Aug 2026· Journal of Autoimmunity· Vol 163, pp.
103606
· 0 citations· 51 references
Medicine
TL;DR
UHI exposure may be a modifiable environmental risk factor for RA and provide new insights into the biological mechanisms underlying this association, with proteomic analyses suggested that this association may involve not only canonical immune-inflammatory pathways, but also hypoxia response and protein transport.
Abstract
Background
Urban heat island (UHI) exposure is an increasingly common consequence of urbanization and climate warming, but its association with rheumatoid arthritis (RA) risk remains unclear.
Objective
To investigate the association between UHI exposure and incident RA, and to further assess the roles of genetic susceptibility and plasma proteomic profiles in this association.
Methods
This study included 400,628 urban residents with UHI exposure data and free of RA at baseline. Cox proportional hazards models were used to evaluate the association between UHI exposure and incident RA. Polygenic risk scores were used to assess effect modification by genetic susceptibility. Proteomic analyses identified candidate proteins and enriched pathways underlying the association. Mendelian randomization, colocalization, and mediation analyses assessed causal relevance and mediation.
Results
Over a median follow-up of 14.05 years, 5397 incident RA cases were documented. Each standard-deviation increase in UHI exposure was associated with a 17% higher risk of RA. This association was more pronounced among older adults and individuals with lower socioeconomic status. An additive interaction was observed between UHI exposure and genetic risk for RA. Proteomic analyses suggested that this association may involve not only canonical immune-inflammatory pathways, but also hypoxia response and protein transport, with CD40, VCAM1, and SUGP1 emerging as potential molecular mediators.
Conclusions
UHI exposure may be a modifiable environmental risk factor for RA and provide new insights into the biological mechanisms underlying this association.
Background Arthritis, characterised by inflammation, pain, and joint stiffness, is emerging as a significant health concern in ageing populations worldwide. While many studies have examined the effects of outdoor environmental risk factors on arthritis, fewer have investigated the effects of residential ecological factors. Methods We included 2,511 participants in the cohort study and 13,886 participants in the cross-sectional study from the Chinese Longitudinal Healthy Longevity Survey. We employed multivariate Cox proportional hazards and logistic regression to analyse the cohort and cross-sectional data. For each residential environment factor, we developed three models, with model three adjusted for all potential confounders collected in our study, which we considered our primary model. Results We identified a significant association between the use of non-clean fuel and a higher risk of arthritis (hazard ratio (HR) = 1.42; 95% confidence interval (CI) = 1.05–1.93) through cohort analysis. Conversely, residing in high-rise buildings with elevators was associated with a reduced risk of arthritis (HR = 0.12; 95% CI = 0.02–0.86). Our cross-sectional study reflects a reduced risk of arthritis associated with summer window ventilation for one to five times/week (odds ratio (OR) = 0.70; 95% CI = 0.50–0.97) and more than five times/week (OR = 0.66; 95% CI = 0.48–0.90). Additionally, distance of >300 m was also associated with a lower risk of arthritis (OR = 0.79; 95% CI = 0.69–0.89). Conclusion These findings underscore the crucial role of residential environmental factors in the risk of arthritis development. These results have important implications for public health policies aimed at reducing the prevalence of arthritis.
Xiangbin Jia, Xin Xiong, Zichao Jiang et al.· Journal of Global Health· 0 citations
BACKGROUND AND AIMS
Previous hypothesis-driven studies focused on limited risk factors for ischemic stroke (IS) with inconsistent findings. We aimed to systematically identify modifiable factors, assess causality, quantify joint impact, and evaluate public health implications of conservative versus radical multi-domain IS intervention strategies.
METHODS
We conducted a large prospective cohort study using UK Biobank and a bidirectional two-sample Mendelian randomization study. Exposures included 323 modifiable factors in seven domains: early life, health and medical history, lifestyle, local environment, physical measures, psychosocial factors, and sociodemographics. Exposome-wide association scan was used to investigate the associations between exposures and IS onset, and multivariable Cox models to assess their joint impact. Weighted population attributable fraction was estimated by partially eliminating exposures.
RESULTS
497,856 IS-free UK adults were enrolled during 2006-2010, and 10,593 experienced IS with a median follow-up of 14.53 years. Among 68 modifiable factors significantly associated with IS, 18 demonstrated robust causality. Controlling for the overlapping hazards of exposures, worsening risk profiles in each domain independently increased IS risk. Eradicating all modifiable risk factors would prevent 72.21% (95% CI 69.37%-74.83%) of IS, whereas a one-third reduction in the exposure levels of modifiable factors would prevent 68.19% (95% CI 64.22%-71.81%). These findings remained consistent across populations stratified by age, sex, and polygenic risk score-based genetic risk.
CONCLUSIONS
This hypothesis-free and triangulated study provided comprehensive information on the modifiable exposome for IS. Our findings suggested that a moderate reduction in risk factor exposure achieved a preventive impact comparable to total eradication, supporting the potential for efficient public health strategies.
The complex interplay of genetic, metabolic and immune-related processes influences chronic diseases. Even though genomic research has found susceptibility loci to specific conditions, there have been fewer studies that combine variant level, pathway-level, and cumulative risk using a single framework to examine chronic disease susceptibility. This study aimed to investigate genomic variation associated with susceptibility patterns and examine its implications for personalised medicine using type 2 diabetes mellitus as a model chronic disease. A secondary analysis was conducted using SNP genotype data from the GEO dataset GSE226084. Following quality control, principal component analysis was applied for dimensionality reduction, and K-means clustering was used to derive genomic susceptibility groups. Logistic regression identified associated SNPs, which were subsequently annotated, aggregated at the gene level, and evaluated through pathway enrichment analysis. A weighted genetic risk score was calculated to assess cumulative genetic burden across clusters. Two distinct genomic clusters were identified, comprising 63 and 243 individuals. Among 3,733 tested SNPs, 871 remained significant after false discovery rate correction. Key loci included HLA-DPA1, ETV6, TRIM15, TRIM26, and TCF7L2, with notable signal concentration on chromosome 6. Enrichment analysis revealed pathways related to immune regulation, inflammatory response, and cellular signaling. Genetic risk scores differed markedly between clusters, with one group exhibiting consistently higher cumulative genetic burden. These findings demonstrate that genomic susceptibility is organised into biologically distinct profiles defined by coordinated variant, gene, pathway, and cumulative risk signals. Integrating these layers provides a practical basis for risk stratification and supports the application of genomics in personalised medicine.
Dr. Bana Sarahbibi Mohmedsalim, Subhabrata Sarkar, D. Bandyopadhyay et al.· Genetics and Molecular Resea...· 0 citations
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
Cardiovascular disease (CVD) is the result of a complex interaction between genetic, lifestyle, and environmental factors, which may vary over the course of life. Traditional risk factors do not explain all patients’ risks; thus, the research of additional biomarkers to refine cardiovascular risk prediction has attracted considerable interest in recent years. Genetic factors, as well as air pollution exposure, among nontraditional factors, have been found to be critical determinants of CV risk. Accordingly, this narrative review aims to provide a comprehensive summary of the literature (PubMed) on the combined effects of air pollution and genetic susceptibility on CVD risk. Although different limitations and pitfalls are still to be solved, available evidence suggests that genetic variants (especially genes related to detoxification and inflammation) are involved in the association between air pollution exposure and CV adverse events. Thus, this research area could provide further knowledge of the etiology of CVD, offering new tools for targeted prevention and treatment of more susceptible subjects from a more personalized medicine point of view.
M. Gaggini, C. Vassalle· International Journal of Mol...· 0 citations
Long-term exposure to air pollutants, particularly NO2 and PM10, is associated with an increased risk of microvascular complications among individuals with diabetes, and the observed risk appears to be persistent and may begin at relatively low exposure levels, underscoring the need for preventive strategies targeting environmental risk factors.
