.3; p < 0.001). Moreover, analysis of covariance adjusting for confounders showed that nutritional status was significantly associated with specific BPSD, including “verbal aggressiveness/emotional disinhibition” (F = 5.87, p = 0.016) and “apathy/memory impairment” (F = 15.38, p < 0.001), which were revealed by factor analysis of DBD. Our results suggest that malnutrition is common among older adults with mild cognitive decline, and possibility that nutritional problems are associated with individual BPSD.

Risk Factors for Falls in Older Adults With Mild Cognitive Impairment and Mild Alzheimer Disease.

Understanding fall risk factors in people with mild cognitive impairment (MCI) and Alzheimer disease (AD) can help to establish specific plans for prevention of falls. The purpose of this study was to identify fall risk factors in older adults with MCI and mild AD. A prospective study was conducted with community-dwelling older adults (40 MCI; 38 mild AD). The assessments consisted of sociodemographic and health variables, caloric expenditure, functional status, functional mobility (10-m walk test, dual-task test, and transition Timed Up and Go phases), cognitive domains, and depressive symptoms. Falls were recorded for 6 months by a falls calendar and monthly telephone calls. Falls were reported in 52.6% and 51.4% of people with MCI and mild AD, respectively. Among people with MCI, lower functional status, higher time spent on walk and dual task tests, and higher depressive symptom scores were associated with falls. Higher time spent on the dual-task test was independently associated with falls. Among people with mild AD, falls were associated with lower time spent on the walk test and turn-to-sit phase, and a higher visuospatial domain score. Lower time spent on the turn-to-sit phase was identified as an independent predictor of falls. Careful attention should be given to dual-task and turn-to-sit activities when detecting risk of falls among older people with MCI and mild AD.