Essentials of the proper diagnoses of mild cognitive impairment, dementia, and major subtypes of dementia

Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline-A Systematic Review

Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.

Protective effect of bilingualism on aging, MCI, and dementia: A community-based study

INTRODUCTION

Lifelong bilingualism is associated with a delayed age at onset of dementia, but evidence from community-based studies is limited. We investigated the relationship between bilingualism and the prevalence of cognitive impairment in a linguistically diverse community.

METHODS

A door-to-door community study was conducted from January to December 2021 in urban Bengaluru, India. 1234 individuals aged ≥60 years participated in the study. Participants were diagnosed with no cognitive impairment (NCI), mild cognitive impairment (MCI), or dementia using established diagnostic criteria.

RESULTS

Dementia prevalence was higher in monolinguals (4.9%) than bilinguals (0.4%) (P = .001). The prevalence of MCI was also higher in monolinguals (8.5%) than bilinguals (5.3%) (P = .001). The study also revealed better cognitive function in bilinguals than monolinguals with NCI, after controlling for confounding variables.

DISCUSSION

The current study provides significant support for the protective effect of bilingualism on cognitive impairment in an urban community with extensive bilingual interactional contexts in everyday life.

HIGHLIGHTS

Bilingualism has been demonstrated to protect against dementia and mild cognitive impairment in a linguistically diverse community with extensive code-switching contexts. Bilingual older individuals had superior baseline cognitive performance compared to monolingual older individuals. Bilingualism was found to have an independent effect on general cognition after adjusting for major social determinants of health in the group without cognitive impairment.

Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review

In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician's workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians' roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.

Brief instrument for direct complex functionality assessment: a new ecological tool

Background

The Direct Assessment of Functional Status (DAFS) is the only instrument validated in Brazil that assesses functionality directly with the patient. However, this clinical tool takes a long time to be administered. This limits its use in hospitals and outpatient clinics that require brief assessment instruments. Additionally, we need to count with a direct assessment because the number of older adults living alone is increasing and we thus lack reliable informants.

Objective

This study aimed to present the development and content validity evidence of a direct complex functionality test for older adults, the Brief Instrument for Direct Functionality Assessment (BIDFA).

Method

A total sample of 30 older adults and eight expert judges took part in the study stages. The BIDFA construction stages were: (1) literature review of functionality instruments; (2) development of seven ecological tasks to evaluate the performance of daily complex activities with the older adults; (3) content analysis by eight expert judges; (4) pilot study with 30 older adults; (5) the ecological analysis of items; (6) focus group analysis; and (7) final version of the BIDFA.

Results

The BIDFA had evidence of content validity with an agreement index of 96.5%. The final version of BIDFA was left with six domains of complex functionality divided into semantic memory and time orientation; shopping skills; executive attention, math and finance skills; organization; planning and procedural memory; and problem-solving. The complex functionality score by BIDFA ranges from 0 to 100 points.

Conclusion

The BIDFA was found to have good content validity by the expert judges and by the ecological analysis of the items by the older adults. The new instrument is expected to help assess the functional status of older adults, in an abbreviated context including complex functionality demands, with a wider range of total and subdomain scores.

Speech and language processing with deep learning for dementia diagnosis: A systematic review

Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its progression or reduce its burdens. However, the diagnostic process of dementia is often complex and resource intensive. Access to diagnostic services is also an issue in low and middle-income countries. The abundance and easy accessibility of speech and language data have created new possibilities for utilizing Deep Learning (DL) technologies to be part of the dementia diagnostic process. This systematic review included studies published between 2012-2022 that utilized such technologies to aid in diagnosing dementia. We identified 72 studies using the PRISMA 2020 protocol, extracted and analyzed data from these studies and reported the related DL technologies. We found these technologies effectively differentiated between healthy individuals and those with a dementia diagnosis, highlighting their potential in the diagnosis of dementia. This systematic review provides insights into the contributions of DL-based speech and language techniques to support the dementia diagnostic process. It also offers an understanding of the advancements made in this field thus far and highlights some challenges that still need to be addressed.

Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics

Introduction

Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.

Methods

In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.

Results

The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.

Conclusion

Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data.. [DOI: 10.21203/rs.3.rs-3250006/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression. We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.

Nootropic Herbs, Shrubs, and Trees as Potential Cognitive Enhancers

Plant-based nootropics are a diverse group of natural drugs that can improve cognitive abilities through various physiological mechanisms, especially in cases where these functions are weakened or impaired. In many cases, the nootropics enhance erythrocyte plasticity and inhibit aggregation, which improves the blood's rheological properties and increases its flow to the brain. Many of these formulations possess antioxidant activity that protects brain tissue from neurotoxicity and improves the brain's oxygen supply. They can induce the synthesis of neuronal proteins, nucleic acids, and phospholipids for constructing and repairing neurohormonal membranes. These natural compounds can potentially be present in a great variety of herbs, shrubs, and even some trees and vines. The plant species reviewed here were selected based on the availability of verifiable experimental data and clinical trials investigating potential nootropic effects. Original research articles, relevant animal studies, meta-analyses, systematic reviews, and clinical trials were included in this review. Selected representatives of this heterogeneous group included Bacopa monnieri (L.) Wettst., Centella asiatica (L.) Urban, Eleutherococcus senticosus (Rupr. & Maxim.) Maxim., Ginkgo biloba L., Lepidium meyenii Walp., Panax ginseng C.A. Meyer, Paullinia cupana Kunth, Rhodiola rosea L., Schisandra chinensis (Turcz.) Baill., and Withania somnifera (L.) Dunal. The species are depicted and described, together with their active components and nootropic effects, and evidence of their efficacy is presented. The study provides brief descriptions of the representative species, their occurrence, history, and the chemical composition of the principle medicinal compounds, with uses, indications, experimental treatments, dosages, possible side effects, and contraindications. Most plant nootropics must be taken at optimal doses for extended periods before measurable improvement occurs, but they are generally very well tolerated. Their psychoactive properties are not produced by a single molecule but by a synergistic combination of several compounds. The available data suggest that including extracts from these plants in medicinal products to treat cognitive disorders can have substantial potential therapeutic benefits.

Regularized Buckley-James method for right-censored outcomes with block-missing multimodal covariates

High-dimensional data with censored outcomes of interest are prevalent in medical research. To analyze such data, the regularized Buckley-James estimator has been successfully applied to build accurate predictive models and conduct variable selection. In this paper, we consider the problem of parameter estimation and variable selection for the semiparametric accelerated failure time model for high-dimensional block-missing multimodal neuroimaging data with censored outcomes. We propose a penalized Buckley-James method that can simultaneously handle block-wise missing covariates and censored outcomes. This method can also perform variable selection. The proposed method is evaluated by simulations and applied to a multimodal neuroimaging dataset and obtains meaningful results.

Can mild cognitive impairment and Alzheimer's disease be diagnosed by monitoring a miRNA triad in the blood?

Objectively diagnosing age‐related cognitive impairment (ACI), mild cognitive impairment (MCI), and early‐stage Alzheimer's disease (AD) is a difficult task, as most cognitive impairment is clinically established via questionnaires, history, and physical examinations. A recent study has suggested that monitoring a miRNA triad, miR‐181a‐5p, miR‐146a‐5p, and miR‐148a‐3p can identify ACI and its progression to MCI and AD (Islam et al., EMBO Mol Med. 13: e14997, 2021). This commentary deliberates findings from this article, such as elevated levels of the miRNA triad in the brain impairing neural plasticity and cognitive function, the efficiency of measuring the miRNA triad in the circulating blood diagnosing MCI and AD, and the promise for improving cognitive function in MCI and AD by inhibiting this miRNA triad. Additional studies required prior to employing this miRNA triad in clinical practice are also discussed.

Behavioral Variant Frontotemporal Dementia

PURPOSE OF REVIEW

This article reviews many of the complex facets of behavioral variant frontotemporal dementia (bvFTD) and frontotemporal lobar degeneration (FTLD). A particular focus is on improving diagnostic accuracy to reduce the arduous diagnostic odyssey that so many patients and families endure. Strategies to promote diagnostic accuracy and approach the management of problematic symptoms are also discussed.

RECENT FINDINGS

Although the International Consensus Criteria for bvFTD were published more than a decade ago and clinicopathologic studies have confirmed their utility, diagnostic confusion continues. This article presents updated data along with illustrative cases to emphasize the clinical pearls that are most useful for clinicians. Although accurate prediction of the underlying proteinopathy remains a challenge, the ability to differentiate bvFTD from atypical Alzheimer disease, psychiatric disorders, and other mimickers has improved. Knowledge about the genetic underpinnings in a significant minority of individuals with familial FTLD is enabling early and accurate diagnosis. Therapeutic optimism has also increased, particularly in familial FTLD, with a few clinical trials in progress and several more planned, some of which are designed to slow progression or delay the onset of symptoms, or both.

SUMMARY

The diagnosis and management of bvFTD is challenging for clinicians and particularly for patients and their families. Although much progress has been gained over recent years, several key research questions persist. Treatments that significantly improve symptoms or alter the course of FTLD remain elusive, but optimism is increasing as pathobiology is better understood and novel therapies are being developed.

DNA Double-Strand Breaks as Pathogenic Lesions in Neurological Disorders

The damage and repair of DNA is a continuous process required to maintain genomic integrity. DNA double-strand breaks (DSBs) are the most lethal type of DNA damage and require timely repair by dedicated machinery. DSB repair is uniquely important to nondividing, post-mitotic cells of the central nervous system (CNS). These long-lived cells must rely on the intact genome for a lifetime while maintaining high metabolic activity. When these mechanisms fail, the loss of certain neuronal populations upset delicate neural networks required for higher cognition and disrupt vital motor functions. Mammalian cells engage with several different strategies to recognize and repair chromosomal DSBs based on the cellular context and cell cycle phase, including homologous recombination (HR)/homology-directed repair (HDR), microhomology-mediated end-joining (MMEJ), and the classic non-homologous end-joining (NHEJ). In addition to these repair pathways, a growing body of evidence has emphasized the importance of DNA damage response (DDR) signaling, and the involvement of heterogeneous nuclear ribonucleoprotein (hnRNP) family proteins in the repair of neuronal DSBs, many of which are linked to age-associated neurological disorders. In this review, we describe contemporary research characterizing the mechanistic roles of these non-canonical proteins in neuronal DSB repair, as well as their contributions to the etiopathogenesis of selected common neurological diseases.

AMPK activators for the prevention and treatment of neurodegenerative diseases

INTRODUCTION

As the global population ages at an unprecedented rate, the burden of neurodegenerative diseases is expected to grow. Given the profound impact illness like dementia exert on individuals and society writ large, researchers, physicians, and scientific organizations have called for increased investigation into their treatment and prevention. Both metformin and aspirin have been associated with improved cognitive outcomes. These agents are related in their ability to stimulate AMP kinase (AMPK). Momordica charantia, another AMPK activator, is a component of traditional medicines and a novel agent for the treatment of cancer. It is also being evaluated as a nootropic agent.

AREAS COVERED

This article is a comprehensive review which examines the role of AMPK activation in neuroprotection and the role that AMPK activators may have in the management of dementia and cognitive impairment. It evaluates the interaction of metformin, aspirin, and Momordica charantia, with AMPK, and reviews the literature characterizing these agents' impact on neurodegeneration.

EXPERT OPINION

We suggest that AMPK activators should be considered for the treatment and prevention of neurodegenerative diseases. We identify multiple areas of future investigation which may have a profound impact on patients worldwide.

Virtual Reality (VR)-Based Environmental Enrichment in Older Adults with Mild Cognitive Impairment (MCI) and Mild Dementia

Background: Despite an alarming rise in the global prevalence of dementia, the available modalities for improving cognition and mental wellbeing of dementia patients remain limited. Environmental enrichment is an experimental paradigm that has shown promising anti-depressive and memory-enhancing effects in pre-clinical studies. However, its clinical utility has remained limited due to the lack of effective implementation strategies. Objective: The primary objective of this study was to evaluate the usability (tolerability and interactivity) of a long-term virtual reality (VR)- based environmental enrichment training program in older adults with mild cognitive impairment (MCI) and mild dementia. A secondary objective was to assess the effect of VR-based environmental enrichment on stabilization of cognitive functioning and improvement of mental wellbeing in older adults with MCI and mild dementia. Methods: A total of seven participants (four patients with MCI and three with mild dementia) received biweekly VR-based environmental enrichment over a course of 6 months. The tolerability and interactivity of the participants in the VR training was serially assessed via virtual reality sickness questionnaire (VRSQ) and recording of input-error ratio. Cognitive functioning was assessed through Montreal cognitive assessment (MoCA) before and after the study. Mental wellbeing was assessed through Warwick-Edinburgh Mental Well Being Scale (WEMWBS). Results: VR-based environmental enrichment was well-tolerated by the patients with significant decrease in VRSQ scores (p < 0.01) and input-error ratio (p < 0.001) overtime. VR training was also effective in stabilization of MoCA scores over the course of therapy (non-significant difference in the MoCA scores before and after the therapy) and was associated with a trend (p < 0.1) towards improvement in WEMWBS scores between the first and the last assessments. Qualitative observations by the care-givers further corroborated a noticeable improvement in mental wellbeing of patients. Conclusions: This pilot study shows that VR can be a feasible, tolerable, and potentially effective tool in long-term support of older adults with MCI and mild dementia.

Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature

BACKGROUND

Cerebral small vessel disease (CSVD) is a common neurological disease present in the ageing population that is associated with an increased risk of dementia and stroke. Damage to white matter tracts compromises the substrate for interneuronal connectivity. Analysing resting-state functional magnetic resonance imaging (fMRI) can reveal dysfunctional patterns of brain connectivity and contribute to explaining the pathophysiology of clinical phenotypes in CSVD.

MATERIALS AND METHODS

This systematic review provides an overview of methods and results of recent resting-state functional MRI studies in patients with CSVD. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, a systematic search of the literature was performed.

RESULTS

Of 493 studies that were screened, 44 reports were identified that investigated resting-state fMRI connectivity in the context of cerebral small vessel disease. The risk of bias and heterogeneity of results were moderate to high. Patterns associated with CSVD included disturbed connectivity within and between intrinsic brain networks, in particular the default mode, dorsal attention, frontoparietal control, and salience networks; decoupling of neuronal activity along an anterior-posterior axis; and increases in functional connectivity in the early stage of the disease.

CONCLUSION

The recent literature provides further evidence for a functional disconnection model of cognitive impairment in CSVD. We suggest that the salience network might play a hitherto underappreciated role in this model. Low quality of evidence and the lack of preregistered multi-centre studies remain challenges to be overcome in the future.

Rapid Neurocognitive Deterioration and Mortality in a Healthcare Professional With Spongiform Encephalopathy: Implications for Neurologic and End-of-Life Care

Spongiform encephalopathy (SE) is a rare prion disorder characterized by progressive cognitive dysfunction and mortality. Affected patients can observe a wide variety of neurological symptoms, such as myoclonus, dementia, cerebellar signs, and others. We present a case of laboratory-confirmed SE in an otherwise healthy 57-year-old medical professional who initially presented with nonspecific and unique "head in a fish-bowl" dissociation and cognitive decline. No social risk factors were ever identified other than his healthcare career, but subsequent neuroimaging, serology, and lumbar puncture confirmed a diagnosis of sporadic SE due to unknown etiology. He was then treated symptomatically and referred ultimately to palliative care. The patient passed while in hospice care with time from the initial diagnosis to mortality being only 42 days. Given his vague but uniquely rapid deterioration and subsequent mortality, we highlight an opportunity to discuss diagnosis, management, quality improvement, and ethical concerns associated with SE prognosis. We aim to help primary care physicians and neurologists better elucidate the risk factors, signs and symptoms, and pathophysiology of SE to make an early diagnosis. Symptoms can then be managed effectively and palliative services coordinated via a legal and compassionate shared decision-making approach. We recommend that once a diagnosis is made, a discussion with the patient and their family about advance directives and end-of-life care be coordinated as soon as reasonably possible. This should be carried out by a multidisciplinary team consisting of the patient's primary care physician and neurologist, as well as a social worker, palliative care physician, and counselor (spiritual or otherwise). It is our hope that through a better understanding of these factors in SE care, quality of life improvement protocols in similarly-debilitating neurocognitive diseases can be developed.

Evaluation of visual evoked potentials and brain-stem auditory evoked response in patients of chronic kidney disease

Chronic kidney disease (CKD) is associated with functional changes in the central nervous system (CNS) which, in the initial stages do not manifest clinically. Early involvement of the CNS can be identified by the assessment of the electrocortical activity. Visual evoked potential (VEP) and brain-stem auditory evoked response (BAER) are useful tests for the early diagnosis of CNS involvement in CKD and are more sensitive compared to electroencephalography. One hundred adult CKD patients (stage 3-5 and 5D) and 50 controls were included in the study. Clinical and biochemical parameters were assessed and all the patients and controls underwent VEP and BAER evaluation. Evaluation of the VEP showed prolonged latencies of all the three peaks (N75, P100, and N145) compared to controls. Furthermore, all the absolute and interpeak BAER latencies for the CKD patients were similarly prolonged compared to controls. CNS dysfunction is common in CKD patients. The electrophysiological tests of VEP and BAER can be used for the early diagnosis of these disorders, even in the sub-clinical stages, thus allowing for their better management.

The Clinical Utility and Ecological Validity of the Medication Management Ability Assessment in Older Adults with and without Dementia

OBJECTIVE

Older adults are susceptible to medication nonadherence, which may signify functional decline. Thus, performance-based proxies of medication-taking behavior may help diagnose dementia. We assessed the Medication Management Ability Assessment's (MMAA) clinical utility and ecological validity.

METHOD

This was a retrospective chart review of 180 outpatients (age = 72 ± 8 years) who completed the MMAA during clinical evaluations. Forty-seven were cognitively normal (CN), 103 had mild cognitive impairment (MCI), and 30 had dementia. Most (136) were independent in medication management, whereas 28 were assisted and 16 were dependent. Kruskal-Wallis tests assessed whether MMAA scores differed by diagnosis and independence. Receiver operating characteristic (ROC) analyses identified diagnostic cut-offs. Classification accuracy estimates were derived.

RESULTS

MMAA performance differed across diagnosis as expected (p's < .001). Those who were independent in medication management outperformed assisted and dependent counterparts (p's < .001). Assisted and dependent cases were no different. At a cut-off = 23, the MMAA was good-to-strong in distinguishing dementia from CN cases (Sn = 0.96, Sp = 0.83), dementia from MCI (Sn = 0.70, Sp = 0.83), and dementia from functionally unimpaired cases (Sn = 0.78, Sp = 0.83). At a cut-off = 27, it had good sensitivity but weaker specificity when distinguishing both MCI and all cognitively impaired patients (MCI and dementia) from CN cases (Sn = 0.81, Sp = 0.66 and Sn = 0.81, Sp = 0.72, respectively).

CONCLUSIONS

The MMAA has ecological validity and clinical utility in identifying dementia. Its inclusion in neuropsychological practice may be especially useful when medication mismanagement is suspected.

Evaluating the Real-World Representativeness of Participants with Mild Cognitive Impairment in Canadian Research Protocols: a Comparison of the Characteristics of a Memory Clinic Patients and Research Samples

Background

Studies of mild cognitive impairment (MCI) employ rigorous eligibility criteria, resulting in sampling that may not be representative of the broader clinical population.

Objective

To compare the characteristics of MCI patients in a Calgary memory clinic to those of MCI participants in published Canadian studies.

Methods

Clinic participants included 555 MCI patients from the PROspective Registry of Persons with Memory SyMPToms (PROMPT) registry in Calgary. Research participants included 4,981 individuals with MCI pooled from a systematic literature review of 112 original, English-language peer-reviewed Canadian studies. Both samples were compared on baseline sociodemographic variables, medical and psychiatric comorbidities, and cognitive performance for MCI due to Alzheimer’s disease and Parkinson’s disease.

Results

Overall, clinic patients tended to be younger, more often male, and more educated than research participants. Psychiatric disorders, traumatic brain injury, and sensory impairment were commonplace in PROMPT (up to 83% affected) but > 80% studies in the systematic review excluded these conditions. PROMPT patients also performed worse on global cognition measures than did research participants.

Conclusion

Stringent eligibility criteria in Canadian research studies excluded a considerable subset of MCI patients with comorbid medical or psychiatric conditions. This exclusion may contribute to differences in cognitive performance and outcomes compared to real-world clinical samples.

3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI

We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R² value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.

Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases

Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1-weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume-based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble-learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time-to-conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state-of-the-art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.

Cognitive Dysfunction in Patients with Chronic Kidney Disease

Cognitive impairment is a common entity in patients with chronic kidney disease (CKD), which plays an important role in increasing the morbidity in these patients. This study was performed to evaluate cognitive dysfunction and its severity in different stages of CKD and identify the correlation with factors affecting this dysfunction. A cross-sectional design study was conducted on 100 patients with CKD Stage III to V-D fulfilling the eligibility criteria. Cognitive status was assessed using the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) tests. The patients were divided into four groups according to their estimated glomerular filtration rate (eGFR); Group A with eGFR ranging between 30 and 59 mL/min/1.73 m², Group B with eGFR between 15 and 29 mL/min/1.73 m², Group C with eGFR <15 mL/min/1.73 m² not on hemodialysis (HD), and Group D with eGFR <15 mL/min/1.73 m² and on HD for the past six months. Factors affecting MMSE and MoCA scores were assessed using univariate and multivariate linear regression analysis. Mean MMSE score was 28.72 ± 1.37 in Group A, 26.00 ± 3.67 in Group B, 20.76 ± 4.84 in Group C, and 17.28 ± 3.32 in Group D, which showed a statistically significant difference. The mean MoCA score also showed a statistically significant decline from Group A to Group D with values being 26.36 ± 1.91, 24.56 ± 1.94, 22.08 ± 2.12 and 20.12 ± 1.81, respectively (P < 0.05). The MMSE and MoCA scores were found to have a statistically significant positive correlation with hemoglobin; serum corrected calcium and eGFR (P < 0.01) and statistically significant negative correlation with blood urea, serum creatinine, serum uric acid, serum phosphate, serum potassium and stage of CKD (P < 0.01). By raising the clinician's awareness about cognitive dysfunction in CKD patients and its potential effects on medication, fluid and, dietary compliance improved quality of care is expected. Early intervention will improve the patient's quality of life.

Methods to identify dementia in the electronic health record: Comparing cognitive test scores with dementia algorithms

BACKGROUND

Epidemiologic studies often use diagnosis codes to identify dementia outcomes. It remains unknown to what extent cognitive screening test results add value in identifying dementia cases in big data studies leveraging electronic health record (EHR) data. We examined test scores from EHR data and compared results with dementia algorithms.

METHODS

This retrospective cohort study included patients 60+ years of age from Kaiser Permanente Washington (KPWA) during 2013-2018 and the Veterans Health Affairs (VHA) during 2012-2015. Results from the Mini Mental State Examination (MMSE) and the Saint Louis University Mental Status Examination (SLUMS) cognitive screening exams, were classified as showing dementia or not. Multiple dementia algorithms were created using combinations of diagnosis codes, pharmacy records, and specialty care visits. Correlations between test scores and algorithms were assessed.

RESULTS

3,690 of 112,917 KPWA patients and 2,981 of 102,981 VHA patients had cognitive test results in the EHR. In KPWA, dementia prevalence ranged from 6.4%-8.1% depending on the algorithm used and in the VHA, 8.9%-12.1%. The algorithm which best agreed with test scores required ≥2 dementia diagnosis codes in 12 months; at KPWA, 14.8% of people meeting this algorithm had an MMSE score, of whom 65% had a score indicating dementia. Within VHA, those figures were 6.2% and 77% respectively.

CONCLUSIONS

Although cognitive test results were rarely available, agreement was good with algorithms requiring ≥2 dementia diagnosis codes, supporting the accuracy of this algorithm.

IMPLICATIONS

These scores may add value in identifying dementia cases for EHR-based research studies.

Functional Connectivity in Neurodegenerative Disorders: Alzheimer's Disease and Frontotemporal Dementia

Neurodegenerative disorders are a growing cause of morbidity and mortality worldwide. Onset is typically insidious and clinical symptoms of behavioral change, memory loss, or cognitive dysfunction may not be evident early in the disease process. Efforts have been made to discover biomarkers that allow for earlier diagnosis of neurodegenerative disorders, to initiate treatment that may slow the course of clinical deterioration. Neuronal dysfunction occurs earlier than clinical symptoms manifest. Thus, assessment of neuronal function using functional brain imaging has been examined as a potential biomarker. While most early studies used task-functional magnetic resonance imaging (fMRI), with the more recent technique of resting-state fMRI, "intrinsic" relationships between brain regions or brain networks have been studied in greater detail in neurodegenerative disorders. In Alzheimer's disease, the most common neurodegenerative disorder, and frontotemporal dementia, another of the common dementias, specific brain networks may be particularly susceptible to dysfunction. In this review, we highlight the major findings of functional connectivity assessed by resting state fMRI in Alzheimer's disease and frontotemporal dementia.

Obesity Is Less Frequently Associated with Cognitive Impairment in Elderly Individuals: A Cross-Sectional Study in Yogyakarta, Indonesia

Obesity is one of the factors associated with cognitive impairment. However, obesity may differently affect cognitive function in different age groups, and scarce data are available from low- and middle-income countries. This cross-sectional study aimed to identify the association between obesity and cognitive impairment among 143 elderly individuals in Yogyakarta. We recorded the sociodemographic factors and some comorbidities, also measured the body mass index as a parameter of obesity, cognitive function using Montreal Cognitive Assessment—Indonesia, mood condition and depression status using geriatric depression scale-short form, as well as the daily life function using Activity of Daily Living and Instrumental Activity of Daily Living. After adjustment for the sociodemographic and comorbidities, we found that subjects with older age were more likely to have cognitive impairment (odds ratio [OR] 3.544, 95%CI: 1.36–9.22, p < 0.01) and compared with elderly individuals with normal weight, obese elderly individuals were 40% less likely to have cognitive impairment (OR 0.604, 95%CI: 0.39–0.95, p < 0.05). This study suggests that obesity in elderly individuals is less frequently associated with cognitive impairment. These findings support the reverse causation mechanism related to body mass index (BMI) and cognitive impairment in low/middle-income countries.

How to Assess Executive Functions in a Low-Educated and Multicultural Population Using a Switching Verbal Fluency Test (the TFA-93) in Neurodegenerative Diseases?

Few neuropsychological tests are available to assess executive dysfunction in low-educated and multicultural populations. To address this issue, the TFA-93, a switching verbal fluency test to assess cognitive flexibility, was administered to 70 healthy controls, 57 patients with a clinical diagnosis of Alzheimer's disease, and 21 with a clinical diagnosis of a neurodegenerative disease associated with frontal disorders. Most of the participants were low-educated and nonnative French speakers. The TFA-93 comprises 2 categorical fluency tasks (animals and fruits) and a fluency task in which participants have to switch between animals and fruits. Correct responses and errors were collected, and a flexibility index expressed the switching cost. Results showed that correct responses were lower, and the switching cost was greater in both patient groups. In low-educated and multicultural populations, the TFA-93 seems to be a good alternative to assess flexibility compared to the standard neuropsychological tools based on academic abilities.

A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction

Background: Alzheimer's disease (AD) is the most common type of dementia. Scientists have discovered that the causes of AD may include a combination of genetic, lifestyle, and environmental factors, but the exact cause has not yet been elucidated. Effective strategies to prevent and treat AD therefore remain elusive. The identified genetic causes of AD mainly focus on individual genes, but growing evidence has shown that complex diseases are usually affected by the interaction of genes in a network. Few studies have focused on the interactions and correlations between genes and how they are gradually destroyed or disappear during AD progression. A differential network analysis has been recognized as an essential tool for identifying the underlying pathogenic mechanisms and significant genes for prediction analysis. We therefore aim to conduct a differential network analysis to reveal potential networks involved in the neuropathogenesis of AD and identify genes for AD prediction. Methods: In this paper, we selected 365 samples from the Religious Orders Study and the Rush Memory and Aging Project, including 193 clinically and neuropathologically confirmed AD subjects and 172 no cognitive impairment (NCI) controls. Then, we selected 158 genes belonging to the AD pathway (hsa05010) of the Kyoto Encyclopedia of Genes and Genomes. We employed a machine learning method, namely, joint density-based non-parametric differential interaction network analysis and classification (JDINAC), in the analysis of gene expression data (RNA-seq data). We searched for the differential networks in the RNA-seq data with a pathological diagnosis of AD. Finally, an optimal prediction model was built through cross-validation, which showed good discrimination and calibration for AD prediction. Results: We used JDINAC to derive a gene co-expression network and to explore the relationship between the interaction of gene pairs and AD, and the top 10 differential gene pairs were identified. We then compared the prediction performance between JDINAC and individual genes based on prediction methods. JDINAC provides better accuracy of classification than the latest methods, such as random forest and penalized logistic regression. Conclusions: The interaction between gene pairs is related to AD and can provide more insight than the individual genes in AD prediction.

The impacts of family involvement on general hospital care experiences for people living with dementia: An ethnographic study

BACKGROUND

A quarter of people in general hospitals have dementia. Limited existing studies suggest that hospital care experiences of people living with dementia, and the involvement of their families in care, may be suboptimal.

OBJECTIVES

The objectives of this study were to explore how family involvement impacts upon experiences of hospital care for people living with dementia.

DESIGN

A qualitative ethnographic study.

METHODS

Ethnographic data were collected from two care of older people general hospital wards. Data were collected via observations, conversations and interviews with people living with dementia, families and staff. In total, 400 hours of observation and 46 interviews were conducted across two 7-9 month periods.

RESULTS

People living with dementia could experience a lack of connection on multiple levels - from pre-hospital life as well as life on the wards - where they could spend long periods of time without interacting with anyone. There was great variation in the degree to which staff used opportunities to involve families in improving connections and care. When used, the knowledge and expertise of families played a crucial role in facilitating more meaningful interactions, demonstrating how person-centred connections and care are possible in busy hospital settings. Despite such benefits, the involvement of families and their knowledge was not routine. Care was required to ensure that family involvement did not override the needs and wishes of people living with dementia.

CONCLUSIONS

This study demonstrates the benefits of involving families and their knowledge in care, advocating for family involvement, alongside the involvement of people living with dementia, to become a more routine component of hospital care.

Language in Amnestic Mild Cognitive Impairment and Dementia of Alzheimer’s Type: Quantitatively or Qualitatively Different?

Background/Aims: The purpose of this study was to explore language differences between individuals diagnosed with amnestic mild cognitive impairment multiple domain (aMCIm) and those with probable Alzheimer’s disease, with a goal of (i) characterizing the language profile of aMCIm and (ii) determining whether the profiles of dementia of Alzheimer’s type (DAT) and aMCIm individuals are on a continuum of one diagnostic entity or represent two distinct ­cognitive disorders. Methods: Language data from 28 patients with consensus diagnosis of aMCIm and DAT derived from a retrospective chart review were compared to that of healthy controls. Results: A non-parametric statistic established that there was no significant difference between groups in age, years of education or duration of symptoms and that expressive language was found to be relatively intact in both patient groups. In contrast, both groups exhibited significant impairments on receptive language tests and on linguistically complex tasks that rely on episodic memory and executive functions. Individuals with aMCIm and DAT present with configurations of language features that are largely in parallel to each other and reflect predominantly quantitative differences. Conclusion: Language tests provide an important contribution to the diagnostic process in their capacity to identify language impairments at an early stage. Understanding the nature of language decline is critically important to the intervention process as this information would inform cognitive intervention approaches aimed at promoting quality of life in people living with MCI and dementia.

Effect of a home based, low intensity, physical exercise program in older adults dialysis patients: a secondary analysis of the EXCITE trial

Background

Older adults dialysis patients represent the frailest subgroup of the End Stage Renal Disease (ESRD) population and physical exercise program may mitigate the age-related decline in muscle mass and function.

Methods

Dialysis patients of the EXCITE trial aged > 65 years (n = 115, active arm, n = 53; control arm, n = 62) were submitted in random order to a home based, low intensity physical exercise program. At baseline and 6 months after exercise training 6-min walking distance (6MWD) and 5-time sit-to-stand test (5STS) were performed, and quality of life (QoL) was tested.

Results

The training program improved both the 6MWD (6-months: 327 ± 86 m versus baseline: 294 ± 74 m; P < 0.001) and the 5STS time (6-months: 19.8 ± 5.6 s versus baseline: 22.5 ± 5.1 s; P < 0.001) in the exercise group whereas they did not change in the control group (P = 0.98 and 0.25, respectively). The between-arms differences (6 months-baseline) in the 6MWD (+ 34.0 m, 95% CI: 14.4 to 53.5 m) and in the 5STS time changes (− 1.9 s, 95% CI: -3.6 to − 0.3 s) were both statistically significant (P = 0.001 and P = 0.024, respectively). The cognitive function dimension of QoL significantly reduced in the control arm (P = 0.04) while it remained unchanged in the active arm (P = 0.78) (between groups difference P = 0.05). No patient died during the trial and the training program was well tolerated.

Conclusions

This secondary analysis of the EXCITE trial shows that a home-based, exercise program improves physical performance and is well tolerated in elderly ESRD patients.

Trial registration

The trial was registered in ClinicalTrials.Gov (Clinicaltrials.gov identifier: NCT01255969) on December 8, 2010.

Electronic supplementary material

The online version of this article (10.1186/s12877-018-0938-5) contains supplementary material, which is available to authorized users.

Using Base Rate of Low Scores to Identify Progression from Amnestic Mild Cognitive Impairment to Alzheimer's Disease

OBJECTIVES

To investigate the implications of obtaining one or more low scores on a battery of cognitive tests on diagnosing mild cognitive impairment (MCI).

DESIGN

Observational longitudinal study.

SETTING

Alzheimer's Disease Neuroimaging Initiative.

PARTICIPANTS

Normal controls (NC, n = 280) and participants with MCI (n = 415) according to Petersen criteria were reclassified using the Jak/Bondi criteria and number of impaired tests (NIT) criteria.

MEASUREMENTS

Diagnostic statistics and hazard ratios of progression to Alzheimer's disease (AD) were compared according to diagnostic criteria.

RESULTS

The NIT criteria were a better predictor of progression to AD than the Petersen or Jak/Bondi criteria, with optimal sensitivity, specificity, and positive and negative predictive value.

CONCLUSION

Considering normal variability in cognitive test performance when diagnosing MCI may help identify individuals at greatest risk of progression to AD with greater certainty.

A Novel Method for Direct Assessment of Everyday Competence Among Older Adults

BACKGROUND

Recent findings indicate that impairments in functional performance do occur among individuals diagnosed with mild cognitive impairment (MCI). Most assessment strategies for everyday competence are associated with challenges with reliability, are typically in paper and pencil format, or require in-person administration by a trained professional.

OBJECTIVE

This paper reports on a novel technology-based assessment battery of everyday competence that includes ecologically valid simulations of daily activities important to independence.

METHODS

The sample included 85 non-cognitively impaired older adults aged 65+ and 62 older adults diagnosed with amnestic MCI (aMCI). Participants completed standard measures of cognitive abilities and the computerized battery of everyday tasks, which included simulations of a doctor's visit; and medication and financial management tasks.

RESULTS

The older adults with aMCI performed significantly poorer on all three tasks in the everyday task battery. Performance on these measures were also moderately correlated with standard measures of cognitive abilities and showed good test-retest reliability.

CONCLUSIONS

The results show that it is feasible to use a technology-based assessment battery of everyday tasks with both non-cognitively impaired older adults and older adults with MCI. The use of this type of battery can overcome many of the logistic constraints associated with current functional assessment protocols.

Low-grade systemic inflammation is associated with functional disability in elderly people affected by dementia

The decline in basic and instrumental activities of daily living (BADLs and IADLs, respectively) is a well-established clinical hallmark of dementia. Growing evidence has shown that systemic subclinical inflammation may be related to functional impairment. We evaluated the possible association between low-grade systemic inflammation and functional disability in older individuals affected by dementia. We explored the association between high-sensitivity C-reactive protein (hs-CRP) levels and BADLs/IADLs in older individuals affected by late onset Alzheimer's disease (LOAD; n 110), "mixed" dementia (n 135), or mild cognitive impairment (MCI; n 258), and compared them with 75 normal Controls. Independent of age, gender, comorbidity, and other potential confounders, higher hs-CRP was significantly associated with poorer BADLs (loss ≥ 1 function) in people with LOAD (odds ratio [OR] 3.14, 95% confidence interval [CI], 1.33-7.33) and mixed dementia (OR 2.48, 95%CI 1.12-5.55), but not in those with MCI (OR 1.38, 95%CI 0.83-2.45) or Controls (OR 2.98, 95%CI 0.54-10.10). No association emerged between hs-CRP and IADLs in any of the sub-group. Our data suggest that systemic low-grade inflammation may contribute to functional disability in older patients with dementia.

Neurogranin as a predictor of memory and executive function decline in MCI patients

OBJECTIVE

To determine whether high CSF levels of neurogranin (Ng) predict longitudinal decline in memory and executive function during early-stage Alzheimer disease (AD).

METHODS

Baseline levels of CSF Ng were studied in relation to cross-sectional and longitudinal cognitive performance over 8 years. Data were obtained from the Alzheimer's Disease Neuroimaging Initiative database, and participants with normal cognition (n = 111) and mild cognitive impairment (MCI) (n = 193) were included.

RESULTS

High levels of CSF Ng were associated with poor baseline memory scores (β = -0.21, p < 0.0001). CSF Ng predicted both memory and executive function decline over time (β = -0.0313, p = 0.0068 and β = -0.0346, p = 0.0169, respectively) independently of age, sex, education, and APOE ε4 status. When the rate of decline by tertiles was examined, CSF Ng was a level-dependent predictor of memory function, whereby the group with highest levels of Ng showed the fastest rates of decline in both memory and executive function. When examined separately, elevated Ng was associated with cognitive decline in participants with MCI but not in those with normal cognition. The levels of CSF Ng were not associated with cognitive measures when tau and amyloid 42 (Aβ₄₂) were controlled for in these analyses.

CONCLUSIONS

High CSF Ng associates with poor memory scores in participants with MCI cross-sectionally and with poor memory and executive function longitudinally. The association of Ng with cognitive measures disappears when tau and Aβ₄₂ are included in the statistical models. Our findings suggest that CSF Ng may serve as a biomarker of cognition. Synaptic dysfunction contributes to cognitive impairment in early-stage AD.

Hyperplanar Morphological Clustering of a Hippocampus by Using Volumetric Computerized Tomography in Early Alzheimer's Disease

Background: On diagnosing Alzheimer’s disease (AD), most existing imaging-based schemes have relied on analyzing the hippocampus and its peripheral structures. Recent studies have confirmed that volumetric variations are one of the primary indicators in differentiating symptomatic AD from healthy aging. In this study, we focused on deriving discriminative shape-based parameters that could effectively identify early AD from volumetric computerized tomography (VCT) delineation, which was previously almost intangible. Methods: Participants were 63 volunteers of Thai nationality, whose ages were between 40 and 90 years old. Thirty subjects (age 68.51 ± 5.5) were diagnosed with early AD, by using Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria and the National Institute of Neurological and Communicative Disorders and the Stroke and the Alzheimer’s disease and Related Disorders Association (NINCDS-ADRDA) criteria, while the remaining 33 were in the healthy control group (age 67.93 ± 5.5). The structural imaging study was conducted by using VCT. Three uninformed readers were asked to draw left and right hippocampal outlines on a coronal section. The resultant shapes were aligned and then analyzed with statistical shape analysis to obtain the first few dominant variational parameters, residing in hyperplanes. A supervised machine learning, i.e., support vector machine (SVM) was then employed to elucidate the proposed scheme. Results: Provided trivial delineations, relatively as low as 5 to 7 implicit model parameters could be extracted and used as discriminants. Clinical verification showed that the model could differentiate early AD from aging, with high sensitivity, specificity, accuracy and F-measure of 0.970, 0.968, 0.983 and 0.983, respectively, with no apparent effect of left-right asymmetry. Thanks to a less laborious task required, yet high discriminating capability, the proposed scheme is expected to be applicable in a typical clinical setting, equipped with only a moderate-specs VCT.

Advantages of Structure-Based Drug Design Approaches in Neurological Disorders

OBJECTIVE

The purpose of the review is to portray the theoretical concept on neurological disorders from research data.

BACKGROUND

The freak changes in chemical response of nerve impulse causes neurological disorders. The research evidence of the effort done in the older history suggests that the biological drug targets and their effective feature with responsive drugs could be valuable in promoting the future development of health statistics structure for improved treatment for curing the nervous disorders.

METHODS

In this review, we summarized the most iterative theoretical concept of structure based drug design approaches in various neurological disorders to unfathomable understanding of reported information for future drug design and development.

RESULTS

On the premise of reported information we analyzed the model of theoretical drug designing process for understanding the mechanism and pathology of the neurological diseases which covers the development of potentially effective inhibitors against the biological drug targets. Finally, it also suggests the management and implementation of the current treatment in improving the human health system behaviors.

CONCLUSION

With the survey of reported information we concluded the development strategies of diagnosis and treatment against neurological diseases which leads to supportive progress in the drug discovery.

A comprehensive intervention following the clinical pathway of eating and swallowing disorder in the elderly with dementia: historically controlled study

Background

Eating problems in patients with advanced dementia are strongly associated with their deteriorating survival. Food and drink intake in people with dementia may be supported by specific interventions, but the effectiveness of such interventions is backed by almost no evidence. However, comprehensive geriatric assessment (CGA) might potentially clarify the etiology of decreased oral intake in people with dementia; thus improving their clinical outcomes.

Methods

This study was a single-arm, non-randomized trial that included historically controlled patients for comparison. We defined elderly patients with both severely decreased oral intake depending on artificial hydration and/or nutrition (AHN) and dementia as “Eating and Swallowing Disorder of the Elderly with Dementia (ESDED)”. In the intervention group, participants received CGA through the original clinical pathway with multidisciplinary interventions. This was followed by individualized therapeutic interventions according to assessment of the etiology of their eating problems.

Results

During the intervention period (between 1st April 2013 and 31st March 2015), 102 cases of ESDED were enrolled in the study and 90 patients had completed receiving CGA. Conversely, 124 ESDED patient controls were selected from the same hospital enrolled during the historical period (between 1st April 2011 and 31st March 2012). Most participants in both groups were bedridden with severe cognitive impairment. For the intervention group, an average of 4.3 interventional strategies was recommended per participant after CGA. Serological tests, diagnostic imaging and other diagnostic examinations were much more frequently performed in the intervention group. Recovery rate from ESDED in the intervention group was significantly higher than that in the historical group (51% v.s. 34%, respectively, P = 0.02). The 1-year AHN-free survival in the intervention group was significantly higher than that in the historical group (28% v.s. 15%, respectively, P = 0.01). No significant difference between the two groups was found for 1-year overall survival (37% v.s. 28%, respectively, P = 0.08).

Conclusions

Use of CGA with multidisciplinary interventions could improve the functional status of eating and allow elderly patients with severe eating problems and dementia to survive independently without the need for AHN.

Trial registration

ISRCTN57646445, this trial was retrospectively registered on 8th December 2015.

Electronic supplementary material

The online version of this article (doi:10.1186/s12877-017-0531-3) contains supplementary material, which is available to authorized users.

Dementia: A complex disease with multiple etiologies and multiple treatments

As the population of the United States ages, the rates of dementia are also likely to increase. Clinicians will, therefore, likely be asked to evaluate and treat an escalating number of patients experiencing a decline in multiple domains of cognitive function, which is the hallmark of neurocognitive disorders. It is also probable that clinicians will be confronted with management dilemmas related to the myriad of psychological and behavioral problems that often occur as a consequence of the neurocognitive impairment. In fact, these behavioral and psychological issues might be the initial symptoms that lead the patient to present to the clinician. Dementia has multiple potential etiologies, and a careful diagnostic assessment is imperative to best characterize the specific type of dementia impacting the patient. This is important, as knowing the type of dementia helps the clinician choose the most effective treatment. Potential treatments should be interdisciplinary in scope, patient/family-centered, and may include both nonpharmacologic and pharmacologic treatments.

Cognitive Impairment in Chronic Kidney Disease: Vascular Milieu and the Potential Therapeutic Role of Exercise

Chronic kidney disease (CKD) is considered a model of accelerated aging. More specifically, CKD leads to reduced physical functioning and increased frailty, increased vascular dysfunction, vascular calcification and arterial stiffness, high levels of systemic inflammation, and oxidative stress, as well as increased cognitive impairment. Increasing evidence suggests that the cognitive impairment associated with CKD may be related to cerebral small vessel disease and overall impairment in white matter integrity. The triad of poor physical function, vascular dysfunction, and cognitive impairment places patients living with CKD at an increased risk for loss of independence, poor health-related quality of life, morbidity, and mortality. The purpose of this review is to discuss the available evidence of cerebrovascular-renal axis and its interconnection with early and accelerated cognitive impairment in patients with CKD and the plausible role of exercise as a therapeutic modality. Understanding the cerebrovascular-renal axis pathophysiological link and its interconnection with physical function is important for clinicians in order to minimize the risk of loss of independence and improve quality of life in patients with CKD.

Reference Intervals for Plasma Amyloid β in Korean Adults Without Cognitive Impairment

Amyloid β (Aβ) peptides are important components of plaques in patients with Alzheimer's disease (AD). Recent studies suggest that a low plasma ratio of Aβ42 to Aβ40 may precede the development of the sporadic form of AD. The aim of this study was to establish reference intervals for plasma Aβ in Korean adults. A total of 370 apparently healthy individuals (181 males and 189 females aged 40-69 yr) without cognitive impairment were enrolled. Plasma concentrations of Aβ40 and Aβ42 were measured by using a human amyloid β assay kit (Immuno-Biological Laboratories, Japan). Reference intervals were established according to the "CLSI guidelines for defining, establishing, and verifying reference intervals in the clinical laboratory". There was no need to partition the data with respect to gender or age group. The 95th percentile reference intervals for Aβ40 and Aβ42 were 127-331 pg/mL and 2.31-19.84 pg/mL, respectively. The reference interval for the Aβ42/Aβ40 ratio was 0.011-0.092. Plasma Aβ concentrations obtained in this study could be used as reference intervals for clinical purposes.

(1)H-MRS asymmetry changes in the anterior and posterior cingulate gyrus in patients with mild cognitive impairment and mild Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia worldwide. Amnestic mild cognitive impairment (aMCI) is often the prodromal stage to AD. Most patients with aMCI harbor the pathologic changes of AD and demonstrate transition to AD at a rate of 10%-15% per year. Patients with AD and aMCI experience progressive brain metabolite changes. Accumulating evidence indicates that the asymmetry changes of left and right brain happen in the early stage of AD. However, the features of asymmetry changes in both anterior cingulate gyrus (ACG) and posterior cingulate gyrus (PCG) are still unclear. Here, we examine the left-right asymmetry changes of metabolites in ACG and PCG. Fifteen cases of mild AD patients meeting criteria for probable AD of NINDS-ADRDA, thirteen cases of aMCI according to the Mayo Clinic Alzheimer's Disease Research Center criteria, and sixteen cases of age-matched normal controls (NC) received Proton magnetic resonance spectroscopy ((1)H-MRS) for measurement of NAA/mI, NAA/Cr, Cho/Cr, and mI/Cr ratios in the PCG and ACG bilaterally. We analyzed (1)H-MRS data by paired t-test to validate the left-right asymmetry of (1)H-MRS data in the PCG and ACG. In AD, there was a significant difference in mI/Cr between the left and right ACG (P<0.001) and the left and right PCG (P=0.007). In aMCI, there was a significant difference in mI/Cr between the left and right ACG (P<0.001). In NC, there were no differences in the ratio value of metabolites NAA/mI, NAA/Cr, Cho/Cr, and mI/Cr between the left and right ACG and PCG. Thus, the left-right asymmetry of mI/Cr in the ACG and PCG may be an important biological indicator of mild AD.

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment

INTRODUCTION

The objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD).

METHOD

Verbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness. Over 4 years of follow-up, 24 MCI patients converted to AD. Using conversion as the outcome variable, ensemble classifiers were constructed by training classifiers on the individual groups of scores and then entering predictions from the primary classifiers into regularized logistic regression models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was measured for classifiers trained with five groups of available variables.

RESULTS

Classifiers trained with novel scores outperformed those trained with raw scores (AUC 0.872 vs 0.735; P < .05 by DeLong test). Addition of structural brain measurements did not improve performance based on novel scores alone.

CONCLUSION

The brevity and cost profile of verbal fluency tasks recommends their use for clinical decision making. The word lists generated are a rich source of information for predicting outcomes in MCI. Further work is needed to assess the utility of verbal fluency for early AD.

Autoimmune dementia and encephalopathy

Autoimmune dementia and encephalopathies (ADE) are complex disorders that can cause immune-mediated cognitive deficits and have confusing nomenclature. Presentation varies from acute limbic encephalitis to subacute or chronic disorders of cognition mimicking neurodegenerative dementia. It may occur as a paraneoplastic phenomenon or an idiopathic autoimmune phenomenon. The presence of a personal/family history of autoimmunity, inflammatory spinal fluid, serologic evidence of autoimmunity (neural or nonorgan-specific), or mesial temporal magnetic resonance imaging abnormalities are clues to diagnosis. Bedside cognitive assessment and/or detailed neuropsychologic testing are useful. Neural-specific autoantibodies, mostly discovered in the past two decades, may bind antigens on the cell surface (e.g., N-methyl-d-aspartate receptor autoantibodies) and are likely to be pathogenic, with treatment aimed at antibody-depleting agents often with success, while antibodies binding intracellular antigens (e.g., antineuronal nuclear autoantibody type 1 (ANNA1 or anti-Hu)) are a marker of a T-cell-mediated process and treated with T-cell-depleting immunotherapies, with variable responses. Detection and treatment of cancer (when present) are essential. High-dose corticosteroids are the initial treatment in most patients and may serve as a diagnostic test when the diagnosis is uncertain. Repeat cognitive testing after immunotherapy helps document objective improvements. Maintenance immunotherapy is recommended in those at risk for relapse. Prognosis is variable, but paraneoplastic ADE with antibodies to intracellular antigens have a worse prognosis. The field is still developing and future studies should provide guidelines for diagnosis and treatments.