Guía de tratamiento de la alteración aguda del estado mental. Academia Iberoamericana de Neurología Pediátrica / Guide for the treatment of acute altered mental status. Iberoamerican Academy of Pediatric Neurology

La alteración aguda del estado mental en pediatría se refiere a un cambio repentino y significativo en la función cerebral y el nivel de conciencia de un niño. Puede manifestarse como confusión, desorientación, agitación, letargo o incluso pérdida de la conciencia. Esta condición es una emergencia médica, y requiere una evaluación y una atención inmediatas. Existen diversas causas de alteración aguda del estado mental en niños, algunas de las cuales incluyen infecciones del sistema nervioso central, como la meningitis o la encefalitis, los traumatismos craneoencefálicos, los trastornos metabólicos, las convulsiones o las intoxicaciones, entre otras. Este estudio tuvo como objetivo analizar, preparar y calificar la bibliografía actual para determinar las mejores recomendaciones sobre el tratamiento ante casos de alteración aguda del estado mental en pediatría de diferentes causas. El estudio se basó en la calificación de expertos en el campo para poder determinar la calificación de las recomendaciones, además de ser sometido a la revisión por parte del comité científico de la Academia Iberoamericana de Neurología Pediátrica. Nuestra guía representa una ayuda para el tratamiento de este síntoma inespecífico desde un enfoque básico y avanzado, aplicable por cualquier neurólogo pediatra.(AU)

In pediatric patients, an acute altered mental status refers to a sudden and significant change in a child’s brain function and level of consciousness. It may manifest as confusion, disorientation, agitation, lethargy or even a loss of consciousness. This condition is a medical emergency, and requires immediate evaluation and attention. There are several causes of acute altered mental status in children, including infections of the central nervous system such as meningitis or encephalitis, traumatic brain injury, metabolic disorders, seizures and poisoning, among others. The aim of this study was to analyse, prepare and classify the current literature in order to determine the best recommendations for the treatment of cases of acute altered mental status with various causes in pediatric patients. The study was based on opinions from experts in the field in order to classify the recommendations, and was submitted to the scientific committee of the Iberoamerican Academy of Pediatric Neurology for review. Our guide is an aid for the treatment of this non-specific symptom based on a basic and advanced approach, which can be applied by any pediatric neurologist.(AU)

Association of socioeconomic status and poststroke cognitive function: A systematic review and meta-analysis.

BACKGROUND: Stroke survivors are at high risk of coping with cognitive problems after stroke. In recent decades, the relationship between socioeconomic status (SES) and health-related outcomes has been a topic of considerable interest. Learning more about the potential impact of SES on poststroke cognitive dysfunction is of great importance. OBJECTIVE: The purpose of this systematic review and meta-analysis was to summarize the association between SES and poststroke cognitive function by quantifying the effect sizes of the existing studies. METHOD: We searched studies from PubMed, Ovid, Embase, Cochrane, Scopus, and PsychINFO up to January 30th 2024 and the references of relevant reviews. Studies reporting the risk of poststroke cognitive dysfunction as assessed by categorized SES indicators were included. The Newcastle-Ottawa scale and the Agency for Healthcare Research and Quality were used to evaluate the study quality. Meta-analyses using fixed-effect models or random-effect models based on study heterogeneity were performed to estimate the influence of SES on cognitive function after stroke, followed by subgroup analyses stratified by study characteristics. RESULTS: Thirty-four studies were eligible for this systematic review and meta-analysis. Of which, 19 studies reported poststroke cognitive impairment (PSCI) as the outcome, 13 reported poststroke dementia (PSD), one reported both PSCI and PSD, and one reported vascular cognitive impairment no dementia. The findings showed that individuals with lower SES levels had a higher risk of combined poststroke cognitive dysfunction (odds ratio (OR) = 1.91, 95% confidence interval (CI) = 1.59-2.29), PSCI (OR = 2.09, 95% CI = 1.57-2.78), and PSD (OR = 1.95, 95% CI = 1.48-2.57). Subgroup analyses stratified by SES indicators demonstrated the protective effects of education and occupation against the diagnoses of combined poststroke cognitive dysfunction, PSCI, and PSD. CONCLUSIONS: Stroke survivors belonging to a low SES are at high risk of poststroke cognitive dysfunction. Our findings add evidence for public health strategies to reduce the risk of poststroke cognitive dysfunction by reducing SES inequalities.

Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer's Disease Neuroimaging Initiative cohort.

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10-8 and 4.3 × 10-7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

A household survey of the prevalence of subjective cognitive decline and mild cognitive impairment among urban community-dwelling adults aged 30 to 65.

While it is possible to detect cognitive decline before the age of 60, and there is a report indicating that certain cognitive abilities peak in one's 30s, the evidence regarding cognitive problems in populations younger than 65 years is scarce. This study aims to (1) determine the proportion of community-dwelling adults with different cognitive status, and (2) determine the prevalence of neuropsychiatric behaviors. A population-based survey was conducted in Chiang Mai, Thailand. Individuals aged 30 to 65 were recruited and assessed for demographic data, memory complaints, cognitive performance, and neuropsychiatric symptoms using self-reported questionnaires. In a total of 539 participants, 33.95% had mild cognitive impairment (MCI), 7.05% had subjective cognitive decline (SCD), and 52.50% had neuropsychiatric symptoms. The risk of MCI increased with age, and neuropsychiatric symptoms were significantly higher in those with MCI or SCD than in those without (p < 0.001). The most common complaints were sleep problems, anxiety, and irritability. Screening for MCI in adults aged < 65 years might be useful. However, further investigation on the appropriate age to screen and the program's cost-effectiveness is suggested.

Associations between breakfast skipping and outcomes in neuropsychiatric disorders, cognitive performance, and frailty: a Mendelian randomization study.

BACKGROUND: Prior studies have identified a correlation between breakfast skipping and a heightened risk of mental health issues. This investigation aimed to employ a Two-Sample Mendelian Randomization (MR) approach to explore the potential causal links between breakfast skipping and various psychiatric, neurological disorders, cognitive performance, and frailty. METHODS: Utilizing data from genome-wide association studies within European demographics, this research scrutinized the association between breakfast habits and several neuropsychiatric conditions and physical health outcomes, including Alzheimer's disease (AD), Attention Deficit Hyperactivity Disorder (ADHD), Bipolar Disorder (BD), Major Depressive Disorder (MDD), Narcolepsy, Insomnia, cognitive performance, and frailty. In this MR analysis, the Inverse Variance Weighted (IVW) method was primarily utilized for evaluation. Outcomes were reported as Odds Ratios (OR) and regression coefficients (ß), and underwent validation through False Discovery Rate (FDR) corrections, thereby offering a rigorous evaluation of the effects of breakfast habits on both mental and physical health dimensions. RESULTS: Findings demonstrate a significant causal link between skipping breakfast and an increased risk of ADHD (OR = 2.74, 95%CI: 1.54-4.88, PFDR = 0.003) and MDD (OR = 1.7, 95%CI: 1.22-2.37, PFDR = 0.005). Conversely, no substantial causal associations were identified between breakfast skipping and AD, BD, narcolepsy, or insomnia (PFDR > 0.05). Moreover, a notable causal relationship was established between skipping breakfast and a reduction in cognitive performance (ß = -0.16, 95%CI: -0.29-0.04, PFDR = 0.024) and an increase in frailty (ß = 0.29, 95%CI: 0.12-0.45, PFDR = 0.003). CONCLUSION: The MR analysis reveals that skipping breakfast is associated with an increased risk of ADHD, MDD, decreased cognitive performance, and greater frailty, while showing no associations were found with AD, BD, narcolepsy, or insomnia. These findings warrant further investigation into the underlying mechanisms and emphasize the importance of regular breakfast consumption for mental and physical well-being.

Prevalence of Motoric Cognitive Risk Syndrome Among Community-Dwelling Older Adults: A Systematic Review and Meta-Analysis.

PURPOSE: To systematically review the prevalence of motoric cognitive risk syndrome (MCR) among community-dwelling older adults and provide evidence-based support for policymakers planning health and social care policies. METHOD: Web of Science, PubMed, and Cochrane Library databases were searched for cross-sectional, prospective cohort, or population-based longitudinal studies of community-dwelling older adults aged ≥60 years with MCR from inception of the database through December 18, 2021. RESULTS: Seventeen studies were included. Pooled prevalence of MCR was found to be 10% (95% confidence interval [8%, 12%], I2 = 98.4%). Results of a subgroup analysis revealed a combined prevalence of MCR of 8.2% in males and 9.2% in females. Pooled prevalence of MCR was 9.7% in Asia and 10.2% in other regions. CONCLUSION: Prevalence of MCR in community-dwelling older adults is high. Our research may improve the epidemiological understanding of MCR, draw attention to older adults with MCR, and thus promote research of MCR and the formulation of relevant public health policies. With early identification and intervention of MCR, cognitive function can be improved, and the onset of dementia can be delayed or prevented. [Journal of Gerontological Nursing, 50(4), 16-24.].

Protocol for a construct and clinical validation study of MyCog Mobile: a remote smartphone-based cognitive screener for older adults.

INTRODUCTION: Annual cognitive screening in older adults is essential for early detection of cognitive impairment, yet primary care settings face time constraints that present barriers to routine screening. A remote cognitive screener completed on a patient's personal smartphone before a visit has the potential to save primary care clinics time, encourage broader screening practices and increase early detection of cognitive decline. MyCog Mobile is a promising new remote smartphone-based cognitive screening app for primary care settings. We propose a combined construct and clinical validation study of MyCog Mobile. METHODS AND ANALYSIS: We will recruit a total sample of 300 adult participants aged 65 years and older. A subsample of 200 healthy adult participants and a subsample of 100 adults with a cognitive impairment diagnosis (ie, dementia, mild cognitive impairment, cognitive deficits or other memory loss) will be recruited from the general population and specialty memory care centres, respectively. To evaluate the construct validity of MyCog Mobile, the healthy control sample will self-administer MyCog Mobile on study-provided smartphones and be administered a battery of gold-standard neuropsychological assessments. We will compare correlations between performance on MyCog Mobile and measures of similar and dissimilar constructs to evaluate convergent and discriminant validity. To assess clinical validity, participants in the clinical sample will self-administer MyCog Mobile on a smartphone and be administered a Mini-Cog screener and these data will be combined with the healthy control sample. We will then apply several supervised model types to determine the best predictors of cognitive impairment within the sample. Area under the receiver operating characteristic curve, accuracy, sensitivity and specificity will be the primary performance metrics for clinical validity. ETHICS AND DISSEMINATION: The Institutional Review Board at Northwestern University (STU00214921) approved this study protocol. Results will be published in peer-reviewed journals and summaries provided to the study's funders.

[Association between the severity of neurocognitive disorders and the seriousness of falls in the elderly]. / Association entre la sévérité des troubles neurocognitifs et la gravité des chutes chez les personnes âgées.

Falls cause severe morbidity and mortality in people over 65 years old in all countries. Cognitive frailty is considered to be one of the risk factors for falls in the elderly. Approximately 60% of the elderly with neurocognitive disorders fall annually and this is two times more compared to elderly with no cognitive impairment. We already know that neurocognitive disorders and their severity are a risk factor for falls in older people. Few studies are conducted to investigate the association between the severity of neurocognitive disorders and the severity of falls. This study is therefore interested in investigating the association between the severity of neurocognitive disorders and the serious falls in the elderly. This is a non-interventional retrospective study of 100 patients admitted for fall in a geriatric hospital. The correlation between MMSE and fall severity remains uncertain. Serious falls are more frequent in patients with Parkinsonian syndromes, but this result is not statically significant. Polypharmacy remains very prevalent in our population with 70 % of patients having more than four drugs. Polydrug use in our study was very high, with 70% of patients taking more than four medications. We did not find a statistically significant association between the severity of neurocognitive disorders evaluated with the MMSE and the serious falls. More studies with tailored neurocognitive testing are needed to investigate the link between executive function disorders and the serious of falls.

[The reversible share of cognitive deficits in older adults]. / La part réversible des déficits cognitifs chez les seniors.

Cognitive performance of older adults is very often inferior to that of younger adults on a variety of laboratory tests assessing basic functions such as memory, inhibition, or attention. Classic hypotheses and theories share the idea that these cognitive deficits are irreversible, due to profound cerebral changes. In this review article, we develop a more positive conception of aging, according to which cognitive deficits are not all irreversible, and can even be partially if not completely reversible. To this end, we present some of the most illustrative research on the reversibility of the effects of aging on cognition. We show how subtle contextual manipulations can change older adults' motivation and strategy, which improve their cognitive performance. We also show that guidance toward the selection of the most appropriate strategy, whether explicit as in selectivity paradigms or implicit as in dual-task procedures, can increase older adults' cognitive performance. We finally describe the hypotheses and theories that both account for low cognitive performance in old age and ways to reverse the effects of cognitive aging.

A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties.

Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia.

[Association between working in small-scale cultivated land as a daily-life task and the physical and cognitive functions among elderly people in hilly and mountainous areas].

AIM: This study aimed to verify whether working in cultivated land as a daily-life task contributes to the maintenance and improvement of physical and cognitive functions. METHODS: The participants were 91 elderly people of ≥65 years of age who owned cultivated land in the mountainous Koyadaira district in Tokushima Prefecture. Sex, age, body mass index (BMI), walking speed as a physical function, and the mini-mental status examination (MMSE) score as a cognitive function were measured and analyzed in addition to the total working hours per week (WH) in cultivated land. RESULTS: The participants were 31 males and 60 females (mean age 78.5±6.6 years). The average values of the evaluated variables were as follows: WH, 18.0±13.2; BMI, 23.4±3.0 kg/m2; walking speed, 0.95±0.28 m/s; and MMSE score, 26.6±3.1 points. In addition, the Mann-Whitney U test and the Chi-square test showed no significant differences between sexes for each item. A logistic regression analysis showed that WH was significantly associated with MMSE (1, ≥28 points; 0, <28 points), and the odds ratio was 1.054 (p=0.010) in the model adjusted for age and BMI, while it was not significantly associated with walking speed (1, ≥1 m/s; 0, <1 m/s). CONCLUSIONS: Working on small-scale cultivated land was significantly associated with the cognitive function but not the physical function. Routine work on small-scale cultivated land as a daily-life task would contribute to the suppression of cognitive decline in older people living in hilly and mountainous areas.

Development of chitosan lipid nanoparticles to alleviate the pharmacological activity of piperine in the management of cognitive deficit in diabetic rats.

The aim of the present study was to prepare and evaluate Piperine (PP) loaded chitosan lipid nanoparticles (PP-CLNPs) to evaluate its biological activity alone or in combination with the antidiabetic drug Metformin (MET) in the management of cognitive deficit in diabetic rats. Piperine was successfully loaded on CLNPs prepared using chitosan, stearic acid, Tween 80 and Tripolyphosphate (TPP) at different concentrations. The developed CLNPs exhibited high entrapment efficiency that ranged from 85.12 to 97.41%, a particle size in the range of 59.56-414 nm and a negatively charged zeta potential values (- 20.1 to - 43.9 mV). In vitro release study revealed enhanced PP release from CLNPs compared to that from free PP suspensions for up to 24 h. In vivo studies revealed that treatment with the optimized PP-CLNPs formulation (F2) exerted a cognitive enhancing effect and ameliorated the oxidative stress associated with diabetes. PP-CLNPs acted as an effective bio-enhancer which increased the potency of metformin in protecting brain tissue from diabetes-induced neuroinflammation and memory deterioration. These results suggested that CLNPs could be a promising drug delivery system for encapsulating PP and thus can be used as an adjuvant therapy in the management of high-risk diabetic cognitive impairment conditions.

Spontaneous brain activity in the hippocampal regions could characterize cognitive impairment in patients with Parkinson's disease.

OBJECTIVE: This study aimed to investigate whether spontaneous brain activity can be used as a prospective indicator to identify cognitive impairment in patients with Parkinson's disease (PD). METHODS: Resting-state functional magnetic resonance imaging (RS-fMRI) was performed on PD patients. The cognitive level of patients was assessed by the Montreal Cognitive Assessment (MoCA) scale. The fractional amplitude of low-frequency fluctuation (fALFF) was applied to measure the strength of spontaneous brain activity. Correlation analysis and between-group comparisons of fMRI data were conducted using Rest 1.8. By overlaying cognitively characterized brain regions and defining regions of interest (ROIs) based on their spatial distribution for subsequent cognitive stratification studies. RESULTS: A total of 58 PD patients were enrolled in this study. They were divided into three groups: normal cognition (NC) group (27 patients, average MoCA was 27.96), mild cognitive impairment (MCI) group (21 patients, average MoCA was 23.52), and severe cognitive impairment (SCI) group (10 patients, average MoCA was 17.3). It is noteworthy to mention that those within the SCI group exhibited the most advanced chronological age, with an average of 74.4 years, whereas the MCI group displayed a higher prevalence of male participants at 85.7%. It was found hippocampal regions were a stable representative brain region of cognition according to the correlation analysis between the fALFF of the whole brain and cognition, and the comparison of fALFF between different cognitive groups. The parahippocampal gyrus was the only region with statistically significant differences in fALFF among the three cognitive groups, and it was also the only brain region to identify MCI from NC, with an AUC of 0.673. The paracentral lobule, postcentral gyrus was the region that identified SCI from NC, with an AUC of 0.941. The midbrain, hippocampus, and parahippocampa gyrus was the region that identified SCI from MCI, with an AUC of 0.926. CONCLUSION: The parahippocampal gyrus was the potential brain region for recognizing cognitive impairment in PD, specifically for identifying MCI. Thus, the fALFF of parahippocampal gyrus is expected to contribute to future study as a multimodal fingerprint for early warning.

Cannabidiol improves the cognitive function of SAMP8 AD model mice involving the microbiota-gut-brain axis.

Cannabidiol (CBD), a natural component extracted from Cannabis sativa L. exerts neuroprotective, antioxidant, and anti-inflammatory effects in Alzheimer's disease (AD), a disease characterized by impaired cognition and accumulation of amyloid-B peptides (Aß). Interactions between the gut and central nervous system (microbiota-gut-brain axis) play a critical role in the pathogenesis of neurodegenerative disorder AD. At present investigations into the mechanisms underlying the neuroprotective action of CBD in AD are not conclusive. The aim of this study was thus to examine the influence of CBD on cognition and involvement of the microbiota-gut-brain axis using a senescence-accelerated mouse prone 8 (SAMP8) model. Data demonstrated that administration of CBD to SAMP8 mice improved cognitive function as evidenced from the Morris water maze test and increased hippocampal activated microglia shift from M1 to M2. In addition, CBD elevated levels of Bacteriodetes associated with a fall in Firmicutes providing morphologically a protective intestinal barrier which subsequently reduced leakage of intestinal toxic metabolites. Further, CBD was found to reduce the levels of hippocampal and colon epithelial cells lipopolysaccharide (LPS), known to be increased in AD leading to impaired gastrointestinal motility, thereby promoting neuroinflammation and subsequent neuronal death. Our findings demonstrated that CBD may be considered a beneficial therapeutic drug to counteract AD-mediated cognitive impairment and restore gut microbial functions associated with the observed neuroprotective mechanisms.

Regional MRI volumetry using NeuroQuant versus visual rating scales in patients with cognitive impairment and dementia.

BACKGROUND AND PURPOSE: The aims were to compare the novel regional brain volumetric measures derived by the automatic software NeuroQuant (NQ) with clinically used visual rating scales of medial temporal lobe atrophy (MTA), global cortical atrophy-frontal (GCA-f), and posterior atrophy (PA) brain regions, assessing their diagnostic validity, and to explore if combining automatic and visual methods would increase diagnostic prediction accuracy. METHODS: Brain magnetic resonance imaging (MRI) examinations from 86 patients with subjective and mild cognitive impairment (i.e., non-dementia, n = 41) and dementia (n = 45) from the Memory Clinic at Oslo University Hospital were assessed using NQ volumetry and with visual rating scales. Correlations, receiver operating characteristic analyses calculating area under the curves (AUCs) for diagnostic accuracy, and logistic regression analyses were performed. RESULTS: The correlations between NQ volumetrics and visual ratings of corresponding regions were generally high between NQ hippocampi/temporal volumes and MTA (r = -0.72/-0.65) and between NQ frontal volume and GCA-f (r = -0.62) but lower between NQ parietal/occipital volumes and PA (r = -0.49/-0.37). AUCs of each region, separating non-dementia from dementia, were generally comparable between the two methods, except that NQ hippocampi volume did substantially better than visual MTA (AUC = 0.80 vs. 0.69). Combining both MRI methods increased only the explained variance of the diagnostic prediction substantially regarding the posterior brain region. CONCLUSIONS: The findings of this study encourage the use of regional automatic volumetry in locations lacking neuroradiologists with experience in the rating of atrophy typical of neurodegenerative diseases, and in primary care settings.

Data-driven clustering approach to identify novel clusters of high cognitive impairment risk among Chinese community-dwelling elderly people with normal cognition: A national cohort study.

Background: Cognitive impairment is a highly heterogeneous disorder that necessitates further investigation into the distinct characteristics of populations at varying risk levels of cognitive impairment. Using a large-scale registry cohort of elderly individuals, we applied a data-driven approach to identify novel clusters based on diverse sociodemographic features. Methods: A prospective cohort of 6398 elderly people from the Chinese Longitudinal Healthy Longevity Survey, followed between 2008-14, was used to develop and validate the model. Participants were aged ≥60 years, community-dwelling, and the Chinese version of the Mini-Mental State Examination (MMSE) score ≥18 were included. Sixty-nine sociodemographic features were included in the analysis. The total population was divided into two-thirds for the derivation cohort (n = 4265) and one-third for the validation cohort (n = 2133). In the derivation cohort, an unsupervised Gaussian mixture model was applied to categorise participants into distinct clusters. A classifier was developed based on the most important 10 factors and was applied to categorise participants into their corresponding clusters in a validation cohort. The difference in the three-year risk of cognitive impairment was compared across the clusters. Results: We identified four clusters with distinct features in the derivation cohort. Cluster 1 was associated with the worst life independence, longest sleep duration, and the oldest age. Cluster 2 demonstrated the highest loneliness, characterised by non-marital status and living alone. Cluster 3 was characterised by the lowest sense of loneliness and the highest proportions in marital status and family co-residence. Cluster 4 demonstrated heightened engagement in exercise and leisure activity, along with independent decision-making, hygiene, and a diverse diet. In comparison to Cluster 4, Cluster 1 exhibited the highest three-year cognitive impairment risk (adjusted odds ratio (aOR) = 3.31; 95% confidence interval (CI) = 1.81-6.05), followed by Cluster 2 and Cluster 3 after adjustment for baseline MMSE, residence, sex, age, years of education, drinking, smoking, hypertension, diabetes, heart disease and stroke or cardiovascular diseases. Conclusions: A data-driven approach can be instrumental in identifying individuals at high risk of cognitive impairment among cognitively normal elderly populations. Based on various sociodemographic features, these clusters can suggest individualised intervention plans.

A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN.

This study aims to develop an optimally performing convolutional neural network to classify Alzheimer's disease into mild cognitive impairment, normal controls, or Alzheimer's disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer's disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer's disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer's disease versus normal controls, Alzheimer's disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.