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BMC Geriatrics Jun 2024Critical wandering occurs when an individual living with dementia leaves a location and is unaware of place or time. Critical wandering incidents are expected to...
BACKGROUND
Critical wandering occurs when an individual living with dementia leaves a location and is unaware of place or time. Critical wandering incidents are expected to increase with the growing prevalence of persons living with dementia worldwide. We investigated the association between demographic, psychopathological, and environmental factors and a history of critical wandering among Medic-Alert subscribers, both with and without dementia.
METHODS
Our retrospective study included data of 25,785 Canadian Medic-Alert subscribers who were aged 40 years or older. We used multivariable logistic regression analysis to examine the associations between a history of critical wandering and dementia status as psychopathological independent variable, controlled by demographic (age, ethnic background, sex at birth, Canadian languages spoken) and environmental (living arrangement, population density) factors.
RESULTS
The overall study sample comprised of mainly older adults (77.4%). Medic-Alert subscribers who were older, male sex at birth, living with dementia, of a minority ethnic group and who did not have proficiency in an official Canadian language had a higher likelihood of a history of critical wandering. Residing in an urban environment, in an institution or with a family member, were environmental factors associated with a higher likelihood of a history of critical wandering.
CONCLUSIONS
People living with dementia experience a higher likelihood of a history of critical wandering compared to those without dementia. Medic-Alert and similar organizations can develop algorithms based on the associated factors that can be used to flag risks of critical wandering. This can inform preventative strategies at the individual and community levels.
Topics: Humans; Male; Female; Retrospective Studies; Aged; Dementia; Aged, 80 and over; Middle Aged; Wandering Behavior; Adult; Risk Factors; Canada
PubMed: 38943089
DOI: 10.1186/s12877-024-05162-3 -
Scientific Reports Jun 2024While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), the Apolipoprotein E locus E4 haplotype is still...
While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), the Apolipoprotein E locus E4 haplotype is still the biggest driver of risk, with odds ratios for neuropathologically confirmed E44 carriers exceeding 30 (95% confidence interval 16.59-58.75). We sought to address whether the APOE E4 haplotype modifies expression globally through networks of expression to increase LOAD risk. We have used the Human Brainome data to build expression networks comparing APOE E4 carriers to non-carriers using scalable mixed-datatypes Bayesian network (BN) modeling. We have found that VGF had the greatest explanatory weight. High expression of VGF is a protective signal, even on the background of APOE E4 alleles. LOAD risk signals, considering an APOE background, include high levels of SPECC1L, HLA-DRA and RANBP3L. Our findings nominate several new transcripts, taking a combined approach to network building including known LOAD risk loci.
Topics: Humans; Alzheimer Disease; Genetic Predisposition to Disease; Apolipoprotein E4; HLA-DR alpha-Chains; Female; Male; Aged; Adaptor Proteins, Signal Transducing; Alleles; Haplotypes; Bayes Theorem; Risk Factors; Nuclear Proteins; Aged, 80 and over
PubMed: 38942763
DOI: 10.1038/s41598-024-65010-7 -
Radiography (London, England : 1995) Jun 2024Alzheimer's disease (AD), the most common cause of dementia, presents a global health crisis with its prevalence expected to triple worldwide by 2050, emphasizing the...
INTRODUCTION
Alzheimer's disease (AD), the most common cause of dementia, presents a global health crisis with its prevalence expected to triple worldwide by 2050, emphasizing the urgent need for early diagnosis to delay progression and improve patient quality of life. Our project aims to detect AD in its early phase by identifying subtle neuroanatomical changes with Radiomics features, offering a more accurate diagnosis.
METHODS
The AssemblyNet segmentation model was used to analyze brain changes by employing anonymized T1 MRI scans from 416 patients. For each segmented label we extracted Radiomic features. After preprocessing of Radiomic features we trained four models, Gradient Booster, Random Forest, Support Vector Classifier, and XGBoost, in a 70%/20%/10% train, validation and test split. All models were hyperparameter tuned with GridSearch, Cross validation and evaluated with accuracy on the test data.
RESULTS
208 T1-weighted MRI scans were segmented, with 132 segmentation labels per patient, 1130 Radiomic features per segmentation, totalling in over 31 million features. For all four models we achieved accuracies between 0.71 and 0.86, and the machine learning model with highest accuracy were XGBoost, achieving an accuracy at 0.86 on the segmentation of the left inferior lateral ventricle.
CONCLUSION
Our study's use of segmentation on T1-weighted MRI scans resulted promising accuracies for early AD diagnosis with the machine learning model XGBoost, peaking at 0.86 accuracy. Future research should aim to expand datasets and refine methodologies for broader applicability.
IMPLICATION FOR PRACTICE
Implementing Radiomics for early AD detection using T1-weighted MRI scans could substantially improve diagnostic accuracy, enabling earlier interventions that may delay disease progression and improve outcomes, thereby requiring radiographers to adopt more advanced imaging techniques and analysis tools, as well as additional training to effectively interpret complex Radiomic data.
PubMed: 38942647
DOI: 10.1016/j.radi.2024.06.016 -
Aging Jun 2024Down Syndrome (DS) is a common genetic disorder characterized by an extra copy of chromosome 21, leading to dysregulation of various metabolic pathways. Oxidative stress...
Down Syndrome (DS) is a common genetic disorder characterized by an extra copy of chromosome 21, leading to dysregulation of various metabolic pathways. Oxidative stress in DS is associated with neurodevelopmental defects, neuronal dysfunction, and a dementia onset resembling Alzheimer's disease. Additionally, chronic oxidative stress contributes to cardiovascular diseases and certain cancers prevalent in DS individuals. This study investigates the impact of ageing on oxidative stress and liver fibrosis using a DS murine model (Ts2Cje mice). Our results show that DS mice show increased liver oxidative stress and impaired antioxidant defenses, as evidenced by reduced glutathione levels and increased lipid peroxidation. Therefore, DS liver exhibits an altered inflammatory response and mitochondrial fitness as we showed by assaying the expression of HMOX1, CLPP, and the heat shock proteins Hsp90 and Hsp60. DS liver also displays dysregulated lipid metabolism, indicated by altered expression of PPARα, PPARγ, FATP5, and CTP2. Consistently, these changes might contribute to non-alcoholic fatty liver disease development, a condition characterized by liver fat accumulation. Consistently, histological analysis of DS liver reveals increased fibrosis and steatosis, as showed by Col1a1 increased expression, indicative of potential progression to liver cirrhosis. Therefore, our findings suggest an increased risk of liver pathologies in DS individuals, particularly when combined with the higher prevalence of obesity and metabolic dysfunctions in DS patients. These results shed a light on the liver's role in DS-associated pathologies and suggest potential therapeutic strategies targeting oxidative stress and lipid metabolism to prevent or mitigate liver-related complications in DS individuals.
PubMed: 38942607
DOI: 10.18632/aging.205970 -
The Lancet. Public Health Jul 2024Some cohort studies have reported a decline in dementia prevalence and incidence over time, although these findings have not been consistent across studies. We reviewed... (Review)
Review
BACKGROUND
Some cohort studies have reported a decline in dementia prevalence and incidence over time, although these findings have not been consistent across studies. We reviewed evidence on changes in dementia prevalence and incidence over time using published population-based cohort studies that had used consistent methods with each wave and aimed to quantify associated changes in risk factors over time using population attributable fractions (PAFs).
METHODS
We searched for systematic reviews of cohort studies examining changes in dementia prevalence or incidence over time. We searched PubMed for publications from database inception up to Jan 12, 2023, using the search terms "systematic review" AND "dementia" AND ("prevalence" OR "incidence"), with no language restrictions. We repeated this search on March 28, 2024. From eligible systematic reviews, we searched the references and selected peer-reviewed publications about cohort studies where dementia prevalence or incidence was measured in the same geographical location, at a minimum of two timepoints, and that reported age-standardised prevalence or incidence of dementia. Additionally, data had to be from population-based samples, in which participants' cognitive status was assessed and where validated criteria were used to diagnose dementia. We extracted summary-level data from each paper about dementia risk factors, contacting authors when such data were not available in the published paper, and calculated PAFs for each risk factor at all available timepoints. Where possible, we linked changes in dementia prevalence or incidence with changes in the prevalence of risk factors.
FINDINGS
We identified 1925 records in our initial search, of which five eligible systematic reviews were identified. Within these systematic reviews, we identified 71 potentially eligible primary papers, of which 27 were included in our analysis. 13 (48%) of 27 primary papers reported change in prevalence of dementia, ten (37%) reported change in incidence of dementia, and four (15%) reported change in both incidence and prevalence of dementia. Studies reporting change in dementia incidence over time in Europe (n=5) and the USA (n=5) consistently reported a declining incidence in dementia. One study from Japan reported an increase in dementia prevalence and incidence and a stable incidence was reported in one study from Nigeria. Overall, across studies, the PAFs for less education or smoking, or both, generally declined over time, whereas PAFs for obesity, hypertension, and diabetes generally increased. The decrease in PAFs for less education and smoking was associated with a decline in the incidence of dementia in the Framingham study (Framingham, MA, USA, 1997-2013), the only study with sufficient data to allow analysis.
INTERPRETATION
Our findings suggest that lifestyle interventions such as compulsory education and reducing rates of smoking through country-level policy changes could be associated with an observed reduction, and therefore future reduction, in the incidence of dementia. More studies are needed in low-income and middle-income countries, where the burden of dementia is highest, and continues to increase.
FUNDING
National Institute for Health and Care Research Three Schools' Dementia Research Programme.
Topics: Humans; Cohort Studies; Dementia; Incidence; Prevalence; Risk Factors; Systematic Reviews as Topic
PubMed: 38942556
DOI: 10.1016/S2468-2667(24)00120-8 -
Brain Research Jun 2024Oxidative stress plays a pivotal role in various neurological disorders, encompassing both neurodegenerative diseases such as Alzheimer's and Parkinson's, and mood...
Oxidative stress plays a pivotal role in various neurological disorders, encompassing both neurodegenerative diseases such as Alzheimer's and Parkinson's, and mood disorders like depression. The balance between the generation of reactive oxygen species (ROS) and the cell's antioxidant defenses, when disrupted, can lead to neuronal damage and neurologic dysfunction. In this study, we focused on the pathogenic role of oxidative stress in various neurologic disease models in vitro and investigated the neuroprotective capabilities of some novel bicyclic γ-butyrolactone compounds, with particular emphasis on the compound designated as 'bd'. Our investigation leveraged the HT22 and SH-SY5Y cells to model oxidative stress induced by HO or corticosterone (CORT), common triggers of neuronal damage in neurodegenerative and mood disorders. We discovered that compound bd robustly reduced ROS production and suppressed neuronal apoptosis, suggesting its potential in treating a wider array of neurological conditions influenced by oxidative stress. In conclusion, our research underscores the importance of addressing oxidative stress in the context of diverse neurological disorders. The identification of compound bd as a neuroprotective agent with potential efficacy against ROS-induced apoptosis in neural cells opens new horizons for therapeutic development, offering hope for patients suffering from neurodegenerative diseases, depression, and other stress-related neurological conditions.
PubMed: 38942352
DOI: 10.1016/j.brainres.2024.149099 -
NeuroImage Jun 2024The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has...
BACKGROUND
The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI).
METHODS
We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques.
RESULTS
Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures.
CONCLUSION
This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.
PubMed: 38942101
DOI: 10.1016/j.neuroimage.2024.120695 -
Biomedicine & Pharmacotherapy =... Jun 2024Alzheimer's disease (AD) is characterized by cognitive impairment, loss of learning and memory, and abnormal behaviors. Scopolamine (SCOP) is a non-selective antagonist...
Ameliorative effect of vanillic acid against scopolamine-induced learning and memory impairment in rat via attenuation of oxidative stress and dysfunctional synaptic plasticity.
Alzheimer's disease (AD) is characterized by cognitive impairment, loss of learning and memory, and abnormal behaviors. Scopolamine (SCOP) is a non-selective antagonist of muscarinic acetylcholine receptors that exhibits the behavioral and molecular hallmarks of AD. Vanillic acid (VA), a phenolic compound, is obtained from the roots of a traditional plant called Angelica sinensis, and has several pharmacologic effects, including antimicrobial, anti-inflammatory, anti-angiogenic, anti-metastatic, and antioxidant properties. Nevertheless, VA's neuroprotective potential associated with the memory has not been thoroughly investigated. Therefore, this study investigated whether VA treatment has an ameliorative effect on the learning and memory impairment induced by SCOP in rats. Behavioral experiments were utilized to assess the learning and memory performance associated with the hippocampus. Using western blotting analysis and assay kits, the neuronal damage, oxidative stress, and acetylcholinesterase activity responses of hippocampus were evaluated. Additionally, the measurement of long-term potentiation was used to determine the function of synaptic plasticity in organotypic hippocampal slice cultures. In addition, the synaptic vesicles' density and the length and width of the postsynaptic density were evaluated using electron microscopy. Consequently, the behavioral, biochemical, electrophysiological, and ultrastructural analyses revealed that VA treatment prevents learning and memory impairments caused by SCOP in rats. The study's findings suggest that VA has a neuroprotective effect on SCOP-induced learning and memory impairment linked to the hippocampal cholinergic system, oxidative damage, and synaptic plasticity. Therefore, VA may be a prospective therapeutic agent for treating AD.
PubMed: 38941895
DOI: 10.1016/j.biopha.2024.117000 -
NeuroImage. Clinical Jun 2024Advanced age is the most important risk factor for Alzheimer's disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic...
Advanced age is the most important risk factor for Alzheimer's disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD. To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia. Brain region centralities within functional networks, computed as eigenvector centrality, were tested for multivariate associations with chronological age, APOE4 carrier status and clinical stage (as well as their interactions) by partial least square analysis (PLSC). By PLSC analysis two distinct brain activity patterns could be identified, which reflected interactive effects of age, APOE4 and clinical disease stage. A first component including sensorimotor regions and parietal regions correlated with age and AD clinical stage (p < 0.001). A second component focused on medial-frontal regions and was specifically related to the interaction between age and APOE4 (p = 0.032). Our findings are consistent with earlier reports on altered network connectivity in APOE4 carriers. Results of our study highlight promise of graph-theory based network centrality to identify brain connectivity linked to genetic risk, clinical stage and age. Our data suggest the existence of brain network activity patterns that characterize APOE4 carriers across clinical stages of AD.
PubMed: 38941766
DOI: 10.1016/j.nicl.2024.103635 -
Journal of Medical Internet Research Jun 2024Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the...
BACKGROUND
Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings.
OBJECTIVE
This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts.
METHODS
Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score.
RESULTS
Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=-93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = -31.51, P=.002) and summer (β of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer.
CONCLUSIONS
Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.
Topics: Humans; Seasons; Female; Circadian Rhythm; Male; Wearable Electronic Devices; Adult; Longitudinal Studies; Depression; Middle Aged; Retrospective Studies; Telemedicine
PubMed: 38941600
DOI: 10.2196/55302