Exposure to benzene poses significant health risks, with cardiovascular toxicity emerging as a primary adverse effect. However, the exact influence of benzene on the development of atrial fibrillation (AF) remains unclear. The occurrence of AF is influenced by environmental factors and genetic factors that should not be overlooked. Previous studies have found an association between accelerated biological aging and benzene and AF. This study explored the effects of benzene exposure on the risk of incident AF later in life, examining the interaction with genetic susceptibility and the mediating role of accelerated biological aging. From 2006 to 2010, 432,522 participants in the UK Biobank who were free of cardiovascular disease at baseline were included. The annual average benzene concentrations during the follow-up period were assessed using air dispersion models. AF patients were identified using the International Classification of Diseases 10th Revision (ICD-10) code. Cox proportional hazard models were employed to explore the association between benzene exposure and incident AF. Restricted cubic spline models were utilized to estimate the exposureresponse relationships. The polygenic risk score (PRS) for AF was used to examine the gene-environment interactions related to AF risk. Exposure to benzene significantly increased the risk of AF, with the hazard ratio (95 % confidence interval) for a standard deviation increase in benzene concentration reaching 1.02 (1.00, 1.04). Participants with high benzene exposure and high genetic risk had a greater risk of AF than those with low benzene exposure and low genetic risk (HR, 3.12; 95 % CI, 2.82, 3.46). Accelerated biological aging significantly mediated the association between benzene exposure and AF. Exposure to benzene is associated with an increased risk of AF, particularly in populations at high genetic risk. Accelerated biological aging partially mediated these associations.

Background: Combining genetic risk factors and plasma fatty acids (FAs) can be used as an effective method of precision medicine to prevent hypertension risk.Methods: A total of 195,250 participants in the UK Biobank cohort were included in this study from 2006–2010. Polygenic risk scores (PRSs) were calculated for hypertension using single-nucleotide polymorphisms (SNPs). Concentrations of plasma FAs, including polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs) and saturated fatty acids (SFAs), were tested by nuclear magnetic resonance. The Cox model was used to test for the main effects of PRS, different plasma FAs and their joint effects on hypertension. Relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP) were used to test the additive interaction.Results: Plasma PUFAs, n-3 PUFAs, MUFAs and SFAs were related to the risk of hypertension (PUFAs: HR, 0.878; 95% CI, 0.868–0.888; MUFAs: HR, 1.13; 95% CI, 1.123–1.150; SFAs: HR, 1.086; 95% CI, 1.074–1.098; n-3 PUFAs: HR, 0.984; 95% CI, 0.973–0.995). Moreover, an additive interaction was found between PRS and plasma FAs, which could contribute to an approximately 10–18% risk of hypertension, and the associations between high plasma MUFAs and a high PRS of hypertension were the strongest positive [RERI: 0.178 (95% CI: 0.062, 0.294), AP: 0.079 (95% CI: 0.027, 0.130)].Conclusion: Increased plasma MUFAs or SFAs and decreased plasma PUFAs or n-3 PUFAs were associated with hypertension risk, especially among people at high genetic risk.

Background: Adverse reproductive outcomes (AROs) in women can lead to the occurrence of a variety of diseases later in life. However, research on AROs and dementia risk in women has not been reported. Objective: This study explored the effects of miscarriage and stillbirth on future dementia risk in women. Methods: The Cox proportional hazards model was used to clarify the association between miscarriage, stillbirth, and dementia risk. In this cohort, only women with a history of miscarriage and stillbirth were selected. A genetic risk score for dementia was constructed, and the combined effect of miscarriage, stillbirth, and the genetic risk score for dementia on the future risk of incident dementia was determined. Results: For each increase in the number of miscarriages and stillbirths, the risk of dementia increased by 5% and 22%, respectively. Compared to women who had a low genetic risk score, no miscarriages and at least one live-born infant, women with more than 3 miscarriages and a high genetic risk score had a significantly increased risk of dementia. Conclusions: Our results indicate that miscarriage and stillbirth are associated with an increased risk of dementia, especially in women with a high genetic risk score.

Background: Sudden cardiac death (SCD) remains difficult to predict in the general population, and proteomics may offer additional prognostic information. We aimed to develop and evaluate a plasma proteomic signature for SCD risk prediction in the UK Biobank. Methods: We analyzed prospective data from 52,826 UK Biobank participants, including 408 incident SCD cases. A proteomic signature was derived using repeated LASSO-Cox regression. Its biological relevance was explored by GO enrichment and PPI analyses. Predictive performance was assessed by discrimination, reclassification, calibration, and decision curve analysis. Results: A 71-protein signature was identified. Each 1-SD increase in the proteomic signature was associated with a higher risk of incident SCD (HR 3.25, 95% CI 2.94–3.58). Although non-proportional hazards were detected, the time-dependent association remained positive throughout follow-up. The proteomic-signature model showed substantially better discrimination than the clinical model (AUC 0.871 vs. 0.770), whereas the combined model provided only minimal additional improvement (AUC 0.872). Compared with the clinical model alone, the combined model improved reclassification (IDI 0.033, 95% CI 0.022–0.050; continuous NRI 0.403, 95% CI 0.351–0.459; both p < 0.001). Calibration was good, and net clinical benefit was mainly observed at relatively low threshold probabilities. Sensitivity analyses supported the robustness of the findings. Conclusion: The proteomic signature was independently associated with incident SCD and improved risk stratification beyond the clinical model. However, its clinical utility should be interpreted cautiously, and external validation is required before clinical application.

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders during pregnancy. Women with GDM face an increased risk of type 2 diabete