Association between multiple-heavy-metal exposures and systemic immune inflammation in a middle-aged and elderly Chinese general population

Abstract

Background: Exposure to heavy metals alone or in combination can promote systemic inflammation. The aim of this study was to investigate potential associations between multiple plasma heavy metals and markers of systemic immune inflammation.

Methods: Using a cross-sectional study, routine blood tests were performed on 3355 participants in Guangxi, China. Eight heavy metal elements in plasma were determined by inductively coupled plasma mass spectrometry. Immunoinflammatory markers were calculated based on peripheral blood WBC and its subtype counts. A generalised linear regression model was used to analyse the association of each metal with the immunoinflammatory markers, and the association of the metal mixtures with the immunoinflammatory markers was further assessed using weighted quantile sum (WQS) regression.

Results: In the single-metal model, plasma metal Fe (log10) was significantly negatively correlated with the levels of immune-inflammatory markers SII, NLR and PLR, and plasma metal Cu (log10) was significantly positively correlated with the levels of immune-inflammatory markers SII and PLR. In addition, plasma metal Mn (log10 conversion) was positively correlated with the levels of immune inflammatory markers NLR and PLR. The above associations remained after multiple corrections. In the mixed-metal model, after WQS regression analysis, plasma metal Cu was found to have the greatest weight in the positive effects of metal mixtures on SII and PLR, while plasma metals Mn and Fe had the greatest weight in the positive effects of metal mixtures on NLR and LMR, respectively. In addition, blood Fe had the greatest weight in the negative effects of the metal mixtures for SII, PLR and NLR.

Conclusion: Plasma metals Cu and Mn were positively correlated with immunoinflammatory markers SII, NLR and PLR. While plasma metal Fe was negatively correlated with immunoinflammatory markers SII, NLR, and PLR.

Keywords: Heavy metals; Immunoinflammatory markers; Mixed exposure.

Fig. 1

Flowchart of population included in our final analysis (N = 3355)

Fig. 2

Heat map of association between plasma heavy metal concentrations (log₁₀ conversion) and immune-inflammatory biomarkers in 3355 participants. ELR: eosinophil-lymphocyte ratio; LMR: lymphocyte-monocyte ratio; PLR: platelet-lymphocyte ratio; NLR: neutrophil–lymphocyte ratio; SII: Systemic Immune-Inflammation Index

Fig. 3

Generalized linear regression model-based investigation of the effect of single heavy metal exposures (all log10 transformed) on immunoinflammatory biomarkers. All above are adjusted for gender, age, Ethnicity, Educated, BMI, Smoking, Alcohol consumption, Marital status, Diabates, Dyslipidemia, and Hypertension. β is the change in the standardized systemic inflammatory index per 1 SD increase in log₁₀-converted plasma heavy metal concentrations. The value of β at the blue dashed line is 0. P trend was examined by using the median of each quartile of plasma heavy metals as a continuous variable in the model. ^*P value/P trend in multiple testing. ELR: eosinophil-lymphocyte ratio; LMR: lymphocyte-monocyte ratio; PLR: platelet-lymphocyte ratio; NLR: neutrophil–lymphocyte ratio; SII: Systemic Immune-Inflammation Index

Fig. 4

Positive or negative associations between heavy metal mixture exposure (all log transformed) and immunoinflammatory biomarkers were explored based on WQS regression modeling. The model was adjusted for gender, age, Ethnicity, Educated, BMI, Smoking, Alcohol consumption, Marital status, Diabates, Dyslipidemia, and Hypertension. ELR: eosinophil-lymphocyte ratio; LMR: lymphocyte-monocyte ratio; PLR: platelet-lymphocyte ratio; NLR: neutrophil–lymphocyte ratio; SII: Systemic Immune-Inflammation Index