BACKGROUND

Type 2 Diabetes Mellitus (T2DM) patients are predisposed to cognitive decline and dementia. The cooccurrence of the two diseases translate to a higher medical cost. Identification of factors contributing to cognitive impairment is warranted.

To determine the predictors of cognitive impairment among Filipino patients with Type 2 Diabetes Mellitus.

This is a cross-sectional analytical study involving Filipino patients diagnosed with T2DM in the outpatient clinic. A total of 171 patients were included and were screened using AD8-P tool.

A total of 171 adult patients were included and screened for cognitive impairment.19.3% were cognitively impaired, with mean age of 59.6 years old (vs. 55.5 years old, p < 0.029), and two-thirds were female. The mean duration of the patient’s diabetes was 11.2 years. After adjusting for confounders and multi-collinearity, the duration of diabetes was significantly associated with cognitive impairment with odds of developing cognitive impairment increasing as the duration reach 10 years above. Those with T2DM for at least ten years were 2.5 times more likely to develop cognitive impairment, holding the age constant. (OR = 2.5, 95% CI – 1.0 to 5.8, p < 0.043).

19.3% of Filipino patients with Type 2 Diabetes Mellitus in a tertiary government hospital are cognitively impaired and this can occur even in less than 65 years old. The ten years or longer duration of T2DM increases the risk of developing cognitive impairment by 2.5%.

Intermittent theta burst stimulation (iTBS), a time-saving and cost-effective repetitive transcranial magnetic stimulation regime, has been shown to improve cognition in patients with Alzheimer's disease (AD). However, the specific mechanism underlying iTBS-induced cognitive enhancement remains unknown. Previous studies suggested that mitochondrial functions are modulated by magnetic stimulation. Here, we showed that iTBS upregulates the expression of iron-sulfur cluster assembly 1 (ISCA1, an essential regulatory factor for mitochondrial respiration) in the brain of APP/PS1 mice. In vivo and in vitro studies revealed that iTBS modulates mitochondrial iron-sulfur cluster assembly to facilitate mitochondrial respiration and function, which is required for ISCA1. Moreover, iTBS rescues cognitive decline and attenuates AD-type pathologies in APP/PS1 mice. The present study uncovers a novel mechanism by which iTBS modulates mitochondrial respiration and function via ISCA1-mediated iron-sulfur cluster assembly to alleviate cognitive impairments and pathologies in AD. We provide the mechanistic target of iTBS that warrants its therapeutic potential for AD patients.
Humans ; Mice ; Animals ; Transcranial Magnetic Stimulation ; Alzheimer Disease/therapy* ; Cognitive Dysfunction/therapy* ; Cognition ; Sulfur ; Iron ; Iron-Sulfur Proteins ; Mitochondrial Proteins

BACKGROUND: Chronic illnesses such as Type 2 diabetes mellitus (T2DM) and obesity have been implicated as risk factors in the development of cognitive impairment (CI), but despite this, definite association between the two conditions in increasing cognitive impairment risk is not well defined. OBJECTIVE: This study aims to examine the association between body mass index (BMI) and cognitive impairment (CI) in elderly patients with Type 2 diabetes mellitus. METHODS: This is a cross-sectional study conducted in the outpatient clinics of a private hospital in Manila which included elderly patients with Type 2 diabetes. BMI categories of the subjects were determined using the Asia-Pacific criteria and the Montreal Cognitive Assessment – Philippines (MOCA-P) was administered to subjects who fulfilled the inclusion criteria. Descriptive statistics were used to determine the prevalence of impaired cognition among subjects while risk ratio analysis was used to determine the correlation between BMI and CI. Correlation analysis and linear regression analysis were used to determine the presence of association between cognition (measured by MOCA-P scores) and BMI. For all analysis, a 95% level of significance was used. RESULTS: A total of 109 subjects from the outpatient clinics were included in the study. A high percentage of the study population (90.83%) had CI based on MOCA-P scores. Subjects that belonged to the extremes of BMI- underweight and obese class 2 had higher incidence of CI compared to the other groups. Underweight subjects had 1.103 (95% CI: 1.038 to 1.172) times likelihood of having impaired cognition (p-value 0.0016), while obese 2 subjects had 1.110 (95% CI: 1.040 to 1.184) times likelihood of having impaired cognition (p-value 0.0016). Regression analysis revealed that in subjects with diabetes of less than 10 years, cognition scores were negatively correlated to BMI (p-value 0.0454). Correlation analysis revealed that at the general population level, regardless of the external factors, increasing or decreasing BMI did not have significant effect on cognition scores. CONCLUSION Subjects who belonged to the extremes of BMI-underweight and obese class 2 – had higher incidence of CI compared to the other BMI groups. Among subjects with T2DM duration of less than 10 years, cognition scores tend to be negatively correlated to BMI.
diabetes mellitus, Type 2 ; cognitive impairment ; cognitive dysfunction ; Body Mass Index

Background: The number of individuals with mild cognitive impairment (MCI), or those people without dementia who are experiencing age-related cognitive decline, has increased in recent years. Conveniently, several interventions to delay cognitive decline exist, where cognitively stimulating activities (CSA) have been receiving too much attention. However, its beneficial effects have not been well established among older people with MCI due to conflicting findings. Objectives: This study aimed to assess and summarize the available evidence on the effects of CSA on the overall cognitive functioning of older people with MCI. Specifically, it sought to answer the PICO question, “In older people with MCI, does engagement in cognitively stimulating activities improve cognitive function?” Methods: A systematic review and meta-analysis of randomized controlled trials examining the effects of CSA on older people with MCI were conducted. Three studies met the inclusion criteria from the 1,328 records from BioMed Central, CINAHL, Cochrane Library, Health Source: Nursing/Academic Edition, MEDLINE, and PubMed databases and 156 articles from WorldCat, DSpace Saint Louis University, and Google Scholar databases and catalogs. Effect size values were inspected using the random-effects model. Data were summarized as standardized mean difference (SMD) with corresponding 95% confidence intervals in the forest plot. Results: This meta-analysis which compared studies that employed similar methodologies, found that CSA has a significant, large effect in improving cognitive functioning among older people with MCI, evidenced by an SMD of 0.798 (95% CI = 0.510-1.085, p = 0.001). While its superiority over other interventions that improve cognitive function was not observed in this study, it was still found that using CSA was helpful in terms of its cost-effectiveness. Also, heterogeneity across studies was non-significant (Cochran’s Q = 0.151, df = 2, p = 0.927, I2 = 0.00%). These results mean that clinical heterogeneity was absent even though a diverse range of CSA was employed. Additionally, methodological diversity was not present since there were no variations in the study design and minimal variability in the risk of bias assessment. Conclusion Overall, it is acknowledged that CSA are effective and practical, inexpensive, non-pharmacologic cognitive training approaches to delay cognitive decline among older people with MCI. However, interpreting this study’s significant, large effect, and non-significant heterogeneity warrants caution.
Cognition ; Cognitive Dysfunction ; Meta-Analysis

Humans ; Cardiovascular Diseases/therapy* ; China ; Cognitive Dysfunction/therapy* ; Consensus

Tripterygium glycosides liposome(TPGL) were prepared by thin film-dispersion method, which were optimized accor-ding to their morphological structures, average particle size and encapsulation rate. The measured particle size was(137.39±2.28) nm, and the encapsulation rate was 88.33%±1.82%. The mouse model of central nervous system inflammation was established by stereotaxic injection of lipopolysaccharide(LPS). TPGL and tripterygium glycosides(TPG) were administered intranasally for 21 days. The effects of intranasal administration of TPG and TPGL on behavioral cognitive impairment of mice due to LPS-induced central ner-vous system inflammation were estimated by animal behavioral tests, hematoxylin-eosin(HE) staining of hippocampus, real-time quantitative polymerase chain reaction(RT-qPCR) and immunofluorescence. Compared with TPG, TPGL caused less damage to the nasal mucosa, olfactory bulb, liver and kidney of mice administered intranasally. The behavioral performance of treated mice was significantly improved in water maze, Y maze and nesting experiment. Neuronal cell damage was reduced, and the expression levels of inflammation and apoptosis related genes [tumor necrosis factor-α(TNF-α), interleukin-1β(IL-1β), BCL2-associated X(Bax), etc.] and glial activation markers [ionized calcium binding adaptor molecule 1(IBA1) and glial fibrillary acidic protein(GFAP)] were decreased. These results indicated that liposome technique combined with nasal delivery alleviated the toxic side effects of TPG, and also significantly ameliorated the cognitive impairment of mice induced by central nervous system inflammation.
Mice ; Animals ; Tripterygium ; Liposomes ; Glycosides/therapeutic use* ; Administration, Intranasal ; Lipopolysaccharides ; Central Nervous System ; Cognitive Dysfunction/drug therapy* ; Inflammation/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Cardiac Glycosides

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.
Humans ; Bayes Theorem ; Neural Networks, Computer ; Algorithms ; Brain ; Cognitive Dysfunction/diagnosis*

China is facing the peak of an ageing population, and there is an increase in demand for intelligent healthcare services for the elderly. The metaverse, as a new internet social communication space, has shown infinite potential for application. This paper focuses on the application of the metaverse in medicine in the intervention of cognitive decline in the elderly population. The problems in assessment and intervention of cognitive decline in the elderly group were analyzed. The basic data required to construct the metaverse in medicine was introduced. Moreover, it is demonstrated that the elderly users can conduct self-monitoring, experience immersive self-healing and health-care through the metaverse in medicine technology. Furthermore, we proposed that it is feasible that the metaverse in medicine has obvious advantages in prediction and diagnosis, prevention and rehabilitation, as well as assisting patients with cognitive decline. Risks for its application were pointed out as well. The metaverse in medicine technology solves the problem of non-face-to-face social communication for elderly users, which may help to reconstruct the social medical system and service mode for the elderly population.
Aged ; Humans ; Cognitive Dysfunction/prevention & control* ; Aging ; China ; Internet ; Technology

Non-suicidal self-injury (NSSI) is becoming increasingly common in adolescents and seriously affects their physical and mental health, and it is also a major risk factor for suicide among adolescents. NSSI has now become a public health issue of general concern; however, the identification of cognitive dysfunction in NSSI is still based on neuropsychological cognitive assessment and subjective questionnaire assessment, with a lack of objective evaluation indicators. As a method for studying the cognitive neural mechanism of NSSI, electroencephalography is a reliable tool for finding objective biomarkers of NSSI. This article reviews the recent research on electrophysiology associated with cognitive dysfunction in adolescents with NSSI.
Humans ; Adolescent ; Self-Injurious Behavior ; Cognitive Dysfunction ; Electroencephalography ; Neuropsychological Tests ; Risk Factors