PubMed HealthSearch

PubMed · 41650091

Causality between noise pollution and Alzheimer disease: A Mendelian randomization analysis.

Abstract

The role of noise pollution as a risk factor for Alzheimer disease (AD) is unclear, with observational studies yielding conflicting results susceptible to confounding and reverse causality. To clarify this relationship, we performed a 2-sample Mendelian randomization (MR) study using summary statistics from large-scale genome-wide association studies of European populations. Genetically predicted daytime and evening noise exposure was used as an instrumental variable to assess a causal effect on AD risk. The primary analysis was conducted using the inverse-variance weighted method, with weighted median and MR-Egger methods as key sensitivity analyses. We assessed instrument validity and pleiotropy using the Cochran Q test, the MR-Egger intercept, and leave-one-out analysis. Our MR analysis found no evidence of a causal association between genetically predicted daytime noise (odds ratio [95% confidence interval] = 0.999 [0.993-1.006], P = .819) or evening noise (odds ratio [95% confidence interval] = 0.999 [0.993-1.005], P = .643) and the risk of AD. Sensitivity analyses were consistent, with no evidence of heterogeneity or directional pleiotropy. In conclusion, this study does not support a direct causal link between noise and AD. While our findings mitigate common observational biases, they do not preclude indirect mechanisms whereby noise may influence AD pathogenesis via established risk pathways, such as chronic sleep disruption and cardiovascular stress. Studies are needed to focus on disentangling these potential indirect effects.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ze-Hao Chen, Lian-Xin Wang, Ran Li, Yu-Hang Jiang, Guan-Hua Zong, Zong-Xi Yi, Xin-Yu Ren, Bao-Hui Jia. 2026-02-06. Causality between noise pollution and Alzheimer disease: A Mendelian randomization analysis.. https://doi.org/10.1097/md.0000000000047612

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

KEEP EXPLORING

Related citations

Non-coding RNAs and Mitochondrial Dysfunction in Alzheimer's Disease: A Systematic Review.

Alzheimer's disease (AD) is responsible for 70% of dementia cases worldwide, with tau hyperphosphorylation and amyloid-β plaque accumulation representing its core pathological hallmarks. Genetic predisposition, oxidative stress, and neuroinflammation contribute to disease onset and progression. Non-coding ribonucleic acids (ncRNAs) are a class of RNAs which control gene expression and whose dysregulation in AD patients has been linked to amyloid production, neuroinflammation, and mitochondrial dysfunction, which ranges from impaired energy metabolism to disrupted mitochondrial biogenesis and dynamics. Our descriptive systematic review surveyed the involvement of ncRNAs in mitochondrial dysfunction in AD across experimental and clinical literature. We identified multiple microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) that directly regulate mitophagy, mitochondrial biogenesis, mitochondrial autophagic, and apoptotic pathways, mitochondrial dynamics, and protein import mechanisms in AD models. Among the most important candidates demonstrating clinical dysregulation, miR-140 and lncRNA NEAT1 regulate mitophagy, while miR-9, miR-34a, miR-146a, miR-155, and miR-485 are implicated in mitochondrial biogenesis and miR-204 in mitochondrial autophagy. LncRNA BDNF-AS, miR-148a-3p, miR-21-5p, and miR-103a-3p emerged as regulators of the mitochondrial apoptosis pathway with confirmed clinical dysregulation. Multiple ncRNAs control mitochondrial dynamics, of which miR-195, miR-124, and miR-455-3p have also been studied in AD patients. Additionally, several ncRNAs were found to indirectly regulate mitochondrial fission, autophagy, and apoptosis, although the underlying mechanisms require further characterization. Thus, while ncRNA-centered AD research is in its early stages, current mechanistic and translational evidence supports mitochondrially relevant ncRNAs as promising candidates for biomarker and therapeutic development.

Alzheimer Disease

Uncovering heterogeneous effects via localized feature selection.

Identifying features that interact to trigger disease, while accounting for heterogeneity across diverse populations, is essential for the development of precision and targeted medicine. Despite the availability of vast and complex health-related datasets, most existing works focus on identifying disease-associated features at the population level or within a few subpopulations, often overlooking individual-level heterogeneity within these groups. To address this limitation, we propose a framework that utilizes localized test statistics to identify disease-associated features tailored to individual profiles. Our method leverages the recently developed knockoffs methodology to control the noise level of the selection set so that the results are replicable. Moreover, it allows for the discovery of hidden heterogeneous effects within the data, as demonstrated in an application to single-cell RNA sequencing data for Alzheimer's disease. By aggregating localized feature selection results, our framework also enables powerful population-level feature selection. Our framework provides a powerful tool for exploratory studies of precision medicine, offering the potential to generate novel hypotheses for confirmatory biological experiments.

Alzheimer Disease

Cross-tissue multi-omics integration highlights BPHL and mitochondrial targets in Alzheimer's disease.

BACKGROUND: Mitochondrial dysfunction is a hallmark of Alzheimer's disease (AD), yet specific molecular targets remain to be fully characterized. METHODS: A summary-data-based Mendelian randomization (SMR) framework integrated AD genome-wide association study (GWAS) statistics (39,918 cases) with blood DNA methylation quantitative trait loci (mQTL), gene expression (eQTL), and protein (pQTL) data for 1136 mitochondria-related genes. Associations were assessed using Bayesian colocalization and HEIDI testing. Tissue relevance was evaluated in four brain regions (hippocampus, amygdala, cortex, frontal cortex) using GTEx and external transcriptomic datasets. RESULTS: Screening identified eight candidates supported across blood mQTL and eQTL layers. Stepwise central nervous system (CNS) evaluation singled out biphenyl hydrolase-like (BPHL) as the consistent candidate. Higher genetically predicted BPHL expression was associated with reduced AD risk across the hippocampus (OR=0.920, 95% CI 0.873-0.970), amygdala (OR=0.925, 95%CI 0.880-0.973), cortex (OR=0.943, 95% CI 0.908-0.978), and frontal cortex (OR=0.938, 95%CI 0.901-0.976). These findings aligned with protein-protein interactions connecting BPHL to respiratory complexes and lower BPHL expression in independent AD brains. Functional enrichment converged on oxidative phosphorylation pathways. CONCLUSIONS: By integrating multi-omics data with tissue-specific validation, this study nominates BPHL as a consistent protective candidate in the brain. These findings provide genetic support for mitochondrial molecular perturbations in AD, offering insights for future validation.

Alzheimer Disease