Investigation of the relationship between lead exposure in heavy metals mixtures and the prevalence of stroke: a cross-sectional study.

BACKGROUND

The adverse effects of environmental toxic metal exposure on human health are well-documented. However, the specific influence of heavy metal exposure on stroke prevalence remains underexplored.

METHODS

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning from 2011 to 2018 to investigate the association between blood metal concentrations and the incidence of stroke. Four analytical approaches-logistic regression, Restricted Cubic Splines (RCS), Weighted Quantile Sum regression (WQS), and Bayesian Kernel Machine Regression (BKMR)-were employed to assess the relationship, with a mediation analysis conducted to explore the role of inflammatory markers in Pb exposure-induced stroke.

RESULTS

Among the 9,399 participants in this project, 421 (4.4%) were diagnosed with stroke. After adjusting for covariates, a multivariable logistic regression model identified a positive association between the logarithmic concentration of Pb and the incidence of stroke. Besides, the analysis conducted using both WQS and BKMR methodologies found a consistent positive association between the composite exposure to heavy metals and the frequency of stroke cases, with Pb emerging as the predominant factor in this relationship. An evident saturation phenomenon was noted in the correlation between lead exposure and the risk of stroke. Additionally, the interplay between Pb exposure and stroke manifestation was found to be partially mediated by inflammatory markers, which were responsible for 6.9% of the observed effect (95%CI:0.01, 0.24, P = 0.03).

CONCLUSION

These findings indicate a notable contribution of Pb exposure to stroke risk, highlighting inflammation as a significant intermediary mechanism in the Pb exposure-stroke association.