Exploring the common pathogenesis of Alzheimer's disease and type 2 diabetes mellitus via microarray data analysis

Scientometric analysis and historical review of diabetic encephalopathy research: Trends and hotspots (2004-2023)

BACKGROUND

Diabetic encephalopathy (DE) is a common and serious complication of diabetes that can cause death in many patients and significantly affects the lives of individuals and society. Multiple studies investigating the pathogenesis of DE have been reported. However, few studies have focused on scientometric analysis of DE.

AIM

To analyze literature on DE using scientometrics to provide a comprehensive picture of research directions and progress in this field.

METHODS

We reviewed studies on DE or cognitive impairment published between 2004 and 2023. The latter were used to identify the most frequent keywords in the keyword analysis and explore the hotspots and trends of DE.

RESULTS

Scientometric analysis revealed 1308 research papers on DE, a number that increased annually over the past 20 years, and that the primary topics explored were domain distribution, knowledge structure, evolution, and emergence of research topics related to DE. The inducing factors, comorbidities, pathogenesis, treatment, and animal models of DE help clarify its occurrence, development, and treatment. An increasing number of studies on DE may be a result of the recent increase in patients with diabetes, unhealthy lifestyles, and unhealthy eating habits, which have aggravated the incidence of this disease.

CONCLUSION

We identified the main inducing factors and comorbidities of DE, though other complex factors undoubtedly increase social and economic burdens. These findings provide vital references for future studies.

Identifying Hub Genes and miRNAs Associated with Alzheimer's Disease: A Bioinformatics Pathway to Novel Therapeutic Strategies

Alzheimer's disease (AD) is a neurodegenerative disorder that mainly affects the elderly population. It is characterized by cognitive impairment and dementia due to abnormal levels of amyloid beta peptide (Aβ) and axonal Tau protein in the brain. However, the complex underlying mechanisms affecting this disease are not yet known, and there is a lack of standardized biomarkers and therapeutic targets. Therefore, in this study, by means of bioinformatics analysis, AD-affected brain tissue was analyzed using the GSE138260 dataset, identifying 612 differentially expressed genes (DEGs). Functional analysis revealed 388 upregulated DEGs associated with sensory perception and 224 downregulated DEGs linked to the regulation and modulation of synaptic processes. Protein-protein interaction network analysis identified 20 hub genes. Furthermore, miRNA target gene networks revealed 1767 miRNAs linked to hub genes, among which hsa-mir-106a-5p, hsa-mir-17-5p, hsa-mir-26a-5p, hsa-mir-27a-3p and hsa-mir-34a-5p were the most relevant. This study presents novel biomarkers and therapeutic targets for AD by analyzing the information obtained with a comprehensive literature review, providing new potential targets to study their role in AD.

Beneficial Effects of Echinacoside on Cognitive Impairment and Diabetes in Type 2 Diabetic db/db Mice

INTRODUCTION

Cognitive dysfunction is an important comorbidity of diabetes. Insulin resistance may play a critical role in diabetes-related cognitive impairment. Echinacoside (ECH), a natural phenylethanoid glycoside, is the active component of anti-diabetes prescriptions in traditional Chinese medicine. Its effect on modulating insulin resistance has been confirmed but modulating neurodegenerative disease remains unclear.

METHODS

Db/db mice, a spontaneous type 2 diabetes mode, were intragastrically administered ECH by 300 mg/kg or an equivalent volume of saline. Weight, blood glucose, and insulin resistance index were measured. Morris water maze test was performed to observe the compound effects on cognition. Hippocampal lesions were observed by histochemical analysis.

RESULTS

In db/db mice, ECH alleviated diabetes symptoms, memory loss, and hippocampal neuronal damage. Next, the expression of CD44 and phosphorylated tau was upregulated in diabetic mice. In addition, the insulin receptor substrate-1/phosphatidylinositol 3-kinase /protein kinase B signaling pathway was dysregulated in diabetic mice. All these dysregulations could be reversed by ECH.

DISCUSSION

This study provides theoretical support and experimental evidence for the future application of ECH in diabetic cognition dysfunction treatment, promoting the development of traditional medicines.

Network analysis-guided drug repurposing strategies targeting LPAR receptor in the interplay of COVID, Alzheimer's, and diabetes

The COVID-19 pandemic caused by the SARS-CoV-2 virus has greatly affected global health. Emerging evidence suggests a complex interplay between Alzheimer's disease (AD), diabetes (DM), and COVID-19. Given COVID-19's involvement in the increased risk of other diseases, there is an urgent need to identify novel targets and drugs to combat these interconnected health challenges. Lysophosphatidic acid receptors (LPARs), belonging to the G protein-coupled receptor family, have been implicated in various pathological conditions, including inflammation. In this regard, the study aimed to investigate the involvement of LPARs (specifically LPAR1, 3, 6) in the tri-directional relationship between AD, DM, and COVID-19 through network analysis, as well as explore the therapeutic potential of selected anti-AD, anti-DM drugs as LPAR, SPIKE antagonists. We used the Coremine Medical database to identify genes related to DM, AD, and COVID-19. Furthermore, STRING analysis was used to identify the interacting partners of LPAR1, LPAR3, and LPAR6. Additionally, a literature search revealed 78 drugs on the market or in clinical studies that were used for treating either AD or DM. We carried out docking analysis of these drugs against the LPAR1, LPAR3, and LPAR6. Furthermore, we modeled the LPAR1, LPAR3, and LPAR6 in a complex with the COVID-19 spike protein and performed a docking study of selected drugs with the LPAR-Spike complex. The analysis revealed 177 common genes implicated in AD, DM, and COVID-19. Protein-protein docking analysis demonstrated that LPAR (1,3 & 6) efficiently binds with the viral SPIKE protein, suggesting them as targets for viral infection. Furthermore, docking analysis of the anti-AD and anti-DM drugs against LPARs, SPIKE protein, and the LPARs-SPIKE complex revealed promising candidates, including lupron, neflamapimod, and nilotinib, stating the importance of drug repurposing in the drug discovery process. These drugs exhibited the ability to bind and inhibit the LPAR receptor activity and the SPIKE protein and interfere with LPAR-SPIKE protein interaction. Through a combined network and targeted-based therapeutic intervention approach, this study has identified several drugs that could be repurposed for treating COVID-19 due to their expected interference with LPAR(1, 3, and 6) and spike protein complexes. In addition, it can also be hypothesized that the co-administration of these identified drugs during COVID-19 infection may not only help mitigate the impact of the virus but also potentially contribute to the prevention or management of post-COVID complications related to AD and DM.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Identification of the Shared Gene Signatures Between Alzheimer's Disease and Diabetes-Associated Cognitive Dysfunction by Bioinformatics Analysis Combined with Biological Experiment

Background

The connection between diabetes-associated cognitive dysfunction (DACD) and Alzheimer's disease (AD) has been shown in several observational studies. However, it remains controversial as to how the two related.

Objective

To explore shared genes and pathways between DACD and AD using bioinformatics analysis combined with biological experiment.

Methods

We analyzed GEO microarray data to identify DEGs in AD and type 2 diabetes mellitus (T2DM) induced-DACD datasets. Weighted gene co-expression network analysis was used to find modules, while R packages identified overlapping genes. A robust protein-protein interaction network was constructed, and hub genes were identified with Gene ontology enrichment and Kyoto Encyclopedia of Genome and Genome pathway analyses. HT22 cells were cultured under high glucose and amyloid-β 25-35 (Aβ25-35) conditions to establish DACD and AD models. Quantitative polymerase chain reaction with reverse transcription verification analysis was then performed on intersection genes.

Results

Three modules each in AD and T2DM induced-DACD were identified as the most relevant and 10 hub genes were screened, with analysis revealing enrichment in pathways such as synaptic vesicle cycle and GABAergic synapse. Through biological experimentation verification, 6 key genes were identified.

Conclusions

This study is the first to use bioinformatics tools to uncover the genetic link between AD and DACD. GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2 were identified as key genes connecting AD and DACD. These findings offer new insights into the diseases' pathogenesis and potential diagnostic and therapeutic targets.