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

Abstract

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.

Keywords: Heavy metals; Inflammation; Lead; NHANES; Stroke.

Fig. 1

Flowchart of participants included in this study. NHANES, National Health and Nutrition Examination Survey; BMI, body mass index; PIR, family poverty income ratio; WBC, white blood cell; ALP, alkaline phosphatase; GGT, gamma glutamyl transferase

Fig. 2

Subgroup analysis of the association between Pb exposure and stroke prevalence. On the left side of the figure was the subgroup analysis of the crude model, while on the right side was the subgroup analysis of adjusting all covariates and remaining heavy metals. BMI, body mass index; PIR, family poverty income ratio

Fig. 3

Effects of heavy metal model WQS weights on the prevalence of stroke. (a) Positive and negative impacts of heavy metal model WQS weights on the prevalence of stroke in the crude model (“pwqs” stands for “positive weight quantile sum”, and “nwqs” stands for “negative weight quantile sum”. (b) Positive impact of heavy metal model WQS weights on the prevalence of stroke in the model adjusted for all covariates. WQS: Weighted quantile sum

Fig. 4

Analysis of stroke risk and heavy metals exposure using the BKMR model. (a) Evaluates the collective effect of mixed heavy metals on stroke occurrence, comparing various heavy metal percentiles against the median (50th percentile) within the BKMR model. (b) Investigates the response of stroke prevalence to individual heavy metals in the BKMR model, with a focus on elevating the concentration of one heavy metal to the 75th percentile, while keeping the levels of other metals constant at the 25th, 50th, and 75th percentiles. (c) Examines the univariate response of individual heavy metals to stroke prevalence, maintaining the concentrations of all other metals at the median level within the BKMR model. Adjustments were made for all covariates across the models. BKMR: Bayesian kernel machine regression