Overview and Current Status of Alzheimer's Disease in Bangladesh

A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimer's disease

Alzheimer's disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

Coupling of Alzheimer's Disease Genetic Risk Factors with Viral Susceptibility and Inflammation

Alzheimer's disease (AD) is a neurodegenerative disease characterized by persistent cognitive decline. Amyloid plaque deposition and neurofibrillary tangles are the main pathological features of AD brain, though mechanisms leading to the formation of lesions remain to be understood. Genetic efforts through genome-wide association studies (GWAS) have identified dozens of risk genes influencing the pathogenesis and progression of AD, some of which have been revealed in close association with increased viral susceptibilities and abnormal inflammatory responses in AD patients. In the present study, we try to present a list of AD candidate genes that have been shown to affect viral infection and inflammatory responses. Understanding of how AD susceptibility genes interact with the viral life cycle and potential inflammatory pathways would provide possible therapeutic targets for both AD and infectious diseases.

Knowledge, Awareness, and Perceived Threat Toward Alzheimer's Disease Among the Family Caregivers of Individuals with Chronic Physical Conditions in Bangladesh

Background

Alzheimer's disease (AD) is a growing public health concern affecting millions of individuals worldwide. This disease burden is exacerbated in countries like Bangladesh, where public awareness and knowledge about the condition remains limited.

Objective

This study aimed to assess the level of AD knowledge, awareness, and perceived threat among family caregivers of individuals with chronic physical conditions of Bangladesh.

Methods

A descriptive cross-sectional study was conducted among the caregivers of patients selected via convenience sampling in six public hospitals between October 2022 and March 2023. The data was collected via face-to-face interviews employing a semi-structured questionnaire that contained information about socio-demographic characteristics, personal experiences with AD, knowledge of AD, level of awareness regarding AD, and perceived threat toward AD.

Results

The findings revealed a poor knowledge of AD among the study participants. Regarding AD awareness, 52.5% considered memory loss in older individuals abnormal, 26.2% attributed AD to causes like black magic, psychological distress, or bad eyes. Regarding perceived threat, 60.2% wanted to assess their AD risk, while 68.2% disagreed with the likelihood of developing the disease. Multiple linear regression analysis revealed that age, place of residence, education, and employment status significantly contribute to AD knowledge scores.

Conclusions

This study highlights the urgent need for public education and awareness campaigns to address the knowledge gap and reduce stigmatization associated with AD in Bangladesh. Targeted efforts, particularly among older age groups and rural communities, can enhance understanding and improve the quality of care and support for AD patients and their caregivers.

A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer's Disease

Alzheimer's disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer's is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. The main cause of Alzheimer's disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. The main aim is to recognize Dementia among various patients. This paper represents the result and analysis regarding detecting Dementia from various machine learning models. The Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. The dataset is small, but it has some significant values. The dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. The system is simple and can easily help people by detecting Dementia among them.

(-)-Oleocanthal Nutraceuticals for Alzheimer's Disease Amyloid Pathology: Novel Oral Formulations, Therapeutic, and Molecular Insights in 5xFAD Transgenic Mice Model

Alzheimer’s disease (AD) is a complex progressive neurodegenerative disorder affecting humans mainly through the deposition of Aβ-amyloid (Aβ) fibrils and accumulation of neurofibrillary tangles in the brain. Currently available AD treatments only exhibit symptomatic relief but do not generally intervene with the amyloid and tau pathologies. The extra-virgin olive oil (EVOO) monophenolic secoiridoid S-(–)-oleocanthal (OC) showed anti-inflammatory activity through COX system inhibition with potency comparable to the standard non-steroidal anti-inflammatory drug (NSAID) like ibuprofen. OC also showed positive in vitro, in vivo, and clinical therapeutic effects against cardiovascular diseases, many malignancies, and AD. Due to its pungent, astringent, and irritant taste, OC should be formulated in acceptable dosage form before its oral use as a potential nutraceutical. The objective of this study is to develop new OC oral formulations, assess whether they maintained OC activity on the attenuation of β-amyloid pathology in a 5xFAD mouse model upon 4-month oral dosing use. Exploration of potential OC formulations underlying molecular mechanism is also within this study scope. OC powder formulation (OC-PF) and OC-solid dispersion formulation with erythritol (OC-SD) were prepared and characterized using FT-IR spectroscopy, powder X-ray diffraction, and scanning electron microscopy (ScEM) analyses. Both formulations showed an improved OC dissolution profile. OC-PF and OC-SD improved memory deficits of 5xFAD mice in behavioral studies. OC-PF and OC-SD exhibited significant attenuation of the accumulation of Aβ plaques and tau phosphorylation in the brain of 5xFAD female mice. Both formulations markedly suppressed C3AR1 (complement component 3a receptor 1) activity by targeting the downstream marker STAT3. Collectively, these results demonstrate the potential for the application of OC-PF as a prospective nutraceutical or dietary supplement to control the progression of amyloid pathogenesis associated with AD.

Analyzing the chance of developing dementia among geriatric people: a cross-sectional pilot study in Bangladesh

AIM

Alzheimer's disease is the most common form of dementia, representing 60-80% of cases, and ageing is the primary risk factor for the development of Alzheimer's disease. The objective of this study was to examine the chance of developing dementia (i.e. mild cognitive impairment (MCI), Alzheimer's disease) among geriatric people in Bangladesh.

METHODS

This study included 390 adult citizens of Bangladesh (age range: 60-70 years). The Takeda Three Colors Combination (TTCC) test was used to detect the prevalence of MCI and mild dementia among the subjects, and then the Clinical Dementia Rating was used to determine the level of dementia.

RESULTS

The subjects who were aged 60-65 years included 154 with MCI, 76 with mild dementia, 1 with moderate dementia, 4 with severe dementia, and 29 without dementia. The subjects who were aged 66-70 years included 75 with MCI, 36 with mild dementia, 0 with moderate dementia, 2 with severe dementia, and 13 without dementia. The sensitivity of the TTCC was 75% and 58% for the mild dementia and MCI groups, respectively, and the specificity was 52%. The odds ratio of incorrect responses to the TTCC was 3.42 (95% confidence interval: 1.63-7.21) for subjects with mild dementia compared those without dementia. However, the TTCC outcomes revealed no significant differences between the MCI and non-dementia groups. The results showed no significant associations between cognitive decline/developing dementia and social status/occupation.

CONCLUSION

The outcomes of this study indicated that most of the subjects had MCI or mild dementia and were farmers aged 60-65 years.