AlzRiskMR database: an online database for the impact of exposure factors on Alzheimer’s disease

Wang, Zhe; Meng, Lei; Liu, Hong; Shen, Liang; Ji, Hong‐Fang

doi:10.1093/bib/bbaa213

Cited by 3 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, a similar systemic MR study has been conducted for AD, and they summarized their results in a database called AlzRiskMR 15 . Our study is new and novel in the following two respects.…”

Section: Discussionmentioning

confidence: 96%

“…Recently, a similar systemic MR study has been conducted for AD, and they summarized their results in a database called AlzRiskMR. 15 Our study is new and novel in the following two respects. First, we carefully extracted the instrumental variables to alleviate the concern of horizontal pleiotropy by excluding genetic variants that were genome-wide significant for AD.…”

Section: Comparison To Existing Literaturementioning

confidence: 92%

“…However, they typically focus on a few pre‐selected risk factors, and the results may be over‐optimistic due to publication bias. One recent study systemically investigated many exposures simultaneously 15 . However, this study may suffer from horizontal pleiotropy.…”

Section: Introductionmentioning

confidence: 99%

“…One recent study systemically investigated many exposures simultaneously. 15 However, this study may suffer from horizontal pleiotropy. More importantly, recently it has been proposed that MR could be applied to predict promising drug repurposing options.…”

mentioning

confidence: 97%

See 3 more Smart Citations

Systematic identification of risk factors and drug repurposing options for Alzheimer's disease

Wang

et al. 2021

A&D Transl Res & Clin Interv

View full text Add to dashboard Cite

Introduction:Several Mendelian randomization studies have been conducted that identified multiple risk factors for Alzheimer's disease (AD). However, they typically focus on a few pre-selected risk factors. Methods:A two-sample Mendelian randomization (MR) study was used to systematically examine the potential causal associations of 1037 risk factors/medical conditions and 31 drugs with the risk of late-onset AD. To correct for multiple comparisons, the false discovery rate was set at < 0.05. Results:There was strong evidence of a causal association between glioma risk, reduced trunk fat-free mass, lower education levels, lower intelligence and a higher risk of AD. For 31 investigated treatments (such as antihypertensive drugs), we found limited evidence for their associations.Discussion: MR found robust evidence of causal associations between glioma, trunk fat-free, and AD. Our study also confirms that higher educational attainment and higher intelligence are associated with a reduced risk of AD.

show abstract

Section: Discussionmentioning

confidence: 96%

Section: Comparison To Existing Literaturementioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 97%

See 2 more Smart Citations

Systematic identification of risk factors and drug repurposing options for Alzheimer's disease

Wang

et al. 2021

A&D Transl Res & Clin Interv

View full text Add to dashboard Cite

show abstract

Deciphering the genetic interplay between depression and dysmenorrhea: a Mendelian randomization study

Liu,

Wei,

Carr

et al. 2024

Briefings in Bioinformatics

View full text Add to dashboard Cite

Background This study aims to explore the link between depression and dysmenorrhea by using an integrated and innovative approach that combines genomic, transcriptomic, and protein interaction data/information from various resources. Methods A two-sample, bidirectional, and multivariate Mendelian randomization (MR) approach was applied to determine causality between dysmenorrhea and depression. Genome-wide association study (GWAS) data were used to identify genetic variants associated with both dysmenorrhea and depression, followed by colocalization analysis of shared genetic influences. Expression quantitative trait locus (eQTL) data were analyzed from public databases to pinpoint target genes in relevant tissues. Additionally, a protein–protein interaction (PPI) network was constructed using the STRING database to analyze interactions among identified proteins. Results MR analysis confirmed a significant causal effect of depression on dysmenorrhea [‘odds ratio’ (95% confidence interval) = 1.51 (1.19, 1.91), P = 7.26 × 10−4]. Conversely, no evidence was found to support a causal effect of dysmenorrhea on depression (P = .74). Genetic analysis, using GWAS and eQTL data, identified single-nucleotide polymorphisms in several genes, including GRK4, TRAIP, and RNF123, indicating that depression may impact reproductive function through these genetic pathways, with a detailed picture presented by way of analysis in the PPI network. Colocalization analysis highlighted rs34341246(RBMS3) as a potential shared causal variant. Conclusions This study suggests that depression significantly affects dysmenorrhea and identifies key genes and proteins involved in this interaction. The findings underline the need for integrated clinical and public health approaches that screen for depression among women presenting with dysmenorrhea and suggest new targeted preventive strategies.

show abstract

Biomarkers for Alzheimer’s Disease in the Current State: A Narrative Review

Gunes

Aizawa

Sugashi

et al. 2022

IJMS

View full text Add to dashboard Cite

Alzheimer’s disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.

show abstract

AlzRiskMR database: an online database for the impact of exposure factors on Alzheimer’s disease

Cited by 3 publications

References 19 publications

Systematic identification of risk factors and drug repurposing options for Alzheimer's disease

Systematic identification of risk factors and drug repurposing options for Alzheimer's disease

Deciphering the genetic interplay between depression and dysmenorrhea: a Mendelian randomization study

Biomarkers for Alzheimer’s Disease in the Current State: A Narrative Review

Contact Info

Product

Resources

About