Aspirin Use and Risk of Alzheimer's Disease: A 2-Sample Mendelian Randomization Study

The causal relationship between cholecystectomy and IBD/IBS and the role of bile acids and gut microbiota: a two-sample Mendelian randomization study

OBJECTIVE

This study aims to explore the causal relationship between cholecystectomy and inflammatory bowel disease (IBD)/irritable bowel syndrome (IBS) and the role of serum bile acids and gut microbiota in this context.

METHODS

Utilizing genetic variant data from previous Genome-Wide Association Studies (GWAS), this study employed a two-sample MR approach to assess the causal effect of cholecystectomy on IBD/IBS.

RESULTS

The MR analysis suggested a potential negative causal relationship between cholecystectomy and UC (p = 0.0233, OR 0.9773, 95%CI 0.9581-0.9969) and a positive causal relationship between cholecystectomy and IBS (p = 0.0395, OR 4.077, 95%CI 1.0699-15.5362). Various sensitivity analyses reinforced the reliability of the causal relationship. However, the analysis did not find definitive results between serum bile acids or gut microbiota and cholecystectomy or IBD/IBS, possibly due to insufficient statistical power. MVMR find a causal relationship between bile acids and IBS (p = 0.0015, b = 0.4085) and UC (p = 0.0198, b = 0.0029).

CONCLUSION

This study provides evidence of a causal relationship between cholecystectomy and IBD/IBS, highlighting the potential risk reduction for UC and increased risk for IBS following cholecystectomy. The role of bile acids and gut microbiota in this relationship remains unclear, necessitating further research to validate the causality and explore underlying mechanisms.

A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs and diseases within a graph. However, labeled data for drug indication prediction is often limited and expensive to acquire. Contrastive learning addresses this challenge by aligning similar drug-disease pairs and separating dissimilar pairs in the embedding space. Thus, we developed a model called DrIGCL for drug indication prediction, which utilizes graph convolutional networks and contrastive learning. DrIGCL incorporates drug structure, disease comorbidities, and known drug indications to extract representations of drugs and diseases. By combining contrastive and classification losses, DrIGCL predicts drug indications effectively. In multiple runs of hold-out validation experiments, DrIGCL consistently outperformed existing computational methods for drug indication prediction, particularly in terms of top-k. Furthermore, our ablation study has demonstrated a significant improvement in the predictive capabilities of our model when utilizing contrastive learning. Finally, we validated the practical usefulness of DrIGCL by examining the predicted novel indications of Aspirin.

CSF proteomic profiling with amyloid/tau positivity identifies distinctive sex-different alteration of multiple proteins involved in Alzheimer's disease

In Alzheimer's disease (AD), the most common cause of dementia, females have higher prevalence and faster progression, but sex-specific molecular findings in AD are limited. Here, we comprehensively examined and validated 7,006 aptamers targeting 6,162 proteins in cerebral spinal fluid (CSF) from 2,077 amyloid/tau positive cases and controls to identify sex-specific proteomic signatures of AD. In discovery (N=1,766), we identified 330 male-specific and 121 female-specific proteomic alternations in CSF (FDR <0.05). These sex-specific proteins strongly predicted amyloid/tau positivity (AUC=0.98 in males; 0.99 in females), significantly higher than those with age, sex, and APOE-ε4 (AUC=0.85). The identified sex-specific proteins were well validated (r≥0.5) in the Stanford study (N=108) and Emory study (N=148). Biological follow-up of these proteins led to sex differences in cell-type specificity, pathways, interaction networks, and drug targets. Male-specific proteins, enriched in astrocytes and oligodendrocytes, were involved in postsynaptic and axon-genesis. The male network exhibited direct connections among 152 proteins and highlighted PTEN, NOTCH1, FYN, and MAPK8 as hubs. Drug target suggested melatonin (used for sleep-wake cycle regulation), nabumetone (used for pain), daunorubicin, and verteporfin for treating AD males. In contrast, female-specific proteins, enriched in neurons, were involved in phosphoserine residue binding including cytokine activities. The female network exhibits strong connections among 51 proteins and highlighted JUN and 14-3-3 proteins (YWHAG and YWHAZ) as hubs. Drug target suggested biperiden (for muscle control of Parkinson's disease), nimodipine (for cerebral vasospasm), quinostatin and ethaverine for treating AD females. Together, our findings provide mechanistic understanding of sex differences for AD risk and insights into clinically translatable interventions.

Association between aspirin use and risk of dementia: a systematic review and meta-analysis

PURPOSE

Dementia and Alzheimer's disease (AD) pose significant challenges to public health globally with no effective treatment strategies available. Therefore, the research focuses on developing effective prophylaxis to prevent the onset of these diseases. Recent studies have suggested that low-dose aspirin may help reduce the risk of dementia. Nonetheless, evidence regarding the correlation between aspirin consumption and the onset of dementia and AD is limited. This review aims to provide an up-to-date summary of the existing evidence and evaluate the association between aspirin and the onset of dementia and Alzheimer's disease.

METHODS

A systematic search of PubMed, Embase, Web of Science, PsycINFO, and CINAHL databases was conducted to find eligible studies published until April 2023. A random-effects meta-analysis of the eligible studies was then performed to assess the link between aspirin use and the onset of dementia and Alzheimer's disease. Additionally, we conducted subgroup analyses to evaluate the overall effect of low-dose (75-100 mg) aspirin consumption on the onset of dementia and AD.

RESULTS

A total of 875 studies were identified, with only 22 meeting the inclusion criteria. There was no statistically significant impact of aspirin consumption on the onset of dementia (HR 1.13, 11 studies) or Alzheimer's disease (HR 0.91, 3 studies). Additionally, subgroup analysis showed that taking low doses of aspirin (75-100 mg) did not significantly affect the onset of either dementia (HR 0.96, 13 studies) or Alzheimer's disease (HR 0.85, 2 studies).

CONCLUSIONS

Aspirin use does not decrease the risk of dementia or AD, even when taken in low doses. However, the quality of the studies analyzed was inadequate, with only three randomized controlled trials included in the review. Future high-quality studies are needed to assess the effect of aspirin consumption on these diseases. These findings may assist clinicians in selecting appropriate prophylactic strategies for patients at risk of developing dementia and AD.

Association of thrombopoietin-related drugs with thromboembolic events: Mendelian randomization and a real-world study

Background

Studies have shown conflicting results when using thrombopoietin-related drugs (TPORD) for thromboembolic events (TEEs). Our study aimed to explore the correlation between TPORDs and TEEs.

Method

Drug-targeted Mendelian randomization (MR) and multivariate MR (MVMR) analysis were used to explore the causal relationship between TPORDs and TEEs such as venous thromboembolism (VTE), deep vein thrombosis (DVT), pulmonary embolism (PE), myocardial infarction (MI) and ischemic stroke (STR). At the same time, a real-world study was conducted by extracting adverse events (AEs) from the FDA Adverse Event Reporting System database included in AERSMine to further validate our findings.

Outcome

In drug-target MR, TPORDs were associated with VTE (OR = 1.193, 95% confidence interval (CI): 1.001-1.423, p = 0.049], DVT (OR = 1.321, 95% CI: 1.027-1.700, p = 0.030), MI (OR = 1.216, 95% CI: 1.010-1.464, p = 0.039), STR (OR = 1.224, 95% CI: 1.021-1.468, p = 0.029). VTE/DVT/STR remained stable in MVMR (VTE: OR = 1.3, 95% CI: 1.187-1.422, p < 0.001; DVT: OR = 1.465,95% CI:1.285-1.671, p < 0.001; STR: OR = 1.119, 95% CI: 1.018-1.229, p = 0.019) and real-world studies [lower bound of proportional reporting ratio (ROR) greater than 1]. The significance of myocardial infarction disappeared in MVMR (OR = 0.996, 95% CI: 0.894-1.109, p = 0.942) and in real-world studies (lower ROR lower than 1). There was no evidence of a causal relationship between TPORD and PE (OR = 1.244, 95% CI: 0.969-1.597, p = 0.087), but it generated a signal from a real-world study (lower bound of ROR greater than 1).

Conclusion

This study suggests that TPORDs may be associated with an increased risk of TEEs, particularly AEs leading to VTE/DVT/STR. In addition, the relationship between TPORDs and PE/MI is debatable and requires more research.

Causal relationship between Alzheimer's disease and cardiovascular disease: a bidirectional Mendelian randomization analysis

Observational studies suggest that cardiovascular disease (CVD) increases the risk of developing Alzheimer's disease (AD). However, the causal relationship between the two is not clear. This study applied a two-sample bidirectional Mendelian randomization method to explore the causal relationship between CVD and AD. Genome-wide association study (GWAS) data from 46 datasets of European populations (21,982 cases of AD and 41,944 controls) were utilized to obtain genetic instrumental variables for AD. In addition, genetic instrumental variables for atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), coronary heart disease (CHD), angina pectoris (AP), and ischemic stroke (IS) (including large-artery atherosclerotic stroke [LAS] and cardioembolic stroke [CES]) were selected from GWAS data of European populations (P < 5E-8). The inverse variance weighting method was employed as the major Mendelian randomization analysis method. Genetically predicted AD odds ratios (OR) (1.06) (95% CI: 1.02-1.10, P = 0.003) were linked to higher AP analysis. A higher genetically predicted OR for CES (0.9) (95% CI 0.82-0.99, P = 0.02) was linked to a decreased AD risk. This Mendelian randomized study identified AD as a risk factor for AP. In addition, CES was related to a reduced incidence of AD. Therefore, these modifiable risk factors are crucial targets for preventing and treating AD.