Search

Abstract Background A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. Methods Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. Results BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. Conclusions We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

Background: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. Methods: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. Results: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. Conclusions: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia. [ABSTRACT FROM AUTHOR]

Dementia is a major global health issue that significantly impacts millions of individuals, families, and societies worldwide, creating a substantial burden on healthcare systems. This study introduces a novel approach for predicting dementia by employing the Logistic Regression (LR) model, enhanced with Recursive Feature Elimination (RFE), applied to a unique dataset comprising 1000 patients, with 49.60% male and 50.40% female. The LR model, recognized for its simplicity and effectiveness in binary classification tasks, is optimized through RFE, a technique that iteratively eliminates less significant features to improve model performance. The model’s effectiveness was assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Kappa score. Furthermore, SHapley Additive exPlanations (SHAP) values were employed to increase the interpretability of the model, providing insights into the most influential features for dementia prediction. To address the issue of overfitting, a standardization technique was implemented, which enhanced the model’s predictive performance. The findings of this study hold potential implications for early dementia detection, informing intervention strategies, and optimizing healthcare resource allocation.

The objective of this study was to find variables that showed significant associations with cognitive decline and memory loss in visual, auditory, olfactory, and balance functions. Seventysix elderly people aged 65 or older participated in the experiment, received the aforementioned functions' assessments, and underwent cognitive screening. Participants were divided into the normal cognitive (NC) and lower cognitive (LC) groups on the basis of cognitive function test scores, and the LC group was divided into the lower-MIS and normal-MIS groups on the basis of memory index scores (MISs). The LC group had less balance, hearing, and olfactory functions than the NC group, and the lower-MIS group had less visual and auditory functions than the normal-MIS group. The performance of the classification model made of variables was evaluated as the area under the receiver operating characteristic curve. The performance was shown to be good. Therefore, this study can be the basis for the development of tools for the early diagnosis of dementia and the creation of a smart environment. [ABSTRACT FROM AUTHOR]

In the last decades the health care developments highly rise the level of ages of world population. This improvement was accompanied by increasing the diseases related with elder like Dementia, which Alzheimer's disease represents the most common form. The present studies aim to design and implementation a medical system for improving the life of Alzheimer's disease persons and ease the burden of their caregivers. AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patient's future health, and recommend better treatments. AI goes beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. Diagnosis is about identifying disease. By building an algorithm we can diagnosis chest X-ray and determine whether it contains disease, another algorithm that will look at brain MRIs and identify the location of tumours in those brain MRIs health of the patients lab values and their demographics and use those to predict the risk of an event. A Smart IOT Interactive Assistance is a technological device that continuously monitors the stability of an Alzheimer's patient, indicates their position on a map, automatically reminds them to take their prescriptions, and has a call button for any emergencies they could experience during the day. The system has two components, one of which the patient wears and the other of which is an IoT platform application utilized by the caregiver. The motion processing unit sensor, GPS, heart rate sensor with microcontrollers, and LCD display were used to construct the wearable device. An Internet of Things (IoT) platform supports this device, allowing the caregiver to communicate with the patient from any location. [ABSTRACT FROM AUTHOR]

Accurate classification of Magnetic Resonance Images (MRI) is essential to accurately predict Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion. Meanwhile, deep learning has been successfully implemented to classify and predict dementia disease. However, the accuracy of MRI image classification is low. This paper aims to increase the accuracy and reduce the processing time of classification through Deep Learning Architecture by using Elastic Net Regularisation in Feature Selection. The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularisation. Initially, the MRI images are fed into CNN for features extraction through convolutional layers alternate with pooling layers, and then through a fully connected layer. After that, the features extracted are subjected to Principle Component Analysis (PCA) and Elastic Net Regularisation for feature selection. Finally, the selected features are used as an input to Extreme Machine Learning (EML) for the classification of MRI images. The result shows that the accuracy of the proposed solution is better than the current system. In addition to that, the proposed method has improved the classification accuracy by 5% on average and reduced the processing time by 30 ~ 40 seconds on average. The proposed system is focused on improving the accuracy and processing time of MCI converters/non-converters classification. It consists of features extraction, feature selection, and classification using CNN, FreeSurfer, PCA, Elastic Net, and Extreme Machine Learning. Finally, this study enhances the accuracy and the processing time by using Elastic Net Regularisation, which provides important selected features for classification. [ABSTRACT FROM AUTHOR]

Dementia is a neurodegenerative disease that causes a progressive deterioration of thinking, memory, and the ability to perform daily tasks. Other common symptoms include emotional disorders, language disorders, and reduced mobility; however, self-consciousness is unaffected. Dementia is irreversible, and medicine can only slow but not stop the degeneration. However, if dementia could be predicted, its onset may be preventable. Thus, this study proposes a revolutionary transfer-learning machine-learning model to predict dementia from magnetic resonance imaging data. In training, k-fold cross-validation and various parameter optimization algorithms were used to increase prediction accuracy. Synthetic minority oversampling was used for data augmentation. The final model achieved an accuracy of 90.7%, superior to that of competing methods on the same data set. This study’s model facilitates the early diagnosis of dementia, which is key to arresting neurological deterioration from the disease, and is useful for underserved regions where many do not have access to a human physician. In the future, the proposed system can be used to plan rehabilitation therapy programs for patients.

Background: Motoric cognitive risk (MCR) is a syndrome characterised by measured slow gait speed and self‐reported cognitive complaints. MCR is a high‐risk state for adverse health outcomes in older adults, particularly cognitive impairment and dementia. Previous studies have identified risk factors for MCR, but the effect of socioeconomic status has, to date, been insufficiently examined. This study explored the association between MCR and socioeconomic status, as determined by occupational social class and years of education. Methods: Some 692 community‐based adults of the Lothian Birth Cohort 1936 (LBC1936), aged 70 years at baseline, were followed up after 6 years and classified into non‐MCR and MCR groups. We applied logistic regression analyses adjusting for demographic, lifestyle, and health covariates to investigate the association between MCR and years of education and occupational social class, categorised into manual versus non‐manual occupations. Results: MCR prevalence at age 76 years was 5.6% (95% CI 4.0–7.6). After multivariate adjustment, participants of lower socioeconomic status (manual occupation) had a greater than three‐fold increased likelihood of MCR (adjusted odds ratio 3.55, 95% CI 1.46–8.74; p = 0.005) compared with those of higher socioeconomic status (non‐manual occupation). Conclusions: Working in a manual job earlier in life triples the risk of MCR later in life, regardless of education. Unravelling this association will likely reveal important pathophysiological mechanisms underlying MCR and may unearth modifiable risk factors which could be targeted to reduce the incidence of MCR and, ultimately, dementia. Policy and healthcare practice addressing dementia risks such as MCR in their social context and early in the lifecourse could be effective strategies for reducing health inequalities in older age. [ABSTRACT FROM AUTHOR]

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset. [ABSTRACT FROM AUTHOR]

Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations. [ABSTRACT FROM AUTHOR]

Mental health-related disorders are common in elderly populations. Among the various mental health disorders, one most significant threat is dementia, and prediction of dementia has become an important issue related to well-being in old age, because the disease progression of dementia can be slowed by early diagnosis and disease control. In this paper, we propose an unobtrusive dementia-prediction system for monitoring physical activities of elderly persons either living alone or as a couple in different house structures, achieved through passive infrared (PIR) motion sensors combined with data processing. The proposed feature extraction algorithm extracts feature values related to physical activities from simple passive infrared sensors located in each room space. We then apply a variety of common popular classification models, including Deep Neural Networks (DNNs), to predict the risk of dementia in a sensor-enabled home. We implemented and validated algorithms on data collected for over a month from 18 participants who were engaged with a variety of living conditions. The proposed system was effective in predicting dementia risk, with up to an 0.99 area under the curve (AUC) using DNN with principal component analysis (PCA) and a quantile transformer scaler. In terms of the result based on leave-one-subject-out (LOSO) analysis, an accuracy of 63.38% was achieved using DNN with PCA and a standard scaler. The proposed methodology is non-invasive and cost-effective, and can be used for a variety of long-term monitoring and early symptom detection systems, helping caregivers provide optimal interventions to elderly individuals at risk for dementia.

Background: Motoric Cognitive Risk (MCR) syndrome is a high-risk state for adverse health outcomes in older adults characterised by measured slow gait speed and self-reported cognitive complaints. The recent addition to the Lothian Birth Cohort 1936 of robust dementia outcomes enabled us to assess the prognostic value of MCR for dementia and explore the various trajectories of participants diagnosed with MCR. Methods: We classified 680 community-dwelling participants free from dementia into non-MCR or MCR groups at mean [SD] age 76.3 [0.8] years. We used Cox and competing risk regression methods, adjusted for potential confounders, to evaluate the risk of developing all-cause incident dementia over 10 years of follow-up. Secondarily, we followed the trajectories for individuals with and without MCR at baseline and categorised them into subgroups based on whether MCR was still present at the next research wave, three years later. Results: The presence of MCR increased the risk of incident dementia (adjusted HR 2.34, 95%CI 1.14–4.78, p = 0.020), as did fewer years of education and higher depression symptoms. However, MCR has a heterogenous progression trajectory. The MCR progression subgroups each have different prognostic values for incident dementia. Conclusion: MCR showed similar prognostic ability for dementia in a Scottish cohort as for other populations. MCR could identify a target group for early interventions of modifiable risk factors to prevent incident dementia. This study illustrates the heterogeneous nature of MCR progression. Exploring the underlying reasons will be important work in future work.

Background: Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable. Method: We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains. Results: The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu. Conclusion: For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder. [ABSTRACT FROM AUTHOR]

Abstract Background Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable. Method We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains. Results The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu. Conclusion For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder.

Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%. [ABSTRACT FROM AUTHOR]

It is difficult to provide information to patients because the cause of Alzheimer's disease is not accurately identified. Therefore, there are difficulties in management and prevention. However, if one can manage the basic influencing factors, one can maintain a healthy brain. Therefore, this study proposes a prediction support model for dementia based on regression analysis using an image style transfer. The proposed method collects images of factors extracted from text information about Alzheimer's disease, images of a normal brain, and images of a brain with Alzheimer's disease to provide precautions for the factors affecting Alzheimer's disease. Accordingly, it transforms the brain's style by transferring image features of the factors affecting it onto the normal brain image. The transformed results allow for discovery of the factors that affect Alzheimer's disease, compared to the brain with Alzheimer's disease, and allow the medical team or the patients themselves to prevent and manage it. In addition, performance evaluation compares the similarities in style transmission results for factors affecting it according to each stage of the dementia condition. A comparison of similarities shows that a brain with cerebral hemorrhage and the brain of an alcoholic have the highest similarities to all stages of dementia. [ABSTRACT FROM AUTHOR]

Abstract Background Biomarker research is gaining increasing attention focusing on the preclinical stages of the disease. Such interest requires special attention for communication and disclosure in clinical contexts. Many countries give dementia a high health policy priority by developing national strategies and by improving guidelines addressing disclosure of a diagnosis; however, risk communication is often neglected. Main text This paper aims to identify the challenges of disclosure in the context of dementia prediction and to find out whether existing clinical guidelines sufficiently address the issues of disclosing a dementia diagnosis and of disclosing the risk of developing dementia in asymptomatic and MCI stage. We will examine clinical guidelines and recommendations of three countries (USA, Canada and Germany) regarding predictive testing and diagnostic disclosure in dementia and Mild Cognitive Impairment (MCI) to show their potentials and limits. This will provide a background to address ethical implications of predictive information and to identify ways how to proceed further. We will start by examining the guidelines and recommendations by focusing on what there is already and what is missing regarding the challenges of disclosing dementia prediction and MCI. Then, we will highlight the novel ethical issues generated by the shift to identify preclinical stages of the disease by biomarkers. We will argue for the need to develop guidelines for disclosing a risk status, which requires different considerations then disclosing a diagnosis of dementia. Finally, we will make some suggestions on how to address the gap and challenges raised by referring to German Stakeholder Conference, which presents us a good starting point to the applicability of involving stakeholders. Conclusions This paper underlines the need to develop empirically based guidelines that address the ethical and social strategies for risk communication of dementia prediction by genetic as well as non-genetic biomarkers. According to our analysis, the guidelines do not address the new developments sufficiently. International efforts should aim for specific guidelines on counseling, communicating risk and disclosing results. We argue that guidelines on (risk) disclosure should be developed by involving various stakeholders and should be informed by socio-empirical studies involving laypersons’ needs and wishes regarding risk communication.

Dementia is a neurological condition that primarily affects older adults and there is still no cure or therapy available to cure it. The symptoms of dementia can appear as early as 10 years before the beginning of actual diagnosed dementia. Hence, machine learning (ML) researchers have presented several methods for early detection of dementia based on symptoms. However, these techniques suffer from two major flaws. The first issue is the bias of ML models caused by imbalanced classes in the dataset. Past research did not address this issue well and did not take preventative precautions. Different ML models were developed to illustrate this bias. To alleviate the problem of bias, we deployed a synthetic minority oversampling technique (SMOTE) to balance the training process of the proposed ML model. The second issue is the poor classification accuracy of ML models, which leads to a limited clinical significance. To improve dementia prediction accuracy, we proposed an intelligent learning system that is a hybrid of an autoencoder and adaptive boost model. The autoencoder is used to extract relevant features from the feature space and the Adaboost model is deployed for the classification of dementia by using an extracted subset of features. The hyperparameters of the Adaboost model are fine-tuned using a grid search algorithm. Experimental findings reveal that the suggested learning system outperforms eleven similar systems which were proposed in the literature. Furthermore, it was also observed that the proposed learning system improves the strength of the conventional Adaboost model by 9.8% and reduces its time complexity. Lastly, the proposed learning system achieved classification accuracy of 90.23%, sensitivity of 98.00% and specificity of 96.65%.

Background Cognitive dispersion, or inconsistencies in performance across cognitive domains, has been posited as a cost-effective tool to predict conversion to dementia in older adults. However, there is a dearth of studies exploring cognitive dispersion in the oldest-old (>80 years) and its relationship to dementia incidence. Objective The main aim of this study was to examine whether higher cognitive dispersion at baseline was associated with dementia incidence within an 8-year follow-up of very old adults, while controlling for established risk factors and suggested protective factors for dementia. Methods Participants (n  = 468) were from the Origins of Variance in the Old-Old: Octogenarian Twins study, based on the Swedish Twin Registry. Cox regression analyses were performed to assess the association between baseline cognitive dispersion scores and dementia incidence, while controlling for sociodemographic variables, ApoEe4 carrier status, co-morbidities, zygosity and lifestyle engagement scores. An additional model included a composite of average cognitive performance. Results Cognitive dispersion and ApoEe4 were significantly associated with dementia diagnosis. These variables remained statistically significant when global cognitive performance was entered into the model. Likelihood ratio tests revealed that cognitive dispersion and cognitive composite scores entered together in the same model was superior to either predictor alone in the full model. Conclusions The study underscores the usefulness of cognitive dispersion metrics for dementia prediction in the oldest-old and highlights the influence of ApoEe4 on cognition in very late age. Our findings concur with others suggesting that health and lifestyle factors pose little impact upon cognition in very advanced age. [ABSTRACT FROM AUTHOR]

It is difficult to provide information to patients because the cause of Alzheimer’s disease is not accurately identified. Therefore, there are difficulties in management and prevention. However, if one can manage the basic influencing factors, one can maintain a healthy brain. Therefore, this study proposes a prediction support model for dementia based on regression analysis using an image style transfer. The proposed method collects images of factors extracted from text information about Alzheimer’s disease, images of a normal brain, and images of a brain with Alzheimer’s disease to provide precautions for the factors affecting Alzheimer’s disease. Accordingly, it transforms the brain’s style by transferring image features of the factors affecting it onto the normal brain image. The transformed results allow for discovery of the factors that affect Alzheimer’s disease, compared to the brain with Alzheimer’s disease, and allow the medical team or the patients themselves to prevent and manage it. In addition, performance evaluation compares the similarities in style transmission results for factors affecting it according to each stage of the dementia condition. A comparison of similarities shows that a brain with cerebral hemorrhage and the brain of an alcoholic have the highest similarities to all stages of dementia.

Background: Biomarker research is gaining increasing attention focusing on the preclinical stages of the disease. Such interest requires special attention for communication and disclosure in clinical contexts. Many countries give dementia a high health policy priority by developing national strategies and by improving guidelines addressing disclosure of a diagnosis; however, risk communication is often neglected.Main Text: This paper aims to identify the challenges of disclosure in the context of dementia prediction and to find out whether existing clinical guidelines sufficiently address the issues of disclosing a dementia diagnosis and of disclosing the risk of developing dementia in asymptomatic and MCI stage. We will examine clinical guidelines and recommendations of three countries (USA, Canada and Germany) regarding predictive testing and diagnostic disclosure in dementia and Mild Cognitive Impairment (MCI) to show their potentials and limits. This will provide a background to address ethical implications of predictive information and to identify ways how to proceed further. We will start by examining the guidelines and recommendations by focusing on what there is already and what is missing regarding the challenges of disclosing dementia prediction and MCI. Then, we will highlight the novel ethical issues generated by the shift to identify preclinical stages of the disease by biomarkers. We will argue for the need to develop guidelines for disclosing a risk status, which requires different considerations then disclosing a diagnosis of dementia. Finally, we will make some suggestions on how to address the gap and challenges raised by referring to German Stakeholder Conference, which presents us a good starting point to the applicability of involving stakeholders.Conclusions: This paper underlines the need to develop empirically based guidelines that address the ethical and social strategies for risk communication of dementia prediction by genetic as well as non-genetic biomarkers. According to our analysis, the guidelines do not address the new developments sufficiently. International efforts should aim for specific guidelines on counseling, communicating risk and disclosing results. We argue that guidelines on (risk) disclosure should be developed by involving various stakeholders and should be informed by socio-empirical studies involving laypersons' needs and wishes regarding risk communication. [ABSTRACT FROM AUTHOR]

Abstract Background The CAIDE (Cardiovascular Risk Factors, Aging, and Incidence of Dementia) Dementia Risk Score is a validated tool to predict late‐life dementia risk (20 years later), based on midlife vascular risk factors. The goal was to render this prediction tool widely accessible. Methods The CAIDE Risk Score (mobile application) App was developed based on the CAIDE Dementia Risk Score, involving information on age, educational level, hypertension, hypercholesterolemia, obesity, and physical inactivity. Results The CAIDE Risk Score App is an evidence‐based practical tool, which allows users to detect their individual risk, provides guidance for risk modification, and suggests consulting a health care practitioner if needed. Moreover, it allows practitioners to discuss preventive measures and monitor risk reduction. Conclusions The CAIDE Risk Score App is the first to predict the risk for dementia through an important evidence‐based tool. The App can encourage users to actively decrease their modifiable risk factors and postpone cognitive impairment and dementia.

The vascular hypothesis of Alzheimer's disease (VHAD) was proposed 24 years ago from observations made in our laboratory using aging rats subjected to chronic brain hypoperfusion. In recent years, VHAD has become a mother-lode to numerous neuroimaging studies targeting cerebral hemodynamic changes, particularly brain hypoperfusion in elderly patients at risk of developing Alzheimer's disease (AD). There is a growing consensus among neuroradiologists that brain hypoperfusion is likely involved in the pathogenesis of AD and that disturbed cerebral blood flow (CBF) can serve as a key biomarker for predicting conversion of mild cognitive impairment to AD. The use of cerebral hypoperfusion as a preclinical predictor of AD is becoming decisive in stratifying low and high risk patients that may develop cognitive decline and for assessing the effectiveness of therapeutic interventions. There is currently an international research drive from neuroimaging groups to seek new perspectives that can broaden our understanding of AD and improve lifestyle. Diverse neuroimaging methods are currently being used to monitor normal and dyscognitive brain activity. Some techniques are very powerful and can detect, diagnose, quantify, prognose, and predict cognitive decline before AD onset, even from a healthy cognitive state. Multimodal imaging offers new insights in the treatment and prevention of cognitive decline during advanced aging and better understanding of the functional and structural organization of the human brain. This review discusses the impact the VHAD and CBF are having on the neuroimaging technology that can usher practical strategies to help prevent AD. [ABSTRACT FROM AUTHOR]

• A multimodal DL architecture combining text and audio to predict dementia. • The system was evaluated on the DementiaBank Pitt Corpus dataset. • An analysis of the corpus has been made for explainability reasons. • We extracted how the models make their predictions and find patterns on data. This paper proposes a multimodal deep learning architecture combining text and audio information to predict dementia, a disease which affects around 55 million people all over the world and makes them in some cases dependent people. The system was evaluated on the DementiaBank Pitt Corpus dataset, which includes audio recordings as well as their transcriptions for healthy people and people with dementia. Different models have been used and tested, including Convolutional Neural Networks (CNN) for audio classification, Transformers for text classification, and a combination of both in a multimodal ensemble. These models have been evaluated on a test set, obtaining the best results by using the text modality, achieving 90.36% accuracy on the task of detecting dementia. Additionally, an analysis of the corpus has been conducted for the sake of explainability, aiming to obtain more information about how the models generate their predictions and identify patterns in the data. [ABSTRACT FROM AUTHOR]

Objective: Mild cognitive impairment (MCI) is considered an intermediate stage between normal aging and dementia. It is diagnosed in the presence of subjective cognitive decline and objective cognitive impairment without significant functional impairment, although there are no standard operationalizations for each of these criteria. The objective of this study is to determine which operationalization of the MCI criteria is most accurate at predicting dementia.Design: Six-year longitudinal study, part of the Sydney Memory and Ageing Study.Setting: Community-based.Participants: 873 community-dwelling dementia-free adults between 70 and 90 years of age. Persons from a non-English speaking background were excluded.Measurements: Seven different operationalizations for subjective cognitive decline and eight measures of objective cognitive impairment (resulting in 56 different MCI operational algorithms) were applied. The accuracy of each algorithm to predict progression to dementia over 6 years was examined for 618 individuals.Results: Baseline MCI prevalence varied between 0.4% and 30.2% and dementia conversion between 15.9% and 61.9% across different algorithms. The predictive accuracy for progression to dementia was poor. The highest accuracy was achieved based on objective cognitive impairment alone. Inclusion of subjective cognitive decline or mild functional impairment did not improve dementia prediction accuracy.Conclusions: Not MCI, but objective cognitive impairment alone, is the best predictor for progression to dementia in a community sample. Nevertheless, clinical assessment procedures need to be refined to improve the identification of pre-dementia individuals. [ABSTRACT FROM AUTHOR]

Background: Motoric Cognitive Risk (MCR) syndrome is a high-risk state for adverse health outcomes in older adults characterised by measured slow gait speed and self-reported cognitive complaints. The recent addition to the Lothian Birth Cohort 1936 of robust dementia outcomes enabled us to assess the prognostic value of MCR for dementia and explore the various trajectories of participants diagnosed with MCR., Methods: We classified 680 community-dwelling participants free from dementia into non-MCR or MCR groups at mean [SD] age 76.3 [0.8] years. We used Cox and competing risk regression methods, adjusted for potential confounders, to evaluate the risk of developing all-cause incident dementia over 10 years of follow-up. Secondarily, we followed the trajectories for individuals with and without MCR at baseline and categorised them into subgroups based on whether MCR was still present at the next research wave, three years later., Results: The presence of MCR increased the risk of incident dementia (adjusted HR 2.34, 95%CI 1.14-4.78, p  = 0.020), as did fewer years of education and higher depression symptoms. However, MCR has a heterogenous progression trajectory. The MCR progression subgroups each have different prognostic values for incident dementia., Conclusion: MCR showed similar prognostic ability for dementia in a Scottish cohort as for other populations. MCR could identify a target group for early interventions of modifiable risk factors to prevent incident dementia. This study illustrates the heterogeneous nature of MCR progression. Exploring the underlying reasons will be important work in future work., Competing Interests: The authors have stated explicitly that there are no conflicts of interest in connection with this article. DAG is a part-time employee of Optima partners, a health data science consultancy company. Optima had no role or influence in this study., (© 2023 The Authors. Published by Elsevier B.V.)

Objective: The association between common electrocardiogram (ECG) markers and Alzheimer's disease has been scarcely investigated, and it is unknown if ECG markers can improve risk prediction. Thus, we aimed to examine the association between common ECG markers and Alzheimer's disease in a large population., Methods: We studied the association between ECG markers and Alzheimer's disease using Cox models with adjustment for age, sex, and comorbidities using a large primary care population of patients aged 60 years or more., Results: We followed 172,236 subjects for a median of 7.5 years. Increased PR interval (hazard ratio for PR > 188 ms: 0.76 [95% confidence interval: 0.69-0.83, p < 0.001) and increased QTc interval (hazard ratio for QTc = [426;439]: 0.90 [0.83-0.98], p = 0.02) were associated with a decreased rate of Alzheimer's disease. A positive Sokolow-Lyon index >35 mm (1.22 [1.13-1.33], p < 0.001) and increased T-wave amplitude >4.1 mm (1.15 [1.04-1.27]) were associated with an increased rate of Alzheimer's disease. Upon addition of ECG markers to a reference model, 10-year prediction area under the receiver-operator characteristics curve (AUC) improved by 0.39 [0.06-0.67] %-points. The 10-year absolute risk of Alzheimer's disease was 6.5% and 5.2% for an 82-year old female and a male, respectively, with a favorable ECG, and 12% and 9.2%, respectively, with an unfavorable ECG, almost twice as high., Conclusions: We identified several common ECG markers which were associated with Alzheimer's disease, and which improved risk prediction for Alzheimer's disease., Competing Interests: Declaration of Competing Interest A.G.H. is an employee of Acesion Pharma. J.B·N is employed by Regeneron Pharmaceuticals, Inc. The remaining authors have no conflict of interests to declare., (Copyright © 2023. Published by Elsevier B.V.)

Background: animal studies suggest a neuroprotective role for leptin, but human studies have shown mixed results. We examined whether plasma leptin levels in individuals with mild cognitive impairment (MCI) were related to cognitive function at baseline and whether higher leptin levels were associated with reduced risk of dementia.Methods: we categorised 352 MCI participants into sex-specific tertiles based on log-transformed fasting plasma leptin levels. In sex-stratified analyses, we investigated whether cognitive ability differed by leptin tertile. We also examined whether the risk of dementia over a 3-year follow-up period differed by leptin level. Analyses controlled for numerous potential confounding variables, including body mass index, hypertension and levels of blood insulin and C-reactive protein.Results: baseline cognitive ability did not differ as a function of leptin level, nor were higher leptin levels associated with reduced hazard of developing dementia. Controlling for related co-variates did not reveal any significant associations between leptin and dementia risk.Conclusion: in this cohort of older adults with MCI, plasma leptin level was not associated with cognitive function at baseline, nor did it predict risk of dementia. Other biological measures, such as volumetric MRI and cerebrospinal fluid protein levels, have demonstrated robust dementia prediction in this cohort. Thus, the current negative findings suggest that plasma leptin, on its own, is unlikely to become a useful clinical biomarker for Alzheimer's disease. Efforts to develop other blood-based biomarkers are needed. [ABSTRACT FROM PUBLISHER]

Objectives: To determine whether electrocardiogram (ECG) markers are associated with incident non-Alzheimer's dementia (non-AD) and whether these markers also improve risk prediction for non-AD., Materials and Methods: We retrospectively included 170,605 primary care patients aged 60 years or older referred for an ECG by their general practitioner and followed them for a median of 7.6 years. Using Cox regression, we reported hazard ratios (HRs) for electrocardiogram markers. Subsequently, we evaluated if addition of these electrocardiogram markers to a clinical model improved risk prediction for non-AD using change in area under the receiver-operator characteristics curve (AUC)., Results: The 5-year cumulative incidence of non-AD was 3.4 %. Increased heart rate (HR=1.06 pr. 10 bpm [95% confidence interval: 1.04-1.08], p<0.001), shorter QRS duration (HR=1.07 pr. 10 ms [1.05-1.09], p<0.001), elevated J-amplitude (HR=1.16 pr. mm [1.08-1.24], p<0.001), decreased T-peak amplitude (HR=1.02 pr. mm [1.01-1.04], p=0.002), and increased QTc (HR=1.08 pr. 20 ms [1.05-1.10], p<0.001) were associated with an increased rate of non-AD. Atrial fibrillation on the ECG (HR=1.18 [1.08-1.28], p<0.001) Sokolow-Lyon index > 35 mm (HR=1.31 [1.18-1.46], p<0.001) and borderline (HR=1.18 [1.11-1.26], p<0.001) or abnormal (HR=1.40 [1.27-1.55], p<0.001) QRS-T angle were also associated with an increased rate of non-AD. Upon addition of ECG markers to the Cox model, 5-year and 10-year C-statistic (AUC) improved significantly (delta-AUC, 0.36 [0.18-0.50] and 0.20 [0.03-0.35] %-points, respectively)., Conclusions: ECG markers typical of an elevated cardiovascular risk profile were associated with non-AD and improved both 5-year and 10-year risk predictions for non-AD., Competing Interests: Conflict of Interests A.G.H. is an employee of Acesion Pharma. J.B.N is employed by Regeneron Pharmaceuticals, Inc. The remaining authors have no conflict of interests to declare., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Alzheimer's disease (AD) represents an enormous global health burden in terms of human suffering and economic cost. AD management requires a shift from the prevailing paradigm targeting pathogenesis to design and develop effective drugs with adequate success in clinical trials. Therefore, it is of interest to report a review on amyloid beta (Aβ) effects and other multi-targets including cholinesterase, NFTs, tau protein and TNF associated with brain cell death to be neuro-protective from AD. It should be noted that these molecules have been generated either by target-based or phenotypic methods. Hence, the use of recent advancements in nanomedicine and other natural compounds screening tools as a feasible alternative for circumventing specific liabilities is realized. We review recent developments in the design and identification of neuro-degenerative compounds against AD generated using current advancements in computational multi-target modeling algorithms reflected by theragnosis (combination of diagnostic tests and therapy) concern.

Objective: Mild cognitive impairment (MCI) is considered an intermediate stage between normal aging and dementia. It is diagnosed in the presence of subjective cognitive decline and objective cognitive impairment without significant functional impairment, although there are no standard operationalizations for each of these criteria. The objective of this study is to determine which operationalization of the MCI criteria is most accurate at predicting dementia., Design: Six-year longitudinal study, part of the Sydney Memory and Ageing Study., Setting: Community-based., Participants: 873 community-dwelling dementia-free adults between 70 and 90 years of age. Persons from a non-English speaking background were excluded., Measurements: Seven different operationalizations for subjective cognitive decline and eight measures of objective cognitive impairment (resulting in 56 different MCI operational algorithms) were applied. The accuracy of each algorithm to predict progression to dementia over 6 years was examined for 618 individuals., Results: Baseline MCI prevalence varied between 0.4% and 30.2% and dementia conversion between 15.9% and 61.9% across different algorithms. The predictive accuracy for progression to dementia was poor. The highest accuracy was achieved based on objective cognitive impairment alone. Inclusion of subjective cognitive decline or mild functional impairment did not improve dementia prediction accuracy., Conclusions: Not MCI, but objective cognitive impairment alone, is the best predictor for progression to dementia in a community sample. Nevertheless, clinical assessment procedures need to be refined to improve the identification of pre-dementia individuals., (Copyright © 2016 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: The CAIDE (Cardiovascular Risk Factors, Aging, and Incidence of Dementia) Dementia Risk Score is a validated tool to predict late-life dementia risk (20 years later), based on midlife vascular risk factors. The goal was to render this prediction tool widely accessible., Methods: The CAIDE Risk Score (mobile application) App was developed based on the CAIDE Dementia Risk Score, involving information on age, educational level, hypertension, hypercholesterolemia, obesity, and physical inactivity., Results: The CAIDE Risk Score App is an evidence-based practical tool, which allows users to detect their individual risk, provides guidance for risk modification, and suggests consulting a health care practitioner if needed. Moreover, it allows practitioners to discuss preventive measures and monitor risk reduction., Conclusions: The CAIDE Risk Score App is the first to predict the risk for dementia through an important evidence-based tool. The App can encourage users to actively decrease their modifiable risk factors and postpone cognitive impairment and dementia.