Jul 2026· Ecotoxicology and Environmental Safety· Vol 322, pp.
120474
· 0 citations· 53 references
Medicine
TL;DR
It is suggested that air pollution might contribute to T2D risk through inflammation-related proteins, highlighting the potential of proteomics as a tool for precision public health.
Abstract
Despite accumulating evidence linking air pollution to type 2 diabetes (T2D), the underlying mechanisms remain largely unexplored. We aimed to profile proteomic signatures associated with air pollution and examine their relationship to T2D. We conducted proteome-wide association studies on 2911 plasma proteins among 49,134 UK Biobank participants. Exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and benzene were estimated based on residential addresses. Proteomic signatures and their corresponding scores for each air pollutant were identified using linear and elastic net regression models, comprising 368 proteins for PM2.5, 207 for NO2, 206 for SO2, and 236 for benzene. Cox proportional hazards regression models were subsequently used to examine the effect of air pollution and proteomic signature scores on the risk of incident T2D. In both the time-independent and time-dependent Cox models, all four air pollutants were significantly associated with higher T2D risk. In the time-dependent Cox models, the hazard ratios (HRs) and 95% confidence intervals (CIs) were 1.02 (1.00, 1.05) for PM2.5, 1.02 (1.01, 1.02) for NO2, 1.12 (1.06, 1.18) for SO2, and 1.88 (1.38, 2.57) for benzene, respectively. Higher proteomic signature scores of PM2.5, NO2, SO2, and benzene were also associated with an elevated risk of T2D, with HRs (95% CIs) of 1.05 (1.00, 1.09), 1.17 (1.12, 1.23), 1.11 (1.06, 1.16), and 1.11 (1.06, 1.16) for a per-standard-deviation increase, respectively. Moderate mediation effects of the proteomic signature scores were observed. Pathway analyses further implicated systemic inflammation as a potential underlying mechanism. Our findings suggested that air pollution might contribute to T2D risk through inflammation-related proteins, highlighting the potential of proteomics as a tool for precision public health. Building on this, our study also offered a framework to explore molecular pathways connecting modifiable risk factors to diseases.
Introduction Ambient air pollutants have been increasingly implicated in immune-mediated disorders, yet the metabolic perturbations through which long-term exposure may contribute to rheumatoid arthritis (RA) remain insufficiently characterized. Methods We used data from 403,332 RA-free participants in the UK Biobank cohort to investigate the associations among long-term ambient air pollution exposure, plasma metabolic signatures, and incident RA risk. Participants were followed for a median of 14.01 years. Residential annual average concentrations of PM2.5, PM10, NO2, and NOx were linked to participants' addresses, and a composite air pollution score (APS) was calculated to reflect overall exposure burden. Plasma metabolomics data were used to characterize metabolic perturbations related to each pollutant. A machine learning model was applied to identify air pollutant-associated metabolites and construct corresponding metabolic signatures. We then examined the relationships of air pollutants and derived metabolic signatures with RA risk using Cox proportional hazards models. Generalized propensity score analyses were performed to assess whether the findings persisted after covariate balancing, and mediation analyses were conducted to estimate the contribution of metabolic signatures to the relationship of air pollutants and RA. Results Each standard deviation increase in the PM2.5-, NO2-, NOx-, and APS-related metabolic signatures was associated with elevated RA risk, with hazard ratios of 1.208 (95% CI: 1.180, 1.236), 1.141 (95% CI: 1.109, 1.173), 1.174 (95% CI: 1.148, 1.201), and 1.184 (95% CI: 1.156, 1.212), respectively. Similar estimates were observed in generalized propensity score analyses. Metabolic signatures accounted for 15.7% to 21.1% of the associations between air pollution exposure and RA risk. Conclusion These findings suggest that long-term air pollution exposure is associated with incident RA risk and that systemic metabolic perturbations may represent potential biological pathways underlying this association.
Yangchang Zhang, Tian Liang, Yang Pu et al.· Frontiers in Public Health· 0 citations
BACKGROUND
Air pollution is a major environmental risk factor for Atrial Fibrillation (AF). We aimed to derive robust, pollutant-specific proteomic signatures to elucidate the biological mechanisms and evaluate their potential risk-stratification utility.
METHODS
We analyzed plasma levels of approximately 2900 proteins in relation to long-term exposure to seven major air pollutants (PM2.5, PM10, NO2, NOx, SO2, benzene, and O3) within the UK Biobank cohort. A rigorous two-step strategy was employed: (1) proteome-wide screening with Bonferroni correction to identify high-confidence candidates, followed by (2) elastic net regularization to construct pollutant-specific proteomic signatures. Associations with incident AF were assessed using Cox proportional hazards models over a median follow-up of 13.53 years. High-throughput mediation analysis and comprehensive sensitivity analyses were performed to verify robustness.
RESULTS
We derived distinct proteomic signatures for all seven pollutants, which demonstrated superior predictive value for incident AF compared to traditional external exposure metrics. Mediation analysis identified specific pathogenic axes, highlighting ANGPT2, GDF15, and PLAUR as candidate molecular transducers linking pollution to AF risk. Survival analysis further revealed that individuals with high proteomic risk (top 50%) experienced an estimated loss of 1.15-1.25 years of AF-free life compared to low-risk peers. This risk stratification remained significant even after adjusting for ambient pollution concentrations.
CONCLUSIONS
We identified robust pollutant-specific proteomic signatures that effectively bridge the gap between environmental stress and clinical arrhythmia. These signatures offer candidate pathway-level markers for refining AF risk stratification and shifting public health strategies from reactive management toward precision prevention.
Yuyu Chen, Minjia Wu, Zhixi Lu et al.· Ecotoxicology and Environmen...· 0 citations
BACKGROUND
Air pollution has been increasingly associated with epilepsy risk, but the underlying biological mechanisms remain unclear.
METHODS
We conducted a large-scale proteome-wide analysis in a population-based cohort including 51,428 participants and 2254 plasma proteins. Concentrations of particulate matter (PM2.5, PM2.5-10) and gaseous pollutants (NO2 and NO) were estimated using land-use regression models. Principal component analysis was performed to derive an air pollution index (API). Logistic regression and Cox proportional hazards models were applied to examine interactions between plasma proteins and air pollution exposures in relation to epilepsy prevalence and incidence, respectively. Functional enrichment, protein-protein interaction (PPI) network, neuroimaging association, and tissue- and cell-type transcriptomic enrichment analyses were conducted to explore the biological relevance of proteins exhibiting interaction effects.
RESULTS
We identified protein signatures exhibiting consistent interactions with multiple air pollution exposures in relation to epilepsy risk, including CIT-PM₂.₅, PTPRR-NO₂, and BCL2L15-API. Functional enrichment analyses indicated that these proteins were enriched in pathways related to immune regulation, inflammation, and cell death-related processes, which was consistent with PPI network analysis showing a highly interconnected module centered on immune-related proteins. Neuroimaging analyses suggested that these protein-air pollution interactions were associated with structural brain measures, including hippocampal volume and white matter hyperintensities. Additionally, tissue and single-nucleus RNA sequencing analyses showed that the corresponding genes were enriched in immune-related tissues and specific brain cell types, particularly neurons and endothelial cells.
CONCLUSIONS
This study identifies protein signatures that may modify the association between air pollution exposure and epilepsy risk, providing new insights into potential biological links and highlighting candidate molecular signatures for future mechanistic and translational investigations.
Zihua He, Jianqiong Yin, Quan Yang et al.· Ecotoxicology and Environmen...· 0 citations
Air pollution is a leading environmental cause of lung cancer, yet the underlying biological pathways remain poorly understood. Identifying circulating biomarkers that capture early molecular responses may clarify how air pollutants contribute to carcinogenesis and help identify individuals at elevated risk. We conduct a prospective nested case-control study within two Cancer Prevention Study cohorts, profiling more than 1100 metabolites in pre-diagnostic plasma samples from 1357 participants. Residential concentrations of six major air pollutants are estimated at the time of blood draw. Here we show that eight circulating metabolites are associated with both air pollution exposure and subsequent lung cancer risk. Four metabolites, including γ-glutamylglutamine, phenylacetylglutamate, N-(2-furoyl)glycine, and 4-vinylguaiacol glucuronide, significantly mediate associations for particulate matter and ozone (adjusted q-value < 0.2). These findings suggest that air pollution may promote lung cancer partly through metabolic pathways related to inflammatory and oxidative processes, providing key insights into potential mechanisms and targets for prevention. Exposure to ambient air pollution is an established risk factor for lung cancer. In here, the authors report multiple metabolic perturbations that may mediate the association between ambient air pollution exposure and lung cancer risk.
Sabrina S. Chow, Ying Wang, J. Sarnat et al.· Nature Communications· 0 citations
Increasing PM2.5, NO2, NOx, and CO exposures were suggestively associated with increased CRC incidence, particularly among female and Latino participants and for left colon and rectal cancers.
U. Ihenacho, Chiu‐Cheng Tseng, Jun Wu et al.· Environmental Research· 0 citations
Ambient fine particulate matter (PM2.5) is associated with oxidative stress, metabolic dysregulation, and cardiometabolic disease. However, systemic metabolic responses to contrasting real-world ambient air pollution exposure environments remain poorly characterized. In this cross-sectional study, untargeted proton nuclear magnetic resonance (1H-NMR)-based urinary metabolomics was used to investigate metabolic signatures associated with contrasting ambient air pollution exposure environments in Thailand. Adults residing in Chiang Mai (high-exposure region; n = 51) and Songkhla (low-exposure region; n = 52) were recruited during a period of elevated regional air pollution, with long-term residence serving as a proxy for differential exposure to ambient air pollution environments. Partial least squares-discriminant analysis (PLS-DA) demonstrated separation between exposure groups (R2 = 0.873, Q2 = 0.447), indicating good model fit but only modest predictive ability. Eight urinary metabolites differed significantly (p < 0.05), implicating pathways related to tryptophan metabolism, nucleotide metabolism, energy metabolism, and host–microbial co-metabolism. L-arginine and L-cystathionine showed lower relative abundance in the high-exposure group, and six metabolites remained significant after Benjamini–Hochberg false discovery rate correction. Following covariate adjustment, L-tryptophan, hippuric acid, xanthine, and 5-hydroxyindoleacetic acid (5-HIAA) remained significantly associated with the high-exposure group. Contrasting ambient air pollution exposure environments, indexed by regional PM2.5 concentrations, were associated with coordinated urinary metabolic alterations. Because long-term regional residence served as a proxy for individual-level exposure and diet and lifestyle differences between regions were not fully controlled, these findings should be interpreted as associations with contrasting regional exposure environments. Metabolite annotations remain putative and require validation in longitudinal studies with comprehensive pollutant characterization and individual-level exposure assessment.
Blecious Zinan'dala, Anupon Iadnut, Chikondi Maluwa et al.· International Journal of Mol...· 0 citations
