Breast cancer is one of the most common malignant tumors in women worldwide. Inflammation plays an important role in the occurrence and development of breast cancer. This study aimed to evaluate the association between inflammatory markers and breast cancer prevalence and explore their potential biomarker value. Using data from the NHANES, approximately 20,000 participants were analyzed to assess the association between six systemic inflammatory markers and breast cancer prevalence. Statistical methods including multivariable logistic regression, subgroup analysis, and interaction testing were employed. ROC curves were used to evaluate and compare their diagnostic capabilities. This study included 19,734 participants. We observed a significant positive correlation between platelet-to-lymphocyte ratio (PLR) and breast cancer prevalence (OR = 1.35; 95% CI:1.09, 1.67), with PLR demonstrating good predictive performance for breast cancer. Additionally, monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), systemic inflammation response index(SIRI) and aggregate index of systemic inflammation(AISI) were also found to be positively associated with breast cancer prevalence. Subgroup analysis and interaction tests revealed that the association between PLR and BC did not differ significantly among population groups. ROC curve analysis indicated that PLR (AUC = 0.59; 95%CI: 0.56, 0.63) outperformed other inflammatory markers in predicting BC. Systemic inflammatory markers, especially PLR, are significantly associated with BC prevalence and demonstrate potential as biomarkers for early detectionn. People with elevated inflammation markers should pay close attention to the latent prevalence of BC.

Long-term exposure to air pollutants and diabetes are both linked to cancer development. However, their combined effect remains unclear. This study examined the relationship between air pollutants and cancer incidence, with diabetes as a potential mediator. Data from 10,590 participants in the 2015 China Health and Retirement Longitudinal Study (CHARLS) were analyzed. Participants were grouped based on cancer diagnosis, and air pollutant exposure levels were estimated using satellite-based spatiotemporal models. Generalized linear regression and restricted cubic spline (RCS) analysis were used to assess the impact of air pollutants and diabetes in covariates-adjusted models. Further analyses, including conditional independence test, mediation effect and sensitivity analysis based on Bayesian networks, were performed to further analyze specific air pollutants. After adjusting for covariates, particulate matter (PM) (PM ≤ 1 μm in aerodynamic diameter [PM₁], PM_2.5, ammonium (NH₄), nitrate (NO₃) and diabetes showed significant associations with cancer incidence. RCS analysis confirmed significant direct effects of PM_2.5 and PM₁₀ on cancer and the mediated effects of diabetes. The interaction between diabetes and both PM_2.5 and PM₁₀ was further supported by conditional independence tests, highlighting diabetes as a significant mediator in the PM_2.5-cancer relationship. This study offers a novel perspective by identifying diabetes as a key intermediary in the association between PM_2.5 exposure and cancer risk, providing evidence that diabetes plays a significant mediating role in air pollutant-related cancer development.