Diagnostic accuracy of the Clinical Dementia Rating Scale for detecting mild cognitive impairment and dementia: A bivariate meta-analysis

The risk of cognitive decline and dementia in older adults diagnosed with COVID-19: A systematic review and meta-analysis

BACKGROUND

Cognitive impairment can be caused by infections with various pathogens, including SARS-CoV-2. Research has yet to determine the true incidence and course of cognitive impairment in older adults following COVID-19. Furthermore, research has theorised that COVID-19 is associated with dementia progression and diagnosis but this association has yet to be fully described.

METHODS

A systematic review was registered in Prospero and conducted on the databases PubMed, Embase, Ovid, CENTRAL and Cochrane Library. Studies reporting cognitive impairment and dementia outcomes in post-acute and post-COVID-19 patients aged ≥65 years, and which included control data, were included in this review.

RESULTS

15,124 articles were identified by the search strategy. After eliminating duplicate titles and completing title, abstracts and full-text review, 18 studies were included comprising of 412,957 patients with COVID-19 (46.63 % male) and 411,929 patients without COVID-19 (46.59 % male). The overall mean Montreal Cognitive Assessment (MoCA) score in COVID-19 patients was 23.34 out of 30 (95 % CI [22.24, 24.43]). indicating cognitive impairment. The overall proportion of patients identified as having new onset cognitive impairment was 65 % (95 % CI [44,81]). Subgroup analyses indicated that time since infection significantly improves overall MoCA score and reduces proportion of patients with cognitive impairment.

CONCLUSION

This study indicates that cognitive impairment may be an important sequela of COVID-19. Further research with adequate sample sizes is warranted regarding COVID-19's association with new-onset dementia and dementia progression, and the effect of repeat infections. There is a need for development of diagnostic and management protocols for COVID-19 patients with cognitive impairment.

Development of cognitive frailty screening tool among community-dwelling older adults

Purpose

To develop a brief screening tool consisting of twelve items that can be self-administered for rapid identification of older adults at risk of cognitive frailty (CF), named as Cognitive Frailty Screening Tool (CFST).

Patients and methods

A total of 1318 community-dwelling individuals aged 60 years and above were selected and assessed for cognitive frailty using a set of neuropsychology batteries and physical function tests. A binary logistic regression (BLR) was used to identify predictors of CF to be used as items in the screening tool. A suitable cut-off point was developed using receiver operating characteristic analysis.

Results

Twelve items were included in the screening tool, comprising of gender, education years, medical history, depressive symptoms and functional status as well as lifestyle activities. The area under the curve (AUC) was 0.817 (95 % CI:0.774-0.861), indicating an excellent discriminating power. The sensitivity and specificity for cut-off 7 were 80.8 % and 79.0 %, with an acceptable range of positive predictive value (PPV) (73.3 %) and negative predictive value (NPV) (85.2 %) for screening tools. Concurrent validity of CFST score with standard cognitive and frailty assessment tools shows a significant association with the total score of CFST with low to moderate correlation (p < 0.05 for all parameters).

Conclusion

CFST had good sensitivity and specificity and was valid for community-dwelling older adults. There is a need to evaluate further the cost-effectiveness of implementing CFST as a screening for the risk of CF in the community. Its usage in clinical settings needs further validation.

Needs analysis of family caregivers of people living with dementia in Sleman Regency, Yogyakarta

BACKGROUND

Without appropriate support, taking care of people living with dementia may become a burden for family caregivers. Identifying the needs for caregivers can help them minimise the burden of caring and meet quality care for people living with dementia.

METHODS

In the first phase, a content validity test was conducted on the Carers' Needs Assessment of Dementia (CNA-D) in the Indonesian version. The second phase, a sequential explanatory mixed-methods design, was conducted on 65 family caregivers in two stages. The first stage was a cross-sectional study. A correlation test between caregiver problems and caregiver burden was conducted. The caregiver problems that were statistically significant were analyzed to reveal the unmet needs. A needs analysis was also conducted on problems experienced by more than half of the caregivers. In the second stage, we conducted a semi-structured individual interview, and thematic analysis was used to analyze the data.

RESULTS

The result of the validity test of the CNA-D instrument, Indonesian version, obtained a high value for content validity. The main problem of caregivers is a lack of information about dementia; however, it does not have a significant correlation with caregiver burden. The caregiver problem with the highest correlation to caregiver burden is burnout due to caring. More than 50% of caregivers' needs in Sleman Regency were not met in this research. The most essential needs that were not met were counselling and psychotherapy (83.3%-92%). The personal understanding of dementia, spiritual values in caring, cultural values in caring, barriers to accessing healthcare services, and self-care strategies should be considered in fulfilling family caregiver needs.

CONCLUSION

Most of the needs of family caregivers of people living with dementia in Sleman Regency, Yogyakarta, have not been met. Therefore, it requires collaboration with multi-professionals and all stakeholders to fulfil these needs.

Prevention, Assessment, and Management of Malnutrition in Older Adults with Early Stages of Cognitive Disorders

Malnutrition is common in older adults, and its risk is greater in those living with dementia. Relative to cognitively healthy peers, the prevalence of malnutrition is also increased in individuals with early stages of cognitive disorders owing to pathophysiological, cognitive, and psychosocial changes related to cognitive impairment. Malnutrition is associated with adverse health outcomes, including faster cognitive and functional decline. Here, we provide an overview of the prevention, assessment, and management of malnutrition in older adults, with a special focus on the aspects that are important to consider in individuals with early stages of cognitive disorders. Strategies to prevent malnutrition include systematic screening for malnourishment using validated tools to detect those at risk. If the screening reveals an increased risk of malnutrition, a detailed assessment including the individual's nutritional, medical, and functional status as well as dietary intake should be performed. The management of malnutrition in the early stages of cognitive disorders should be based on the findings of a comprehensive assessment and be personalized according to the individual's specific characteristics. In the article, we also provide an overview of the evidence on vitamin supplements and specific dietary patterns to prevent cognitive decline or attenuate its progression.

Feasibility and Preliminary Efficacy of American Elderberry Juice for Improving Cognition and Inflammation in Patients with Mild Cognitive Impairment

Despite data showing that nutritional interventions high in antioxidant/anti-inflammatory properties (anthocyanin-rich foods, such as blueberries/elderberries) may decrease risk of memory loss and cognitive decline, evidence for such effects in mild cognitive impairment (MCI) is limited. This study examined preliminary effects of American elderberry (Sambucus nigra subsp. canadensis) juice on cognition and inflammatory markers in patients with MCI. In a randomized, double-blind, placebo-controlled trial, patients with MCI (n = 24, Mage = 76.33 ± 6.95) received American elderberry (n = 11) or placebo (n = 13) juice (5 mL orally 3 times a day) for 6 months. At baseline, 3 months, and 6 months, patients completed tasks measuring global cognition, verbal memory, language, visuospatial cognitive flexibility/problem solving, and memory. A subsample (n = 12, 7 elderberry/5 placebo) provided blood samples to measure serum inflammatory markers. Multilevel models examined effects of the condition (elderberry/placebo), time (baseline/3 months/6 months), and condition by time interactions on cognition/inflammation outcomes. Attrition rates for elderberry (18%) and placebo (15%) conditions were fairly low. The dosage compliance (elderberry-97%; placebo-97%) and completion of cognitive (elderberry-88%; placebo-87%) and blood-based (elderberry-100%; placebo-100%) assessments was high. Elderberry (not placebo) trended (p = 0.09) towards faster visuospatial problem solving performance from baseline to 6 months. For the elderberry condition, there were significant or significantly trending decreases over time across several markers of low-grade peripheral inflammation, including vasorin, prenylcysteine oxidase 1, and complement Factor D. Only one inflammatory marker showed an increase over time (alpha-2-macroglobin). In contrast, for the placebo, several inflammatory marker levels increased across time (L-lactate dehydrogenase B chain, complement Factor D), with one showing deceased levels over time (L-lactate dehydrogenase A chain). Daily elderberry juice consumption in patients with MCI is feasible and well tolerated and may provide some benefit to visuospatial cognitive flexibility. Preliminary findings suggest elderberry juice may reduce low-grade inflammation compared to a placebo-control. These promising findings support the need for larger, more definitive prospective studies with longer follow-ups to better understand mechanisms of action and the clinical utility of elderberries for potentially mitigating cognitive decline.

Impact of incidental synucleinopathy in mild cognitive impairment due to Alzheimer disease

Recent evidence suggests that the presence of α-synuclein Lewy bodies (LBs) correlates with accelerated disease progression in patients with Alzheimer disease (AD) but it is unclear whether this effect is also exerted in the mild cognitive impairment (MCI) phase of AD. We sought to determine whether incidental LB pathology in patients with MCI due to AD is associated with a faster rate of cognitive decline compared to MCI controls without LB pathology. We identified patients within the National Alzheimer's Coordinating Center (NACC) database with MCI due to AD and stratified the cohort by the presence or absence of synucleinopathy. We utilized a repeated measures longitudinal analysis of Mini-Mental State Examination (MMSE) scores to determine whether the decline in performance occurred at a greater rate in the synucleinopathy patients. A total of 206 participants were studied; 80 had coincident synucleinopathy. The rate of decline in MMSE scores between the groups did not differ. This may suggest that a synergistic effect of LB and AD neuropathology is only appreciable in the later stages of disease progression. Further investigation into the effect of mixed LB and AD pathology in the early stages of cognitive impairment is warranted to highlight opportunities for targeted early intervention in patients.

A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning

The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer's disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively.

Characterization of de novo Dementia with Lewy Body with different duration of rapid eye movement sleep behavior disorder

BACKGROUND

Cognitive disorder, parkinsonism, autonomic dysfunction (AuD) and rapid eye movement sleep behavior disorder (RBD) can occur prior to or simultaneously with Dementia with Lewy Body (DLB) onset. RBD is generally linked with progressive neurodegenerative traits. However, associations between RBD with DLB, RBD without DLB, and RBD duration effects on DLB symptoms remain unclear.

OBJECTIVES

To examine DLB symptom frequency and subtypes in RBD, and explore the effects of different RBD onset times on symptoms in de novo DLB patients.

METHODS

In this multicenter investigation, we consecutively recruited 271 de novo DLB patients. All had standardized clinical and comprehensive neuropsychological evaluations. Subgroup analyses, performed based on the duration of RBD confirmed by polysomnography before the DLB diagnosis, we compared the proportion of patients with cognitive impairment, parkinsonism, and AuD features between groups.

RESULTS

Parkinsonism and AuD incidences were significantly elevated in DLB patients with RBD when compared with patients without RBD. Subgroup analyses indicated no significant differences in parkinsonism between DLB patients who developed RBD ≥10 years prior to the DLB diagnosis and DLB patients without RBD. The incidence of non-tremor-predominant parkinsonism and AuD was significantly higher in DLB patients whose RBD duration before the DLB diagnosis was <10 years when compared with DLB patients without RBD.

CONCLUSIONS

We identified significant symptom and phenotypic variability between DLB patients with and without RBD. Also, different RBD duration effects before the DLB diagnosis had a significant impact on symptomatic phenotypes, suggesting the existence of a slowly progressive DLB neurodegenerative subtype.

An explainable machine learning approach for Alzheimer's disease classification

The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for multiclass task. To enhance model explainability, we employed two rule-extraction approaches: class rule mining and stable and interpretable rule set for classification model. These approaches generated human-understandable rules to assist domain experts in comprehending the key factors involved in AD development. We further validated these rules using SHAP and LIME models, underscoring the significance of factors such as MEMORY, JUDGMENT, COMMUN, and ORIENT in determining AD risk. Our experimental outcomes also shed light on the crucial role of the Clinical Dementia Rating tool in predicting AD.

Meta-analysis and moderator analysis of the prevalence of malnutrition and malnutrition risk among older adults with dementia

BACKGROUND

Aging and dementia are common and closely related health problems in older adults, affecting their ability to maintain a healthy diet and ultimately resulting in malnutrition.

OBJECTIVE

In this study, we estimated the global prevalence of malnutrition and malnutrition risk in older adults with dementia.

DESIGN

Meta-analysis.

DATA SOURCES

Embase, Ovid MEDLINE, PubMed, CINAHL, Scopus, and Web of Science were comprehensively searched for articles published from database inception to October 2022.

METHODS

Pooled prevalence analysis was conducted using a generalized linear mixed model and a random-effects model. I2 and Cochran's Q statistics were used for identifying heterogeneity. Publication bias was evaluated using Peters' regression test and a funnel plot. Moderator analyses were conducted to investigate variations in the prevalence estimates of the included studies. All statistical analyses were conducted using R software.

RESULTS

A total of 16 studies involving a total of 6513 older adults with dementia were included in the analysis. The results indicated that 32.52 % (95 % confidence interval: 19.55-45.49) of all included older adults with dementia had malnutrition, whereas 46.80 % (95 % confidence interval: 38.90-54.70) had a risk of malnutrition. The prevalence of malnutrition was found to be high among older patients living in institutionalized settings (46.59 %) and those with Alzheimer's disease (12.26 %). The factors moderating the prevalence of malnutrition included adequate vitamin B12 consumption, risk behaviors, medical comorbidities, and certain neuropsychiatric symptoms. The prevalence of malnutrition risk was high among women (29.84 %) and patients with Alzheimer's disease (26.29 %). The factors moderating the prevalence of malnutrition risk included total cholesterol level, vitamin B12 consumption, risk behaviors, medical comorbidities, and certain neuropsychiatric symptoms.

CONCLUSIONS

Approximately one-third of older adults with dementia are malnourished and nearly half of older adults are at a risk of malnutrition. Encouraging collaboration among health-care professionals and ensuring early assessment and effective management of malnutrition are crucial for maintaining a favorable nutritional status in older adults with dementia.

REGISTRATION

This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42022369329).

TWEETABLE ABSTRACT

Globally, approximately 32.52 % of older adults with dementia are malnourished and approximately 46.80 % are at a risk of malnutrition.

Assessing Quality of Life, Economic Burden, and Independence Across the Alzheimer's Disease Continuum Using Patient-Caregiver Dyad Surveys

Background

Alzheimer's disease (AD) and mild cognitive impairment (MCI) have negative quality of life (QoL) and economic impacts on patients and their caregivers and may increase along the disease continuum from MCI to mild, moderate, and severe AD.

Objective

To assess how patient and caregiver QoL, indirect and intangible costs are associated with MCI and AD severity.

Methods

An on-line survey of physician-identified patient-caregiver dyads living in the United States was conducted from June-October 2022 and included questions to both patients and their caregivers. Dementia Quality of Life Proxy, the Care-related Quality of Life, Work Productivity and Activity Impairment, and Dependence scale were incorporated into the survey. Regression analyses investigated the association between disease severity and QoL and cost outcomes with adjustment for baseline characteristics.

Results

One-hundred patient-caregiver dyads were assessed with the survey (MCI, n = 27; mild AD, n = 27; moderate AD, n = 25; severe AD, n = 21). Decreased QoL was found with worsening severity in patients (p < 0.01) and in unpaid (informal) caregivers (n = 79; p = 0.02). Dependence increased with disease severity (p < 0.01). Advanced disease severity was associated with higher costs to employers (p = 0.04), but not with indirect costs to caregivers. Patient and unpaid caregiver intangible costs increased with disease severity (p < 0.01). A significant trend of higher summed costs (indirect costs to caregivers, costs to employers, intangible costs to patients and caregivers) in more severe AD was observed (p < 0.01).

Conclusions

Patient QoL and functional independence and unpaid caregiver QoL decrease as AD severity increases. Intangible costs to patients and summed costs increase with disease severity and are highest in severe AD.

Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1

BACKGROUND

The term 'dementia' covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency.

OBJECTIVE

This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer's disease (AD) - a common dementia condition.

METHODS

The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection.

RESULTS

A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository.

CONCLUSION

The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features.

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

Early Alzheimer's Disease Screening Approach Using Plasma Biomarkers

Alzheimer's disease (AD) is the most prevalent dementia, but it shows similar initial symptoms to other neurocognitive diseases (Lewy body disease (LBD) and frontotemporal dementia (FTD)). Thus, the identification of reliable AD plasma biomarkers is required. The aim of this work is to evaluate the use of a few plasma biomarkers to develop an early and specific AD screening method. Plasma p-Tau181, neurofilament light (NfL), and glial fibrillary acid protein (GFAP) were determined by Single Molecule Assay (SIMOA® Quanterix, Billerica, MA, USA) in patients with mild cognitive impairment due to AD (MCI-AD, n = 50), AD dementia (n = 10), FTD (n = 20), LBD (n = 5), and subjective cognitive impairment (SCI (n = 21)). Plasma p-Tau181 and GFAP showed the highest levels in AD dementia, and significant correlations with clinical AD characteristics; meanwhile, NfL showed the highest levels in FTD, but no significant correlations with AD. The partial least squares (PLS) diagnosis model developed between the AD and SCI groups showed good accuracy with a receiver operating characteristic (ROC) area under curve (AUC) of 0.935 (CI 95% 0.87-0.98), sensitivity of 86%, and specificity of 88%. In a first screen, NfL plasma levels could identify FTD patients among subjects with cognitive impairment. Then, the developed PLS model including p-Tau181 and GFAP levels could identify AD patients, constituting a simple, early, and specific diagnosis approach.

Optical Coherence Tomography Assessment of Macular Thickness in Alzheimer's Dementia with Different Neuropsychological Severities

This retrospective case-control study aimed to investigate associations between disease severity of Alzheimer's dementia (AD) and macular thickness. Data of patients with AD who were under medication (n = 192) between 2013 and 2020, as well as an age- and sex-matched control group (n = 200) with normal cognitive function, were included. AD patients were divided into subgroups according to scores of the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). Macular thickness was analyzed via the Early Treatment Diabetic Retinopathy Study (ETDRS) grid map. AD patients had significant reductions in full macula layers, including inner circle, outer inferior area, and outer nasal area of the macula. Similar retinal thinning was noted in ganglion cells and inner plexiform layers. Advanced AD patients (MMSE score < 18 or CDR ≥ 1) showed more advanced reduction of macular thickness than the AD group (CDR = 0.5 or MMSE ≥ 18), indicating that severe cognitive impairment was associated with thinner macular thickness. Advanced AD is associated with significant macula thinning in full retina and inner plexiform layers, especially at the inner circle of the macula. Macular thickness may be a useful biomarker of AD disease severity. Retinal imaging may be a non-invasive, low-cost surrogate for AD.

Stability in cognitive classification as a function of severity of impairment and ethnicity: A longitudinal analysis

OBJECTIVE

The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer's Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans).

METHODS

Biomarkers (structural MRI and amyloid PET scans) were compared between Hispanic/Latino and European American individuals who presented a change in cognitive diagnosis during the second or third follow-up and those who remained stable over time.

RESULTS

There were no significant differences in biomarkers between ethnic groups in any of the diagnostic categories. The frequency of CN and MCI participants who were progressors (progressed to a more severe cognitive diagnosis at follow-up) and non-progressors (either stable through follow-ups or unstable [progressed but later reverted to a diagnosis of CN]) did not significantly differ across ethnic groups. Progressors had greater atrophy in the hippocampus (HP) and entorhinal cortex (ERC) at baseline compared to unstable non-progressors (reverters) for both ethnic groups, and more significant ERC atrophy was observed among progressors of the Hispanic/Latino group. For European Americans diagnosed with MCI, there were 60% more progressors than reverters (reverted from MCI to CN), while among Hispanics/Latinos with MCI, there were 7% more reverters than progressors. Binomial logistic regressions predicting progression, including brain biomarkers, MMSE, and ethnicity, demonstrated that only MMSE was a predictor for CN participants at baseline. However, for MCI participants at baseline, HP atrophy, ERC atrophy, and MMSE predicted progression.

Association of insulin resistance with delirium and CSF biomarkers of Alzheimer's disease in elderly patients with hip fracture

BACKGROUND

Delirium is associated with dementia, which shares symptoms of cognitive dysfunctions. Notably, pathological mechanisms of Alzheimer's disease (AD) appear involved in both conditions. Insulin resistance has been reported to be a risk factor for AD, leading to neurodegeneration and cognitive impairment by affecting amyloid-beta (Aβ) metabolism, tau phosphorylation, and neuro-inflammation. Thus, insulin resistance may provide pathophysiological clues to the occurrence of delirium.

AIM

To investigate the relationship between preoperative insulin resistance, insulin concentrations in the cerebrospinal fluid (CSF), and delirium in elderly patients with hip fracture.

METHODS

The study included 138 elderly patients with or without pre-existing dementia who underwent hip fracture surgery. Delirium was diagnosed with the confusion assessment method performed daily from pre-operation to 5 days post-operation. CSF and blood samples were collected at the beginning of spinal anesthesia. The concentrations of insulin, amyloid-beta1-42 (Aβ42), total tau (t-tau), and phosphorylated tau (p-tau)₁₈₁ were determined by ELISA. Homeostasis model assessment (HOMA-IR) was used to assess insulin resistance.

RESULTS

Sixty-one (44%) of 138 hip fracture patients developed delirium peri-operatively. Compared to non-delirium group, the preoperative HOMA-IR index in delirium was much higher (median 3.3 vs 2.8, p = 0.001), but the CSF insulin concentration was significantly decreased (median 1.5 vs 2.2 mU/L, p < 0.001). Binary logistic regression analysis showed that HOMA-IR index and CSF insulin concentration were independent risk factors for delirium (p < 0.05). HOMA-IR index was negatively correlated with CSF insulin concentrations (rho =  - 0.55, p < 0.001). Multiple linear regression analysis showed that AD core biomarkers were significantly correlated with HOMA-IR index and CSF insulin level (p < 0.05).

CONCLUSION

This study innovatively examined insulin concentrations in serum and cerebrospinal fluid in patients with delirium. Our findings suggest that preoperative insulin resistance may affect the occurrence of delirium. The potential association between insulin resistance and delirium may be related to insulin resistance affecting the metabolism of AD biomarkers.

Identification of biomarkers differentiating Alzheimer's disease from other neurodegenerative diseases by integrated bioinformatic analysis and machine-learning strategies

Background

Alzheimer's disease (AD) is the most common neurodegenerative disease, imposing huge mental and economic burdens on patients and society. The specific molecular pathway(s) and biomarker(s) that distinguish AD from other neurodegenerative diseases and reflect the disease progression are still not well studied.

Methods

Four frontal cortical datasets of AD were integrated to conduct differentially expressed genes (DEGs) and functional gene enrichment analyses. The transcriptional changes after the integrated frontal cortical datasets subtracting the cerebellar dataset of AD were further compared with frontal cortical datasets of frontotemporal dementia and Huntingdon's disease to identify AD-frontal-associated gene expression. Integrated bioinformatic analysis and machine-learning strategies were applied for screening and determining diagnostic biomarkers, which were further validated in another two frontal cortical datasets of AD by receiver operating characteristic (ROC) curves.

Results

Six hundred and twenty-six DEGs were identified as AD frontal associated, including 580 downregulated genes and 46 upregulated genes. The functional enrichment analysis revealed that immune response and oxidative stress were enriched in AD patients. Decorin (DCN) and regulator of G protein signaling 1 (RGS1) were screened as diagnostic biomarkers in distinguishing AD from frontotemporal dementia and Huntingdon's disease of AD. The diagnostic effects of DCN and RGS1 for AD were further validated in another two datasets of AD: the areas under the curve (AUCs) reached 0.8148 and 0.8262 in GSE33000, and 0.8595 and 0.8675 in GSE44770. There was a better value for AD diagnosis when combining performances of DCN and RGS1 with the AUCs of 0.863 and 0.869. Further, DCN mRNA level was correlated to CDR (Clinical Dementia Rating scale) score (r = 0.5066, p = 0.0058) and Braak staging (r = 0.3348, p = 0.0549).

Conclusion

DCN and RGS1 associated with the immune response may be useful biomarkers for diagnosing AD and distinguishing the disease from frontotemporal dementia and Huntingdon's disease. DCN mRNA level reflects the development of the disease.

Structural and functional activities of brain in patients with vascular cognitive impairment: A case-controlled magnetic resonance imaging study

This study aimed to identify abnormal brain regions and imaging indices of vascular cognitive impairment (VCI) and explore specific imaging diagnostic markers of VCI. In this study, 24 patients with VCI were allocated to the VCI group and 25 healthy subjects were assigned to the healthy control (HC) group. Demographic data and neuropsychological test scores were compared using SPSS 25.0. The structural and functional imaging data were post-processed and statistically analyzed using CAT12, DPARSF and SPM12 software, based on the MATLAB platform. The structural and functional indices of gray matter volume (GMV) and regional homogeneity (ReHo) were obtained, and inter-group data were analyzed using an independent-sample t test. Sex, age, years of education, and total brain volume were used as covariates. Compared to the HC group, the GMV of VCI in the VCI group decreased significantly in the rectus muscles of the bilateral gyrus, left superior temporal gyrus, left supplementary motor area (SMA), right insula, right superior temporal gyrus, right anterior cuneiform lobe, and right anterior central gyrus (PRECG) (P < .05, FWE correction), without GMV enlargement in the brain area. ReHo decreased in the right inferior temporal gyrus (ITG), right parahippocampal gyrus, and left temporal pole (middle temporal gyrus, right lingual gyrus, left posterior central gyrus, and right middle temporal gyrus), the areas of increased ReHo were the left caudate nucleus, left rectus gyrus, right anterior cingulate gyrus and lateral cingulate gyrus (P < .05, FWE correction). Correlation analysis showed that the GMV of the left superior temporal gyrus was positively correlated with the Montreal Cognitive Assessment (MoCA) score (P < .05), and the GMV of the right insula was positively correlated with the MESE and long delayed memory scores (P < .05). There was a significant positive correlation between the ReHo and short-term delayed memory scores in the middle temporal gyrus of the left temporal pole (P < .05). The volume of GMV and ReHo decreased in VCI patients, suggesting that impairment of brain structure and function in specific regions is the central mechanism of cognitive impairment in these patients. Meanwhile, the functional indices of some brain regions were increased, which may be a compensatory mechanism for the cognitive impairment associated with VCI.

Convolutional Neural Networks to Classify Alzheimer’s Disease Severity Based on SPECT Images: A Comparative Study

Image recognition and neuroimaging are increasingly being used to understand the progression of Alzheimer’s disease (AD). However, image data from single-photon emission computed tomography (SPECT) are limited. Medical image analysis requires large, labeled training datasets. Therefore, studies have focused on overcoming this problem. In this study, the detection performance of five convolutional neural network (CNN) models (MobileNet V2 and NASNetMobile (lightweight models); VGG16, Inception V3, and ResNet (heavier weight models)) on medical images was compared to establish a classification model for epidemiological research. Brain scan image data were collected from 99 subjects, and 4711 images were used. Demographic data were compared using the chi-squared test and one-way analysis of variance with Bonferroni’s post hoc test. Accuracy and loss functions were used to evaluate the performance of CNN models. The cognitive abilities screening instrument and mini mental state exam scores of subjects with a clinical dementia rating (CDR) of 2 were considerably lower than those of subjects with a CDR of 1 or 0.5. This study analyzed the classification performance of various CNN models for medical images and proved the effectiveness of transfer learning in identifying the mild cognitive impairment, mild AD, and moderate AD scoring based on SPECT images.

Importance of telemedicine in mild cognitive impairment and Alzheimer disease patients population during admission to emergency departments with COVID-19

In March 2020, the World Health Organization declared a global pandemic due to the new coronavirus SARS-CoV-2, and several governments have planned a national quarantine to control the spread of the virus. Acute psychological effects during hospitalization in frail elderly individuals with special needs, such as patients with dementia, have been little studied. The greatest distress manifested by these kinds of patients was isolation from their families during hospitalization. Thus, structured video call interventions were carried out to family caregivers of patients diagnosed with dementia during their hospitalization in the COVID-19 ward. The purpose of this quasi-experimental study was to assess changes in cognitive and behavioral symptoms in both patients and caregivers. All study participants underwent psychological assessments. Specifically, the psychological well-being states of patients and their caregivers were measured at admission (T0) and discharge (T1) using psychometric tests and clinical scales. Each participant received an electronic device to access video calls in addition meetings were scheduled with the psychologist and medical team to keep caregivers updated on the health status of their relatives. A psychological support and cognitive rehabilitation service was also provided. Significant differences were found in all clinical variables of the caregiver group. Results showed a significant relationship in the quality of life score between the patient and caregiver groups. The results of this study has highlighted the importance of maintaining significantly effective relationships during the hospitalization period of patients admitted to COVID wards.

Assessment of Plasma and Cerebrospinal Fluid Biomarkers in Different Stages of Alzheimer's Disease and Frontotemporal Dementia

Alzheimer's disease (AD) is the primary type of dementia, followed by frontotemporal lobar degeneration (FTLD). They share some clinical characteristics, mainly at the early stages. So, the identification of early, specific, and minimally invasive biomarkers is required. In this study, some plasma biomarkers (Amyloid β42, p-Tau181, t-Tau, neurofilament light (NfL), TAR DNA-binding protein 43 (TDP-43)) were determined by single molecule array technology (SIMOA^®) in control subjects (n = 22), mild cognitive impairment due to AD (MCI-AD, n = 33), mild dementia due to AD (n = 12), and FTLD (n = 11) patients. The correlations between plasma and cerebrospinal fluid (CSF) levels and the accuracy of plasma biomarkers for AD early diagnosis and discriminating from FTLD were analyzed. As result, plasma p-Tau181 and NfL levels correlated with the corresponding CSF levels. Additionally, plasma p-Tau181 showed good accuracy for distinguishing between the controls and AD, as well as discriminating between AD and FTLD. Moreover, plasma NfL could discriminate dementia-AD vs. controls, FTLD vs. controls, and MCI-AD vs. dementia-AD. Therefore, the determination of these biomarkers in plasma is potentially helpful in AD spectrum diagnosis, but also discriminating from FTLD. In addition, the accessibility of these potential early and specific biomarkers may be useful for AD screening protocols in the future.

Change in the Quick Dementia Rating System Across Time in Older Adults with and without Cognitive Impairment

BACKGROUND

The Quick Dementia Rating System (QDRS) is a brief, informant-reported dementia staging tool that approximates scores on the Clinical Dementia Rating Scale in patients with Alzheimer's disease (AD).

OBJECTIVE

The current study sought to examine change in the QDRS across time, which is necessary for clinical and research efforts.

METHODS

One-hundred ten older adults (intact, mild cognitive impairment [MCI], mild AD, classified with Alzheimer's Disease Neuroimaging Initiative criteria) were rated on the QDRS by an informant and had an amyloid positron emission tomography scan at baseline. The informant re-rated each participant on the QDRS after one year. Dependent t-tests compared the entire sample and various subgroups (e.g., cognitive status, amyloid status) on baseline and follow-up QDRS scores.

RESULTS

In the entire sample, the Total score on the QDRS significantly increased (i.e., worsened) on follow-up (p < 0.001). When subgroups were analyzed, the MCI and mild AD subjects showed increasing (i.e., worsening) QDRS Total scores (both p < 0.001), but the intact subjects remained stable over time (p = 0.28). Additionally, those classified as being amyloid positive at baseline showed significantly increased QDRS Total scores at follow-up (p < 0.001) compared to those who were amyloid negative at baseline, whose QDRS Total scores remained stable over time (p = 0.63).

CONCLUSION

The QDRS can potentially demonstrate worsening functioning status across one year, especially in those who have MCI or mild AD and those who are amyloid positive. Therefore, the current results preliminarily suggest that the QDRS may provide an efficient tool for tracking progression in clinical trials in AD.

The Anti-Amyloid Monoclonal Antibody Lecanemab: 16 Cautionary Notes

After the CLARITY-AD clinical trial results of lecanemab were interpreted as positive, and supporting the amyloid hypothesis, the drug received accelerated Food and Drug Administration approval. However, we argue that benefits of lecanemab treatment are uncertain and may yield net harm for some patients, and that the data do not support the amyloid hypothesis. We note potential biases from inclusion, unblinding, dropouts, and other issues. Given substantial adverse effects and subgroup heterogeneity, we conclude that lecanemab's efficacy is not clinically meaningful, consistent with numerous analyses suggesting that amyloid-β and its derivatives are not the main causative agents of Alzheimer's disease dementia.

Validation of a rapid remote digital test for impaired cognition using clinical dementia rating and mini-mental state examination: An observational research study

Background

The Clinical Dementia Rating (CDR) and Mini-Mental State Examination (MMSE) are useful screening tools for mild cognitive impairment (MCI). However, these tests require qualified in-person supervision and the CDR can take up to 60 min to complete. We developed a digital cognitive screening test (M-CogScore) that can be completed remotely in under 5 min without supervision. We set out to validate M-CogScore in head-to-head comparisons with CDR and MMSE.

Methods

To ascertain the validity of the M-CogScore, we enrolled participants as healthy controls or impaired cognition, matched for age, sex, and education. Participants completed the 30-item paper MMSE Second Edition Standard Version (MMSE-2), paper CDR, and smartphone-based M-CogScore. The digital M-CogScore test is based on time-normalised scores from smartphone-adapted Stroop (M-Stroop), digit-symbols (M-Symbols), and delayed recall tests (M-Memory). We used Spearman's correlation coefficient to determine the convergent validity between M-CogScore and the 30-item MMSE-2, and non-parametric tests to determine its discriminative validity with a CDR label of normal (CDR 0) or impaired cognition (CDR 0.5 or 1). M-CogScore was further compared to MMSE-2 using area under the receiver operating characteristic curves (AUC) with corresponding optimal cut-offs.

Results

72 participants completed all three tests. The M-CogScore correlated with both MMSE-2 (rho = 0.54, p < 0.0001) and impaired cognition on CDR (Mann Whitney U = 187, p < 0.001). M-CogScore achieved an AUC of 0.85 (95% bootstrapped CI [0.80, 0.91]), when differentiating between normal and impaired cognition, compared to an AUC of 0.78 [0.72, 0.84] for MMSE-2 (p = 0.21).

Conclusion

Digital screening tests such as M-CogScore are desirable to aid in rapid and remote clinical cognitive evaluations. M-CogScore was significantly correlated with established cognitive tests, including CDR and MMSE-2. M-CogScore can be taken remotely without supervision, is automatically scored, has less of a ceiling effect than the MMSE-2, and takes significantly less time to complete.

Differentiation of patients with mild cognitive impairment and healthy controls based on computer assisted hand movement analysis: a proof-of-concept study

Mild cognitive impairment (MCI) is the prodromal phase of dementia, and it is highly underdiagnosed in the community. We aimed to develop an automated, rapid (< 5 min), electronic screening tool for the recognition of MCI based on hand movement analysis. Sixty-eight individuals participated in our study, 46 healthy controls and 22 patients with clinically defined MCI. All participants underwent a detailed medical assessment including neuropsychology and brain MRI. Significant differences were found between controls and MCI groups in mouse movement characteristics. Patients showed higher level of entropy for both the left (F = 5.24; p = 0.001) and the right hand (F = 8.46; p < 0.001). Longer time was required in MCI to perform the fine motor task (p < 0.005). Furthermore, we also found significant correlations between mouse movement parameters and neuropsychological test scores. Correlation was the strongest between motor parameters and Clinical Dementia Rating scale (CDR) score (average r: - 0.36, all p's < 0.001). Importantly, motor parameters were not influenced by age, gender, or anxiety effect (all p's > 0.05). Our study draws attention to the utility of hand movement analysis, especially to the estimation of entropy in the early recognition of MCI. It also suggests that our system might provide a promising tool for the cognitive screening of large populations.

Dementia and risk of visual impairment in Chinese older adults

We had previously identified visual impairment increasing risk of incident dementia. While a bi-directional vision-cognition association has subsequently been proposed, no study has specifically examined the longitudinal association between dementia and incidence of clinically defined visual impairment. In this territory-wide community cohort study of 10,806 visually unimpaired older adults, we examined their visual acuity annually for 6 years and tested if dementia at baseline was independently associated with higher risk of incident visual impairment (LogMAR ≥ 0.50 in the better eye despite best correction, which is equivalent to moderate visual impairment according to the World Health Organization definition). By the end of Year 6, a total of 3151 (29.2%) participants developed visual impairment. However, we did not find baseline dementia associating with higher risk of incident visual impairment, after controlling for baseline visual acuity, cataract, glaucoma, diabetes, hypertension, hypercholesterolemia, heart diseases, stroke, Parkinson's disease, depression, hearing and physical impairments, physical, intellectual and social activities, diet, smoking, age, sex, educational level, and socioeconomic status. Among different covariables, baseline visual acuity appears to be more important than dementia in contributing to the development of visual impairment. Our present findings highlight the need for re-evaluating whether dementia is indeed a risk factor for visual impairment.

Factors associated with social participation in Brazilian older adults with Alzheimer's disease: A correlational, cross-sectional study

The occurrence of Alzheimer's disease (AD) can exert a negative impact in social participation in affected older adults. The purpose of this study was to investigate whether social participation in older adults with AD is associated with disease stage and cognitive function as well as the quality of life and depressive symptoms in their caregivers. A correlational, cross-sectional study was conducted in 40 older adults with AD (28 women and 12 men) and 40 caregivers (30 women and 10 men). Social participation was assessed using the 'social participation' domain of the Activities of Daily Living Questionnaire. Disease stage was determined using the Clinical Dementia Rating scale and cognitive function was assessed using Addenbrooke's Cognitive Examination. Quality of life and depressive symptoms in the caregivers were evaluated using the Quality of Life Assessment Scale on Alzheimer's Disease and Beck Depression Inventory respectively. The older adults with AD had a mean percentage of 59.4% on the social participation domain and a mean score of 49.0 for cognitive function. The caregivers had mean scores of 39.1 for quality of life and 9.9 for depressive symptoms. The stepwise backward multiple linear regression model indicated that the predictors analysed together explained 48% of the variability in social participation among older adults with AD. Therefore, lower social participation among older adults with AD is associated with more advanced stages of the disease and cognitive decline in these individuals as well as a lower perception of quality of life and greater levels of depressive symptoms in their caregivers.

A novel framework to estimate cognitive impairment via finger interaction with digital devices

Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer’s Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of questionnaires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery of patterns in the time series of keystroke dynamics, i.e. the time required to press and release keys, by machine learning algorithms. In this work, our hypothesis is that the typing patterns generated from user-device interaction may reflect relevant features of the effects of cognitive impairment caused by neurodegeneration. We use machine learning algorithms to estimate cognitive performance through the analysis of keystroke dynamic patterns that were extracted from mechanical and touchscreen keyboard use in a dataset of cognitively normal (n = 39, 51% male) and cognitively impaired subjects (n = 38, 60% male). These algorithms are trained and evaluated using a novel framework that integrates items from multiple neuropsychological and clinical scales into cognitive subdomains to generate a more holistic representation of multifaceted clinical signs. In our results, we see that these models based on typing input achieve moderate correlations with verbal memory, non-verbal memory and executive function subdomains [Spearman’s ρ between 0.54 (P < 0.001) and 0.42 (P < 0.001)] and a weak correlation with language/verbal skills [Spearman’s ρ 0.30 (P < 0.05)]. In addition, we observe a moderate correlation between our typing-based approach and the Total Montreal Cognitive Assessment score [Spearman’s ρ 0.48 (P < 0.001)]. Finally, we show that these machine learning models can perform better by using our subdomain framework that integrates the information from multiple neuropsychological scales as opposed to using the individual items that make up these scales. Our results support our hypothesis that typing patterns are able to reflect the effects of neurodegeneration in mild cognitive impairment and Alzheimer’s disease and that this new subdomain framework both helps the development of machine learning models and improves their interpretability.

Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study

Alzheimer’s Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process. The diagnosis is currently established following an evaluation of the protein biomarkers in cerebrospinal fluid (CSF), brain imaging, cognitive tests, and the medical history of the individuals. While diagnostic tools based on CSF collections are invasive, the tools used for acquiring brain scans are expensive. Taking these into account, an early predictive system, based on Artificial Intelligence (AI) approaches, targeting the diagnosis of this condition, as well as the identification of lead biomarkers becomes an important research direction. In this survey, we review the state-of-the-art research on machine learning (ML) techniques used for the detection of AD and Mild Cognitive Impairment (MCI). We attempt to identify the most accurate and efficient diagnostic approaches, which employ ML techniques and therefore, the ones most suitable to be used in practice. Research is still ongoing to determine the best biomarkers for the task of AD classification. At the beginning of this survey, after an introductory part, we enumerate several available resources, which can be used to build ML models targeting the diagnosis and classification of AD, as well as their main characteristics. After that, we discuss the candidate markers which were used to build AI models with the best results in terms of diagnostic accuracy, as well as their limitations.

The Quick Dementia Rating System and Its Relationship to Biomarkers of Alzheimer's Disease and Neuropsychological Performance

INTRODUCTION

The Quick Dementia Rating System (QDRS) is a brief, patient-reported dementia staging tool that has approximated scores on the Clinical Dementia Rating Scale in patients with Alzheimer's disease (AD). However, no studies have examined its relationship with AD-related biomarkers.

METHODS

One-hundred twenty-one older adults (intact, amnestic mild cognitive impairment, mild AD) completed the QDRS, and three biomarkers (amyloid deposition via positron emission tomography, hippocampal volume via magnetic resonance imaging, and apolipoprotein [APOE] ε4 status).

RESULTS

The Total score on the QDRS was statistically significantly related to all three biomarkers (after controlling for age, education, sex, and race), with greater levels of dementia severity being associated with greater amyloid deposition, smaller hippocampi, and having copies of APOE ε4 allele.

DISCUSSION

In participants across the cognitive spectrum, the QDRS showed modest relationships with amyloid deposition, hippocampal volumes, and APOE status. Therefore, the QDRS may offer a cost-effective screening method for clinical trials in AD.

Functional connectome analysis in Mild Cognitive Impairment: Comparing AD continuum and Suspected Non-Alzheimer Pathology

INTRODUCTION

Research in brain resting-state functional connectivity (FC) analysis in mild cognitive impairment (MCI) has conflicting results. This work intends to find differences in resting-state FC of MCI subjects due to Alzheimer´s disease continuum (MCI-AD) or suspected non-Alzheimer pathology (MCI-SNAP).

METHODS

92 subjects over 55 years old were enrolled. MCI and controls were grouped using clinical dementia rating and neuropsychological data. CSF biomarkers were collected from MCI subjects, resulting in 32 MCI-AD, 25 MCI-SNAP, and 35 controls. A ROI-to-ROI analysis was carried out looking at inter and intranetwork interactions selecting the following networks: default mode (DMN), salience (SN), visuospatial (VN), and executive. Pearson correlation coefficients, converted to Z-scores were compared by T-tests with alpha set to 0.05, FDR corrected.

RESULTS

Groups were similar in age, education and demographic measures, there were no differences in neuropsychological data between the MCI groups. The ROI-to-ROI analysis MCI-AD versus MCI-SNAP showed no differences. MCI-AD versus controls showed decreased FC between ROIs of the SN and between ROIs from SN and VN. MCI-SNAP versus controls showed increased FC between a ROI of DMN and VN.

DISCUSSION

SN, DMN, and VN are multimodal networks with high value/high cost and may be more vulnerable to AD pathogenic processes. SN and VN were affected in the MCI-AD group, with maintained anticorrelation between DMN and VN. This may indicate subthreshold DMN dysfunction. The result in MCI-SNAP, although discrete, reflects a rearrangement of brain FC, as DMN and VN are expected to be anticorrelated. More research is necessary to confirm these findings.

Clinical features of dementia cases ascertained by ICD coding in LIMBIC-CENC multicenter study of mild traumatic brain injury

OBJECTIVE

Describe dementia cases identified through International Classification of Diseases (ICD) coding in the Long-term Impact of Military-relevant Brain Injury Consortium - Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC) multicenter prospective longitudinal study (PLS) of mild traumatic brain injury (mTBI).

DESIGN

Descriptive case series using cross-sectional data.

METHODS

Veterans Affairs (VA) health system data including ICD codes were obtained for 1563 PLS participants through the VA Informatics and Computing Infrastructure (VINCI). Demographic, injury, and clinical characteristics of Dementia positive and negative cases are described.

RESULTS

Five cases of dementia were identified, all under 65 years old. The dementia cases all had a history of blast-related mTBI and all had self-reported functional problems and four had PTSD symptomatology at the clinical disorder range. Cognitive testing revealed some deficits especially in the visual memory and verbal learning and memory domains, and that two of the cases might be false positives.

CONCLUSIONS

ICD codes for early dementia in the VA system have specificity concerns, but could be indicative of cognitive performance and self-reported cognitive function. Further research is needed to better determine links to blast exposure, blast-related mTBI, and PTSD to early dementia in the military population.

Association Plasma Aβ42 Levels with Alzheimer's Disease and Its Influencing Factors in Chinese Elderly Population

BACKGROUND AND PURPOSE

Intracerebral Aβ protein deposition is an important pathological mechanism of Alzheimer's disease (AD) and is one of the indicators of early diagnosis of AD. However, invasive lumbar puncture and Aβ PET are difficult to perform in primary units, resulting delays in early diagnosis of AD. In recent years, it has been found that plasma Aβ can reflect the pathological state of AD in early stage, but the results are not consistent. The objective of this study was to explore the association between plasma Aβ42 levels and AD cognitive impairment and its influencing factors in Chinese elderly population, so as to provide guidance for the clinical application of plasma Aβ42 as a blood biomarker of AD.

METHODS

This is a cross-sectional study based on the community population. Plasma samples were collected from 604 healthy controls (HC), 508 mild cognitive impairment (MCI) and 202 dementia with Alzheimer's type (DAT) patients from three cities. We analyzed the correlation between plasma Aβ42 levels and cognitive function and the influence of confounding factors on the relationship between plasma Aβ42 levels and AD. The independent influencing factors of plasma Aβ42 levels were determined by covariance and linear regression analysis.

RESULTS

Our results suggest that there is a special linear relationship between plasma Aβ42 and cognitive impairment of AD in Chinese elderly population, with Aβ42 levels slightly decreased in early AD and significantly increased in moderate-to-severe AD (P<0.01). There are many factors influencing the association between plasma Aβ42 levels and AD cognitive impairment, and sample source, gender and BMI are independent influencing factors of plasma Aβ42.

CONCLUSION

This indentifies that plasma Aβ42 may be a peripheral biomarker for AD screening in Chinese elderly population, but it is necessary to establish standardized detection methods and establish different demarcation criteria for various influencing factors.

Incident epilepsy in the cognitively normal geriatric population, irrespective of seizure control, impairs quality of life

PURPOSE OF THE RESEARCH

The geriatric population is the fastest-growing population in the United States and the impact of incident epilepsy on the cognitively intact geriatric population is not well-studied. Understanding how epilepsy affects the elderly is important to improve the quality of treatment and care for our aging population. This study sought to address the impact of incident epilepsy on the perceived Quality of Life (QOL) in cognitively intact elderly using the SF-36 questionnaire.

METHODS

Nine hundred and twenty-seven participants were assessed from a community-based cohort. Based on a history of subsequent development of new-onset seizures, participants were divided into two groups, an incident seizure group that developed new-onset seizures after 65 years of age and the control group without incident seizures. Of this, six hundred eleven were analyzed with the SF-36 questionnaire after excluding for cognitive decline and inconsistent medical data.

PRINCIPAL RESULTS

Statistically significant differences were found in 9 items on SF-36, involving perception of increased physical disability (p < 0.01; t-test), frailty (p < 0.04; t-test), emotional health limitations (p < 0.03; t-test), anxiety and sadness (p < 0.04; t-test), problems interfering with social activities (p < 0.0001; t-test). No between-group differences were found for demographic variables including age, education, gender, or minority status. Among the 611 subjects who remained cognitively normal across all longitudinal visits, 12 reported a history of new-onset seizures. Ten of these 12 subjects were seizure free as a result of treatment, with only 2 experiencing recent seizures. The incidence of seizures in our population was 300 per 100,000 person years.

MAJOR CONCLUSIONS

This study identified the elderly population with incident epilepsy as a subgroup with an unmet health need, and healthcare professionals should address the potential impact of seizures with their geriatric patients to ensure comprehensive care.

Wrist accelerometry for monitoring dementia agitation behaviour in clinical settings: A scoping review

Agitated behaviour among elderly people with dementia is a challenge in clinical management. Wrist accelerometry could be a versatile tool for making objective, quantitative, and long-term assessments. The objective of this review was to summarise the clinical application of wrist accelerometry to agitation assessments and ways of analysing the data. Two authors independently searched the electronic databases CINAHL, PubMed, PsycInfo, EMBASE, and Web of Science. Nine (n = 9) articles were eligible for a review. Our review found a significant association between the activity levels (frequency and entropy) measured by accelerometers and the benchmark instrument of agitated behaviour. However, the performance of wrist accelerometry in identifying the occurrence of agitation episodes was unsatisfactory. Elderly people with dementia have also been monitored in existing studies by investigating the at-risk time for their agitation episodes (daytime and evening). Consideration may be given in future studies on wrist accelerometry to unifying the parameters of interest and the cut-off and measurement periods, and to using a sampling window to standardise the protocol for assessing agitated behaviour through wrist accelerometry.

Practical Considerations in the Administration of Aducanumab for the Neurologist

Aducanumab (Aduhelm), developed by the biotechnology firm Biogen in Cambridge, MA, was approved using the less common accelerated approval pathway by the Federal Drug Administration (FDA) reserved for treatments that fill a significant unmet need.¹ Its approval on June 7, 2021, has been met with an outpouring of opinions from prescribers, insurers, advocacy groups, and hospital systems regarding its risk-benefit profile.^2-4 Originally approved for all forms of Alzheimer disease (AD), the FDA updated aducanumab's labeling on July 8, 2021, for “treatment in patients with mild cognitive impairment (MCI) or mild dementia stage of disease, the population in which treatment was initiated in clinical trials.”⁵ With 6 million people nationally in the United States who suffer from AD and an anticipated one-third of those who may now fulfill the criteria under the revised labeling, the implications of aducanumab's approval continue to generate national interest.⁶

Differential item functioning of the Beck Anxiety Inventory in a rural, multi-ethnic cohort

BACKGROUND

Evaluating measurement bias is vital to ensure equivalent assessment across diverse groups. One approach for evaluating test bias, differential item functioning (DIF), assesses item-level bias across specified groups by comparing item-level responses between groups that have the same overall score. Previous DIF studies of the Beck Anxiety Inventory (BAI) have only assessed bias across age, sex, and disease duration in monolingual samples. We expand this literature through DIF analysis of the BAI across age, sex, education, ethnicity, cognitive status, and test language.

METHODS

BAI data from a sample (n = 527, mean age=61.4 ± 12.7, mean education=10.9 ± 4.3, 69.3% female, 41.9% Hispanic/Latin American) from rural communities in West Texas, USA were analyzed. Item response theory (IRT) / logistic ordinal regression DIF was conducted across dichotomized demographic grouping factors. The Mann-Whitney U test and Hedge's g standardized mean differences were calculated before and after adjusting for the impact of DIF.

RESULTS

Significant DIF was demonstrated in 10/21 items. An adverse impact of DIF was not identified when demographics were assessed individually. Adverse DIF was identified for only one participant (1/527, 0.2%) when all demographics were aggregated.

LIMITATIONS

These results might not be generalizable to a sample with broader racial representation, more severe cognitive impairment, and higher levels of anxiety.

CONCLUSIONS

Minimal item-level bias was identified across demographic factors considered. These results support prior evidence that the BAI is valid for assessing anxiety across age and sex while contributing new evidence of its clinical relevance across education, ethnicity, cognitive status, and English/Spanish test language.

The association among apathy, leisure activity participation, and severity of dementia in nursing home residents with Alzheimer's disease: A cross-sectional study

The purpose of this study was to examine 1) the relationship between apathy and leisure activity participation in nursing home residents with Alzheimer disease (AD) and 2) the moderator effect of the severity of dementia on this relationship. Data were collected from 290 residents with AD using the Apathy Evaluation Scale-informant version (AES-I), Leisure Activities Questionnaire (LAQ), and Clinical Dementia Rating scale (CDR). The multiple linear regression model showed that leisure activity participation (β=-0.452, p<0.001) was negatively associated with apathy, while the severity of dementia (β=0.515, p<0.001) was positively associated with apathy. The severity of dementia moderated the effect of leisure activity participation on apathy (β=-0.108, p=0.015). The results indicate that the effects of leisure activity participation on apathy diminish with the aggravation of AD. The severity of dementia should be considered when designing and delivering leisure activity interventions to manage apathy in nursing home residents with AD.

Relation of Literacy and Music Literacy to Dementia in Older Black and White Brazilians

BACKGROUND

Literacy is more consistently reported than education as protective against dementia in developing regions.

OBJECTIVE

To study the association of verbal literacy, numeracy, and music literacy with dementia in older Black and White Brazilians with a broad spectrum of education.

METHODS

We studied 1,818 Black, Mixed-race, and White deceased Brazilians 65 years or older at death (mean = 79.64). Data were retrospectively obtained within 36 hours after death in a face-to-face interview with an informant, usually a family member. Dementia was classified using the Clinical Dementia Rating (CDR) scale. Three forms of literacy were ascertained: verbal literacy (10 questions: reading and writing), numeracy (3 questions: multiplication, percentages, and use of a calculator), and music literacy (1 question: reading music). Black (11%) and Mixed-race (23%) older adults were combined in analyses. Models adjusted for age and sex.

RESULTS

Dementia was identified in 531 people. Participants had 0 to 25 years of education (median = 4). More literacy was associated with lower odds of dementia (all p≤0.039). Participants that read music had about half the odds of having dementia. Participants in the highest quartile of numeracy and verbal literacy had respectively 27%and 15%lower odds of having dementia compared to the lowest quartile. Literacy was lower in Blacks (p < 0.001, except music p = 0.894) but the effect of literacy on dementia was similar (interaction p > 0.237). In secondary analyses, playing instruments without reading music was not associated with dementia (p = 0.887).

CONCLUSION

In a large sample of Brazilians, verbal literacy, numeracy, and music literacy were associated with lower odds of dementia. The effect was similar across races.

Neuropsychiatric symptoms in early stage of Alzheimer's and non-Alzheimer's dementia, and the risk of progression to severe dementia

BACKGROUND

Neuropsychiatric symptoms (NPSs) in early dementia have been suggested to predict a higher risk of dementia progression. However, the literature is not yet clear whether the risk is similar across Alzheimer's dementia (AD) and non-Alzheimer's dementia (non-AD), as well as across different NPSs. This study examined the association between NPSs in early dementia and the risk of progression to severe dementia, specifically in AD and non-AD, as well as across various NPSs.

METHOD

This cohort study included 7,594 participants who were ≥65 years and had early dementia (global Clinical Dementia Rating [CDR] = 1). Participants completed Neuropsychiatric-Inventory-Questionnaire at baseline and were followed-up almost annually for progression to severe dementia (global CDR = 3) (median follow-up = 3.5 years; interquartile range = 2.1-5.9 years). Cox regression was used to examine progression risk, stratified by AD and non-AD.

RESULTS

The presence of NPSs was associated with risk of progression to severe dementia, but primarily in AD (HR 1.4, 95% confidence interval [CI]: 1.1-1.6) and not in non-AD (HR 0.9, 95% CI: 0.5-1.5). When comparing across various NPSs, seven NPSs in AD were associated with disease progression, and they were depression, anxiety, apathy, delusions, hallucinations, irritability and motor disturbance (HR 1.2-1.6). In contrast, only hallucinations and delusions were associated with disease progression in non-AD (HR 1.7-1.9).

CONCLUSIONS

NPSs in early dementia-especially among individuals with AD-can be useful prognostic markers of disease progression. They may inform discussion on advanced care planning and prompt clinical review to incorporate evidence-based interventions that may address disease progression.

Operational Determination of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using Sum of Boxes of the Clinical Dementia Rating Scale

Objectives: The Clinical Dementia Rating (CDR) Scale is the gold standard for the staging of dementia due to Alzheimer's disease (AD). However, the application of CDR for the staging of subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in AD remains controversial. This study aimed to use the sum of boxes of the CDR (CDR-SB) plus an SCD single questionnaire to operationally determine the different stages of cognitive impairment (CI) due to AD and non-AD. Methods: This was a two-phase study, and we retrospectively analyzed the Show Chwan Dementia registry database using the data selected from 2015 to 2020. Individuals with normal cognition (NC), SCD, MCI, and mild dementia (MD) due to AD or non-AD with a CDR < 2 were included in the analysis. Results: A total of 6,946 individuals were studied, including 875, 1,009, 1,585, and 3,447 with NC, SCD, MCI, and MD, respectively. The cutoff scores of CDR-SB for NC/SCD, SCD/MCI, and MCI/dementia were 0/0.5, 0.5/1.0, and 2.5/3.0, respectively. The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) values of the test groups were 0.85, 0.90, and 0.92 for discriminating NC from SCD, SCD from MCI, and MCI from dementia, respectively. Compared with the Cognitive Abilities Screening Instrument or the Montreal Cognitive Assessment, the use of CDR-SB is less influenced by age and education. Conclusion: Our study showed that the operational determination of SCD, MCI, and dementia using the CDR-SB is practical and can be applied in clinical settings and research on CI or dementia.

Screening and predicting progression from high-risk mild cognitive impairment to Alzheimer's disease

Individuals with mild cognitive impairment (MCI) are clinically heterogeneous, with different risks of progression to Alzheimer's disease. Regular follow-up and examination may be time-consuming and costly, especially for MRI and PET. Therefore, it is necessary to identify a more precise MRI population. In this study, a two-stage screening frame was proposed for evaluating the predictive utility of additional MRI measurements among high-risk MCI subjects. In the first stage, the K-means cluster was performed for trajectory-template based on two clinical assessments. In the second stage, high-risk individuals were filtered out and imputed into prognosis models with varying strategies. As a result, the ADAS-13 was more sensitive for filtering out high-risk individuals among patients with MCI. The optimal model included a change rate of clinical assessments and three neuroimaging measurements and was significantly associated with a net reclassification improvement (NRI) of 0.246 (95% CI 0.021, 0.848) and integrated discrimination improvement (IDI) of 0.090 (95% CI - 0.062, 0.170). The ADAS-13 longitudinal models had the best discrimination performance (Optimism-corrected concordance index = 0.830), as validated by the bootstrap method. Considering the limited medical and financial resources, our findings recommend follow-up MRI examination 1 year after identification for high-risk individuals, while regular clinical assessments for low-risk individuals.

A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation

Background

In the current study, we aimed to develop an algorithm based on biomarkers obtained through non- or minimally invasive procedures to identify healthy elderly subjects who have an increased risk of abnormal cerebrospinal fluid (CSF) amyloid beta42 (Aβ) levels consistent with the presence of Alzheimer’s disease (AD) pathology. The use of the algorithm may help to identify subjects with preclinical AD who are eligible for potential participation in trials with disease modifying compounds being developed for AD. Due to this pre-selection, fewer lumbar punctures will be needed, decreasing overall burden for study subjects and costs.

Methods

Healthy elderly subjects (n = 200; age 65–70 (N = 100) and age > 70 (N = 100)) with an MMSE > 24 were recruited. An automated central nervous system test battery was used for cognitive profiling. CSF Aβ1-42 concentrations, plasma Aβ1-40, Aβ1-42, neurofilament light, and total Tau concentrations were measured. Aβ1-42/1-40 ratio was calculated for plasma. The neuroinflammation biomarker YKL-40 and APOE ε4 status were determined in plasma. Different mathematical models were evaluated on their sensitivity, specificity, and positive predictive value. A logistic regression algorithm described the data best. Data were analyzed using a 5-fold cross validation logistic regression classifier.

Results

Two hundred healthy elderly subjects were enrolled in this study. Data of 154 subjects were used for the per protocol analysis. The average age of the 154 subjects was 72.1 (65–86) years. Twenty-four (27.3%) were Aβ positive for AD (age 65–83). The results of the logistic regression classifier showed that predictive features for Aβ positivity/negativity in CSF consist of sex, 7 CNS tests, and 1 plasma-based assay. The model achieved a sensitivity of 70.82% (± 4.35) and a specificity of 89.25% (± 4.35) with respect to identifying abnormal CSF in healthy elderly subjects. The receiver operating characteristic curve showed an AUC of 65% (± 0.10).

Conclusion

This algorithm would allow for a 70% reduction of lumbar punctures needed to identify subjects with abnormal CSF Aβ levels consistent with AD. The use of this algorithm can be expected to lower overall subject burden and costs of identifying subjects with preclinical AD and therefore of total study costs.

Trial registration

ISRCTN.org identifier: ISRCTN79036545 (retrospectively registered).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00874-9.

Performance of the Rowland Universal Dementia Assessment Scale for the Detection of Mild Cognitive Impairment and Dementia in a Diverse Cohort of Illiterate Persons From Rural Communities in Peru

Background: The accurate diagnosis of neurocognitive disorders in illiterate Peruvian populations is challenging, largely owing to scarcity of brief cognitive screening tools (BCST) validated in these diverse populations. The Peruvian version of the Rowland Universal Dementia Assessment Scale (RUDAS-PE) is a BCST that relies minimally on educational attainment and has shown good diagnostic accuracy in an urban illiterate population in Peru, yet its psychometric properties in illiterate populations in rural settings of the country have not been previously investigated. Objectives: To establish the diagnostic accuracy of the RUDAS-PE compared to expert clinical diagnosis using the Clinical Dementia Rating (CDR) Scale in healthy and cognitively impaired illiterate persons living in two culturally and geographically distinct rural communities of Peru. Methods: A cross-sectional, population-based study of residents ≥ 50 years of age living in the Peruvian rural communities of Santa Clotilde and Chuquibambilla. A total of 129 subjects (76 from Santa Clotilde and 53 from Chuquibambilla) were included in this study. Gold standard diagnostic neurocognitive evaluation was based on expert neurological history and examination and administration of the CDR. Receiver operating characteristics, areas under the curve (AUC), and logistic regression analyses were conducted to determine the performance of RUDAS-PE compared to expert gold standard diagnosis. Results: Compared to gold standard diagnosis, the RUDAS-PE was better at correctly discriminating between MCI and dementia than discriminating between MCI and controls in both sites (97.0% vs. 76.2% correct classification in Chuquibambilla; 90.0% vs. 64.7% in Santa Clotilde). In Chuquibambilla, the area under the curve (AUC) of the RUDAS to discriminate between dementia and MCI was 99.4% (optimal cutoff at <18), whereas between MCI and controls it was 82.8% (optimal cutoff at <22). In Santa Clotilde, the area under the curve (AUC) of the RUDAS to discriminate between dementia and MCI was 99.1% (optimal cutoff at <17), whereas between MCI and controls it was 75.5% (optimal cutoff at <21). Conclusions: The RUDAS-PE has acceptable psychometric properties and performed well in its ability to discriminate MCI and dementia in two cohorts of illiterate older adults from two distinct rural Peruvian communities.

Neuroticism, negative life events, and dementia in older White and Black Brazilians

OBJECTIVE

Exposure to negative life events (NLEs) and neuroticism are associated with dementia. It is unknown whether neuroticism explains or modifies the association of NLEs with dementia in older Black and White Brazilians.

METHODS

A total of 1747 decedents 65 years and older White and Black (11% Black and 23% Mixed) Brazilians, 53% women, were included in the analyses. Data were obtained in a face-to-face interview with an informant (71% their children) who knew the decedents for 47 years on average. Dementia was classified using the Clinical Dementia Rating. NLEs were assessed with a 10-item scale involving common problems (e.g., death, illness, alcoholism, and financial). Neuroticism was assessed with a 6-item neuroticism scale adapted from the NEO Five-Factor Inventory. Models adjusted for age, sex, and education. Black and mixed-race were combined in the analyses.

RESULTS

NLEs (median of 2) were more common in Blacks than Whites (2.04 vs. 1.82, p = 0.007). More NLEs increased the odds of dementia (OR = 1.112, β = 0.106, p = 0.002), similarly in Blacks and Whites (β interaction  = 0.046, p = 0.526). More NLEs were also associated with higher neuroticism (β = 0.071, p < 0.0001), in Whites but not in Blacks (β interaction  = -0.048, p = 0.006). Neuroticism was associated with higher odds of dementia (OR = 1.658, β = 0.506, p=<0.001), in Whites but not in Blacks (β interaction  = -0.420, p = 0.040). Overall, 34% of the effect of NLEs on dementia was associated with the underlying neuroticism trait in Whites (65%, Indirect OR = 1.060, p < 0.001) but no association was evident in Blacks (6%, Indirect OR = 1.008, p = 0.326). Neuroticism did not moderate the association of NLEs with dementia (OR = 0.979, β = -0.021, p = 0.717).

CONCLUSION

The association of NLEs and dementia is partially explained by neuroticism in older White but not in Blacks Brazilians.

Predicting the Severity of Parkinson's Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression Model

In this study, we measured the convergence rate using the mean-squared error (MSE) of the standardized neuropsychological test to determine the severity of Parkinson's disease dementia (PDD), which is based on support vector machine (SVM) regression (SVR) and present baseline data in order to develop a model to predict the severity of PDD. We analyzed 328 individuals with PDD who were 60 years or older. To identify the SVR with the best prediction power, we compared the classification performance (convergence rate) of eight SVR models (Eps-SVR and Nu-SVR with four kernel functions (a radial basis function (RBF), linear algorithm, polynomial algorithm, and sigmoid)). Among the eight models, the MSE of Nu-SVR-RBF was the lowest (0.078), with the highest convergence rate, whereas the MSE of Eps-SVR-sigmoid was 0.110, with the lowest convergence rate. The results of this study imply that this approach could be useful for measuring the severity of dementia by comprehensively examining axial atypical features, the Korean instrumental activities of daily living (K-IADL), changes in rapid eye movement sleep behavior disorder (RBD), etc. for optimal intervention and caring of the elderly living alone or patients with PDD residing in medically vulnerable areas.