Causal association between non-steroidal anti-inflammatory drugs use and the risk of benign prostatic hyperplasia: a univariable and multivariable Mendelian randomization study.

Zi-He Peng; Ming-Rui Li; Min-Xin He; Jing Liu; Jia-Hao Dou; Ya-Wen Wang; Yao Dong; Chong Yan; Zi-Hao Li; Tie Chong; Zhao-Lun Li

doi:10.1186/s12920-025-02128-1

Home/Archive/PubMed

PubMed · 40165277

Causal association between non-steroidal anti-inflammatory drugs use and the risk of benign prostatic hyperplasia: a univariable and multivariable Mendelian randomization study.

Zi-He Peng·Ming-Rui Li·Min-Xin He·Jing Liu·Jia-Hao Dou·Ya-Wen Wang·Yao Dong·Chong Yan·Zi-Hao Li·Tie Chong·Zhao-Lun Li

View original source ↗Export metadata ↓

Abstract

BACKGROUND: The results of earlier observational research on the relationships between the usage of non-steroidal anti-inflammatory medicines (NSAIDs) and the risk of benign prostatic hyperplasia (BPH) have been inconsistent. METHODS: To assess these associations, we performed both univariable and multivariable Mendelian randomization (MR) studies. Instrumental variables (IVs) associated with exposures at the significance level (p < 5 × 10-6) were selected from a comprehensive meta-analysis conducted by the United Kingdom Biobank (UKB). Summary data for BPH were obtained from the FinnGen consortium, which comprised 30,066 cases and 119,297 controls. Sensitivity analyses were performed to evaluate heterogeneity and pleiotropy. RESULTS: We found evidence by univariable MR (UVMR) that genetically predicted NSAIDs use increased the risk of BPH (odds ratio [OR] per unit increase in log odds NSAIDs use: 1.164, 95% confidence interval [CI]: 1.041-1.302, p = 0.008). After controlling for inflammation in multivariable MR (MVMR), the link persisted (OR: 1.165, 95% CI: 1.049-1.293, p = 0.004). There were no indications of potential heterogeneity and pleiotropy in UVMR and MVMR analyses. CONCLUSION: The results of the MR estimates suggest that genetically predicted NSAIDs use may elevate the risk of BPH. This outcome prompts the imperative for deeper exploration into potential underlying mechanisms.

Explore related subjects

Humans Male Prostatic Hyperplasia Anti-Inflammatory Agents, Non-Steroidal Mendelian Randomization Analysis Risk Factors Multivariate Analysis Benign prostatic hyperplasia Causal association Genetic epidemiology Mendelian randomization Non-steroidal anti-inflammatory drugs Journal Article Research Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Zi-He Peng, Ming-Rui Li, Min-Xin He, Jing Liu, Jia-Hao Dou, Ya-Wen Wang, Yao Dong, Chong Yan, Zi-Hao Li, Tie Chong, Zhao-Lun Li. 2025-03-31. Causal association between non-steroidal anti-inflammatory drugs use and the risk of benign prostatic hyperplasia: a univariable and multivariable Mendelian randomization study.. https://doi.org/10.1186/s12920-025-02128-1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2027

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗

PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

PubMed2027

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗