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Studies on the interaction among genetic susceptibility, blue space exposure, and rheumatoid arthritis (RA) risk have been lacking. Therefore, we examined the association between blue space exposure and RA incidence and assess the modifying effect of genetic susceptibility and air pollutants. Form the UK Biobank, 322,783 participants without RA were enrolled in this study. The association between blue space exposure and RA incidence was estimated using a cox proportional hazards model. The combined effect of blue space and genetic factors on the risk of RA was further evaluated. The polygenic risk score (PRS) for RA was calculated to represent individual genetic risk, and the potential modification effect of air pollution on the relationship between blue space, PRS, and RA were explored. During a median follow-up of 12.4 years, 3659 RA cases were identified. A 10 % increase in blue space300 m was associated with a 22.6 % reduction in RA incidence (HR=0.774, 95 % CI: 0.670, 0.895), exhibiting a consistent downward trend in the exposure-response curve. A high PRS was an independent risk factor for RA (HR=1.393, 95 % CI: 1.347, 1.439). The associations between blue space exposure, PRS, and the risk of RA were dose-dependent, with the lowest risk observed among those with high levels of blue space and lower PRS (HRbluespace300m=0.501, 95 % CI: 0.431, 0.583; HRbluespace1000m=0.476, 95 % CI: 0.408, 0.555). Interaction analysis indicated that increased concentrations of air pollutants strengthened the relationship between PRS and RA. Blue space exposure mitigated the risk of RA development, particularly in individuals with low genetic risk.

Introduction: Air pollution is speculated to increase the risk of Coronavirus disease-2019 (COVID-19). Nevertheless, the results remain inconsistent and inconclusive. This study aimed to explore the association between ambient air pollution (AAP) and COVID-19 risks using a meta-analysis with meta-regression modelling. Methods: The inclusion criteria were: original studies quantifying the association using effect sizes and 95 % confidence intervals (CIs); time-series, cohort, ecological or case-crossover peer-reviewed studies in English. Exclusion criteria encompassed non-original studies, animal studies, and data with common errors. PubMed, Web of Science, Embase and Google Scholar electronic databases were systemically searched for eligible literature, up to 31, March 2023. The risk of bias (ROB) was assessed following the Agency for Healthcare Research and Quality parameters. A random-effects model was used to calculate pooled risk ratios (RRs) and their 95 % CIs. Results: A total of 58 studies, between 2020 and 2023, met the inclusion criteria. The global representation was skewed, with major contributions from the USA (24.1 %) and China (22.4 %). The distribution included studies on short-term (43.1 %) and long-term (56.9 %) air pollution exposure. Ecological studies constituted 51.7 %, time-series-27.6 %, cohorts-17.2 %, and case crossover-3.4 %. ROB assessment showed low (86.2 %) and moderate (13.8 %) risk. The COVID-19 incidences increased with a 10 μg/m3 increase in PM2.5 [RR = 4.9045; 95 % CI (4.1548–5.7895)], PM10 [RR = 2.9427: (2.2290–3.8850)], NO2 [RR = 3.2750: (3.1420–3.4136)], SO2 [RR = 3.3400: (2.7931–3.9940)], CO [RR = 2.6244: (2.5208–2.7322)] and O3 [RR = 2.4008: (2.1859–2.6368)] concentrations. A 10 μg/m3 increase in concentrations of PM2.5 [RR = 3.0418: (2.7344–3.3838)], PM10 [RR = 2.6202: (2.1602–3.1781)], NO2 [RR = 3.2226: (2.1411–4.8504)], CO [RR = 1.8021 (0.8045–4.0370)] and O3 [RR = 2.3270 (1.5906–3.4045)] was significantly associated with COVID-19 mortality. Stratified analysis showed that study design, exposure period, and country influenced exposure-response associations. Meta-regression model indicated significant predictors for air pollution-COVID-19 incidence associations. Conclusion: The study, while robust, lacks causality demonstration and focuses only on the USA and China, limiting its generalizability. Regardless, the study provides a strong evidence base for air pollution-COVID-19-risks associations, offering valuable insights for intervention measures for COVID-19.

A number of studies from the literature have suggested that exposure to air pollutants is associated with a declined bone mineral density (BMD), and increased risks of osteoporosis (OP) and bone fractures. This study was performed to systemically assess the genetically causal associations of air pollutants with site-/age-specific BMD and risk of bone fractures with the implementation of two-sample Mendelian randomization (TSMR) and multivariate Mendelian randomization (MVMR). The TSMR analysis was implemented to infer the causal associations between air pollutants and BMD and the risk of bone fractures, additional MVMR analysis was used to further estimate the direct causal effects between air pollutants and BMD, the occurrence of OP, and bone fractures. The results showed that NOx exposure contributed to lower femoral neck BMD (FN-BMD) (β = −0.71, 95%CI: −1.22, −0.20, p = 0.006) and total body BMD (TB-BMD) (β = −0.55, 95%CI: −0.90, −0.21, p = 0.002). Additionally, exposure to PM10 was found to be associated with a decreased TB-BMD (B β = −0.42, 95%CI: −0.66, −0.18, p = 0.001), further age-specific subgroup analysis demonstrated the causal effect of PM10 exposure on the decreased TB-BMD in a subgroup aged 45 to 60 years (β = −0.70, 95%CI: −1.12, −0.29, p = 0.001). Moreover, the findings of the MVMR analysis implied that there was a direct causal effect between PM10 exposure and the decreased TB-BMD (45 < age < 60), after adjusting for PM2.5 and PM2.5 —10 exposure. Our study provides additional evidence to support the causal associations of higher concentrations of air pollutant exposure with decreased BMD, especially in those populations aged between 45 to 60 years, suggesting that early intervention measures and public policy should be considered to improve public health awareness and promote bone health.