Independent and Combined Associations between Metals Exposure and Inflammatory Markers among the General U.S. Adults

Abstract

Exposure to metals can trigger a series of diseases by dysregulating the human immune system, but there is still a lack of systematic studies assessing the independent and combined effects of exposure to metals on immune function in the general population, particularly concerning inflammation markers. This cross-sectional study was designed to mainly examine the associations between urinary metal mixtures and inflammatory markers, including white blood cell (WBC), platelet count (PLT), mean platelet volume (MPV), MPV/PLT ratio (MPR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR). A total of 3451 participants aged ≥20 years were selected from the 2013-2016 National Health and Nutrition Examination Survey. Generalized linear models were used to investigate the relationships of exposure to single metals on inflammatory markers. Associations between coexposure to multiple metals and inflammatory markers were determined using weighted quantile sum regression and quantile g-computation. Barium, cadmium, lead, thallium, and cobalt showed significant associations with MPV, PLR, and NLR. Metal mixtures showed a negative association with MPV, while they had positive associations with PLR and NLR. Overall, our study highlights the significant effects of multiple metals exposure on inflammation markers, including MPV, PLR, and NLR, among U.S. adults. Thereinto, uranium, cadmium, and cobalt were identified as major contributors. Further prospective studies representative of other countries are warranted to either validate or refute our findings.

Figure 1

Geometric mean levels of inflammatory markers in urinary metals under quartiles (adjusted for age, sex, race, education level, FIPR, BMI, smoking, drinking, urine creatinine, survey period) *: Indicates a significant difference from Q1. #: Indicates a significant difference from Q2. $: Indicates a significant difference from Q3.

Figure 2

Associations between urinary metals and inflammatory markers in NHANES 2013–2016. The model was adjusted for age, sex, race, education level, FIPR, BMI, smoking, drinking, urine creatinine, and survey period.

Figure 3

(a) Association of total 12 urinary metals coexposure with 6 inflammatory markers levels by the positive WQS regression model. (b) Association of total 12 urinary metals coexposure with 6 inflammatory markers levels by the quantile g-computation model. These models were adjusted for age, sex, race, education level, FIPR, BMI, smoking, drinking, urine creatinine, and survey period.

Figure 4

(a) Weights in the positive WQS regression model between NLR and WQS index of a total 12 urinary metals. (b–d) Weights corresponding to the proportion of the effect per metal mixtures on MPV, PLR, and NLR in the quantile g-computation model, respectively. All models were adjusted for age, sex, race, education level, FIPR, BMI, smoking, drinking, urine creatinine, and survey period.