Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer's disease

Glycation in Alzheimer's Disease and Type 2 Diabetes: The Prospect of Dual Drug Approaches for Therapeutic Interventions

As global life expectancy increases, the prevalence of neurodegenerative diseases like Alzheimer's disease (AD) continues to rise. Since therapeutic options are minimal, a deeper understanding of the pathophysiology is essential for improved diagnosis and treatments. AD is marked by the aggregation of Aβ proteins, tau hyperphosphorylation, and progressive neuronal loss, though its precise origins remain poorly understood. Meanwhile, type 2 diabetes mellitus (T2DM) is characterized by chronic hyperglycemia, leading to the formation of advanced glycation end products (AGEs), which are implicated in tissue damage and neurotoxicity. These AGEs can be resistant to proteolysis and, therefore, accumulate, exacerbating AD pathology and accelerating neurodegeneration. Insulin resistance, a hallmark of T2DM, further complicates AD pathogenesis by promoting tau hyperphosphorylation and Aβ plaque accumulation. Additionally, gut microbiome dysbiosis in T2DM fosters AGE accumulation and neuroinflammation, underscoring the intricate relationship between metabolic disorders, gut health, and neurodegenerative processes. This complex interplay presents both a challenge and a potential avenue for therapeutic intervention. Emerging evidence suggests that antidiabetic medications may offer cognitive benefits in AD, as well as in other neurodegenerative conditions, pointing to a shared pathophysiology. Thus, we posit that targeting AGEs, insulin signaling, and gut microbiota dynamics presents promising opportunities for innovative treatment approaches in AD and T2DM.

Alzheimer's disease in the Kingdom of Saudi Arabia: Current perspectives and genetic insights

The prevalence of dementia and mild cognitive disorder has markedly risen in recent years. Alzheimer's disease (AD) stands out as the most common form of neurodegenerative dementia among the elderly, featuring progressive memory loss and cognitive decline. Although the exact biological causes of AD are complex and multifactorial, genetics is considered a prominent contributor. To date, around 80 genetic loci have been identified, primarily in European ancestry groups, though a considerable portion of AD's genetic architecture remains elusive. Recognizing the impending rise in AD cases, both governmental and private sectors in Saudi Arabia are making efforts to enhance formal care and services for older adults. While few studies have investigated AD-susceptible genes within the Saudi population, further attention is needed to explore the genetic background and identify molecular biomarkers associated with AD. This review provides an overview of the current understanding of AD and recent genetic research in Saudi Arabia.

Aronia Melanocarpa Elliot Anthocyanins Inhibits Alcoholic Liver Disease by Activation of α7nAChR

The study wanted to explore the preventative effects of Aornia melanocarpa Elliot anthocyanins (AMA) to Alcoholic liver disease (ALD) by bioinformatics prediction and experimental verification. We founded 419 differentially expressed genes (DEGs) in GSE28619 related to ALD from GEO database, COL1A1 was selected by the core gene module construction and molecular docking. Mice were treated by intragastric administration of gradient 50% ethanol, AMA alleviated liver injury by ALD and ameliorated the model's body weight, lessened the liver inflammation according to histopathological evaluation, increased serum liver biochemical index (AST, ALT, TC, TG and LDL-C) and decreased HDL-C, reversed the expression of enzymes (ALDH and GSH-PX), decreased cytokines expression (Ki67, TNF-α and IL-6), reversed the expression of α7nAChR and collagen I, downregulated the PI3K-Akt pathway and Keap1/HO-1 pathway (p-PI3K, PI3K, p-Akt, Akt, Keap1, Nrf2, HO-1,GSK-3β and Bcl-2), indicated that α7nAChR and collagen I may be the AMA action targets.

Alzheimer's Disease as Type 3 Diabetes: Understanding the Link and Implications

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are two prevalent conditions that present considerable public health issue in aging populations worldwide. Recent research has proposed a novel conceptualization of AD as "type 3 diabetes", highlighting the critical roles of insulin resistance and impaired glucose metabolism in the pathogenesis of the disease. This article examines the implications of this association, exploring potential new avenues for treatment and preventive strategies for AD. Key evidence linking diabetes to AD emphasizes critical metabolic processes that contribute to neurodegeneration, including inflammation, oxidative stress, and alterations in insulin signaling pathways. By framing AD within this metabolic context, we can enhance our understanding of its etiology, which in turn may influence early diagnosis, treatment plans, and preventive measures. Understanding AD as a manifestation of diabetes opens up the possibility of employing novel therapeutic strategies that incorporate lifestyle modifications and the use of antidiabetic medications to mitigate cognitive decline. This integrated approach has the potential to improve patient outcomes and deepen our comprehension of the intricate relationship between neurodegenerative diseases and metabolic disorders.

Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications

Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein-protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.

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.

Decision of the Optimal Rank of a Nonnegative Matrix Factorization Model for Gene Expression Data Sets Utilizing the Unit Invariant Knee Method: Development and Evaluation of the Elbow Method for Rank Selection

BACKGROUND

There is a great need to develop a computational approach to analyze and exploit the information contained in gene expression data. The recent utilization of nonnegative matrix factorization (NMF) in computational biology has demonstrated the capability to derive essential details from a high amount of data in particular gene expression microarrays. A common problem in NMF is finding the proper number rank (r) of factors of the degraded demonstration, but no agreement exists on which technique is most appropriate to utilize for this purpose. Thus, various techniques have been suggested to select the optimal value of rank factorization (r).

OBJECTIVE

In this work, a new metric for rank selection is proposed based on the elbow method, which was methodically compared against the cophenetic metric.

METHODS

To decide the optimum number rank (r), this study focused on the unit invariant knee (UIK) method of the NMF on gene expression data sets. Since the UIK method requires an extremum distance estimator that is eventually employed for inflection and identification of a knee point, the proposed method finds the first inflection point of the curvature of the residual sum of squares of the proposed algorithms using the UIK method on gene expression data sets as a target matrix.

RESULTS

Computation was conducted for the UIK task using gene expression data of acute lymphoblastic leukemia and acute myeloid leukemia samples. Consequently, the distinct results of NMF were subjected to comparison on different algorithms. The proposed UIK method is easy to perform, fast, free of a priori rank value input, and does not require initial parameters that significantly influence the model's functionality.

CONCLUSIONS

This study demonstrates that the elbow method provides a credible prediction for both gene expression data and for precisely estimating simulated mutational processes data with known dimensions. The proposed UIK method is faster than conventional methods, including metrics utilizing the consensus matrix as a criterion for rank selection, while achieving significantly better computational efficiency without visual inspection on the curvatives. Finally, the suggested rank tuning method based on the elbow method for gene expression data is arguably theoretically superior to the cophenetic measure.