Exploring causal associations between autoimmune diseases and hearing loss: a mendelian randomization study.

Yutong Huang; Shengli Gao; Renzhong Luo

doi:10.1016/j.bjorl.2026.101837

Home/Archive/PubMed

PubMed · 42208083

Exploring causal associations between autoimmune diseases and hearing loss: a mendelian randomization study.

Yutong Huang·Shengli Gao·Renzhong Luo

View original source ↗Export metadata ↓

Abstract

OBJECTIVE: The causal relationship between Autoimmune Diseases (ADs) and Hearing Loss (HL) remains unclear. This study investigates whether genetic predispositions associated with ADs contribute to HL risk. METHODS: Mendelian Randomization (MR) analysis was conducted to explore the causal effects of ADs on HL. SNPs from Genome-Wide Association Studies (GWAS) were used as instrumental variables for ADs, including Rheumatoid Arthritis (RA), Type 1 Diabetes (T1D), Systemic Lupus Erythematosus (SLE), Sjögren's Syndrome (SS), Ankylosing Spondylitis (AS), Multiple Sclerosis (MS), Crohn's Disease (CD), and Ulcerative Colitis (UC). Outcome data included Sensorineural Hearing Loss (SNHL), Conductive Hearing Loss (CHL), Mixed conductive and sensorineural Hearing Loss (MHL), and Sudden Idiopathic Hearing Loss (SIHL). MR analyses employed Inverse Variance Weighted (IVW) as the primary method, supplemented with MR-Egger, weighted median, and weighted mode. Heterogeneity, pleiotropy, and sensitivity were evaluated using Cochran's Q test, MR-Egger regression, MR-PRESSO, and leave-one-out analysis. RESULTS: The IVW method identified nine significant associations: MS-SIHL (OR = 1.0494, 95% CI 1.0072-1.0934), AS-CHL (OR = 1.2832, 95% CI 1.0643-1.5472), AS-MHL (OR = 1.5994, 95% CI 1.3696-1.8678), AS-SNHL (OR = 1.1903, 95% CI 1.1104-1.276), AS-SIHL (OR = 1.481, 95% CI 1.22-1.798), SLE-CHL (OR = 1.0593, 95% CI 1.0116-1.1092), UC-MHL (OR = 1.0907, 95% CI 1.0027-1.1865), CD-CHL (OR = 1.0529, 95%CI: 1.0074-1.1005), and CD-SIHL (OR = 1.0597, 95% CI 1.0177-1.1034). Among these, outliers were detected only in AS-SNHL. After outlier removal, the AS-SNHL association remained significant (OR = 1.1722, p < 0.00001), with resolved heterogeneity and pleiotropy. No heterogeneity and pleiotropy were found for the other associations. CONCLUSION: This study identified nine significant AD-HL associations, emphasizing the need for targeted screening and management of HL in individuals with AD. LEVEL OF EVIDENCE: Level 5.

Explore related subjects

Humans Mendelian Randomization Analysis Autoimmune Diseases Polymorphism, Single Nucleotide Genetic Predisposition to Disease Genome-Wide Association Study Hearing Loss Risk Factors Autoimmune diseases Genome-wide association study Hearing loss Mendelian randomization analysis Sensorineural hearing loss Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Yutong Huang, Shengli Gao, Renzhong Luo. 2026-05-28. Exploring causal associations between autoimmune diseases and hearing loss: a mendelian randomization study.. https://doi.org/10.1016/j.bjorl.2026.101837

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↗