Regulation of interleukin 6 by a polymorphic CpG within the frontal cortex in Alzheimer's disease

Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.

The Role of IL-6 in Neurodegenerative Disorders

"Neurodegenerative disorder" is an umbrella term for a group of fatal progressive neurological illnesses characterized by neuronal loss and inflammation. Interleukin-6 (IL-6), a pleiotropic cytokine, significantly affects the activities of nerve cells and plays a pivotal role in neuroinflammation. Furthermore, as high levels of IL-6 have been frequently observed in association with several neurodegenerative disorders, it may potentially be used as a biomarker for the progression and prognosis of these diseases. This review summarizes the production and function of IL-6 as well as its downstream signaling pathways. Moreover, we make a comprehensive review on the roles of IL-6 in neurodegenerative disorders and its potential clinical application.

Interleukin 6 polymorphisms are associated with cardiovascular risk factors in premature coronary artery disease patients and healthy controls of the GEA Mexican study

BACKGROUND AND AIMS

Interleukin-6 (IL-6) is an acute-phase protein that plays an important role in the inflammatory response, vascular inflammation, and atherosclerosis process. The study aimed to establish whether IL-6 gene polymorphisms and IL-6 concentrations are associated with premature coronary artery disease (pCAD) and cardiovascular risk factors.

METHODS

The IL-6 concentrations and the rs2069827, rs1800796, and rs1800795 IL-6 polymorphisms were determined in 1150 pCAD patients and 1083 healthy controls (coronary artery calcium equal to zero determined by tomography).

RESULTS

The IL-6 polymorphisms studied were not associated with pCAD, but they were associated with cardiovascular risk factors in patients and controls. In controls, under the dominant model, the rs1800795 C allele and the rs2069827 T allele were associated with a low risk of central obesity (OR = 0.401, p = 0.017 and OR = 0.577, p = 0.031, respectively), hypoalphalipoproteinemia (OR = 0.581, p = 0.027 and OR = 0.700, p = 0.014, respectively) and hypertriglyceridemia (OR = 0.575, p = 0.030 and OR = 0.728, p = 0.033, respectively). In pCAD, the rs1800795 C allele was associated with an increased risk of hypoalphalipoproteinemia (OR = 1.370, padditive = 0.025) and increased C-reactive protein (CRP) concentrations (OR = 1.491, padditive = 0.007). pCAD patients had significantly higher serum IL-6 concentrations compared to controls (p = 0.002). In the total population, individuals carrying the rs1800795 GC + CC genotypes had higher levels of IL-6 than carriers of the GG genotype (p = 0.025). In control individuals carrying the C allele (CG + CC), an inverse correlation was observed between IL-6 and HDL-cholesterol levels (p = 0.003).

CONCLUSIONS

In summary, the IL-6 polymorphisms were not associated with pCAD, however, they were associated with cardiovascular risk factors in pCAD patients and healthy controls. Individuals carrying the rs1800795 GC + CC genotypes had higher levels of IL-6 than carriers of the GG genotype.

DNA methylation: The epigenetic mechanism of Alzheimer's disease

Nowadays, with the development of the social health care system, there is an increasing trend towards an aging society. The incidence of Alzheimer's disease (AD) is also on the rise. AD is a kind of neurodegenerative disease that can be found in any age group. For years, scientists have been committing to discovering the cause of AD. DNA methylation is one of the most common epigenetic mechanisms in mammals and plays a vital role in the pathogenesis of several diseases, including tumors. Studying chemical changes in the epigenome, or DNA methylation can help us understand the effects of our environment and life on diseases, such as smoking, depression, and menopause, which may affect people's chances of developing Alzheimer's or other diseases. Recent studies have identified some crucial genes like ANK1, RHBDF2, ABCA7, and BIN1, linking DNA methylation to AD. This review focuses on elucidating the relationship between DNA methylation and the pathogenesis of AD and provides an outlook on possible targeted therapeutic modalities.

From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs

Purpose

Large Language Models (LLMs) have shown exceptional performance in various natural language processing tasks, benefiting from their language generation capabilities and ability to acquire knowledge from unstructured text. However, in the biomedical domain, LLMs face limitations that lead to inaccurate and inconsistent answers. Knowledge Graphs (KGs) have emerged as valuable resources for organizing structured information. Biomedical Knowledge Graphs (BKGs) have gained significant attention for managing diverse and large-scale biomedical knowledge. The objective of this study is to assess and compare the capabilities of ChatGPT and existing BKGs in question-answering, biomedical knowledge discovery, and reasoning tasks within the biomedical domain.

Methods

We conducted a series of experiments to assess the performance of ChatGPT and the BKGs in various aspects of querying existing biomedical knowledge, knowledge discovery, and knowledge reasoning. Firstly, we tasked ChatGPT with answering questions sourced from the "Alternative Medicine" sub-category of Yahoo! Answers and recorded the responses. Additionally, we queried BKG to retrieve the relevant knowledge records corresponding to the questions and assessed them manually. In another experiment, we formulated a prediction scenario to assess ChatGPT's ability to suggest potential drug/dietary supplement repurposing candidates. Simultaneously, we utilized BKG to perform link prediction for the same task. The outcomes of ChatGPT and BKG were compared and analyzed. Furthermore, we evaluated ChatGPT and BKG's capabilities in establishing associations between pairs of proposed entities. This evaluation aimed to assess their reasoning abilities and the extent to which they can infer connections within the knowledge domain.

Results

The results indicate that ChatGPT with GPT-4.0 outperforms both GPT-3.5 and BKGs in providing existing information. However, BKGs demonstrate higher reliability in terms of information accuracy. ChatGPT exhibits limitations in performing novel discoveries and reasoning, particularly in establishing structured links between entities compared to BKGs.

Conclusions

To address the limitations observed, future research should focus on integrating LLMs and BKGs to leverage the strengths of both approaches. Such integration would optimize task performance and mitigate potential risks, leading to advancements in knowledge within the biomedical field and contributing to the overall well-being of individuals.

Repurposing Non-pharmacological Interventions for Alzheimer's Diseases through Link Prediction on Biomedical Literature

Recently, computational drug repurposing has emerged as a promising method for identifying new pharmaceutical interventions (PI) for Alzheimer's Disease (AD). Non-pharmaceutical interventions (NPI), such as Vitamin E and Music therapy, have great potential to improve cognitive function and slow the progression of AD, but have largely been unexplored. This study predicts novel NPIs for AD through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement domain knowledge graph, SuppKG, with semantic relations from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set and was used to generate the score tables of the link prediction task. Discovery patterns were applied to generate mechanism pathways for high scoring triples. Our ADInt had 162,213 nodes and 1,017,319 edges. The graph convolutional network model, R-GCN, performed best in both the Time Slicing test set (MR = 7.099, MRR = 0.5007, Hits@1 = 0.4112, Hits@3 = 0.5058, Hits@10 = 0.6804) and the Clinical Trials test set (MR = 1.731, MRR = 0.8582, Hits@1 = 0.7906, Hits@3 = 0.9033, Hits@10 = 0.9848). Among high scoring triples in the link prediction results, we found the plausible mechanism pathways of (Photodynamic therapy, PREVENTS, Alzheimer's Disease) and (Choerospondias axillaris, PREVENTS, Alzheimer's Disease) by discovery patterns and discussed them further. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover NPIs (dietary supplements (DS) and complementary and integrative health (CIH)) for AD. We used discovery patterns to find mechanisms for predicted triples to solve the poor interpretability of artificial neural networks. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions.

Cellular and molecular influencers of neuroinflammation in Alzheimer's disease: Recent concepts & roles

Alzheimer's disease (AD), an extremely common neurodegenerative disorder of the older generation, is one of the leading causes of death globally. Besides the conventional hallmarks i.e. Amyloid-β (Aβ) plaques and neurofibrillary tangles (NFTs), neuroinflammation also serves as a major contributing factor in the pathogenesis of AD. There are mounting evidences to support the fundamental role of cellular (microglia, astrocytes, mast cells, and T-cells) and molecular (cytokines, chemokines, caspases, and complement proteins) influencers of neuroinflammation in producing/promoting neurodegeneration and dementia in AD. Genome-wide association studies (GWAS) have revealed the involvement of various single nucleotide polymorphisms (SNPs) of genes related to neuroinflammation with the risk of developing AD. Modulating the release of the neuroinflammatory molecules and targeting their relevant mechanisms may have beneficial effects on the onset, progress and severity of the disease. Here, we review the distinct role of various mediators and modulators of neuroinflammation that impact the pathogenesis and progression of AD as well as incite further research efforts for the treatment of AD through a neuroinflammatory approach.